System

Abstract

An object of a system according to an embodiment is to solve the problems of manpower shortage and multilingual support and to ensure smooth communication with a customer.SOLUTION: A system according to an embodiment includes a natural language processor, a database reference unit, and a cloud-based service providing unit. The natural language processor analyzes a natural language. The database reference unit provides specialized information based on the information analyzed by the natural language processing unit. The cloud-based service providing unit operates the entire system based on the information provided by the database reference 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] With conventional technology, there are issues such as labor shortages and multilingual support in the restaurant and tourism industries, and there is room for improvement in ensuring smooth communication with customers.

[0005] The system according to the embodiment aims to solve the problems of labor shortages and multilingual support, and ensure smooth communication with customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a natural language processing unit, a database reference unit, and a cloud-based service providing unit. The natural language processing unit analyzes natural language. The database reference unit provides specialized information based on the information analyzed by the natural language processing unit. The cloud-based service providing unit operates the entire system based on the information provided by the database reference unit. [Effects of the Invention]

[0007] The system according to the embodiment can solve the problems of labor shortages and multilingual support, and ensure smooth communication with customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes AI technology to address labor shortages while minimizing opportunity losses in the restaurant and tourism industries. This system uses AI to provide multilingual support in situations requiring specialized expertise, ensuring smooth communication with customers. Furthermore, its low-cost implementation aims to expand the scope of its adoption. For example, in the restaurant and tourism industries, AI supports communication with customers through multilingual support. For example, when a foreign tourist visits, AI can respond in their native language, providing orders and tourist information. This enables smooth communication across language barriers. Next, AI also provides support in situations requiring specialized expertise. For example, in restaurants, AI can provide menu explanations and allergy information, and in tourist destinations, AI can provide explanations of history and culture, providing specialized knowledge. This enables the provision of high-quality service to customers. Furthermore, AI can be implemented at low cost, making it easy for even small and medium-sized restaurants and tourism businesses to adopt. This is expected to expand adoption and promote growth throughout the industry. This system addresses labor shortages while minimizing opportunity losses in the restaurant and tourism industries. This allows the system to address labor shortages while minimizing opportunity losses in the restaurant and tourism industries. For example, AI support during busy periods can reduce the burden on staff and improve customer satisfaction. In addition, AI's multilingual support can accommodate the increasing number of foreign tourists. In this way, utilizing AI technology can solve issues in the restaurant and tourism industries and aim for growth for the entire industry.

[0029] A system according to an embodiment includes a natural language processing unit, a database reference unit, and a cloud-based service providing unit. The natural language processing unit supports multiple languages ​​using natural language processing technology. For example, the natural language processing unit can analyze sentences using morphological analysis. The natural language processing unit can also analyze sentence structure using grammatical analysis. The natural language processing unit can also understand the meaning of sentences using semantic analysis. The database reference unit references a pre-built database to provide specialized information. For example, the database reference unit can provide medical information. The database reference unit can also provide legal information. The database reference unit can also provide technical information. The cloud-based service providing unit uses cloud-based services to reduce initial investment. For example, the cloud-based service providing unit can use Software as a Service (SaaS). The cloud-based service providing unit can also use Platform as a Service (PaaS). The cloud-based service providing unit can also use Infrastructure as a Service (IaaS). This enables the system according to an embodiment to perform natural language processing, provide specialized information, and provide cloud-based services.

[0030] The natural language processing unit can support multiple languages ​​using natural language processing technology. The natural language processing unit can analyze sentences using, for example, morphological analysis. The natural language processing unit can also analyze the structure of a sentence using grammatical analysis. The natural language processing unit can also understand the meaning of a sentence using semantic analysis. This enables multilingual support. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI (text generation AI or multimodal generation AI), or may be performed without using a generation AI. For example, the natural language processing unit can provide multilingual text data as input to a generation AI, which can then analyze the text data and translate it into the corresponding language.

[0031] The database reference unit can refer to a pre-constructed database and provide specialized information. The database reference unit can provide, for example, medical information. The database reference unit can also provide legal information. The database reference unit can also provide technical information. This makes it possible to provide specialized information. Some or all of the above-described processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide specialized information as input to the generation AI, which can then analyze the information and extract and provide the necessary information.

[0032] The cloud-based service providing unit can reduce initial investment by using a cloud-based service. The cloud-based service providing unit can use, for example, SaaS (Software as a Service). The cloud-based service providing unit can also use PaaS (Platform as a Service). The cloud-based service providing unit can also use IaaS (Infrastructure as a Service). This makes it possible to reduce initial investment. Some or all of the above-mentioned processing in the cloud-based service providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide configuration information for the cloud-based service as input to the generation AI, which can then analyze the information and perform optimal configuration.

[0033] The natural language processing unit can provide a specific scenario of interactions when ordering or tourist information. The natural language processing unit can provide, for example, the flow of interactions when ordering. The natural language processing unit can also provide details of tourist information. The natural language processing unit can also provide the content of a specific scenario. This makes it possible to provide a specific scenario. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide a scenario of interactions when ordering or tourist information as input to the generation AI, and the generation AI can analyze the scenario and provide an optimal scenario.

[0034] The natural language processing unit can ensure the accuracy or reliability of the translation and provide a method for dealing with mistranslations when they occur. The natural language processing unit can, for example, provide criteria for evaluating the accuracy of the translation. The natural language processing unit can also provide criteria for evaluating reliability. The natural language processing unit can also provide a method for dealing with mistranslations when they occur. This improves the accuracy and reliability of the translation and makes it possible to deal with mistranslations. Some or all of the above-mentioned processing in the natural language processing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the natural language processing unit can provide evaluation criteria for the accuracy and reliability of the translation to the generation AI as input, and the generation AI can analyze the criteria and make an optimal evaluation.

[0035] During natural language processing, the natural language processing unit can generate an optimal response by referring to the user's past dialogue history. For example, the natural language processing unit provides relevant information based on questions the user has previously asked. The natural language processing unit can also provide recommended content based on the user's past favorite menus or tourist spots. The natural language processing unit can also adjust the response content to avoid points that the user has previously expressed dissatisfaction with. This makes it possible to provide an optimal response based on the past dialogue history. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's past dialogue history as input to the generation AI, which can then analyze the history and generate an optimal response.

[0036] During natural language processing, the natural language processing unit can adjust the timing of a response based on the user's speaking speed or tone. For example, if the user speaks quickly, the natural language processing unit allows the AI ​​to respond quickly and match the tempo. Furthermore, if the user speaks slowly, the natural language processing unit can also allow the AI ​​to respond slowly and provide a calm conversation. Furthermore, if the user speaks in an emotional tone, the natural language processing unit can also allow the AI ​​to respond at an appropriate time and empathize with the user's emotions. This enables a response that is appropriate for the user's speaking speed and tone. Some or all of the above-described processing in the natural language processing unit may be performed using, or without, a generation AI. For example, the natural language processing unit can provide the generation AI with data on the user's speaking speed and tone as input, and the generation AI can analyze the data and generate the optimal response timing.

[0037] During natural language processing, the natural language processing unit can select appropriate expressions based on the user's cultural background. For example, if the user is Japanese, the natural language processing unit uses honorific and polite expressions. Furthermore, if the user is American, the natural language processing unit can also use casual expressions. Furthermore, if the user is French, the natural language processing unit can also use expressions that take French culture into consideration. This makes it possible to use appropriate expressions according to the cultural background. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide data on the user's cultural background as input to the generation AI, which can then analyze the data and generate optimal expressions.

[0038] During natural language processing, the natural language processing unit can provide region-specific information based on the user's geographical location information. For example, if the user is in Tokyo, the natural language processing unit can provide information about tourist spots and restaurants in Tokyo. Furthermore, if the user is in New York, the natural language processing unit can provide information about tourist spots and restaurants in New York. Furthermore, if the user is in Paris, the natural language processing unit can provide information about tourist spots and restaurants in Paris. This makes it possible to provide region-specific information based on geographical location information. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific information.

[0039] During natural language processing, the natural language processing unit can analyze a user's social media activities and provide related information. For example, the natural language processing unit can provide information about places where the user has checked in on social media. The natural language processing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The natural language processing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on social media activities. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide data on the user's social media activities as input to the generation AI, which can then analyze the data and provide optimal related information.

[0040] During natural language processing, the natural language processing unit can customize the response content by reflecting the user's past feedback. For example, the natural language processing unit provides recommended content based on the user's past favorite menus or tourist spots. The natural language processing unit can also adjust the response content to avoid points that the user has previously expressed dissatisfaction with. The natural language processing unit can also provide more appropriate information based on the user's past feedback. This makes it possible to customize the response content based on past feedback. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's past feedback as input to the generation AI, which can then analyze the feedback and generate optimal response content.

[0041] When referencing the database, the database reference unit can provide optimal information by referring to the user's past search history. The database reference unit can provide relevant information based on, for example, menus or tourist spots that the user has previously searched for. The database reference unit can also prioritize providing relevant information from the user's past search history. The database reference unit can also provide optimal information based on the content of the user's past searches. This makes it possible to provide optimal information based on the past search history. Some or all of the above-mentioned processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide the user's past search history as input to the generation AI, which can then analyze the history and provide optimal information.

[0042] When referencing the database, the database reference unit can prioritize providing highly relevant information based on the user's current situation. The database reference unit can provide relevant information based on, for example, the user's current location. The database reference unit can also provide relevant information based on the user's current time zone. The database reference unit can also provide optimal information based on the user's current situation. This makes it possible to provide highly relevant information based on the current situation. Some or all of the above-mentioned processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide data on the user's current situation as input to the generation AI, which can then analyze the data and provide optimal relevant information.

[0043] When referencing the database, the database reference unit can adjust the level of detail of the information according to the user's level of expertise. For example, if the user has specialized knowledge, the database reference unit provides detailed information. Furthermore, if the user has general knowledge, the database reference unit can also provide basic information. Furthermore, if the user is a beginner, the database reference unit can also provide simple information. This makes it possible to provide information according to the level of expertise. Some or all of the above-mentioned processing in the database reference unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database reference unit can provide data on the user's level of expertise as input to the generation AI, which can then analyze the data and adjust the optimal level of detail of the information.

[0044] When referencing the database, the database reference unit can provide region-specific information based on the user's geographical location information. For example, if the user is in Tokyo, the database reference unit can provide information on tourist spots and restaurants in Tokyo. Furthermore, if the user is in New York, the database reference unit can provide information on tourist spots and restaurants in New York. Furthermore, if the user is in Paris, the database reference unit can provide information on tourist spots and restaurants in Paris. This enables the provision of region-specific information based on geographical location information. Some or all of the above-described processing in the database reference unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database reference unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific information.

[0045] When referencing the database, the database reference unit can analyze the user's social media activity and provide related information. For example, the database reference unit can provide information about places where the user has checked in on social media. The database reference unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The database reference unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-described processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide data on the user's social media activity as input to the generation AI, which can then analyze the data and provide optimal related information.

[0046] When referencing the database, the database reference unit can customize the information provision method by reflecting the user's past feedback. The database reference unit provides recommended content based on, for example, the user's previously preferred menu items or tourist spots. The database reference unit can also adjust the information provision method to avoid points that the user has previously expressed dissatisfaction with. The database reference unit can also provide more appropriate information based on the user's past feedback. This makes it possible to customize the information provision method based on past feedback. Some or all of the above-mentioned processing in the database reference unit may be performed using, or without, a generation AI. For example, the database reference unit can provide the user's past feedback as input to the generation AI, which can then analyze the feedback and generate an optimal information provision method.

[0047] When providing a service, the cloud-based service providing unit can provide the optimal service by referring to the user's past usage history. For example, the cloud-based service providing unit can provide a related service based on services the user has used in the past. The cloud-based service providing unit can also prioritize related services based on the user's past usage history. The cloud-based service providing unit can also provide the optimal service based on the content the user has used in the past. This makes it possible to provide the optimal service based on the past usage history. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, or without, a generation AI. For example, the cloud-based service providing unit can provide the user's past usage history as input to the generation AI, which can then analyze the history and provide the optimal service.

[0048] The cloud-based service providing unit can customize the content of the service based on the user's current situation when providing the service. The cloud-based service providing unit can, for example, provide a relevant service based on the user's current location. The cloud-based service providing unit can also provide a relevant service based on the user's current time zone. The cloud-based service providing unit can also provide an optimal service based on the user's current situation. This enables customization of the service content based on the current situation. Some or all of the above-described processing in the cloud-based service providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide data on the user's current situation as input to the generation AI, which can then analyze the data and generate optimal service content.

[0049] The cloud-based service providing unit can improve the quality of the service by reflecting user feedback when providing the service. For example, the cloud-based service providing unit can improve the content of the service based on feedback previously provided by the user. The cloud-based service providing unit can also reflect user feedback in real time to immediately improve the quality of the service. The cloud-based service providing unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to improve the quality of the service based on the feedback. Some or all of the above-mentioned processing in the cloud-based service providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cloud-based service providing unit can provide user feedback as input to the generation AI, which can then analyze the feedback and generate optimal service improvement measures.

[0050] The cloud-based service providing unit can provide region-specific services based on the user's geographical location information when providing services. For example, if the user is in Tokyo, the cloud-based service providing unit can provide information on tourist attractions and restaurants in Tokyo. Furthermore, if the user is in New York, the cloud-based service providing unit can provide information on tourist attractions and restaurants in New York. Furthermore, if the user is in Paris, the cloud-based service providing unit can provide information on tourist attractions and restaurants in Paris. This enables the provision of region-specific services based on geographical location information. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cloud-based service providing unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific services.

[0051] The cloud-based service providing unit can analyze a user's social media activities and provide related services when providing services. For example, the cloud-based service providing unit can provide services related to locations where the user has checked in on social media. The cloud-based service providing unit can also analyze the content of the user's social media posts and provide related services. The cloud-based service providing unit can also refer to the activities of the user's friends on social media. This makes it possible to provide related services based on social media activities. Some or all of the above-described processing in the cloud-based service providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide data on the user's social media activities as input to the generation AI, which can then analyze the data and provide optimal related services.

[0052] The cloud-based service providing unit can customize the service provision method by reflecting the user's past feedback when providing the service. The cloud-based service providing unit can improve the service provision method, for example, based on feedback provided by the user in the past. The cloud-based service providing unit can also reflect the user's feedback in real time and instantly adjust the service provision method. The cloud-based service providing unit can also analyze the user's feedback, identify common problems, and improve the provision method. This enables customization of the service provision method based on the past feedback. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, or without, a generation AI. For example, the cloud-based service providing unit can provide the user's feedback as input to the generation AI, which can then analyze the feedback and generate an optimal service provision method.

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

[0054] The natural language processor can analyze the content of a user's speech in real time and infer the user's intention. For example, if a user uses an ambiguous expression, the natural language processor analyzes the context and provides the most appropriate interpretation. It can also provide additional relevant information when a user asks a question. Furthermore, if a user asks multiple questions at once, it can respond to each question individually. This makes it possible to respond based on the user's intention.

[0055] The database reference unit can automatically recommend related information based on the user's search history. For example, it can provide event information related to tourist spots that the user has previously searched for. It can also provide information about new menu items at restaurants that the user has previously searched for. It can also provide related news articles based on information that the user has previously searched for. This makes it possible to provide information based on the user's search history.

[0056] The cloud-based service provider can dynamically change the way services are provided depending on the user's usage. For example, it can prioritize the display of functions that the user uses frequently. It can also automatically optimize the services that the user uses during specific time periods. It can also provide customized services based on the user's usage patterns. This makes it possible to provide services that are tailored to the user's usage.

[0057] The natural language processing unit can adjust the timing of responses based on the user's speaking speed and tone. For example, if the user speaks quickly, the AI ​​will respond quickly and match the tempo. Also, if the user speaks slowly, the AI ​​can respond slowly and provide a calm conversation. Furthermore, if the user speaks in an emotional tone, the AI ​​can respond at an appropriate time and be considerate of the user's emotions. This makes it possible to respond according to the user's speaking speed and tone.

[0058] The database reference unit can adjust the level of detail of information according to the user's level of expertise. For example, if the user has specialized knowledge, detailed information can be provided. If the user has general knowledge, basic information can be provided. Furthermore, if the user is a beginner, simple information can be provided. This makes it possible to provide information according to the level of expertise.

[0059] The cloud-based service providing unit can provide optimal services by referring to the user's past usage history. For example, it can provide related services based on the services the user has used in the past. It can also provide related services with priority based on the user's past usage history. It can also provide optimal services based on the content the user has used in the past. This makes it possible to provide optimal services based on the user's past usage history.

[0060] The natural language processing unit can select appropriate expressions based on the user's cultural background. For example, if the user is Japanese, honorific and polite expressions can be used. If the user is American, casual expressions can be used. Furthermore, if the user is French, expressions that take French culture into consideration can be used. This makes it possible to use appropriate expressions according to cultural background.

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

[0062] Step 1: The natural language processing unit uses natural language processing technology to handle multiple languages. For example, it analyzes sentences using morphological analysis, analyzes sentence structure using grammatical analysis, and understands the meaning of sentences using semantic analysis. Step 2: The database reference section refers to a pre-built database and provides specialized information, such as medical information, legal information, and technical information. Step 3: The cloud-based service provider operates the entire system using cloud-based services, such as SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service).

[0063] (Example 2) A system according to an embodiment of the present invention utilizes AI technology to address labor shortages while minimizing opportunity losses in the restaurant and tourism industries. This system uses AI to provide multilingual support in situations requiring specialized expertise, ensuring smooth communication with customers. Furthermore, its low-cost implementation aims to expand the scope of its adoption. For example, in the restaurant and tourism industries, AI supports communication with customers through multilingual support. For example, when a foreign tourist visits, AI can respond in their native language, providing orders and tourist information. This enables smooth communication across language barriers. Next, AI also provides support in situations requiring specialized expertise. For example, in restaurants, AI can provide menu explanations and allergy information, and in tourist destinations, AI can provide explanations of history and culture, providing specialized knowledge. This enables the provision of high-quality service to customers. Furthermore, AI can be implemented at low cost, making it easy for even small and medium-sized restaurants and tourism businesses to adopt. This is expected to expand adoption and promote growth throughout the industry. This system addresses labor shortages while minimizing opportunity losses in the restaurant and tourism industries. This allows the system to address labor shortages while minimizing opportunity losses in the restaurant and tourism industries. For example, AI support during busy periods can reduce the burden on staff and improve customer satisfaction. In addition, AI's multilingual support can accommodate the increasing number of foreign tourists. In this way, utilizing AI technology can solve issues in the restaurant and tourism industries and aim for growth for the entire industry.

[0064] A system according to an embodiment includes a natural language processing unit, a database reference unit, and a cloud-based service providing unit. The natural language processing unit supports multiple languages ​​using natural language processing technology. For example, the natural language processing unit can analyze sentences using morphological analysis. The natural language processing unit can also analyze sentence structure using grammatical analysis. The natural language processing unit can also understand the meaning of sentences using semantic analysis. The database reference unit references a pre-built database to provide specialized information. For example, the database reference unit can provide medical information. The database reference unit can also provide legal information. The database reference unit can also provide technical information. The cloud-based service providing unit uses cloud-based services to reduce initial investment. For example, the cloud-based service providing unit can use Software as a Service (SaaS). The cloud-based service providing unit can also use Platform as a Service (PaaS). The cloud-based service providing unit can also use Infrastructure as a Service (IaaS). This enables the system according to an embodiment to perform natural language processing, provide specialized information, and provide cloud-based services.

[0065] The natural language processing unit can support multiple languages ​​using natural language processing technology. The natural language processing unit can analyze sentences using, for example, morphological analysis. The natural language processing unit can also analyze the structure of a sentence using grammatical analysis. The natural language processing unit can also understand the meaning of a sentence using semantic analysis. This enables multilingual support. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI (text generation AI or multimodal generation AI), or may be performed without using a generation AI. For example, the natural language processing unit can provide multilingual text data as input to a generation AI, which can then analyze the text data and translate it into the corresponding language.

[0066] The database reference unit can refer to a pre-constructed database and provide specialized information. The database reference unit can provide, for example, medical information. The database reference unit can also provide legal information. The database reference unit can also provide technical information. This makes it possible to provide specialized information. Some or all of the above-described processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide specialized information as input to the generation AI, which can then analyze the information and extract and provide the necessary information.

[0067] The cloud-based service providing unit can reduce initial investment by using a cloud-based service. The cloud-based service providing unit can use, for example, SaaS (Software as a Service). The cloud-based service providing unit can also use PaaS (Platform as a Service). The cloud-based service providing unit can also use IaaS (Infrastructure as a Service). This makes it possible to reduce initial investment. Some or all of the above-mentioned processing in the cloud-based service providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide configuration information for the cloud-based service as input to the generation AI, which can then analyze the information and perform optimal configuration.

[0068] The natural language processing unit can provide a specific scenario of interactions when ordering or tourist information. The natural language processing unit can provide, for example, the flow of interactions when ordering. The natural language processing unit can also provide details of tourist information. The natural language processing unit can also provide the content of a specific scenario. This makes it possible to provide a specific scenario. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide a scenario of interactions when ordering or tourist information as input to the generation AI, and the generation AI can analyze the scenario and provide an optimal scenario.

[0069] The natural language processing unit can ensure the accuracy or reliability of the translation and provide a method for dealing with mistranslations when they occur. The natural language processing unit can, for example, provide criteria for evaluating the accuracy of the translation. The natural language processing unit can also provide criteria for evaluating reliability. The natural language processing unit can also provide a method for dealing with mistranslations when they occur. This improves the accuracy and reliability of the translation and makes it possible to deal with mistranslations. Some or all of the above-mentioned processing in the natural language processing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the natural language processing unit can provide evaluation criteria for the accuracy and reliability of the translation to the generation AI as input, and the generation AI can analyze the criteria and make an optimal evaluation.

[0070] The natural language processing unit can estimate the user's emotions and adjust the natural language processing response content based on the estimated user emotions. For example, if the user is frustrated, the natural language processing unit can have the AI ​​respond using polite language and make suggestions to solve the problem. If the user is excited, the natural language processing unit can also respond in a calm tone and provide content to calm the user. If the user is relaxed, the natural language processing unit can also respond in a friendly tone and engage in a friendly conversation. This enables a response that is appropriate for the user's emotions. Emotion estimation is achieved 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-mentioned processing in the natural language processing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the natural language processing unit can provide the user's emotion data as input to the generation AI, which can then analyze the data and generate an optimal response content.

[0071] During natural language processing, the natural language processing unit can generate an optimal response by referring to the user's past dialogue history. For example, the natural language processing unit provides relevant information based on questions the user has previously asked. The natural language processing unit can also provide recommended content based on the user's past favorite menus or tourist spots. The natural language processing unit can also adjust the response content to avoid points that the user has previously expressed dissatisfaction with. This makes it possible to provide an optimal response based on the past dialogue history. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's past dialogue history as input to the generation AI, which can then analyze the history and generate an optimal response.

[0072] During natural language processing, the natural language processing unit can adjust the timing of a response based on the user's speaking speed or tone. For example, if the user speaks quickly, the natural language processing unit allows the AI ​​to respond quickly and match the tempo. Furthermore, if the user speaks slowly, the natural language processing unit can also allow the AI ​​to respond slowly and provide a calm conversation. Furthermore, if the user speaks in an emotional tone, the natural language processing unit can also allow the AI ​​to respond at an appropriate time and empathize with the user's emotions. This enables a response that is appropriate for the user's speaking speed and tone. Some or all of the above-described processing in the natural language processing unit may be performed using, or without, a generation AI. For example, the natural language processing unit can provide the generation AI with data on the user's speaking speed and tone as input, and the generation AI can analyze the data and generate the optimal response timing.

[0073] During natural language processing, the natural language processing unit can select appropriate expressions based on the user's cultural background. For example, if the user is Japanese, the natural language processing unit uses honorific and polite expressions. Furthermore, if the user is American, the natural language processing unit can also use casual expressions. Furthermore, if the user is French, the natural language processing unit can also use expressions that take French culture into consideration. This makes it possible to use appropriate expressions according to the cultural background. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide data on the user's cultural background as input to the generation AI, which can then analyze the data and generate optimal expressions.

[0074] The natural language processing unit can estimate the user's emotions and prioritize responses based on the estimated user emotions. For example, if the user has an urgent question, the natural language processing unit prioritizes the AI's response. Furthermore, if the user is relaxed, the natural language processing unit can also enable the AI ​​to respond at a normal response speed. Furthermore, if the user is dissatisfied, the natural language processing unit can also enable the AI ​​to quickly respond to resolve the problem. This enables the priority of responses to be determined 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 natural language processing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the natural language processing unit can provide the user's emotion data as input to the generation AI, which then analyzes the data and prioritizes optimal responses.

[0075] During natural language processing, the natural language processing unit can provide region-specific information based on the user's geographical location information. For example, if the user is in Tokyo, the natural language processing unit can provide information about tourist spots and restaurants in Tokyo. Furthermore, if the user is in New York, the natural language processing unit can provide information about tourist spots and restaurants in New York. Furthermore, if the user is in Paris, the natural language processing unit can provide information about tourist spots and restaurants in Paris. This makes it possible to provide region-specific information based on geographical location information. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific information.

[0076] During natural language processing, the natural language processing unit can analyze a user's social media activities and provide related information. For example, the natural language processing unit can provide information about places where the user has checked in on social media. The natural language processing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The natural language processing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on social media activities. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide data on the user's social media activities as input to the generation AI, which can then analyze the data and provide optimal related information.

[0077] During natural language processing, the natural language processing unit can customize the response content by reflecting the user's past feedback. For example, the natural language processing unit provides recommended content based on the user's past favorite menus or tourist spots. The natural language processing unit can also adjust the response content to avoid points that the user has previously expressed dissatisfaction with. The natural language processing unit can also provide more appropriate information based on the user's past feedback. This makes it possible to customize the response content based on past feedback. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the natural language processing unit can provide the user's past feedback as input to the generation AI, which can then analyze the feedback and generate optimal response content.

[0078] The database reference unit can estimate the user's emotions and determine the priority of data to be referenced based on the estimated user emotions. For example, if the user has an urgent question, the database reference unit can prioritize referencing related data. Furthermore, if the user is relaxed, the database reference unit can also prioritize referencing data aimed at solving the problem if the user is dissatisfied. This makes it possible to determine the priority of data reference 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 database reference unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the database reference unit can provide the user's emotion data as input to the generation AI, which can then analyze the data and determine the optimal data reference priority.

[0079] When referencing the database, the database reference unit can provide optimal information by referring to the user's past search history. The database reference unit can provide relevant information based on, for example, menus or tourist spots that the user has previously searched for. The database reference unit can also prioritize providing relevant information from the user's past search history. The database reference unit can also provide optimal information based on the content of the user's past searches. This makes it possible to provide optimal information based on the past search history. Some or all of the above-mentioned processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide the user's past search history as input to the generation AI, which can then analyze the history and provide optimal information.

[0080] When referencing the database, the database reference unit can prioritize providing highly relevant information based on the user's current situation. The database reference unit can provide relevant information based on, for example, the user's current location. The database reference unit can also provide relevant information based on the user's current time zone. The database reference unit can also provide optimal information based on the user's current situation. This makes it possible to provide highly relevant information based on the current situation. Some or all of the above-mentioned processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide data on the user's current situation as input to the generation AI, which can then analyze the data and provide optimal relevant information.

[0081] When referencing the database, the database reference unit can adjust the level of detail of the information according to the user's level of expertise. For example, if the user has specialized knowledge, the database reference unit provides detailed information. Furthermore, if the user has general knowledge, the database reference unit can also provide basic information. Furthermore, if the user is a beginner, the database reference unit can also provide simple information. This makes it possible to provide information according to the level of expertise. Some or all of the above-mentioned processing in the database reference unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database reference unit can provide data on the user's level of expertise as input to the generation AI, which can then analyze the data and adjust the optimal level of detail of the information.

[0082] The database reference unit can estimate the user's emotions and adjust the display method of the referenced data based on the estimated user emotions. For example, if the user is nervous, the database reference unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the database reference unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the database reference unit can provide a display method that focuses on the main points. This enables the data display method to be adjusted 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 database reference unit can be performed using, for example, the generation AI, or without the generation AI. For example, the database reference unit can provide the user's emotion data as input to the generation AI, which can then analyze the data and generate an optimal data display method.

[0083] When referencing the database, the database reference unit can provide region-specific information based on the user's geographical location information. For example, if the user is in Tokyo, the database reference unit can provide information on tourist spots and restaurants in Tokyo. Furthermore, if the user is in New York, the database reference unit can provide information on tourist spots and restaurants in New York. Furthermore, if the user is in Paris, the database reference unit can provide information on tourist spots and restaurants in Paris. This enables the provision of region-specific information based on geographical location information. Some or all of the above-described processing in the database reference unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the database reference unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific information.

[0084] When referencing the database, the database reference unit can analyze the user's social media activity and provide related information. For example, the database reference unit can provide information about places where the user has checked in on social media. The database reference unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The database reference unit can also provide information about related places and events by referring to the activities of the user's friends on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-described processing in the database reference unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the database reference unit can provide data on the user's social media activity as input to the generation AI, which can then analyze the data and provide optimal related information.

[0085] When referencing the database, the database reference unit can customize the information provision method by reflecting the user's past feedback. The database reference unit provides recommended content based on, for example, the user's previously preferred menu items or tourist spots. The database reference unit can also adjust the information provision method to avoid points that the user has previously expressed dissatisfaction with. The database reference unit can also provide more appropriate information based on the user's past feedback. This makes it possible to customize the information provision method based on past feedback. Some or all of the above-mentioned processing in the database reference unit may be performed using, or without, a generation AI. For example, the database reference unit can provide the user's past feedback as input to the generation AI, which can then analyze the feedback and generate an optimal information provision method.

[0086] The cloud-based service providing unit can estimate a user's emotions and adjust a service provision method based on the estimated user's emotions. For example, if the user is nervous, the cloud-based service providing unit can provide a simple, highly visible service provision method. Furthermore, if the user is relaxed, the cloud-based service providing unit can provide a service provision method that includes detailed information. Furthermore, if the user is in a hurry, the cloud-based service providing unit can provide a service provision method that focuses on the main points. This enables the service provision method to be adjusted 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 cloud-based service providing unit can be performed using, for example, the generation AI. For example, the cloud-based service providing unit can provide user emotion data as input to the generation AI, which can then analyze the data and generate an optimal service provision method.

[0087] When providing a service, the cloud-based service providing unit can provide the optimal service by referring to the user's past usage history. For example, the cloud-based service providing unit can provide a related service based on services the user has used in the past. The cloud-based service providing unit can also prioritize related services based on the user's past usage history. The cloud-based service providing unit can also provide the optimal service based on the content the user has used in the past. This makes it possible to provide the optimal service based on the past usage history. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, or without, a generation AI. For example, the cloud-based service providing unit can provide the user's past usage history as input to the generation AI, which can then analyze the history and provide the optimal service.

[0088] The cloud-based service providing unit can customize the content of the service based on the user's current situation when providing the service. The cloud-based service providing unit can, for example, provide a relevant service based on the user's current location. The cloud-based service providing unit can also provide a relevant service based on the user's current time zone. The cloud-based service providing unit can also provide an optimal service based on the user's current situation. This enables customization of the service content based on the current situation. Some or all of the above-described processing in the cloud-based service providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide data on the user's current situation as input to the generation AI, which can then analyze the data and generate optimal service content.

[0089] The cloud-based service providing unit can improve the quality of the service by reflecting user feedback when providing the service. For example, the cloud-based service providing unit can improve the content of the service based on feedback previously provided by the user. The cloud-based service providing unit can also reflect user feedback in real time to immediately improve the quality of the service. The cloud-based service providing unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to improve the quality of the service based on the feedback. Some or all of the above-mentioned processing in the cloud-based service providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cloud-based service providing unit can provide user feedback as input to the generation AI, which can then analyze the feedback and generate optimal service improvement measures.

[0090] The cloud-based service providing unit can estimate a user's emotions and determine the priority of service provision based on the estimated user emotions. For example, the cloud-based service providing unit can prioritize services when the user has an urgent request. The cloud-based service providing unit can also provide services with normal priority when the user is relaxed. The cloud-based service providing unit can also provide services aimed at quickly resolving the problem when the user is dissatisfied. This enables the priority of service provision to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 cloud-based service providing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the cloud-based service providing unit can provide user emotion data as input to the generation AI, which can then analyze the data and determine the optimal priority of service provision.

[0091] The cloud-based service providing unit can provide region-specific services based on the user's geographical location information when providing services. For example, if the user is in Tokyo, the cloud-based service providing unit can provide information on tourist attractions and restaurants in Tokyo. Furthermore, if the user is in New York, the cloud-based service providing unit can provide information on tourist attractions and restaurants in New York. Furthermore, if the user is in Paris, the cloud-based service providing unit can provide information on tourist attractions and restaurants in Paris. This enables the provision of region-specific services based on geographical location information. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cloud-based service providing unit can provide the user's geographical location information as input to the generation AI, which can then analyze the information and provide optimal region-specific services.

[0092] The cloud-based service providing unit can analyze a user's social media activities and provide related services when providing services. For example, the cloud-based service providing unit can provide services related to locations where the user has checked in on social media. The cloud-based service providing unit can also analyze the content of the user's social media posts and provide related services. The cloud-based service providing unit can also refer to the activities of the user's friends on social media. This makes it possible to provide related services based on social media activities. Some or all of the above-described processing in the cloud-based service providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the cloud-based service providing unit can provide data on the user's social media activities as input to the generation AI, which can then analyze the data and provide optimal related services.

[0093] The cloud-based service providing unit can customize the service provision method by reflecting the user's past feedback when providing the service. The cloud-based service providing unit can improve the service provision method, for example, based on feedback provided by the user in the past. The cloud-based service providing unit can also reflect the user's feedback in real time and instantly adjust the service provision method. The cloud-based service providing unit can also analyze the user's feedback, identify common problems, and improve the provision method. This enables customization of the service provision method based on the past feedback. Some or all of the above-described processing in the cloud-based service providing unit may be performed using, or without, a generation AI. For example, the cloud-based service providing unit can provide the user's feedback as input to the generation AI, which can then analyze the feedback and generate an optimal service provision method. === Hard Collateral 1-1 === Each of the multiple elements, including the natural language processing unit, database reference unit, and cloud-based service providing unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the natural language processing unit is realized in either the smart device 14 or the data processing device 12. For example, the processor 46 of the smart device 14 performs natural language processing and can perform morphological analysis, grammatical analysis, and semantic analysis. The database reference unit is realized by the specific processing unit 290 of the data processing device 12 and provides specialized information by referring to a pre-constructed database 24. The cloud-based service providing unit is realized by the data processing device 12 using SaaS, PaaS, or IaaS on the cloud. === Hard Collateral 1-2 === Each of the multiple elements, including the natural language processing unit, database reference unit, and cloud-based service providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the natural language processing unit is realized by either the smart glasses 214 or the data processing device 12. For example, the processor 46 of the smart glasses 214 performs natural language processing and can perform morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the database reference unit is realized by the specific processing unit 290 of the data processing device 12 and provides specialized information by referring to a pre-constructed database 24. The cloud-based service providing unit is realized by the data processing device 12 using SaaS, PaaS, or IaaS on the cloud. === Hard Collateral 1-3 === Each of the multiple elements including the natural language processing unit, database reference unit, and cloud-based service providing unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the natural language processing unit is realized in either the headset type terminal 314 or the data processing device 12. For example, the processor 46 of the headset type terminal 314 performs natural language processing and can execute morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the database reference unit is realized by the specific processing unit 290 of the data processing device 12 and provides specialized information by referring to a pre-constructed database 24. The cloud-based service providing unit is realized by the data processing device 12 using SaaS, PaaS, or IaaS on the cloud. === Hard Collateral 1-4 === Each of the multiple elements including the natural language processing unit, database reference unit, and cloud-based service providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the natural language processing unit is realized by either the robot 414 or the data processing device 12. For example, the processor 46 of the robot 414 performs natural language processing and can execute morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the database reference unit is realized by the specific processing unit 290 of the data processing device 12 and provides specialized information by referring to a pre-constructed database 24. The cloud-based service providing unit is realized by the data processing device 12 using SaaS, PaaS, or IaaS on the cloud.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The natural language processor can analyze the content of a user's speech in real time and infer the user's intention. For example, if a user uses an ambiguous expression, the natural language processor analyzes the context and provides the most appropriate interpretation. It can also provide additional relevant information when a user asks a question. Furthermore, if a user asks multiple questions at once, it can respond to each question individually. This makes it possible to respond based on the user's intention.

[0096] The database reference unit can automatically recommend related information based on the user's search history. For example, it can provide event information related to tourist spots that the user has previously searched for. It can also provide information about new menu items at restaurants that the user has previously searched for. It can also provide related news articles based on information that the user has previously searched for. This makes it possible to provide information based on the user's search history.

[0097] The cloud-based service provider can dynamically change the way services are provided depending on the user's usage. For example, it can prioritize the display of functions that the user uses frequently. It can also automatically optimize the services that the user uses during specific time periods. It can also provide customized services based on the user's usage patterns. This makes it possible to provide services that are tailored to the user's usage.

[0098] The natural language processing unit can estimate the user's emotions and adjust the tone of the response based on the estimated emotions. For example, if the user is angry, the AI ​​can respond in a calm and collected tone and offer suggestions to solve the problem. If the user is sad, the AI ​​can respond in a gentle tone and offer comforting content. Furthermore, if the user is happy, the AI ​​can respond in a bright tone and show empathy. This makes it possible to respond according to the user's emotions.

[0099] The database reference unit can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to display information according to the user's emotions.

[0100] The cloud-based service provider can estimate the user's emotions and adjust the service delivery method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible service delivery method can be provided. If the user is relaxed, a service delivery method including detailed information can be provided. Furthermore, if the user is in a hurry, a service delivery method that focuses on the main points can be provided. This makes it possible to provide services according to the user's emotions.

[0101] The natural language processing unit can adjust the timing of responses based on the user's speaking speed and tone. For example, if the user speaks quickly, the AI ​​will respond quickly and match the tempo. Also, if the user speaks slowly, the AI ​​can respond slowly and provide a calm conversation. Furthermore, if the user speaks in an emotional tone, the AI ​​can respond at an appropriate time and be considerate of the user's emotions. This makes it possible to respond according to the user's speaking speed and tone.

[0102] The database reference unit can adjust the level of detail of information according to the user's level of expertise. For example, if the user has specialized knowledge, detailed information can be provided. If the user has general knowledge, basic information can be provided. Furthermore, if the user is a beginner, simple information can be provided. This makes it possible to provide information according to the level of expertise.

[0103] The cloud-based service providing unit can provide optimal services by referring to the user's past usage history. For example, it can provide related services based on the services the user has used in the past. It can also provide related services with priority based on the user's past usage history. It can also provide optimal services based on the content the user has used in the past. This makes it possible to provide optimal services based on the user's past usage history.

[0104] The natural language processing unit can select appropriate expressions based on the user's cultural background. For example, if the user is Japanese, honorific and polite expressions can be used. If the user is American, casual expressions can be used. Furthermore, if the user is French, expressions that take French culture into consideration can be used. This makes it possible to use appropriate expressions according to cultural background.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The natural language processing unit uses natural language processing technology to handle multiple languages. For example, it analyzes sentences using morphological analysis, analyzes sentence structure using grammatical analysis, and understands the meaning of sentences using semantic analysis. Step 2: The database reference section refers to a pre-built database and provides specialized information, such as medical information, legal information, and technical information. Step 3: The cloud-based service provider operates the entire system using cloud-based services, such as SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 natural language processing unit; a database reference unit that provides specialized information based on the information analyzed by the natural language processing unit; a cloud-based service providing unit that operates the entire system based on the information provided by the database reference unit. A system characterized by:

2. The natural language processing unit Supports multiple languages ​​using natural language processing technology 2. The system of claim 1.

3. The database reference unit Refer to a pre-built database to provide expert information 2. The system of claim 1.

4. The cloud-based service provider Use cloud-based services to reduce initial investment 2. The system of claim 1.

5. The natural language processing unit Providing specific scenarios for ordering or tourist information 2. The system of claim 1.

6. The natural language processing unit Ensure accuracy or reliability of translations and provide solutions in case of mistranslations 2. The system of claim 1.

7. The natural language processing unit Inferring user emotions and adjusting natural language processing responses based on the estimated user emotions 2. The system of claim 1.

8. The natural language processing unit When processing natural language, generate optimal responses by referring to the user's past dialogue history 2. The system of claim 1.

Images