system

Abstract

The system according to the embodiment aims to provide services that meet the needs of users by utilizing voice input. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives voice input. The analysis unit analyzes the voice data received by the reception unit. The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The provision unit provides the user with the service identified by the identification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized voice input to provide services that meet the needs of users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide services that meet the needs of users by utilizing voice input. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives voice input. The analysis unit analyzes the voice data received by the reception unit. The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The provision unit provides the user with the service identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide services according to the needs of the user by using voice input. [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 service provision system using voice input according to an embodiment of the present invention is a system that uses voice input from a device to recommend various services and meet customer needs. In this system, a user inputs questions or requests into a device by voice, and AI analyzes the voice to understand the user's intent and recommends optimal services based on the user's needs. For example, if a user voice-inputs, "Tell me about nearby restaurants," the AI ​​identifies the user's current location and recommends nearby restaurants. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?" the AI ​​considers the user's past travel history and preferences to suggest optimal travel destinations. This system allows users to easily use voice input to receive services tailored to their needs. Furthermore, privacy is also protected in the handling of voice data and the use of user location information. For example, the voice data is encrypted, and the user's location information is anonymized. This protects the user's privacy. This allows the service provision system using voice input to provide services tailored to the user's needs.

[0029] A service provision system using voice input according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives questions or requests input by voice into a device from a user. For example, if a user inputs "Tell me about nearby restaurants," the reception unit can accept the voice. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?", the reception unit can accept the voice. The analysis unit analyzes the voice data received by the reception unit. The analysis unit converts the voice data into text data and understands the user's intention. For example, the analysis unit can convert the voice data into text data using voice recognition technology and analyze the user's intention. The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The identification unit identifies optimal services taking into account the user's past history and preferences. For example, the system can identify recommended travel destinations for the user's next vacation based on the user's past travel history and preferences. The provision unit provides the user with the services identified by the identification unit. The provision unit displays the identified services on the user's device. For example, the system can display information about nearby restaurants on the user's device. As a result, the service providing system using voice input according to the embodiment can provide services that meet the needs of the user.

[0030] The reception unit can accept specific questions or requests input by voice into the device. Specific questions or requests include, but are not limited to, FAQ-style questions and customer support requests. For example, if a user inputs "Tell me about nearby restaurants," the reception unit can accept the voice. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?" the reception unit can accept the voice. In this way, questions and requests input by the user by voice into the device can be accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the voice-input data into AI, which can analyze the voice data to identify the question or request.

[0031] The analysis unit can convert voice data into text data and understand the user's intent. The analysis unit can, for example, use voice recognition technology to convert voice data into text data. For example, the analysis unit can convert voice data into text data using a voice recognition engine. After converting voice data into text data, the analysis unit can also use natural language processing technology to understand the user's intent. For example, the analysis unit can analyze the text data and apply an algorithm to identify the user's intent. This allows the voice data to be converted into text data and the user's intent to be understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input voice data into AI, which then converts the voice data into text data and analyzes the user's intent.

[0032] The identification unit can identify an appropriate service by taking into account the user's past history and preferences. For example, the identification unit can identify recommended travel destinations for the user's next vacation by taking into account the user's past travel history and preferences. For example, the identification unit can retrieve the user's past travel history from a database and analyze the user's preferences. The identification unit can also identify the optimal service based on the user's past purchase history and browsing history. For example, the identification unit can analyze the user's past purchase history and identify products that the user prefers. This makes it possible to identify the optimal service by taking into account the user's past history and preferences. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past history data into AI, which can analyze the data to identify the optimal service.

[0033] The providing unit can display the identified service on the user's device. The providing unit, for example, displays the identified service on the user's device. For example, the providing unit can display information about nearby restaurants on the user's device. The providing unit can also display recommended travel destinations for the user's next vacation on the user's device. For example, the providing unit displays information about the travel destination on the user's device so that the user can check detailed information. This allows the identified service to be displayed on the user's device. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the identified service to AI, which analyzes the information and displays it on the user's device.

[0034] The system can protect privacy when handling voice data and when using user location information. For example, the system encrypts the voice data and anonymizes the user location information. For example, the system can use encryption technology such as AES (Advanced Encryption Standard) to encrypt the voice data. The system can also use technology that blurs location information within a specific range to anonymize the user location information. This allows privacy protection to be taken into consideration when handling voice data and when using user location information. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can input voice data into AI, which then encrypts the data and anonymizes the location information.

[0035] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit can prioritize and suggest a voice input method that the user has frequently used in the past. For example, the reception unit can acquire the user's past voice input history from a database and identify a frequently used voice input method. The reception unit can also select a reception method suitable for a specific time period from the user's past voice input history. For example, the reception unit can analyze the user's past voice input history and suggest the optimal reception method for a specific time period. Furthermore, the reception unit can analyze the user's past voice input history and suggest the most efficient reception method. For example, the reception unit can identify an efficient reception method based on the user's past voice input history. This makes it possible to analyze the user's past voice input history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice input history into AI, which can analyze the data and select the optimal reception method.

[0036] When receiving a voice input, the reception unit can filter the voice input based on the user's current situation and areas of interest. The reception unit, for example, prioritizes receiving a voice input that is highly relevant to the user's current situation. For example, the reception unit can prioritize receiving a voice input related to nearby services based on the user's current location information. The reception unit can also filter specific voice inputs and provide appropriate services based on the user's areas of interest. For example, the reception unit can prioritize receiving a voice input related to the user's areas of interest based on the user's past search history and browsing history. Furthermore, the reception unit can optimize the reception of the voice input by taking the user's current situation and areas of interest into consideration. For example, the reception unit can analyze the user's current situation and areas of interest in real time and receive the optimal voice input. This allows the voice input to be filtered based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI, which can then analyze the data and filter the voice input.

[0037] When receiving a voice input, the reception unit can prioritize receiving a highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving a voice input related to that area. For example, the reception unit can acquire the user's current location from GPS data and prioritize receiving a voice input related to that area. The reception unit can also prioritize receiving a voice input related to nearby services based on the user's current location. For example, the reception unit can prioritize receiving a voice input related to nearby restaurants and stores based on the user's current location. Furthermore, the reception unit can prioritize receiving an optimal voice input in consideration of the user's geographical location information. For example, the reception unit can analyze the user's geographical location information in real time and prioritize receiving a highly relevant voice input. This makes it possible to prioritize receiving a highly relevant voice input in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information into the AI, which can then analyze the data and prioritize voice input.

[0038] The reception unit can analyze the user's social media activity when receiving a voice input and receive related input. The reception unit, for example, preferentially receives voice input related to topics of interest from the user's social media activity. For example, the reception unit can analyze the content of the user's social media posts and preferentially receive voice input related to topics of interest. The reception unit can also preferentially receive optimal voice input in consideration of the user's social media activity. For example, the reception unit can preferentially receive related voice input based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and receive related voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and preferentially receive voice input.

[0039] When analyzing voice data, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit performs a detailed analysis on input content with high importance. For example, the analysis unit can analyze the user's voice data and perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on input content with low importance. For example, the analysis unit can analyze the user's voice data and perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the level of detail of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data to AI, which analyzes the data and performs an analysis according to the importance.

[0040] When analyzing voice data, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specific analysis algorithm to input content related to restaurants. For example, the analysis unit can analyze voice data related to restaurants and apply a specific analysis algorithm. The analysis unit can also apply a different analysis algorithm to input content related to travel. For example, the analysis unit can analyze voice data related to travel and apply a different analysis algorithm. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the category of the input content. For example, the analysis unit can analyze the user's voice data in real time and apply an optimal analysis algorithm depending on the category. This makes it possible to apply an optimal analysis algorithm depending on the category of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data into AI, which analyzes the data and applies an analysis algorithm depending on the category.

[0041] When analyzing voice data, the analysis unit can determine the analysis priority based on the submission date of the input content. The analysis unit, for example, prioritizes analysis of recently submitted input content. For example, the analysis unit can prioritize analysis of recently submitted content based on the submission date and time of the user's voice data. The analysis unit can also lower the priority of older submitted input content for analysis. For example, the analysis unit can lower the priority of older content based on the submission date and time of the user's voice data. Furthermore, the analysis unit can adjust the analysis priority based on the submission date and time of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the analysis priority according to the submission date and time. This makes it possible to determine the analysis priority based on the submission date of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's voice data into AI, which can analyze the data and determine the priority based on the submission date.

[0042] When analyzing voice data, the analysis unit can adjust the order of analysis based on the relevance of the input content. The analysis unit, for example, prioritizes analysis of highly relevant input content. For example, the analysis unit can analyze the user's voice data and prioritize analysis of highly relevant content. The analysis unit can also postpone the order of analysis of less relevant input content. For example, the analysis unit can analyze the user's voice data and postpone the order of analysis of less relevant content. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data to AI, and the AI ​​can analyze the data and determine the order of analysis based on the relevance.

[0043] When identifying a service, the identification unit can analyze the user's past history and select the optimal service. The identification unit selects the optimal service, for example, based on services the user has used in the past. For example, the identification unit can obtain the user's past usage history from a database and select the optimal service based on the services used in the past. The identification unit can also select a service that matches the user's preferences from the user's past history. For example, the identification unit can select a service that matches the user's preferences based on the user's past purchase history and browsing history. The identification unit can also analyze the user's past history and select the most suitable service. For example, the identification unit can analyze the user's past behavioral data and select the most suitable service. This makes it possible to analyze the user's past history and select the optimal service. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past history data into AI, which can analyze the data and select the optimal service.

[0044] When identifying a service, the identification unit can customize the specific means based on the user's current situation. The identification unit, for example, customizes the optimal service according to the user's current situation. For example, the identification unit can customize the optimal service based on the user's current location information and recent behavior history. The identification unit can also adjust the specific means taking the user's current situation into consideration. For example, the identification unit can analyze the user's current situation in real time and adjust the specific means. Furthermore, the identification unit can optimize the specific means based on the user's current situation. For example, the identification unit can select the optimal service provision means based on the user's current situation. This allows the specific means to be customized based on the user's current situation. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's current situation data into AI, which can analyze the data and customize the specific means.

[0045] When identifying a service, the identification unit can select an optimal service by taking into account the user's geographical location information. For example, if the user is in a specific area, the identification unit selects a service related to that area. For example, the identification unit can obtain the user's current location from GPS data and select a service related to that area. The identification unit can also select nearby services based on the user's current location. For example, the identification unit can select services related to nearby restaurants and stores based on the user's current location. Furthermore, the identification unit can select an optimal service by taking into account the user's geographical location information. For example, the identification unit can analyze the user's geographical location information in real time and select an optimal service. This makes it possible to select an optimal service by taking into account the user's geographical location information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's geographical location information into AI, which can analyze the data and select an optimal service.

[0046] When identifying a service, the identification unit can analyze the user's social media activity and suggest a specific means. The identification unit, for example, identifies a service of interest from the user's social media activity. For example, the identification unit can analyze the content of the user's social media posts to identify a service of interest. The identification unit can also identify the optimal service by taking the user's social media activity into consideration. For example, the identification unit can identify related services based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and suggest a specific means. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the user's social media activity into AI, which can analyze the data to identify the optimal service.

[0047] When providing a service, the providing unit can analyze the user's past usage history and select the optimal provision method. The providing unit selects the optimal provision method, for example, based on services the user has used in the past. For example, the providing unit can obtain the user's past usage history from a database and select the optimal provision method based on the services used in the past. The providing unit can also select a provision method that suits the user's preferences based on the user's past usage history. For example, the providing unit can select a provision method that suits the user's preferences based on the user's past purchase history and browsing history. Furthermore, the providing unit can analyze the user's past usage history and select the most appropriate provision method. For example, the providing unit can analyze the user's past behavioral data and select the most appropriate provision method. This makes it possible to analyze the user's past usage history and select the optimal provision method. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past usage history data into AI, which can analyze the data and select the optimal provision method.

[0048] The providing unit can customize the means of providing the service based on the user's current situation when providing the service. The providing unit customizes the optimal means of providing the service according to the user's current situation, for example. For example, the providing unit can customize the optimal means of providing the service based on the user's current location information and recent behavioral history. The providing unit can also adjust the means of providing the service taking the user's current situation into consideration. For example, the providing unit can analyze the user's current situation in real time and adjust the means of providing the service. Furthermore, the providing unit can optimize the means of providing the service based on the user's current situation. For example, the providing unit can select the optimal means of providing the service based on the user's current situation. This allows the means of providing the service to be customized based on the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into AI, which can analyze the data and customize the means of providing the service.

[0049] When providing a service, the providing unit can select an optimal service provision method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a service related to that area. For example, the providing unit can obtain the user's current location from GPS data and provide a service related to the area. The providing unit can also provide nearby services based on the user's current location. For example, the providing unit can provide services related to nearby restaurants and stores based on the user's current location. Furthermore, the providing unit can select an optimal service provision method by taking into account the user's geographical location information. For example, the providing unit can analyze the user's geographical location information in real time and select an optimal service provision method. This makes it possible to select an optimal service provision method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can analyze the data and select an optimal service provision method.

[0050] When providing a service, the provision unit can analyze the user's social media activity and suggest a means of provision. The provision unit, for example, provides a service of interest based on the user's social media activity. For example, the provision unit can analyze the content of the user's social media posts and provide a service of interest. The provision unit can also suggest an optimal means of providing the service by taking the user's social media activity into consideration. For example, the provision unit can provide a related service based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and suggest a means of provision. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal means of providing the service.

[0051] During privacy protection, the privacy protection unit can analyze a user's past data usage history and select an optimal protection method. The privacy protection unit, for example, selects an optimal privacy protection method from the user's past data usage history. For example, the privacy protection unit can retrieve the user's past data usage history from a database and select an optimal privacy protection method. The privacy protection unit can also analyze the user's past data usage history and select a privacy protection method that suits the user's preferences. For example, the privacy protection unit can select a privacy protection method that suits the user's preferences based on the user's past data usage history. Furthermore, the privacy protection unit can select the most appropriate privacy protection method based on the user's past data usage history. For example, the privacy protection unit can analyze the user's past data usage history and select the most appropriate privacy protection method. In this way, the user's past data usage history can be analyzed and the optimal protection method can be selected. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's past data usage history into AI, which can analyze the data and select the optimal privacy protection method.

[0052] The privacy protection unit may select an optimal privacy protection method by taking into account the user's geographical location information during privacy protection. For example, if the user is in a specific area, the privacy protection unit may select a privacy protection method related to that area. For example, the privacy protection unit may obtain the user's current location from GPS data and select a privacy protection method related to the area. The privacy protection unit may also select an optimal privacy protection method based on the user's current location. For example, the privacy protection unit may select an optimal privacy protection method based on the user's current location. Furthermore, the privacy protection unit may select an optimal privacy protection method by taking into account the user's geographical location information. For example, the privacy protection unit may analyze the user's geographical location information in real time and select an optimal privacy protection method. This allows the optimal protection method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input the user's geographical location information into AI, which may analyze the data and select an optimal privacy protection 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 reception unit can provide real-time feedback to the user's voice input. For example, if the user voice-inputs, "Tell me about nearby restaurants," the reception unit can immediately return feedback such as, "I'm searching for nearby restaurants." Also, if the user asks, "What travel destinations do you recommend for my next vacation?", the reception unit can return feedback such as, "Checking your past travel history." Furthermore, if the user's voice input is unclear, the reception unit can return a request such as, "Please say it again." This allows the user to check in real time whether their voice input is being correctly received.

[0055] When analyzing voice data, the analysis unit can learn the user's past voice input patterns and improve the analysis accuracy. For example, the analysis unit can learn phrases and expressions that the user has frequently used in the past and analyze the voice data based on them. The analysis unit can also learn the user's pronunciation characteristics and improve the accuracy of voice recognition. Furthermore, the analysis unit can learn the voice input tendencies in specific situations based on the user's past voice input history and improve the analysis accuracy. In this way, the analysis unit can learn the user's past voice input patterns and improve the analysis accuracy.

[0056] When displaying the identified service on the user's device, the providing unit can customize the display method by taking into account the user's past browsing history. For example, the providing unit can prioritize the use of a display format that the user has frequently used in the past. For example, the providing unit can acquire the user's past browsing history from a database and identify the frequently used display format. The providing unit can also select a display method suitable for a specific time period from the user's past browsing history. For example, the providing unit can analyze the user's past browsing history and suggest a display method that is optimal for a specific time period. Furthermore, the providing unit can analyze the user's past browsing history and suggest the most efficient display method. For example, the providing unit can identify an efficient display method based on the user's past browsing history. This makes it possible to customize the display method by taking into account the user's past browsing history.

[0057] The reception unit can provide voice feedback in response to the user's voice input. For example, if the user voice inputs, "Tell me about nearby restaurants," the reception unit can return voice feedback such as, "I'm looking for nearby restaurants." Also, if the user asks, "What travel destinations do you recommend for my next vacation?", the reception unit can return voice feedback such as, "Checking your past travel history." Furthermore, if the user's voice input is unclear, the reception unit can return voice feedback such as, "Please say it again." This allows the user to confirm by voice whether their voice input has been correctly received.

[0058] The identification unit can customize the content of the identified service based on the user's current situation. For example, when the user is in a specific area, it can prioritize the identification of services related to that area. For example, the identification unit can acquire the user's current location from GPS data and identify services related to that area. Furthermore, when the user is in a specific time period, it can also identify services suitable for that time period. For example, the identification unit can identify the optimal service based on the user's current time period. Furthermore, when the user is in a specific situation, it can also identify a service according to that situation. For example, the identification unit can analyze the user's current situation in real time and identify the optimal service. This makes it possible to customize the content of the identified service based on the user's current situation.

[0059] When displaying the identified services on the user's device, the providing unit can customize the display content by taking into account the user's social media activity. For example, the providing unit can prioritize the display of services related to topics that the user frequently mentions on social media. For example, the providing unit can analyze the content of the user's comments on social media to identify services related to topics of interest to the user. The providing unit can also display related services based on the user's social media trends and interests. For example, the providing unit can analyze the user's social media trends and display related services. Furthermore, the providing unit can select an optimal display method by taking into account the user's social media activity. For example, the providing unit can select an optimal display method based on the user's social media activity. This makes it possible to customize the display content by taking into account the user's social media activity.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts questions or requests that the user types into the device by voice. For example, if the user types, "Tell me about nearby restaurants," the reception unit can accept that voice. Similarly, if the user asks, "What are some recommended travel destinations for my next vacation?", the reception unit can accept that voice. Step 2: The analysis unit analyzes the voice data received by the reception unit. The analysis unit converts the voice data into text data and understands the user's intent. For example, voice recognition technology can be used to convert the voice data into text data and analyze the user's intent. Step 3: The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The identification unit identifies the most suitable service taking into account the user's past history and preferences. For example, the identification unit can identify recommended travel destinations for the user's next vacation based on the user's past travel history and preferences. Step 4: The providing unit provides the service identified by the identifying unit to the user. The providing unit displays the identified service on the user's device. For example, information about nearby restaurants can be displayed on the user's device.

[0062] (Example 2) A service provision system using voice input according to an embodiment of the present invention is a system that uses voice input from a device to recommend various services and meet customer needs. In this system, a user inputs questions or requests into a device by voice, and AI analyzes the voice to understand the user's intent and recommends optimal services based on the user's needs. For example, if a user voice-inputs, "Tell me about nearby restaurants," the AI ​​identifies the user's current location and recommends nearby restaurants. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?" the AI ​​considers the user's past travel history and preferences to suggest optimal travel destinations. This system allows users to easily use voice input to receive services tailored to their needs. Furthermore, privacy is also protected in the handling of voice data and the use of user location information. For example, the voice data is encrypted, and the user's location information is anonymized. This protects the user's privacy. This allows the service provision system using voice input to provide services tailored to the user's needs.

[0063] A service provision system using voice input according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives questions or requests input by voice into a device from a user. For example, if a user inputs "Tell me about nearby restaurants," the reception unit can accept the voice. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?", the reception unit can accept the voice. The analysis unit analyzes the voice data received by the reception unit. The analysis unit converts the voice data into text data and understands the user's intention. For example, the analysis unit can convert the voice data into text data using voice recognition technology and analyze the user's intention. The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The identification unit identifies optimal services taking into account the user's past history and preferences. For example, the system can identify recommended travel destinations for the user's next vacation based on the user's past travel history and preferences. The provision unit provides the user with the services identified by the identification unit. The provision unit displays the identified services on the user's device. For example, the system can display information about nearby restaurants on the user's device. As a result, the service providing system using voice input according to the embodiment can provide services that meet the needs of the user.

[0064] The reception unit can accept specific questions or requests input by voice into the device. Specific questions or requests include, but are not limited to, FAQ-style questions and customer support requests. For example, if a user inputs "Tell me about nearby restaurants," the reception unit can accept the voice. Similarly, if a user asks, "What are some recommended travel destinations for my next vacation?" the reception unit can accept the voice. In this way, questions and requests input by the user by voice into the device can be accepted. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the voice-input data into AI, which can analyze the voice data to identify the question or request.

[0065] The analysis unit can convert voice data into text data and understand the user's intent. The analysis unit can, for example, use voice recognition technology to convert voice data into text data. For example, the analysis unit can convert voice data into text data using a voice recognition engine. After converting voice data into text data, the analysis unit can also use natural language processing technology to understand the user's intent. For example, the analysis unit can analyze the text data and apply an algorithm to identify the user's intent. This allows the voice data to be converted into text data and the user's intent to be understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input voice data into AI, which then converts the voice data into text data and analyzes the user's intent.

[0066] The identification unit can identify an appropriate service by taking into account the user's past history and preferences. For example, the identification unit can identify recommended travel destinations for the user's next vacation by taking into account the user's past travel history and preferences. For example, the identification unit can retrieve the user's past travel history from a database and analyze the user's preferences. The identification unit can also identify the optimal service based on the user's past purchase history and browsing history. For example, the identification unit can analyze the user's past purchase history and identify products that the user prefers. This makes it possible to identify the optimal service by taking into account the user's past history and preferences. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past history data into AI, which can analyze the data to identify the optimal service.

[0067] The providing unit can display the identified service on the user's device. The providing unit, for example, displays the identified service on the user's device. For example, the providing unit can display information about nearby restaurants on the user's device. The providing unit can also display recommended travel destinations for the user's next vacation on the user's device. For example, the providing unit displays information about the travel destination on the user's device so that the user can check detailed information. This allows the identified service to be displayed on the user's device. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the identified service to AI, which analyzes the information and displays it on the user's device.

[0068] The system can protect privacy when handling voice data and when using user location information. For example, the system encrypts the voice data and anonymizes the user location information. For example, the system can use encryption technology such as AES (Advanced Encryption Standard) to encrypt the voice data. The system can also use technology that blurs location information within a specific range to anonymize the user location information. This allows privacy protection to be taken into consideration when handling voice data and when using user location information. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can input voice data into AI, which then encrypts the data and anonymizes the location information.

[0069] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of voice input reception to allow the user to input in a relaxed state. For example, the reception unit can analyze the user's voice tone to estimate the stress level. Furthermore, if the user is relaxed, the reception unit can immediately accept the voice input to provide smooth operation. For example, the reception unit can analyze the user's facial expression using a camera to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of voice input reception to quickly process the request. For example, the reception unit can analyze the user's speech speed to determine whether the user is in a hurry. This allows the timing of voice input reception to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's voice data into AI, which may infer the user's emotions and adjust the reception timing.

[0070] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit can prioritize and suggest a voice input method that the user has frequently used in the past. For example, the reception unit can acquire the user's past voice input history from a database and identify a frequently used voice input method. The reception unit can also select a reception method suitable for a specific time period from the user's past voice input history. For example, the reception unit can analyze the user's past voice input history and suggest the optimal reception method for a specific time period. Furthermore, the reception unit can analyze the user's past voice input history and suggest the most efficient reception method. For example, the reception unit can identify an efficient reception method based on the user's past voice input history. This makes it possible to analyze the user's past voice input history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice input history into AI, which can analyze the data and select the optimal reception method.

[0071] When receiving a voice input, the reception unit can filter the voice input based on the user's current situation and areas of interest. The reception unit, for example, prioritizes receiving a voice input that is highly relevant to the user's current situation. For example, the reception unit can prioritize receiving a voice input related to nearby services based on the user's current location information. The reception unit can also filter specific voice inputs and provide appropriate services based on the user's areas of interest. For example, the reception unit can prioritize receiving a voice input related to the user's areas of interest based on the user's past search history and browsing history. Furthermore, the reception unit can optimize the reception of the voice input by taking the user's current situation and areas of interest into consideration. For example, the reception unit can analyze the user's current situation and areas of interest in real time and receive the optimal voice input. This allows the voice input to be filtered based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI, which can then analyze the data and filter the voice input.

[0072] The reception unit can estimate the user's emotion and determine the priority of voice inputs to be received based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can prioritize receiving voice inputs of higher importance. For example, the reception unit can analyze the user's voice tone to estimate the stress level. Furthermore, if the user is relaxed, the reception unit can equally accept all voice inputs. For example, the reception unit can analyze the user's facial expression using a camera to determine whether the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving voice inputs of higher urgency. For example, the reception unit can analyze the user's speech speed to determine whether the user is in a hurry. This allows the priority of voice inputs to be determined according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data into AI, which can then estimate emotions and determine the priority of the voice input.

[0073] When receiving a voice input, the reception unit can prioritize receiving a highly relevant input in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving a voice input related to that area. For example, the reception unit can acquire the user's current location from GPS data and prioritize receiving a voice input related to that area. The reception unit can also prioritize receiving a voice input related to nearby services based on the user's current location. For example, the reception unit can prioritize receiving a voice input related to nearby restaurants and stores based on the user's current location. Furthermore, the reception unit can prioritize receiving an optimal voice input in consideration of the user's geographical location information. For example, the reception unit can analyze the user's geographical location information in real time and prioritize receiving a highly relevant voice input. This makes it possible to prioritize receiving a highly relevant voice input in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information into the AI, which can then analyze the data and prioritize voice input.

[0074] The reception unit can analyze the user's social media activity when receiving a voice input and receive related input. The reception unit, for example, preferentially receives voice input related to topics of interest from the user's social media activity. For example, the reception unit can analyze the content of the user's social media posts and preferentially receive voice input related to topics of interest. The reception unit can also preferentially receive optimal voice input in consideration of the user's social media activity. For example, the reception unit can preferentially receive related voice input based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and receive related voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and preferentially receive voice input.

[0075] The analysis unit can estimate the user's emotion and adjust the analysis method of the voice data based on the estimated user's emotion. For example, if the user is stressed, the analysis unit applies a simplified analysis method. For example, the analysis unit can analyze the user's voice tone, estimate the stress level, and apply the simplified analysis method. Furthermore, if the user is relaxed, the analysis unit can apply a detailed analysis method. For example, the analysis unit can analyze the user's facial expression using a camera, determine whether the user is relaxed, and apply the detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can apply a rapid analysis method. For example, the analysis unit can analyze the user's speech speed, determine whether the user is in a hurry, and apply the rapid analysis method. This allows the analysis method of the voice data to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data into AI, which can then infer emotions and adjust the analysis method.

[0076] When analyzing voice data, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit performs a detailed analysis on input content with high importance. For example, the analysis unit can analyze the user's voice data and perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on input content with low importance. For example, the analysis unit can analyze the user's voice data and perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the level of detail of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data to AI, which analyzes the data and performs an analysis according to the importance.

[0077] When analyzing voice data, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specific analysis algorithm to input content related to restaurants. For example, the analysis unit can analyze voice data related to restaurants and apply a specific analysis algorithm. The analysis unit can also apply a different analysis algorithm to input content related to travel. For example, the analysis unit can analyze voice data related to travel and apply a different analysis algorithm. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the category of the input content. For example, the analysis unit can analyze the user's voice data in real time and apply an optimal analysis algorithm depending on the category. This makes it possible to apply an optimal analysis algorithm depending on the category of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data into AI, which analyzes the data and applies an analysis algorithm depending on the category.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, the analysis unit can analyze the user's voice tone to estimate the stress level and provide a simple display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, the analysis unit can analyze the user's facial expressions using a camera, determine whether the user is relaxed, and provide a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit can analyze the user's speech speed, determine whether the user is in a hurry, and provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's voice data into AI, which may infer emotions and adjust the display method of the analysis results.

[0079] When analyzing voice data, the analysis unit can determine the analysis priority based on the submission date of the input content. The analysis unit, for example, prioritizes analysis of recently submitted input content. For example, the analysis unit can prioritize analysis of recently submitted content based on the submission date and time of the user's voice data. The analysis unit can also lower the priority of older submitted input content for analysis. For example, the analysis unit can lower the priority of older content based on the submission date and time of the user's voice data. Furthermore, the analysis unit can adjust the analysis priority based on the submission date and time of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the analysis priority according to the submission date and time. This makes it possible to determine the analysis priority based on the submission date of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's voice data into AI, which can analyze the data and determine the priority based on the submission date.

[0080] When analyzing voice data, the analysis unit can adjust the order of analysis based on the relevance of the input content. The analysis unit, for example, prioritizes analysis of highly relevant input content. For example, the analysis unit can analyze the user's voice data and prioritize analysis of highly relevant content. The analysis unit can also postpone the order of analysis of less relevant input content. For example, the analysis unit can analyze the user's voice data and postpone the order of analysis of less relevant content. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the input content. For example, the analysis unit can analyze the user's voice data in real time and adjust the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's voice data to AI, and the AI ​​can analyze the data and determine the order of analysis based on the relevance.

[0081] The identification unit can estimate the user's emotions and determine the priority of services to be identified based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit can prioritize services that help the user relax. For example, the identification unit can analyze the user's voice tone to estimate the stress level and prioritize services that help the user relax. Furthermore, if the user is relaxed, the identification unit can equally prioritize all services. For example, the identification unit can analyze the user's facial expressions using a camera to determine whether the user is relaxed and prioritize all services. Furthermore, if the user is in a hurry, the identification unit can prioritize services that can be provided quickly. For example, the identification unit can analyze the user's speech speed to determine whether the user is in a hurry and prioritize services that can be provided quickly. This makes it possible to prioritize services to be identified based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input user voice data to AI, which may infer emotions and determine service priorities.

[0082] When identifying a service, the identification unit can analyze the user's past history and select the optimal service. The identification unit selects the optimal service, for example, based on services the user has used in the past. For example, the identification unit can obtain the user's past usage history from a database and select the optimal service based on the services used in the past. The identification unit can also select a service that matches the user's preferences from the user's past history. For example, the identification unit can select a service that matches the user's preferences based on the user's past purchase history and browsing history. The identification unit can also analyze the user's past history and select the most suitable service. For example, the identification unit can analyze the user's past behavioral data and select the most suitable service. This makes it possible to analyze the user's past history and select the optimal service. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past history data into AI, which can analyze the data and select the optimal service.

[0083] When identifying a service, the identification unit can customize the specific means based on the user's current situation. The identification unit, for example, customizes the optimal service according to the user's current situation. For example, the identification unit can customize the optimal service based on the user's current location information and recent behavior history. The identification unit can also adjust the specific means taking the user's current situation into consideration. For example, the identification unit can analyze the user's current situation in real time and adjust the specific means. Furthermore, the identification unit can optimize the specific means based on the user's current situation. For example, the identification unit can select the optimal service provision means based on the user's current situation. This allows the specific means to be customized based on the user's current situation. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's current situation data into AI, which can analyze the data and customize the specific means.

[0084] The identification unit can estimate the user's emotions and adjust the display method of the identified service based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit can provide a simple, highly visible display method. For example, the identification unit can analyze the user's voice tone to estimate the stress level and provide a simple display method. Furthermore, if the user is relaxed, the identification unit can provide a display method including detailed information. For example, the identification unit can analyze the user's facial expressions using a camera, determine whether the user is relaxed, and provide a detailed display method. Furthermore, if the user is in a hurry, the identification unit can provide a display method that focuses on the main points. For example, the identification unit can analyze the user's speech speed, determine whether the user is in a hurry, and provide a display method that focuses on the main points. This makes it possible to adjust the display method of the identified service according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input user voice data to AI, which may infer emotions and adjust the display method of the service.

[0085] When identifying a service, the identification unit can select an optimal service by taking into account the user's geographical location information. For example, if the user is in a specific area, the identification unit selects a service related to that area. For example, the identification unit can obtain the user's current location from GPS data and select a service related to that area. The identification unit can also select nearby services based on the user's current location. For example, the identification unit can select services related to nearby restaurants and stores based on the user's current location. Furthermore, the identification unit can select an optimal service by taking into account the user's geographical location information. For example, the identification unit can analyze the user's geographical location information in real time and select an optimal service. This makes it possible to select an optimal service by taking into account the user's geographical location information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's geographical location information into AI, which can analyze the data and select an optimal service.

[0086] When identifying a service, the identification unit can analyze the user's social media activity and suggest a specific means. The identification unit, for example, identifies a service of interest from the user's social media activity. For example, the identification unit can analyze the content of the user's social media posts to identify a service of interest. The identification unit can also identify the optimal service by taking the user's social media activity into consideration. For example, the identification unit can identify related services based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and suggest a specific means. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the user's social media activity into AI, which can analyze the data to identify the optimal service.

[0087] The providing unit can estimate the user's emotions and adjust the service provision method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible service provision method. For example, the providing unit can analyze the user's voice tone to estimate the stress level and provide a simple service provision method. Furthermore, if the user is relaxed, the providing unit can provide a service provision method including detailed information. For example, the providing unit can analyze the user's facial expressions using a camera to determine whether the user is relaxed and provide a detailed service provision method. Furthermore, if the user is in a hurry, the providing unit can provide a service provision method that focuses on the main points. For example, the providing unit can analyze the user's speech speed to determine whether the user is in a hurry and provide a service provision method that focuses on the main points. This makes it possible to adjust the service provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's voice data into AI, which may infer the user's emotions and adjust the method of providing the service.

[0088] When providing a service, the providing unit can analyze the user's past usage history and select the optimal provision method. The providing unit selects the optimal provision method, for example, based on services the user has used in the past. For example, the providing unit can obtain the user's past usage history from a database and select the optimal provision method based on the services used in the past. The providing unit can also select a provision method that suits the user's preferences based on the user's past usage history. For example, the providing unit can select a provision method that suits the user's preferences based on the user's past purchase history and browsing history. Furthermore, the providing unit can analyze the user's past usage history and select the most appropriate provision method. For example, the providing unit can analyze the user's past behavioral data and select the most appropriate provision method. This makes it possible to analyze the user's past usage history and select the optimal provision method. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past usage history data into AI, which can analyze the data and select the optimal provision method.

[0089] The providing unit can customize the means of providing the service based on the user's current situation when providing the service. The providing unit customizes the optimal means of providing the service according to the user's current situation, for example. For example, the providing unit can customize the optimal means of providing the service based on the user's current location information and recent behavioral history. The providing unit can also adjust the means of providing the service taking the user's current situation into consideration. For example, the providing unit can analyze the user's current situation in real time and adjust the means of providing the service. Furthermore, the providing unit can optimize the means of providing the service based on the user's current situation. For example, the providing unit can select the optimal means of providing the service based on the user's current situation. This allows the means of providing the service to be customized based on the user's current situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into AI, which can analyze the data and customize the means of providing the service.

[0090] The providing unit can estimate the user's emotions and determine the order in which services are provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing services that help the user relax. For example, the providing unit can analyze the user's voice tone to estimate the stress level and prioritize providing services that help the user relax. The providing unit can also provide all services equally if the user is relaxed. For example, the providing unit can analyze the user's facial expressions using a camera to determine whether the user is relaxed and provide all services equally. Furthermore, if the user is in a hurry, the providing unit can prioritize providing services that can be provided quickly. For example, the providing unit can analyze the user's speech speed to determine whether the user is in a hurry and prioritize providing services that can be provided quickly. This makes it possible to determine the order in which services are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user voice data to AI, which may infer emotions and determine the order in which services are provided.

[0091] When providing a service, the providing unit can select an optimal service provision method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a service related to that area. For example, the providing unit can obtain the user's current location from GPS data and provide a service related to the area. The providing unit can also provide nearby services based on the user's current location. For example, the providing unit can provide services related to nearby restaurants and stores based on the user's current location. Furthermore, the providing unit can select an optimal service provision method by taking into account the user's geographical location information. For example, the providing unit can analyze the user's geographical location information in real time and select an optimal service provision method. This makes it possible to select an optimal service provision method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can analyze the data and select an optimal service provision method.

[0092] When providing a service, the provision unit can analyze the user's social media activity and suggest a means of provision. The provision unit, for example, provides a service of interest based on the user's social media activity. For example, the provision unit can analyze the content of the user's social media posts and provide a service of interest. The provision unit can also suggest an optimal means of providing the service by taking the user's social media activity into consideration. For example, the provision unit can provide a related service based on the user's social media trends and interests. This makes it possible to analyze the user's social media activity and suggest a means of provision. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal means of providing the service.

[0093] The privacy protection unit can estimate a user's emotions and adjust the privacy protection method based on the estimated user emotions. For example, if the user is feeling stressed, the privacy protection unit can increase the level of privacy protection. For example, the privacy protection unit can analyze the user's voice tone to estimate the stress level and increase the level of privacy protection. The privacy protection unit can also apply a standard privacy protection method if the user is relaxed. For example, the privacy protection unit can analyze the user's facial expressions using a camera to determine whether the user is relaxed and apply the standard privacy protection method. Furthermore, the privacy protection unit can quickly perform privacy protection if the user is in a hurry. For example, the privacy protection unit can analyze the user's speech speed to determine whether the user is in a hurry and quickly perform privacy protection. This allows the privacy protection method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit may be performed using AI, or may be performed without AI. For example, the privacy protection unit may input the user's voice data into AI, which may infer emotions and adjust the privacy protection method.

[0094] During privacy protection, the privacy protection unit can analyze a user's past data usage history and select an optimal protection method. The privacy protection unit, for example, selects an optimal privacy protection method from the user's past data usage history. For example, the privacy protection unit can retrieve the user's past data usage history from a database and select an optimal privacy protection method. The privacy protection unit can also analyze the user's past data usage history and select a privacy protection method that suits the user's preferences. For example, the privacy protection unit can select a privacy protection method that suits the user's preferences based on the user's past data usage history. Furthermore, the privacy protection unit can select the most appropriate privacy protection method based on the user's past data usage history. For example, the privacy protection unit can analyze the user's past data usage history and select the most appropriate privacy protection method. In this way, the user's past data usage history can be analyzed and the optimal protection method can be selected. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's past data usage history into AI, which can analyze the data and select the optimal privacy protection method.

[0095] The privacy protection unit can estimate a user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, if the user is feeling stressed, the privacy protection unit can increase the priority of privacy protection. For example, the privacy protection unit can analyze the user's voice tone to estimate the stress level and increase the priority of privacy protection. The privacy protection unit can also apply a standard priority of privacy protection if the user is relaxed. For example, the privacy protection unit can analyze the user's facial expressions using a camera, determine whether the user is relaxed, and apply a standard priority of privacy protection. Furthermore, the privacy protection unit can quickly perform privacy protection if the user is in a hurry. For example, the privacy protection unit can analyze the user's speech speed to determine whether the user is in a hurry and quickly perform privacy protection. This makes it possible to determine the priority of privacy protection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input user voice data into AI, which may infer emotions and determine privacy protection priorities.

[0096] The privacy protection unit may select an optimal privacy protection method by taking into account the user's geographical location information during privacy protection. For example, if the user is in a specific area, the privacy protection unit may select a privacy protection method related to that area. For example, the privacy protection unit may obtain the user's current location from GPS data and select a privacy protection method related to the area. The privacy protection unit may also select an optimal privacy protection method based on the user's current location. For example, the privacy protection unit may select an optimal privacy protection method based on the user's current location. Furthermore, the privacy protection unit may select an optimal privacy protection method by taking into account the user's geographical location information. For example, the privacy protection unit may analyze the user's geographical location information in real time and select an optimal privacy protection method. This allows the optimal protection method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input the user's geographical location information into AI, which may analyze the data and select an optimal privacy protection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives the user's voice using the microphone 38B of the smart device 14 and transmits the voice data to the data processing device 12 via the control unit 46A. The analysis unit converts the voice data into text data via the identification processing unit 290 of the data processing device 12 and analyzes the user's intentions. For example, the identification unit identifies the user's needs via the identification processing unit 290 of the data processing device 12 and identifies the optimal service taking into account past history and preferences. The provision unit displays the identified service to the user via the display 40A of the smart device 14, for example. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives the user's voice using the microphone 238 of the smart glasses 214 and transmits the voice data to the data processing device 12 via the control unit 46A. The analysis unit converts the voice data into text data via the identification processing unit 290 of the data processing device 12 and analyzes the user's intentions, for example. The identification unit identifies the user's needs via the identification processing unit 290 of the data processing device 12 and identifies the optimal service taking into account past history and preferences. The provision unit displays the identified service to the user via the display of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives the user's voice using the microphone 238 of the headset type terminal 314 and transmits the voice data to the data processing device 12 by the control unit 46A. For example, the analysis unit converts the voice data into text data by the identification processing unit 290 of the data processing device 12 and analyzes the user's intention. For example, the identification unit identifies the user's needs by the identification processing unit 290 of the data processing device 12 and identifies the optimal service taking into consideration past history and preferences. The provision unit displays the identified service to the user on the display 343 of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the user's voice using the microphone 238 of the robot 414 and transmits the voice data to the data processing device 12 by the control unit 46A. The analysis unit converts the voice data into text data by the identification processing unit 290 of the data processing device 12, for example, and analyzes the user's intention. For example, the identification unit identifies the user's needs by the identification processing unit 290 of the data processing device 12, and identifies the optimal service taking into consideration past history and preferences. The provision unit displays the identified service to the user on the display of the robot 414, for example.

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

[0098] The reception unit can provide real-time feedback to the user's voice input. For example, if the user voice-inputs, "Tell me about nearby restaurants," the reception unit can immediately return feedback such as, "I'm searching for nearby restaurants." Also, if the user asks, "What travel destinations do you recommend for my next vacation?", the reception unit can return feedback such as, "Checking your past travel history." Furthermore, if the user's voice input is unclear, the reception unit can return a request such as, "Please say it again." This allows the user to check in real time whether their voice input is being correctly received.

[0099] When analyzing voice data, the analysis unit can learn the user's past voice input patterns and improve the analysis accuracy. For example, the analysis unit can learn phrases and expressions that the user has frequently used in the past and analyze the voice data based on them. The analysis unit can also learn the user's pronunciation characteristics and improve the accuracy of voice recognition. Furthermore, the analysis unit can learn the voice input tendencies in specific situations based on the user's past voice input history and improve the analysis accuracy. In this way, the analysis unit can learn the user's past voice input patterns and improve the analysis accuracy.

[0100] The identification unit can estimate the user's emotions and customize the content of the identified service based on the estimated user's emotions. For example, if the user is feeling stressed, a service that helps the user relax can be identified. For example, the identification unit can analyze the user's voice tone to estimate the stress level and identify a service that helps the user relax. Furthermore, if the user is relaxed, an active service can be identified. For example, the identification unit can analyze the user's facial expression using a camera to determine whether the user is relaxed and identify an active service. Furthermore, if the user is in a hurry, a service that can be provided quickly can be identified. For example, the identification unit can analyze the user's speech speed to determine whether the user is in a hurry and identify a service that can be provided quickly. This makes it possible to customize the content of the identified service according to the user's emotions.

[0101] When displaying the identified service on the user's device, the providing unit can customize the display method by taking into account the user's past browsing history. For example, the providing unit can prioritize the use of a display format that the user has frequently used in the past. For example, the providing unit can acquire the user's past browsing history from a database and identify the frequently used display format. The providing unit can also select a display method suitable for a specific time period from the user's past browsing history. For example, the providing unit can analyze the user's past browsing history and suggest a display method that is optimal for a specific time period. Furthermore, the providing unit can analyze the user's past browsing history and suggest the most efficient display method. For example, the providing unit can identify an efficient display method based on the user's past browsing history. This makes it possible to customize the display method by taking into account the user's past browsing history.

[0102] When displaying the identified service on the user's device, the providing unit can estimate the user's emotions and adjust the display content based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. For example, the providing unit can analyze the user's voice tone to estimate the stress level and provide a simple display method. Furthermore, if the user is relaxed, a display method including detailed information can be provided. For example, the providing unit can analyze the user's facial expressions with a camera, determine whether the user is relaxed, and provide a detailed display method. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. For example, the providing unit can analyze the user's speech speed to determine whether the user is in a hurry, and provide a display method that focuses on the main points. This makes it possible to adjust the display content according to the user's emotions.

[0103] The reception unit can provide voice feedback in response to the user's voice input. For example, if the user voice inputs, "Tell me about nearby restaurants," the reception unit can return voice feedback such as, "I'm looking for nearby restaurants." Also, if the user asks, "What travel destinations do you recommend for my next vacation?", the reception unit can return voice feedback such as, "Checking your past travel history." Furthermore, if the user's voice input is unclear, the reception unit can return voice feedback such as, "Please say it again." This allows the user to confirm by voice whether their voice input has been correctly received.

[0104] When analyzing the voice data, the analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated emotions. For example, if the user is feeling stressed, a simplified analysis method can be applied. For example, the analysis unit can analyze the user's voice tone to estimate the stress level and apply the simplified analysis method. Also, if the user is relaxed, a detailed analysis method can be applied. For example, the analysis unit can analyze the user's facial expressions using a camera, determine whether the user is relaxed, and apply the detailed analysis method. Furthermore, if the user is in a hurry, a rapid analysis method can be applied. For example, the analysis unit can analyze the user's speech speed, determine whether the user is in a hurry, and apply the rapid analysis method. In this way, the level of analysis detail can be adjusted according to the user's emotions.

[0105] The identification unit can customize the content of the identified service based on the user's current situation. For example, when the user is in a specific area, it can prioritize the identification of services related to that area. For example, the identification unit can acquire the user's current location from GPS data and identify services related to that area. Furthermore, when the user is in a specific time period, it can also identify services suitable for that time period. For example, the identification unit can identify the optimal service based on the user's current time period. Furthermore, when the user is in a specific situation, it can also identify a service according to that situation. For example, the identification unit can analyze the user's current situation in real time and identify the optimal service. This makes it possible to customize the content of the identified service based on the user's current situation.

[0106] When displaying the identified services on the user's device, the providing unit can customize the display content by taking into account the user's social media activity. For example, the providing unit can prioritize the display of services related to topics that the user frequently mentions on social media. For example, the providing unit can analyze the content of the user's comments on social media to identify services related to topics of interest to the user. The providing unit can also display related services based on the user's social media trends and interests. For example, the providing unit can analyze the user's social media trends and display related services. Furthermore, the providing unit can select an optimal display method by taking into account the user's social media activity. For example, the providing unit can select an optimal display method based on the user's social media activity. This makes it possible to customize the display content by taking into account the user's social media activity.

[0107] When displaying the identified services on the user's device, the providing unit can estimate the user's emotions and adjust the display order based on the estimated emotions. For example, if the user is feeling stressed, services that help the user relax can be displayed preferentially. For example, the providing unit can analyze the user's voice tone to estimate the stress level and display services that help the user relax preferentially. Furthermore, if the user is relaxed, all services can be displayed equally. For example, the providing unit can analyze the user's facial expression using a camera to determine whether the user is relaxed and display all services equally. Furthermore, if the user is in a hurry, services that can be provided quickly can be displayed preferentially. For example, the providing unit can analyze the user's speech speed to determine whether the user is in a hurry and display services that can be provided quickly preferentially. In this way, the display order can be adjusted according to the user's emotions.

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

[0109] Step 1: The reception unit accepts questions or requests that the user types into the device by voice. For example, if the user types, "Tell me about nearby restaurants," the reception unit can accept that voice. Similarly, if the user asks, "What are some recommended travel destinations for my next vacation?", the reception unit can accept that voice. Step 2: The analysis unit analyzes the voice data received by the reception unit. The analysis unit converts the voice data into text data and understands the user's intent. For example, voice recognition technology can be used to convert the voice data into text data and analyze the user's intent. Step 3: The identification unit identifies the user's needs based on the data analyzed by the analysis unit. The identification unit identifies the most suitable service taking into account the user's past history and preferences. For example, the identification unit can identify recommended travel destinations for the user's next vacation based on the user's past travel history and preferences. Step 4: The providing unit provides the service identified by the identifying unit to the user. The providing unit displays the identified service on the user's device. For example, information about nearby restaurants can be displayed on the user's device.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice input; an analysis unit that analyzes the voice data received by the reception unit; an identification unit that identifies user needs based on the data analyzed by the analysis unit; a providing unit that provides the service identified by the identifying unit to a user; Equipped with A system characterized by:

2. The reception unit Accept specific questions or requests spoken into the device 2. The system of claim 1.

3. The analysis unit Converting voice data into text data and understanding user intent 2. The system of claim 1.

4. The identification unit Identify appropriate services based on user history and preferences 2. The system of claim 1.

5. The providing unit Displaying identified services on the user's device 2. The system of claim 1.

6. The system comprises: Protecting privacy regarding the handling of voice data and the use of user location information 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1.

9. The reception unit When accepting voice input, it filters it based on the user's current situation and interests.

2. The system of claim 1.

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