System

Abstract

To provide a system that allows the completion of smartphone operations through natural language queries even by users unfamiliar with smartphone operation.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, an execution unit, and a feedback unit. The reception unit receives inquiries in natural language from a user. The analysis unit analyzes the inquiries in natural language received by the reception unit and identifies a suitable operation. The execution unit executes the operation identified by the analysis unit. The feedback unit provides the user with feedback on the result of the operation executed by the execution unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem that it was difficult for elderly people and others who are not good at operating smartphones to perform complex operations.

[0005] The system according to the embodiment aims to enable even users who are not good at operating smartphones to complete operations by making inquiries in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a feedback unit. The reception unit receives a natural language query from a user. The analysis unit analyzes the natural language query received by the reception unit and identifies an appropriate operation. The execution unit executes the operation identified by the analysis unit. The feedback unit feeds back to the user a result of the operation executed by the execution unit. Effect of the Invention

[0007] The system according to the embodiment enables even users who are not good at operating smartphones to complete operations by making inquiries in natural language. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The desktop application system according to the embodiment of the present invention is a system that allows elderly people and people who are not good at operating smartphones to complete all operations using a generation AI. In this system, the user makes an inquiry to the generation AI in natural language, and the generation AI automatically executes complex operations and feeds back the results. For example, when a user makes an inquiry such as "I want to send a message to a friend" or "I want to make a phone call," the generation AI analyzes the inquiry and executes an appropriate operation. This allows the user to intuitively use the desktop application without performing complex operations. For example, when a user makes an inquiry to the generation AI of "I want to send a message to a friend," the generation AI launches a messenger app and executes an operation to send a message to the specified friend. In addition, in response to an inquiry of "I want to make a phone call," the generation AI launches a phone app and executes an operation to make a call to the specified person. This mechanism allows the user to intuitively use the desktop application without performing complex operations. For example, elderly people and people who are not good at operating smartphones can complete all operations, such as operating a messenger app and making a phone call, simply by making an inquiry to the generation AI in natural language. This allows the desktop application system to simplify the user's complex operations and provide intuitive operations.

[0029] The desktop application system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a feedback unit. The reception unit receives a natural language inquiry from a user. The natural language inquiry from a user includes, but is not limited to, questions, instructions, and requests. For example, when a user makes an inquiry such as "I want to send a message to a friend," the reception unit receives the inquiry. The analysis unit uses a generation AI to analyze the natural language inquiry received by the reception unit and identifies an appropriate operation. The analysis unit understands the user's intention using, for example, a natural language processing technique and identifies an appropriate operation. For example, in response to the inquiry "I want to send a message to a friend," the analysis unit identifies an operation to launch a messenger app and send a message to a specified friend. The execution unit executes the operation identified by the analysis unit. The execution unit executes a specific operation, such as, for example, an operation of the messenger app or making a phone call. For example, the execution unit executes an operation to launch a messenger app and send a message to a specified friend. In addition, the execution unit executes an operation to launch a phone app and make a phone call to a specified person. The feedback unit feeds back to the user the result of the operation executed by the execution unit. For example, the feedback unit notifies the user that the operation has been completed successfully and suggests an appropriate response if an error occurs. For example, the feedback unit notifies the user that a message has been sent successfully and suggests a retry if an error occurs. In this way, the desktop application system according to the embodiment can simplify complicated operations for the user and provide an intuitive operation.

[0030] The reception unit receives inquiries in natural language from the user. The inquiries in natural language from the user include, but are not limited to, questions, instructions, requests, and the like. For example, when the user makes an inquiry such as "I want to send a message to my friend," the reception unit receives the inquiry. Specifically, the reception unit supports both voice input and text input, and can appropriately receive an inquiry whether the user makes an inquiry by voice through a microphone or by text using a keyboard. In the case of voice input, the reception unit converts the voice into text using voice recognition technology and passes the text to the analysis unit. In the case of text input, the text entered by the user is passed directly to the analysis unit. Furthermore, when receiving an inquiry from a user, the reception unit can temporarily store the content of the inquiry and refer to the history of past inquiries as necessary. This makes it possible to associate inquiries made by the user in the past with current inquiries and process them. For example, when a user makes an inquiry such as "Resend yesterday's message," the reception unit can refer to the history of past inquiries and identify the content of the message sent yesterday. This allows the reception unit to efficiently and accurately receive inquiries from users.

[0031] The analysis unit uses the generation AI to analyze the natural language inquiry received by the reception unit and identify an appropriate operation. The analysis unit uses, for example, natural language processing technology to understand the user's intention and identify an appropriate operation. Specifically, the generation AI makes full use of natural language processing technologies such as tokenization, morphological analysis, and context understanding to analyze the user's inquiry. For example, in response to an inquiry such as "I want to send a message to a friend," the generation AI extracts keywords such as "friend," "message," and "want to send," and analyzes the relationship between these keywords. Furthermore, the generation AI can refer to past inquiry history and user profile information to understand the context of the user's inquiry. This allows the user to accurately understand the intention of sending a message to a specific friend and identify an appropriate operation. For example, the generation AI identifies an operation to launch a messenger app based on the user's inquiry and send a message to the specified friend. In addition, the generation AI can ask additional questions to confirm the user's intention when the user's inquiry is ambiguous or multiple interpretations are possible. This allows the analysis unit to accurately analyze the user's inquiry and identify an appropriate operation.

[0032] The execution unit executes the operation specified by the analysis unit. The execution unit executes specific operations, such as, for example, operating a messenger app or making a phone call. Specifically, the execution unit launches a corresponding application or system based on the operation instruction received from the analysis unit, and automatically performs the necessary operation. For example, the execution unit launches a messenger app and executes an operation of sending a message to a specified friend. At this time, the execution unit automatically performs a series of operations, such as searching for the specified friend from the user's contact list, entering the contents of the message, and clicking the send button. In addition, the execution unit launches a phone app and executes an operation of making a call to the specified other party. At this time, the execution unit automatically performs a series of operations, such as searching for the phone number of the specified other party from the user's contact list, and clicking the call button. Furthermore, the execution unit can also cooperate with other applications and systems in the user's desktop environment, and can execute, for example, an operation of launching a calendar app to add an event, or an operation of launching a mail app to send an email. As a result, the execution unit can accurately and quickly execute the operation specified by the analysis unit and respond to the user's request.

[0033] The feedback unit feeds back the result of the operation performed by the execution unit to the user. For example, the feedback unit notifies the user that the operation has been completed successfully, and suggests an appropriate response if an error occurs. Specifically, the feedback unit receives the result of the operation from the execution unit and conveys the result to the user in an easy-to-understand manner. For example, the feedback unit notifies the user that the message has been sent successfully, and suggests retrying if an error occurs. At this time, the feedback unit explains the content and cause of the error message in detail to help the user take an appropriate response. For example, if sending a message fails due to a network connection problem, the feedback unit suggests the user to check the network connection. The feedback unit can also provide additional suggestions and actions based on the result of the operation. For example, a suggestion can be made to add a related event to a calendar after the user has sent a message. Furthermore, the feedback unit can collect user feedback and use it to improve the system. For example, a questionnaire is provided to evaluate whether the user is satisfied with the result of the operation, and the system's functions and operability are improved based on the result. As a result, the feedback unit can appropriately feed back the result of the operation performed by the execution unit to the user, thereby increasing the user's satisfaction.

[0034] The reception unit can provide an interface through which the user makes an inquiry in natural language. The interface includes, for example, a graphical user interface (GUI) and a voice interface, but is not limited to such examples. The reception unit provides, for example, an interface through which the user makes an inquiry in natural language. For example, the reception unit provides an interface through which the user can make an inquiry using text input or voice input. The reception unit can also provide a simple and easy-to-use interface so that the user can intuitively operate. As a result, an interface through which the user can make an inquiry in natural language is provided, thereby enabling intuitive operation. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input to AI and cause the AI ​​to provide the interface.

[0035] The analysis unit can understand the user's intention and identify an appropriate operation using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. The analysis unit uses natural language processing technology, for example, when analyzing a user's inquiry. For example, the analysis unit analyzes the user's inquiry using morphological analysis, understands the structure of the sentence using grammatical analysis, and understands the user's intention using semantic analysis. The analysis unit can also analyze the user's inquiry and identify an appropriate operation using a generation AI. For example, the analysis unit executes an algorithm in which the generation AI analyzes the user's inquiry and identifies an appropriate operation. As a result, the user's intention can be accurately understood and an appropriate operation can be identified by using natural language processing technology. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's inquiry to the generation AI and obtains the analysis result from the generation AI.

[0036] The execution unit can execute a specific operation, such as operating a messenger app or making a phone call. The specific operation includes, for example, operating a messenger app, making a phone call, setting a calendar, sending an email, and the like, but is not limited to such examples. For example, the execution unit executes an operation of starting a messenger app and sending a message to a specified friend. The execution unit can also execute an operation of starting a phone app and making a phone call to a specified person. Furthermore, the execution unit can execute a wide range of operations, such as setting a calendar and sending an email. For example, the execution unit executes an operation of starting a calendar app and adding an event at a specified date and time. The execution unit can also execute an operation of starting an email app and sending an email to a specified person. As a result, by executing specific operations, various operations according to the user's request are possible. Part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the operation content to the generation AI, and the generation AI executes the operation.

[0037] The feedback unit can notify the user that the operation has been completed successfully and can suggest an appropriate response in the event of an error. The appropriate response includes, but is not limited to, displaying an error message, suggesting a retry, contacting support, and the like. The feedback unit, for example, notifies the user that the operation has been completed successfully. For example, the feedback unit notifies the user that the message has been sent successfully. The feedback unit also suggests an appropriate response in the event of an error. For example, the feedback unit displays an error message and suggests a retry. The feedback unit can also suggest contacting support. This makes it easier for the user to grasp the status of the operation by feeding back the result of the operation to the user. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the operation result to the generation AI, and the generation AI provides feedback.

[0038] The analysis unit can use natural language processing technology when analyzing the user's inquiry. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. The analysis unit uses natural language processing technology, for example, when analyzing the user's inquiry. For example, the analysis unit analyzes the user's inquiry using morphological analysis, understands the structure of the sentence using grammatical analysis, and understands the user's intention using semantic analysis. The analysis unit can also analyze the user's inquiry and identify an appropriate operation using a generation AI. For example, the analysis unit executes an algorithm in which the generation AI analyzes the user's inquiry and identifies an appropriate operation. In this way, the user's inquiry can be accurately analyzed by using natural language processing technology. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's inquiry to the generation AI and obtains the analysis result from the generation AI.

[0039] The execution unit can execute multiple operations such as setting a calendar or sending an email, in addition to the messenger app or the phone app. The multiple operations include, for example, setting a calendar, sending an email, managing files, setting reminders, and the like, but are not limited to such examples. For example, the execution unit executes an operation of launching a calendar app and adding an event at a specified date and time. The execution unit can also execute an operation of launching an email app and sending an email to a specified recipient. Furthermore, the execution unit can execute a wide range of operations such as managing files and setting reminders. For example, the execution unit executes an operation of launching a file management app and moving or deleting a specified file. The execution unit can also execute an operation of launching a reminder app and setting a reminder at a specified date and time. This makes it possible to respond to various requests from users by executing a wide range of operations. Part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the operation content to the generation AI, and the generation AI executes the operation.

[0040] The reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, the reception unit automatically displays the inquiry contents that the user frequently made in the past as candidates. The reception unit can also preferentially suggest the inquiry method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and suggest the inquiry method to be used in a specific time period from the user's past inquiry history. In this way, by analyzing the past inquiry history, it is possible to provide the user with the optimal reception method. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past inquiry history into the generation AI and causes the generation AI to select the optimal reception method.

[0041] The reception unit can perform filtering based on the user's current situation and field of interest when receiving an inquiry. For example, when the user inputs the current situation, the reception unit preferentially receives the inquiry content according to the situation. The reception unit can also automatically filter the related inquiry content based on the user's field of interest. Furthermore, when the user is in a specific situation, the reception unit can also suggest an inquiry method that is optimal for that situation. As a result, by filtering based on the user's current situation and field of interest, it is possible to receive more relevant inquiries. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data on the user's current situation and field of interest into the generation AI and has the generation AI perform filtering.

[0042] The reception unit can preferentially receive inquiries with high relevance in consideration of the geographical location information of the user when receiving an inquiry. For example, when the user is in a specific area, the reception unit preferentially receives inquiries related to that area. The reception unit can also suggest an optimal inquiry method based on the user's current location. Furthermore, when the user is moving, the reception unit can also preferentially receive inquiry content according to the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI and causes the generation AI to select inquiries with high relevance.

[0043] The reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. For example, the reception unit analyzes the user's current interests from the user's social media activity and preferentially receives related inquiries. The reception unit can also suggest an optimal inquiry method based on the content mentioned by the user on social media. Furthermore, the reception unit can also analyze the user's social media activity and automatically filter the related inquiry content. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related inquiries. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI and causes the generation AI to select related inquiries.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, the analysis unit performs a detailed analysis on an inquiry with a high level of importance. The analysis unit can also perform a concise analysis on an inquiry with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. As a result, by adjusting the level of detail of the analysis based on the importance of the inquiry, a detailed analysis can be performed on an important inquiry. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance data of the inquiry to the generation AI and causes the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit applies a natural language processing algorithm to an inquiry regarding the operation of a messenger app. The analysis unit can also apply a voice recognition algorithm to an inquiry regarding making a phone call. Furthermore, the analysis unit can apply a schedule analysis algorithm to an inquiry regarding calendar settings. In this way, by applying different analysis algorithms depending on the category of the inquiry, more appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs inquiry category data to the generation AI and causes the generation AI to apply the analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the time of submission of the inquiry during the analysis. For example, the analysis unit performs analysis with the highest priority for urgent inquiries. The analysis unit can also perform analysis with normal priority for normal inquiries. Furthermore, the analysis unit can postpone analysis for past inquiries. In this way, by determining the priority of the analysis based on the time of submission of the inquiry, inquiries with high urgency can be dealt with quickly. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the submission time data of the inquiry to the generation AI and causes the generation AI to determine the priority of the analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the query during analysis. For example, the analysis unit prioritizes analysis of queries with high relevance. The analysis unit can also postpone analysis of queries with low relevance. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the query. This allows queries with high relevance to be dealt with preferentially by adjusting the order of analysis based on the relevance of the query. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs query relevance data to the generation AI and causes the generation AI to adjust the order of analysis.

[0048] At the time of execution, the execution unit can select the optimal execution method by analyzing the user's past operation history. For example, the execution unit preferentially executes an operation method that the user frequently used in the past. The execution unit can also suggest the optimal execution method from the user's past operation history. Furthermore, the execution unit can also select the execution method by referring to the operation method that the user used in the past. In this way, the optimal execution method can be provided by analyzing the user's past operation history. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's past operation history data to the generation AI and causes the generation AI to select the optimal execution method.

[0049] The execution unit can customize the means of execution based on the user's current situation at the time of execution. For example, when the user inputs the current situation, the execution unit selects an execution method according to the situation. The execution unit can also suggest the optimal execution means based on the user's current situation. Furthermore, when the user is in a specific situation, the execution unit can also provide an execution method optimal for that situation. In this way, by customizing the means of execution based on the user's current situation, a more appropriate execution method can be provided. Part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's current situation data into the generation AI and causes the generation AI to customize the execution means.

[0050] The execution unit can select the optimal execution method in consideration of the geographical location information of the user at the time of execution. For example, when the user is in a specific area, the execution unit preferentially executes operations related to that area. The execution unit can also suggest the optimal execution method based on the user's current location. Furthermore, when the user is moving, the execution unit can also select an execution method according to the current location. In this way, the optimal execution method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the user's geographical location information data to the generation AI and causes the generation AI to select the optimal execution method.

[0051] At the time of execution, the execution unit can analyze the user's social media activity and suggest a means of execution. For example, the execution unit analyzes the user's current interests from the user's social media activity and executes related operations with priority. The execution unit can also suggest an optimal execution method based on the contents mentioned by the user on social media. Furthermore, the execution unit can also analyze the user's social media activity and automatically filter related operation contents. In this way, by analyzing the user's social media activity, related operations can be executed with priority. A part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the user's social media activity data into the generation AI and causes the generation AI to execute the proposal of the execution means.

[0052] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides optimal feedback by referring to the feedback content received by the user in the past. The feedback unit can also analyze a specific pattern from the user's past feedback history and suggest appropriate feedback. Furthermore, the feedback unit can also provide a feedback format that the user has preferred in the past with priority. In this way, optimal feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's past feedback history data into the generation AI and causes the generation AI to provide optimal feedback.

[0053] The feedback unit can customize the feedback means based on the user's current situation at the time of feedback. For example, when the user inputs the current situation, the feedback unit selects a feedback method according to the situation. The feedback unit can also suggest the optimal feedback means based on the user's current situation. Furthermore, when the user is in a specific situation, the feedback unit can also provide the optimal feedback method for that situation. This makes it possible to provide more appropriate feedback by customizing the feedback means based on the user's current situation. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's current situation data to the generation AI and causes the generation AI to customize the feedback means.

[0054] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. For example, when the user is in a specific area, the feedback unit preferentially provides feedback related to the area. The feedback unit can also suggest the optimal feedback method based on the user's current location. Furthermore, when the user is moving, the feedback unit can also select a feedback method according to the current location. In this way, the optimal feedback method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's geographical location information data to the generation AI and causes the generation AI to select the optimal feedback method.

[0055] The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. For example, the feedback unit analyzes the user's current interests from the user's social media activity and provides related feedback preferentially. The feedback unit can also suggest an optimal feedback method based on the content mentioned by the user on social media. Furthermore, the feedback unit can also analyze the user's social media activity and automatically filter related feedback content. In this way, by analyzing the user's social media activity, it is possible to provide related feedback preferentially. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's social media activity data into the generation AI and causes the generation AI to execute the suggestion of a feedback means.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, the contents of inquiries frequently made by the user in the past are automatically displayed as candidates. The reception unit can also preferentially suggest inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an inquiry method to be used in a specific time period from the user's past inquiry history. In this way, the reception unit can provide the user with the optimal reception method by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past inquiry history into the generation AI and causes the generation AI to select the optimal reception method.

[0058] The reception unit can perform filtering based on the user's current situation and field of interest. For example, when the user inputs the current situation, the reception unit preferentially receives inquiry content according to the situation. The reception unit can also automatically filter related inquiry content based on the user's field of interest. Furthermore, when the user is in a specific situation, the reception unit can also suggest an inquiry method that is optimal for that situation. As a result, by filtering based on the user's current situation and field of interest, more relevant inquiries can be received. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data on the user's current situation and field of interest into the generation AI and has the generation AI perform filtering.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, a detailed analysis is performed for an inquiry with a high level of importance. The analysis unit can also perform a concise analysis for an inquiry with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. As a result, by adjusting the level of detail of the analysis based on the importance of the inquiry, a detailed analysis can be performed for an important inquiry. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the importance data of the inquiry to the generation AI and causes the generation AI to adjust the level of detail of the analysis.

[0060] At the time of execution, the execution unit can select the optimal execution method by analyzing the user's past operation history. For example, the execution unit preferentially executes operation methods that the user frequently used in the past. The execution unit can also suggest the optimal execution method from the user's past operation history. Furthermore, the execution unit can also select the execution method by referring to operation methods that the user used in the past. In this way, the optimal execution method can be provided by analyzing the user's past operation history. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's past operation history data into the generation AI and causes the generation AI to select the optimal execution method.

[0061] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides optimal feedback by referring to the feedback content received by the user in the past. The feedback unit can also analyze a specific pattern from the user's past feedback history and suggest appropriate feedback. Furthermore, the feedback unit can also provide a feedback format that the user has preferred in the past with priority. In this way, optimal feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's past feedback history data into the generation AI and causes the generation AI to provide optimal feedback.

[0062] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. For example, when the user is in a specific area, feedback related to that area is preferentially provided. The feedback unit can also suggest the optimal feedback method based on the user's current location. Furthermore, when the user is moving, the feedback unit can also select a feedback method according to the current location. In this way, the optimal feedback method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's geographical location information data into the generation AI and causes the generation AI to select the optimal feedback method.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives a natural language inquiry from a user. A natural language inquiry from a user includes questions, instructions, requests, etc. For example, when a user makes an inquiry such as "I want to send a message to a friend," the inquiry is received. Step 2: The analysis unit uses the generation AI to analyze the natural language inquiry received by the reception unit and identify an appropriate operation. The analysis unit uses natural language processing technology to understand the user's intention and identify an appropriate operation. For example, in response to an inquiry such as "I want to send a message to a friend," the analysis unit identifies the operation of launching a messenger app and sending a message to the specified friend. Step 3: The execution unit executes the operation identified by the analysis unit. For example, the execution unit executes a specific operation such as operating a messenger app or making a phone call. For example, the execution unit executes an operation to launch a messenger app and send a message to a specified friend, or executes an operation to launch a phone app and make a phone call to a specified person. Step 4: The feedback unit feeds back to the user the results of the operation performed by the execution unit, e.g., notifying the user that the operation was completed successfully and suggesting an appropriate response if an error occurred, e.g., notifying the user that the message was sent successfully and suggesting a retry if an error occurred.

[0065] (Example 2) The desktop application system according to the embodiment of the present invention is a system that allows elderly people and people who are not good at operating smartphones to complete all operations using a generation AI. In this system, the user makes an inquiry to the generation AI in natural language, and the generation AI automatically executes complex operations and feeds back the results. For example, when a user makes an inquiry such as "I want to send a message to a friend" or "I want to make a phone call," the generation AI analyzes the inquiry and executes an appropriate operation. This allows the user to intuitively use the desktop application without performing complex operations. For example, when a user makes an inquiry to the generation AI of "I want to send a message to a friend," the generation AI launches a messenger app and executes an operation to send a message to the specified friend. In addition, in response to an inquiry of "I want to make a phone call," the generation AI launches a phone app and executes an operation to make a call to the specified person. This mechanism allows the user to intuitively use the desktop application without performing complex operations. For example, elderly people and people who are not good at operating smartphones can complete all operations, such as operating a messenger app and making a phone call, simply by making an inquiry to the generation AI in natural language. This allows the desktop application system to simplify the user's complex operations and provide intuitive operations.

[0066] The desktop application system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a feedback unit. The reception unit receives a natural language inquiry from a user. The natural language inquiry from a user includes, but is not limited to, questions, instructions, and requests. For example, when a user makes an inquiry such as "I want to send a message to a friend," the reception unit receives the inquiry. The analysis unit uses a generation AI to analyze the natural language inquiry received by the reception unit and identifies an appropriate operation. The analysis unit understands the user's intention using, for example, a natural language processing technique and identifies an appropriate operation. For example, in response to the inquiry "I want to send a message to a friend," the analysis unit identifies an operation to launch a messenger app and send a message to a specified friend. The execution unit executes the operation identified by the analysis unit. The execution unit executes a specific operation, such as, for example, an operation of the messenger app or making a phone call. For example, the execution unit executes an operation to launch a messenger app and send a message to a specified friend. In addition, the execution unit executes an operation to launch a phone app and make a phone call to a specified person. The feedback unit feeds back to the user the result of the operation executed by the execution unit. For example, the feedback unit notifies the user that the operation has been completed successfully and suggests an appropriate response if an error occurs. For example, the feedback unit notifies the user that a message has been sent successfully and suggests a retry if an error occurs. In this way, the desktop application system according to the embodiment can simplify complicated operations for the user and provide an intuitive operation.

[0067] The reception unit receives inquiries in natural language from the user. The inquiries in natural language from the user include, but are not limited to, questions, instructions, requests, and the like. For example, when the user makes an inquiry such as "I want to send a message to my friend," the reception unit receives the inquiry. Specifically, the reception unit supports both voice input and text input, and can appropriately receive an inquiry whether the user makes an inquiry by voice through a microphone or by text using a keyboard. In the case of voice input, the reception unit converts the voice into text using voice recognition technology and passes the text to the analysis unit. In the case of text input, the text entered by the user is passed directly to the analysis unit. Furthermore, when receiving an inquiry from a user, the reception unit can temporarily store the content of the inquiry and refer to the history of past inquiries as necessary. This makes it possible to associate inquiries made by the user in the past with current inquiries and process them. For example, when a user makes an inquiry such as "Resend yesterday's message," the reception unit can refer to the history of past inquiries and identify the content of the message sent yesterday. This allows the reception unit to efficiently and accurately receive inquiries from users.

[0068] The analysis unit uses the generation AI to analyze the natural language inquiry received by the reception unit and identify an appropriate operation. The analysis unit uses, for example, natural language processing technology to understand the user's intention and identify an appropriate operation. Specifically, the generation AI makes full use of natural language processing technologies such as tokenization, morphological analysis, and context understanding to analyze the user's inquiry. For example, in response to an inquiry such as "I want to send a message to a friend," the generation AI extracts keywords such as "friend," "message," and "want to send," and analyzes the relationship between these keywords. Furthermore, the generation AI can refer to past inquiry history and user profile information to understand the context of the user's inquiry. This allows the user to accurately understand the intention of sending a message to a specific friend and identify an appropriate operation. For example, the generation AI identifies an operation to launch a messenger app based on the user's inquiry and send a message to the specified friend. In addition, the generation AI can ask additional questions to confirm the user's intention when the user's inquiry is ambiguous or multiple interpretations are possible. This allows the analysis unit to accurately analyze the user's inquiry and identify an appropriate operation.

[0069] The execution unit executes the operation specified by the analysis unit. The execution unit executes specific operations, such as, for example, operating a messenger app or making a phone call. Specifically, the execution unit launches a corresponding application or system based on the operation instruction received from the analysis unit, and automatically performs the necessary operation. For example, the execution unit launches a messenger app and executes an operation of sending a message to a specified friend. At this time, the execution unit automatically performs a series of operations, such as searching for the specified friend from the user's contact list, entering the contents of the message, and clicking the send button. In addition, the execution unit launches a phone app and executes an operation of making a call to the specified other party. At this time, the execution unit automatically performs a series of operations, such as searching for the phone number of the specified other party from the user's contact list, and clicking the call button. Furthermore, the execution unit can also cooperate with other applications and systems in the user's desktop environment, and can execute, for example, an operation of launching a calendar app to add an event, or an operation of launching a mail app to send an email. As a result, the execution unit can accurately and quickly execute the operation specified by the analysis unit and respond to the user's request.

[0070] The feedback unit feeds back the result of the operation performed by the execution unit to the user. For example, the feedback unit notifies the user that the operation has been completed successfully, and suggests an appropriate response if an error occurs. Specifically, the feedback unit receives the result of the operation from the execution unit and conveys the result to the user in an easy-to-understand manner. For example, the feedback unit notifies the user that the message has been sent successfully, and suggests retrying if an error occurs. At this time, the feedback unit explains the content and cause of the error message in detail to help the user take an appropriate response. For example, if sending a message fails due to a network connection problem, the feedback unit suggests the user to check the network connection. The feedback unit can also provide additional suggestions and actions based on the result of the operation. For example, a suggestion can be made to add a related event to a calendar after the user has sent a message. Furthermore, the feedback unit can collect user feedback and use it to improve the system. For example, a questionnaire is provided to evaluate whether the user is satisfied with the result of the operation, and the system's functions and operability are improved based on the result. As a result, the feedback unit can appropriately feed back the result of the operation performed by the execution unit to the user, thereby increasing the user's satisfaction.

[0071] The reception unit can provide an interface through which the user makes an inquiry in natural language. The interface includes, for example, a graphical user interface (GUI) and a voice interface, but is not limited to such examples. The reception unit provides, for example, an interface through which the user makes an inquiry in natural language. For example, the reception unit provides an interface through which the user can make an inquiry using text input or voice input. The reception unit can also provide a simple and easy-to-use interface so that the user can intuitively operate. As a result, an interface through which the user can make an inquiry in natural language is provided, thereby enabling intuitive operation. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input to AI and cause the AI ​​to provide the interface.

[0072] The analysis unit can understand the user's intention and identify an appropriate operation using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. The analysis unit uses natural language processing technology, for example, when analyzing a user's inquiry. For example, the analysis unit analyzes the user's inquiry using morphological analysis, understands the structure of the sentence using grammatical analysis, and understands the user's intention using semantic analysis. The analysis unit can also analyze the user's inquiry and identify an appropriate operation using a generation AI. For example, the analysis unit executes an algorithm in which the generation AI analyzes the user's inquiry and identifies an appropriate operation. As a result, the user's intention can be accurately understood and an appropriate operation can be identified by using natural language processing technology. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's inquiry to the generation AI and obtains the analysis result from the generation AI.

[0073] The execution unit can execute a specific operation, such as operating a messenger app or making a phone call. The specific operation includes, for example, operating a messenger app, making a phone call, setting a calendar, sending an email, and the like, but is not limited to such examples. For example, the execution unit executes an operation of starting a messenger app and sending a message to a specified friend. The execution unit can also execute an operation of starting a phone app and making a phone call to a specified person. Furthermore, the execution unit can execute a wide range of operations, such as setting a calendar and sending an email. For example, the execution unit executes an operation of starting a calendar app and adding an event at a specified date and time. The execution unit can also execute an operation of starting an email app and sending an email to a specified person. As a result, by executing specific operations, various operations according to the user's request are possible. Part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the operation content to the generation AI, and the generation AI executes the operation.

[0074] The feedback unit can notify the user that the operation has been completed successfully and can suggest an appropriate response in the event of an error. The appropriate response includes, but is not limited to, displaying an error message, suggesting a retry, contacting support, and the like. The feedback unit, for example, notifies the user that the operation has been completed successfully. For example, the feedback unit notifies the user that the message has been sent successfully. The feedback unit also suggests an appropriate response in the event of an error. For example, the feedback unit displays an error message and suggests a retry. The feedback unit can also suggest contacting support. This makes it easier for the user to grasp the status of the operation by feeding back the result of the operation to the user. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the operation result to the generation AI, and the generation AI provides feedback.

[0075] The analysis unit can use natural language processing technology when analyzing the user's inquiry. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. The analysis unit uses natural language processing technology, for example, when analyzing the user's inquiry. For example, the analysis unit analyzes the user's inquiry using morphological analysis, understands the structure of the sentence using grammatical analysis, and understands the user's intention using semantic analysis. The analysis unit can also analyze the user's inquiry and identify an appropriate operation using a generation AI. For example, the analysis unit executes an algorithm in which the generation AI analyzes the user's inquiry and identifies an appropriate operation. In this way, the user's inquiry can be accurately analyzed by using natural language processing technology. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's inquiry to the generation AI and obtains the analysis result from the generation AI.

[0076] The execution unit can execute multiple operations such as setting a calendar or sending an email, in addition to the messenger app or the phone app. The multiple operations include, for example, setting a calendar, sending an email, managing files, setting reminders, and the like, but are not limited to such examples. For example, the execution unit executes an operation of launching a calendar app and adding an event at a specified date and time. The execution unit can also execute an operation of launching an email app and sending an email to a specified recipient. Furthermore, the execution unit can execute a wide range of operations such as managing files and setting reminders. For example, the execution unit executes an operation of launching a file management app and moving or deleting a specified file. The execution unit can also execute an operation of launching a reminder app and setting a reminder at a specified date and time. This makes it possible to respond to various requests from users by executing a wide range of operations. Part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the operation content to the generation AI, and the generation AI executes the operation.

[0077] The reception unit can estimate the user's emotion and adjust the inquiry reception method based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept inquiries. This allows for more appropriate responses by adjusting the inquiry reception method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0078] The reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, the reception unit automatically displays the inquiry contents that the user frequently made in the past as candidates. The reception unit can also preferentially suggest the inquiry method (voice, text, etc.) that the user used in the past. Furthermore, the reception unit can predict and suggest the inquiry method to be used in a specific time period from the user's past inquiry history. In this way, by analyzing the past inquiry history, it is possible to provide the user with the optimal reception method. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past inquiry history into the generation AI and causes the generation AI to select the optimal reception method.

[0079] The reception unit can perform filtering based on the user's current situation and field of interest when receiving an inquiry. For example, when the user inputs the current situation, the reception unit preferentially receives the inquiry content according to the situation. The reception unit can also automatically filter the related inquiry content based on the user's field of interest. Furthermore, when the user is in a specific situation, the reception unit can also suggest an inquiry method that is optimal for that situation. As a result, by filtering based on the user's current situation and field of interest, it is possible to receive more relevant inquiries. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data on the user's current situation and field of interest into the generation AI and has the generation AI perform filtering.

[0080] The reception unit can estimate the user's emotion and determine the priority of the received inquiry based on the estimated user's emotion. For example, when the user makes an urgent inquiry, the reception unit receives the inquiry with the highest priority. Also, when the user is relaxed, the reception unit can also receive the inquiry with normal priority. Furthermore, when the user is stressed, the reception unit can raise the priority to respond quickly. In this way, by determining the priority of the inquiry according to the user's emotion, it is possible to quickly respond to an inquiry with a high degree of urgency. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0081] The reception unit can preferentially receive inquiries with high relevance in consideration of the geographical location information of the user when receiving an inquiry. For example, when the user is in a specific area, the reception unit preferentially receives inquiries related to that area. The reception unit can also suggest an optimal inquiry method based on the user's current location. Furthermore, when the user is moving, the reception unit can also preferentially receive inquiry content according to the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI and causes the generation AI to select inquiries with high relevance.

[0082] The reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. For example, the reception unit analyzes the user's current interests from the user's social media activity and preferentially receives related inquiries. The reception unit can also suggest an optimal inquiry method based on the content mentioned by the user on social media. Furthermore, the reception unit can also analyze the user's social media activity and automatically filter the related inquiry content. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related inquiries. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI and causes the generation AI to select related inquiries.

[0083] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. In addition, when the user is in a hurry, the analysis unit can also provide a concise analysis result. Furthermore, when the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This makes it possible to provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and causes the generation AI to execute emotion estimation.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, the analysis unit performs a detailed analysis on an inquiry with a high level of importance. The analysis unit can also perform a concise analysis on an inquiry with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. As a result, by adjusting the level of detail of the analysis based on the importance of the inquiry, a detailed analysis can be performed on an important inquiry. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance data of the inquiry to the generation AI and causes the generation AI to adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit applies a natural language processing algorithm to an inquiry regarding the operation of a messenger app. The analysis unit can also apply a voice recognition algorithm to an inquiry regarding making a phone call. Furthermore, the analysis unit can apply a schedule analysis algorithm to an inquiry regarding calendar settings. In this way, by applying different analysis algorithms depending on the category of the inquiry, more appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs inquiry category data to the generation AI and causes the generation AI to apply the analysis algorithm.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This makes it possible to provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and causes the generation AI to execute emotion estimation.

[0087] The analysis unit can determine the priority of the analysis based on the time of submission of the inquiry during the analysis. For example, the analysis unit performs analysis with the highest priority for urgent inquiries. The analysis unit can also perform analysis with normal priority for normal inquiries. Furthermore, the analysis unit can postpone analysis for past inquiries. In this way, by determining the priority of the analysis based on the time of submission of the inquiry, inquiries with high urgency can be dealt with quickly. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the submission time data of the inquiry to the generation AI and causes the generation AI to determine the priority of the analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the query during analysis. For example, the analysis unit prioritizes analysis of queries with high relevance. The analysis unit can also postpone analysis of queries with low relevance. Furthermore, the analysis unit can determine the order of analysis according to the relevance of the query. This allows queries with high relevance to be dealt with preferentially by adjusting the order of analysis based on the relevance of the query. A part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs query relevance data to the generation AI and causes the generation AI to adjust the order of analysis.

[0089] The execution unit can estimate the user's emotion and adjust the execution method based on the estimated user's emotion. For example, when the user is relaxed, the execution unit executes in a normal manner. Also, when the user is in a hurry, the execution unit can execute quickly. Furthermore, when the user is stressed, the execution unit can execute in a visually easy-to-understand manner. This makes it possible to provide a more appropriate execution method by adjusting the execution method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0090] At the time of execution, the execution unit can select the optimal execution method by analyzing the user's past operation history. For example, the execution unit preferentially executes an operation method that the user frequently used in the past. The execution unit can also suggest the optimal execution method from the user's past operation history. Furthermore, the execution unit can also select the execution method by referring to the operation method that the user used in the past. In this way, the optimal execution method can be provided by analyzing the user's past operation history. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's past operation history data to the generation AI and causes the generation AI to select the optimal execution method.

[0091] The execution unit can customize the means of execution based on the user's current situation at the time of execution. For example, when the user inputs the current situation, the execution unit selects an execution method according to the situation. The execution unit can also suggest the optimal execution means based on the user's current situation. Furthermore, when the user is in a specific situation, the execution unit can also provide an execution method optimal for that situation. In this way, by customizing the means of execution based on the user's current situation, a more appropriate execution method can be provided. Part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's current situation data into the generation AI and causes the generation AI to customize the execution means.

[0092] The execution unit can estimate the user's emotion and determine the priority of execution based on the estimated user's emotion. For example, when the user requests an urgent operation, the execution unit executes the operation with the highest priority. In addition, when the user is relaxed, the execution unit can also execute with normal priority. Furthermore, when the user is stressed, the execution unit can raise the priority to respond quickly. In this way, by determining the priority of execution according to the user's emotion, it is possible to quickly respond to an operation with high urgency. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0093] The execution unit can select the optimal execution method in consideration of the geographical location information of the user at the time of execution. For example, when the user is in a specific area, the execution unit preferentially executes operations related to that area. The execution unit can also suggest the optimal execution method based on the user's current location. Furthermore, when the user is moving, the execution unit can also select an execution method according to the current location. In this way, the optimal execution method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the user's geographical location information data to the generation AI and causes the generation AI to select the optimal execution method.

[0094] At the time of execution, the execution unit can analyze the user's social media activity and suggest a means of execution. For example, the execution unit analyzes the user's current interests from the user's social media activity and executes related operations with priority. The execution unit can also suggest an optimal execution method based on the contents mentioned by the user on social media. Furthermore, the execution unit can also analyze the user's social media activity and automatically filter related operation contents. In this way, by analyzing the user's social media activity, related operations can be executed with priority. A part or all of the above-mentioned processing in the execution unit is performed using a generation AI. For example, the execution unit inputs the user's social media activity data into the generation AI and causes the generation AI to execute the proposal of the execution means.

[0095] The feedback unit can estimate the user's emotion and adjust the feedback method based on the estimated user's emotion. For example, the feedback unit provides detailed feedback when the user is relaxed. Also, the feedback unit can provide brief feedback when the user is in a hurry. Furthermore, the feedback unit can provide visually easy-to-understand feedback when the user is stressed. This allows more appropriate feedback to be provided by adjusting the feedback method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the user's emotion data into the generation AI and causes the generation AI to execute emotion estimation.

[0096] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides optimal feedback by referring to the feedback content received by the user in the past. The feedback unit can also analyze a specific pattern from the user's past feedback history and suggest appropriate feedback. Furthermore, the feedback unit can also provide a feedback format that the user has preferred in the past with priority. In this way, optimal feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's past feedback history data into the generation AI and causes the generation AI to provide optimal feedback.

[0097] The feedback unit can customize the feedback means based on the user's current situation at the time of feedback. For example, when the user inputs the current situation, the feedback unit selects a feedback method according to the situation. The feedback unit can also suggest the optimal feedback means based on the user's current situation. Furthermore, when the user is in a specific situation, the feedback unit can also provide the optimal feedback method for that situation. This makes it possible to provide more appropriate feedback by customizing the feedback means based on the user's current situation. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's current situation data to the generation AI and causes the generation AI to customize the feedback means.

[0098] The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. For example, when the user needs urgent feedback, the feedback unit provides the feedback with the highest priority. Also, when the user is relaxed, the feedback unit can provide the feedback with normal priority. Furthermore, when the user is stressed, the feedback unit can raise the priority in order to respond quickly. In this way, by determining the priority of the feedback according to the user's emotion, it is possible to quickly respond to feedback with high urgency. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0099] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. For example, when the user is in a specific area, the feedback unit preferentially provides feedback related to the area. The feedback unit can also suggest the optimal feedback method based on the user's current location. Furthermore, when the user is moving, the feedback unit can also select a feedback method according to the current location. In this way, the optimal feedback method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's geographical location information data to the generation AI and causes the generation AI to select the optimal feedback method.

[0100] The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. For example, the feedback unit analyzes the user's current interests from the user's social media activity and provides related feedback preferentially. The feedback unit can also suggest an optimal feedback method based on the content mentioned by the user on social media. Furthermore, the feedback unit can also analyze the user's social media activity and automatically filter related feedback content. In this way, by analyzing the user's social media activity, it is possible to provide related feedback preferentially. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's social media activity data into the generation AI and causes the generation AI to execute the suggestion of a feedback means.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, the contents of inquiries frequently made by the user in the past are automatically displayed as candidates. The reception unit can also preferentially suggest inquiry methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an inquiry method to be used in a specific time period from the user's past inquiry history. In this way, the reception unit can provide the user with the optimal reception method by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past inquiry history into the generation AI and causes the generation AI to select the optimal reception method.

[0103] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis result is provided. In addition, when the user is in a hurry, the analysis unit can also provide a concise analysis result. Furthermore, when the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. As a result, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotion data into the generation AI and causes the generation AI to execute emotion estimation.

[0104] The execution unit can estimate the user's emotion and adjust the execution method based on the estimated user's emotion. For example, when the user is relaxed, the execution unit executes in a normal manner. Also, when the user is in a hurry, the execution unit can execute quickly. Furthermore, when the user is stressed, the execution unit can execute in a visually easy-to-understand manner. This makes it possible to provide a more appropriate execution method by adjusting the execution method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0105] The feedback unit can estimate the user's emotion and adjust the feedback method based on the estimated user's emotion. For example, when the user is relaxed, detailed feedback is provided. Also, when the user is in a hurry, the feedback unit can provide brief feedback. Furthermore, when the user is stressed, the feedback unit can provide visually easy-to-understand feedback. This allows more appropriate feedback to be provided by adjusting the feedback method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the user's emotion data into the generation AI and causes the generation AI to execute emotion estimation.

[0106] The reception unit can perform filtering based on the user's current situation and field of interest. For example, when the user inputs the current situation, the reception unit preferentially receives inquiry content according to the situation. The reception unit can also automatically filter related inquiry content based on the user's field of interest. Furthermore, when the user is in a specific situation, the reception unit can also suggest an inquiry method that is optimal for that situation. As a result, by filtering based on the user's current situation and field of interest, more relevant inquiries can be received. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data on the user's current situation and field of interest into the generation AI and has the generation AI perform filtering.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, a detailed analysis is performed for an inquiry with a high level of importance. The analysis unit can also perform a concise analysis for an inquiry with a low level of importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the inquiry. As a result, by adjusting the level of detail of the analysis based on the importance of the inquiry, a detailed analysis can be performed for an important inquiry. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the importance data of the inquiry to the generation AI and causes the generation AI to adjust the level of detail of the analysis.

[0108] At the time of execution, the execution unit can select the optimal execution method by analyzing the user's past operation history. For example, the execution unit preferentially executes operation methods that the user frequently used in the past. The execution unit can also suggest the optimal execution method from the user's past operation history. Furthermore, the execution unit can also select the execution method by referring to operation methods that the user used in the past. In this way, the optimal execution method can be provided by analyzing the user's past operation history. A part or all of the above-mentioned processing in the execution unit is performed using the generation AI. For example, the execution unit inputs the user's past operation history data into the generation AI and causes the generation AI to select the optimal execution method.

[0109] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides optimal feedback by referring to the feedback content received by the user in the past. The feedback unit can also analyze a specific pattern from the user's past feedback history and suggest appropriate feedback. Furthermore, the feedback unit can also provide a feedback format that the user has preferred in the past with priority. In this way, optimal feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's past feedback history data into the generation AI and causes the generation AI to provide optimal feedback.

[0110] The reception unit can estimate the user's emotion and determine the priority of the inquiries to be received based on the estimated user's emotion. For example, when the user makes an urgent inquiry, the reception unit receives the inquiry with the highest priority. In addition, when the user is relaxed, the reception unit can also receive an inquiry with normal priority. Furthermore, when the user is stressed, the reception unit can raise the priority to respond quickly. In this way, by determining the priority of the inquiries according to the user's emotion, it is possible to quickly respond to inquiries with high urgency. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data to the generation AI and causes the generation AI to execute emotion estimation.

[0111] The feedback unit can select the optimal feedback method in consideration of the geographical location information of the user at the time of feedback. For example, when the user is in a specific area, feedback related to that area is preferentially provided. The feedback unit can also suggest the optimal feedback method based on the user's current location. Furthermore, when the user is moving, the feedback unit can also select a feedback method according to the current location. In this way, the optimal feedback method can be provided by considering the user's geographical location information. A part or all of the above-mentioned processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the user's geographical location information data into the generation AI and causes the generation AI to select the optimal feedback method.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit receives a natural language inquiry from a user. A natural language inquiry from a user includes questions, instructions, requests, etc. For example, when a user makes an inquiry such as "I want to send a message to a friend," the inquiry is received. Step 2: The analysis unit uses the generation AI to analyze the natural language inquiry received by the reception unit and identify an appropriate operation. The analysis unit uses natural language processing technology to understand the user's intention and identify an appropriate operation. For example, in response to an inquiry such as "I want to send a message to a friend," the analysis unit identifies the operation of launching a messenger app and sending a message to the specified friend. Step 3: The execution unit executes the operation identified by the analysis unit. For example, the execution unit executes a specific operation such as operating a messenger app or making a phone call. For example, the execution unit executes an operation to launch a messenger app and send a message to a specified friend, or executes an operation to launch a phone app and make a phone call to a specified person. Step 4: The feedback unit feeds back to the user the results of the operation performed by the execution unit, e.g., notifying the user that the operation was completed successfully and suggesting an appropriate response if an error occurred, e.g., notifying the user that the message was sent successfully and suggesting a retry if an error occurred.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0115] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and feedback 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 a natural language inquiry from the user using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the natural language inquiry using a generation AI and identifies an appropriate operation. The execution unit is realized, for example, by the control unit 46A of the smart device 14, and executes the operation identified by the analysis unit. The feedback unit feeds back the result of the operation to the user using, for example, the display 40A or speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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, the RAM 30, and the 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.

[0121] 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.

[0122] 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 the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] 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 performed in a secure state.

[0125] Fig. 4 shows an example of 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0131] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and feedback 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 a natural language inquiry from a user using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the natural language inquiry using a generation AI to identify an appropriate operation. The execution unit is realized, for example, by the control unit 46A of the smart glasses 214, and executes the operation identified by the analysis unit. The feedback unit feeds back the result of the operation to the user using, for example, the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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, the RAM 30, and the 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.

[0137] 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.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] 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 performed in a secure state.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and feedback 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 a natural language inquiry from a user using the microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the natural language inquiry using a generation AI and identifies an appropriate operation. The execution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes the operation identified by the analysis unit. The feedback unit feeds back the result of the operation to the user using, for example, the speaker 240 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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, the RAM 30, and the 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.

[0153] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] 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 the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] 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 performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0162] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] 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 a voice indicating a user input for 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.

[0164] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0166] Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and feedback 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 a natural language inquiry from a user using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the natural language inquiry using a generation AI and identifies an appropriate operation. The execution unit is realized, for example, by the control unit 46A of the robot 414, and executes the operation identified by the analysis unit. The feedback unit feeds back the result of the operation to the user using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0174] 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.

[0175] 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 Universal Serial Bus (USB) 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.

[0176] In addition, 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 upon request from the data processing device 12.

[0177] 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.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0180] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) a reception unit for receiving a query in natural language from a user; an analysis unit that analyzes the natural language inquiry received by the reception unit and identifies an appropriate operation; an execution unit that executes the operation specified by the analysis unit; A feedback unit that feeds back to a user a result of the operation executed by the execution unit. A system characterized by: (Appendix 2) The reception unit is Provide an interface for users to make inquiries in natural language 2. The system of claim 1, (Appendix 3) The analysis unit is Uses natural language processing technology to understand user intent and identify appropriate actions 2. The system of claim 1, (Appendix 4) The execution unit is Perform certain actions, such as using a messenger app or making a phone call 2. The system of claim 1, (Appendix 5) The feedback unit is Notify you when an operation is completed successfully and suggest appropriate actions if an error occurs 2. The system of claim 1, (Appendix 6) The analysis unit is Use natural language processing techniques when analyzing user queries 2. The system of claim 1, (Appendix 7) The execution unit is Apart from the messenger or phone app, perform multiple actions such as setting up a calendar or sending emails 2. The system of claim 1, (Appendix 8) The reception unit is To estimate a user's emotions and adjust a method of accepting an inquiry based on the estimated user's emotions 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past inquiry history and select the optimal reception method 2. The system of claim 1, (Appendix 10) The reception unit is Filtering inquiries based on the user's current situation and interests 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize inquiries based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting inquiries, the system takes into account the user's geographic location information to prioritize inquiries that are more relevant to the user. 2. The system of claim 1, (Appendix 13) The reception unit is When receiving an inquiry, analyze the user's social media activity and receive related inquiries 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the query 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, apply different parsing algorithms depending on the category of the query 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when the query was submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During parsing, adjust the order of parsing based on the relevance of the query 2. The system of claim 1, (Appendix 20) The execution unit is Inferring a user's emotions and adjusting the execution method based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The execution unit is At runtime, the system analyzes the user's past operation history to select the optimal execution method. 2. The system of claim 1, (Appendix 22) The execution unit is At run time, customize the execution method based on the user's current situation 2. The system of claim 1, (Appendix 23) The execution unit is Inferring user emotions and determining execution priorities based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The execution unit is At runtime, the optimal execution method is chosen taking into account the user's geographic location. 2. The system of claim 1, (Appendix 25) The execution unit is At runtime, it analyzes the user's social media activity and suggests actions to take. 2. The system of claim 1, (Appendix 26) The feedback unit is Estimate user's emotions and adjust the feedback method based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The feedback unit is When providing feedback, provide optimal feedback by referring to the user's past feedback history 2. The system of claim 1, (Appendix 28) The feedback unit is When providing feedback, customize the feedback medium based on the user's current situation 2. The system of claim 1, (Appendix 29) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 30) The feedback unit is When providing feedback, the system selects the optimal feedback method by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The feedback unit is When providing feedback, analyze users' social media activity to suggest avenues for feedback 2. The system of claim 1, [Explanation of symbols]

[0186] 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 for receiving a query in natural language from a user; an analysis unit that analyzes the natural language inquiry received by the reception unit and identifies an appropriate operation; an execution unit that executes the operation specified by the analysis unit; A feedback unit that feeds back to a user a result of the operation executed by the execution unit. A system characterized by:

2. The reception unit is Provide an interface for users to make inquiries in natural language 2. The system of claim 1 .

3. The analysis unit is Uses natural language processing technology to understand user intent and identify appropriate actions 2. The system of claim 1 .

4. The execution unit is Perform certain actions, such as using a messenger app or making a phone call 2. The system of claim 1 .

5. The feedback unit is Notify you when an operation is completed successfully and suggest appropriate actions if an error occurs 2. The system of claim 1 .

6. The analysis unit is Use natural language processing techniques when analyzing user queries 2. The system of claim 1 .

7. The execution unit is Apart from the messenger or phone app, perform multiple actions such as setting up a calendar or sending emails 2. The system of claim 1 .

8. The reception unit is To estimate a user's emotions and adjust a method of accepting an inquiry based on the estimated user's emotions 2. The system of claim 1 .

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