system

Abstract

The system according to the embodiment aims to provide support to enable users to use the system efficiently. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user operations. The analysis unit analyzes the operation history received by the reception unit. The provision unit provides support based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that support for using the system is insufficient, and it takes time for users to understand how to operate the system.

[0005] The system according to the embodiment aims to provide support to enable users to use the system efficiently. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives user operations. The analysis unit analyzes the operation history received by the reception unit. The provision unit provides support based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide support to enable users to utilize the system efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) In an embodiment of the AI ​​Navigator system of the present invention, the AI ​​Navigator guides users as they use the system, as if they were sitting next to a colleague who is knowledgeable about the system. This eliminates the need to pore over thick manuals or general FAQs. It also eliminates the need to email inquiries or wait for a call back from a contact point. For example, when a user attempts to use a specific system function, the AI ​​Navigator explains how to use that function and guides them through the necessary steps. This allows the user to use the system smoothly. Furthermore, the AI ​​Navigator analyzes the user's operation history and system usage status to provide optimal support. For example, it predicts the next necessary operation based on the user's past functions and operation methods and provides appropriate advice. This allows the user to use the system efficiently. The AI ​​Navigator also provides instant answers to user questions. For example, if a user asks about a specific system function, the AI ​​Navigator answers the question and provides the necessary information. This allows the user to quickly resolve problems. In this way, the AI ​​Navigator is a powerful tool for supporting system usage and can significantly improve user convenience. This allows the AI ​​navigator system to efficiently accept, analyze, and provide support to users' operations.

[0029] The AI ​​navigator system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user operations. User operations include, but are not limited to, clicks, taps, and inputs. The reception unit receives, for example, operations when the user attempts to use a specific function of the system. The reception unit can also receive operations when the user changes system settings. The reception unit can also receive operations when the user logs into the system. For example, the reception unit receives an operation in which the user enters a username and password for logging into the system. The analysis unit analyzes the operation history received by the reception unit. The operation history includes, but is not limited to, the date and time of the operation, the operation content, and the order of the operation. The analysis unit can analyze the operation history using, for example, data mining technology. The analysis unit can also analyze the operation history using statistical analysis technology. The analysis unit can also analyze the operation history using a machine learning algorithm. For example, the analysis unit analyzes the user's operation patterns based on the operation history and predicts the next required operation. The provision unit provides support based on the analysis results obtained by the analysis unit. Examples of support include, but are not limited to, providing FAQs, chat support, and guidelines. For example, when a user asks a question about a specific function of the system, the providing unit provides an answer to the question. The providing unit can also guide the user through the necessary procedures when changing system settings. Furthermore, the providing unit can provide appropriate advice when the user has trouble using the system. For example, when a user uses a specific function of the system, the providing unit explains how to use the function and guides the user through the necessary procedures. This allows the AI ​​navigator system according to the embodiment to efficiently accept and analyze user operations and provide support.

[0030] The service provider can provide answers to user questions. For example, if a user asks about a specific function of the system, the service provider will provide an answer to that question. For example, if a user asks about how to configure the system, the service provider will explain how to configure it. The service provider can also explain how to use the system if a user asks about how to use it. For example, when a user uses a specific function of the system, the service provider will explain how to use that function. This allows for quick answers to user questions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's question into a generating AI, and the generating AI can generate an answer to that question.

[0031] The analysis unit may include a recording unit that records the user's operation history. The recording unit, for example, stores the user's operation history in a database. The operation history includes, but is not limited to, the date and time of the operation, the content of the operation, and the order of the operations. The recording unit may, for example, generate a log file to record the operation history. The recording unit can also record the operation history in real time. For example, the recording unit records the operation history each time a user uses the system. This improves the accuracy of the analysis by recording the user's operation history. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's operation history into a generating AI, and the generating AI can record that operation history.

[0032] The providing unit can predict the next required operation based on the functions and operation methods used by the user in the past and provide advice. The providing unit can predict the next required operation based on, for example, the functions and operation methods used by the user in the past. For example, the providing unit can analyze the history of functions used by the user in the past and predict the next required operation. The providing unit can also predict the next required operation based on operation methods used by the user in the past. For example, the providing unit can analyze operation methods used by the user in the past and predict the next required operation. Furthermore, the providing unit can provide appropriate advice based on the predicted operation. For example, the providing unit can specifically explain the operation the user should perform next. This can predict the next required operation for the user and provide appropriate advice, thereby supporting efficient system use. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history to a generating AI, which can predict the next required operation and provide advice.

[0033] When a user attempts to use a specific function of the system, the providing unit can explain how to use the function and guide the user through the necessary procedures. For example, when a user attempts to use a specific function of the system, the providing unit can explain how to use the function. For example, when a user attempts to use a search function of the system, the providing unit can explain how to search. Furthermore, when a user attempts to use an editing function of the system, the providing unit can explain how to edit. For example, when a user attempts to use a setting function of the system, the providing unit can explain how to set up. Furthermore, the providing unit can guide the user through the necessary procedures. For example, the providing unit can guide the user through step-by-step procedures for using a specific function of the system. This allows the user to operate the specific function of the system smoothly. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a user's operation into a generating AI, and the generating AI can guide the user through the procedures for that operation.

[0034] The reception unit can analyze the user's past operation history and select a reception method. For example, the reception unit automatically displays operations that the user frequently performed in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest operations to be used in a specific time period based on the user's past operation history. For example, the reception unit predicts and suggests operations to be used in a specific time period based on the user's past operation history. In this way, the optimal reception method can be provided by analyzing the user's past operation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past operation history into a generation AI, which analyzes the operation history and selects the optimal reception method.

[0035] When receiving an operation, the reception unit can filter the operations based on the user's current situation and areas of interest. For example, the reception unit preferentially displays operations related to a project the user is currently working on. For example, the reception unit filters and displays related operations based on the user's areas of interest. The reception unit can also suggest appropriate operations depending on the user's current situation (for example, in a meeting or on the move). For example, if the user is in a meeting, the reception unit preferentially displays operations related to the meeting. This makes it possible to suggest appropriate operations based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI, and the generation AI can filter operations based on the data.

[0036] The reception unit can prioritize operations that are highly relevant to the user, taking into account the user's geographical location information when receiving an operation request. For example, if the user is in a specific location, the reception unit will prioritize operations related to that location. For example, if the user is on the move, the reception unit will prioritize operations related to movement. The reception unit can also prioritize operations that can be performed at home if the user is at home. For example, if the reception unit is at home, the reception unit will prioritize operations that can be performed at home. This allows the system to suggest appropriate operations based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize operations that are highly relevant based on that geographical location information.

[0037] The reception unit can analyze the user's social media activity and accept relevant operations when an operation is received. For example, the reception unit can suggest relevant operations based on information the user has shared on social media. For example, the reception unit can predict and suggest operations of interest based on the user's social media activity. The reception unit can also suggest relevant operations based on the accounts the user follows on social media. For example, the reception unit can suggest relevant operations based on the accounts the user follows on social media. This allows for the suggestion of appropriate operations based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant operations based on that data.

[0038] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the operation history. For example, the analysis unit performs a detailed analysis on important operation history. For example, the analysis unit performs a simplified analysis on general operation history. The analysis unit can also perform a detailed analysis on operations frequently performed by the user. For example, the analysis unit performs a detailed analysis on operations frequently performed by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the operation history to the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the operation. For example, the analysis unit applies an input error analysis algorithm to a data input operation. For example, the analysis unit applies a search efficiency analysis algorithm to a data search operation. The analysis unit can also apply an update history analysis algorithm to a data update operation. For example, the analysis unit applies an update history analysis algorithm to a data update operation. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation category data to a generation AI, and the generation AI can apply an appropriate analysis algorithm based on that data.

[0040] During analysis, the analysis unit can determine the order of analysis based on the submission times of the operations. For example, the analysis unit prioritizes analysis of recently submitted operations. For example, the analysis unit postpones operations that were submitted earlier. The analysis unit can also dynamically adjust the order of analysis based on the submission times. For example, the analysis unit dynamically adjusts the order of analysis based on the submission times. This enables efficient analysis by adjusting the order of analysis based on the submission times of the operations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission times of the operations to a generation AI, and the generation AI can determine the order of analysis based on that data.

[0041] During analysis, the analysis unit can determine the order of analysis based on the relevance of operations. For example, the analysis unit prioritizes analysis of highly relevant operations. For example, the analysis unit postpones analysis of less relevant operations. The analysis unit can also dynamically adjust the order of analysis based on the relevance of operations. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of operations. This enables efficient analysis by adjusting the order of analysis based on the relevance of operations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation relevance data to a generation AI, and the generation AI can determine the order of analysis based on that data.

[0042] When providing support, the providing unit can select the optimal support method by analyzing the user's past operation history. The providing unit, for example, proposes the optimal support method based on support methods used by the user in the past. For example, the providing unit selects the most effective support method from the user's past operation history. The providing unit can also analyze the user's past operation history and predict the next support required. For example, the providing unit predicts the next support required based on the user's past operation history. In this way, the optimal support method can be provided by analyzing the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into a generating AI, and the generating AI can select the optimal support method based on the data.

[0043] When providing support, the providing unit can select a means of support based on the user's current situation. For example, when the user is in a meeting, the providing unit provides brief support. For example, when the user is on the move, the providing unit provides audio support. The providing unit can also provide detailed support when the user is at home. For example, when the user is at home, the providing unit provides detailed support. This makes it possible to provide appropriate support according to the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into a generating AI, and the generating AI can select a means of support based on that data.

[0044] The support unit can select the optimal support method when providing support, taking into account the user's geographical location information. For example, if the user is in a specific location, the support unit can provide support related to that location. For example, if the user is on the move, the support unit can provide support related to the move. The support unit can also provide support that can be performed at home if the user is at home. For example, if the support unit is at home, the support unit can provide support that can be performed at home. This allows for the provision of appropriate support based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal support method based on that data.

[0045] The support unit can analyze the user's social media activity and propose support measures when providing support. For example, the support unit can propose relevant support based on information shared by the user on social media. For example, the support unit can predict and propose support that the user is interested in based on their social media activity. The support unit can also propose relevant support based on the accounts the user follows on social media. For example, the support unit can propose relevant support based on the accounts the user follows on social media. This allows for the provision of appropriate support based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into a generating AI, and the generating AI can propose support measures based on that data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception desk not only receives user input but can also infer the user's intent. For example, when a user attempts to use a specific function, it can infer the intent of that operation and suggest related actions. Furthermore, the reception desk can automatically correct user errors if the user makes a mistake. In addition, the reception desk can display questions to confirm the user's intent as they perform an operation, clarifying their intentions. By inferring the user's intent and providing appropriate support, user errors can be reduced.

[0048] The service provider not only provides answers to user questions but also understands the context of those questions and provides relevant information. For example, if a user asks about a specific feature, the service provider will also explain other related features and settings. Furthermore, the service provider can suggest relevant FAQs and guidelines based on the user's questions. In addition, the service provider can predict the next necessary actions based on the user's questions and provide appropriate advice. This allows for comprehensive answers to user questions and deepens user understanding.

[0049] The analysis unit not only includes a recording unit to record the user's operation history, but can also analyze patterns in the operation history to understand the user's operation trends. For example, the analysis unit analyzes operations that users frequently perform and operations performed during specific time periods to understand the user's operation trends. Furthermore, the analysis unit can make suggestions to improve the efficiency of operations based on the user's operation history. In addition, the analysis unit can predict operation errors based on the user's operation history and provide advice to prevent errors. In this way, the efficiency of operations can be improved by analyzing the user's operation history.

[0050] The providing unit can predict the next required operation based on the functions and operation methods the user has used in the past and provide advice, as well as evaluate the user's operation proficiency and provide appropriate support. For example, when the user uses a specific function, the providing unit can evaluate the user's proficiency with that function and provide guidelines for beginners and advice for advanced users. The providing unit can also adjust the difficulty of the operation according to the user's operation proficiency. Furthermore, the providing unit can suggest the next function or operation method to learn based on the user's operation proficiency. This makes it possible to provide support according to the user's operation proficiency and help improve the user's skills.

[0051] When a user attempts to use a specific function of the system, the providing unit not only explains how to use the function and guides the user through the necessary procedures, but also monitors the progress of the user's operation in real time and provides support at an appropriate time. For example, when the user performs an operation, the providing unit monitors the progress of the operation and guides the user in real time on the next operation to be performed. The providing unit can also provide immediate support when the user has difficulty with an operation. Furthermore, the providing unit can provide advice to improve the efficiency of the operation based on the progress of the user's operation. In this way, by monitoring the progress of the user's operation in real time and providing appropriate support, the user's operation efficiency can be improved.

[0052] The reception unit can not only analyze the user's past operation history and select a reception method, but also dynamically adjust the priority of operations based on the user's operation history. For example, the reception unit can prioritize and display operations that the user performs frequently, thereby improving operation efficiency. The reception unit can also predict operations that will be performed during a specific time period based on the user's operation history and suggest them at an appropriate time. Furthermore, the reception unit can evaluate the importance of operations based on the user's operation history and prioritize receiving important operations. This allows for efficient operation reception by dynamically adjusting the priority of operations based on the user's operation history.

[0053] When receiving an operation, the reception unit not only filters the operations based on the user's current situation and areas of interest, but also suggests an appropriate operation taking into consideration the progress of the user's current task. For example, the reception unit monitors the progress of the task the user is currently working on and suggests the next operation to be performed. The reception unit can also preferentially display related operations based on the user's areas of interest. Furthermore, the reception unit can suggest an appropriate operation depending on the user's current situation (e.g., in a meeting or on the move). This makes it possible to improve the user's work efficiency by suggesting an appropriate operation based on the user's progress of the task and areas of interest.

[0054] When receiving an operation, the reception unit not only prioritizes receiving highly relevant operations in consideration of the user's geographical location information, but can also analyze the user's movement pattern and suggest appropriate operations. For example, when the user is in a specific location, the reception unit prioritizes receiving operations related to that location. The reception unit can also analyze the user's movement pattern and suggest operations to be performed while traveling. Furthermore, when the user is at home, the reception unit can prioritize receiving operations to be performed at home. This makes it possible to improve the user's operation efficiency by suggesting appropriate operations based on the user's geographical location information and movement pattern.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit accepts user operations. User operations include clicking, tapping, inputting, etc. For example, the reception unit accepts operations when the user attempts to use a specific function of the system, changes system settings, or logs in to the system. Specifically, the reception unit accepts operations when the user enters a username and password to log in to the system. Step 2: The analysis unit analyzes the operation history received by the reception unit. The operation history includes the date and time of the operation, the content of the operation, and the order of the operations. The analysis unit analyzes the operation history using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it analyzes the user's operation patterns based on the operation history and predicts the next necessary operation. Step 3: The service provider provides support based on the analysis results obtained by the analysis provider. This support includes providing FAQs, chat support, and guidelines. For example, if a user asks a question about a specific function of the system, the service provider will provide an answer to that question. They will also guide the user through the necessary steps when the user changes system settings. Furthermore, they will provide appropriate advice if the user encounters difficulties using the system. Specifically, when a user uses a particular function of the system, the service provider will explain how to use that function and guide the user through the necessary steps.

[0057] (Example 2) In an embodiment of the AI ​​Navigator system of the present invention, the AI ​​Navigator guides users as they use the system, as if they were sitting next to a colleague who is knowledgeable about the system. This eliminates the need to pore over thick manuals or general FAQs. It also eliminates the need to email inquiries or wait for a call back from a contact point. For example, when a user attempts to use a specific system function, the AI ​​Navigator explains how to use that function and guides them through the necessary steps. This allows the user to use the system smoothly. Furthermore, the AI ​​Navigator analyzes the user's operation history and system usage status to provide optimal support. For example, it predicts the next necessary operation based on the user's past functions and operation methods and provides appropriate advice. This allows the user to use the system efficiently. The AI ​​Navigator also provides instant answers to user questions. For example, if a user asks about a specific system function, the AI ​​Navigator answers the question and provides the necessary information. This allows the user to quickly resolve problems. In this way, the AI ​​Navigator is a powerful tool for supporting system usage and can significantly improve user convenience. This allows the AI ​​navigator system to efficiently accept, analyze, and provide support to users' operations.

[0058] The AI ​​navigator system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user operations. User operations include, but are not limited to, clicks, taps, and inputs. The reception unit receives, for example, operations when the user attempts to use a specific function of the system. The reception unit can also receive operations when the user changes system settings. The reception unit can also receive operations when the user logs into the system. For example, the reception unit receives an operation in which the user enters a username and password for logging into the system. The analysis unit analyzes the operation history received by the reception unit. The operation history includes, but is not limited to, the date and time of the operation, the operation content, and the order of the operation. The analysis unit can analyze the operation history using, for example, data mining technology. The analysis unit can also analyze the operation history using statistical analysis technology. The analysis unit can also analyze the operation history using a machine learning algorithm. For example, the analysis unit analyzes the user's operation patterns based on the operation history and predicts the next required operation. The provision unit provides support based on the analysis results obtained by the analysis unit. Examples of support include, but are not limited to, providing FAQs, chat support, and guidelines. For example, when a user asks a question about a specific function of the system, the providing unit provides an answer to the question. The providing unit can also guide the user through the necessary procedures when changing system settings. Furthermore, the providing unit can provide appropriate advice when the user has trouble using the system. For example, when a user uses a specific function of the system, the providing unit explains how to use the function and guides the user through the necessary procedures. This allows the AI ​​navigator system according to the embodiment to efficiently accept and analyze user operations and provide support.

[0059] The service provider can provide answers to user questions. For example, if a user asks about a specific function of the system, the service provider will provide an answer to that question. For example, if a user asks about how to configure the system, the service provider will explain how to configure it. The service provider can also explain how to use the system if a user asks about how to use it. For example, when a user uses a specific function of the system, the service provider will explain how to use that function. This allows for quick answers to user questions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's question into a generating AI, and the generating AI can generate an answer to that question.

[0060] The analysis unit may include a recording unit that records the user's operation history. The recording unit, for example, stores the user's operation history in a database. The operation history includes, but is not limited to, the date and time of the operation, the content of the operation, and the order of the operations. The recording unit may, for example, generate a log file to record the operation history. The recording unit can also record the operation history in real time. For example, the recording unit records the operation history each time a user uses the system. This improves the accuracy of the analysis by recording the user's operation history. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's operation history into a generating AI, and the generating AI can record that operation history.

[0061] The providing unit can predict the next required operation based on the functions and operation methods used by the user in the past and provide advice. The providing unit can predict the next required operation based on, for example, the functions and operation methods used by the user in the past. For example, the providing unit can analyze the history of functions used by the user in the past and predict the next required operation. The providing unit can also predict the next required operation based on operation methods used by the user in the past. For example, the providing unit can analyze operation methods used by the user in the past and predict the next required operation. Furthermore, the providing unit can provide appropriate advice based on the predicted operation. For example, the providing unit can specifically explain the operation the user should perform next. This can predict the next required operation for the user and provide appropriate advice, thereby supporting efficient system use. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history to a generating AI, which can predict the next required operation and provide advice.

[0062] When a user attempts to use a specific function of the system, the providing unit can explain how to use the function and guide the user through the necessary procedures. For example, when a user attempts to use a specific function of the system, the providing unit can explain how to use the function. For example, when a user attempts to use a search function of the system, the providing unit can explain how to search. Furthermore, when a user attempts to use an editing function of the system, the providing unit can explain how to edit. For example, when a user attempts to use a setting function of the system, the providing unit can explain how to set up. Furthermore, the providing unit can guide the user through the necessary procedures. For example, the providing unit can guide the user through step-by-step procedures for using a specific function of the system. This allows the user to operate the specific function of the system smoothly. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a user's operation into a generating AI, and the generating AI can guide the user through the procedures for that operation.

[0063] The reception unit can estimate the user's emotions and adjust the operation reception method based on the estimated user emotions. For example, when the user is stressed, the reception unit provides a simple interface and minimizes operation steps. For example, when the user is relaxed, the reception unit provides detailed operation options and suggests customizable operation methods. The reception unit can also prioritize voice input and quickly accept operations when the user is in a hurry. For example, when the user is in a hurry, the reception unit prioritizes voice input and quickly accepts operations. This allows for more appropriate operation reception by adjusting the operation reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, and the generation AI can adjust the operation reception method based on the emotion data.

[0064] The reception unit can analyze the user's past operation history and select a reception method. For example, the reception unit automatically displays operations that the user frequently performed in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest operations to be used in a specific time period based on the user's past operation history. For example, the reception unit predicts and suggests operations to be used in a specific time period based on the user's past operation history. In this way, the optimal reception method can be provided by analyzing the user's past operation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past operation history into a generation AI, which analyzes the operation history and selects the optimal reception method.

[0065] When receiving an operation, the reception unit can filter the operations based on the user's current situation and areas of interest. For example, the reception unit preferentially displays operations related to a project the user is currently working on. For example, the reception unit filters and displays related operations based on the user's areas of interest. The reception unit can also suggest appropriate operations depending on the user's current situation (for example, in a meeting or on the move). For example, if the user is in a meeting, the reception unit preferentially displays operations related to the meeting. This makes it possible to suggest appropriate operations based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI, and the generation AI can filter operations based on the data.

[0066] The reception unit can estimate the user's emotions and determine the priority of operations to process based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize important operations. For example, if the user is relaxed, the reception unit will prioritize detailed operations. The reception unit can also prioritize operations that can be completed quickly if the user is in a hurry. For example, if the reception unit is in a hurry, the reception unit will prioritize operations that can be completed quickly. This allows for more appropriate operation processing by determining the priority of operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can then determine the priority of operations based on that emotion data.

[0067] The reception unit can prioritize operations that are highly relevant to the user, taking into account the user's geographical location information when receiving an operation request. For example, if the user is in a specific location, the reception unit will prioritize operations related to that location. For example, if the user is on the move, the reception unit will prioritize operations related to movement. The reception unit can also prioritize operations that can be performed at home if the user is at home. For example, if the reception unit is at home, the reception unit will prioritize operations that can be performed at home. This allows the system to suggest appropriate operations based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize operations that are highly relevant based on that geographical location information.

[0068] The reception unit can analyze the user's social media activity and accept relevant operations when an operation is received. For example, the reception unit can suggest relevant operations based on information the user has shared on social media. For example, the reception unit can predict and suggest operations of interest based on the user's social media activity. The reception unit can also suggest relevant operations based on the accounts the user follows on social media. For example, the reception unit can suggest relevant operations based on the accounts the user follows on social media. This allows for the suggestion of appropriate operations based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant operations based on that data.

[0069] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit performs a simplified analysis. The analysis unit can also provide visually stimulating analysis results if the user is excited. For example, if the analysis unit provides visually stimulating analysis results if the user is excited, the analysis unit will provide visually stimulating analysis results. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis method based on that emotion data.

[0070] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the operation history. For example, the analysis unit performs a detailed analysis on important operation history. For example, the analysis unit performs a simplified analysis on general operation history. The analysis unit can also perform a detailed analysis on operations frequently performed by the user. For example, the analysis unit performs a detailed analysis on operations frequently performed by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the operation history to the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the operation. For example, the analysis unit applies an input error analysis algorithm to a data input operation. For example, the analysis unit applies a search efficiency analysis algorithm to a data search operation. The analysis unit can also apply an update history analysis algorithm to a data update operation. For example, the analysis unit applies an update history analysis algorithm to a data update operation. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation category data to a generation AI, and the generation AI can apply an appropriate analysis algorithm based on that data.

[0072] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize important analyses. For example, if the user is relaxed, the analysis unit can prioritize detailed analyses. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyses that can be completed quickly. For example, if the user is in a hurry, the analysis unit can prioritize analyses that can be completed quickly. This enables more appropriate analysis by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of analysis based on the emotion data.

[0073] During analysis, the analysis unit can determine the order of analysis based on the submission times of the operations. For example, the analysis unit prioritizes analysis of recently submitted operations. For example, the analysis unit postpones operations that were submitted earlier. The analysis unit can also dynamically adjust the order of analysis based on the submission times. For example, the analysis unit dynamically adjusts the order of analysis based on the submission times. This enables efficient analysis by adjusting the order of analysis based on the submission times of the operations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission times of the operations to a generation AI, and the generation AI can determine the order of analysis based on that data.

[0074] During analysis, the analysis unit can determine the order of analysis based on the relevance of operations. For example, the analysis unit prioritizes analysis of highly relevant operations. For example, the analysis unit postpones analysis of less relevant operations. The analysis unit can also dynamically adjust the order of analysis based on the relevance of operations. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of operations. This enables efficient analysis by adjusting the order of analysis based on the relevance of operations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation relevance data to a generation AI, and the generation AI can determine the order of analysis based on that data.

[0075] The providing unit can estimate the user's emotions and adjust the support provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides simple, highly visible support. For example, if the user is relaxed, the providing unit provides support including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide support that focuses on the main points. For example, if the user is in a hurry, the providing unit provides support that focuses on the main points. This enables more appropriate support by adjusting the support provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the support provision method based on the emotion data.

[0076] When providing support, the providing unit can select the optimal support method by analyzing the user's past operation history. The providing unit, for example, proposes the optimal support method based on support methods used by the user in the past. For example, the providing unit selects the most effective support method from the user's past operation history. The providing unit can also analyze the user's past operation history and predict the next support required. For example, the providing unit predicts the next support required based on the user's past operation history. In this way, the optimal support method can be provided by analyzing the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into a generating AI, and the generating AI can select the optimal support method based on the data.

[0077] When providing support, the providing unit can select a means of support based on the user's current situation. For example, when the user is in a meeting, the providing unit provides brief support. For example, when the user is on the move, the providing unit provides audio support. The providing unit can also provide detailed support when the user is at home. For example, when the user is at home, the providing unit provides detailed support. This makes it possible to provide appropriate support according to the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into a generating AI, and the generating AI can select a means of support based on that data.

[0078] The providing unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important support. For example, if the user is relaxed, the providing unit can prioritize providing detailed support. Furthermore, if the user is in a hurry, the providing unit can prioritize providing support that can be completed quickly. For example, if the user is in a hurry, the providing unit can prioritize providing support that can be completed quickly. This enables more appropriate support by determining the priority of support according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of support based on the emotion data.

[0079] The support unit can select the optimal support method when providing support, taking into account the user's geographical location information. For example, if the user is in a specific location, the support unit can provide support related to that location. For example, if the user is on the move, the support unit can provide support related to the move. The support unit can also provide support that can be performed at home if the user is at home. For example, if the support unit is at home, the support unit can provide support that can be performed at home. This allows for the provision of appropriate support based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal support method based on that data.

[0080] The support unit can analyze the user's social media activity and propose support measures when providing support. For example, the support unit can propose relevant support based on information shared by the user on social media. For example, the support unit can predict and propose support that the user is interested in based on their social media activity. The support unit can also propose relevant support based on the accounts the user follows on social media. For example, the support unit can propose relevant support based on the accounts the user follows on social media. This allows for the provision of appropriate support based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into a generating AI, and the generating AI can propose support measures based on that data. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user operations using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the operation history. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides support based on the analysis results. The reception unit may be realized by the specific processing unit 290 of the data processing device 12, and the provision unit may be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's operation using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the operation history. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides support based on the analysis results. The reception unit may be realized by the specific processing unit 290 of the data processing device 12, and the provision unit may be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's operation using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the operation history. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides support based on the analysis results. The reception unit may be realized by the specific processing unit 290 of the data processing device 12, and the provision unit may be realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's operation using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the operation history. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides support based on the analysis results. The reception unit may be realized by the specific processing unit 290 of the data processing device 12, and the provision unit may be realized by the control unit 46A of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception desk not only receives user input but can also infer the user's intent. For example, when a user attempts to use a specific function, it can infer the intent of that operation and suggest related actions. Furthermore, the reception desk can automatically correct user errors if the user makes a mistake. In addition, the reception desk can display questions to confirm the user's intent as they perform an operation, clarifying their intentions. By inferring the user's intent and providing appropriate support, user errors can be reduced.

[0083] The service provider not only provides answers to user questions but also understands the context of those questions and provides relevant information. For example, if a user asks about a specific feature, the service provider will also explain other related features and settings. Furthermore, the service provider can suggest relevant FAQs and guidelines based on the user's questions. In addition, the service provider can predict the next necessary actions based on the user's questions and provide appropriate advice. This allows for comprehensive answers to user questions and deepens user understanding.

[0084] The analysis unit not only includes a recording unit to record the user's operation history, but can also analyze patterns in the operation history to understand the user's operation trends. For example, the analysis unit analyzes operations that users frequently perform and operations performed during specific time periods to understand the user's operation trends. Furthermore, the analysis unit can make suggestions to improve the efficiency of operations based on the user's operation history. In addition, the analysis unit can predict operation errors based on the user's operation history and provide advice to prevent errors. In this way, the efficiency of operations can be improved by analyzing the user's operation history.

[0085] The providing unit can predict the next required operation based on the functions and operation methods the user has used in the past and provide advice, as well as evaluate the user's operation proficiency and provide appropriate support. For example, when the user uses a specific function, the providing unit can evaluate the user's proficiency with that function and provide guidelines for beginners and advice for advanced users. The providing unit can also adjust the difficulty of the operation according to the user's operation proficiency. Furthermore, the providing unit can suggest the next function or operation method to learn based on the user's operation proficiency. This makes it possible to provide support according to the user's operation proficiency and help improve the user's skills.

[0086] When a user attempts to use a specific function of the system, the providing unit not only explains how to use the function and guides the user through the necessary procedures, but also monitors the progress of the user's operation in real time and provides support at an appropriate time. For example, when the user performs an operation, the providing unit monitors the progress of the operation and guides the user in real time on the next operation to be performed. The providing unit can also provide immediate support when the user has difficulty with an operation. Furthermore, the providing unit can provide advice to improve the efficiency of the operation based on the progress of the user's operation. In this way, by monitoring the progress of the user's operation in real time and providing appropriate support, the user's operation efficiency can be improved.

[0087] The reception unit not only estimates the user's emotions and adjusts the operation reception method based on the estimated user emotions, but also monitors changes in the user's emotions in real time and changes the interface at an appropriate time. For example, if the user is feeling stressed, the reception unit simplifies the interface and minimizes operation steps. Alternatively, if the user is relaxed, the reception unit can provide detailed operation options and suggest customizable operation methods. Furthermore, the reception unit can change the design and color of the interface in response to changes in the user's emotions. This makes it possible to improve the user's operation experience by monitoring changes in the user's emotions in real time and providing an appropriate interface.

[0088] The reception unit can not only analyze the user's past operation history and select a reception method, but also dynamically adjust the priority of operations based on the user's operation history. For example, the reception unit can prioritize and display operations that the user performs frequently, thereby improving operation efficiency. The reception unit can also predict operations that will be performed during a specific time period based on the user's operation history and suggest them at an appropriate time. Furthermore, the reception unit can evaluate the importance of operations based on the user's operation history and prioritize receiving important operations. This allows for efficient operation reception by dynamically adjusting the priority of operations based on the user's operation history.

[0089] When receiving an operation, the reception unit not only filters the operations based on the user's current situation and areas of interest, but also suggests an appropriate operation taking into consideration the progress of the user's current task. For example, the reception unit monitors the progress of the task the user is currently working on and suggests the next operation to be performed. The reception unit can also preferentially display related operations based on the user's areas of interest. Furthermore, the reception unit can suggest an appropriate operation depending on the user's current situation (e.g., in a meeting or on the move). This makes it possible to improve the user's work efficiency by suggesting an appropriate operation based on the user's progress of the task and areas of interest.

[0090] The reception desk can estimate the user's emotions and prioritize the operations to be handled based on those estimates. Furthermore, it can predict changes in the user's emotions and suggest appropriate operations in advance. For example, if the user is tense, the reception desk can suggest relaxing operations to stabilize their emotions. If the user is relaxed, it can suggest challenging operations to help improve their skills. Additionally, if the user is in a hurry, it can suggest operations that can be completed quickly, reducing their stress. This allows the system to improve the user experience by predicting changes in the user's emotions and suggesting appropriate operations in advance.

[0091] When receiving an operation, the reception unit not only prioritizes receiving highly relevant operations in consideration of the user's geographical location information, but can also analyze the user's movement pattern and suggest appropriate operations. For example, when the user is in a specific location, the reception unit prioritizes receiving operations related to that location. The reception unit can also analyze the user's movement pattern and suggest operations to be performed while traveling. Furthermore, when the user is at home, the reception unit can prioritize receiving operations to be performed at home. This makes it possible to improve the user's operation efficiency by suggesting appropriate operations based on the user's geographical location information and movement pattern.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit accepts user operations. User operations include clicking, tapping, inputting, etc. For example, the reception unit accepts operations when the user attempts to use a specific function of the system, changes system settings, or logs in to the system. Specifically, the reception unit accepts operations when the user enters a username and password to log in to the system. Step 2: The analysis unit analyzes the operation history received by the reception unit. The operation history includes the date and time of the operation, the content of the operation, and the order of the operations. The analysis unit analyzes the operation history using data mining techniques, statistical analysis techniques, and machine learning algorithms. For example, it analyzes the user's operation patterns based on the operation history and predicts the next necessary operation. Step 3: The service provider provides support based on the analysis results obtained by the analysis provider. This support includes providing FAQs, chat support, and guidelines. For example, if a user asks a question about a specific function of the system, the service provider will provide an answer to that question. They will also guide the user through the necessary steps when the user changes system settings. Furthermore, they will provide appropriate advice if the user encounters difficulties using the system. Specifically, when a user uses a particular function of the system, the service provider will explain how to use that function and guide the user through the necessary steps.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0117] The 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.

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

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

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

[0121] Fig. 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.

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

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

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] 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.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives user operations; an analysis unit that analyzes the operation history accepted by the acceptance unit; a providing unit that provides support based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The providing unit Providing answers to user questions The system of claim 1 .

3. The analysis unit Equipped with a recording unit that records the user's operation history The system of claim 1 .

4. The providing unit Based on the functions and operations the user has used in the past, the system predicts the next operation that will be required and provides advice. The system of claim 1 .

5. The providing unit When a user attempts to use a particular function of the system, they are given instructions on how to use that function and are guided through the necessary steps. The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the operation acceptance method based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyze the user's past operation history and select the reception method The system of claim 1 .

8. The reception unit When accepting operations, filtering is performed based on the user's current situation and areas of interest. The system of claim 1 .

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