system

Abstract

The system according to the embodiment aims to provide appropriate help information based on a user's operation. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user operation logs. The analysis unit analyzes the operation logs collected by the collection unit. The provision unit provides help information 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 techniques have not adequately provided appropriate help information in response to user operations, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate help information based on a user's operation. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user operation logs. The analysis unit analyzes the operation logs collected by the collection unit. The provision unit provides help information based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate help information based on the user's operation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The help character system of an embodiment of the present invention utilizes modern AI technology to evolve the traditional Office help character, making it easier to use. This help character system uses AI to analyze the status of the application the user is using in real time and provide appropriate help information based on the user's operation history and current operation. The help information is designed to minimize disruption to the user's operations by optimizing the display position and timing of the character. The system also includes a function that allows the user to easily hide the character if they wish. For example, when a user is using word processing software, the AI ​​analyzes which function the user is using and what part of the application they are having trouble with. The AI ​​then provides appropriate help information based on the user's operation history and current operation. For example, if a user is trying to use a specific function but does not know how to operate it, the AI ​​displays a character that explains how to use that function. The character is displayed in a location that does not interfere with the user's operation, providing only the necessary information. Furthermore, the user can easily hide the character if they wish. For example, the character can be hidden by simply selecting "Hide" from the character's right-click menu. The character can also be easily displayed again if desired. This mechanism allows users to obtain the necessary help information without being interrupted. For example, if a user encounters a problem while creating a document, the character will provide help information at the appropriate time, minimizing interruptions to the work. Furthermore, the function to hide the character allows the user to customize the operating environment. This allows the help character system to provide necessary help information without interfering with the user's operations.

[0029] A help character system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects a user's operation log. The user's operation log includes, but is not limited to, clicks, scrolls, and inputs. The collection unit collects data such as the user's click position and number of clicks, the scroll distance and direction, and input text and commands. The collection unit can also record the user's operation history and analyze past operation patterns. For example, the collection unit records which functions the user has frequently used in the past and which operations the user has most frequently encountered. The analysis unit analyzes the operation log collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit extracts specific patterns or trends from the operation log to understand the user's operation status. The analysis unit can also predict future operations based on the user's operation history. For example, the analysis unit predicts which function the user is likely to use next and prepares appropriate help information. The provision unit provides help information based on the analysis results obtained by the analysis unit. The help information may be provided in the form of, for example, text, images, videos, or the like, but is not limited to these examples. For example, when a user attempts to use a specific function, the providing unit displays text on how to use the function. The providing unit may also display images or videos showing operation methods. Furthermore, the providing unit provides the help information through a character. For example, the providing unit adjusts the character to be displayed in a position that does not interfere with the user's operation and to provide only the necessary information. In this way, the help character system according to the embodiment can provide necessary help information without interfering with the user's operation.

[0030] The providing unit may include an adjusting unit that adjusts the display position of the character. The adjusting unit optimizes the display position of the character. For example, the adjusting unit may dynamically change the display position of the character depending on the user's operation status. For example, when the user is using word processing software, the adjusting unit may display the character in a corner of the screen so as not to interfere with the user's operation. Furthermore, when the user is performing a specific operation, the adjusting unit may display the character in a position related to the operation. For example, when the user is opening a menu, the adjusting unit may display the character near the menu and provide information indicating how to operate it. Furthermore, the adjusting unit may optimize the display position of the character based on the user's operation history. For example, the adjusting unit may record positions where the user has previously displayed the character and select an optimal position. In this way, the display position of the character can be optimized so as not to interfere with the user's operation.

[0031] The providing unit may include a hiding unit that enables a user to hide a character. The hiding unit provides a method for the user to hide a character. For example, the hiding unit may provide an option to select "hide" from a right-click menu of the character. The hiding unit may also include a function to automatically hide a character after a certain period of inactivity. For example, if a user does not perform an operation on a character for a certain period of time, the hiding unit may automatically hide the character. Furthermore, the hiding unit may select an optimal hiding method based on the user's operation history. For example, the hiding unit may record hiding methods used by the user in the past and suggest an optimal method. This allows the user to easily hide a character.

[0032] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the collection unit prioritizes collecting operation logs related to functions that the user frequently used in the past. For example, the collection unit collects operation logs related to buttons and menus that the user frequently clicked in the past. The collection unit can also analyze the user's past operation patterns and select the most efficient collection method. For example, the collection unit analyzes the order in which the user performed operations in the past and collects operation logs based on that order. Furthermore, the collection unit can prioritize collecting logs related to operations that the user often had trouble with in the past. For example, the collection unit collects logs related to operations in which the user frequently caused errors in the past. This enables efficient data collection by selecting the optimal collection method based on the user's past operation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past operation history data to a generation AI and have the generation AI select the optimal collection method.

[0033] When collecting operation logs, the collection unit can filter them based on the user's current application usage. For example, the collection unit collects only operation logs related to functions currently being used by the user. For example, the collection unit collects operation logs related to specific functions of word processing software currently being used by the user. Furthermore, when the user is using a specific application, the collection unit can prioritize collecting operation logs related to that application. For example, the collection unit collects operation logs of spreadsheet software currently being used by the user. Furthermore, when the user is using multiple applications simultaneously, the collection unit can prioritize collecting operation logs of the most important application. For example, the collection unit prioritizes collecting operation logs of word processing software among word processing software and spreadsheet software simultaneously being used by the user. This allows highly relevant data to be collected by filtering the operation logs based on the user's current application usage. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input current application usage data to a generation AI and have the generation AI perform filtering.

[0034] When collecting operation logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting operation logs related to functions that the user frequently uses in a specific location. For example, the collection unit collects operation logs related to functions that the user frequently uses in a specific office. The collection unit can also prioritize collecting operation logs related to functions that the user uses while traveling. For example, the collection unit collects operation logs of mobile applications that the user uses while traveling. The collection unit can also prioritize collecting operation logs related to applications that the user uses in a specific area. For example, the collection unit collects operation logs of applications that the user uses in a specific city. This enables more accurate data collection by collecting highly relevant operation logs based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize collecting highly relevant logs.

[0035] When collecting operation logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit prioritizes collecting operation logs related to information shared by the user on social media. For example, the collection unit collects operation logs related to links and posts shared by the user on social media. The collection unit can also prioritize collecting operation logs related to functions frequently used by the user on social media. For example, the collection unit collects operation logs related to a message sending function frequently used by the user on social media. Furthermore, the collection unit can also collect related operation logs based on the user's social media activities. For example, the collection unit collects operation logs related to a specific topic based on the user's social media activities. This enables more comprehensive data collection by collecting related operation logs based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input social media activity data to a generation AI and cause the generation AI to collect related logs.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation log. For example, the analysis unit performs a detailed analysis on important operation logs. For example, the analysis unit performs a detailed analysis on logs when a user performs an important operation. The analysis unit can also perform a standard analysis on general operation logs. For example, the analysis unit performs a standard analysis on logs when a user performs a general operation. The analysis unit can also perform a simplified analysis on operation logs with low importance. For example, the analysis unit simplifies and analyzes logs when a user performs an operation with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation log. 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 log to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the operation log. For example, the analysis unit applies a document analysis algorithm to an operation log related to document creation. For example, the analysis unit applies a document analysis algorithm to an operation log when a user is using word processing software. The analysis unit can also apply a data analysis algorithm to an operation log related to spreadsheets. For example, the analysis unit applies a data analysis algorithm to an operation log when a user is using spreadsheet software. The analysis unit can also apply a slide analysis algorithm to an operation log related to presentations. For example, the analysis unit applies a slide analysis algorithm to an operation log when a user is using presentation software. This enables more accurate analysis by applying different analysis algorithms depending on the category of the operation log. 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 category data of the operation log to a generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can determine the analysis priority based on the submission time of the operation logs. The analysis unit, for example, prioritizes analyzing the most recent operation logs. For example, the analysis unit prioritizes analyzing operation logs recently performed by the user. The analysis unit can also prioritize the most recent operation logs while referring to past operation logs. For example, the analysis unit prioritizes analyzing the most recent operation logs while referring to operation logs previously performed by the user. The analysis unit can also prioritize analyzing operation logs submitted within a specific period. For example, the analysis unit prioritizes analyzing operation logs performed by the user within a specific period. In this way, by determining the analysis priority based on the submission time of the operation logs, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the operation logs to the generation AI and cause the generation AI to determine the analysis priority.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operation logs. For example, the analysis unit prioritizes analysis of highly relevant operation logs. For example, the analysis unit prioritizes analysis of particularly highly relevant operation logs among operations performed by the user. The analysis unit can also postpone analysis of less relevant operation logs. For example, the analysis unit postpones analysis of less relevant operation logs among operations performed by the user. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the operation logs. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the operations performed by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the operation logs to the generation AI and cause the generation AI to adjust the order of analysis.

[0040] When providing help information, the providing unit can provide appropriate information by referring to the user's past operation history. For example, the providing unit can prioritize providing help information related to operations that the user has had difficulty with in the past. For example, the providing unit can prioritize providing help information related to operations that the user has had difficulty with in the past. The providing unit can also provide the most relevant help information based on the user's past operation history. For example, the providing unit can provide the most relevant help information based on the user's past operation history. Furthermore, the providing unit can also prioritize providing help information related to functions that the user has used in the past. For example, the providing unit can prioritize providing help information related to functions that the user has used in the past. This enables efficient support by providing optimal help information based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past operation history data to a generation AI and cause the generation AI to provide appropriate information.

[0041] When providing help information, the providing unit can adjust the level of detail of the information based on the user's current operation status. The providing unit, for example, provides help information related to a function currently being used by the user. For example, the providing unit provides help information related to a specific function of word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the providing unit can also provide detailed help information related to the operation. For example, when the user is performing a specific operation, the providing unit provides detailed help information related to the operation. Furthermore, when the user is performing multiple operations simultaneously, the providing unit can prioritize providing help information related to the most important operation. For example, when the user is performing multiple operations simultaneously, the providing unit prioritizes providing help information related to the most important operation. In this way, by adjusting the level of detail of the information based on the user's current operation status, appropriate help information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input current operation status data to a generation AI and cause the generation AI to adjust the level of detail of the information.

[0042] When providing help information, the providing unit can provide appropriate information taking into account the user's geographical location information. For example, the providing unit can prioritize providing help information related to functions used by the user in a specific location. For example, the providing unit can provide help information related to functions used by the user in a specific office. The providing unit can also prioritize providing help information related to functions used by the user while traveling. For example, the providing unit can provide help information for mobile applications used by the user while traveling. Furthermore, the providing unit can prioritize providing help information related to applications used by the user in a specific region. For example, the providing unit can provide help information for applications used by the user in a specific city. This enables more appropriate support by providing optimal help information based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to provide appropriate information.

[0043] When providing help information, the providing unit may analyze the user's social media activity and provide relevant information. For example, the providing unit may prioritize providing help information related to information shared by the user on social media. For example, the providing unit may provide help information related to links and posts shared by the user on social media. The providing unit may also prioritize providing help information related to functions frequently used by the user on social media. For example, the providing unit may provide help information related to a message sending function frequently used by the user on social media. Furthermore, the providing unit may also provide relevant help information based on the user's social media activity. For example, the providing unit may provide help information related to a specific topic based on the user's social media activity. This enables more comprehensive support by providing relevant help information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input social media activity data to a generation AI and cause the generation AI to provide relevant information.

[0044] When optimizing the display position of a character, the optimization unit can select an appropriate position by referring to the user's past operation history. The optimization unit, for example, selects the optimal position based on positions where the user has previously displayed the character. For example, the optimization unit records positions where the user has previously displayed the character and selects the optimal position based on those positions. The optimization unit can also select a position where the character will not get in the way based on the user's past operation history. For example, the optimization unit selects a position where the character will not get in the way based on the user's past operation history. Furthermore, the optimization unit can also select a position related to an operation that the user has had difficulty with in the past. For example, the optimization unit selects a position related to an operation that the user has had difficulty with in the past. In this way, by optimizing the display position of the character based on the user's past operation history, it is possible to avoid getting in the way of user operations. Some or all of the above-described processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit can input past operation history data to a generation AI and cause the generation AI to select an appropriate position.

[0045] When optimizing the character display position, the optimization unit can customize the position based on the user's current operation status. The optimization unit, for example, displays the character at a position related to a function currently being used by the user. For example, the optimization unit displays the character at a position related to a specific function of word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the optimization unit can also display the character at a position related to the operation. For example, when the user is performing a specific operation, the optimization unit displays the character at a position related to the operation. Furthermore, when the user is performing multiple operations simultaneously, the optimization unit can also display the character at a position related to the most important operation. For example, when the user is performing multiple operations simultaneously, the optimization unit displays the character at a position related to the most important operation. In this way, customizing the character display position based on the user's current operation status can prevent the character from interfering with the user's operations. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without AI. For example, the optimization unit can input current operation status data to a generation AI and cause the generation AI to customize the position.

[0046] When optimizing the display position of the character, the optimization unit can select an appropriate position by taking into consideration the user's geographical location information. For example, the optimization unit displays the character at a position related to a function frequently used by the user in a specific location. For example, the optimization unit displays the character at a position related to a function frequently used by the user in a specific office. The optimization unit can also display the character at a position related to a function used by the user while traveling. For example, the optimization unit displays the character at a position related to a mobile application used by the user while traveling. The optimization unit can also display the character at a position related to an application used by the user in a specific area. For example, the optimization unit displays the character at a position related to an application used by the user in a specific city. In this way, optimizing the display position of the character based on the user's geographical location information can avoid interfering with the user's operations. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input geographical location information data to the generation AI and cause the generation AI to select an appropriate position.

[0047] When optimizing the display position of the character, the optimization unit can analyze the user's social media activity and suggest an optimal position. For example, the optimization unit displays the character in a position related to information shared by the user on social media. For example, the optimization unit displays the character in a position related to links or posts shared by the user on social media. The optimization unit can also display the character in a position related to functions frequently used by the user on social media. For example, the optimization unit displays the character in a position related to a message sending function frequently used by the user on social media. Furthermore, the optimization unit can display the character in a position related to the user's activity on social media. For example, the optimization unit displays the character in a position related to a specific topic based on the user's activity on social media. This makes it possible to optimize the display position of the character based on the user's social media activity so as not to interfere with user operations. Some or all of the above-described processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit may input social media activity data to the generation AI and cause the generation AI to suggest an optimal position.

[0048] When hiding a character, the hiding unit can select an appropriate hiding method by referring to the user's past operation history. The hiding unit, for example, suggests an optimal hiding method based on the hiding methods used by the user in the past. For example, the hiding unit records the hiding methods used by the user in the past and suggests an optimal hiding method based on those methods. The hiding unit can also select the most efficient hiding method based on the user's past operation history. For example, the hiding unit selects the most efficient hiding method based on the user's past operation history. Furthermore, the hiding unit can also suggest a hiding method related to an operation that the user has had difficulty with in the past. For example, the hiding unit suggests a hiding method related to an operation that the user has had difficulty with in the past. By optimizing the character hiding method based on the user's past operation history, it is possible to avoid interfering with the user's operation. Some or all of the above-described processing in the hiding unit may be performed using, or without, AI. For example, the hiding unit can input past operation history data into a generation AI and cause the generation AI to select an appropriate hiding method.

[0049] When hiding a character, the hiding unit can customize the hiding method based on the user's current operating status. The hiding unit, for example, provides a hiding method related to a function currently being used by the user. For example, the hiding unit provides a hiding method related to a specific function of the word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the hiding unit can also provide a hiding method related to that operation. For example, when the user is performing a specific operation, the hiding unit provides a hiding method related to that operation. Furthermore, when the user is performing multiple operations simultaneously, the hiding unit can also provide a hiding method related to the most important operation. For example, when the user is performing multiple operations simultaneously, the hiding unit provides a hiding method related to the most important operation. This allows the character hiding method to be customized based on the user's current operating status so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using, for example, AI, or may be performed without AI. For example, the hiding unit can input current operating status data to a generating AI and cause the generating AI to customize the hiding method.

[0050] When hiding a character, the hiding unit can select an appropriate hiding method by taking into account the user's geographical location information. The hiding unit, for example, provides a hiding method related to a function frequently used by the user in a specific location. For example, the hiding unit provides a hiding method related to a function frequently used by the user in a specific office. The hiding unit can also provide a hiding method related to a function used by the user while traveling. For example, the hiding unit provides a hiding method for a mobile application used by the user while traveling. The hiding unit can also provide a hiding method related to an application used by the user in a specific region. For example, the hiding unit provides a hiding method for an application used by the user in a specific city. This makes it possible to optimize the character hiding method based on the user's geographical location information so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using AI, for example, or may be performed without using AI. For example, the hiding unit can input geographical location information data to the generation AI and cause the generation AI to select an appropriate hiding method.

[0051] When hiding a character, the hiding unit can analyze the user's social media activity and suggest an optimal hiding method. The hiding unit, for example, provides a hiding method related to information shared by the user on social media. For example, the hiding unit provides a hiding method related to links or posts shared by the user on social media. The hiding unit can also provide a hiding method related to functions frequently used by the user on social media. For example, the hiding unit provides a hiding method related to a message sending function frequently used by the user on social media. The hiding unit can also provide a related hiding method based on the user's social media activity. For example, the hiding unit provides a hiding method related to a specific topic based on the user's social media activity. This makes it possible to optimize the character hiding method based on the user's social media activity so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using, or without, AI. For example, the hiding unit can input social media activity data into a generation AI and cause the generation AI to suggest an optimal hiding method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can predict the function the user is likely to need next based on the user's operation history and prepare help information related to that function in advance. For example, it can analyze functions that the user has frequently used in the past and predict the function the user is likely to use next based on that pattern. The analysis unit can also immediately provide help information related to a specific operation when the user performs that operation. Furthermore, the analysis unit can prioritize providing help information related to operations that the user has had trouble with in the past. This allows the help information the user needs to be prepared in advance and provided quickly, thereby smoothly supporting the user's operations.

[0054] The adjustment unit can dynamically change the display position of the character according to the user's operation speed. For example, when the user is operating quickly, the character can be displayed in the corner of the screen so as not to interfere with the operation. When the user is operating slowly, the character can be displayed in the center of the screen so that help information stands out. Furthermore, when the user is performing a specific operation, the adjustment unit can display the character in a position related to that operation. This makes it possible to support the user's operation by optimizing the display position of the character according to the user's operation speed.

[0055] The hidden section can adjust the transparency of a character when the user hides the character. For example, the user can make the character semi-transparent rather than completely hiding it, so that the character can be displayed immediately when needed. The hidden section can also reduce the size of the character when the user hides it. For example, the character can be made small and displayed in a corner of the screen so as not to interfere with operation. Furthermore, the hidden section can also shorten the display time of the character when the user hides it. This allows for flexible adjustment when the user hides a character.

[0056] The collection unit can analyze the user's past operation history and understand operation patterns during specific time periods. For example, it can prioritize collection of operation logs related to functions that the user frequently uses during specific time periods. The collection unit can also focus on collecting logs related to operations performed by the user on specific days of the week. Furthermore, the collection unit can also collect operation logs related to specific events (e.g., creating a report at the end of the month) by the user. This allows for efficient data collection by selecting the optimal collection method based on the user's past operation history.

[0057] When collecting operation logs, the collection unit can filter the operation logs based on the user's current application usage status. For example, the collection unit collects only operation logs related to functions currently being used by the user. In addition, when the user is using a specific application, the collection unit can also prioritize collecting operation logs related to that application. Furthermore, when the user is using multiple applications simultaneously, the collection unit can also prioritize collecting operation logs of the most important application. In this way, by filtering the operation logs based on the user's current application usage status, highly relevant data can be collected.

[0058] When collecting operation logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting operation logs related to functions that the user frequently uses in a specific location. The collection unit can also prioritize collecting operation logs related to functions that the user uses while traveling. Furthermore, the collection unit can also prioritize collecting operation logs related to applications that the user uses in a specific area. This allows for more accurate data collection by collecting highly relevant operation logs based on the user's geographical location information.

[0059] When collecting operation logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit can prioritize collecting operation logs related to information shared by the user on social media. The collection unit can also prioritize collecting operation logs related to functions frequently used by the user on social media. Furthermore, the collection unit can also collect related operation logs based on the user's social media activities. This allows for more comprehensive data collection by collecting related operation logs based on the user's social media activities.

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

[0061] Step 1: The collection unit collects the user's operation log. The user's operation log includes clicks, scrolling, input, etc. The collection unit collects data such as the location and number of clicks the user made, the distance and direction they scrolled, and the text and commands they entered. The collection unit can also record the user's operation history and analyze past operation patterns. For example, it can record which functions the user frequently used in the past and which operations they often had trouble with. Step 2: The analysis unit analyzes the operation logs collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit extracts specific patterns and trends from the operation logs to understand the user's operation status. The analysis unit can also predict future operations based on the user's operation history. For example, it can predict which function the user is likely to use next and prepare appropriate help information. Step 3: The providing unit provides help information based on the analysis results obtained by the analyzing unit. The help information is provided in the form of text, images, videos, etc. For example, when the user is trying to use a specific function, the providing unit displays text on how to use that function. The providing unit can also display images or videos showing how to operate the function. Furthermore, the providing unit provides help information through characters. For example, the characters are displayed in a position that does not interfere with the user's operations, and are adjusted to provide only the necessary information.

[0062] (Example 2) The help character system of an embodiment of the present invention utilizes modern AI technology to evolve the traditional Office help character, making it easier to use. This help character system uses AI to analyze the status of the application the user is using in real time and provide appropriate help information based on the user's operation history and current operation. The help information is designed to minimize disruption to the user's operations by optimizing the display position and timing of the character. The system also includes a function that allows the user to easily hide the character if they wish. For example, when a user is using word processing software, the AI ​​analyzes which function the user is using and what part of the application they are having trouble with. The AI ​​then provides appropriate help information based on the user's operation history and current operation. For example, if a user is trying to use a specific function but does not know how to operate it, the AI ​​displays a character that explains how to use that function. The character is displayed in a location that does not interfere with the user's operation, providing only the necessary information. Furthermore, the user can easily hide the character if they wish. For example, the character can be hidden by simply selecting "Hide" from the character's right-click menu. The character can also be easily displayed again if desired. This mechanism allows users to obtain the necessary help information without being interrupted. For example, if a user encounters a problem while creating a document, the character will provide help information at the appropriate time, minimizing interruptions to the work. Furthermore, the function to hide the character allows the user to customize the operating environment. This allows the help character system to provide necessary help information without interfering with the user's operations.

[0063] A help character system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects a user's operation log. The user's operation log includes, but is not limited to, clicks, scrolls, and inputs. The collection unit collects data such as the user's click position and number of clicks, the scroll distance and direction, and input text and commands. The collection unit can also record the user's operation history and analyze past operation patterns. For example, the collection unit records which functions the user has frequently used in the past and which operations the user has most frequently encountered. The analysis unit analyzes the operation log collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit extracts specific patterns or trends from the operation log to understand the user's operation status. The analysis unit can also predict future operations based on the user's operation history. For example, the analysis unit predicts which function the user is likely to use next and prepares appropriate help information. The provision unit provides help information based on the analysis results obtained by the analysis unit. The help information may be provided in the form of, for example, text, images, videos, or the like, but is not limited to these examples. For example, when a user attempts to use a specific function, the providing unit displays text on how to use the function. The providing unit may also display images or videos showing operation methods. Furthermore, the providing unit provides the help information through a character. For example, the providing unit adjusts the character to be displayed in a position that does not interfere with the user's operation and to provide only the necessary information. In this way, the help character system according to the embodiment can provide necessary help information without interfering with the user's operation.

[0064] The providing unit may include an adjusting unit that adjusts the display position of the character. The adjusting unit optimizes the display position of the character. For example, the adjusting unit may dynamically change the display position of the character depending on the user's operation status. For example, when the user is using word processing software, the adjusting unit may display the character in a corner of the screen so as not to interfere with the user's operation. Furthermore, when the user is performing a specific operation, the adjusting unit may display the character in a position related to the operation. For example, when the user is opening a menu, the adjusting unit may display the character near the menu and provide information indicating how to operate it. Furthermore, the adjusting unit may optimize the display position of the character based on the user's operation history. For example, the adjusting unit may record positions where the user has previously displayed the character and select an optimal position. In this way, the display position of the character can be optimized so as not to interfere with the user's operation.

[0065] The providing unit may include a hiding unit that enables a user to hide a character. The hiding unit provides a method for the user to hide a character. For example, the hiding unit may provide an option to select "hide" from a right-click menu of the character. The hiding unit may also include a function to automatically hide a character after a certain period of inactivity. For example, if a user does not perform an operation on a character for a certain period of time, the hiding unit may automatically hide the character. Furthermore, the hiding unit may select an optimal hiding method based on the user's operation history. For example, the hiding unit may record hiding methods used by the user in the past and suggest an optimal method. This allows the user to easily hide a character.

[0066] The collection unit can estimate the user's emotions and adjust the timing of collecting operation logs based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows the timing of collecting operation logs to be adjusted according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0067] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the collection unit prioritizes collecting operation logs related to functions that the user frequently used in the past. For example, the collection unit collects operation logs related to buttons and menus that the user frequently clicked in the past. The collection unit can also analyze the user's past operation patterns and select the most efficient collection method. For example, the collection unit analyzes the order in which the user performed operations in the past and collects operation logs based on that order. Furthermore, the collection unit can prioritize collecting logs related to operations that the user often had trouble with in the past. For example, the collection unit collects logs related to operations in which the user frequently caused errors in the past. This enables efficient data collection by selecting the optimal collection method based on the user's past operation history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past operation history data to a generation AI and have the generation AI select the optimal collection method.

[0068] When collecting operation logs, the collection unit can filter them based on the user's current application usage. For example, the collection unit collects only operation logs related to functions currently being used by the user. For example, the collection unit collects operation logs related to specific functions of word processing software currently being used by the user. Furthermore, when the user is using a specific application, the collection unit can prioritize collecting operation logs related to that application. For example, the collection unit collects operation logs of spreadsheet software currently being used by the user. Furthermore, when the user is using multiple applications simultaneously, the collection unit can prioritize collecting operation logs of the most important application. For example, the collection unit prioritizes collecting operation logs of word processing software among word processing software and spreadsheet software simultaneously being used by the user. This allows highly relevant data to be collected by filtering the operation logs based on the user's current application usage. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input current application usage data to a generation AI and have the generation AI perform filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of operation logs to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important operation logs. For example, the collection unit collects particularly important operation logs among operations performed when the user is stressed. The collection unit can also prioritize collecting detailed operation logs if the user is relaxed. For example, the collection unit collects detailed operation logs among operations performed when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting operation logs that can be collected quickly. For example, the collection unit prioritizes collecting operation logs among operations performed when the user is in a hurry. In this way, by determining the priority of operation logs according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0070] When collecting operation logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting operation logs related to functions that the user frequently uses in a specific location. For example, the collection unit collects operation logs related to functions that the user frequently uses in a specific office. The collection unit can also prioritize collecting operation logs related to functions that the user uses while traveling. For example, the collection unit collects operation logs of mobile applications that the user uses while traveling. The collection unit can also prioritize collecting operation logs related to applications that the user uses in a specific area. For example, the collection unit collects operation logs of applications that the user uses in a specific city. This enables more accurate data collection by collecting highly relevant operation logs based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize collecting highly relevant logs.

[0071] When collecting operation logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit prioritizes collecting operation logs related to information shared by the user on social media. For example, the collection unit collects operation logs related to links and posts shared by the user on social media. The collection unit can also prioritize collecting operation logs related to functions frequently used by the user on social media. For example, the collection unit collects operation logs related to a message sending function frequently used by the user on social media. Furthermore, the collection unit can also collect related operation logs based on the user's social media activities. For example, the collection unit collects operation logs related to a specific topic based on the user's social media activities. This enables more comprehensive data collection by collecting related operation logs based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input social media activity data to a generation AI and cause the generation AI to collect related logs.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit applies a simplified analysis method. For example, the analysis unit simplifies and analyzes the operation log performed when the user is feeling stressed. Furthermore, when the user is relaxed, the analysis unit can also apply a detailed analysis method. For example, the analysis unit analyzes in detail the operation log performed when the user is relaxed. Furthermore, when the user is in a hurry, the analysis unit can also apply an analysis method that can quickly obtain results. For example, the analysis unit quickly analyzes the operation log performed when the user is in a hurry. This allows for more appropriate analysis results to be obtained by adjusting the analysis method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation log. For example, the analysis unit performs a detailed analysis on important operation logs. For example, the analysis unit performs a detailed analysis on logs when a user performs an important operation. The analysis unit can also perform a standard analysis on general operation logs. For example, the analysis unit performs a standard analysis on logs when a user performs a general operation. The analysis unit can also perform a simplified analysis on operation logs with low importance. For example, the analysis unit simplifies and analyzes logs when a user performs an operation with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operation log. 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 log to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the operation log. For example, the analysis unit applies a document analysis algorithm to an operation log related to document creation. For example, the analysis unit applies a document analysis algorithm to an operation log when a user is using word processing software. The analysis unit can also apply a data analysis algorithm to an operation log related to spreadsheets. For example, the analysis unit applies a data analysis algorithm to an operation log when a user is using spreadsheet software. The analysis unit can also apply a slide analysis algorithm to an operation log related to presentations. For example, the analysis unit applies a slide analysis algorithm to an operation log when a user is using presentation software. This enables more accurate analysis by applying different analysis algorithms depending on the category of the operation log. 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 category data of the operation log to a generation AI and cause the generation AI to apply different analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is stressed, the analysis unit prioritizes the analysis of important operation logs. For example, the analysis unit prioritizes the analysis of particularly important operation logs among operations performed when the user is stressed. The analysis unit can also prioritize detailed analysis if the user is relaxed. For example, the analysis unit prioritizes detailed analysis among operations performed when the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis that can provide quick results. For example, the analysis unit prioritizes analysis that can provide quick results among operations performed when the user is in a hurry. In this way, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission time of the operation logs. The analysis unit, for example, prioritizes analyzing the most recent operation logs. For example, the analysis unit prioritizes analyzing operation logs recently performed by the user. The analysis unit can also prioritize the most recent operation logs while referring to past operation logs. For example, the analysis unit prioritizes analyzing the most recent operation logs while referring to operation logs previously performed by the user. The analysis unit can also prioritize analyzing operation logs submitted within a specific period. For example, the analysis unit prioritizes analyzing operation logs performed by the user within a specific period. In this way, by determining the analysis priority based on the submission time of the operation logs, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the operation logs to the generation AI and cause the generation AI to determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operation logs. For example, the analysis unit prioritizes analysis of highly relevant operation logs. For example, the analysis unit prioritizes analysis of particularly highly relevant operation logs among operations performed by the user. The analysis unit can also postpone analysis of less relevant operation logs. For example, the analysis unit postpones analysis of less relevant operation logs among operations performed by the user. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the operation logs. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the operations performed by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the operation logs to the generation AI and cause the generation AI to adjust the order of analysis.

[0078] The providing unit can estimate the user's emotion and adjust the method of providing help information based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit provides concise and easy-to-understand help information. For example, when the user is feeling stressed, the providing unit provides concise and easy-to-understand help information. The providing unit can also provide detailed help information when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed help information. Furthermore, when the user is in a hurry, the providing unit can also provide help information that can be quickly understood. For example, when the user is in a hurry, the providing unit provides help information that can be quickly understood. This allows the method of providing help information to be adjusted according to the user's emotion, thereby providing more appropriate help information. Emotion estimation is realized 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 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 image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0079] When providing help information, the providing unit can provide appropriate information by referring to the user's past operation history. For example, the providing unit can prioritize providing help information related to operations that the user has had difficulty with in the past. For example, the providing unit can prioritize providing help information related to operations that the user has had difficulty with in the past. The providing unit can also provide the most relevant help information based on the user's past operation history. For example, the providing unit can provide the most relevant help information based on the user's past operation history. Furthermore, the providing unit can also prioritize providing help information related to functions that the user has used in the past. For example, the providing unit can prioritize providing help information related to functions that the user has used in the past. This enables efficient support by providing optimal help information based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past operation history data to a generation AI and cause the generation AI to provide appropriate information.

[0080] When providing help information, the providing unit can adjust the level of detail of the information based on the user's current operation status. The providing unit, for example, provides help information related to a function currently being used by the user. For example, the providing unit provides help information related to a specific function of word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the providing unit can also provide detailed help information related to the operation. For example, when the user is performing a specific operation, the providing unit provides detailed help information related to the operation. Furthermore, when the user is performing multiple operations simultaneously, the providing unit can prioritize providing help information related to the most important operation. For example, when the user is performing multiple operations simultaneously, the providing unit prioritizes providing help information related to the most important operation. In this way, by adjusting the level of detail of the information based on the user's current operation status, appropriate help information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input current operation status data to a generation AI and cause the generation AI to adjust the level of detail of the information.

[0081] The providing unit can estimate the user's emotions and determine the priority of help information based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important help information. For example, when the user is feeling stressed, the providing unit can prioritize providing particularly important help information. The providing unit can also prioritize providing detailed help information when the user is relaxed. For example, when the user is relaxed, the providing unit can prioritize providing detailed help information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing help information that can be quickly understood. For example, when the user is in a hurry, the providing unit prioritizes providing help information that can be quickly understood. In this way, by determining the priority of help information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate the user's emotions.

[0082] When providing help information, the providing unit can provide appropriate information taking into account the user's geographical location information. For example, the providing unit can prioritize providing help information related to functions used by the user in a specific location. For example, the providing unit can provide help information related to functions used by the user in a specific office. The providing unit can also prioritize providing help information related to functions used by the user while traveling. For example, the providing unit can provide help information for mobile applications used by the user while traveling. Furthermore, the providing unit can prioritize providing help information related to applications used by the user in a specific region. For example, the providing unit can provide help information for applications used by the user in a specific city. This enables more appropriate support by providing optimal help information based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to provide appropriate information.

[0083] When providing help information, the providing unit may analyze the user's social media activity and provide relevant information. For example, the providing unit may prioritize providing help information related to information shared by the user on social media. For example, the providing unit may provide help information related to links and posts shared by the user on social media. The providing unit may also prioritize providing help information related to functions frequently used by the user on social media. For example, the providing unit may provide help information related to a message sending function frequently used by the user on social media. Furthermore, the providing unit may also provide relevant help information based on the user's social media activity. For example, the providing unit may provide help information related to a specific topic based on the user's social media activity. This enables more comprehensive support by providing relevant help information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input social media activity data to a generation AI and cause the generation AI to provide relevant information.

[0084] The optimization unit can estimate the user's emotion and adjust the display position of the character based on the estimated user's emotion. For example, when the user is feeling stressed, the optimization unit displays the character in an inconspicuous position. For example, when the user is feeling stressed, the optimization unit displays the character in a corner of the screen. Furthermore, when the user is relaxed, the optimization unit can also display the character in a prominent position. For example, when the user is relaxed, the optimization unit displays the character in the center of the screen. Furthermore, when the user is in a hurry, the optimization unit can display the character in a position that is easily accessible. For example, when the user is in a hurry, the optimization unit displays the character near the operation panel. In this way, by adjusting the display position of the character according to the user's emotion, it is possible to avoid interfering with the user's operation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0085] When optimizing the display position of a character, the optimization unit can select an appropriate position by referring to the user's past operation history. The optimization unit, for example, selects the optimal position based on positions where the user has previously displayed the character. For example, the optimization unit records positions where the user has previously displayed the character and selects the optimal position based on those positions. The optimization unit can also select a position where the character will not get in the way based on the user's past operation history. For example, the optimization unit selects a position where the character will not get in the way based on the user's past operation history. Furthermore, the optimization unit can also select a position related to an operation that the user has had difficulty with in the past. For example, the optimization unit selects a position related to an operation that the user has had difficulty with in the past. In this way, by optimizing the display position of the character based on the user's past operation history, it is possible to avoid getting in the way of user operations. Some or all of the above-described processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit can input past operation history data to a generation AI and cause the generation AI to select an appropriate position.

[0086] When optimizing the character display position, the optimization unit can customize the position based on the user's current operation status. The optimization unit, for example, displays the character at a position related to a function currently being used by the user. For example, the optimization unit displays the character at a position related to a specific function of word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the optimization unit can also display the character at a position related to the operation. For example, when the user is performing a specific operation, the optimization unit displays the character at a position related to the operation. Furthermore, when the user is performing multiple operations simultaneously, the optimization unit can also display the character at a position related to the most important operation. For example, when the user is performing multiple operations simultaneously, the optimization unit displays the character at a position related to the most important operation. In this way, customizing the character display position based on the user's current operation status can prevent the character from interfering with the user's operations. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without AI. For example, the optimization unit can input current operation status data to a generation AI and cause the generation AI to customize the position.

[0087] The optimization unit can estimate the user's emotions and determine the priority of the character's display position based on the estimated user's emotions. For example, when the user is feeling stressed, the optimization unit displays the character in an inconspicuous position. For example, when the user is feeling stressed, the optimization unit displays the character in a corner of the screen. Furthermore, when the user is relaxed, the optimization unit can also display the character in a prominent position. For example, when the user is relaxed, the optimization unit displays the character in the center of the screen. Furthermore, when the user is in a hurry, the optimization unit can display the character in a position that is easily accessible. For example, when the user is in a hurry, the optimization unit displays the character near the operation panel. In this way, by determining the priority of the character's display position according to the user's emotions, it is possible to avoid interfering with the user's operations. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or without using an AI. For example, the optimization unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] When optimizing the display position of the character, the optimization unit can select an appropriate position by taking into consideration the user's geographical location information. For example, the optimization unit displays the character at a position related to a function frequently used by the user in a specific location. For example, the optimization unit displays the character at a position related to a function frequently used by the user in a specific office. The optimization unit can also display the character at a position related to a function used by the user while traveling. For example, the optimization unit displays the character at a position related to a mobile application used by the user while traveling. The optimization unit can also display the character at a position related to an application used by the user in a specific area. For example, the optimization unit displays the character at a position related to an application used by the user in a specific city. In this way, optimizing the display position of the character based on the user's geographical location information can avoid interfering with the user's operations. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input geographical location information data to the generation AI and cause the generation AI to select an appropriate position.

[0089] When optimizing the display position of the character, the optimization unit can analyze the user's social media activity and suggest an optimal position. For example, the optimization unit displays the character in a position related to information shared by the user on social media. For example, the optimization unit displays the character in a position related to links or posts shared by the user on social media. The optimization unit can also display the character in a position related to functions frequently used by the user on social media. For example, the optimization unit displays the character in a position related to a message sending function frequently used by the user on social media. Furthermore, the optimization unit can display the character in a position related to the user's activity on social media. For example, the optimization unit displays the character in a position related to a specific topic based on the user's activity on social media. This makes it possible to optimize the display position of the character based on the user's social media activity so as not to interfere with user operations. Some or all of the above-described processing in the optimization unit may be performed using, or without, AI. For example, the optimization unit may input social media activity data to the generation AI and cause the generation AI to suggest an optimal position.

[0090] The hiding unit can estimate the user's emotions and adjust how the character is hidden based on the estimated user's emotions. For example, the hiding unit provides an option to easily hide the character when the user is feeling stressed. For example, the hiding unit provides an option to easily hide the character when the user is feeling stressed. The hiding unit can also provide detailed instructions on how to hide the character when the user is relaxed. For example, the hiding unit can provide detailed instructions on how to hide the character when the user is relaxed. Furthermore, the hiding unit can also provide a shortcut to quickly hide the character when the user is in a hurry. For example, the hiding unit provides a shortcut to quickly hide the character when the user is in a hurry. This allows the character's hiding method to be adjusted according to the user's emotions, thereby preventing interference with user operations. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the hiding unit can be performed using, for example, an AI, or without an AI. For example, the non-display unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0091] When hiding a character, the hiding unit can select an appropriate hiding method by referring to the user's past operation history. The hiding unit, for example, suggests an optimal hiding method based on the hiding methods used by the user in the past. For example, the hiding unit records the hiding methods used by the user in the past and suggests an optimal hiding method based on those methods. The hiding unit can also select the most efficient hiding method based on the user's past operation history. For example, the hiding unit selects the most efficient hiding method based on the user's past operation history. Furthermore, the hiding unit can also suggest a hiding method related to an operation that the user has had difficulty with in the past. For example, the hiding unit suggests a hiding method related to an operation that the user has had difficulty with in the past. By optimizing the character hiding method based on the user's past operation history, it is possible to avoid interfering with the user's operation. Some or all of the above-described processing in the hiding unit may be performed using, or without, AI. For example, the hiding unit can input past operation history data into a generation AI and cause the generation AI to select an appropriate hiding method.

[0092] When hiding a character, the hiding unit can customize the hiding method based on the user's current operating status. The hiding unit, for example, provides a hiding method related to a function currently being used by the user. For example, the hiding unit provides a hiding method related to a specific function of the word processing software currently being used by the user. Furthermore, when the user is performing a specific operation, the hiding unit can also provide a hiding method related to that operation. For example, when the user is performing a specific operation, the hiding unit provides a hiding method related to that operation. Furthermore, when the user is performing multiple operations simultaneously, the hiding unit can also provide a hiding method related to the most important operation. For example, when the user is performing multiple operations simultaneously, the hiding unit provides a hiding method related to the most important operation. This allows the character hiding method to be customized based on the user's current operating status so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using, for example, AI, or may be performed without AI. For example, the hiding unit can input current operating status data to a generating AI and cause the generating AI to customize the hiding method.

[0093] The hiding unit can estimate the user's emotions and determine a priority for hiding characters based on the estimated user's emotions. For example, the hiding unit quickly hides characters when the user is feeling stressed. Furthermore, the hiding unit can provide a hiding method with detailed instructions when the user is relaxed. For example, the hiding unit provides a hiding method with detailed instructions when the user is relaxed. Furthermore, the hiding unit can provide an option to easily hide characters when the user is in a hurry. For example, the hiding unit provides an option to easily hide characters when the user is in a hurry. This allows the user's operation to be uninterrupted by determining the priority for hiding characters according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, 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 hiding unit can be performed using, for example, an AI, or without an AI. For example, the non-display unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0094] When hiding a character, the hiding unit can select an appropriate hiding method by taking into account the user's geographical location information. The hiding unit, for example, provides a hiding method related to a function frequently used by the user in a specific location. For example, the hiding unit provides a hiding method related to a function frequently used by the user in a specific office. The hiding unit can also provide a hiding method related to a function used by the user while traveling. For example, the hiding unit provides a hiding method for a mobile application used by the user while traveling. The hiding unit can also provide a hiding method related to an application used by the user in a specific region. For example, the hiding unit provides a hiding method for an application used by the user in a specific city. This makes it possible to optimize the character hiding method based on the user's geographical location information so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using AI, for example, or may be performed without using AI. For example, the hiding unit can input geographical location information data to the generation AI and cause the generation AI to select an appropriate hiding method.

[0095] When hiding a character, the hiding unit can analyze the user's social media activity and suggest an optimal hiding method. The hiding unit, for example, provides a hiding method related to information shared by the user on social media. For example, the hiding unit provides a hiding method related to links or posts shared by the user on social media. The hiding unit can also provide a hiding method related to functions frequently used by the user on social media. For example, the hiding unit provides a hiding method related to a message sending function frequently used by the user on social media. The hiding unit can also provide a related hiding method based on the user's social media activity. For example, the hiding unit provides a hiding method related to a specific topic based on the user's social media activity. This makes it possible to optimize the character hiding method based on the user's social media activity so as not to interfere with the user's operations. Some or all of the above-described processing in the hiding unit may be performed using, or without, AI. For example, the hiding unit can input social media activity data into a generation AI and cause the generation AI to suggest an optimal hiding method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user operation logs using the control unit 46A of the smart device 14 and analyzes the collected data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected operation logs, and grasps the user's operation status. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides help information based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection 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 collection unit collects a user's operation log using the control unit 46A of the smart glasses 214 and analyzes the collected data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected operation log, and grasps the user's operation status. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides help information based on the analysis result. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection 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 collection unit collects user operation logs by the control unit 46A of the headset type terminal 314, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected operation logs to grasp the user's operation status. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides help information based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection 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 collection unit collects user operation logs using the control unit 46A of the robot 414, and analyzes the collected data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected operation logs to grasp the user's operation status. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides help information based on the analysis results.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can predict the function the user is likely to need next based on the user's operation history and prepare help information related to that function in advance. For example, it can analyze functions that the user has frequently used in the past and predict the function the user is likely to use next based on that pattern. The analysis unit can also immediately provide help information related to a specific operation when the user performs that operation. Furthermore, the analysis unit can prioritize providing help information related to operations that the user has had trouble with in the past. This allows the help information the user needs to be prepared in advance and provided quickly, thereby smoothly supporting the user's operations.

[0098] The adjustment unit can dynamically change the display position of the character according to the user's operation speed. For example, when the user is operating quickly, the character can be displayed in the corner of the screen so as not to interfere with the operation. When the user is operating slowly, the character can be displayed in the center of the screen so that help information stands out. Furthermore, when the user is performing a specific operation, the adjustment unit can display the character in a position related to that operation. This makes it possible to support the user's operation by optimizing the display position of the character according to the user's operation speed.

[0099] The hidden section can adjust the transparency of a character when the user hides the character. For example, the user can make the character semi-transparent rather than completely hiding it, so that the character can be displayed immediately when needed. The hidden section can also reduce the size of the character when the user hides it. For example, the character can be made small and displayed in a corner of the screen so as not to interfere with operation. Furthermore, the hidden section can also shorten the display time of the character when the user hides it. This allows for flexible adjustment when the user hides a character.

[0100] The collection unit can estimate the user's emotions and adjust the method of collecting operation logs based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of collecting operation logs to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of collecting operation logs to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting operation logs that can be collected quickly. This allows for efficient data collection by adjusting the method of collecting operation logs according to the user's emotions.

[0101] The collection unit can analyze the user's past operation history and understand operation patterns during specific time periods. For example, it can prioritize collection of operation logs related to functions that the user frequently uses during specific time periods. The collection unit can also focus on collecting logs related to operations performed by the user on specific days of the week. Furthermore, the collection unit can also collect operation logs related to specific events (e.g., creating a report at the end of the month) by the user. This allows for efficient data collection by selecting the optimal collection method based on the user's past operation history.

[0102] When collecting operation logs, the collection unit can filter the operation logs based on the user's current application usage status. For example, the collection unit collects only operation logs related to functions currently being used by the user. In addition, when the user is using a specific application, the collection unit can also prioritize collecting operation logs related to that application. Furthermore, when the user is using multiple applications simultaneously, the collection unit can also prioritize collecting operation logs of the most important application. In this way, by filtering the operation logs based on the user's current application usage status, highly relevant data can be collected.

[0103] The collection unit can estimate the user's emotions and determine the priority of operation logs to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, only important operation logs can be collected with priority. Also, if the user is relaxed, detailed operation logs can be collected with priority. Furthermore, if the user is in a hurry, operation logs that can be collected quickly can be collected with priority. In this way, by determining the priority of operation logs according to the user's emotions, important data can be collected with priority.

[0104] When collecting operation logs, the collection unit can prioritize collecting highly relevant logs by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting operation logs related to functions that the user frequently uses in a specific location. The collection unit can also prioritize collecting operation logs related to functions that the user uses while traveling. Furthermore, the collection unit can also prioritize collecting operation logs related to applications that the user uses in a specific area. This allows for more accurate data collection by collecting highly relevant operation logs based on the user's geographical location information.

[0105] When collecting operation logs, the collection unit can analyze the user's social media activities and collect related logs. For example, the collection unit can prioritize collecting operation logs related to information shared by the user on social media. The collection unit can also prioritize collecting operation logs related to functions frequently used by the user on social media. Furthermore, the collection unit can also collect related operation logs based on the user's social media activities. This allows for more comprehensive data collection by collecting related operation logs based on the user's social media activities.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling stressed, a simplified analysis method can be applied. If the user is relaxed, a detailed analysis method can be applied. Furthermore, if the user is in a hurry, an analysis method that can obtain results quickly can be applied. In this way, by adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects the user's operation log. The user's operation log includes clicks, scrolling, input, etc. The collection unit collects data such as the location and number of clicks the user made, the distance and direction they scrolled, and the text and commands they entered. The collection unit can also record the user's operation history and analyze past operation patterns. For example, it can record which functions the user frequently used in the past and which operations they often had trouble with. Step 2: The analysis unit analyzes the operation logs collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit extracts specific patterns and trends from the operation logs to understand the user's operation status. The analysis unit can also predict future operations based on the user's operation history. For example, it can predict which function the user is likely to use next and prepare appropriate help information. Step 3: The providing unit provides help information based on the analysis results obtained by the analyzing unit. The help information is provided in the form of text, images, videos, etc. For example, when the user is trying to use a specific function, the providing unit displays text on how to use that function. The providing unit can also display images or videos showing how to operate the function. Furthermore, the providing unit provides help information through characters. For example, the characters are displayed in a position that does not interfere with the user's operations, and are adjusted to provide only the necessary information.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 collection unit that collects user operation logs; an analysis unit that analyzes the operation logs collected by the collection unit; a providing unit that provides help information based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The providing unit Equipped with an adjustment section to adjust the character display position The system of claim 1 .

3. The providing unit It has a hidden section that allows users to hide the character. The system of claim 1 .

4. The collecting unit Estimates user emotions and adjusts the timing of collecting operation logs based on the estimated user emotions. The system of claim 1 .

5. The collecting unit Analyze the user's past operation history and select the appropriate collection method The system of claim 1 .

6. The collecting unit When collecting operation logs, filtering is performed based on the user's current application usage. The system of claim 1 .

7. The collecting unit The user's emotions are estimated, and the priority of the operation logs to be collected is determined based on the estimated user's emotions. The system of claim 1 .

8. The collecting unit When collecting operation logs, the system takes into account the user's geographical location information to prioritize the collection of highly relevant logs. The system of claim 1 .

9. The collecting unit When collecting operation logs, analyze users' social media activities and collect related logs. The system of claim 1 .

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