System

Abstract

An object of the system according to the embodiment is to analyze an operation log of a user, find an optimal procedure, and generate an optimal help page.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, an optimization unit, and a generation unit. The collection unit collects an operation log of the user in real time. The analysis unit analyzes the operation log collected by the collection unit. The optimization unit finds an optimal procedure based on the operation log analyzed by the analysis unit. The generation unit generates the best help page based on the optimal procedure found by the optimization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately identify optimal procedures based on user operation logs and generate appropriate help pages, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's operation log, find the optimal procedure, and generate the best possible help page. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an optimization unit, and a generation unit. The collection unit collects user operation logs in real time. The analysis unit analyzes the operation logs collected by the collection unit. The optimization unit finds an optimal procedure based on the operation logs analyzed by the analysis unit. The generation unit generates an optimal help page based on the optimal procedure found by the optimization unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's operation log, find the optimal procedure, and generate the best possible help page. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention optimizes the process of achieving a goal by having AI learn from user operation logs. This system collects user operation logs in real time, analyzes them using AI, finds optimal procedures, and generates optimal help pages. For example, the system records user operations in real time, and uses AI to analyze the collected operation logs to identify the user's behavioral patterns. Next, based on the analyzed operation logs, the AI ​​finds optimal procedures for the user to achieve their goal and generates optimal help pages. This allows users to achieve their goals without hesitation and assists in improving the app. This enables the system to efficiently collect, analyze, optimize, and generate user operation logs. For example, users can use apps intuitively without complex operations. Furthermore, app developers can identify areas for improvement based on user operation logs and provide easier-to-use apps.

[0029] An optimization system according to an embodiment includes a collection unit, an analysis unit, an optimization unit, and a generation unit. The collection unit collects user operation logs in real time. For example, the collection unit collects detailed data such as which buttons the user clicked and which pages the user viewed. The collection unit can also record a series of operations performed by the user within an app. The analysis unit analyzes the collected operation logs using AI. For example, the analysis unit analyzes the collected operation logs to identify user behavior patterns. If a specific operation is performed frequently, the AI ​​can identify that the operation is important. The optimization unit finds the optimal procedure for the user to achieve their goal based on the analyzed operation logs. For example, the optimization unit can identify the operation procedure that allows the user to achieve their goal in the shortest time. The generation unit generates a help page based on the optimal procedure, allowing the user to operate the app without hesitation. For example, the generation unit can display operation procedures using diagrams or videos to enable the user to intuitively understand them. This enables the optimization system according to an embodiment to efficiently collect, analyze, optimize, and generate user operation logs. For example, the user can intuitively use the app without performing complex operations. In addition, app developers can identify areas for improvement in their apps based on user operation logs and provide apps that are easier to use.

[0030] The collection unit can record user operations in real time. The collection unit records detailed data, such as which buttons the user clicked and which pages the user viewed, in real time. For example, the collection unit can record a series of operations performed by the user within the app. This allows for the collection of detailed operation logs by recording user operations in real time. 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 can input user operation data into AI and have the AI ​​execute a process of recording the data in real time.

[0031] The analysis unit can analyze the collected operation logs using AI and understand the user's behavioral patterns. The analysis unit, for example, analyzes the collected operation logs using AI. For example, the analysis unit analyzes the collected operation logs and understands the user's behavioral patterns. If a specific operation is performed frequently, the AI ​​can identify that the operation is important. For example, the analysis unit can analyze the user's operation logs using AI and identify frequently performed operations. The analysis unit can also analyze the user's operation logs using AI and understand the order and pattern of operations. In this way, the use of AI can accurately understand the user's behavioral patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected operation logs to AI and have the AI ​​analyze the behavioral patterns.

[0032] The optimization unit can find the optimal procedure for the user to achieve their goal based on the analyzed operation log. The optimization unit finds the optimal procedure for the user to achieve their goal based on, for example, the analyzed operation log. For example, the optimization unit can identify the operation procedure for the user to achieve their goal in the shortest time. The optimization unit can also use AI to find the optimal procedure for the user to achieve their goal based on the analyzed operation log. For example, the optimization unit uses AI to analyze the user's operation log and identify the optimal procedure. This makes it possible to find the optimal procedure for the user to achieve their goal. Some or all of the above-mentioned 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 the analyzed operation log into AI and have the AI ​​identify the optimal procedure.

[0033] The generation unit can generate a help page based on an optimal procedure that allows the user to operate without hesitation. The generation unit generates a help page based on the optimal procedure that allows the user to operate without hesitation, for example. For example, the generation unit can show the operation procedure using illustrations or videos, so that the user can intuitively understand. The generation unit can also use AI to generate a help page based on the optimal procedure that allows the user to operate without hesitation. For example, the generation unit uses AI to generate a help page that shows the operation procedure using illustrations or videos. This makes it possible to generate a help page that allows the user to operate without hesitation. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the optimal procedure into AI and have the AI ​​generate the help page.

[0034] The generation unit can generate a help page that shows the operation procedures using diagrams or videos. The generation unit generates, for example, a help page that shows the operation procedures using diagrams or videos. For example, the generation unit can show the operation procedures using GIF or MP4 format videos. The generation unit can also show the operation procedures using still images or illustrations. This allows the user to intuitively understand the operation procedures by visually showing them. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the operation procedures into AI and have the AI ​​generate a help page that shows the operation procedures using diagrams or videos.

[0035] The collection unit can analyze the user's past operation history and select the optimal collection method. The collection unit, for example, analyzes the user's past operation history and selects the optimal collection method. For example, the collection unit prioritizes collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and concentrate collection during that time period. The collection unit can also detect specific operation patterns based on the user's past operation history and adjust the collection method based on the pattern. 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 using AI. For example, the collection unit can input the user's past operation history into AI and have the AI ​​select the optimal collection method.

[0036] The collection unit may filter the operation logs based on the user's current app usage status and areas of interest when collecting the operation logs. For example, when collecting the operation logs, the collection unit may filter the operation logs based on the user's current app usage status and areas of interest. For example, when the user is using a specific function, the collection unit may preferentially collect operation logs related to that function. Furthermore, when the user is performing an operation related to a specific area of ​​interest, the collection unit may collect detailed operation logs. Furthermore, when the user is performing a specific task within an app, the collection unit may collect operation logs related to that task. By filtering the operation logs based on the user's current status and areas of interest, highly relevant data can be collected. 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 data on the user's current app usage status and areas of interest into AI and have the AI ​​perform the filtering.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting operation logs. For example, when collecting operation logs, the collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the collection unit prioritizes collecting operation logs related to voice input. Furthermore, when the user uses text input, the collection unit can also collect operation logs related to text input in detail. Furthermore, when the user uses gesture input, the collection unit can also collect operation logs related to gesture input. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the optimal collection means.

[0038] 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, when collecting operation logs, the collection unit prioritizes collecting highly relevant logs by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting operation logs related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting operation logs related to the user's destination. Furthermore, when the user is staying in a specific location, the collection unit can prioritize collecting operation logs related to that location. In this way, highly relevant data can be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's geographical location information into AI and cause the AI ​​to collect highly relevant logs.

[0039] The collection unit can analyze the user's social media activities and collect related logs when collecting operation logs. For example, the collection unit analyzes the user's social media activities and collects related logs when collecting operation logs. For example, the collection unit collects operation logs related to content shared by the user on social media. The collection unit can also collect operation logs related to topics of interest from the user's social media activities. The collection unit can also collect related operation logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. 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 data on the user's social media activities into AI and cause the AI ​​to collect related logs.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting operation logs. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting operation logs. For example, the collection unit adjusts the type of operation logs to be collected based on feedback provided by the user in the past. The collection unit can also prioritize collecting logs related to specific operations based on the user's past feedback. The collection unit can also customize the collection method by referring to the user's feedback and collect data efficiently. This enables efficient data collection by reflecting the user's past feedback. 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 the user's past feedback data into AI and have the AI ​​customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation log during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the operation log during analysis. For example, the analysis unit performs a detailed analysis on important operation logs. The analysis unit can also perform a simplified analysis on less important operation logs. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the operation log. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation log. 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 importance of the operation log to AI and have the AI ​​adjust the level of detail.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the operation log during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the operation log during analysis. For example, the analysis unit applies a specific analysis algorithm to operation logs related to a user interface. The analysis unit can also apply a different analysis algorithm to operation logs related to system performance. The analysis unit can also apply an even different analysis algorithm to operation logs related to security. In this way, by applying an appropriate analysis algorithm depending on the category of the operation log, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the category of the operation log into AI and have the AI ​​apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm and improve accuracy by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the collection time of the operation logs during analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the operation logs during analysis. For example, the analysis unit prioritizes the analysis of the most recent operation logs. The analysis unit can also prioritize the analysis of operation logs collected during a specific period. The analysis unit can also gradually adjust the analysis priority according to the collection time of the operation logs. This enables efficient analysis by determining the analysis priority based on the collection time 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 data on the collection time of the operation logs into AI and have the AI ​​determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the operation logs during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the operation logs during analysis. For example, the analysis unit prioritizes analysis of highly relevant operation logs. The analysis unit can also postpone analysis of less relevant operation logs. The analysis unit can also gradually adjust the order of analysis according to the relevance of the operation logs. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the operation logs to AI and have the AI ​​adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also gradually adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's level of expertise into AI and have the AI ​​execute the use of technical terms.

[0047] The optimization unit can improve the accuracy of optimization by taking into account the interrelationships between operation logs during optimization. The optimization unit, for example, improves the accuracy of optimization by taking into account the interrelationships between operation logs during optimization. For example, the optimization unit analyzes the interrelationships between operation logs and groups and optimizes related operations. The optimization unit can also find an optimal operation procedure by taking into account the interrelationships between operation logs. The optimization unit can also adjust the optimization algorithm based on the interrelationships between operation logs to improve accuracy. In this way, the accuracy of optimization is improved by taking into account the interrelationships between operation logs. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data on the interrelationships between operation logs into AI and cause the AI ​​to improve the accuracy of optimization.

[0048] The optimization unit can perform optimization while taking into account attribute information of the submitter of the operation log. For example, the optimization unit performs optimization while taking into account attribute information of the submitter of the operation log. For example, the optimization unit finds the optimal operation procedure while taking into account the age and gender of the submitter of the operation log. The optimization unit can also find the optimal operation procedure while taking into account the device used by the submitter of the operation log. The optimization unit can also find the optimal operation procedure while taking into account the frequency of use by the submitter of the operation log. In this way, by taking into account the attribute information of the submitter of the operation log, it is possible to provide the optimal operation procedure for the user. 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 data of attribute information of the submitter of the operation log into AI and cause the AI ​​to perform optimization.

[0049] The optimization unit can weight the optimization based on the frequency of submission of the operation logs during optimization. The optimization unit, for example, weights the optimization based on the frequency of submission of the operation logs during optimization. For example, the optimization unit sets a high weight for operation logs that are submitted frequently. The optimization unit can also set a low weight for operation logs that are submitted infrequently. The optimization unit can also adjust the optimization weight in stages according to the frequency of submission of the operation logs. This enables efficient optimization by weighting the optimization based on the frequency of submission of the operation logs. 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 data on the frequency of submission of operation logs to AI and have the AI ​​perform the weighting.

[0050] The optimization unit can perform optimization taking into account the geographical distribution of the operation logs. For example, the optimization unit performs optimization taking into account the geographical distribution of the operation logs. For example, the optimization unit prioritizes optimization of operations that are frequently performed in a specific region. The optimization unit can also provide different optimization procedures for each region based on the geographical distribution. The optimization unit can also adjust the optimization algorithm and improve accuracy by taking into account the geographical distribution. In this way, by taking the geographical distribution of the operation logs into account, it is possible to provide an optimal operation procedure for each region. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data on the geographical distribution of the operation logs into AI and have the AI ​​perform the optimization.

[0051] The optimization unit can improve the accuracy of optimization by referring to related literature in the operation log during optimization. The optimization unit, for example, improves the accuracy of optimization by referring to related literature in the operation log during optimization. For example, the optimization unit refers to literature related to the operation log to find an optimal procedure. The optimization unit can also adjust the optimization algorithm based on the related literature to improve accuracy. The optimization unit can also reflect information from the related literature in the operation log to improve the accuracy of optimization. In this way, by referring to the related literature in the operation log, the accuracy of optimization is improved. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data from related literature in the operation log into AI and cause the AI ​​to improve the accuracy of optimization.

[0052] The optimization unit can perform optimization taking into account the market value of the operation log during optimization. For example, the optimization unit performs optimization taking into account the market value of the operation log during optimization. For example, the optimization unit prioritizes optimization for operation logs with high market value. The optimization unit can also perform simplified optimization for operation logs with low market value. The optimization unit can also gradually adjust the priority of optimization according to the market value of the operation log. In this way, by taking the market value of the operation log into consideration, it is possible to provide operation procedures with high value preferentially. Some or all of the above-mentioned 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 data on the market value of the operation log into AI and have the AI ​​perform optimization.

[0053] The generation unit can adjust the level of detail of the help page based on the importance of the optimal procedure at the time of generation. The generation unit, for example, adjusts the level of detail of the help page based on the importance of the optimal procedure at the time of generation. For example, the generation unit generates a help page including detailed explanations for important procedures. The generation unit can also generate a simplified help page for procedures with low importance. The generation unit can also gradually adjust the level of detail of the help page according to the importance of the procedures. In this way, by adjusting the level of detail of the help page based on the importance of the optimal procedure, appropriate information can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the optimal procedure to AI and cause the AI ​​to adjust the level of detail.

[0054] The generation unit can apply different generation algorithms depending on the category of the optimal procedure during generation. For example, the generation unit applies different generation algorithms depending on the category of the optimal procedure during generation. For example, the generation unit applies a specific generation algorithm to procedures related to the user interface. The generation unit can also apply a different generation algorithm to procedures related to system performance. The generation unit can also apply yet another generation algorithm to procedures related to security. In this way, by applying an appropriate generation algorithm depending on the category of the optimal procedure, the accuracy of the help page is improved. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data of the category of the optimal procedure into AI and cause the AI ​​to apply the generation algorithm.

[0055] The generation unit can improve the accuracy of generation by referring to the user's past help page usage history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past help page usage history during generation. For example, the generation unit corrects the current help page based on the user's past help page usage history. The generation unit can also extract specific patterns from the user's past usage history and reflect them in the current generation. The generation unit can also adjust the generation algorithm by referring to the user's past usage history to improve accuracy. In this way, the accuracy of generation is improved by referring to the user's past help page usage history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's past help page usage history into AI and cause the AI ​​to improve the accuracy of generation.

[0056] The generation unit can determine the priority of help pages based on the optimal submission time of the procedure at the time of generation. The generation unit, for example, determines the priority of help pages based on the optimal submission time of the procedure at the time of generation. For example, the generation unit preferentially reflects the most recent procedure on the help page. The generation unit can also preferentially reflect procedures submitted within a specific period on the help page. The generation unit can also gradually adjust the priority of help pages depending on the submission time of the procedure. In this way, the latest information can be provided by determining the priority of help pages based on the optimal submission time of the procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the optimal submission time of the procedure into AI and have the AI ​​determine the priority.

[0057] The generation unit can adjust the order of help pages based on the optimal relevance of procedures at the time of generation. The generation unit, for example, adjusts the order of help pages based on the optimal relevance of procedures at the time of generation. For example, the generation unit prioritizes reflecting highly relevant procedures in the help pages. The generation unit can also reflect less relevant procedures in the help pages later. The generation unit can also gradually adjust the order of help pages according to the relevance of procedures. In this way, by adjusting the order of help pages based on the optimal relevance of procedures, it is possible to provide information that is easy for users to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the optimal relevance of procedures into AI and have the AI ​​adjust the order.

[0058] The generation unit can adjust the use of technical terms in the help page according to the user's level of expertise during generation. For example, the generation unit can adjust the use of technical terms in the help page according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit generates a help page that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can also generate a help page in simple language. The generation unit can also gradually adjust the expression method of the help page according to the user's level of expertise. This allows for the provision of information that is easy for the user to understand by adjusting the use of technical terms in the help page according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into AI and have the AI ​​execute the use of technical terms.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting a user's operation log, the collection unit can adjust the collection frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the collection frequency can be reduced to reduce battery consumption. Also, when the remaining battery level is sufficient, the collection frequency can be increased to collect detailed data. Furthermore, when the remaining battery level is medium, the collection frequency can be adjusted appropriately to achieve a balance. This enables efficient data collection according to the battery status of the user's device.

[0061] When analyzing a user's operation log, the analysis unit can adjust the level of analysis detail taking into account the user's Internet connection speed. For example, if the Internet connection speed is slow, a simplified analysis result can be provided. If the connection speed is fast, a detailed analysis result can be provided. Furthermore, if the connection speed is medium, an analysis result with an appropriate level of detail can be provided. This allows the analysis unit to provide appropriate analysis results according to the user's Internet connection status.

[0062] When finding the optimal procedure based on the user's operation log, the optimization unit can adjust the optimization criteria by taking into account the user's past success rate. For example, it can prioritize optimization of procedures that have had a high success rate in the past. It can also suggest procedures to improve procedures with a low success rate. Furthermore, it can perform detailed analysis of procedures with a medium success rate to improve the accuracy of optimization. This makes it possible to provide the optimal procedure based on the user's past success rate.

[0063] When generating a help page, the generation unit can adjust the display method taking into account the screen size of the user's device. For example, if the screen size is small, a simple, highly visible layout can be adopted. If the screen size is large, a layout including detailed information can be adopted. Furthermore, if the screen size is medium, a layout that combines an appropriate amount of information with visibility can be adopted. This makes it possible to provide the optimal help page according to the screen size of the user's device.

[0064] When collecting user operation logs, the collection unit can adjust the collection method taking into account the user's device usage status. For example, if the device is in a high load state, the collection frequency can be reduced to maintain device performance. Also, if the device is in a low load state, the collection frequency can be increased to collect detailed data. Furthermore, if the device is in a medium load state, the collection frequency can be adjusted appropriately to achieve a balance. This enables efficient data collection according to the user's device usage status.

[0065] When analyzing a user's operation log, the analysis unit can adjust the analysis method taking into account the complexity of the user's operation. For example, if the operation is complex, a detailed analysis is performed to find the optimal procedure for the user. If the operation is simple, a simplified analysis can be performed. Furthermore, if the operation is medium in complexity, an analysis with an appropriate level of detail can be performed. This makes it possible to provide an appropriate analysis method according to the complexity of the user's operation.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects user operation logs in real time. For example, the collection unit collects detailed data such as which buttons the user clicked and which pages they viewed. The collection unit can also record the sequence of operations the user performed within the app. Step 2: The analysis unit uses AI to analyze the collected operation logs. For example, the analysis unit analyzes the collected operation logs to understand the user's behavioral patterns. If a particular operation is performed frequently, the AI ​​can identify that the operation is important. Step 3: The optimization unit finds the optimal procedure for the user to achieve their goal based on the analyzed operation log. For example, the optimization unit can identify the operation procedure that will allow the user to achieve their goal in the shortest time. Step 4: The generator generates a help page based on the optimal procedure, allowing the user to operate the device without hesitation. For example, the generator may use illustrations or videos to help users intuitively understand the operation procedures.

[0068] (Example 2) A system according to an embodiment of the present invention optimizes the process of achieving a goal by having AI learn from user operation logs. This system collects user operation logs in real time, analyzes them using AI, finds optimal procedures, and generates optimal help pages. For example, the system records user operations in real time, and uses AI to analyze the collected operation logs to identify the user's behavioral patterns. Next, based on the analyzed operation logs, the AI ​​finds optimal procedures for the user to achieve their goal and generates optimal help pages. This allows users to achieve their goals without hesitation and assists in improving the app. This enables the system to efficiently collect, analyze, optimize, and generate user operation logs. For example, users can use apps intuitively without complex operations. Furthermore, app developers can identify areas for improvement based on user operation logs and provide easier-to-use apps.

[0069] An optimization system according to an embodiment includes a collection unit, an analysis unit, an optimization unit, and a generation unit. The collection unit collects user operation logs in real time. For example, the collection unit collects detailed data such as which buttons the user clicked and which pages the user viewed. The collection unit can also record a series of operations performed by the user within an app. The analysis unit analyzes the collected operation logs using AI. For example, the analysis unit analyzes the collected operation logs to identify user behavior patterns. If a specific operation is performed frequently, the AI ​​can identify that the operation is important. The optimization unit finds the optimal procedure for the user to achieve their goal based on the analyzed operation logs. For example, the optimization unit can identify the operation procedure that allows the user to achieve their goal in the shortest time. The generation unit generates a help page based on the optimal procedure, allowing the user to operate the app without hesitation. For example, the generation unit can display operation procedures using diagrams or videos to enable the user to intuitively understand them. This enables the optimization system according to an embodiment to efficiently collect, analyze, optimize, and generate user operation logs. For example, the user can intuitively use the app without performing complex operations. In addition, app developers can identify areas for improvement in their apps based on user operation logs and provide apps that are easier to use.

[0070] The collection unit can record user operations in real time. The collection unit records detailed data, such as which buttons the user clicked and which pages the user viewed, in real time. For example, the collection unit can record a series of operations performed by the user within the app. This allows for the collection of detailed operation logs by recording user operations in real time. 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 can input user operation data into AI and have the AI ​​execute a process of recording the data in real time.

[0071] The analysis unit can analyze the collected operation logs using AI and understand the user's behavioral patterns. The analysis unit, for example, analyzes the collected operation logs using AI. For example, the analysis unit analyzes the collected operation logs and understands the user's behavioral patterns. If a specific operation is performed frequently, the AI ​​can identify that the operation is important. For example, the analysis unit can analyze the user's operation logs using AI and identify frequently performed operations. The analysis unit can also analyze the user's operation logs using AI and understand the order and pattern of operations. In this way, the use of AI can accurately understand the user's behavioral patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected operation logs to AI and have the AI ​​analyze the behavioral patterns.

[0072] The optimization unit can find the optimal procedure for the user to achieve their goal based on the analyzed operation log. The optimization unit finds the optimal procedure for the user to achieve their goal based on, for example, the analyzed operation log. For example, the optimization unit can identify the operation procedure for the user to achieve their goal in the shortest time. The optimization unit can also use AI to find the optimal procedure for the user to achieve their goal based on the analyzed operation log. For example, the optimization unit uses AI to analyze the user's operation log and identify the optimal procedure. This makes it possible to find the optimal procedure for the user to achieve their goal. Some or all of the above-mentioned 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 the analyzed operation log into AI and have the AI ​​identify the optimal procedure.

[0073] The generation unit can generate a help page based on an optimal procedure that allows the user to operate without hesitation. The generation unit generates a help page based on the optimal procedure that allows the user to operate without hesitation, for example. For example, the generation unit can show the operation procedure using illustrations or videos, so that the user can intuitively understand. The generation unit can also use AI to generate a help page based on the optimal procedure that allows the user to operate without hesitation. For example, the generation unit uses AI to generate a help page that shows the operation procedure using illustrations or videos. This makes it possible to generate a help page that allows the user to operate without hesitation. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the optimal procedure into AI and have the AI ​​generate the help page.

[0074] The generation unit can generate a help page that shows the operation procedures using diagrams or videos. The generation unit generates, for example, a help page that shows the operation procedures using diagrams or videos. For example, the generation unit can show the operation procedures using GIF or MP4 format videos. The generation unit can also show the operation procedures using still images or illustrations. This allows the user to intuitively understand the operation procedures by visually showing them. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the operation procedures into AI and have the AI ​​generate a help page that shows the operation procedures using diagrams or videos.

[0075] The collection unit can estimate the user's emotions and adjust the timing of collecting operation logs based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting operation logs based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of collecting operation logs to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can increase the frequency of collecting operation logs to collect more detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important operations to efficiently collect data. This reduces the user's burden by adjusting the timing of collecting operation logs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection timing.

[0076] The collection unit can analyze the user's past operation history and select the optimal collection method. The collection unit, for example, analyzes the user's past operation history and selects the optimal collection method. For example, the collection unit prioritizes collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and concentrate collection during that time period. The collection unit can also detect specific operation patterns based on the user's past operation history and adjust the collection method based on the pattern. 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 using AI. For example, the collection unit can input the user's past operation history into AI and have the AI ​​select the optimal collection method.

[0077] The collection unit may filter the operation logs based on the user's current app usage status and areas of interest when collecting the operation logs. For example, when collecting the operation logs, the collection unit may filter the operation logs based on the user's current app usage status and areas of interest. For example, when the user is using a specific function, the collection unit may preferentially collect operation logs related to that function. Furthermore, when the user is performing an operation related to a specific area of ​​interest, the collection unit may collect detailed operation logs. Furthermore, when the user is performing a specific task within an app, the collection unit may collect operation logs related to that task. By filtering the operation logs based on the user's current status and areas of interest, highly relevant data can be collected. 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 data on the user's current app usage status and areas of interest into AI and have the AI ​​perform the filtering.

[0078] The collection unit can select the optimal collection means depending on the user's input method when collecting operation logs. For example, when collecting operation logs, the collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user uses voice input, the collection unit prioritizes collecting operation logs related to voice input. Furthermore, when the user uses text input, the collection unit can also collect operation logs related to text input in detail. Furthermore, when the user uses gesture input, the collection unit can also collect operation logs related to gesture input. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of the operation logs to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the 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. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed operation logs. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting operation logs that can be collected quickly. In this way, by determining the priority of the 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-mentioned processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority.

[0080] 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, when collecting operation logs, the collection unit prioritizes collecting highly relevant logs by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting operation logs related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting operation logs related to the user's destination. Furthermore, when the user is staying in a specific location, the collection unit can prioritize collecting operation logs related to that location. In this way, highly relevant data can be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's geographical location information into AI and cause the AI ​​to collect highly relevant logs.

[0081] The collection unit can analyze the user's social media activities and collect related logs when collecting operation logs. For example, the collection unit analyzes the user's social media activities and collects related logs when collecting operation logs. For example, the collection unit collects operation logs related to content shared by the user on social media. The collection unit can also collect operation logs related to topics of interest from the user's social media activities. The collection unit can also collect related operation logs by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. 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 data on the user's social media activities into AI and cause the AI ​​to collect related logs.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting operation logs. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting operation logs. For example, the collection unit adjusts the type of operation logs to be collected based on feedback provided by the user in the past. The collection unit can also prioritize collecting logs related to specific operations based on the user's past feedback. The collection unit can also customize the collection method by referring to the user's feedback and collect data efficiently. This enables efficient data collection by reflecting the user's past feedback. 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 the user's past feedback data into AI and have the AI ​​customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. 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-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the operation log during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the operation log during analysis. For example, the analysis unit performs a detailed analysis on important operation logs. The analysis unit can also perform a simplified analysis on less important operation logs. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the operation log. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation log. 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 importance of the operation log to AI and have the AI ​​adjust the level of detail.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the operation log during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the operation log during analysis. For example, the analysis unit applies a specific analysis algorithm to operation logs related to a user interface. The analysis unit can also apply a different analysis algorithm to operation logs related to system performance. The analysis unit can also apply an even different analysis algorithm to operation logs related to security. In this way, by applying an appropriate analysis algorithm depending on the category of the operation log, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the category of the operation log into AI and have the AI ​​apply the analysis algorithm.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also adjust the analysis algorithm and improve accuracy by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an appropriate analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-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 the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0088] The analysis unit can determine the analysis priority based on the collection time of the operation logs during analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the operation logs during analysis. For example, the analysis unit prioritizes the analysis of the most recent operation logs. The analysis unit can also prioritize the analysis of operation logs collected during a specific period. The analysis unit can also gradually adjust the analysis priority according to the collection time of the operation logs. This enables efficient analysis by determining the analysis priority based on the collection time 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 data on the collection time of the operation logs into AI and have the AI ​​determine the priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the operation logs during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the operation logs during analysis. For example, the analysis unit prioritizes analysis of highly relevant operation logs. The analysis unit can also postpone analysis of less relevant operation logs. The analysis unit can also gradually adjust the order of analysis according to the relevance of the operation logs. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operation logs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the operation logs to AI and have the AI ​​adjust the order of analysis.

[0090] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also gradually adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's level of expertise into AI and have the AI ​​execute the use of technical terms.

[0091] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user emotions. For example, the optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user emotions. For example, if the user is stressed, the optimization unit prioritizes simple and intuitive operation procedures. Furthermore, if the user is relaxed, the optimization unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the optimization unit can prioritize operation procedures that enable the user to quickly achieve their goals. By adjusting the optimization criteria according to the user's emotions, the optimization unit can provide the optimal operation procedures for the user. The 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 optimization unit can be performed using, for example, an AI. For example, the optimization unit can input user emotion data into an AI and have the AI ​​adjust the optimization criteria.

[0092] The optimization unit can improve the accuracy of optimization by taking into account the interrelationships between operation logs during optimization. The optimization unit, for example, improves the accuracy of optimization by taking into account the interrelationships between operation logs during optimization. For example, the optimization unit analyzes the interrelationships between operation logs and groups and optimizes related operations. The optimization unit can also find an optimal operation procedure by taking into account the interrelationships between operation logs. The optimization unit can also adjust the optimization algorithm based on the interrelationships between operation logs to improve accuracy. In this way, the accuracy of optimization is improved by taking into account the interrelationships between operation logs. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data on the interrelationships between operation logs into AI and cause the AI ​​to improve the accuracy of optimization.

[0093] The optimization unit can perform optimization while taking into account attribute information of the submitter of the operation log. For example, the optimization unit performs optimization while taking into account attribute information of the submitter of the operation log. For example, the optimization unit finds the optimal operation procedure while taking into account the age and gender of the submitter of the operation log. The optimization unit can also find the optimal operation procedure while taking into account the device used by the submitter of the operation log. The optimization unit can also find the optimal operation procedure while taking into account the frequency of use by the submitter of the operation log. In this way, by taking into account the attribute information of the submitter of the operation log, it is possible to provide the optimal operation procedure for the user. 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 data of attribute information of the submitter of the operation log into AI and cause the AI ​​to perform optimization.

[0094] The optimization unit can weight the optimization based on the frequency of submission of the operation logs during optimization. The optimization unit, for example, weights the optimization based on the frequency of submission of the operation logs during optimization. For example, the optimization unit sets a high weight for operation logs that are submitted frequently. The optimization unit can also set a low weight for operation logs that are submitted infrequently. The optimization unit can also adjust the optimization weight in stages according to the frequency of submission of the operation logs. This enables efficient optimization by weighting the optimization based on the frequency of submission of the operation logs. 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 data on the frequency of submission of operation logs to AI and have the AI ​​perform the weighting.

[0095] The optimization unit can estimate the user's emotions and adjust the order in which optimization results are displayed based on the estimated user emotions. The optimization unit, for example, estimates the user's emotions and adjusts the order in which optimization results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit can display important results first. Furthermore, if the user is relaxed, the optimization unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the optimization unit can display results that are easy to understand first. By adjusting the order in which optimization results are displayed based on the user's emotions, it is possible to provide results that are easy for the user to understand. The 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-described processing in the optimization unit may be performed using, for example, an AI, or without an AI. For example, the optimization unit can input user emotion data into an AI and have the AI ​​adjust the display order of the results.

[0096] The optimization unit can perform optimization taking into account the geographical distribution of the operation logs. For example, the optimization unit performs optimization taking into account the geographical distribution of the operation logs. For example, the optimization unit prioritizes optimization of operations that are frequently performed in a specific region. The optimization unit can also provide different optimization procedures for each region based on the geographical distribution. The optimization unit can also adjust the optimization algorithm and improve accuracy by taking into account the geographical distribution. In this way, by taking the geographical distribution of the operation logs into account, it is possible to provide an optimal operation procedure for each region. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data on the geographical distribution of the operation logs into AI and have the AI ​​perform the optimization.

[0097] The optimization unit can improve the accuracy of optimization by referring to related literature in the operation log during optimization. The optimization unit, for example, improves the accuracy of optimization by referring to related literature in the operation log during optimization. For example, the optimization unit refers to literature related to the operation log to find an optimal procedure. The optimization unit can also adjust the optimization algorithm based on the related literature to improve accuracy. The optimization unit can also reflect information from the related literature in the operation log to improve the accuracy of optimization. In this way, by referring to the related literature in the operation log, the accuracy of optimization is improved. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input data from related literature in the operation log into AI and cause the AI ​​to improve the accuracy of optimization.

[0098] The optimization unit can perform optimization taking into account the market value of the operation log during optimization. For example, the optimization unit performs optimization taking into account the market value of the operation log during optimization. For example, the optimization unit prioritizes optimization for operation logs with high market value. The optimization unit can also perform simplified optimization for operation logs with low market value. The optimization unit can also gradually adjust the priority of optimization according to the market value of the operation log. In this way, by taking the market value of the operation log into consideration, it is possible to provide operation procedures with high value preferentially. Some or all of the above-mentioned 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 data on the market value of the operation log into AI and have the AI ​​perform optimization.

[0099] The generation unit can estimate the user's emotions and adjust the expression method of the generated help page based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the expression method of the generated help page based on the estimated user emotions. For example, if the user is nervous, the generation unit generates a simple, highly visible help page. If the user is relaxed, the generation unit can generate a help page containing detailed information. If the user is in a hurry, the generation unit can generate a help page that focuses on the main points. This allows the user to easily understand the help page by adjusting the expression method of the help page according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 generation unit may be performed using an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the expression method.

[0100] The generation unit can adjust the level of detail of the help page based on the importance of the optimal procedure at the time of generation. The generation unit, for example, adjusts the level of detail of the help page based on the importance of the optimal procedure at the time of generation. For example, the generation unit generates a help page including detailed explanations for important procedures. The generation unit can also generate a simplified help page for procedures with low importance. The generation unit can also gradually adjust the level of detail of the help page according to the importance of the procedures. In this way, by adjusting the level of detail of the help page based on the importance of the optimal procedure, appropriate information can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the optimal procedure to AI and cause the AI ​​to adjust the level of detail.

[0101] The generation unit can apply different generation algorithms depending on the category of the optimal procedure during generation. For example, the generation unit applies different generation algorithms depending on the category of the optimal procedure during generation. For example, the generation unit applies a specific generation algorithm to procedures related to the user interface. The generation unit can also apply a different generation algorithm to procedures related to system performance. The generation unit can also apply yet another generation algorithm to procedures related to security. In this way, by applying an appropriate generation algorithm depending on the category of the optimal procedure, the accuracy of the help page is improved. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data of the category of the optimal procedure into AI and cause the AI ​​to apply the generation algorithm.

[0102] The generation unit can improve the accuracy of generation by referring to the user's past help page usage history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past help page usage history during generation. For example, the generation unit corrects the current help page based on the user's past help page usage history. The generation unit can also extract specific patterns from the user's past usage history and reflect them in the current generation. The generation unit can also adjust the generation algorithm by referring to the user's past usage history to improve accuracy. In this way, the accuracy of generation is improved by referring to the user's past help page usage history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the user's past help page usage history into AI and cause the AI ​​to improve the accuracy of generation.

[0103] The generation unit can estimate the user's emotion and adjust the length of the generated help page based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the generated help page based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point help page. If the user is relaxed, the generation unit can generate a longer help page with detailed explanations. If the user is excited, the generation unit can generate a help page with visually stimulating effects. By adjusting the length of the help page according to the user's emotion, appropriate information can be provided to the user. The 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 these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the help page.

[0104] The generation unit can determine the priority of help pages based on the optimal submission time of the procedure at the time of generation. The generation unit, for example, determines the priority of help pages based on the optimal submission time of the procedure at the time of generation. For example, the generation unit preferentially reflects the most recent procedure on the help page. The generation unit can also preferentially reflect procedures submitted within a specific period on the help page. The generation unit can also gradually adjust the priority of help pages depending on the submission time of the procedure. In this way, the latest information can be provided by determining the priority of help pages based on the optimal submission time of the procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the optimal submission time of the procedure into AI and have the AI ​​determine the priority.

[0105] The generation unit can adjust the order of help pages based on the optimal relevance of procedures at the time of generation. The generation unit, for example, adjusts the order of help pages based on the optimal relevance of procedures at the time of generation. For example, the generation unit prioritizes reflecting highly relevant procedures in the help pages. The generation unit can also reflect less relevant procedures in the help pages later. The generation unit can also gradually adjust the order of help pages according to the relevance of procedures. In this way, by adjusting the order of help pages based on the optimal relevance of procedures, it is possible to provide information that is easy for users to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the optimal relevance of procedures into AI and have the AI ​​adjust the order.

[0106] The generation unit can adjust the use of technical terms in the help page according to the user's level of expertise during generation. For example, the generation unit can adjust the use of technical terms in the help page according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit generates a help page that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can also generate a help page in simple language. The generation unit can also gradually adjust the expression method of the help page according to the user's level of expertise. This allows for the provision of information that is easy for the user to understand by adjusting the use of technical terms in the help page according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's level of expertise into AI and have the AI ​​execute the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, optimization unit, and generation 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 in real time using the control unit 46A of the smart device 14. For example, the analysis unit analyzes the operation logs collected by the specific processing unit 290 of the data processing device 12 using AI. For example, the optimization unit finds an optimal procedure based on the operation logs analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a help page based on the optimal procedure using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described collection unit, analysis unit, optimization unit, and generation 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 user operation logs in real time using the control unit 46A of the smart glasses 214. For example, the analysis unit analyzes the operation logs collected by the specific processing unit 290 of the data processing device 12 using AI. For example, the optimization unit finds an optimal procedure based on the operation logs analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a help page based on the optimal procedure using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, optimization unit, and generation unit described above 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 in real time using the control unit 46A of the headset type terminal 314. For example, the analysis unit analyzes the operation logs collected by the specific processing unit 290 of the data processing device 12 using AI. For example, the optimization unit finds an optimal procedure based on the operation logs analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a help page based on the optimal procedure using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, optimization unit, and generation unit described above 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 in real time using the control unit 46A of the robot 414. For example, the analysis unit analyzes the operation logs collected by the specific processing unit 290 of the data processing device 12 using AI. For example, the optimization unit finds an optimal procedure based on the operation logs analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates a help page based on the optimal procedure using the control unit 46A of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When collecting a user's operation log, the collection unit can adjust the collection frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the collection frequency can be reduced to reduce battery consumption. Also, when the remaining battery level is sufficient, the collection frequency can be increased to collect detailed data. Furthermore, when the remaining battery level is medium, the collection frequency can be adjusted appropriately to achieve a balance. This enables efficient data collection according to the battery status of the user's device.

[0109] When analyzing a user's operation log, the analysis unit can adjust the level of analysis detail taking into account the user's Internet connection speed. For example, if the Internet connection speed is slow, a simplified analysis result can be provided. If the connection speed is fast, a detailed analysis result can be provided. Furthermore, if the connection speed is medium, an analysis result with an appropriate level of detail can be provided. This allows the analysis unit to provide appropriate analysis results according to the user's Internet connection status.

[0110] When finding the optimal procedure based on the user's operation log, the optimization unit can adjust the optimization criteria by taking into account the user's past success rate. For example, it can prioritize optimization of procedures that have had a high success rate in the past. It can also suggest procedures to improve procedures with a low success rate. Furthermore, it can perform detailed analysis of procedures with a medium success rate to improve the accuracy of optimization. This makes it possible to provide the optimal procedure based on the user's past success rate.

[0111] When generating a help page, the generation unit can adjust the display method taking into account the screen size of the user's device. For example, if the screen size is small, a simple, highly visible layout can be adopted. If the screen size is large, a layout including detailed information can be adopted. Furthermore, if the screen size is medium, a layout that combines an appropriate amount of information with visibility can be adopted. This makes it possible to provide the optimal help page according to the screen size of the user's device.

[0112] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, only important operation logs can be collected. Also, if the user is relaxed, detailed operation logs can be collected. Furthermore, if the user is in a hurry, data that can be collected quickly can be collected with priority. This allows for efficient data collection by adjusting the type of data to be collected according to the user's emotions.

[0113] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can be delayed to reduce the burden on the user. Also, if the user is relaxed, the analysis can be performed in real time. Furthermore, if the user is in a hurry, the analysis can be performed quickly and the results can be provided. In this way, by adjusting the timing of the analysis according to the user's emotions, it is possible to provide the user with the optimal analysis results.

[0114] The optimization unit can estimate the user's emotions and customize the optimization procedure based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize simple and intuitive procedures. If the user is relaxed, it can also provide detailed procedures. Furthermore, if the user is in a hurry, it can also prioritize procedures that allow the user to achieve the goal quickly. In this way, by customizing the optimization procedure according to the user's emotions, it is possible to provide the optimal procedure for the user.

[0115] The generation unit can estimate the user's emotions and adjust the content of the generated help page based on the estimated user emotions. For example, if the user is nervous, simple, highly visible content can be provided. If the user is relaxed, content including detailed information can be provided. Furthermore, if the user is in a hurry, content that focuses on the main points can be provided. In this way, by adjusting the content of the help page according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0116] When collecting user operation logs, the collection unit can adjust the collection method taking into account the user's device usage status. For example, if the device is in a high load state, the collection frequency can be reduced to maintain device performance. Also, if the device is in a low load state, the collection frequency can be increased to collect detailed data. Furthermore, if the device is in a medium load state, the collection frequency can be adjusted appropriately to achieve a balance. This enables efficient data collection according to the user's device usage status.

[0117] When analyzing a user's operation log, the analysis unit can adjust the analysis method taking into account the complexity of the user's operation. For example, if the operation is complex, a detailed analysis is performed to find the optimal procedure for the user. If the operation is simple, a simplified analysis can be performed. Furthermore, if the operation is medium in complexity, an analysis with an appropriate level of detail can be performed. This makes it possible to provide an appropriate analysis method according to the complexity of the user's operation.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects user operation logs in real time. For example, the collection unit collects detailed data such as which buttons the user clicked and which pages they viewed. The collection unit can also record the sequence of operations the user performed within the app. Step 2: The analysis unit uses AI to analyze the collected operation logs. For example, the analysis unit analyzes the collected operation logs to understand the user's behavioral patterns. If a particular operation is performed frequently, the AI ​​can identify that the operation is important. Step 3: The optimization unit finds the optimal procedure for the user to achieve their goal based on the analyzed operation log. For example, the optimization unit can identify the operation procedure that will allow the user to achieve their goal in the shortest time. Step 4: The generator generates a help page based on the optimal procedure, allowing the user to operate the device without hesitation. For example, the generator may use illustrations or videos to help users intuitively understand the operation procedures.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 in real time; an analysis unit that analyzes the operation logs collected by the collection unit; an optimization unit that finds an optimal procedure based on the operation log analyzed by the analysis unit; a generator that generates an optimal help page based on the optimal procedure found by the optimizer; Equipped with A system characterized by:

2. The collecting unit Record user actions in real time 2. The system of claim 1.

3. The analysis unit The collected operation logs are analyzed using AI to understand user behavior patterns.

2. The system of claim 1.

4. The optimization unit Based on the analyzed operation logs, the system finds the optimal procedure for users to achieve their goals.

2. The system of claim 1.

5. The generation unit Generate a help page based on the optimal procedure to help users operate without hesitation 2. The system of claim 1.

6. The generation unit Generate help pages with diagrams and videos showing how to do things 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts the timing of collecting operation logs based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past operation history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting operation logs, filtering is performed based on the user's current app usage and areas of interest.

2. The system of claim 1.

Images