system

Abstract

The system according to this embodiment aims to make it easy for users to intuitively understand the operation flow. [Solution] The system according to the embodiment comprises an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the operation history. The selection unit selects an operation flow based on the results analyzed by the analysis unit. The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for users to intuitively understand the operation flow of websites or applications.

[0005] The system according to the embodiment aims to make it easier for users to intuitively understand the operation flow.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the operation history. The selection unit selects an operation flow based on the result analyzed by the analysis unit. The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit.

Effects of the Invention

[0007] The system according to this embodiment makes it easy for users to intuitively understand the operation flow. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that plugs into a website or app, where AI selects the optimal operation flow and uses image recognition technology to highlight buttons that should be operated, thereby providing users with intuitive operation. When a user uses a website or app, the system's AI analyzes the user's operation history and behavior patterns to select the optimal operation flow. Next, it uses image recognition technology to highlight buttons and links that should be operated. This allows users to operate intuitively without getting lost. For example, on an online shopping site, when a user purchases a product, the AI ​​analyzes the user's past purchase and browsing history and proposes the optimal purchase flow. Furthermore, by using image recognition technology to highlight purchase buttons and add-to-cart buttons, the user can proceed with the purchase process smoothly. Similarly, in an educational app, the AI ​​analyzes the user's learning history and progress and proposes the optimal learning flow. By using image recognition technology to highlight the next content or problem to be studied, the user can efficiently progress through their learning. In this way, the system, by combining AI and image recognition technology, can provide users with an intuitive and efficient operating experience. This allows the system to analyze the user's operation history, select the optimal operation flow, and highlight the buttons that should be operated, thereby providing intuitive operation.

[0029] The system according to the embodiment comprises an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the user's operation history. The user's operation history includes, but is not limited to, click history, input history, page transition history, etc. The analysis unit analyzes the operation history using, for example, data mining techniques or statistical analysis methods. The analysis unit can also analyze the user's behavior patterns. For example, it can analyze behavior patterns using frequency analysis or sequence analysis. The selection unit selects an operation flow based on the results analyzed by the analysis unit. The operation flow includes, for example, a series of operation procedures according to the user's purpose, but is not limited to, such. The selection unit selects an operation flow based on, for example, statistical indicators or the discovery of patterns. The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit. The highlighting includes, for example, color changes, animation effects, size changes, etc. The display unit highlights buttons using, for example, color changes. The display unit can also highlight buttons using animation effects. Furthermore, the display unit can highlight buttons by changing its size. This allows the system according to the embodiment to analyze the user's operation history, select the optimal operation flow, and highlight the buttons to be operated, thereby providing intuitive operation.

[0030] The analysis unit can analyze the user's operation history or behavior patterns. For example, the analysis unit can analyze the user's click history. For example, the analysis unit records which buttons the user clicked and performs analysis based on that data. The analysis unit can also analyze the user's input history. For example, the analysis unit collects text data entered by the user and performs analysis based on that data. The analysis unit can also analyze the user's page transition history. For example, the analysis unit records which pages the user visited and performs analysis based on that data. By analyzing the user's operation history and behavior patterns, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's operation history data into a generating AI and have the generating AI perform the analysis of the operation history.

[0031] The selection unit can select an operation flow based on the analysis results. The selection unit can select an operation flow based on statistical indicators, for example. For example, the selection unit can select the most frequently performed operation flow based on the user's operation history data. The selection unit can also select an operation flow based on pattern discovery, for example. The selection unit can analyze the user's behavior patterns and select the optimal operation flow. The selection unit can also select an operation flow that suits the user's purpose, for example. The selection unit can select the operation flow necessary for the user to achieve a specific purpose. By selecting the optimal operation flow based on the analysis results, the selection unit can provide the user with the most suitable operation flow. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the analysis result data into a generating AI and have the generating AI perform the operation flow selection.

[0032] The display unit can highlight buttons or links that should be operated. For example, the display unit can highlight buttons by changing their color. For instance, it can change the color of a button to be operated, visually highlighting it to the user. The display unit can also highlight buttons using animation effects. For example, it can add animation effects to buttons to be operated, visually highlighting them to the user. Furthermore, the display unit can highlight buttons by changing their size. For example, it can change the size of a button to be operated, visually highlighting it to the user. This allows users to operate intuitively by highlighting buttons and links that should be operated. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input data on buttons to be operated into a generating AI, and have the generating AI perform the button highlighting.

[0033] The analysis unit can analyze a user's past purchase or browsing history on an online shopping site. For example, the analysis unit can analyze a user's purchase history. For example, the analysis unit records the types of products the user has purchased in the past and the date and time of purchase, and performs analysis based on that data. The analysis unit can also analyze a user's browsing history. For example, the analysis unit records the types of products the user has viewed in the past and the date and time of viewing, and performs analysis based on that data. By analyzing a user's past purchase and browsing history on an online shopping site, it is possible to propose a more appropriate purchase flow. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's purchase history data into a generating AI and have the generating AI perform the purchase history analysis.

[0034] The selection unit can select a purchase flow on an online shopping site. For example, the selection unit can select a purchase flow based on the user's purchase history data. For example, the selection unit can select the optimal purchase flow based on the types of products the user has purchased in the past and the date and time of purchase. The selection unit can also select a purchase flow based on the user's browsing history data. For example, the selection unit can select the optimal purchase flow based on the types of products the user has viewed in the past and the date and time of viewing. By selecting the optimal purchase flow on the online shopping site, the user can proceed with the purchase process smoothly. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's purchase history data into a generating AI and have the generating AI perform the purchase flow selection.

[0035] The display unit can highlight the purchase button and the add-to-cart button on an online shopping site. For example, the display unit can highlight the purchase button. For example, the display unit can change the color of the purchase button to visually highlight it to the user. The display unit can also highlight the add-to-cart button. For example, the display unit can change the color of the add-to-cart button to visually highlight it to the user. The display unit can also add animation effects to the purchase button and the add-to-cart button. For example, the display unit can add animation effects to the purchase button and the add-to-cart button to visually highlight them to the user. This allows users to proceed with the purchase process smoothly by highlighting the purchase button and the add-to-cart button on an online shopping site. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data for the purchase button and the add-to-cart button into a generating AI and have the generating AI perform the button highlighting.

[0036] The analysis unit can analyze a user's learning history and progress in an educational app. For example, the analysis unit can analyze a user's learning history. For instance, it can record the content and time a user has previously studied and perform analysis based on that data. The analysis unit can also analyze a user's progress. For example, it can record the user's learning achievement level and incomplete tasks and perform analysis based on that data. By analyzing a user's learning history and progress in an educational app, a more appropriate learning flow can be proposed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning history data into a generating AI and have the generating AI perform the analysis of the learning history.

[0037] The selection unit can select a learning flow in an educational app. For example, the selection unit can select a learning flow based on the user's learning history data. For example, the selection unit can select the optimal learning flow based on the content and time the user has previously studied. The selection unit can also select a learning flow based on the user's progress data. For example, the selection unit can select the optimal learning flow based on the user's learning achievement level and incomplete tasks. By selecting the optimal learning flow in the educational app, the user can learn efficiently. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning history data into a generating AI and have the generating AI perform the selection of the learning flow.

[0038] The display unit can highlight the next content or problem to be studied in the educational app. For example, the display unit can highlight the next content to be studied. For example, the display unit can change the color of the next content to be studied to visually highlight it to the user. The display unit can also highlight the next problem to be studied. For example, the display unit can change the color of the next problem to be studied to visually highlight it to the user. The display unit can also add animation effects to the next content or problem to be studied. For example, the display unit can add animation effects to the next content or problem to be studied to visually highlight it to the user. This allows users to progress through their learning more efficiently by highlighting the next content or problem to be studied in the educational app. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the next content or problem to be studied into a generating AI and have the generating AI perform the highlighting.

[0039] The analysis unit can improve the accuracy of its analysis based on the user's device usage history when analyzing operation history. For example, the analysis unit considers the types of devices the user has used in the past and reflects the operation patterns for each device in the analysis. For example, the analysis unit records the types of devices the user has used in the past and performs analysis based on that data. The analysis unit can also perform analysis specifically for devices if the user frequently uses a particular device. For example, the analysis unit focuses on analyzing the operation history of devices that the user frequently uses. The analysis unit can also integrate the operation history of each device if the user uses multiple devices and perform analysis based on that data. For example, the analysis unit integrates the operation history of multiple devices used by the user and performs analysis based on that data. This improves the accuracy of the analysis by considering the user's device usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's device usage history data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0040] The analysis unit can optimize its analysis algorithm based on the user's past error history when analyzing operation history. For example, the analysis unit can identify errors that the user has frequently made in the past and perform analysis to avoid those errors. For example, the analysis unit can record the types of errors the user has made in the past and the circumstances under which they occurred, and perform analysis based on that data. The analysis unit can also focus its analysis on the history related to a particular operation if the user has made an error during that operation. For example, the analysis unit can collect the history of errors the user has made during a particular operation and perform analysis based on that data. The analysis unit can also identify the cause of errors based on the user's error history and improve the analysis algorithm. For example, the analysis unit can analyze the user's error history, identify the cause of the errors, and develop an analysis algorithm to avoid that cause. This allows the analysis algorithm to be optimized by referring to the user's past error history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's error history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0041] The analysis unit can perform analysis based on the user's geographical location information when analyzing operation history. For example, if the user frequently performs operations in a particular area, the analysis unit can perform region-specific analysis. For example, if the user frequently performs operations in a particular area, the analysis unit can collect region-specific operation history and perform analysis based on that data. The analysis unit can also perform analysis considering the travel route if the user is moving. For example, if the user is moving, the analysis unit can record the travel route and perform analysis based on that data. The analysis unit can also reflect region-specific operation patterns in the analysis if the user is operating in different areas. For example, if the user is operating in different areas, the analysis unit can collect operation patterns for each area and perform analysis based on that data. This improves the accuracy of the analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0042] The analysis unit can analyze the user's social media activity and analyze relevant operation history when analyzing operation history. For example, the analysis unit can analyze operation history based on information shared by the user on social media. For example, the analysis unit can collect information shared by the user on social media and analyze operation history based on that data. The analysis unit can also consider the frequency of the user's activity on social media and focus its analysis on relevant operation history. For example, the analysis unit can record the frequency of the user's activity on social media and analyze operation history based on that data. The analysis unit can also adjust the operation history analysis algorithm based on the content of the user's social media activity. For example, the analysis unit can analyze the content of the user's social media activity and adjust the operation history analysis algorithm based on that data. This allows for the analysis of relevant operation history by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the operation history analysis.

[0043] The selection unit can optimize its selection algorithm based on the user's past selection history when selecting an operation flow. For example, the selection unit can propose the optimal operation flow based on the operation flows the user has selected in the past. For example, the selection unit can collect data on operation flows the user has selected in the past and propose the optimal operation flow based on that data. The selection unit can also prioritize suggesting operation flows that the user frequently selects. For example, the selection unit can collect data on operation flows that the user frequently selects and propose them preferentially based on that data. The selection unit can also analyze the user's past selection history and improve its selection algorithm. For example, the selection unit can analyze the user's past selection history and improve its selection algorithm based on that data. This allows the selection algorithm to be optimized by referring to the user's past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0044] The selection unit can customize the selection method based on the user's current operation status when selecting an operation flow. For example, the selection unit can propose the optimal operation flow according to the operation the user is currently performing. For example, the selection unit can collect data on the operation the user is currently performing and propose the optimal operation flow based on that data. The selection unit can also prioritize suggesting flows related to a specific operation if the user is performing that operation. For example, the selection unit can collect data on flows related to a specific operation if the user is performing that operation and prioritize suggesting flows based on that data. The selection unit can also analyze the user's current operation status in real time and adjust the selection method. For example, the selection unit can analyze the user's current operation status in real time and adjust the selection method based on that data. This allows for the provision of a more appropriate operation flow by customizing the selection method based on the user's current operation status. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current operation status data into a generating AI and have the generating AI perform the customization of the selection method.

[0045] The selection unit can select an operation flow based on the user's geographical location information. For example, if the user is performing an operation in a specific region, the selection unit can select an operation flow specific to that region. For example, if the user is performing an operation in a specific region, the selection unit can collect data on operation flows specific to that region and select an operation flow based on that data. The selection unit can also select an operation flow considering the user's travel route if the user is performing an operation while on the move. For example, if the user is performing an operation while on the move, the selection unit can record the travel route and select an operation flow based on that data. The selection unit can also select operation flows for each region if the user is performing an operation in different regions. For example, if the user is performing an operation in different regions, the selection unit can collect data on operation flows for each region and select an operation flow based on that data. This allows the system to provide the optimal operation flow by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the operation flow selection.

[0046] The selection unit can analyze the user's social media activity and select relevant operation flows when selecting operation flows. For example, the selection unit can select operation flows based on information shared by the user on social media. For example, the selection unit can collect information shared by the user on social media and select operation flows based on that data. The selection unit can also consider the frequency of the user's social media activity and prioritize the selection of relevant operation flows. For example, the selection unit can record the frequency of the user's social media activity and select operation flows based on that data. The selection unit can also adjust the operation flow selection algorithm based on the content of the user's social media activity. For example, the selection unit can analyze the content of the user's social media activity and adjust the operation flow selection algorithm based on that data. In this way, relevant operation flows can be provided by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the operation flow selection.

[0047] The display unit can optimize its display algorithm by referring to the user's past display history when highlighting buttons. For example, the display unit can prioritize highlighting buttons that the user has frequently operated in the past. For example, the display unit can collect data on buttons that the user has frequently operated in the past and prioritize highlighting them based on that data. The display unit can also highlight buttons that the user used when performing a specific operation. For example, the display unit can collect data on buttons that the user used when performing a specific operation and prioritize highlighting them based on that data. Furthermore, the display unit can improve its display algorithm based on the user's past display history. For example, the display unit can analyze the user's past display history and improve its display algorithm based on that data. In this way, the display algorithm can be optimized by referring to the user's past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's display history data into a generating AI and have the generating AI perform the optimization of the display algorithm.

[0048] The display unit can customize the display means based on the user's current operation status when highlighting buttons. For example, the display unit can highlight the most suitable button according to the operation the user is currently performing. For example, the display unit can collect data on the user's current operation and highlight the most suitable button based on that data. The display unit can also highlight buttons related to a specific operation if the user is performing that operation. For example, the display unit can collect data on buttons related to a specific operation if the user is performing that operation and highlight them based on that data. The display unit can also analyze the user's current operation status in real time and adjust the display means. For example, the display unit can analyze the user's current operation status in real time and adjust the display means based on that data. This allows for more appropriate operation by customizing the display means based on the user's current operation status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's current operation status data into a generating AI and have the generating AI perform the customization of the display means.

[0049] The display unit can highlight the most suitable button when highlighting buttons, taking into account the user's geographical location information. For example, if the user is operating in a specific region, the display unit can highlight buttons specific to that region. For example, if the user is operating in a specific region, the display unit can collect data on buttons specific to that region and highlight buttons based on that data. The display unit can also highlight buttons considering the user's travel route if the user is operating while moving. For example, if the user is operating while moving, the display unit can record the travel route and highlight buttons based on that data. The display unit can also highlight buttons specific to each region if the user is operating in different regions. For example, if the user is operating in different regions, the display unit can collect data on buttons for each region and highlight buttons based on that data. This allows the display unit to provide the most suitable button by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information data into a generating AI and have the generating AI perform the button highlighting.

[0050] The display unit can analyze the user's social media activity and highlight relevant buttons when highlighting buttons. For example, the display unit can highlight relevant buttons based on information shared by the user on social media. For example, the display unit can collect information shared by the user on social media and highlight relevant buttons based on that data. The display unit can also consider the frequency of the user's social media activity and prioritize highlighting relevant buttons. For example, the display unit can record the frequency of the user's social media activity and highlight relevant buttons based on that data. The display unit can also adjust the button highlighting algorithm based on the content of the user's social media activity. For example, the display unit can analyze the content of the user's social media activity and adjust the button highlighting algorithm based on that data. This allows the display unit to provide relevant buttons by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform button highlighting.

[0051] The display unit can analyze the user's social media activity and highlight relevant buttons when highlighting buttons. For example, the display unit can highlight relevant buttons based on information shared by the user on social media. For example, the display unit can collect information shared by the user on social media and highlight relevant buttons based on that data. The display unit can also consider the frequency of the user's social media activity and prioritize highlighting relevant buttons. For example, the display unit can record the frequency of the user's social media activity and highlight relevant buttons based on that data. The display unit can also adjust the button highlighting algorithm based on the content of the user's social media activity. For example, the display unit can analyze the content of the user's social media activity and adjust the button highlighting algorithm based on that data. This allows the display unit to provide relevant buttons by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform button highlighting.

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

[0053] The analysis unit can analyze not only the user's operation history but also the user's biometric information. For example, the analysis unit monitors the user's heart rate and skin electrical activity and analyzes the operation history based on that data. The analysis unit can also collect the user's eye-tracking data and analyze which parts of the body the user is focusing on. Furthermore, the analysis unit can analyze the user's voice data to detect voice commands and changes in emotion. By considering the user's biometric information, more accurate analysis becomes possible.

[0054] The analysis unit can estimate a user's preferences and tastes based on their operation history. For example, the analysis unit collects data on categories and products that a user frequently accesses and uses that data to estimate the user's preferences. The analysis unit can also analyze the operations a user performs during specific time periods and estimate their preferences during those times. Furthermore, the analysis unit can analyze the user's operation patterns when using a specific device and estimate their preferences for that device. This enables analysis that takes user preferences and tastes into account.

[0055] The selection unit can consider past user feedback when selecting operation flows based on analysis results. For example, the selection unit can collect feedback data previously provided by users and select operation flows based on that data. Furthermore, if a user has given a high rating to a particular operation flow, the selection unit can prioritize selecting that flow. In addition, the selection unit can avoid operation flows that users have given low ratings to. This makes it possible to select operation flows that reflect user feedback.

[0056] The display unit can take into account the user's visual preferences when highlighting buttons and links that should be operated. For example, the display unit can highlight buttons based on the user's preferred colors and font styles. It can also customize buttons based on themes and designs previously selected by the user. Furthermore, the display unit can display buttons with appropriate contrast and brightness, taking into account the user's visual fatigue. This enables highlighting tailored to the user's visual preferences.

[0057] The analytics unit can analyze users' purchase and browsing history on online shopping sites, as well as their social media activity. For example, the analytics unit collects product information and reviews shared by users on social media and performs analysis based on that data. It can also analyze information about brands and influencers that users follow on social media. Furthermore, the analytics unit can estimate purchase intent based on the frequency and engagement of users' social media activity. This enables analysis that takes social media activity into account.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The analysis unit analyzes the user's operation history. This history includes click history, input history, page transition history, etc. The analysis unit can also analyze the operation history using data mining techniques and statistical analysis methods to analyze the user's behavior patterns. For example, it can analyze behavior patterns using frequency analysis or sequence analysis. Step 2: The selection unit selects an operation flow based on the results analyzed by the analysis unit. The operation flow includes a series of operating procedures tailored to the user's purpose. The selection unit selects the operation flow based on statistical indicators and the discovery of patterns. Step 3: The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit. Highlighting includes color changes, animation effects, and size changes. The display unit can highlight buttons using color changes, animation effects, and size changes.

[0060] (Example of form 2) The system according to an embodiment of the present invention is a system that plugs into a website or app, where AI selects the optimal operation flow and uses image recognition technology to highlight buttons that should be operated, thereby providing users with intuitive operation. When a user uses a website or app, the system's AI analyzes the user's operation history and behavior patterns to select the optimal operation flow. Next, it uses image recognition technology to highlight buttons and links that should be operated. This allows users to operate intuitively without getting lost. For example, on an online shopping site, when a user purchases a product, the AI ​​analyzes the user's past purchase and browsing history and proposes the optimal purchase flow. Furthermore, by using image recognition technology to highlight purchase buttons and add-to-cart buttons, the user can proceed with the purchase process smoothly. Similarly, in an educational app, the AI ​​analyzes the user's learning history and progress and proposes the optimal learning flow. By using image recognition technology to highlight the next content or problem to be studied, the user can efficiently progress through their learning. In this way, the system, by combining AI and image recognition technology, can provide users with an intuitive and efficient operating experience. This allows the system to analyze the user's operation history, select the optimal operation flow, and highlight the buttons that should be operated, thereby providing intuitive operation.

[0061] The system according to the embodiment comprises an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the user's operation history. The user's operation history includes, but is not limited to, click history, input history, page transition history, etc. The analysis unit analyzes the operation history using, for example, data mining techniques or statistical analysis methods. The analysis unit can also analyze the user's behavior patterns. For example, it can analyze behavior patterns using frequency analysis or sequence analysis. The selection unit selects an operation flow based on the results analyzed by the analysis unit. The operation flow includes, for example, a series of operation procedures according to the user's purpose, but is not limited to, such. The selection unit selects an operation flow based on, for example, statistical indicators or the discovery of patterns. The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit. The highlighting includes, for example, color changes, animation effects, size changes, etc. The display unit highlights buttons using, for example, color changes. The display unit can also highlight buttons using animation effects. Furthermore, the display unit can highlight buttons by changing its size. This allows the system according to the embodiment to analyze the user's operation history, select the optimal operation flow, and highlight the buttons to be operated, thereby providing intuitive operation.

[0062] The analysis unit can analyze the user's operation history or behavior patterns. For example, the analysis unit can analyze the user's click history. For example, the analysis unit records which buttons the user clicked and performs analysis based on that data. The analysis unit can also analyze the user's input history. For example, the analysis unit collects text data entered by the user and performs analysis based on that data. The analysis unit can also analyze the user's page transition history. For example, the analysis unit records which pages the user visited and performs analysis based on that data. By analyzing the user's operation history and behavior patterns, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's operation history data into a generating AI and have the generating AI perform the analysis of the operation history.

[0063] The selection unit can select an operation flow based on the analysis results. The selection unit can select an operation flow based on statistical indicators, for example. For example, the selection unit can select the most frequently performed operation flow based on the user's operation history data. The selection unit can also select an operation flow based on pattern discovery, for example. The selection unit can analyze the user's behavior patterns and select the optimal operation flow. The selection unit can also select an operation flow that suits the user's purpose, for example. The selection unit can select the operation flow necessary for the user to achieve a specific purpose. By selecting the optimal operation flow based on the analysis results, the selection unit can provide the user with the most suitable operation flow. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the analysis result data into a generating AI and have the generating AI perform the operation flow selection.

[0064] The display unit can highlight buttons or links that should be operated. For example, the display unit can highlight buttons by changing their color. For instance, it can change the color of a button to be operated, visually highlighting it to the user. The display unit can also highlight buttons using animation effects. For example, it can add animation effects to buttons to be operated, visually highlighting them to the user. Furthermore, the display unit can highlight buttons by changing their size. For example, it can change the size of a button to be operated, visually highlighting it to the user. This allows users to operate intuitively by highlighting buttons and links that should be operated. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input data on buttons to be operated into a generating AI, and have the generating AI perform the button highlighting.

[0065] The analysis unit can analyze a user's past purchase or browsing history on an online shopping site. For example, the analysis unit can analyze a user's purchase history. For example, the analysis unit records the types of products the user has purchased in the past and the date and time of purchase, and performs analysis based on that data. The analysis unit can also analyze a user's browsing history. For example, the analysis unit records the types of products the user has viewed in the past and the date and time of viewing, and performs analysis based on that data. By analyzing a user's past purchase and browsing history on an online shopping site, it is possible to propose a more appropriate purchase flow. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's purchase history data into a generating AI and have the generating AI perform the purchase history analysis.

[0066] The selection unit can select a purchase flow on an online shopping site. For example, the selection unit can select a purchase flow based on the user's purchase history data. For example, the selection unit can select the optimal purchase flow based on the types of products the user has purchased in the past and the date and time of purchase. The selection unit can also select a purchase flow based on the user's browsing history data. For example, the selection unit can select the optimal purchase flow based on the types of products the user has viewed in the past and the date and time of viewing. By selecting the optimal purchase flow on the online shopping site, the user can proceed with the purchase process smoothly. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's purchase history data into a generating AI and have the generating AI perform the purchase flow selection.

[0067] The display unit can highlight the purchase button and the add-to-cart button on an online shopping site. For example, the display unit can highlight the purchase button. For example, the display unit can change the color of the purchase button to visually highlight it to the user. The display unit can also highlight the add-to-cart button. For example, the display unit can change the color of the add-to-cart button to visually highlight it to the user. The display unit can also add animation effects to the purchase button and the add-to-cart button. For example, the display unit can add animation effects to the purchase button and the add-to-cart button to visually highlight them to the user. This allows users to proceed with the purchase process smoothly by highlighting the purchase button and the add-to-cart button on an online shopping site. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data for the purchase button and the add-to-cart button into a generating AI and have the generating AI perform the button highlighting.

[0068] The analysis unit can analyze a user's learning history and progress in an educational app. For example, the analysis unit can analyze a user's learning history. For instance, it can record the content and time a user has previously studied and perform analysis based on that data. The analysis unit can also analyze a user's progress. For example, it can record the user's learning achievement level and incomplete tasks and perform analysis based on that data. By analyzing a user's learning history and progress in an educational app, a more appropriate learning flow can be proposed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning history data into a generating AI and have the generating AI perform the analysis of the learning history.

[0069] The selection unit can select a learning flow in an educational app. For example, the selection unit can select a learning flow based on the user's learning history data. For example, the selection unit can select the optimal learning flow based on the content and time the user has previously studied. The selection unit can also select a learning flow based on the user's progress data. For example, the selection unit can select the optimal learning flow based on the user's learning achievement level and incomplete tasks. By selecting the optimal learning flow in the educational app, the user can learn efficiently. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning history data into a generating AI and have the generating AI perform the selection of the learning flow.

[0070] The display unit can highlight the next content or problem to be studied in the educational app. For example, the display unit can highlight the next content to be studied. For example, the display unit can change the color of the next content to be studied to visually highlight it to the user. The display unit can also highlight the next problem to be studied. For example, the display unit can change the color of the next problem to be studied to visually highlight it to the user. The display unit can also add animation effects to the next content or problem to be studied. For example, the display unit can add animation effects to the next content or problem to be studied to visually highlight it to the user. This allows users to progress through their learning more efficiently by highlighting the next content or problem to be studied in the educational app. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the next content or problem to be studied into a generating AI and have the generating AI perform the highlighting.

[0071] The analysis unit can improve the accuracy of its analysis based on the user's device usage history when analyzing operation history. For example, the analysis unit considers the types of devices the user has used in the past and reflects the operation patterns for each device in the analysis. For example, the analysis unit records the types of devices the user has used in the past and performs analysis based on that data. The analysis unit can also perform analysis specifically for devices if the user frequently uses a particular device. For example, the analysis unit focuses on analyzing the operation history of devices that the user frequently uses. The analysis unit can also integrate the operation history of each device if the user uses multiple devices and perform analysis based on that data. For example, the analysis unit integrates the operation history of multiple devices used by the user and performs analysis based on that data. This improves the accuracy of the analysis by considering the user's device usage history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's device usage history data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0072] The analysis unit can optimize its analysis algorithm based on the user's past error history when analyzing operation history. For example, the analysis unit can identify errors that the user has frequently made in the past and perform analysis to avoid those errors. For example, the analysis unit can record the types of errors the user has made in the past and the circumstances under which they occurred, and perform analysis based on that data. The analysis unit can also focus its analysis on the history related to a particular operation if the user has made an error during that operation. For example, the analysis unit can collect the history of errors the user has made during a particular operation and perform analysis based on that data. The analysis unit can also identify the cause of errors based on the user's error history and improve the analysis algorithm. For example, the analysis unit can analyze the user's error history, identify the cause of the errors, and develop an analysis algorithm to avoid that cause. This allows the analysis algorithm to be optimized by referring to the user's past error history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's error history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying important analysis results. For example, if the analysis unit estimates the user's emotions and they are feeling stressed, it will prioritize displaying important analysis results. The analysis unit can also sequentially display detailed analysis results if the user is relaxed. For example, if the analysis unit estimates the user's emotions and they are relaxed, it will sequentially display detailed analysis results. The analysis unit can also prioritize displaying analysis results that require immediate attention if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and they are in a hurry, it will prioritize displaying analysis results that require immediate attention. In this way, by prioritizing the analysis results according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is 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 processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of the analysis results.

[0074] The analysis unit can perform analysis based on the user's geographical location information when analyzing operation history. For example, if the user frequently performs operations in a particular area, the analysis unit can perform region-specific analysis. For example, if the user frequently performs operations in a particular area, the analysis unit can collect region-specific operation history and perform analysis based on that data. The analysis unit can also perform analysis considering the travel route if the user is moving. For example, if the user is moving, the analysis unit can record the travel route and perform analysis based on that data. The analysis unit can also reflect region-specific operation patterns in the analysis if the user is operating in different areas. For example, if the user is operating in different areas, the analysis unit can collect operation patterns for each area and perform analysis based on that data. This improves the accuracy of the analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0075] The analysis unit can analyze the user's social media activity and analyze relevant operation history when analyzing operation history. For example, the analysis unit can analyze operation history based on information shared by the user on social media. For example, the analysis unit can collect information shared by the user on social media and analyze operation history based on that data. The analysis unit can also consider the frequency of the user's activity on social media and focus its analysis on relevant operation history. For example, the analysis unit can record the frequency of the user's activity on social media and analyze operation history based on that data. The analysis unit can also adjust the operation history analysis algorithm based on the content of the user's social media activity. For example, the analysis unit can analyze the content of the user's social media activity and adjust the operation history analysis algorithm based on that data. This allows for the analysis of relevant operation history by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the operation history analysis.

[0076] The selection unit can estimate the user's emotions and adjust the method for selecting the optimal operation flow based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit will select a simple and quick operation flow. For example, if the selection unit estimates the user's emotions and, if stressed, will select a simple and quick operation flow. The selection unit can also select a detailed operation flow if the user is relaxed. For example, if the selection unit estimates the user's emotions and, if relaxed, will select a detailed operation flow. The selection unit can also select an operation flow that can be completed in the shortest time if the user is in a hurry. For example, if the selection unit estimates the user's emotions and, if in a hurry, will select an operation flow that can be completed in the shortest time. In this way, by adjusting the method for selecting the optimal operation flow according to the user's emotions, a more appropriate operation flow can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generating AI and have the generating AI adjust the method for selecting the operation flow.

[0077] The selection unit can optimize its selection algorithm based on the user's past selection history when selecting an operation flow. For example, the selection unit can propose the optimal operation flow based on the operation flows the user has selected in the past. For example, the selection unit can collect data on operation flows the user has selected in the past and propose the optimal operation flow based on that data. The selection unit can also prioritize suggesting operation flows that the user frequently selects. For example, the selection unit can collect data on operation flows that the user frequently selects and propose them preferentially based on that data. The selection unit can also analyze the user's past selection history and improve its selection algorithm. For example, the selection unit can analyze the user's past selection history and improve its selection algorithm based on that data. This allows the selection algorithm to be optimized by referring to the user's past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0078] The selection unit can customize the selection method based on the user's current operation status when selecting an operation flow. For example, the selection unit can propose the optimal operation flow according to the operation the user is currently performing. For example, the selection unit can collect data on the operation the user is currently performing and propose the optimal operation flow based on that data. The selection unit can also prioritize suggesting flows related to a specific operation if the user is performing that operation. For example, the selection unit can collect data on flows related to a specific operation if the user is performing that operation and prioritize suggesting flows based on that data. The selection unit can also analyze the user's current operation status in real time and adjust the selection method. For example, the selection unit can analyze the user's current operation status in real time and adjust the selection method based on that data. This allows for the provision of a more appropriate operation flow by customizing the selection method based on the user's current operation status. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current operation status data into a generating AI and have the generating AI perform the customization of the selection method.

[0079] The selection unit can estimate the user's emotions and determine the priority of the operation flow to be selected based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit will prioritize selecting important operation flows. For example, if the selection unit estimates the user's emotions and is feeling stressed, it will prioritize selecting important operation flows. The selection unit can also sequentially select detailed operation flows if the user is relaxed. For example, if the selection unit estimates the user's emotions and is relaxed, it will sequentially select detailed operation flows. The selection unit can also prioritize operation flows that require a quick response if the user is in a hurry. For example, if the selection unit estimates the user's emotions and is in a hurry, it will prioritize operation flows that require a quick response. In this way, by determining the priority of operation flows according to the user's emotions, a more appropriate operation flow can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the operation flow.

[0080] The selection unit can select an operation flow based on the user's geographical location information. For example, if the user is performing an operation in a specific region, the selection unit can select an operation flow specific to that region. For example, if the user is performing an operation in a specific region, the selection unit can collect data on operation flows specific to that region and select an operation flow based on that data. The selection unit can also select an operation flow considering the user's travel route if the user is performing an operation while on the move. For example, if the user is performing an operation while on the move, the selection unit can record the travel route and select an operation flow based on that data. The selection unit can also select operation flows for each region if the user is performing an operation in different regions. For example, if the user is performing an operation in different regions, the selection unit can collect data on operation flows for each region and select an operation flow based on that data. This allows the system to provide the optimal operation flow by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the operation flow selection.

[0081] The selection unit can analyze the user's social media activity and select relevant operation flows when selecting operation flows. For example, the selection unit can select operation flows based on information shared by the user on social media. For example, the selection unit can collect information shared by the user on social media and select operation flows based on that data. The selection unit can also consider the frequency of the user's social media activity and prioritize the selection of relevant operation flows. For example, the selection unit can record the frequency of the user's social media activity and select operation flows based on that data. The selection unit can also adjust the operation flow selection algorithm based on the content of the user's social media activity. For example, the selection unit can analyze the content of the user's social media activity and adjust the operation flow selection algorithm based on that data. In this way, relevant operation flows can be provided by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the operation flow selection.

[0082] The display unit can estimate the user's emotions and adjust how buttons are highlighted based on the estimated emotions. For example, if the user is stressed, the display unit can highlight important buttons to allow for quick operation. For example, the display unit can estimate the user's emotions and, if stressed, highlight important buttons. The display unit can also highlight buttons containing detailed information if the user is relaxed. For example, the display unit can estimate the user's emotions and, if relaxed, highlight buttons containing detailed information. The display unit can also prioritize highlighting the most important buttons if the user is in a hurry. For example, the display unit can estimate the user's emotions and, if in a hurry, prioritize highlighting the most important buttons. This allows for more appropriate operation by adjusting how buttons are highlighted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI adjust the way buttons are highlighted.

[0083] The display unit can optimize its display algorithm by referring to the user's past display history when highlighting buttons. For example, the display unit can prioritize highlighting buttons that the user has frequently operated in the past. For example, the display unit can collect data on buttons that the user has frequently operated in the past and prioritize highlighting them based on that data. The display unit can also highlight buttons that the user used when performing a specific operation. For example, the display unit can collect data on buttons that the user used when performing a specific operation and prioritize highlighting them based on that data. Furthermore, the display unit can improve its display algorithm based on the user's past display history. For example, the display unit can analyze the user's past display history and improve its display algorithm based on that data. In this way, the display algorithm can be optimized by referring to the user's past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's display history data into a generating AI and have the generating AI perform the optimization of the display algorithm.

[0084] The display unit can customize the display means based on the user's current operation status when highlighting buttons. For example, the display unit can highlight the most suitable button according to the operation the user is currently performing. For example, the display unit can collect data on the user's current operation and highlight the most suitable button based on that data. The display unit can also highlight buttons related to a specific operation if the user is performing that operation. For example, the display unit can collect data on buttons related to a specific operation if the user is performing that operation and highlight them based on that data. The display unit can also analyze the user's current operation status in real time and adjust the display means. For example, the display unit can analyze the user's current operation status in real time and adjust the display means based on that data. This allows for more appropriate operation by customizing the display means based on the user's current operation status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's current operation status data into a generating AI and have the generating AI perform the customization of the display means.

[0085] The display unit can estimate the user's emotions and determine the priority of buttons to highlight based on the estimated emotions. For example, if the user is stressed, the display unit will prioritize highlighting important buttons. For example, if the display unit estimates the user's emotions and they are stressed, it will prioritize highlighting important buttons. The display unit can also sequentially highlight buttons containing detailed information if the user is relaxed. For example, if the display unit estimates the user's emotions and they are relaxed, it will sequentially highlight buttons containing detailed information. The display unit can also prioritize highlighting buttons that require immediate attention if the user is in a hurry. For example, if the display unit estimates the user's emotions and they are in a hurry, it will prioritize highlighting buttons that require immediate attention. This allows for more appropriate operation by prioritizing buttons according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI determine the priority of buttons.

[0086] The display unit can highlight the most suitable button when highlighting buttons, taking into account the user's geographical location information. For example, if the user is operating in a specific region, the display unit can highlight buttons specific to that region. For example, if the user is operating in a specific region, the display unit can collect data on buttons specific to that region and highlight buttons based on that data. The display unit can also highlight buttons considering the user's travel route if the user is operating while moving. For example, if the user is operating while moving, the display unit can record the travel route and highlight buttons based on that data. The display unit can also highlight buttons specific to each region if the user is operating in different regions. For example, if the user is operating in different regions, the display unit can collect data on buttons for each region and highlight buttons based on that data. This allows the display unit to provide the most suitable button by taking into account the user's geographical location information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information data into a generating AI and have the generating AI perform the button highlighting.

[0087] The display unit can analyze the user's social media activity and highlight relevant buttons when highlighting buttons. For example, the display unit can highlight relevant buttons based on information shared by the user on social media. For example, the display unit can collect information shared by the user on social media and highlight relevant buttons based on that data. The display unit can also consider the frequency of the user's social media activity and prioritize highlighting relevant buttons. For example, the display unit can record the frequency of the user's social media activity and highlight relevant buttons based on that data. The display unit can also adjust the button highlighting algorithm based on the content of the user's social media activity. For example, the display unit can analyze the content of the user's social media activity and adjust the button highlighting algorithm based on that data. This allows the display unit to provide relevant buttons by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform button highlighting.

[0088] The display unit can analyze the user's social media activity and highlight relevant buttons when highlighting buttons. For example, the display unit can highlight relevant buttons based on information shared by the user on social media. For example, the display unit can collect information shared by the user on social media and highlight relevant buttons based on that data. The display unit can also consider the frequency of the user's social media activity and prioritize highlighting relevant buttons. For example, the display unit can record the frequency of the user's social media activity and highlight relevant buttons based on that data. The display unit can also adjust the button highlighting algorithm based on the content of the user's social media activity. For example, the display unit can analyze the content of the user's social media activity and adjust the button highlighting algorithm based on that data. This allows the display unit to provide relevant buttons by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform button highlighting. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, selection unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's operation history. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an operation flow based on the analysis results. The display unit is implemented by the control unit 46A of the smart device 14 and highlights the buttons to be operated based on the selected operation flow. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, selection unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's operation history. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects an operation flow based on the analysis results. The display unit is implemented, for example, by the control unit 46A of the smart glasses 214 and highlights the buttons to be operated based on the selected operation flow. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, selection unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's operation history. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an operation flow based on the analysis results. The display unit is implemented by the control unit 46A of the headset terminal 314 and highlights the buttons to be operated based on the selected operation flow. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, selection unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's operation history. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an operation flow based on the analysis results. The display unit is implemented by the control unit 46A of the robot 414 and highlights the buttons to be operated based on the selected operation flow.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The analysis unit can analyze not only the user's operation history but also the user's biometric information. For example, the analysis unit monitors the user's heart rate and skin electrical activity and analyzes the operation history based on that data. The analysis unit can also collect the user's eye-tracking data and analyze which parts of the body the user is focusing on. Furthermore, the analysis unit can analyze the user's voice data to detect voice commands and changes in emotion. By considering the user's biometric information, more accurate analysis becomes possible.

[0091] The analysis unit can estimate a user's preferences and tastes based on their operation history. For example, the analysis unit collects data on categories and products that a user frequently accesses and uses that data to estimate the user's preferences. The analysis unit can also analyze the operations a user performs during specific time periods and estimate their preferences during those times. Furthermore, the analysis unit can analyze the user's operation patterns when using a specific device and estimate their preferences for that device. This enables analysis that takes user preferences and tastes into account.

[0092] The selection unit can consider past user feedback when selecting operation flows based on analysis results. For example, the selection unit can collect feedback data previously provided by users and select operation flows based on that data. Furthermore, if a user has given a high rating to a particular operation flow, the selection unit can prioritize selecting that flow. In addition, the selection unit can avoid operation flows that users have given low ratings to. This makes it possible to select operation flows that reflect user feedback.

[0093] The display unit can take into account the user's visual preferences when highlighting buttons and links that should be operated. For example, the display unit can highlight buttons based on the user's preferred colors and font styles. It can also customize buttons based on themes and designs previously selected by the user. Furthermore, the display unit can display buttons with appropriate contrast and brightness, taking into account the user's visual fatigue. This enables highlighting tailored to the user's visual preferences.

[0094] The analytics unit can analyze users' purchase and browsing history on online shopping sites, as well as their social media activity. For example, the analytics unit collects product information and reviews shared by users on social media and performs analysis based on that data. It can also analyze information about brands and influencers that users follow on social media. Furthermore, the analytics unit can estimate purchase intent based on the frequency and engagement of users' social media activity. This enables analysis that takes social media activity into account.

[0095] The selection process can estimate the user's emotions when selecting a purchase flow on an online shopping site and make selections based on those emotions. For example, if the user is feeling stressed, the selection process can select a simple and fast purchase flow. If the user is relaxed, the selection process can select a purchase flow that provides detailed information. Furthermore, if the user is in a hurry, the selection process can select a flow that allows them to complete the purchase in the shortest possible time. This makes it possible to select a purchase flow that responds to the user's emotions.

[0096] The display unit can estimate the user's emotions when highlighting purchase buttons or add-to-cart buttons on an online shopping site, and adjust the display method based on those emotions. For example, if the user is stressed, the display unit can highlight important buttons to allow for quick operation. If the user is relaxed, the display unit can also highlight buttons containing detailed information. Furthermore, if the user is in a hurry, the display unit can prioritize highlighting the most important buttons. This enables button highlighting that responds to the user's emotions.

[0097] The analytics unit in the educational app can estimate the user's emotions in addition to their learning history and progress, and perform analysis based on those estimated emotions. For example, if the user is feeling stressed, the analytics unit can perform analysis to reduce the burden of learning. If the user is relaxed, the analytics unit can also suggest more challenging learning content. Furthermore, if the user is focused, the analytics unit can perform analysis to help them learn more efficiently. This enables learning analysis tailored to the user's emotions.

[0098] The selection unit can estimate the user's emotions when selecting a learning flow in an educational app and make selections based on those emotions. For example, if the user is feeling stressed, the selection unit will select a simple and low-burden learning flow. If the user is relaxed, the selection unit can also select a learning flow that provides detailed information. Furthermore, if the user is focused, the selection unit can select a flow that allows for efficient learning. This makes it possible to select a learning flow that responds to the user's emotions.

[0099] The display unit can estimate the user's emotions and adjust the display method based on those emotions when highlighting the next content or problem to be learned in an educational app. For example, if the user is feeling stressed, the display unit can highlight important content to enable faster learning. If the user is relaxed, the display unit can also highlight content containing detailed information. Furthermore, if the user is focused, the display unit can prioritize highlighting the most important content. This enables content highlighting that responds to the user's emotions.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The analysis unit analyzes the user's operation history. This history includes click history, input history, page transition history, etc. The analysis unit can also analyze the operation history using data mining techniques and statistical analysis methods to analyze the user's behavior patterns. For example, it can analyze behavior patterns using frequency analysis or sequence analysis. Step 2: The selection unit selects an operation flow based on the results analyzed by the analysis unit. The operation flow includes a series of operating procedures tailored to the user's purpose. The selection unit selects the operation flow based on statistical indicators and the discovery of patterns. Step 3: The display unit highlights the buttons to be operated based on the operation flow selected by the selection unit. Highlighting includes color changes, animation effects, and size changes. The display unit can highlight buttons using color changes, animation effects, and size changes.

[0102] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0105] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the operation history, A selection unit selects an operation flow based on the results analyzed by the aforementioned analysis unit, A display unit that highlights the buttons to be operated based on the operation flow selected by the selection unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the user's operation history or behavioral patterns. The system according to feature 1.

3. The aforementioned selection unit is Select the operation flow based on the analysis results. The system according to feature 1.

4. The aforementioned display unit is Highlight the button or link that needs to be operated. The system according to feature 1.

5. The aforementioned analysis unit, Analyzing users' past purchase or browsing history on online shopping sites. The system according to feature 1.

6. The aforementioned selection unit is Selecting a purchase flow on an online shopping site. The system according to feature 1.

7. The aforementioned display unit is Highlighting the purchase button or add to cart button on an online shopping site. The system according to feature 1.

8. The aforementioned analysis unit, Analyzing users' learning history and progress in educational apps. The system according to feature 1.

9. The aforementioned selection unit is Selecting a learning flow in an educational app. The system according to feature 1.

10. The aforementioned display unit is Highlight the next content or questions to study in the educational app. The system according to feature 1.

Images