system

Abstract

The system according to the embodiment aims to efficiently manage the different procedural flows for each case and to realize consistent procedures. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects information necessary for the procedures of each case. The generation unit analyzes the information collected by the collection unit and generates an efficient procedure flow. The provision unit provides the procedure flow generated by the generation unit to a user. The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes any necessary adjustments.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was the issue that it was difficult to carry out consistent procedures due to different internal systems for each case and frequent changes.

[0005] The system according to the embodiment aims to efficiently manage the different procedural flows for each case and to realize consistent procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects information necessary for the procedures of each case. The generation unit analyzes the information collected by the collection unit and generates an efficient procedure flow. The provision unit provides the procedure flow generated by the generation unit to a user. The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes any necessary adjustments. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage different procedural flows for each case and realize consistent procedures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention centralizes different internal systems for each case, enabling seamless processing of procedures. This system collects information required for each case, analyzes that information using AI, generates an optimal procedure flow, and provides it to the user. Furthermore, the system monitors the progress of the procedure in real time and makes necessary adjustments. For example, when collecting the information required for each case, detailed data related to the procedure is collected and analyzed by AI. The collected information is then analyzed by AI to generate an optimal procedure flow. The generated procedure flow is provided to the user, who can check each step of the procedure through an intuitive interface and grasp the progress in real time. Furthermore, the system monitors the progress of the procedure in real time and makes necessary adjustments. This ensures efficient and consistent processing. This allows the system to centralize the procedures for each case and efficiently proceed. For example, the system can automatically send reminders when procedures are behind schedule and dynamically change the priority of procedures. This ensures consistency and efficient processing.

[0029] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects information necessary for the procedures of each case. For example, the collection unit collects detailed data related to the procedures of each case. The collection unit can collect information such as documents, evidence, and application forms. The collection unit can also analyze the collected information using AI. The generation unit analyzes the information collected by the collection unit to generate an efficient procedure flow. For example, the generation unit optimizes each procedure step based on the collected data to generate an efficient procedure flow. The generation unit can optimize the order and priority of procedures, for example, and reduce unnecessary steps. The provision unit provides the procedure flow generated by the generation unit to a user. For example, the provision unit provides a dashboard that allows the user to check each procedure step and grasp the progress in real time through an interface that is intuitively understandable to the user. The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes necessary adjustments. For example, the monitoring unit has a function to automatically send reminders if the progress of the procedure is delayed. The monitoring unit also has a function for dynamically changing the priority of procedures, which allows the system according to the embodiment to unify the procedures for each case and proceed efficiently.

[0030] The collection unit can collect detailed data regarding the procedures for each case. Detailed data includes, but is not limited to, documents, evidence, and application forms. For example, the collection unit can collect detailed data regarding the procedures for each case. For example, the collection unit can scan the contents of documents, convert them into digital data, and collect them. The collection unit can also take photographs of evidence and collect them as digital data. Furthermore, the collection unit can input and collect the contents of application forms in digital format. By collecting detailed data regarding the procedures for each case, an overall picture of the procedures can be grasped. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the collected data into AI and have the AI ​​analyze the data.

[0031] The generation unit can streamline each procedure step based on the collected data and generate an efficient procedure flow. The generation unit, for example, optimizes each procedure step based on the collected data and generates an efficient procedure flow. For example, the generation unit can optimize the order and priority of procedures and reduce unnecessary steps. The generation unit can also use AI to streamline the procedure steps. For example, the generation unit can use AI to analyze the collected data and generate an optimal procedure flow. Furthermore, the generation unit can monitor the progress of the procedure in real time and make necessary adjustments to streamline the procedure steps. In this way, an efficient procedure flow can be generated by optimizing the procedure steps. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected data into AI and have the AI ​​generate the procedure flow.

[0032] The providing unit can provide a dashboard that allows a user to check each step of a procedure and grasp the progress status in real time through an interface that is easy for the user to understand. The providing unit can provide, for example, a dashboard that allows a user to check each step of a procedure and grasp the progress status in real time through an interface that is intuitively understandable to the user. For example, the providing unit can provide a dashboard that visually displays each step of a procedure and updates the progress status in real time. The providing unit can also devise an interface design that allows a user to intuitively understand each step of a procedure. For example, the providing unit can display each step of a procedure in a color-coded manner to allow a user to intuitively understand. Furthermore, the providing unit can use AI to grasp the progress status of a procedure in real time. For example, the providing unit can use AI to monitor the progress status of a procedure in real time and make necessary adjustments. This allows a user to intuitively understand each step of a procedure and grasp the progress status in real time. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the progress status of a procedure into AI and have the AI ​​monitor the progress status.

[0033] The monitoring unit may have a function to automatically send a reminder when the progress of the procedure is behind schedule. The monitoring unit may have a function to automatically send a reminder when the progress of the procedure is behind schedule. For example, the monitoring unit may monitor the progress of the procedure in real time and send a reminder when the progress is behind schedule. The monitoring unit may also adjust the timing of sending the reminder. For example, the monitoring unit may adjust the frequency of sending reminders based on the progress of the procedure. Furthermore, the monitoring unit may also select the means of sending the reminder. For example, the monitoring unit may send reminders using means such as email, SMS, or push notification. This allows for automatic sending of reminders when the progress of the procedure is behind schedule, thereby ensuring the consistency of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress of the procedure into AI and have the AI ​​send reminders.

[0034] The monitoring unit may have a function for changing the priority of procedures in real time. The monitoring unit may, for example, have a function for changing the priority of procedures in real time. For example, the monitoring unit may monitor the progress of procedures in real time and dynamically change the priority. The monitoring unit may also use an algorithm for changing the priority based on the progress of procedures. For example, the monitoring unit may increase the priority of a procedure when progress is delayed and decrease the priority when progress is smooth. The monitoring unit may also set a trigger for changing the priority of a procedure. For example, the monitoring unit may set a trigger for changing the priority when a specific condition is met. This allows procedures to be progressed efficiently by dynamically changing the priority of procedures. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input the progress of procedures into AI and have the AI ​​change the priority.

[0035] The collection unit can analyze past data related to the procedures for each case and select the optimal information collection method. The collection unit, for example, analyzes past data related to the procedures for each case and selects the optimal information collection method. For example, the collection unit can identify the most efficient information collection method for a specific procedure from the past data. The collection unit can also optimize the timing and frequency of information collection based on the past data. Furthermore, the collection unit can analyze the past data and prioritize the collection of information required for a specific procedure. In this way, the optimal information collection method can be selected by analyzing the past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data into AI and have the AI ​​select the information collection method.

[0036] The collection unit can filter information according to the type and scale of the case when collecting information. For example, the collection unit can filter information according to the type and scale of the case when collecting information. For example, the collection unit can collect detailed information for large-scale cases and collect the minimum necessary information for small-scale cases. The collection unit can also prioritize the collection of specific information depending on the type of case. Furthermore, the collection unit can adjust the scope of information collection depending on the scale of the case. This allows for efficient information collection by filtering according to the type and scale of the case. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data regarding the type and scale of the case into AI and have the AI ​​perform the filtering.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the user's geographical location information when collecting information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows for efficient information collection by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0039] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI and cause the AI ​​to collect related information.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can adjust the information collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal information collection method. Furthermore, the collection unit can adjust the timing and frequency of information collection by reflecting the user's past feedback. In this way, the optimal information collection method can be selected by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0041] The generation unit can adjust the level of detail of the flow based on the importance of each procedure when generating a procedure flow. For example, the generation unit can adjust the level of detail of the flow based on the importance of each procedure when generating a procedure flow. For example, the generation unit can provide a detailed flow for an important procedure. The generation unit can also provide a simplified flow for a less important procedure. Furthermore, the generation unit can dynamically adjust the level of detail of the flow based on the importance of each procedure. This allows the procedure to proceed efficiently by adjusting the level of detail of the flow based on the importance of each procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input procedure importance data into AI and have the AI ​​adjust the level of detail of the flow.

[0042] The generation unit can apply different generation algorithms depending on the case category when generating a procedural flow. For example, the generation unit can apply different generation algorithms depending on the case category when generating a procedural flow. For example, the generation unit can apply a generation algorithm dedicated to legal matters to legal cases. The generation unit can also apply a generation algorithm dedicated to finance to financial cases. Furthermore, the generation unit can select the optimal generation algorithm depending on the category of each case. This allows procedures to be carried out efficiently by applying the optimal generation algorithm depending on the case category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case category data into AI and have the AI ​​select a generation algorithm.

[0043] When generating a procedure flow, the generation unit can improve the accuracy of the flow by referring to the user's past procedure results. For example, when generating a procedure flow, the generation unit can improve the accuracy of the flow by referring to the user's past procedure results. For example, the generation unit can analyze the user's past procedure results and generate an optimal procedure flow. The generation unit can also improve the accuracy of the flow based on the user's past procedure results. Furthermore, the generation unit can adjust the level of detail of the flow by referring to the user's past procedure results. In this way, the accuracy of the flow can be improved by referring to the user's past procedure results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past procedure result data into AI and have the AI ​​improve the accuracy of the flow.

[0044] The generation unit can determine the priority of flows based on the submission time of cases when generating procedure flows. The generation unit, for example, determines the priority of flows based on the submission time of cases when generating procedure flows. For example, the generation unit can prioritize generating procedure flows for cases with an upcoming submission deadline. The generation unit can also postpone generating procedure flows for cases with a distant submission deadline. Furthermore, the generation unit can dynamically adjust the priority of flows based on the submission time of cases. This allows procedures to proceed efficiently by determining the priority of flows based on the submission time of cases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case submission time data into AI and have the AI ​​determine the priority of flows.

[0045] The generation unit can adjust the order of the flows based on the relevance of the cases when generating a procedure flow. The generation unit, for example, adjusts the order of the flows based on the relevance of the cases when generating a procedure flow. For example, the generation unit can prioritize incorporating highly relevant procedures into the flow. The generation unit can also prioritize incorporating less relevant procedures into the flow. Furthermore, the generation unit can dynamically adjust the order of the flows based on the relevance of the cases. This allows the procedures to proceed efficiently by adjusting the order of the flows based on the relevance of the cases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case relevance data into AI and have the AI ​​adjust the order of the flows.

[0046] The generation unit can adjust the use of technical terms in the flow according to the user's level of expertise when generating a procedural flow. For example, the generation unit can adjust the use of technical terms in the flow according to the user's level of expertise when generating a procedural flow. For example, the generation unit can provide a flow that uses a lot of technical terms to a user with high level of expertise. The generation unit can also provide a flow that avoids technical terms to a user with low level of expertise. Furthermore, the generation unit can dynamically adjust the use of technical terms in the flow according to the user's level of expertise. This allows the provision of a procedural flow that is easy for the user to understand by adjusting the use of technical terms in the flow according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0047] The providing unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the providing unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the providing unit can preferentially provide a display method that the user has used in the past. The providing unit can also select the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into AI and have the AI ​​select the display method.

[0048] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, the providing unit customizes the display content according to the user's current task when displaying the interface. For example, the providing unit can prioritize displaying information related to the user's current task. The providing unit can also dynamically customize the display content according to the user's current task. Furthermore, the providing unit can provide optimal display content based on the user's current task. This allows for efficient information provision by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current task data into AI and have the AI ​​customize the display content.

[0049] The providing unit can improve the display method by reflecting user feedback when displaying the interface. For example, the providing unit can improve the display method by reflecting user feedback when displaying the interface. For example, the providing unit can improve the display method of the interface based on feedback provided by the user. The providing unit can also analyze the user feedback and select an optimal display method. Furthermore, the providing unit can customize the display content of the interface by reflecting user feedback. In this way, an optimal display method can be provided by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into AI and cause the AI ​​to improve the display method.

[0050] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying the interface. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the display method.

[0051] The providing unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. In this way, by making the display content multilingual in accordance with the user's language setting, it is possible to provide display content that is easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI and have the AI ​​perform multilingual support for the display content.

[0052] The providing unit can customize the display content by reflecting the user's past feedback when displaying the interface. For example, the providing unit customizes the display content by reflecting the user's past feedback when displaying the interface. For example, the providing unit can customize the display content based on feedback previously provided by the user. The providing unit can also analyze the user's past feedback and select optimal display content. Furthermore, the providing unit can dynamically adjust the display content by reflecting the user's past feedback. In this way, optimal display content can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past feedback data into AI and have the AI ​​customize the display content.

[0053] The monitoring unit can analyze the progress of the procedure in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the progress of the procedure in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can monitor the progress of the procedure in real time and send an alert if a delay occurs. The monitoring unit can also analyze the progress of the procedure and select the optimal monitoring method. Furthermore, the monitoring unit can adjust the frequency and timing of monitoring based on the progress of the procedure. In this way, the optimal monitoring method can be selected by analyzing the progress of the procedure in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​select the monitoring method.

[0054] The monitoring unit can adjust the frequency of reminder transmissions according to the progress of the procedure during monitoring. The monitoring unit, for example, adjusts the frequency of reminder transmissions according to the progress of the procedure during monitoring. For example, the monitoring unit can increase the frequency of reminder transmissions if the progress of the procedure is delayed. The monitoring unit can also decrease the frequency of reminder transmissions if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically adjust the frequency of reminder transmissions based on the progress of the procedure. This allows the procedure to be progressed efficiently by adjusting the frequency of reminder transmissions according to the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​adjust the frequency of reminder transmissions.

[0055] The monitoring unit can dynamically change the priority based on the progress of the procedure during monitoring. The monitoring unit, for example, dynamically changes the priority based on the progress of the procedure during monitoring. For example, the monitoring unit can raise the priority if the progress of the procedure is delayed. The monitoring unit can also lower the priority if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically change the priority based on the progress of the procedure. This allows the procedure to proceed efficiently by dynamically changing the priority based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​change the priority.

[0056] The monitoring unit visually displays the progress of the procedure during monitoring, allowing the user to intuitively understand it. The monitoring unit visually displays the progress of the procedure during monitoring, allowing the user to intuitively understand it. For example, the monitoring unit can visually display the progress of the procedure using graphs or charts. The monitoring unit can also display the progress of the procedure in a color-coded manner, allowing the user to intuitively understand it. Furthermore, the monitoring unit can update the progress of the procedure in real time to provide the user with the latest information. In this way, by visually displaying the progress of the procedure, the user can intuitively understand it. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress data of the procedure into AI and cause the AI ​​to generate a visual display.

[0057] The monitoring unit can automatically send reminders based on the progress of the procedure during monitoring. The monitoring unit can automatically send reminders based on the progress of the procedure during monitoring, for example. For example, the monitoring unit can automatically send reminders if the progress of the procedure is delayed. The monitoring unit can also adjust the timing of sending reminders based on the progress of the procedure. Furthermore, the monitoring unit can monitor the progress of the procedure in real time and send reminders as necessary. This allows the progress of the procedure to be expedited by automatically sending reminders based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​send reminders.

[0058] The monitoring unit can dynamically change the priority based on the progress of the procedure during monitoring. The monitoring unit, for example, dynamically changes the priority based on the progress of the procedure during monitoring. For example, the monitoring unit can raise the priority if the progress of the procedure is delayed. The monitoring unit can also lower the priority if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically change the priority based on the progress of the procedure. This allows the procedure to proceed efficiently by dynamically changing the priority based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​change the priority.

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

[0060] When generating a procedure flow, the generation unit can adjust the level of detail of the flow based on the importance of each procedure. For example, the generation unit can provide a detailed flow for an important procedure. The generation unit can also provide a simplified flow for a less important procedure. Furthermore, the generation unit can dynamically adjust the level of detail of the flow based on the importance of each procedure. This allows the procedure to proceed efficiently by adjusting the level of detail of the flow based on the importance of each procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input procedure importance data into AI and have the AI ​​adjust the level of detail of the flow.

[0061] The monitoring unit visually displays the progress of the procedure, allowing the user to intuitively understand it. For example, the monitoring unit can visually display the progress of the procedure using graphs or charts. The monitoring unit can also display the progress of the procedure in a color-coded manner, allowing the user to intuitively understand it. Furthermore, the monitoring unit can update the progress of the procedure in real time to provide the user with the latest information. In this way, by visually displaying the progress of the procedure, the user can intuitively understand it. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress data of the procedure into AI and cause the AI ​​to generate a visual display.

[0062] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows for efficient information collection by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0063] The monitoring unit can adjust the frequency of reminder transmission based on the progress of the procedure. For example, the monitoring unit can increase the frequency of reminder transmission if the progress of the procedure is delayed. The monitoring unit can also decrease the frequency of reminder transmission if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically adjust the frequency of reminder transmission based on the progress of the procedure. This allows the procedure to be carried out efficiently by adjusting the frequency of reminder transmission according to the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​adjust the frequency of reminder transmission.

[0064] When displaying an interface, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can prioritize providing display methods that the user has used in the past. The providing unit can also select the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into AI and have the AI ​​select the display method.

[0065] The providing unit can select the optimal display method by taking into account the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the display method.

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

[0067] Step 1: The collection department collects the information necessary for each case procedure. For example, the collection department can collect information such as documents, evidence, and applications. The collection department can also use AI to analyze the collected information. Step 2: The generation unit analyzes the information collected by the collection unit and generates an efficient procedure flow. For example, the generation unit optimizes each procedure step based on the collected data, optimizes the order and priority of procedures, and eliminates unnecessary steps. Step 3: The providing unit provides the procedure flow generated by the generating unit to the user. For example, the providing unit provides a dashboard that allows the user to check each step of the procedure and grasp the progress status in real time through an interface that can be intuitively understood by the user. Step 4: The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes necessary adjustments. For example, the monitoring unit has a function to automatically send reminders if the procedure progress is delayed and a function to dynamically change the priority of the procedure.

[0068] (Example 2) A system according to an embodiment of the present invention centralizes different internal systems for each case, enabling seamless processing of procedures. This system collects information required for each case, analyzes that information using AI, generates an optimal procedure flow, and provides it to the user. Furthermore, the system monitors the progress of the procedure in real time and makes necessary adjustments. For example, when collecting the information required for each case, detailed data related to the procedure is collected and analyzed by AI. The collected information is then analyzed by AI to generate an optimal procedure flow. The generated procedure flow is provided to the user, who can check each step of the procedure through an intuitive interface and grasp the progress in real time. Furthermore, the system monitors the progress of the procedure in real time and makes necessary adjustments. This ensures efficient and consistent processing. This allows the system to centralize the procedures for each case and efficiently proceed. For example, the system can automatically send reminders when procedures are behind schedule and dynamically change the priority of procedures. This ensures consistency and efficient processing.

[0069] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects information necessary for the procedures of each case. For example, the collection unit collects detailed data related to the procedures of each case. The collection unit can collect information such as documents, evidence, and application forms. The collection unit can also analyze the collected information using AI. The generation unit analyzes the information collected by the collection unit to generate an efficient procedure flow. For example, the generation unit optimizes each procedure step based on the collected data to generate an efficient procedure flow. The generation unit can optimize the order and priority of procedures, for example, and reduce unnecessary steps. The provision unit provides the procedure flow generated by the generation unit to a user. For example, the provision unit provides a dashboard that allows the user to check each procedure step and grasp the progress in real time through an interface that is intuitively understandable to the user. The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes necessary adjustments. For example, the monitoring unit has a function to automatically send reminders if the progress of the procedure is delayed. The monitoring unit also has a function for dynamically changing the priority of procedures, which allows the system according to the embodiment to unify the procedures for each case and proceed efficiently.

[0070] The collection unit can collect detailed data regarding the procedures for each case. Detailed data includes, but is not limited to, documents, evidence, and application forms. For example, the collection unit can collect detailed data regarding the procedures for each case. For example, the collection unit can scan the contents of documents, convert them into digital data, and collect them. The collection unit can also take photographs of evidence and collect them as digital data. Furthermore, the collection unit can input and collect the contents of application forms in digital format. By collecting detailed data regarding the procedures for each case, an overall picture of the procedures can be grasped. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the collected data into AI and have the AI ​​analyze the data.

[0071] The generation unit can streamline each procedure step based on the collected data and generate an efficient procedure flow. The generation unit, for example, optimizes each procedure step based on the collected data and generates an efficient procedure flow. For example, the generation unit can optimize the order and priority of procedures and reduce unnecessary steps. The generation unit can also use AI to streamline the procedure steps. For example, the generation unit can use AI to analyze the collected data and generate an optimal procedure flow. Furthermore, the generation unit can monitor the progress of the procedure in real time and make necessary adjustments to streamline the procedure steps. In this way, an efficient procedure flow can be generated by optimizing the procedure steps. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected data into AI and have the AI ​​generate the procedure flow.

[0072] The providing unit can provide a dashboard that allows a user to check each step of a procedure and grasp the progress status in real time through an interface that is easy for the user to understand. The providing unit can provide, for example, a dashboard that allows a user to check each step of a procedure and grasp the progress status in real time through an interface that is intuitively understandable to the user. For example, the providing unit can provide a dashboard that visually displays each step of a procedure and updates the progress status in real time. The providing unit can also devise an interface design that allows a user to intuitively understand each step of a procedure. For example, the providing unit can display each step of a procedure in a color-coded manner to allow a user to intuitively understand. Furthermore, the providing unit can use AI to grasp the progress status of a procedure in real time. For example, the providing unit can use AI to monitor the progress status of a procedure in real time and make necessary adjustments. This allows a user to intuitively understand each step of a procedure and grasp the progress status in real time. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the progress status of a procedure into AI and have the AI ​​monitor the progress status.

[0073] The monitoring unit may have a function to automatically send a reminder when the progress of the procedure is behind schedule. The monitoring unit may have a function to automatically send a reminder when the progress of the procedure is behind schedule. For example, the monitoring unit may monitor the progress of the procedure in real time and send a reminder when the progress is behind schedule. The monitoring unit may also adjust the timing of sending the reminder. For example, the monitoring unit may adjust the frequency of sending reminders based on the progress of the procedure. Furthermore, the monitoring unit may also select the means of sending the reminder. For example, the monitoring unit may send reminders using means such as email, SMS, or push notification. This allows for automatic sending of reminders when the progress of the procedure is behind schedule, thereby ensuring the consistency of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress of the procedure into AI and have the AI ​​send reminders.

[0074] The monitoring unit may have a function for changing the priority of procedures in real time. The monitoring unit may, for example, have a function for changing the priority of procedures in real time. For example, the monitoring unit may monitor the progress of procedures in real time and dynamically change the priority. The monitoring unit may also use an algorithm for changing the priority based on the progress of procedures. For example, the monitoring unit may increase the priority of a procedure when progress is delayed and decrease the priority when progress is smooth. The monitoring unit may also set a trigger for changing the priority of a procedure. For example, the monitoring unit may set a trigger for changing the priority when a specific condition is met. This allows procedures to be progressed efficiently by dynamically changing the priority of procedures. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input the progress of procedures into AI and have the AI ​​change the priority.

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

[0076] The collection unit can analyze past data related to the procedures for each case and select the optimal information collection method. The collection unit, for example, analyzes past data related to the procedures for each case and selects the optimal information collection method. For example, the collection unit can identify the most efficient information collection method for a specific procedure from the past data. The collection unit can also optimize the timing and frequency of information collection based on the past data. Furthermore, the collection unit can analyze the past data and prioritize the collection of information required for a specific procedure. In this way, the optimal information collection method can be selected by analyzing the past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past data into AI and have the AI ​​select the information collection method.

[0077] The collection unit can filter information according to the type and scale of the case when collecting information. For example, the collection unit can filter information according to the type and scale of the case when collecting information. For example, the collection unit can collect detailed information for large-scale cases and collect the minimum necessary information for small-scale cases. The collection unit can also prioritize the collection of specific information depending on the type of case. Furthermore, the collection unit can adjust the scope of information collection depending on the scale of the case. This allows for efficient information collection by filtering according to the type and scale of the case. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data regarding the type and scale of the case into AI and have the AI ​​perform the filtering.

[0078] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting information. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the optimal collection means.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting important information and postpone detailed information. The collection unit can also prioritize collecting detailed information when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the minimum amount of information necessary. This allows information to be collected efficiently by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of information.

[0080] The collection unit can prioritize collecting highly relevant information based on the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information based on the user's geographical location information when collecting information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows for efficient information collection by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0081] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can collect related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI and cause the AI ​​to collect related information.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit can adjust the information collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal information collection method. Furthermore, the collection unit can adjust the timing and frequency of information collection by reflecting the user's past feedback. In this way, the optimal information collection method can be selected by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0083] The generation unit can estimate the user's emotions and adjust the presentation method of the procedure flow based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the presentation method of the procedure flow based on the estimated user emotions. For example, the generation unit can provide a simple, highly visible procedure flow when the user is stressed. The generation unit can also provide a detailed procedure flow when the user is relaxed. Furthermore, the generation unit can provide a procedure flow that focuses on the main points when the user is in a hurry. By adjusting the presentation method of the procedure flow according to the user's emotions, a procedure flow that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using an AI, or may be performed without an AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the presentation method of the procedure flow.

[0084] The generation unit can adjust the level of detail of the flow based on the importance of each procedure when generating a procedure flow. For example, the generation unit can adjust the level of detail of the flow based on the importance of each procedure when generating a procedure flow. For example, the generation unit can provide a detailed flow for an important procedure. The generation unit can also provide a simplified flow for a less important procedure. Furthermore, the generation unit can dynamically adjust the level of detail of the flow based on the importance of each procedure. This allows the procedure to proceed efficiently by adjusting the level of detail of the flow based on the importance of each procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input procedure importance data into AI and have the AI ​​adjust the level of detail of the flow.

[0085] The generation unit can apply different generation algorithms depending on the case category when generating a procedural flow. For example, the generation unit can apply different generation algorithms depending on the case category when generating a procedural flow. For example, the generation unit can apply a generation algorithm dedicated to legal matters to legal cases. The generation unit can also apply a generation algorithm dedicated to finance to financial cases. Furthermore, the generation unit can select the optimal generation algorithm depending on the category of each case. This allows procedures to be carried out efficiently by applying the optimal generation algorithm depending on the case category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case category data into AI and have the AI ​​select a generation algorithm.

[0086] When generating a procedure flow, the generation unit can improve the accuracy of the flow by referring to the user's past procedure results. For example, when generating a procedure flow, the generation unit can improve the accuracy of the flow by referring to the user's past procedure results. For example, the generation unit can analyze the user's past procedure results and generate an optimal procedure flow. The generation unit can also improve the accuracy of the flow based on the user's past procedure results. Furthermore, the generation unit can adjust the level of detail of the flow by referring to the user's past procedure results. In this way, the accuracy of the flow can be improved by referring to the user's past procedure results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past procedure result data into AI and have the AI ​​improve the accuracy of the flow.

[0087] The generation unit can estimate the user's emotions and adjust the length of the procedure flow based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the procedure flow based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide a short, concise procedure flow. If the user is relaxed, the generation unit can provide a longer procedure flow with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide a procedure flow that can be completed quickly. By adjusting the length of the procedure flow according to the user's emotions, the optimal procedure flow can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the procedure flow.

[0088] The generation unit can determine the priority of flows based on the submission time of cases when generating procedure flows. The generation unit, for example, determines the priority of flows based on the submission time of cases when generating procedure flows. For example, the generation unit can prioritize generating procedure flows for cases with an upcoming submission deadline. The generation unit can also postpone generating procedure flows for cases with a distant submission deadline. Furthermore, the generation unit can dynamically adjust the priority of flows based on the submission time of cases. This allows procedures to proceed efficiently by determining the priority of flows based on the submission time of cases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case submission time data into AI and have the AI ​​determine the priority of flows.

[0089] The generation unit can adjust the order of the flows based on the relevance of the cases when generating a procedure flow. The generation unit, for example, adjusts the order of the flows based on the relevance of the cases when generating a procedure flow. For example, the generation unit can prioritize incorporating highly relevant procedures into the flow. The generation unit can also prioritize incorporating less relevant procedures into the flow. Furthermore, the generation unit can dynamically adjust the order of the flows based on the relevance of the cases. This allows the procedures to proceed efficiently by adjusting the order of the flows based on the relevance of the cases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input case relevance data into AI and have the AI ​​adjust the order of the flows.

[0090] The generation unit can adjust the use of technical terms in the flow according to the user's level of expertise when generating a procedural flow. For example, the generation unit can adjust the use of technical terms in the flow according to the user's level of expertise when generating a procedural flow. For example, the generation unit can provide a flow that uses a lot of technical terms to a user with high level of expertise. The generation unit can also provide a flow that avoids technical terms to a user with low level of expertise. Furthermore, the generation unit can dynamically adjust the use of technical terms in the flow according to the user's level of expertise. This allows the provision of a procedural flow that is easy for the user to understand by adjusting the use of technical terms in the flow according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0091] The providing unit can estimate the user's emotion and adjust the interface display method based on the estimated user emotion. For example, the providing unit can estimate the user's emotion and adjust the interface display method based on the estimated user emotion. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible interface. Furthermore, if the user is relaxed, the providing unit can provide an interface that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide an interface that focuses on the main points. This allows the interface display method to be adjusted according to the user's emotion, thereby providing an optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the interface display method.

[0092] The providing unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the providing unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the providing unit can preferentially provide a display method that the user has used in the past. The providing unit can also select the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into AI and have the AI ​​select the display method.

[0093] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, the providing unit customizes the display content according to the user's current task when displaying the interface. For example, the providing unit can prioritize displaying information related to the user's current task. The providing unit can also dynamically customize the display content according to the user's current task. Furthermore, the providing unit can provide optimal display content based on the user's current task. This allows for efficient information provision by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's current task data into AI and have the AI ​​customize the display content.

[0094] The providing unit can improve the display method by reflecting user feedback when displaying the interface. For example, the providing unit can improve the display method by reflecting user feedback when displaying the interface. For example, the providing unit can improve the display method of the interface based on feedback provided by the user. The providing unit can also analyze the user feedback and select an optimal display method. Furthermore, the providing unit can customize the display content of the interface by reflecting user feedback. In this way, an optimal display method can be provided by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into AI and cause the AI ​​to improve the display method.

[0095] The providing unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can simplify the operation procedures to enable intuitive operation. The providing unit can also provide detailed operation procedures when the user is relaxed. Furthermore, if the user is in a hurry, the providing unit can provide procedures that allow quick operation. This allows the interface operation procedures to be adjusted according to the user's emotions, thereby providing the optimal operation procedures for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the operation procedures.

[0096] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying the interface. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the display method.

[0097] The providing unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. For example, the providing unit can automatically set the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. In this way, by making the display content multilingual in accordance with the user's language setting, it is possible to provide display content that is easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into AI and have the AI ​​perform multilingual support for the display content.

[0098] The providing unit can customize the display content by reflecting the user's past feedback when displaying the interface. For example, the providing unit customizes the display content by reflecting the user's past feedback when displaying the interface. For example, the providing unit can customize the display content based on feedback previously provided by the user. The providing unit can also analyze the user's past feedback and select optimal display content. Furthermore, the providing unit can dynamically adjust the display content by reflecting the user's past feedback. In this way, optimal display content can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past feedback data into AI and have the AI ​​customize the display content.

[0099] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the monitoring frequency to reduce the user's burden. Also, if the user is relaxed, the monitoring unit can increase the monitoring frequency and collect more detailed data. Furthermore, if the user is in a hurry, the monitoring unit can speed up the timing of monitoring and quickly collect necessary information. This reduces the user's burden by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI and have the AI ​​adjust the monitoring method.

[0100] The monitoring unit can analyze the progress of the procedure in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the progress of the procedure in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can monitor the progress of the procedure in real time and send an alert if a delay occurs. The monitoring unit can also analyze the progress of the procedure and select the optimal monitoring method. Furthermore, the monitoring unit can adjust the frequency and timing of monitoring based on the progress of the procedure. In this way, the optimal monitoring method can be selected by analyzing the progress of the procedure in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​select the monitoring method.

[0101] The monitoring unit can adjust the frequency of reminder transmissions according to the progress of the procedure during monitoring. The monitoring unit, for example, adjusts the frequency of reminder transmissions according to the progress of the procedure during monitoring. For example, the monitoring unit can increase the frequency of reminder transmissions if the progress of the procedure is delayed. The monitoring unit can also decrease the frequency of reminder transmissions if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically adjust the frequency of reminder transmissions based on the progress of the procedure. This allows the procedure to be progressed efficiently by adjusting the frequency of reminder transmissions according to the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​adjust the frequency of reminder transmissions.

[0102] The monitoring unit can dynamically change the priority based on the progress of the procedure during monitoring. The monitoring unit, for example, dynamically changes the priority based on the progress of the procedure during monitoring. For example, the monitoring unit can raise the priority if the progress of the procedure is delayed. The monitoring unit can also lower the priority if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically change the priority based on the progress of the procedure. This allows the procedure to proceed efficiently by dynamically changing the priority based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​change the priority.

[0103] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines monitoring priorities based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring important procedures. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring detailed procedures. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring procedures that can be completed quickly. This allows procedures to be efficiently progressed by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI and have the AI ​​determine the monitoring priorities.

[0104] The monitoring unit visually displays the progress of the procedure during monitoring, allowing the user to intuitively understand it. The monitoring unit visually displays the progress of the procedure during monitoring, allowing the user to intuitively understand it. For example, the monitoring unit can visually display the progress of the procedure using graphs or charts. The monitoring unit can also display the progress of the procedure in a color-coded manner, allowing the user to intuitively understand it. Furthermore, the monitoring unit can update the progress of the procedure in real time to provide the user with the latest information. In this way, by visually displaying the progress of the procedure, the user can intuitively understand it. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress data of the procedure into AI and cause the AI ​​to generate a visual display.

[0105] The monitoring unit can automatically send reminders based on the progress of the procedure during monitoring. The monitoring unit can automatically send reminders based on the progress of the procedure during monitoring, for example. For example, the monitoring unit can automatically send reminders if the progress of the procedure is delayed. The monitoring unit can also adjust the timing of sending reminders based on the progress of the procedure. Furthermore, the monitoring unit can monitor the progress of the procedure in real time and send reminders as necessary. This allows the progress of the procedure to be expedited by automatically sending reminders based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​send reminders.

[0106] The monitoring unit can dynamically change the priority based on the progress of the procedure during monitoring. The monitoring unit, for example, dynamically changes the priority based on the progress of the procedure during monitoring. For example, the monitoring unit can raise the priority if the progress of the procedure is delayed. The monitoring unit can also lower the priority if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically change the priority based on the progress of the procedure. This allows the procedure to proceed efficiently by dynamically changing the priority based on the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the progress of the procedure into AI and have the AI ​​change the priority. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information necessary for the procedure of each case using the camera 42 and microphone 38B of the smart device 14, and analyzes the information using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an efficient procedure flow based on the collected information. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the procedure flow through an interface that is intuitively understandable to the user. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the progress of the procedure in real time and makes necessary adjustments. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information required for the procedure of each case using the camera 42 and microphone 238 of the smart glasses 214, and analyzes the information using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an efficient procedure flow based on the collected information. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the procedure flow through an interface that is intuitively understandable to the user. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the progress of the procedure in real time and makes necessary adjustments. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned collection unit, generation unit, provision unit, and monitoring unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information necessary for the procedure of each case using the camera 42 and microphone 238 of the headset-type terminal 314, and analyzes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an efficient procedure flow based on the collected information. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the procedure flow through an interface that is intuitively understandable to the user. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the procedure in real time and makes any necessary adjustments. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information required for the procedure of each case using the camera 42 and microphone 238 of the robot 414, and analyzes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an efficient procedure flow based on the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the procedure flow through an interface that is intuitively understandable to the user. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the procedure in real time and makes any necessary adjustments.

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

[0108] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can speed up the timing of information collection to quickly collect necessary information. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of information collection.

[0109] When generating a procedure flow, the generation unit can adjust the level of detail of the flow based on the importance of each procedure. For example, the generation unit can provide a detailed flow for an important procedure. The generation unit can also provide a simplified flow for a less important procedure. Furthermore, the generation unit can dynamically adjust the level of detail of the flow based on the importance of each procedure. This allows the procedure to proceed efficiently by adjusting the level of detail of the flow based on the importance of each procedure. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input procedure importance data into AI and have the AI ​​adjust the level of detail of the flow.

[0110] The providing unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible interface. Furthermore, if the user is relaxed, the providing unit can provide an interface that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide an interface that focuses on the main points. This allows the interface display method to be adjusted according to the user's emotions, thereby providing an optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the interface display method.

[0111] The monitoring unit visually displays the progress of the procedure, allowing the user to intuitively understand it. For example, the monitoring unit can visually display the progress of the procedure using graphs or charts. The monitoring unit can also display the progress of the procedure in a color-coded manner, allowing the user to intuitively understand it. Furthermore, the monitoring unit can update the progress of the procedure in real time to provide the user with the latest information. In this way, by visually displaying the progress of the procedure, the user can intuitively understand it. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the progress data of the procedure into AI and cause the AI ​​to generate a visual display.

[0112] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. This allows for efficient information collection by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0113] The generation unit can estimate the user's emotions and adjust the presentation method of the procedure flow based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide a simple, highly visible procedure flow. Furthermore, if the user is relaxed, the generation unit can provide a detailed procedure flow. Furthermore, if the user is in a hurry, the generation unit can provide a procedure flow that focuses on the main points. By adjusting the presentation method of the procedure flow according to the user's emotions, a procedure flow that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method of the procedure flow.

[0114] The monitoring unit can adjust the frequency of reminder transmission based on the progress of the procedure. For example, the monitoring unit can increase the frequency of reminder transmission if the progress of the procedure is delayed. The monitoring unit can also decrease the frequency of reminder transmission if the procedure is progressing smoothly. Furthermore, the monitoring unit can dynamically adjust the frequency of reminder transmission based on the progress of the procedure. This allows the procedure to be carried out efficiently by adjusting the frequency of reminder transmission according to the progress of the procedure. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input progress data of the procedure into AI and have the AI ​​adjust the frequency of reminder transmission.

[0115] When displaying an interface, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can prioritize providing display methods that the user has used in the past. The providing unit can also select the optimal display method based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into AI and have the AI ​​select the display method.

[0116] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring important procedures. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring detailed procedures. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring procedures that can be completed quickly. This allows procedures to be efficiently completed by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI and have the AI ​​determine the monitoring priorities.

[0117] The providing unit can select the optimal display method by taking into account the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method optimized for a wide screen. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into AI and have the AI ​​select the display method.

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

[0119] Step 1: The collection department collects the information necessary for each case procedure. For example, the collection department can collect information such as documents, evidence, and applications. The collection department can also use AI to analyze the collected information. Step 2: The generation unit analyzes the information collected by the collection unit and generates an efficient procedure flow. For example, the generation unit optimizes each procedure step based on the collected data, optimizes the order and priority of procedures, and eliminates unnecessary steps. Step 3: The providing unit provides the procedure flow generated by the generating unit to the user. For example, the providing unit provides a dashboard that allows the user to check each step of the procedure and grasp the progress status in real time through an interface that can be intuitively understood by the user. Step 4: The monitoring unit monitors the progress of the procedure flow provided by the provision unit in real time and makes necessary adjustments. For example, the monitoring unit has a function to automatically send reminders if the procedure progress is delayed and a function to dynamically change the priority of the procedure.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information necessary for the procedure of each case; a generation unit that analyzes the information collected by the collection unit and generates an efficient procedure flow; a providing unit that provides a user with the procedure flow generated by the generating unit; a monitoring unit that monitors the progress of the procedure flow provided by the providing unit in real time and makes necessary adjustments. A system characterized by:

2. The collecting unit Collect detailed data on the procedures for each case 2. The system of claim 1.

3. The generation unit Based on the collected data, we streamline each procedural step and generate an efficient procedural flow.

2. The system of claim 1.

4. The providing unit Provide users with an easy-to-understand interface to view each step of the process and a dashboard to track progress in real time.

2. The system of claim 1.

5. The monitoring unit Equipped with a function to automatically send reminders if the process is behind schedule 2. The system of claim 1.

6. The monitoring unit Equipped with the ability to change procedure priorities in real time 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze past data on procedures for each case and select the most appropriate information gathering method 2. The system of claim 1.

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