system

Abstract

The system according to this embodiment aims to improve and standardize the skills of human final judgments made after machine analysis. [Solution] The system according to the embodiment comprises a determination unit, a judgment unit, a feedback generation unit, and a re-check unit. The determination unit performs a machine judgment. The judgment unit makes a final judgment based on the judgment result obtained by the determination unit. The feedback generation unit generates feedback for the final judgment made by the judgment unit. The re-check unit performs a re-check based on the feedback generated by the feedback generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the final human judgment made after machine determination is not unified due to variations in skill levels, making it difficult to perform efficient operations.

[0005] The system according to the embodiment aims to improve and unify the final human judgment after machine determination.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a determination unit, a judgment unit, a feedback generation unit, and a re-check unit. The determination unit performs a machine judgment. The judgment unit makes a final judgment based on the judgment result obtained by the determination unit. The feedback generation unit generates feedback for the final judgment made by the judgment unit. The re-check unit performs a re-check based on the feedback generated by the feedback generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve and standardize the final human judgment after machine analysis. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The skill improvement promotion system according to an embodiment of the present invention is a system that uses AI to promote and standardize human skill improvement. In this skill improvement promotion system, first, the AI ​​performs a machine judgment, and a human makes a final judgment based on the result. Next, the AI ​​reads a standards document and generates feedback on the final judgment. Based on this feedback, a human performs a re-check and confirms the final judgment. This mechanism standardizes the skill levels of people and reduces the risk of misjudgment. For example, the AI ​​performs a machine judgment. In this case, the AI ​​uses an internal system to make a judgment of white, gray, or black. For example, if the AI ​​detects an anomaly in patrol work, it determines whether the anomaly is white (no problem), gray (caution required), or black (problem). Next, the AI ​​reads a standards document and generates feedback on the final judgment. For example, the AI ​​checks whether the final judgment conforms to the standards based on the standards document and generates feedback. This feedback is provided to the person who made the final judgment. Furthermore, the person who made the final judgment performs a re-check based on the feedback and confirms the final judgment. For example, based on the AI's feedback, they reconfirm whether the final judgment is appropriate and make corrections as necessary. In this way, the accuracy of final decisions is improved, and the skill levels of individuals are standardized. This mechanism standardizes the skill levels of individuals in patrol and inspection work, reducing the risk of misjudgment. Furthermore, by having AI read the standards manual and generate feedback, the accuracy of final decisions is improved, enabling the provision of safer and more secure services. For example, if the AI ​​detects anomalies in patrol work and verifies whether the anomaly conforms to the standards, the risk of misjudgment is reduced, enabling the provision of safer and more secure services. In this way, the skill improvement promotion system can promote and standardize the skill improvement of individuals using AI.

[0029] The skill improvement promotion system according to the embodiment comprises a judgment unit, a decision unit, a feedback generation unit, and a re-check unit. The judgment unit performs machine judgment. The judgment unit, for example, uses AI to detect anomalies and determines whether the anomaly is white (no problem), gray (caution required), or black (problem exists). For example, the judgment unit uses an internal system to detect anomalies and determines whether the anomaly is white, gray, or black. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, the judgment unit sets and applies different judgment criteria depending on the type of anomaly. The decision unit makes a final judgment based on the judgment results obtained by the judgment unit. The decision unit can also optimize the decision algorithm by referring to past decision data when making a final judgment. For example, the decision unit adjusts the parameters of the decision algorithm based on past decision data. The feedback generation unit generates feedback for the final judgment made by the judgment unit. The feedback generation unit, for example, reads the standards document, verifies whether the final judgment conforms to the standards, and generates feedback. For example, the feedback generation unit verifies whether the final judgment conforms to the standards based on the standards document and generates feedback. The feedback generation unit can also optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, the feedback generation unit adjusts the parameters of the feedback algorithm based on past feedback data. The re-checking unit performs a re-check based on the feedback generated by the feedback generation unit. For example, the re-checking unit re-verifies whether the final judgment is appropriate based on the feedback. For example, the re-checking unit re-verifies whether the final judgment is appropriate based on the feedback. The re-checking unit can also optimize the re-checking algorithm by referring to past re-checking data when performing a re-check. For example, the re-checking unit adjusts the parameters of the re-checking algorithm based on past re-checking data.As a result, the skill improvement promotion system according to this embodiment can use AI to promote and standardize people's skill improvement.

[0030] The judgment unit performs machine-based judgments. For example, the judgment unit uses AI to detect anomalies and determines whether the anomaly is "white" (no problem), "gray" (caution required), or "black" (problem). Specifically, the judgment unit analyzes data collected from internal systems in real time and uses machine learning algorithms to detect signs of anomalies. For example, anomaly detection algorithms and deep learning models are used for anomaly detection, enabling highly accurate identification of anomaly patterns. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, different criteria are set and applied to different types of anomalies such as network anomalies, system errors, and data inconsistencies. This enables appropriate judgment according to the characteristics of the anomaly. Furthermore, to improve the accuracy of anomaly detection, the judgment unit learns from past anomaly data and continuously optimizes the model parameters. For example, the model is retrained using past anomaly data to respond to new anomaly patterns. As a result, the judgment unit can always incorporate the latest anomaly detection technology and perform highly accurate anomaly judgments.

[0031] The judgment unit makes a final decision based on the judgment results obtained by the determination unit. Specifically, the judgment unit determines appropriate countermeasures based on the type and level of the anomaly provided by the determination unit. For example, if it is judged as "clear," no action is taken; if it is judged as "gray," a warning is issued; and if it is judged as "guilty," countermeasures are implemented immediately. In addition, the judgment unit can optimize its judgment algorithm by referring to past judgment data when making a final decision. For example, it can adjust the parameters of the judgment algorithm based on past judgment data to make more accurate judgments. Furthermore, the judgment unit can simulate multiple countermeasures depending on the type and situation of the anomaly and select the optimal countermeasure. This allows the judgment unit to take a quick and appropriate action and minimize the impact of the anomaly.

[0032] The feedback generation unit generates feedback on the final decision made by the judgment unit. For example, the feedback generation unit reads the standards document, checks whether the final decision conforms to the standards, and generates feedback. Specifically, the feedback generation unit refers to the evaluation criteria and procedures described in the standards document and checks whether the final decision was made in accordance with these criteria. For example, if the final decision conforms to the standards, it is evaluated as "appropriate," and if it does not conform to the standards, it is evaluated as "needs improvement." In addition, the feedback generation unit can optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, it can adjust the parameters of the feedback algorithm based on past feedback data to generate more effective feedback. Furthermore, the feedback generation unit can use natural language generation technology to make the feedback content specific and easy to understand. This allows the feedback generation unit to provide users with clear and useful feedback, promoting skill improvement.

[0033] The re-checking unit performs a re-check based on the feedback generated by the feedback generation unit. For example, the re-checking unit reconfirms whether the final decision is appropriate based on the feedback. Specifically, the re-checking unit examines the feedback in detail and re-evaluates whether the final decision was made in accordance with the criteria. For example, if the feedback states "improvement is needed," the re-checking unit reconfirms the accuracy of the final decision procedure and criterion application and makes corrections as necessary. The re-checking unit can also optimize the re-checking algorithm by referring to past re-checking data during the re-checking process. For example, it can adjust the parameters of the re-checking algorithm based on past re-checking data to perform a more accurate re-check. Furthermore, the re-checking unit can feed the re-checking results back to the feedback generation unit, contributing to the improvement of the feedback algorithm. This allows the re-checking unit to ensure the appropriateness of the final decision and improve the overall reliability and accuracy of the system.

[0034] The feedback generation unit can read the standards document and verify whether the final judgment conforms to the standards. For example, the feedback generation unit can verify whether the final judgment conforms to the standards based on the standards document. For example, the feedback generation unit can analyze the contents of the standards document in detail and verify whether the final judgment conforms to the standards. The feedback generation unit can also use a generating AI when analyzing the contents of the standards document. For example, the feedback generation unit inputs the contents of the standards document into the generating AI, and the generating AI analyzes the contents of the standards document. This improves the accuracy of the final judgment. Standards documents include, but are not limited to, work procedure manuals and quality standards documents. The contents of standards documents include, but are not limited to, work procedures and quality standards documents. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the contents of the standards document into AI, and the AI ​​can analyze the contents of the standards document.

[0035] The re-checking unit can reconfirm whether the final decision is appropriate based on the feedback. For example, the re-checking unit reconfirms whether the final decision is appropriate based on the feedback. For example, the re-checking unit analyzes the content of the feedback in detail and reconfirms whether the final decision is appropriate. The re-checking unit can also use AI when analyzing the content of the feedback. For example, the re-checking unit inputs the content of the feedback into the AI, and the AI ​​analyzes the content of the feedback. This improves the accuracy of the final decision. The feedback includes, but is not limited to, the basis for the final decision and points for correction. Some or all of the above processing in the re-checking unit may be performed using, for example, AI, or without AI. For example, the re-checking unit can input the content of the feedback into the AI, and the AI ​​can analyze the content of the feedback.

[0036] The judgment unit can make judgments of white, gray, or black. For example, the judgment unit can use AI to detect an anomaly and determine whether the anomaly is white (no problem), gray (caution needed), or black (problem). For example, the judgment unit can use an internal system to detect an anomaly and determine whether the anomaly is white, gray, or black. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, the judgment unit sets and applies different judgment criteria depending on the type of anomaly. This makes the anomaly determination clearer. The judgment criteria for white, gray, and black are set based on, for example, the severity and scope of impact of the anomaly, but are not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or not using AI. For example, the judgment unit can input different judgment criteria depending on the type of anomaly into the AI, and the AI ​​can apply the judgment criteria.

[0037] The feedback generation unit can analyze the contents of the standards document in detail. For example, the feedback generation unit can analyze the contents of the standards document in detail and confirm whether the final judgment conforms to the standards. For example, the feedback generation unit can analyze the contents of the standards document in detail and confirm whether the final judgment conforms to the standards. The feedback generation unit can also use a generating AI when analyzing the contents of the standards document. For example, the feedback generation unit inputs the contents of the standards document into the generating AI, and the generating AI analyzes the contents of the standards document. This improves the accuracy of the feedback. Standards documents include, but are not limited to, work procedures manuals and quality standards manuals. The contents of standards documents include, but are not limited to, work procedures and quality standards. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the contents of the standards document into the AI, and the AI ​​can analyze the contents of the standards document.

[0038] The feedback generation unit can specifically indicate the content of the feedback. For example, the feedback generation unit can specifically indicate the content of the feedback and confirm whether the final judgment conforms to the criteria. For example, the feedback generation unit can specifically indicate the content of the feedback and confirm whether the final judgment conforms to the criteria. The feedback generation unit can also use a generating AI when indicating the content of the feedback. For example, the feedback generation unit inputs the content of the feedback into the generating AI, and the generating AI indicates the content of the feedback. This deepens the understanding of the feedback. The content of the feedback includes, but is not limited to, the basis for the final judgment and points for correction. The format of the feedback includes, but is not limited to, text format and graph format. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the content of the feedback into AI, and the AI ​​indicates the content of the feedback.

[0039] The judgment unit can optimize the judgment algorithm by referring to past judgment data during the judgment process. For example, the judgment unit can adjust the parameters of the judgment algorithm based on past judgment data. The judgment unit can also analyze past judgment data to identify patterns of misjudgments and improve the algorithm. The judgment unit can also use past judgment data to evaluate the accuracy of the judgment algorithm and make corrections as necessary. This improves the accuracy of the judgment algorithm. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past judgment data into AI, and the AI ​​can optimize the judgment algorithm.

[0040] The judgment unit can apply different judgment criteria depending on the type of anomaly during the judgment process. For example, the judgment unit can set and apply different judgment criteria depending on the type of anomaly. The judgment unit can also dynamically change the judgment criteria for each type of anomaly. For example, the judgment unit can dynamically change the judgment criteria for each type of anomaly. The judgment unit can also adjust the weighting of the judgment criteria depending on the type of anomaly. For example, the judgment unit can adjust the weighting of the judgment criteria depending on the type of anomaly. This enables judgment according to the type of anomaly. Different judgment criteria include, but are not limited to, judgment criteria for each type of anomaly. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input judgment criteria according to the type of anomaly to the AI, and the AI ​​can apply the judgment criteria.

[0041] The determination unit can display the determination result while considering the location where the anomaly occurred. For example, the determination unit can display the determination result on a map based on the location where the anomaly occurred. The determination unit can also change the method of displaying the determination result depending on the location where the anomaly occurred. For example, the determination unit can change the method of displaying the determination result depending on the location where the anomaly occurred. The determination unit can also determine the priority of the determination results while considering the location where the anomaly occurred. For example, the determination unit can determine the priority of the determination results while considering the location where the anomaly occurred. This provides a determination result that corresponds to the location where the anomaly occurred. The location where the anomaly occurred includes, but is not limited to, specific methods for identifying and displaying the location where the anomaly occurred. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the location where the anomaly occurred into the AI, and the AI ​​can display the determination result while considering the location.

[0042] The determination unit can improve the accuracy of its determination by referring to relevant external data during the determination process. For example, the determination unit can improve the accuracy of its determination by referring to external weather data. The determination unit can also improve the accuracy of its determination by referring to external traffic data. The determination unit can also improve the accuracy of its determination by referring to external sensor data. In this way, the accuracy of the determination is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the determination unit may be performed using, for example, AI, or not using AI. For example, the determination unit can input external data into the AI, and the AI ​​can refer to the external data to improve the accuracy of its determination.

[0043] The decision unit can optimize the decision algorithm by referring to past decision data at the time of the final decision. For example, the decision unit can adjust the parameters of the decision algorithm based on past decision data. The decision unit can also analyze past decision data to identify patterns of misjudgments and improve the algorithm. The decision unit can also use past decision data to evaluate the accuracy of the decision algorithm and make corrections as necessary. This improves the accuracy of the decision algorithm. Some or all of the above processes in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input past decision data into AI, and the AI ​​can optimize the decision algorithm.

[0044] The judgment unit can apply different judgment criteria depending on the type of anomaly at the time of final judgment. For example, the judgment unit can set and apply different judgment criteria depending on the type of anomaly. The judgment unit can also dynamically change the judgment criteria for each type of anomaly. The judgment unit can also adjust the weighting of the judgment criteria depending on the type of anomaly. This enables judgments according to the type of anomaly. Different judgment criteria include, but are not limited to, judgment criteria for each type of anomaly. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input judgment criteria according to the type of anomaly into the AI, and the AI ​​can apply the judgment criteria.

[0045] The judgment unit can display the judgment result at the time of final judgment, taking into account the location where the anomaly occurred. For example, the judgment unit can display the judgment result on a map based on the location where the anomaly occurred. The judgment unit can also change the method of displaying the judgment result depending on the location where the anomaly occurred. For example, the judgment unit can change the method of displaying the judgment result depending on the location where the anomaly occurred. The judgment unit can also determine the priority of the judgment results, taking into account the location where the anomaly occurred. For example, the judgment unit can determine the priority of the judgment results, taking into account the location where the anomaly occurred. This provides a judgment result that corresponds to the location where the anomaly occurred. The location where the anomaly occurred includes, but is not limited to, specific methods for identifying and displaying the location where the anomaly occurred. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the location where the anomaly occurred into the AI, and the AI ​​can display the judgment result taking the location into account.

[0046] The decision-making unit can improve the accuracy of its decision by referring to relevant external data at the time of the final decision. For example, the decision-making unit can improve the accuracy of its decision by referring to external weather data. The decision-making unit can also improve the accuracy of its decision by referring to external traffic data. The decision-making unit can also improve the accuracy of its decision by referring to external sensor data. In this way, the accuracy of the decision is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can input external data into the AI, and the AI ​​can improve the accuracy of its decision by referring to the external data.

[0047] The feedback generation unit can optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, the feedback generation unit can adjust the parameters of the feedback algorithm based on past feedback data. The feedback generation unit can also analyze past feedback data to identify patterns of erroneous feedback and improve the algorithm. The feedback generation unit can also use past feedback data to evaluate the accuracy of the feedback algorithm and make corrections as necessary. This improves the accuracy of the feedback algorithm. Some or all of the above processes in the feedback generation unit may be performed using AI, for example, or without AI. For example, the feedback generation unit can input past feedback data into AI, and the AI ​​can optimize the feedback algorithm.

[0048] The feedback generation unit can apply different feedback criteria depending on the type of anomaly when generating feedback. For example, the feedback generation unit can set and apply different feedback criteria depending on the type of anomaly. The feedback generation unit can also dynamically change the feedback criteria for each type of anomaly. For example, the feedback generation unit can dynamically change the feedback criteria for each type of anomaly. The feedback generation unit can also adjust the weighting of the feedback criteria depending on the type of anomaly. For example, the feedback generation unit can adjust the weighting of the feedback criteria depending on the type of anomaly. This provides feedback appropriate to the type of anomaly. Different feedback criteria include, but are not limited to, feedback criteria for each type of anomaly. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input feedback criteria appropriate to the type of anomaly to the AI, and the AI ​​can apply the feedback criteria.

[0049] The feedback generation unit can display feedback content while considering the location of the anomaly when generating feedback. For example, the feedback generation unit can display feedback content on a map based on the location of the anomaly. The feedback generation unit can also change the method of displaying feedback content depending on the location of the anomaly. The feedback generation unit can also determine the priority of feedback content while considering the location of the anomaly. This provides feedback appropriate to the location of the anomaly. The location of the anomaly includes, but is not limited to, specific methods for identifying and displaying the location of the anomaly. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or without AI. For example, the feedback generation unit can input the location of the anomaly into AI, and the AI ​​can display feedback content while considering the location.

[0050] The feedback generation unit can improve the accuracy of the feedback by referring to relevant external data when generating feedback. For example, the feedback generation unit can improve the accuracy of the feedback by referring to external weather data. The feedback generation unit can also improve the accuracy of the feedback by referring to external traffic data. The feedback generation unit can also improve the accuracy of the feedback by referring to external sensor data. In this way, the accuracy of the feedback is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input external data into AI, and the AI ​​can refer to the external data to improve the accuracy of the feedback.

[0051] The re-checking unit can optimize the re-checking algorithm by referring to past re-checking data during the re-checking process. For example, the re-checking unit can adjust the parameters of the re-checking algorithm based on past re-checking data. The re-checking unit can also analyze past re-checking data to identify patterns of incorrect re-checks and improve the algorithm. The re-checking unit can also use past re-checking data to evaluate the accuracy of the re-checking algorithm and make corrections as needed. This improves the accuracy of the re-checking algorithm. Some or all of the above processes in the re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input past re-checking data into AI, which can then optimize the re-checking algorithm.

[0052] The re-checking unit can apply different re-checking criteria depending on the type of anomaly during re-checking. For example, the re-checking unit can set and apply different re-checking criteria depending on the type of anomaly. The re-checking unit can also dynamically change the re-checking criteria for each type of anomaly. The re-checking unit can also adjust the weighting of the re-checking criteria depending on the type of anomaly. This enables re-checking according to the type of anomaly. Different re-checking criteria include, but are not limited to, re-checking criteria for each type of anomaly. Some or all of the above-described processes in the re-checking unit may be performed using, for example, AI, or not using AI. For example, the re-checking unit can input re-checking criteria according to the type of anomaly into the AI, and the AI ​​can apply the re-checking criteria.

[0053] The re-checking unit can display the re-check content while considering the location where the anomaly occurred. For example, the re-checking unit can display the re-check content on a map based on the location where the anomaly occurred. The re-checking unit can also change the display method of the re-check content depending on the location where the anomaly occurred. The re-checking unit can also determine the priority of the re-check content while considering the location where the anomaly occurred. This provides re-check content appropriate to the location where the anomaly occurred. The location of the anomaly includes, but is not limited to, specific methods for identifying and displaying the location of the anomaly. Some or all of the above-described processes in the re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input the location of the anomaly into the AI, and the AI ​​can display the re-check content while considering the location.

[0054] The re-checking unit can improve the accuracy of the re-check by referring to relevant external data during the re-check. For example, the re-checking unit can improve the accuracy of the re-check by referring to external weather data. The re-checking unit can also improve the accuracy of the re-check by referring to external traffic data. The re-checking unit can also improve the accuracy of the re-check by referring to external sensor data. In this way, the accuracy of the re-check is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the re-checking unit may be performed using, for example, AI, or not using AI. For example, the re-checking unit can input external data into AI, and the AI ​​can refer to the external data to improve the accuracy of the re-check.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The skill development promotion system can also include a learning style analysis unit that analyzes the user's learning style and suggests the optimal learning method. For example, if the learning style analysis unit finds that the user prefers visual learning, it can provide learning materials that heavily utilize visual content. If the user prefers auditory learning, it can recommend audio materials or podcasts. Furthermore, if the user prefers practical learning, it can provide practical exercises and simulations. This improves the user's learning efficiency and promotes skill development.

[0057] The skill development acceleration system can also include a progress monitoring unit that monitors the user's progress in real time and provides feedback at the appropriate time. For example, if a user is spending too much time on a particular task, the progress monitoring unit can suggest a more efficient solution. It can also provide positive feedback to boost motivation if the user is progressing well. Furthermore, if a user is falling behind, it can provide supplementary resources. This improves the user's learning efficiency and accelerates skill development.

[0058] The skill development acceleration system can also include a learning plan generation unit that analyzes the user's learning history and generates individualized learning plans. For example, the learning plan generation unit can propose a plan that focuses on areas the user has struggled with in the past. It can also provide a plan that further deepens the user's strengths. Furthermore, it can generate flexible plans that match the user's learning pace. This improves the user's learning efficiency and accelerates skill development.

[0059] The skill development acceleration system can also include a comparative analysis unit that compares the user's learning data with other users and provides benchmarks. For example, the comparative analysis unit can compare the user's learning progress with other users in the same field and show their relative position. It can also provide feedback to encourage further challenges if the user is ahead of other users. Furthermore, if the user is falling behind, it can provide specific advice for improvement. This increases the user's motivation to learn and accelerates skill development.

[0060] The skill development promotion system can also include a reward system that analyzes user learning data and provides rewards according to learning progress. For example, the reward system can award badges or points when a user achieves a specific goal. It can also offer special rewards for users who continue learning consistently. Furthermore, it can offer additional rewards if a user competes against and wins against other users. This increases user motivation to learn and promotes skill development.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The judgment unit performs a machine judgment. For example, it uses AI to detect anomalies and determines whether the anomaly is clear (no problem), gray (caution needed), or black (problem). The judgment unit can also apply different judgment criteria depending on the type of anomaly. Step 2: The decision unit makes a final decision based on the decision result obtained by the judgment unit. For example, the decision algorithm can be optimized by referring to past decision data. Step 3: The feedback generation unit generates feedback on the final decision made by the decision unit. For example, it reads the standards document, checks whether the final decision conforms to the standards, and generates feedback. The feedback algorithm can also be optimized by referring to past feedback data. Step 4: The re-check unit performs a re-check based on the feedback generated by the feedback generation unit. For example, it reconfirms whether the final decision is appropriate based on the feedback. The re-check algorithm can also be optimized by referring to past re-check data.

[0063] (Example of form 2) The skill improvement promotion system according to an embodiment of the present invention is a system that uses AI to promote and standardize human skill improvement. In this skill improvement promotion system, first, the AI ​​performs a machine judgment, and a human makes a final judgment based on the result. Next, the AI ​​reads a standards document and generates feedback on the final judgment. Based on this feedback, a human performs a re-check and confirms the final judgment. This mechanism standardizes the skill levels of people and reduces the risk of misjudgment. For example, the AI ​​performs a machine judgment. In this case, the AI ​​uses an internal system to make a judgment of white, gray, or black. For example, if the AI ​​detects an anomaly in patrol work, it determines whether the anomaly is white (no problem), gray (caution required), or black (problem). Next, the AI ​​reads a standards document and generates feedback on the final judgment. For example, the AI ​​checks whether the final judgment conforms to the standards based on the standards document and generates feedback. This feedback is provided to the person who made the final judgment. Furthermore, the person who made the final judgment performs a re-check based on the feedback and confirms the final judgment. For example, based on the AI's feedback, they reconfirm whether the final judgment is appropriate and make corrections as necessary. In this way, the accuracy of final decisions is improved, and the skill levels of individuals are standardized. This mechanism standardizes the skill levels of individuals in patrol and inspection work, reducing the risk of misjudgment. Furthermore, by having AI read the standards manual and generate feedback, the accuracy of final decisions is improved, enabling the provision of safer and more secure services. For example, if the AI ​​detects anomalies in patrol work and verifies whether the anomaly conforms to the standards, the risk of misjudgment is reduced, enabling the provision of safer and more secure services. In this way, the skill improvement promotion system can promote and standardize the skill improvement of individuals using AI.

[0064] The skill improvement promotion system according to the embodiment comprises a judgment unit, a decision unit, a feedback generation unit, and a re-check unit. The judgment unit performs machine judgment. The judgment unit, for example, uses AI to detect anomalies and determines whether the anomaly is white (no problem), gray (caution required), or black (problem exists). For example, the judgment unit uses an internal system to detect anomalies and determines whether the anomaly is white, gray, or black. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, the judgment unit sets and applies different judgment criteria depending on the type of anomaly. The decision unit makes a final judgment based on the judgment results obtained by the judgment unit. The decision unit can also optimize the decision algorithm by referring to past decision data when making a final judgment. For example, the decision unit adjusts the parameters of the decision algorithm based on past decision data. The feedback generation unit generates feedback for the final judgment made by the judgment unit. The feedback generation unit, for example, reads the standards document, verifies whether the final judgment conforms to the standards, and generates feedback. For example, the feedback generation unit verifies whether the final judgment conforms to the standards based on the standards document and generates feedback. The feedback generation unit can also optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, the feedback generation unit adjusts the parameters of the feedback algorithm based on past feedback data. The re-checking unit performs a re-check based on the feedback generated by the feedback generation unit. For example, the re-checking unit re-verifies whether the final judgment is appropriate based on the feedback. For example, the re-checking unit re-verifies whether the final judgment is appropriate based on the feedback. The re-checking unit can also optimize the re-checking algorithm by referring to past re-checking data when performing a re-check. For example, the re-checking unit adjusts the parameters of the re-checking algorithm based on past re-checking data.As a result, the skill improvement promotion system according to this embodiment can use AI to promote and standardize people's skill improvement.

[0065] The judgment unit performs machine-based judgments. For example, the judgment unit uses AI to detect anomalies and determines whether the anomaly is "white" (no problem), "gray" (caution required), or "black" (problem). Specifically, the judgment unit analyzes data collected from internal systems in real time and uses machine learning algorithms to detect signs of anomalies. For example, anomaly detection algorithms and deep learning models are used for anomaly detection, enabling highly accurate identification of anomaly patterns. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, different criteria are set and applied to different types of anomalies such as network anomalies, system errors, and data inconsistencies. This enables appropriate judgment according to the characteristics of the anomaly. Furthermore, to improve the accuracy of anomaly detection, the judgment unit learns from past anomaly data and continuously optimizes the model parameters. For example, the model is retrained using past anomaly data to respond to new anomaly patterns. As a result, the judgment unit can always incorporate the latest anomaly detection technology and perform highly accurate anomaly judgments.

[0066] The judgment unit makes a final decision based on the judgment results obtained by the determination unit. Specifically, the judgment unit determines appropriate countermeasures based on the type and level of the anomaly provided by the determination unit. For example, if it is judged as "clear," no action is taken; if it is judged as "gray," a warning is issued; and if it is judged as "guilty," countermeasures are implemented immediately. In addition, the judgment unit can optimize its judgment algorithm by referring to past judgment data when making a final decision. For example, it can adjust the parameters of the judgment algorithm based on past judgment data to make more accurate judgments. Furthermore, the judgment unit can simulate multiple countermeasures depending on the type and situation of the anomaly and select the optimal countermeasure. This allows the judgment unit to take a quick and appropriate action and minimize the impact of the anomaly.

[0067] The feedback generation unit generates feedback on the final decision made by the judgment unit. For example, the feedback generation unit reads the standards document, checks whether the final decision conforms to the standards, and generates feedback. Specifically, the feedback generation unit refers to the evaluation criteria and procedures described in the standards document and checks whether the final decision was made in accordance with these criteria. For example, if the final decision conforms to the standards, it is evaluated as "appropriate," and if it does not conform to the standards, it is evaluated as "needs improvement." In addition, the feedback generation unit can optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, it can adjust the parameters of the feedback algorithm based on past feedback data to generate more effective feedback. Furthermore, the feedback generation unit can use natural language generation technology to make the feedback content specific and easy to understand. This allows the feedback generation unit to provide users with clear and useful feedback, promoting skill improvement.

[0068] The re-checking unit performs a re-check based on the feedback generated by the feedback generation unit. For example, the re-checking unit reconfirms whether the final decision is appropriate based on the feedback. Specifically, the re-checking unit examines the feedback in detail and re-evaluates whether the final decision was made in accordance with the criteria. For example, if the feedback states "improvement is needed," the re-checking unit reconfirms the accuracy of the final decision procedure and criterion application and makes corrections as necessary. The re-checking unit can also optimize the re-checking algorithm by referring to past re-checking data during the re-checking process. For example, it can adjust the parameters of the re-checking algorithm based on past re-checking data to perform a more accurate re-check. Furthermore, the re-checking unit can feed the re-checking results back to the feedback generation unit, contributing to the improvement of the feedback algorithm. This allows the re-checking unit to ensure the appropriateness of the final decision and improve the overall reliability and accuracy of the system.

[0069] The feedback generation unit can read the standards document and verify whether the final judgment conforms to the standards. For example, the feedback generation unit can verify whether the final judgment conforms to the standards based on the standards document. For example, the feedback generation unit can analyze the contents of the standards document in detail and verify whether the final judgment conforms to the standards. The feedback generation unit can also use a generating AI when analyzing the contents of the standards document. For example, the feedback generation unit inputs the contents of the standards document into the generating AI, and the generating AI analyzes the contents of the standards document. This improves the accuracy of the final judgment. Standards documents include, but are not limited to, work procedure manuals and quality standards documents. The contents of standards documents include, but are not limited to, work procedures and quality standards documents. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the contents of the standards document into AI, and the AI ​​can analyze the contents of the standards document.

[0070] The re-checking unit can reconfirm whether the final decision is appropriate based on the feedback. For example, the re-checking unit reconfirms whether the final decision is appropriate based on the feedback. For example, the re-checking unit analyzes the content of the feedback in detail and reconfirms whether the final decision is appropriate. The re-checking unit can also use AI when analyzing the content of the feedback. For example, the re-checking unit inputs the content of the feedback into the AI, and the AI ​​analyzes the content of the feedback. This improves the accuracy of the final decision. The feedback includes, but is not limited to, the basis for the final decision and points for correction. Some or all of the above processing in the re-checking unit may be performed using, for example, AI, or without AI. For example, the re-checking unit can input the content of the feedback into the AI, and the AI ​​can analyze the content of the feedback.

[0071] The judgment unit can make judgments of white, gray, or black. For example, the judgment unit can use AI to detect an anomaly and determine whether the anomaly is white (no problem), gray (caution needed), or black (problem). For example, the judgment unit can use an internal system to detect an anomaly and determine whether the anomaly is white, gray, or black. The judgment unit can also apply different judgment criteria depending on the type of anomaly. For example, the judgment unit sets and applies different judgment criteria depending on the type of anomaly. This makes the anomaly determination clearer. The judgment criteria for white, gray, and black are set based on, for example, the severity and scope of impact of the anomaly, but are not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or not using AI. For example, the judgment unit can input different judgment criteria depending on the type of anomaly into the AI, and the AI ​​can apply the judgment criteria.

[0072] The feedback generation unit can analyze the contents of the standards document in detail. For example, the feedback generation unit can analyze the contents of the standards document in detail and confirm whether the final judgment conforms to the standards. For example, the feedback generation unit can analyze the contents of the standards document in detail and confirm whether the final judgment conforms to the standards. The feedback generation unit can also use a generating AI when analyzing the contents of the standards document. For example, the feedback generation unit inputs the contents of the standards document into the generating AI, and the generating AI analyzes the contents of the standards document. This improves the accuracy of the feedback. Standards documents include, but are not limited to, work procedures manuals and quality standards manuals. The contents of standards documents include, but are not limited to, work procedures and quality standards. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the contents of the standards document into the AI, and the AI ​​can analyze the contents of the standards document.

[0073] The feedback generation unit can specifically indicate the content of the feedback. For example, the feedback generation unit can specifically indicate the content of the feedback and confirm whether the final judgment conforms to the criteria. For example, the feedback generation unit can specifically indicate the content of the feedback and confirm whether the final judgment conforms to the criteria. The feedback generation unit can also use a generating AI when indicating the content of the feedback. For example, the feedback generation unit inputs the content of the feedback into the generating AI, and the generating AI indicates the content of the feedback. This deepens the understanding of the feedback. The content of the feedback includes, but is not limited to, the basis for the final judgment and points for correction. The format of the feedback includes, but is not limited to, text format and graph format. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input the content of the feedback into AI, and the AI ​​indicates the content of the feedback.

[0074] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit can provide a simple and highly visible display method. For example, if the user is feeling stressed, the judgment unit can provide a simple and highly visible display method. The judgment unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the judgment unit can provide a display method that includes detailed information. The judgment unit can also provide a display method that gets straight to the point if the user is in a hurry. For example, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. This provides a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the display method.

[0075] The judgment unit can optimize the judgment algorithm by referring to past judgment data during the judgment process. For example, the judgment unit can adjust the parameters of the judgment algorithm based on past judgment data. The judgment unit can also analyze past judgment data to identify patterns of misjudgments and improve the algorithm. The judgment unit can also use past judgment data to evaluate the accuracy of the judgment algorithm and make corrections as necessary. This improves the accuracy of the judgment algorithm. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past judgment data into AI, and the AI ​​can optimize the judgment algorithm.

[0076] The judgment unit can apply different judgment criteria depending on the type of anomaly during the judgment process. For example, the judgment unit can set and apply different judgment criteria depending on the type of anomaly. The judgment unit can also dynamically change the judgment criteria for each type of anomaly. For example, the judgment unit can dynamically change the judgment criteria for each type of anomaly. The judgment unit can also adjust the weighting of the judgment criteria depending on the type of anomaly. For example, the judgment unit can adjust the weighting of the judgment criteria depending on the type of anomaly. This enables judgment according to the type of anomaly. Different judgment criteria include, but are not limited to, judgment criteria for each type of anomaly. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input judgment criteria according to the type of anomaly to the AI, and the AI ​​can apply the judgment criteria.

[0077] The judgment unit can estimate the user's emotions and determine the priority of judgment results based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit can prioritize displaying important judgment results. For example, if the user is feeling stressed, the judgment unit can prioritize displaying important judgment results. The judgment unit can also prioritize displaying detailed judgment results if the user is relaxed. For example, if the user is relaxed, the judgment unit can prioritize displaying detailed judgment results. The judgment unit can also prioritize displaying concise judgment results if the user is in a hurry. For example, if the user is in a hurry, the judgment unit can prioritize displaying concise judgment results. This provides prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input user emotion data into a generating AI, which can then estimate the emotion and determine priorities.

[0078] The determination unit can display the determination result while considering the location where the anomaly occurred. For example, the determination unit can display the determination result on a map based on the location where the anomaly occurred. The determination unit can also change the method of displaying the determination result depending on the location where the anomaly occurred. For example, the determination unit can change the method of displaying the determination result depending on the location where the anomaly occurred. The determination unit can also determine the priority of the determination results while considering the location where the anomaly occurred. For example, the determination unit can determine the priority of the determination results while considering the location where the anomaly occurred. This provides a determination result that corresponds to the location where the anomaly occurred. The location where the anomaly occurred includes, but is not limited to, specific methods for identifying and displaying the location where the anomaly occurred. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the location where the anomaly occurred into the AI, and the AI ​​can display the determination result while considering the location.

[0079] The determination unit can improve the accuracy of its determination by referring to relevant external data during the determination process. For example, the determination unit can improve the accuracy of its determination by referring to external weather data. The determination unit can also improve the accuracy of its determination by referring to external traffic data. The determination unit can also improve the accuracy of its determination by referring to external sensor data. In this way, the accuracy of the determination is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the determination unit may be performed using, for example, AI, or not using AI. For example, the determination unit can input external data into the AI, and the AI ​​can refer to the external data to improve the accuracy of its determination.

[0080] The decision unit can estimate the user's emotions and adjust the expression of the final decision based on the estimated emotions. For example, if the user is nervous, the decision unit can provide a simple and easily understandable expression. For example, if the user is nervous, the decision unit can provide a simple and easily understandable expression. The decision unit can also provide an expression that includes detailed information if the user is relaxed. For example, if the user is relaxed, the decision unit can provide an expression that includes detailed information. The decision unit can also provide a concise expression if the user is in a hurry. For example, if the decision unit is in a hurry, the decision unit can provide a concise expression. This provides an expression that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way it is expressed.

[0081] The decision unit can optimize the decision algorithm by referring to past decision data at the time of the final decision. For example, the decision unit can adjust the parameters of the decision algorithm based on past decision data. The decision unit can also analyze past decision data to identify patterns of misjudgments and improve the algorithm. The decision unit can also use past decision data to evaluate the accuracy of the decision algorithm and make corrections as necessary. This improves the accuracy of the decision algorithm. Some or all of the above processes in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input past decision data into AI, and the AI ​​can optimize the decision algorithm.

[0082] The judgment unit can apply different judgment criteria depending on the type of anomaly at the time of final judgment. For example, the judgment unit can set and apply different judgment criteria depending on the type of anomaly. The judgment unit can also dynamically change the judgment criteria for each type of anomaly. The judgment unit can also adjust the weighting of the judgment criteria depending on the type of anomaly. This enables judgments according to the type of anomaly. Different judgment criteria include, but are not limited to, judgment criteria for each type of anomaly. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input judgment criteria according to the type of anomaly into the AI, and the AI ​​can apply the judgment criteria.

[0083] The decision-making unit can estimate the user's emotions and determine the priority of the final decision based on the estimated emotions. For example, if the user is feeling stressed, the decision-making unit may prioritize displaying important decision results. For example, if the user is feeling stressed, the decision-making unit may prioritize displaying important decision results. The decision-making unit may also prioritize displaying detailed decision results if the user is relaxed. For example, if the user is relaxed, the decision-making unit may prioritize displaying detailed decision results. The decision-making unit may also prioritize displaying concise decision results if the user is in a hurry. For example, if the user is in a hurry, the decision-making unit may prioritize displaying concise decision results. This provides prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input user emotion data into a generating AI, which can then estimate the emotion and determine priorities.

[0084] The judgment unit can display the judgment result at the time of final judgment, taking into account the location where the anomaly occurred. For example, the judgment unit can display the judgment result on a map based on the location where the anomaly occurred. The judgment unit can also change the method of displaying the judgment result depending on the location where the anomaly occurred. For example, the judgment unit can change the method of displaying the judgment result depending on the location where the anomaly occurred. The judgment unit can also determine the priority of the judgment results, taking into account the location where the anomaly occurred. For example, the judgment unit can determine the priority of the judgment results, taking into account the location where the anomaly occurred. This provides a judgment result that corresponds to the location where the anomaly occurred. The location where the anomaly occurred includes, but is not limited to, specific methods for identifying and displaying the location where the anomaly occurred. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the location where the anomaly occurred into the AI, and the AI ​​can display the judgment result taking the location into account.

[0085] The decision-making unit can improve the accuracy of its decision by referring to relevant external data at the time of the final decision. For example, the decision-making unit can improve the accuracy of its decision by referring to external weather data. The decision-making unit can also improve the accuracy of its decision by referring to external traffic data. The decision-making unit can also improve the accuracy of its decision by referring to external sensor data. In this way, the accuracy of the decision is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can input external data into the AI, and the AI ​​can improve the accuracy of its decision by referring to the external data.

[0086] The feedback generation unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions. For example, if the user is nervous, the feedback generation unit can provide simple and easily understandable feedback. For example, if the user is nervous, the feedback generation unit can provide simple and easily understandable feedback. The feedback generation unit can also provide detailed feedback if the user is relaxed. For example, if the user is relaxed, the feedback generation unit can provide detailed feedback. The feedback generation unit can also provide concise feedback if the user is in a hurry. For example, if the user is in a hurry, the feedback generation unit can provide concise feedback. This ensures that feedback is provided that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback generation unit may be performed using AI, for example, or without AI. For example, the feedback generation unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way it is expressed.

[0087] The feedback generation unit can optimize the feedback algorithm by referring to past feedback data when generating feedback. For example, the feedback generation unit can adjust the parameters of the feedback algorithm based on past feedback data. The feedback generation unit can also analyze past feedback data to identify patterns of erroneous feedback and improve the algorithm. The feedback generation unit can also use past feedback data to evaluate the accuracy of the feedback algorithm and make corrections as necessary. This improves the accuracy of the feedback algorithm. Some or all of the above processes in the feedback generation unit may be performed using AI, for example, or without AI. For example, the feedback generation unit can input past feedback data into AI, and the AI ​​can optimize the feedback algorithm.

[0088] The feedback generation unit can apply different feedback criteria depending on the type of anomaly when generating feedback. For example, the feedback generation unit can set and apply different feedback criteria depending on the type of anomaly. The feedback generation unit can also dynamically change the feedback criteria for each type of anomaly. For example, the feedback generation unit can dynamically change the feedback criteria for each type of anomaly. The feedback generation unit can also adjust the weighting of the feedback criteria depending on the type of anomaly. For example, the feedback generation unit can adjust the weighting of the feedback criteria depending on the type of anomaly. This provides feedback appropriate to the type of anomaly. Different feedback criteria include, but are not limited to, feedback criteria for each type of anomaly. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input feedback criteria appropriate to the type of anomaly to the AI, and the AI ​​can apply the feedback criteria.

[0089] The feedback generation unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback generation unit may prioritize displaying important feedback. For example, if the user is relaxed, the feedback generation unit may prioritize displaying detailed feedback. For example, if the user is relaxed, the feedback generation unit may prioritize displaying detailed feedback. For example, if the user is in a hurry, the feedback generation unit may prioritize displaying concise feedback. For example, if the user is in a hurry, the feedback generation unit may prioritize displaying concise feedback. This provides prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback generation unit may be performed using AI, for example, or without AI. For example, the feedback generation unit can input user emotion data into a generating AI, which can then estimate the emotion and determine priorities.

[0090] The feedback generation unit can display feedback content while considering the location of the anomaly when generating feedback. For example, the feedback generation unit can display feedback content on a map based on the location of the anomaly. The feedback generation unit can also change the method of displaying feedback content depending on the location of the anomaly. The feedback generation unit can also determine the priority of feedback content while considering the location of the anomaly. This provides feedback appropriate to the location of the anomaly. The location of the anomaly includes, but is not limited to, specific methods for identifying and displaying the location of the anomaly. Some or all of the above-described processes in the feedback generation unit may be performed using, for example, AI, or without AI. For example, the feedback generation unit can input the location of the anomaly into AI, and the AI ​​can display feedback content while considering the location.

[0091] The feedback generation unit can improve the accuracy of the feedback by referring to relevant external data when generating feedback. For example, the feedback generation unit can improve the accuracy of the feedback by referring to external weather data. The feedback generation unit can also improve the accuracy of the feedback by referring to external traffic data. The feedback generation unit can also improve the accuracy of the feedback by referring to external sensor data. In this way, the accuracy of the feedback is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the feedback generation unit may be performed using, for example, AI, or not using AI. For example, the feedback generation unit can input external data into AI, and the AI ​​can refer to the external data to improve the accuracy of the feedback.

[0092] The re-checking unit can estimate the user's emotions and adjust the re-checking method based on the estimated emotions. For example, if the user is nervous, the re-checking unit can provide a simple and highly visible re-checking method. For example, if the user is nervous, the re-checking unit can provide a simple and highly visible re-checking method. The re-checking unit can also provide a re-checking method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the re-checking unit can provide a re-checking method that includes detailed information. The re-checking unit can also provide a concise re-checking method that gets to the point if the user is in a hurry. For example, if the re-checking unit can provide a re-checking method that gets to the point if the user is in a hurry. This provides a re-checking method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the re-checking method.

[0093] The re-checking unit can optimize the re-checking algorithm by referring to past re-checking data during the re-checking process. For example, the re-checking unit can adjust the parameters of the re-checking algorithm based on past re-checking data. The re-checking unit can also analyze past re-checking data to identify patterns of incorrect re-checks and improve the algorithm. The re-checking unit can also use past re-checking data to evaluate the accuracy of the re-checking algorithm and make corrections as needed. This improves the accuracy of the re-checking algorithm. Some or all of the above processes in the re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input past re-checking data into AI, which can then optimize the re-checking algorithm.

[0094] The re-checking unit can apply different re-checking criteria depending on the type of anomaly during re-checking. For example, the re-checking unit can set and apply different re-checking criteria depending on the type of anomaly. The re-checking unit can also dynamically change the re-checking criteria for each type of anomaly. The re-checking unit can also adjust the weighting of the re-checking criteria depending on the type of anomaly. This enables re-checking according to the type of anomaly. Different re-checking criteria include, but are not limited to, re-checking criteria for each type of anomaly. Some or all of the above-described processes in the re-checking unit may be performed using, for example, AI, or not using AI. For example, the re-checking unit can input re-checking criteria according to the type of anomaly into the AI, and the AI ​​can apply the re-checking criteria.

[0095] The re-checking unit can estimate the user's emotions and determine the priority of re-checks based on the estimated emotions. For example, if the user is stressed, the re-checking unit will prioritize displaying important re-checks. For example, if the user is stressed, the re-checking unit will prioritize displaying important re-checks. For example, if the user is relaxed, the re-checking unit will prioritize displaying detailed re-checks. For example, if the user is relaxed, the re-checking unit will prioritize displaying detailed re-checks. For example, if the user is in a hurry, the re-checking unit will prioritize displaying concise re-checks. For example, if the user is in a hurry, the re-checking unit will prioritize displaying concise re-checks. This provides prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input user emotion data into a generating AI, which can then estimate the emotion and determine priorities.

[0096] The re-checking unit can display the re-check content while considering the location where the anomaly occurred. For example, the re-checking unit can display the re-check content on a map based on the location where the anomaly occurred. The re-checking unit can also change the display method of the re-check content depending on the location where the anomaly occurred. The re-checking unit can also determine the priority of the re-check content while considering the location where the anomaly occurred. This provides re-check content appropriate to the location where the anomaly occurred. The location of the anomaly includes, but is not limited to, specific methods for identifying and displaying the location of the anomaly. Some or all of the above-described processes in the re-checking unit may be performed using AI, for example, or without AI. For example, the re-checking unit can input the location of the anomaly into the AI, and the AI ​​can display the re-check content while considering the location.

[0097] The re-checking unit can improve the accuracy of the re-check by referring to relevant external data during the re-check. For example, the re-checking unit can improve the accuracy of the re-check by referring to external weather data. The re-checking unit can also improve the accuracy of the re-check by referring to external traffic data. The re-checking unit can also improve the accuracy of the re-check by referring to external sensor data. In this way, the accuracy of the re-check is improved by referring to external data. External data includes, but is not limited to, weather data, traffic data, and sensor data. Some or all of the above processing in the re-checking unit may be performed using, for example, AI, or not using AI. For example, the re-checking unit can input external data into AI, and the AI ​​can refer to the external data to improve the accuracy of the re-check.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The skill development promotion system can also include a learning style analysis unit that analyzes the user's learning style and suggests the optimal learning method. For example, if the learning style analysis unit finds that the user prefers visual learning, it can provide learning materials that heavily utilize visual content. If the user prefers auditory learning, it can recommend audio materials or podcasts. Furthermore, if the user prefers practical learning, it can provide practical exercises and simulations. This improves the user's learning efficiency and promotes skill development.

[0100] The skill development acceleration system can also include an emotion-adaptive learning unit that estimates the user's emotions and adjusts the learning content based on those emotions. For example, if the user is feeling stressed, it can provide light learning content that helps them relax. Conversely, if the user is concentrating, it can provide more challenging content. Furthermore, if the user is tired, it can display a message encouraging them to take a break. This improves the user's learning experience and makes skill development more effective.

[0101] The skill development acceleration system can also include a progress monitoring unit that monitors the user's progress in real time and provides feedback at the appropriate time. For example, if a user is spending too much time on a particular task, the progress monitoring unit can suggest a more efficient solution. It can also provide positive feedback to boost motivation if the user is progressing well. Furthermore, if a user is falling behind, it can provide supplementary resources. This improves the user's learning efficiency and accelerates skill development.

[0102] The skill development acceleration system can also include an emotion-adaptive environment unit that estimates the user's emotions and adjusts the learning environment based on those emotions. For example, if the user is feeling stressed, it can play relaxing music. It can also turn off notifications when the user is concentrating, providing an environment conducive to focused learning. Furthermore, if the user is tired, it can display a message encouraging them to take a break. This optimizes the user's learning environment, leading to more effective skill development.

[0103] The skill development acceleration system can also include a learning plan generation unit that analyzes the user's learning history and generates individualized learning plans. For example, the learning plan generation unit can propose a plan that focuses on areas the user has struggled with in the past. It can also provide a plan that further deepens the user's strengths. Furthermore, it can generate flexible plans that match the user's learning pace. This improves the user's learning efficiency and accelerates skill development.

[0104] The skill development promotion system may also include an emotion-adaptive goal-setting unit that estimates the user's emotions and adjusts learning goals based on those emotions. For example, if the user is unmotivated, short-term goals can be set to help them achieve a sense of accomplishment. Conversely, if the user is motivated, challenging goals can be set. Furthermore, if the user is tired, goals can be temporarily eased. This ensures that the user's learning goals are set appropriately, leading to effective skill development.

[0105] The skill development acceleration system can also include a comparative analysis unit that compares the user's learning data with other users and provides benchmarks. For example, the comparative analysis unit can compare the user's learning progress with other users in the same field and show their relative position. It can also provide feedback to encourage further challenges if the user is ahead of other users. Furthermore, if the user is falling behind, it can provide specific advice for improvement. This increases the user's motivation to learn and accelerates skill development.

[0106] The skill development system can also include an emotion-adaptive difficulty section that estimates the user's emotions and adjusts the difficulty level of the learning content based on those emotions. For example, if the user is stressed, it can provide content with a lower difficulty level. Conversely, if the user is relaxed, it can provide content with a higher difficulty level. Furthermore, if the user is focused, it can provide content with an appropriate difficulty level. This improves the user's learning experience and facilitates effective skill development.

[0107] The skill development promotion system can also include a reward system that analyzes user learning data and provides rewards according to learning progress. For example, the reward system can award badges or points when a user achieves a specific goal. It can also offer special rewards for users who continue learning consistently. Furthermore, it can offer additional rewards if a user competes against and wins against other users. This increases user motivation to learn and promotes skill development.

[0108] The skill development acceleration system can further include an emotion-adaptive timing unit that estimates the user's emotions and adjusts the learning timing based on the estimated emotions. For example, if the user is tired, it can temporarily suspend learning and encourage a break. Conversely, if the user is focused, it can encourage them to continue learning. Furthermore, if the user is feeling stressed, it can resume learning at a time when they can relax. This improves the user's learning efficiency and ensures effective skill development.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The judgment unit performs a machine judgment. For example, it uses AI to detect anomalies and determines whether the anomaly is clear (no problem), gray (caution needed), or black (problem). The judgment unit can also apply different judgment criteria depending on the type of anomaly. Step 2: The decision unit makes a final decision based on the decision result obtained by the judgment unit. For example, the decision algorithm can be optimized by referring to past decision data. Step 3: The feedback generation unit generates feedback on the final decision made by the decision unit. For example, it reads the standards document, checks whether the final decision conforms to the standards, and generates feedback. The feedback algorithm can also be optimized by referring to past feedback data. Step 4: The re-check unit performs a re-check based on the feedback generated by the feedback generation unit. For example, it reconfirms whether the final decision is appropriate based on the feedback. The re-check algorithm can also be optimized by referring to past re-check data.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the determination unit, judgment unit, feedback generation unit, and re-check unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the smart device 14, which uses AI to detect anomalies and determines whether the anomaly is positive, negative, or negative. The judgment unit is implemented by the identification processing unit 290 of the data processing device 12, which makes a final decision based on the judgment result obtained by the determination unit. The feedback generation unit is implemented by the identification processing unit 290 of the data processing device 12, which reads a standard document, checks whether the final decision conforms to the standard, and generates feedback. The re-check unit is implemented by the control unit 46A of the smart device 14, which reconfirms whether the final decision is appropriate based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the determination unit, judgment unit, feedback generation unit, and re-check unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the smart glasses 214, which uses AI to detect anomalies and determines whether the anomaly is positive, negative, or negative. The judgment unit is implemented by the identification processing unit 290 of the data processing device 12, which makes a final decision based on the judgment result obtained by the determination unit. The feedback generation unit is implemented by the identification processing unit 290 of the data processing device 12, which reads a standard document, checks whether the final decision conforms to the standard, and generates feedback. The re-check unit is implemented by the control unit 46A of the smart glasses 214, which reconfirms whether the final decision is appropriate based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the determination unit, judgment unit, feedback generation unit, and re-check unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the headset terminal 314, which uses AI to detect anomalies and determines whether the anomaly is positive, negative, or negative. The judgment unit is implemented by the identification processing unit 290 of the data processing device 12, which makes a final decision based on the judgment result obtained by the determination unit. The feedback generation unit is implemented by the identification processing unit 290 of the data processing device 12, which reads a standard document, checks whether the final decision conforms to the standard, and generates feedback. The re-check unit is implemented by the control unit 46A of the headset terminal 314, which reconfirms whether the final decision is appropriate based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the determination unit, judgment unit, feedback generation unit, and re-check unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the robot 414, which uses AI to detect anomalies and determines whether the anomaly is positive, negative, or negative. The judgment unit is implemented by the identification processing unit 290 of the data processing device 12, which makes a final decision based on the judgment result obtained by the determination unit. The feedback generation unit is implemented by the identification processing unit 290 of the data processing device 12, which reads a standard document, checks whether the final decision conforms to the standard, and generates feedback. The re-check unit is implemented by the control unit 46A of the robot 414, which reconfirms whether the final decision is appropriate based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A judgment unit that performs machine judgment, A determination unit that makes a final decision based on the determination result obtained by the determination unit, A feedback generation unit that generates feedback for the final decision made by the aforementioned determination unit, The system includes a re-checking unit that performs a re-check based on the feedback generated by the feedback generation unit. A system characterized by the following features. (Note 2) The aforementioned feedback generation unit, Read the standards document and confirm whether the final decision conforms to the standards. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned re-check unit, Based on the feedback, reconfirm whether the final decision is appropriate. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, Determine whether it is white, gray, or black. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback generation unit, Analyze the contents of the standards document in detail. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback generation unit, Please specify the content of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The determination unit, During the decision-making process, the decision algorithm is optimized by referring to past decision data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The determination unit, When making a judgment, different judgment criteria are applied depending on the type of abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 10) The determination unit, The system estimates the user's emotions and prioritizes the judgment results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The determination unit, When making a judgment, the location where the anomaly occurred is taken into consideration when displaying the judgment result. The system described in Appendix 1, characterized by the features described herein. (Note 12) The determination unit, During the decision-making process, relevant external data is referenced to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 13) The unit that makes the determination said, The system estimates the user's emotions and adjusts the way the final decision is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, At the final decision stage, the decision algorithm is optimized by referring to past decision data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When making a final decision, different criteria will be applied depending on the type of abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, The system estimates the user's emotions and determines the priority of the final decision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, When making a final decision, the result will be displayed taking into account the location where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When making a final decision, we refer to relevant external data to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback generation unit, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback generation unit, When generating feedback, the feedback algorithm is optimized by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback generation unit, When generating feedback, different feedback criteria are applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback generation unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback generation unit, When generating feedback, the content of the feedback should be displayed considering the location where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback generation unit, When generating feedback, we refer to relevant external data to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned re-check unit, We estimate the user's emotions and adjust the re-checking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned re-check unit, During rechecks, the recheck algorithm is optimized by referring to past recheck data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned re-check unit, During the recheck, different recheck criteria will be applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned re-check unit, The system estimates the user's emotions and determines the priority of re-checking based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned re-check unit, During the recheck, the recheck details will be displayed, taking into account the location where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned re-check unit, During rechecks, the accuracy of the rechecks is improved by referring to relevant external data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A judgment unit that performs machine judgment, A determination unit that makes a final decision based on the determination result obtained by the determination unit, A feedback generation unit that generates feedback for the final decision made by the aforementioned determination unit, The system includes a re-checking unit that performs a re-check based on the feedback generated by the feedback generation unit. A system characterized by the following features.

2. The aforementioned feedback generation unit, Read the standards document and confirm whether the final decision conforms to the standards. The system according to feature 1.

3. The aforementioned re-check unit, Based on the feedback, reconfirm whether the final decision is appropriate. The system according to feature 1.

4. The determination unit, Determine whether it is white, gray, or black. The system according to feature 1.

5. The aforementioned feedback generation unit, Analyze the contents of the standards document in detail. The system according to feature 1.

6. The aforementioned feedback generation unit, Please specify the content of the feedback. The system according to feature 1.

7. The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system according to feature 1.

8. The determination unit, During the decision-making process, the decision algorithm is optimized by referring to past decision data. The system according to feature 1.

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