system

Abstract

To provide a system capable of diagnosing the performance of AI and correcting the same properly.SOLUTION: The system in an embodiment includes a collection unit, a diagnostics unit, a correction unit, a fork unit, and a management unit. The collection unit collects data. The diagnostics unit diagnoses the performance of an AI based on the data collected by the collection unit. The correction unit inputs training data to be corrected based on the result diagnosed by the diagnostics unit. The fork unit rebuilds the algorithm of the AI corrected by the correction unit. The management unit manages the diagnosis result or the correction data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to identify the cause of a decline in AI performance and make appropriate corrections.

[0005] The system according to the embodiment aims to diagnose the performance of AI and correct it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a diagnosis unit, a correction unit, a forking unit, and a management unit. The collection unit collects data. The diagnosis unit diagnoses the performance of the AI ​​based on the data collected by the collection unit. The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. The forking unit reconstructs the AI ​​algorithm corrected by the correction unit. The management unit manages the diagnosis results or correction data. [Effects of the Invention]

[0007] The system according to the embodiment can diagnose the performance of AI and make appropriate corrections. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​diagnosis and treatment system according to an embodiment of the present invention is a system designed to prevent biased AI due to learning noise-generating training data when generative AI or general-purpose AI is engaged in a task. This system diagnoses the AI ​​and, if necessary, "treats" it by introducing hard forks or corrective training data. For example, it collects data generated by the AI ​​as it works and diagnoses its performance based on the collected data. This diagnosis is achieved by the AI ​​having functions similar to a self-diagnostic program or antivirus software. If the diagnosis results indicate that the AI ​​is learning in a biased manner, corrective training data is introduced. Furthermore, if necessary, a hard fork is performed to optimize the AI's performance. For example, if the AI ​​is making biased decisions in a specific task, data related to that task is collected and the AI ​​is diagnosed. If the diagnosis results indicate that the AI ​​is learning in a biased manner, corrective training data is introduced to correct the AI's learning. Furthermore, if necessary, a hard fork is performed to optimize the AI's performance. This mechanism prevents biased learning when the AI ​​is engaged in a task, ensuring optimal performance. For example, if the AI ​​is engaged in customer service work, customer inquiry data is collected and the AI ​​is diagnosed. If the AI ​​is found to be responding in a biased manner based on the diagnosis results, corrective training data is input to correct the AI's response. Additionally, a hard fork is performed as necessary to optimize the AI's performance. In this way, by commercializing AI that diagnoses and treats other AI, the AI's performance can be kept at an optimal state at all times. This allows the AI ​​diagnosis and treatment system to always maintain the AI's performance at an optimal state.

[0029] The AI ​​diagnosis and treatment system according to the embodiment includes a collection unit, a diagnosis unit, a correction unit, a forking unit, and a management unit. The collection unit collects data generated when the AI ​​performs its tasks. For example, the collection unit can collect customer inquiry data generated when the AI ​​performs customer service tasks. The collection unit can also collect inspection data generated when the AI ​​performs product inspection tasks. The collection unit can also collect marketing data generated when the AI ​​performs marketing tasks. The diagnosis unit diagnoses the performance of the AI ​​based on the data collected by the collection unit. For example, the diagnosis unit can diagnose whether the AI ​​is responding unbalancedly in customer service tasks. The diagnosis unit can also diagnose whether the AI ​​is producing unbalanced inspection results in product inspection tasks. The diagnosis unit can also diagnose whether the AI ​​is formulating a biased marketing strategy in marketing tasks. The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. For example, if the AI ​​is responding unbalancedly in customer service tasks, the correction unit can input training data to correct the AI's behavior. In addition, if the AI ​​produces biased test results in product inspection work, the correction unit can input training data to correct the test results. Furthermore, if the AI ​​formulates a biased marketing strategy in marketing work, the correction unit can input training data to correct the strategy. The forking unit reconstructs the AI ​​algorithm corrected by the correction unit. For example, if the AI ​​shows a significant bias in customer service work, the forking unit can reconstruct the algorithm. In addition, if the AI ​​shows a significant bias in product inspection work, the forking unit can reconstruct the algorithm. Furthermore, if the AI ​​shows a significant bias in marketing work, the forking unit can reconstruct the algorithm. The management unit manages the diagnosis results and correction data. For example, the management unit can store the diagnosis results and correction data on the cloud so that they can be accessed as needed.The management unit can also encrypt and store diagnostic results and correction data to ensure security. Furthermore, the management unit can set access permissions for diagnostic results and correction data and perform access control. This allows the AI ​​diagnosis and treatment system according to the embodiment to always maintain optimal AI performance.

[0030] The collection department collects data generated when the AI ​​is engaged in its work. Specifically, it can collect customer inquiry data generated when the AI ​​is engaged in customer service tasks. For example, this includes text and voice data when customers make inquiries through the AI ​​chatbot, as well as metadata such as the response content and response time. The collection department can also collect inspection data generated when the AI ​​is engaged in product inspection tasks. In product inspection tasks, when the AI ​​uses image recognition technology to perform visual inspections of products, image data of the inspection results and log data during the inspection process are collected. Furthermore, the collection department can collect marketing data generated when the AI ​​is engaged in marketing tasks. Marketing data includes customer purchase history, website browsing history, and customer response data on social media analyzed by the AI. This data is collected in real time and sent to a central database. The collection department can centrally manage this data and link it to other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the diagnostics department or orthodontics department. In addition, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The Diagnostics Department diagnoses AI performance based on the data collected by the Collection Department. Specifically, it can diagnose whether the AI ​​is responding biasedly in customer service tasks. For example, if the AI ​​consistently responds differently to a specific customer segment, it can identify the cause and evaluate the fairness of the responses. The Diagnostics Department can also diagnose whether the AI ​​is producing biased test results in product inspection tasks. In product inspection tasks, if the AI ​​is applying overly strict standards to a specific product category, it can evaluate the appropriateness of those standards. Furthermore, the Diagnostics Department can diagnose whether the AI ​​is formulating a biased marketing strategy in marketing tasks. In marketing tasks, if the AI ​​is allocating excessive resources to a specific customer segment, it can evaluate the effectiveness of that strategy. To perform these diagnoses, the Diagnostics Department performs detailed analysis of the AI's output results and uses statistical methods and machine learning algorithms to detect bias. Furthermore, the Diagnostics Department can evaluate AI performance by comparing it with past data and benchmark data and identify areas for improvement. This allows the Diagnostics Department to quickly and accurately evaluate AI performance and identify necessary improvements.

[0032] The correction department inputs training data to be corrected based on the results of the diagnosis by the diagnosis department. Specifically, if the AI ​​is responding unbalancedly in customer service operations, training data can be input to correct that response. For example, if the AI ​​is consistently responding differently to a specific customer group, a balanced dataset can be prepared and the AI ​​can be retrained to correct that response. Similarly, if the AI ​​is producing biased inspection results in product inspection operations, the correction department can input training data to correct those inspection results. In product inspection operations, if the AI ​​is applying overly strict standards to a specific product category, a dataset with appropriate standards can be prepared and the AI ​​can be retrained to correct those standards. Furthermore, if the AI ​​is formulating a biased marketing strategy in marketing operations, the correction department can input training data to correct that strategy. In marketing operations, if the AI ​​is allocating excessive resources to a specific customer group, a balanced dataset can be prepared and the AI ​​can be retrained to correct that strategy. To perform these correction tasks efficiently, the correction department selects an appropriate dataset and executes an appropriate learning process for the AI. This allows the correction department to optimize the AI's performance and provide unbiased and fair results.

[0033] The forking department reconstructs the AI ​​algorithm corrected by the correction department. Specifically, if the AI ​​shows significant bias in customer service tasks, it can reconstruct the algorithm. For example, if the AI ​​consistently treats a specific customer group differently, it will review the algorithm and redesign it to provide a fairer response. The forking department can also reconstruct an algorithm if the AI ​​shows significant bias in product inspection tasks. In product inspection tasks, if the AI ​​applies overly strict standards to a specific product category, it will review the algorithm and redesign it to have appropriate standards. Furthermore, the forking department can reconstruct an algorithm if the AI ​​shows significant bias in marketing tasks. In marketing tasks, if the AI ​​allocates excessive resources to a specific customer group, it will review the algorithm and redesign it to create a more balanced strategy. To perform these reconstruction tasks efficiently, the forking department uses appropriate algorithm design methods to optimize the AI's performance. This allows the forking department to reconstruct the AI ​​algorithm and provide unbiased and fair results.

[0034] The management department manages diagnostic results and correction data. Specifically, it can store diagnostic results and correction data on the cloud and make them accessible as needed. For example, it can store diagnostic results and correction data in cloud storage so that the diagnostic department and correction department can access them at any time. The management department can also encrypt and store diagnostic results and correction data to ensure security. This prevents unauthorized access and leaks of data. Furthermore, the management department can set access permissions for diagnostic results and correction data and perform access control. For example, it can grant access rights only to specific users or departments to ensure data security. The management department implements appropriate data management tools and security measures to efficiently perform these management tasks. This allows the management department to safely and efficiently manage diagnostic results and correction data, improving the reliability and security of the entire system.

[0035] The collection unit can collect data generated when the AI ​​engages in work. For example, the collection unit can collect customer inquiry data generated when the AI ​​engages in customer service work. The collection unit can also collect inspection data generated when the AI ​​engages in product inspection work. Furthermore, the collection unit can collect marketing data generated when the AI ​​engages in marketing work. In this way, by collecting data generated when the AI ​​engages in work, it is possible to accurately diagnose the performance of the AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data generated when the AI ​​engages in work into the AI ​​and have the AI ​​collect the data.

[0036] The diagnostic unit has a self-diagnostic program and can periodically check performance. For example, the diagnostic unit can periodically check whether the AI ​​is responding biasedly in customer service operations. The diagnostic unit can also periodically check whether the AI ​​is producing biased test results in product inspection operations. Furthermore, the diagnostic unit can also periodically check whether the AI ​​is formulating biased marketing strategies in marketing operations. In this way, by periodically checking performance, the state of the AI ​​can be constantly understood. Some or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the AI's performance data into the AI ​​and have the AI ​​perform a performance check.

[0037] The correction unit can input training data generated based on past data. For example, if the AI ​​is responding biasedly in customer service operations, the correction unit can input training data to correct the response. Furthermore, if the AI ​​is producing biased inspection results in product inspection operations, the correction unit can input training data to correct the inspection results. Furthermore, if the AI ​​is formulating a biased marketing strategy in marketing operations, the correction unit can input training data to correct the strategy. Thus, by inputting training data generated based on past data, bias in the AI ​​can be corrected. Some or all of the above-described processing in the correction unit may be performed, for example, using AI, or may be performed without using AI. For example, the correction unit can input past data into the AI ​​and have the AI ​​generate and input training data.

[0038] The forking unit can perform a hard fork if the AI ​​shows significant bias. For example, if the AI ​​shows significant bias in customer service operations, the forking unit can perform a hard fork to rebuild the algorithm. Furthermore, if the AI ​​shows significant bias in product inspection operations, the forking unit can also perform a hard fork to rebuild the algorithm. Furthermore, if the AI ​​shows significant bias in marketing operations, the forking unit can also perform a hard fork to rebuild the algorithm. Thus, by performing a hard fork when the AI ​​shows significant bias, the performance of the AI ​​can be optimized. Some or all of the above-described processing in the forking unit may be performed using, or without, an AI. For example, the forking unit can input the AI's algorithm into the AI ​​and leave the execution of the hard fork to the AI.

[0039] The management unit can store diagnostic results or correction data on the cloud and make them accessible as needed. For example, the management unit can store diagnostic results or correction data on the cloud and make them accessible as needed. The management unit can also encrypt and store the diagnostic results or correction data to ensure security. Furthermore, the management unit can set access permissions for the diagnostic results or correction data and perform access control. As a result, by storing the diagnostic results or correction data on the cloud, they can be accessed as needed. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data stored on the cloud into AI and have the AI ​​manage the data.

[0040] The collection unit can analyze the AI's past performance data and select an efficient data collection method. For example, the collection unit can concentrate data collection during a specific time period based on the past performance data. The collection unit can also analyze the past performance data and select an effective data collection method for a specific task. Furthermore, the collection unit can optimize the frequency and timing of data collection based on the past performance data. This enables efficient data collection by selecting the optimal data collection method based on the past performance data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into the AI ​​and have the AI ​​select the data collection method.

[0041] The collection unit can filter data based on the AI's current work status and areas of interest when collecting data. For example, the collection unit can collect only data related to the work the AI ​​is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the AI's areas of interest. Furthermore, the collection unit can filter unnecessary data according to the AI's work status and collect data efficiently. This enables efficient data collection by filtering data based on the AI's work status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the AI's work status and areas of interest into the AI ​​and have the AI ​​perform data filtering.

[0042] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the AI's geographical location information. For example, if the AI ​​is operating in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also filter and collect highly relevant data based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the collection unit can also collect data related to its current location in real time. In this way, by collecting data while taking into account the AI's geographical location information, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the AI. For example, the collection unit can input the AI's geographical location information into the AI ​​and cause the AI ​​to collect data.

[0043] The collection unit can analyze the AI's social media activity and collect relevant data when collecting data. For example, the collection unit can collect data related to topics in which the AI ​​has shown interest on social media. The collection unit can also prioritize the collection of highly relevant data based on the AI's social media activity. Furthermore, the collection unit can collect data related to accounts the AI ​​follows on social media. In this way, by analyzing the AI's social media activity and collecting data, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the AI. For example, the collection unit can input the AI's social media activity data into the AI ​​and have the AI ​​collect the data.

[0044] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the AI ​​during diagnosis. For example, the diagnostic unit can perform a detailed diagnosis for an AI with a high importance level. The diagnostic unit can also perform a brief diagnosis for an AI with a low importance level. Furthermore, the diagnostic unit can adjust the frequency and level of detail of the diagnosis according to the importance of the AI. This enables efficient diagnosis by adjusting the level of detail of the diagnosis based on the importance of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using an AI, for example, or may be performed without using an AI. For example, the diagnostic unit can input the importance data of the AI ​​into the AI ​​and cause the AI ​​to adjust the level of detail of the diagnosis.

[0045] The diagnostic unit can apply different diagnostic algorithms depending on the AI ​​category during diagnosis. For example, the diagnostic unit can apply a diagnostic algorithm that emphasizes customer satisfaction to a customer service AI. The diagnostic unit can also apply a diagnostic algorithm that emphasizes analysis accuracy to a data analysis AI. Furthermore, the diagnostic unit can apply a diagnostic algorithm that emphasizes recognition accuracy to an image recognition AI. This enables appropriate diagnosis by applying a diagnostic algorithm depending on the AI ​​category. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, an AI. For example, the diagnostic unit can input AI category data into the AI ​​and cause the AI ​​to apply a diagnostic algorithm.

[0046] During diagnosis, the diagnostic unit can determine the priority of diagnosis based on the operation period of the AI. For example, the diagnostic unit can prioritize diagnosis for newly introduced AI. The diagnostic unit can also periodically diagnose AI that has been operating for a long time. Furthermore, the diagnostic unit can adjust the frequency and priority of diagnosis according to the operation period. This enables efficient diagnosis by determining the priority of diagnosis based on the operation period of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input data on the operation period of the AI ​​into the AI ​​and have the AI ​​determine the priority of diagnosis.

[0047] The diagnostic unit can adjust the order of diagnoses based on the relevance of the AI ​​during diagnosis. For example, the diagnostic unit can prioritize diagnosis of AIs related to important tasks. The diagnostic unit can also postpone diagnosis of AIs related to less relevant tasks. Furthermore, the diagnostic unit can adjust the order and frequency of diagnoses based on the relevance of the AI. This enables efficient diagnosis by adjusting the order of diagnoses based on the relevance of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input AI relevance data into the AI ​​and have the AI ​​adjust the order of diagnoses.

[0048] During correction, the correction unit can analyze the AI's past learning data to select an efficient correction method. For example, the correction unit can select an effective correction method for a specific task based on the past learning data. The correction unit can also analyze the past learning data and adjust the correction method based on a specific pattern. Furthermore, the correction unit can optimize the frequency and timing of correction based on the past learning data. This enables efficient correction by selecting an optimal correction method based on the past learning data. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input past learning data into the AI ​​and have the AI ​​select a correction method.

[0049] During correction, the correction unit can customize the correction method based on the AI's current work situation. For example, the correction unit can provide a correction method related to the work the AI ​​is currently working on. The correction unit can also customize the correction method according to the AI's work situation. Furthermore, the correction unit can determine the priority of correction based on the AI's work situation. This enables efficient correction by customizing the correction method based on the AI's work situation. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input data on the AI's work situation into the AI ​​and cause the AI ​​to customize the correction method.

[0050] During correction, the correction unit can select the optimal correction method by taking into account the AI's geographical location information. For example, if the AI ​​works in a specific area, the correction unit can select a correction method related to that area. The correction unit can also select a highly relevant correction method based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the correction unit can provide a correction method related to the AI's current location. This enables highly relevant correction by selecting a correction method by taking into account the AI's geographical location information. Some or all of the above-described processing in the correction unit may be performed using, or without, an AI. For example, the correction unit can input the AI's geographical location information into the AI ​​and have the AI ​​select a correction method.

[0051] During correction, the correction unit can analyze the AI's social media activity and suggest correction measures. For example, the correction unit can provide correction methods related to topics in which the AI ​​has shown interest on social media. The correction unit can also suggest highly relevant correction methods based on the AI's social media activity. Furthermore, the correction unit can also provide correction methods related to accounts the AI ​​follows on social media. This enables highly relevant correction by analyzing the AI's social media activity and suggesting correction measures. Some or all of the above-described processing in the correction unit may be performed using, or without, an AI. For example, the correction unit can input the AI's social media activity data into the AI ​​and have the AI ​​execute the suggested correction measures.

[0052] At the time of forking, the forking unit can analyze the AI's past performance data to select the optimal forking method. For example, the forking unit can select a forking method that is effective for a specific task based on past performance data. The forking unit can also analyze past performance data and adjust the forking method based on specific patterns. Furthermore, the forking unit can optimize the frequency and timing of forking based on past performance data. This enables efficient forking by selecting the optimal forking method based on past performance data. Some or all of the above-described processing in the forking unit may be performed using, or without, AI. For example, the forking unit can input past performance data into AI and have the AI ​​select the forking method.

[0053] The forking unit can customize the forking means based on the current work status of the AI ​​when forking. The forking unit can, for example, provide a forking method related to the work the AI ​​is currently working on. The forking unit can also customize the forking means according to the work status of the AI. Furthermore, the forking unit can determine the priority of forking based on the work status of the AI. This enables efficient forking by customizing the forking means based on the work status of the AI. Some or all of the above-mentioned processing in the forking unit may be performed using AI, for example, or may be performed without using AI. For example, the forking unit can input work status data of the AI ​​into the AI ​​and have the AI ​​customize the forking means.

[0054] When forking, the forking unit can select the optimal forking method by taking into account the AI's geographical location information. For example, if the AI ​​is operating in a specific area, the forking unit can select a forking method related to that area. The forking unit can also select a highly relevant forking method based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the forking unit can provide a forking method related to the AI's current location. This enables a highly relevant fork by selecting a forking method by taking into account the AI's geographical location information. Some or all of the above-described processing in the forking unit may be performed using, or without, an AI. For example, the forking unit can input the AI's geographical location information into the AI ​​and have the AI ​​select the forking method.

[0055] At the time of forking, the forking unit can analyze the AI's social media activity and suggest a forking method. For example, the forking unit can provide a forking method related to a topic in which the AI ​​has shown interest on social media. The forking unit can also suggest a highly relevant forking method based on the AI's social media activity. Furthermore, the forking unit can also provide a forking method related to accounts that the AI ​​follows on social media. In this way, by analyzing the AI's social media activity and suggesting a forking method, highly relevant forking is possible. Some or all of the above-described processing in the forking unit may be performed using, or without, the AI. For example, the forking unit can input the AI's social media activity data into the AI ​​and have the AI ​​execute the suggested forking method.

[0056] During management, the management unit can optimize the management algorithm by referring to past management data. For example, the management unit can select an effective management algorithm for a specific task based on the past management data. The management unit can also analyze the past management data and adjust the management algorithm based on a specific pattern. Furthermore, the management unit can optimize the frequency and timing of management based on the past management data. This enables efficient management by optimizing the management algorithm based on the past management data. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input past management data into AI and have the AI ​​optimize the management algorithm.

[0057] The management unit can weight the management data based on the operation period of the AI ​​during management. For example, the management unit can prioritize weighting of the management data for newly introduced AI. The management unit can also periodically weight the management data for AI that has been operating for a long time. Furthermore, the management unit can adjust the weighting of the management data according to the operation period. As a result, weighting of the management data based on the operation period of the AI ​​enables efficient management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, an AI. For example, the management unit can input operation period data of the AI ​​into the AI ​​and have the AI ​​perform weighting of the management data.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The AI ​​diagnosis and treatment system can further include a prediction unit. The prediction unit can predict the future performance of the AI ​​based on the data collected by the collection unit. For example, the prediction unit can predict how the AI ​​will respond in future customer service tasks. The prediction unit can also predict what test results the AI ​​will produce in future product inspection tasks. Furthermore, the prediction unit can predict what marketing strategies the AI ​​will develop in future marketing tasks. In this way, the prediction unit can take measures in advance by predicting the future performance of the AI.

[0060] When collecting AI performance data, the collection unit can evaluate the reliability of the data. For example, the collection unit can check the source of the data and the method of generation, and exclude unreliable data. The collection unit can also check the consistency and integrity of the data and filter out abnormal data. Furthermore, the collection unit can evaluate the recency of the data and exclude old data. This allows the collection unit to collect only reliable data, thereby improving the accuracy of AI performance diagnosis.

[0061] The diagnostic unit can be equipped with an anomaly detection function when diagnosing AI performance. For example, the diagnostic unit can detect an anomaly when the AI ​​deviates from its normal performance. The diagnostic unit can also issue an alert when the AI ​​exhibits abnormal performance in a specific task. Furthermore, the diagnostic unit can perform a detailed analysis of AI performance based on the results of anomaly detection. This allows the diagnostic unit to detect AI anomalies early and take prompt measures.

[0062] The correction unit can be equipped with a real-time feedback function when correcting AI performance. For example, the correction unit can provide real-time feedback to the AI ​​when it performs a task, allowing for immediate correction. The correction unit can also receive real-time feedback from the AI ​​during customer service tasks and correct its responses. Furthermore, the correction unit can receive real-time feedback from the AI ​​during product inspection tasks and correct the inspection results. This allows the correction unit to provide real-time feedback, allowing for rapid correction of AI performance.

[0063] The fork unit can be equipped with a version management function when reconstructing an AI algorithm. For example, the fork unit can manage different versions of the AI ​​algorithm and revert to a previous version as needed. The fork unit can also record the change history of the AI ​​algorithm and track the impact of the changes. Furthermore, the fork unit can compare versions of the AI ​​algorithm and select the optimal version. In this way, the fork unit can maintain stable performance by managing the versions of the AI ​​algorithm.

[0064] The management unit can be equipped with a data visualization function when managing diagnostic results and correction data. For example, the management unit can display diagnostic results and correction data in graphs and charts to make them easier to understand visually. The management unit can also visualize data trends and patterns to grasp fluctuations in AI performance. Furthermore, the management unit can provide data filtering and sorting functions to quickly obtain necessary information. As a result, the management unit can streamline AI performance management by visualizing data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data generated when the AI ​​is engaged in its work. For example, it collects customer inquiry data generated when the AI ​​is engaged in customer service work, inspection data generated when the AI ​​is engaged in product inspection work, and marketing data generated when the AI ​​is engaged in marketing work. Step 2: The diagnostic department diagnoses the AI's performance based on the data collected by the collection department. For example, it diagnoses whether the AI ​​is responding in a biased manner in customer service tasks, producing biased test results in product inspection tasks, or formulating a biased marketing strategy in marketing tasks. Step 3: The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. For example, if the AI ​​is responding biasedly in customer service operations, training data is input to correct that response; if it is producing biased inspection results in product inspection operations, training data is input to correct those inspection results; and if it is formulating a biased marketing strategy in marketing operations, training data is input to correct that strategy. Step 4: The forking department reconstructs the AI ​​algorithm corrected by the correction department. For example, if the AI ​​shows significant bias in customer service, product inspection, or marketing tasks, the algorithm will be reconstructed. Step 5: The management department manages the diagnostic results and correction data. For example, it stores the diagnostic results and correction data on the cloud and makes them accessible as needed. It also encrypts and stores the diagnostic results and correction data to ensure security. It also sets permissions to access the diagnostic results and correction data and controls access.

[0067] (Example 2) The AI ​​diagnosis and treatment system according to an embodiment of the present invention is a system designed to prevent biased AI due to learning noise-generating training data when generative AI or general-purpose AI is engaged in a task. This system diagnoses the AI ​​and, if necessary, "treats" it by introducing hard forks or corrective training data. For example, it collects data generated by the AI ​​as it works and diagnoses its performance based on the collected data. This diagnosis is achieved by the AI ​​having functions similar to a self-diagnostic program or antivirus software. If the diagnosis results indicate that the AI ​​is learning in a biased manner, corrective training data is introduced. Furthermore, if necessary, a hard fork is performed to optimize the AI's performance. For example, if the AI ​​is making biased decisions in a specific task, data related to that task is collected and the AI ​​is diagnosed. If the diagnosis results indicate that the AI ​​is learning in a biased manner, corrective training data is introduced to correct the AI's learning. Furthermore, if necessary, a hard fork is performed to optimize the AI's performance. This mechanism prevents biased learning when the AI ​​is engaged in a task, ensuring optimal performance. For example, if the AI ​​is engaged in customer service work, customer inquiry data is collected and the AI ​​is diagnosed. If the AI ​​is found to be responding in a biased manner based on the diagnosis results, corrective training data is input to correct the AI's response. Additionally, a hard fork is performed as necessary to optimize the AI's performance. In this way, by commercializing AI that diagnoses and treats other AI, the AI's performance can be kept at an optimal state at all times. This allows the AI ​​diagnosis and treatment system to always maintain the AI's performance at an optimal state.

[0068] The AI ​​diagnosis and treatment system according to the embodiment includes a collection unit, a diagnosis unit, a correction unit, a forking unit, and a management unit. The collection unit collects data generated when the AI ​​performs its tasks. For example, the collection unit can collect customer inquiry data generated when the AI ​​performs customer service tasks. The collection unit can also collect inspection data generated when the AI ​​performs product inspection tasks. The collection unit can also collect marketing data generated when the AI ​​performs marketing tasks. The diagnosis unit diagnoses the performance of the AI ​​based on the data collected by the collection unit. For example, the diagnosis unit can diagnose whether the AI ​​is responding unbalancedly in customer service tasks. The diagnosis unit can also diagnose whether the AI ​​is producing unbalanced inspection results in product inspection tasks. The diagnosis unit can also diagnose whether the AI ​​is formulating a biased marketing strategy in marketing tasks. The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. For example, if the AI ​​is responding unbalancedly in customer service tasks, the correction unit can input training data to correct the AI's behavior. In addition, if the AI ​​produces biased test results in product inspection work, the correction unit can input training data to correct the test results. Furthermore, if the AI ​​formulates a biased marketing strategy in marketing work, the correction unit can input training data to correct the strategy. The forking unit reconstructs the AI ​​algorithm corrected by the correction unit. For example, if the AI ​​shows a significant bias in customer service work, the forking unit can reconstruct the algorithm. In addition, if the AI ​​shows a significant bias in product inspection work, the forking unit can reconstruct the algorithm. Furthermore, if the AI ​​shows a significant bias in marketing work, the forking unit can reconstruct the algorithm. The management unit manages the diagnosis results and correction data. For example, the management unit can store the diagnosis results and correction data on the cloud so that they can be accessed as needed.The management unit can also encrypt and store diagnostic results and correction data to ensure security. Furthermore, the management unit can set access permissions for diagnostic results and correction data and perform access control. This allows the AI ​​diagnosis and treatment system according to the embodiment to always maintain optimal AI performance.

[0069] The collection department collects data generated when the AI ​​is engaged in its work. Specifically, it can collect customer inquiry data generated when the AI ​​is engaged in customer service tasks. For example, this includes text and voice data when customers make inquiries through the AI ​​chatbot, as well as metadata such as the response content and response time. The collection department can also collect inspection data generated when the AI ​​is engaged in product inspection tasks. In product inspection tasks, when the AI ​​uses image recognition technology to perform visual inspections of products, image data of the inspection results and log data during the inspection process are collected. Furthermore, the collection department can collect marketing data generated when the AI ​​is engaged in marketing tasks. Marketing data includes customer purchase history, website browsing history, and customer response data on social media analyzed by the AI. This data is collected in real time and sent to a central database. The collection department can centrally manage this data and link it to other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the diagnostics department or orthodontics department. In addition, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0070] The Diagnostics Department diagnoses AI performance based on the data collected by the Collection Department. Specifically, it can diagnose whether the AI ​​is responding biasedly in customer service tasks. For example, if the AI ​​consistently responds differently to a specific customer segment, it can identify the cause and evaluate the fairness of the responses. The Diagnostics Department can also diagnose whether the AI ​​is producing biased test results in product inspection tasks. In product inspection tasks, if the AI ​​is applying overly strict standards to a specific product category, it can evaluate the appropriateness of those standards. Furthermore, the Diagnostics Department can diagnose whether the AI ​​is formulating a biased marketing strategy in marketing tasks. In marketing tasks, if the AI ​​is allocating excessive resources to a specific customer segment, it can evaluate the effectiveness of that strategy. To perform these diagnoses, the Diagnostics Department performs detailed analysis of the AI's output results and uses statistical methods and machine learning algorithms to detect bias. Furthermore, the Diagnostics Department can evaluate AI performance by comparing it with past data and benchmark data and identify areas for improvement. This allows the Diagnostics Department to quickly and accurately evaluate AI performance and identify necessary improvements.

[0071] The correction department inputs training data to be corrected based on the results of the diagnosis by the diagnosis department. Specifically, if the AI ​​is responding unbalancedly in customer service operations, training data can be input to correct that response. For example, if the AI ​​is consistently responding differently to a specific customer group, a balanced dataset can be prepared and the AI ​​can be retrained to correct that response. Similarly, if the AI ​​is producing biased inspection results in product inspection operations, the correction department can input training data to correct those inspection results. In product inspection operations, if the AI ​​is applying overly strict standards to a specific product category, a dataset with appropriate standards can be prepared and the AI ​​can be retrained to correct those standards. Furthermore, if the AI ​​is formulating a biased marketing strategy in marketing operations, the correction department can input training data to correct that strategy. In marketing operations, if the AI ​​is allocating excessive resources to a specific customer group, a balanced dataset can be prepared and the AI ​​can be retrained to correct that strategy. To perform these correction tasks efficiently, the correction department selects an appropriate dataset and executes an appropriate learning process for the AI. This allows the correction department to optimize the AI's performance and provide unbiased and fair results.

[0072] The forking department reconstructs the AI ​​algorithm corrected by the correction department. Specifically, if the AI ​​shows significant bias in customer service tasks, it can reconstruct the algorithm. For example, if the AI ​​consistently treats a specific customer group differently, it will review the algorithm and redesign it to provide a fairer response. The forking department can also reconstruct an algorithm if the AI ​​shows significant bias in product inspection tasks. In product inspection tasks, if the AI ​​applies overly strict standards to a specific product category, it will review the algorithm and redesign it to have appropriate standards. Furthermore, the forking department can reconstruct an algorithm if the AI ​​shows significant bias in marketing tasks. In marketing tasks, if the AI ​​allocates excessive resources to a specific customer group, it will review the algorithm and redesign it to create a more balanced strategy. To perform these reconstruction tasks efficiently, the forking department uses appropriate algorithm design methods to optimize the AI's performance. This allows the forking department to reconstruct the AI ​​algorithm and provide unbiased and fair results.

[0073] The management department manages diagnostic results and correction data. Specifically, it can store diagnostic results and correction data on the cloud and make them accessible as needed. For example, it can store diagnostic results and correction data in cloud storage so that the diagnostic department and correction department can access them at any time. The management department can also encrypt and store diagnostic results and correction data to ensure security. This prevents unauthorized access and leaks of data. Furthermore, the management department can set access permissions for diagnostic results and correction data and perform access control. For example, it can grant access rights only to specific users or departments to ensure data security. The management department implements appropriate data management tools and security measures to efficiently perform these management tasks. This allows the management department to safely and efficiently manage diagnostic results and correction data, improving the reliability and security of the entire system.

[0074] The collection unit can collect data generated when the AI ​​engages in work. For example, the collection unit can collect customer inquiry data generated when the AI ​​engages in customer service work. The collection unit can also collect inspection data generated when the AI ​​engages in product inspection work. Furthermore, the collection unit can collect marketing data generated when the AI ​​engages in marketing work. In this way, by collecting data generated when the AI ​​engages in work, it is possible to accurately diagnose the performance of the AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data generated when the AI ​​engages in work into the AI ​​and have the AI ​​collect the data.

[0075] The diagnostic unit has a self-diagnostic program and can periodically check performance. For example, the diagnostic unit can periodically check whether the AI ​​is responding biasedly in customer service operations. The diagnostic unit can also periodically check whether the AI ​​is producing biased test results in product inspection operations. Furthermore, the diagnostic unit can also periodically check whether the AI ​​is formulating biased marketing strategies in marketing operations. In this way, by periodically checking performance, the state of the AI ​​can be constantly understood. Some or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the AI's performance data into the AI ​​and have the AI ​​perform a performance check.

[0076] The correction unit can input training data generated based on past data. For example, if the AI ​​is responding biasedly in customer service operations, the correction unit can input training data to correct the response. Furthermore, if the AI ​​is producing biased inspection results in product inspection operations, the correction unit can input training data to correct the inspection results. Furthermore, if the AI ​​is formulating a biased marketing strategy in marketing operations, the correction unit can input training data to correct the strategy. Thus, by inputting training data generated based on past data, bias in the AI ​​can be corrected. Some or all of the above-described processing in the correction unit may be performed, for example, using AI, or may be performed without using AI. For example, the correction unit can input past data into the AI ​​and have the AI ​​generate and input training data.

[0077] The forking unit can perform a hard fork if the AI ​​shows significant bias. For example, if the AI ​​shows significant bias in customer service operations, the forking unit can perform a hard fork to rebuild the algorithm. Furthermore, if the AI ​​shows significant bias in product inspection operations, the forking unit can also perform a hard fork to rebuild the algorithm. Furthermore, if the AI ​​shows significant bias in marketing operations, the forking unit can also perform a hard fork to rebuild the algorithm. Thus, by performing a hard fork when the AI ​​shows significant bias, the performance of the AI ​​can be optimized. Some or all of the above-described processing in the forking unit may be performed using, or without, an AI. For example, the forking unit can input the AI's algorithm into the AI ​​and leave the execution of the hard fork to the AI.

[0078] The management unit can store diagnostic results or correction data on the cloud and make them accessible as needed. For example, the management unit can store diagnostic results or correction data on the cloud and make them accessible as needed. The management unit can also encrypt and store the diagnostic results or correction data to ensure security. Furthermore, the management unit can set access permissions for the diagnostic results or correction data and perform access control. As a result, by storing the diagnostic results or correction data on the cloud, they can be accessed as needed. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data stored on the cloud into AI and have the AI ​​manage the data.

[0079] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to collect data quickly. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0080] The collection unit can analyze the AI's past performance data and select an efficient data collection method. For example, the collection unit can concentrate data collection during a specific time period based on the past performance data. The collection unit can also analyze the past performance data and select an effective data collection method for a specific task. Furthermore, the collection unit can optimize the frequency and timing of data collection based on the past performance data. This enables efficient data collection by selecting the optimal data collection method based on the past performance data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into the AI ​​and have the AI ​​select the data collection method.

[0081] The collection unit can filter data based on the AI's current work status and areas of interest when collecting data. For example, the collection unit can collect only data related to the work the AI ​​is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the AI's areas of interest. Furthermore, the collection unit can filter unnecessary data according to the AI's work status and collect data efficiently. This enables efficient data collection by filtering data based on the AI's work status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the AI's work status and areas of interest into the AI ​​and have the AI ​​perform data filtering.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can postpone collection of less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collection of detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collection of more important data. Thus, by determining the priority of data based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0083] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the AI's geographical location information. For example, if the AI ​​is operating in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also filter and collect highly relevant data based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the collection unit can also collect data related to its current location in real time. In this way, by collecting data while taking into account the AI's geographical location information, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the AI. For example, the collection unit can input the AI's geographical location information into the AI ​​and cause the AI ​​to collect data.

[0084] The collection unit can analyze the AI's social media activity and collect relevant data when collecting data. For example, the collection unit can collect data related to topics in which the AI ​​has shown interest on social media. The collection unit can also prioritize the collection of highly relevant data based on the AI's social media activity. Furthermore, the collection unit can collect data related to accounts the AI ​​follows on social media. In this way, by analyzing the AI's social media activity and collecting data, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the AI. For example, the collection unit can input the AI's social media activity data into the AI ​​and have the AI ​​collect the data.

[0085] The diagnostic unit can estimate the user's emotions and adjust the way the diagnosis is presented based on the estimated user's emotions. For example, if the user is nervous, the diagnostic unit can provide a simple, highly visible diagnostic result. Furthermore, if the user is relaxed, the diagnostic unit can provide a detailed diagnostic result. Furthermore, if the user is in a hurry, the diagnostic unit can provide a concise diagnostic result that focuses on the main points. By adjusting the way the diagnosis is presented based on the user's emotions, it is possible to provide a diagnostic result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's emotion data into an AI and have the AI ​​adjust the way the diagnosis is presented.

[0086] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the AI ​​during diagnosis. For example, the diagnostic unit can perform a detailed diagnosis for an AI with a high importance level. The diagnostic unit can also perform a brief diagnosis for an AI with a low importance level. Furthermore, the diagnostic unit can adjust the frequency and level of detail of the diagnosis according to the importance of the AI. This enables efficient diagnosis by adjusting the level of detail of the diagnosis based on the importance of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using an AI, for example, or may be performed without using an AI. For example, the diagnostic unit can input the importance data of the AI ​​into the AI ​​and cause the AI ​​to adjust the level of detail of the diagnosis.

[0087] The diagnostic unit can apply different diagnostic algorithms depending on the AI ​​category during diagnosis. For example, the diagnostic unit can apply a diagnostic algorithm that emphasizes customer satisfaction to a customer service AI. The diagnostic unit can also apply a diagnostic algorithm that emphasizes analysis accuracy to a data analysis AI. Furthermore, the diagnostic unit can apply a diagnostic algorithm that emphasizes recognition accuracy to an image recognition AI. This enables appropriate diagnosis by applying a diagnostic algorithm depending on the AI ​​category. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, an AI. For example, the diagnostic unit can input AI category data into the AI ​​and cause the AI ​​to apply a diagnostic algorithm.

[0088] The diagnosis unit can estimate the user's emotions and adjust the length of the diagnosis based on the estimated user emotions. For example, if the user is in a hurry, the diagnosis unit can provide a short, to-the-point diagnosis. Furthermore, if the user is relaxed, the diagnosis unit can provide a longer diagnosis with detailed explanations. Furthermore, if the user is excited, the diagnosis unit can provide a diagnosis with visually stimulating effects. By adjusting the length of the diagnosis based on the user's emotions, it is possible to provide a diagnosis result appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the diagnosis unit can be performed using, for example, an AI, or without an AI. For example, the diagnosis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the diagnosis.

[0089] During diagnosis, the diagnostic unit can determine the priority of diagnosis based on the operation period of the AI. For example, the diagnostic unit can prioritize diagnosis for newly introduced AI. The diagnostic unit can also periodically diagnose AI that has been operating for a long time. Furthermore, the diagnostic unit can adjust the frequency and priority of diagnosis according to the operation period. This enables efficient diagnosis by determining the priority of diagnosis based on the operation period of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input data on the operation period of the AI ​​into the AI ​​and have the AI ​​determine the priority of diagnosis.

[0090] The diagnostic unit can adjust the order of diagnoses based on the relevance of the AI ​​during diagnosis. For example, the diagnostic unit can prioritize diagnosis of AIs related to important tasks. The diagnostic unit can also postpone diagnosis of AIs related to less relevant tasks. Furthermore, the diagnostic unit can adjust the order and frequency of diagnoses based on the relevance of the AI. This enables efficient diagnosis by adjusting the order of diagnoses based on the relevance of the AI. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input AI relevance data into the AI ​​and have the AI ​​adjust the order of diagnoses.

[0091] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user's emotions. For example, if the user is nervous, the correction unit can provide a simple, highly visible correction method. Furthermore, if the user is relaxed, the correction unit can provide a detailed correction method. Furthermore, if the user is in a hurry, the correction unit can provide a concise correction method that focuses on the key points. By adjusting the correction method based on the user's emotions, an appropriate correction method can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's emotion data into an AI and have the AI ​​adjust the correction method.

[0092] During correction, the correction unit can analyze the AI's past learning data to select an efficient correction method. For example, the correction unit can select an effective correction method for a specific task based on the past learning data. The correction unit can also analyze the past learning data and adjust the correction method based on a specific pattern. Furthermore, the correction unit can optimize the frequency and timing of correction based on the past learning data. This enables efficient correction by selecting an optimal correction method based on the past learning data. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input past learning data into the AI ​​and have the AI ​​select a correction method.

[0093] During correction, the correction unit can customize the correction method based on the AI's current work situation. For example, the correction unit can provide a correction method related to the work the AI ​​is currently working on. The correction unit can also customize the correction method according to the AI's work situation. Furthermore, the correction unit can determine the priority of correction based on the AI's work situation. This enables efficient correction by customizing the correction method based on the AI's work situation. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input data on the AI's work situation into the AI ​​and cause the AI ​​to customize the correction method.

[0094] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, if the user is feeling stressed, the correction unit can postpone corrections of less importance. Furthermore, if the user is relaxed, the correction unit can prioritize detailed corrections. Furthermore, if the user is in a hurry, the correction unit can prioritize corrections of greater importance. Thus, by determining the priority of corrections based on the user's emotions, important corrections can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's emotion data into an AI and have the AI ​​determine the priority of corrections.

[0095] During correction, the correction unit can select the optimal correction method by taking into account the AI's geographical location information. For example, if the AI ​​works in a specific area, the correction unit can select a correction method related to that area. The correction unit can also select a highly relevant correction method based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the correction unit can provide a correction method related to the AI's current location. This enables highly relevant correction by selecting a correction method by taking into account the AI's geographical location information. Some or all of the above-described processing in the correction unit may be performed using, or without, an AI. For example, the correction unit can input the AI's geographical location information into the AI ​​and have the AI ​​select a correction method.

[0096] During correction, the correction unit can analyze the AI's social media activity and suggest correction measures. For example, the correction unit can provide correction methods related to topics in which the AI ​​has shown interest on social media. The correction unit can also suggest highly relevant correction methods based on the AI's social media activity. Furthermore, the correction unit can also provide correction methods related to accounts the AI ​​follows on social media. This enables highly relevant correction by analyzing the AI's social media activity and suggesting correction measures. Some or all of the above-described processing in the correction unit may be performed using, or without, an AI. For example, the correction unit can input the AI's social media activity data into the AI ​​and have the AI ​​execute the suggested correction measures.

[0097] The forking unit can estimate the user's emotions and adjust the forking method based on the estimated user emotions. For example, if the user is nervous, the forking unit can provide a simple, highly visible forking method. Furthermore, if the user is relaxed, the forking unit can provide a detailed forking method. Furthermore, if the user is in a hurry, the forking unit can provide a concise forking method that focuses on the main points. This allows the forking method to be adjusted based on the user's emotions, providing a forking method that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the forking unit can be performed using, for example, an AI, or without an AI. For example, the forking unit can input the user's emotion data into an AI and have the AI ​​adjust the forking method.

[0098] At the time of forking, the forking unit can analyze the AI's past performance data to select the optimal forking method. For example, the forking unit can select a forking method that is effective for a specific task based on past performance data. The forking unit can also analyze past performance data and adjust the forking method based on specific patterns. Furthermore, the forking unit can optimize the frequency and timing of forking based on past performance data. This enables efficient forking by selecting the optimal forking method based on past performance data. Some or all of the above-described processing in the forking unit may be performed using, or without, AI. For example, the forking unit can input past performance data into AI and have the AI ​​select the forking method.

[0099] The forking unit can customize the forking means based on the current work status of the AI ​​when forking. The forking unit can, for example, provide a forking method related to the work the AI ​​is currently working on. The forking unit can also customize the forking means according to the work status of the AI. Furthermore, the forking unit can determine the priority of forking based on the work status of the AI. This enables efficient forking by customizing the forking means based on the work status of the AI. Some or all of the above-mentioned processing in the forking unit may be performed using AI, for example, or may be performed without using AI. For example, the forking unit can input work status data of the AI ​​into the AI ​​and have the AI ​​customize the forking means.

[0100] The forking unit can estimate the user's emotions and determine the priority of forks based on the estimated user's emotions. For example, if the user is stressed, the forking unit can postpone forks with lower importance. Furthermore, if the user is relaxed, the forking unit can prioritize detailed forks. Furthermore, if the user is in a hurry, the forking unit can prioritize forks with higher importance. Thus, by determining the priority of forks based on the user's emotions, important forks can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the forking unit can be performed using, for example, an AI, or without an AI. For example, the forking unit can input user emotion data into an AI and have the AI ​​determine the priority of forks.

[0101] When forking, the forking unit can select the optimal forking method by taking into account the AI's geographical location information. For example, if the AI ​​is operating in a specific area, the forking unit can select a forking method related to that area. The forking unit can also select a highly relevant forking method based on the AI's geographical location information. Furthermore, if the AI ​​is moving, the forking unit can provide a forking method related to the AI's current location. This enables a highly relevant fork by selecting a forking method by taking into account the AI's geographical location information. Some or all of the above-described processing in the forking unit may be performed using, or without, an AI. For example, the forking unit can input the AI's geographical location information into the AI ​​and have the AI ​​select the forking method.

[0102] At the time of forking, the forking unit can analyze the AI's social media activity and suggest a forking method. For example, the forking unit can provide a forking method related to a topic in which the AI ​​has shown interest on social media. The forking unit can also suggest a highly relevant forking method based on the AI's social media activity. Furthermore, the forking unit can also provide a forking method related to accounts that the AI ​​follows on social media. In this way, by analyzing the AI's social media activity and suggesting a forking method, highly relevant forking is possible. Some or all of the above-described processing in the forking unit may be performed using, or without, the AI. For example, the forking unit can input the AI's social media activity data into the AI ​​and have the AI ​​execute the suggested forking method.

[0103] The management unit can estimate the user's emotions and select management data based on the estimated user emotions. For example, if the user is nervous, the management unit can provide simple, highly visible management data. Furthermore, if the user is relaxed, the management unit can provide detailed management data. Furthermore, if the user is in a hurry, the management unit can provide concise management data that focuses on the main points. By selecting management data based on the user's emotions, appropriate management data can be provided for the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into an AI and have the AI ​​select the management data.

[0104] During management, the management unit can optimize the management algorithm by referring to past management data. For example, the management unit can select an effective management algorithm for a specific task based on the past management data. The management unit can also analyze the past management data and adjust the management algorithm based on a specific pattern. Furthermore, the management unit can optimize the frequency and timing of management based on the past management data. This enables efficient management by optimizing the management algorithm based on the past management data. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input past management data into AI and have the AI ​​optimize the management algorithm.

[0105] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can reduce the frequency of management to reduce the user's burden. Furthermore, if the user is relaxed, the management unit can increase the frequency of management and collect detailed data. Furthermore, if the user is in a hurry, the management unit can shorten the frequency of management and quickly collect data. This reduces the user's burden by adjusting the frequency of management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into an AI and have the AI ​​adjust the frequency of management.

[0106] The management unit can weight the management data based on the operation period of the AI ​​during management. For example, the management unit can prioritize weighting of the management data for newly introduced AI. The management unit can also periodically weight the management data for AI that has been operating for a long time. Furthermore, the management unit can adjust the weighting of the management data according to the operation period. As a result, weighting of the management data based on the operation period of the AI ​​enables efficient management. Some or all of the above-mentioned processing in the management unit may be performed using, or without, an AI. For example, the management unit can input operation period data of the AI ​​into the AI ​​and have the AI ​​perform weighting of the management data.

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

[0108] The AI ​​diagnosis and treatment system can further include a prediction unit. The prediction unit can predict the future performance of the AI ​​based on the data collected by the collection unit. For example, the prediction unit can predict how the AI ​​will respond in future customer service tasks. The prediction unit can also predict what test results the AI ​​will produce in future product inspection tasks. Furthermore, the prediction unit can predict what marketing strategies the AI ​​will develop in future marketing tasks. In this way, the prediction unit can take measures in advance by predicting the future performance of the AI.

[0109] When collecting AI performance data, the collection unit can evaluate the reliability of the data. For example, the collection unit can check the source of the data and the method of generation, and exclude unreliable data. The collection unit can also check the consistency and integrity of the data and filter out abnormal data. Furthermore, the collection unit can evaluate the recency of the data and exclude old data. This allows the collection unit to collect only reliable data, thereby improving the accuracy of AI performance diagnosis.

[0110] The diagnostic unit can be equipped with an anomaly detection function when diagnosing AI performance. For example, the diagnostic unit can detect an anomaly when the AI ​​deviates from its normal performance. The diagnostic unit can also issue an alert when the AI ​​exhibits abnormal performance in a specific task. Furthermore, the diagnostic unit can perform a detailed analysis of AI performance based on the results of anomaly detection. This allows the diagnostic unit to detect AI anomalies early and take prompt measures.

[0111] The correction unit can be equipped with a real-time feedback function when correcting AI performance. For example, the correction unit can provide real-time feedback to the AI ​​when it performs a task, allowing for immediate correction. The correction unit can also receive real-time feedback from the AI ​​during customer service tasks and correct its responses. Furthermore, the correction unit can receive real-time feedback from the AI ​​during product inspection tasks and correct the inspection results. This allows the correction unit to provide real-time feedback, allowing for rapid correction of AI performance.

[0112] The fork unit can be equipped with a version management function when reconstructing an AI algorithm. For example, the fork unit can manage different versions of the AI ​​algorithm and revert to a previous version as needed. The fork unit can also record the change history of the AI ​​algorithm and track the impact of the changes. Furthermore, the fork unit can compare versions of the AI ​​algorithm and select the optimal version. In this way, the fork unit can maintain stable performance by managing the versions of the AI ​​algorithm.

[0113] The management unit can be equipped with a data visualization function when managing diagnostic results and correction data. For example, the management unit can display diagnostic results and correction data in graphs and charts to make them easier to understand visually. The management unit can also visualize data trends and patterns to grasp fluctuations in AI performance. Furthermore, the management unit can provide data filtering and sorting functions to quickly obtain necessary information. As a result, the management unit can streamline AI performance management by visualizing data.

[0114] The collection unit can estimate the user's emotions and customize the data collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can simplify the data collection method to reduce the burden on the user. Also, if the user is relaxed, the collection unit can adopt a detailed data collection method to collect more information. Furthermore, if the user is in a hurry, the collection unit can select a method to collect data quickly. In this way, the collection unit can customize the data collection method based on the user's emotions, thereby reducing the burden on the user and achieving efficient data collection.

[0115] The diagnosis unit can estimate the user's emotions and adjust the timing of the diagnosis based on the estimated user emotions. For example, if the user is feeling stressed, the diagnosis unit can delay the timing of the diagnosis to reduce the burden on the user. Furthermore, if the user is relaxed, the diagnosis unit can advance the timing of the diagnosis to quickly provide diagnostic results. Furthermore, if the user is in a hurry, the diagnosis unit can adjust the timing of the diagnosis to quickly perform the diagnosis. In this way, the diagnosis unit can reduce the burden on the user and achieve efficient diagnosis by adjusting the timing of the diagnosis based on the user's emotions.

[0116] The correction unit can estimate the user's emotions and adjust the correction feedback method based on the estimated user's emotions. For example, if the user is nervous, the correction unit can provide simple, highly visible feedback. If the user is relaxed, the correction unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the correction unit can also provide concise feedback that focuses on the main points. In this way, the correction unit can provide appropriate feedback for the user by adjusting the feedback method based on the user's emotions.

[0117] The forking unit can estimate the user's emotions and adjust the timing of forking based on the estimated user's emotions. For example, if the user is feeling stressed, the forking unit can delay the timing of forking to reduce the burden on the user. Also, if the user is relaxed, the forking unit can advance the timing of forking to perform the forking quickly. Furthermore, if the user is in a hurry, the forking unit can adjust the timing of forking to perform the forking quickly. In this way, the forking unit can reduce the burden on the user and achieve efficient forking by adjusting the timing of forking based on the user's emotions.

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

[0119] Step 1: The collection unit collects data generated when the AI ​​is engaged in its work. For example, it collects customer inquiry data generated when the AI ​​is engaged in customer service work, inspection data generated when the AI ​​is engaged in product inspection work, and marketing data generated when the AI ​​is engaged in marketing work. Step 2: The diagnostic department diagnoses the AI's performance based on the data collected by the collection department. For example, it diagnoses whether the AI ​​is responding in a biased manner in customer service tasks, producing biased test results in product inspection tasks, or formulating a biased marketing strategy in marketing tasks. Step 3: The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. For example, if the AI ​​is responding biasedly in customer service operations, training data is input to correct that response; if it is producing biased inspection results in product inspection operations, training data is input to correct those inspection results; and if it is formulating a biased marketing strategy in marketing operations, training data is input to correct that strategy. Step 4: The forking department reconstructs the AI ​​algorithm corrected by the correction department. For example, if the AI ​​shows significant bias in customer service, product inspection, or marketing tasks, the algorithm will be reconstructed. Step 5: The management department manages the diagnostic results and correction data. For example, it stores the diagnostic results and correction data on the cloud and makes them accessible as needed. It also encrypts and stores the diagnostic results and correction data to ensure security. It also sets permissions to access the diagnostic results and correction data and controls access.

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

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

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

[0123] Each of the multiple elements, including the collection unit, diagnosis unit, correction unit, forking unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The diagnosis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, diagnoses AI performance based on the collected data. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, inputs training data to be corrected based on the diagnosis results. The forking unit, realized, for example, by the specific processing unit 290 of the data processing device 12, reconstructs the corrected AI algorithm. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages the diagnosis results and correction data. The correspondence between each unit and the device or control unit is not limited to the above example and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0139] Each of the multiple elements, including the collection unit, diagnosis unit, correction unit, fork unit, and management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and diagnoses AI performance based on the collected data. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs training data to be corrected based on the diagnosis results. The fork unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reconstructs the corrected AI algorithm. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the diagnosis results and correction data. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0155] Each of the multiple elements, including the collection unit, diagnosis unit, correction unit, fork unit, and management unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and diagnoses the performance of the AI ​​based on the collected data. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inputs training data to be corrected based on the diagnosis results. The fork unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reconstructs the corrected AI algorithm. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the diagnosis results and correction data. The correspondence between each unit and the device and control unit is not limited to the above example and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0172] Each of the multiple elements, including the collection unit, diagnosis unit, correction unit, fork unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The diagnosis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, diagnoses the AI's performance based on the collected data. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, inputs training data to be corrected based on the diagnosis results. The fork unit, realized, for example, by the specific processing unit 290 of the data processing device 12, reconstructs the corrected AI algorithm. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages the diagnosis results and correction data. The correspondence between each unit and the device or control unit is not limited to the above example and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0191] (Appendix 1) a collection unit that collects data; a diagnostic unit that diagnoses the performance of the AI ​​based on the data collected by the collection unit; a correction unit that inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit; a fork unit that reconstructs the AI ​​algorithm corrected by the correction unit; a management unit that manages the diagnostic results or correction data; A system characterized by: (Appendix 2) The collecting unit Collect data generated by AI as it works 2. The system of claim 1. (Appendix 3) The diagnostic unit Have a self-diagnosis program and check your performance regularly 2. The system of claim 1. (Appendix 4) The correction unit is Input training data generated based on past data 2. The system of claim 1. (Appendix 5) The fork portion is Hard fork if AI shows significant bias 2. The system of claim 1. (Appendix 6) The management unit Store diagnostic or correctional data on the cloud and make it accessible when needed 2. The system of claim 1. (Appendix 7) The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1. (Appendix 8) The collecting unit Analyze past AI performance data and select an efficient data collection method 2. The system of claim 1. (Appendix 9) The collecting unit As data is collected, it is filtered based on the AI's current business context and areas of interest. 2. The system of claim 1. (Appendix 10) The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1. (Appendix 11) The collecting unit When collecting data, AI takes geographic location into account to prioritize the collection of relevant data. 2. The system of claim 1. (Appendix 12) The collecting unit During data collection, AI analyzes social media activity and collects relevant data 2. The system of claim 1. (Appendix 13) The diagnostic unit Inferring user emotions and adjusting the way the diagnosis is presented based on the estimated user emotions 2. The system of claim 1. (Appendix 14) The diagnostic unit During diagnosis, adjust the diagnostic detail based on the AI's importance level 2. The system of claim 1. (Appendix 15) The diagnostic unit During diagnosis, different diagnostic algorithms are applied depending on the AI ​​category. 2. The system of claim 1. (Appendix 16) The diagnostic unit Inferring the user's emotions and adjusting the length of the diagnosis based on the estimated user emotions 2. The system of claim 1. (Appendix 17) The diagnostic unit During diagnosis, prioritize diagnosis based on when AI is operational. 2. The system of claim 1. (Appendix 18) The diagnostic unit At the time of diagnosis, the order of diagnoses is adjusted based on AI relevance 2. The system of claim 1. (Appendix 19) The correction unit is Estimating the user's emotions and adjusting the correction method based on the estimated user's emotions 2. The system of claim 1. (Appendix 20) The correction unit is When correcting teeth, the AI ​​analyzes past learning data to select the most efficient correction method. 2. The system of claim 1. (Appendix 21) The correction unit is During correction, the AI ​​customizes the correction measures based on the current work situation. 2. The system of claim 1. (Appendix 22) The correction unit is Estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. 2. The system of claim 1. (Appendix 23) The correction unit is When correcting teeth, the AI's geographic location information is taken into consideration to select the optimal correction method. 2. The system of claim 1. (Appendix 24) The correction unit is During correction, AI analyzes social media activity to suggest correction measures. 2. The system of claim 1. (Appendix 25) The fork portion is Inferring user sentiment and adjusting forking strategies based on the estimated user sentiment 2. The system of claim 1. (Appendix 26) The fork portion is When forking, the AI ​​analyzes past performance data to select the optimal forking method. 2. The system of claim 1. (Appendix 27) The fork portion is When forking, customize the forking method based on the AI's current operating situation. 2. The system of claim 1. (Appendix 28) The fork portion is Estimate user sentiment and prioritize forks based on the estimated user sentiment 2. The system of claim 1. (Appendix 29) The fork portion is When forking, the AI's geographic location information is taken into account to select the optimal forking method. 2. The system of claim 1. (Appendix 30) The fork portion is At the time of forking, the AI ​​analyzes social media activity to suggest ways to fork. 2. The system of claim 1. (Appendix 31) The management unit Estimate the user's emotions and select management data based on the estimated user emotions. 2. The system of claim 1. (Appendix 32) The management unit During management, past management data is referenced to optimize the management algorithm. 2. The system of claim 1. (Appendix 33) The management unit Estimate the user's emotions and adjust the frequency of management based on the estimated user emotions. 2. The system of claim 1. (Appendix 34) The management unit During management, the management data is weighted based on when the AI ​​was running. 2. The system of claim 1. [Explanation of symbols]

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

Claims

1. a collection unit that collects data; A diagnosis unit that diagnoses AI performance based on the data collected by the collection unit; a correction unit that inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit; a fork unit that reconstructs the AI ​​algorithm corrected by the correction unit; a management unit that manages the diagnosis results or correction data of the diagnosis unit, The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions A system characterized by:

2. The collecting unit Collect data generated when AI performs its work 2. The system of claim 1.

3. The diagnostic unit Have a self-diagnosis program and check your performance regularly 2. The system of claim 1.

4. The correction unit is Input training data generated based on past data 2. The system of claim 1.

5. The fork portion is A hard fork will be performed if the AI ​​shows significant bias.

2. The system of claim 1.

6. The management unit Store diagnostic or correctional data on the cloud and make it accessible when needed 2. The system of claim 1.

7. The collecting unit Analyze past AI performance data and select efficient data collection methods 2. The system of claim 1.

8. The collecting unit When collecting data, filter it based on the AI's current business context and areas of interest.

2. The system of claim 1.

9. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

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