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 a description and related instruction sentence regarding 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] JP 2022-180282 A 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 AI performance and make appropriate corrections. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a diagnosis unit, a correction unit, a fork 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 teacher data to be corrected based on the results of the diagnosis by the diagnosis unit. The fork unit reconstructs the AI ​​algorithm corrected by the correction unit. The management unit manages the diagnosis results or the correction data. Effect of the Invention

[0007] The system according to the embodiment can diagnose AI performance and correct it appropriately. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. 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 the embodiment of the present invention is a system for preventing the possibility that a generation AI or a general-purpose AI may become biased due to learning of teacher data that becomes noise when engaged in work. This system "treats AI" by diagnosing AI and, if necessary, hard forking or inputting corrective teacher data. For example, data generated when an AI engages in work is collected, and the performance of the AI ​​is diagnosed based on the collected data. This diagnosis is realized by the AI ​​having a function such as a self-diagnosis program or an antivirus. If the AI ​​is learning in a biased manner based on the diagnosis result, teacher data for correction is input. Also, if necessary, a hard fork is performed to optimize the performance of the AI. For example, if an AI makes biased judgments in a specific task, data related to that task is collected and the AI ​​is diagnosed. If it is determined that the AI ​​is learning in a biased manner based on the diagnosis result, teacher data for correction is input and the learning of the AI ​​is corrected. Also, if necessary, a hard fork is performed to optimize the performance of the AI. This mechanism prevents biased learning when an AI engages in work, and always performs optimal performance. For example, if an AI engages in customer service work, customer inquiry data is collected and the AI ​​is diagnosed. If the AI ​​responds in a biased manner based on the diagnosis results, corrective training data is input to correct the AI's response. In addition, a hard fork is performed if necessary to optimize the AI's performance. In this way, by commercializing an AI that diagnoses and treats other AI, the AI's performance can always be kept at an optimal state. This allows the AI ​​diagnosis and treatment system to always keep 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 fork unit, and a management unit. The collection unit collects data generated when the AI ​​engages in work. The collection unit can collect, for example, 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. The diagnosis unit diagnoses the performance of the AI ​​based on the data collected by the collection unit. The diagnosis unit can diagnose, for example, whether the AI ​​responds biasedly in customer service work. The diagnosis unit can also diagnose whether the AI ​​produces biased inspection results in product inspection work. Furthermore, the diagnosis unit can diagnose whether the AI ​​has established a biased marketing strategy in marketing work. The correction unit inputs teacher data to be corrected based on the results diagnosed by the diagnosis unit. For example, if the AI ​​responds biasedly in customer service work, the correction unit can input teacher data to correct the response. In addition, the correction unit can input teacher data to correct the inspection results when the AI ​​produces biased inspection results in product inspection work. Furthermore, in the case where the AI ​​has formulated a biased marketing strategy in marketing work, the correction unit can input teacher data to correct the strategy. The fork unit reconstructs the algorithm of the AI ​​corrected by the correction unit. For example, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in customer service work. In addition, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in product inspection work. Furthermore, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in marketing work. The management unit manages the diagnosis results and the correction data. For example, the management unit can store the diagnosis results and the correction data on the cloud and make them accessible as needed.The management unit can also encrypt and store diagnostic results and correction data to ensure security. Furthermore, the management unit can set the authority to access 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 unit collects data generated when the AI ​​is engaged in work. Specifically, it can collect customer inquiry data generated when the AI ​​is engaged in customer support work. For example, the data includes text data and voice data when a customer makes an inquiry through the AI ​​chatbot, as well as metadata such as the response content and response time. The collection unit can also collect inspection data generated when the AI ​​is engaged in product inspection work. In product inspection work, when the AI ​​uses image recognition technology to perform an appearance inspection of a product, image data of the inspection results and log data during the inspection process are collected. In addition, the collection unit can collect marketing data generated when the AI ​​is engaged in marketing work. Marketing data includes customer purchase history and website browsing history analyzed by the AI, as well as customer response data on social media. These data are collected in real time and sent to a central database. The collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the diagnosis department and correction department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The diagnostic department diagnoses the performance of the AI ​​based on the data collected by the collection department. Specifically, it can diagnose whether the AI ​​is responding biasedly in customer service operations. For example, if the AI ​​consistently responds differently to a specific customer group, it identifies the cause and evaluates the fairness of the response. The diagnostic department can also diagnose whether the AI ​​is producing biased inspection results in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, it evaluates the appropriateness of the standards. Furthermore, the diagnostic department can diagnose whether the AI ​​is formulating a biased marketing strategy in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, it evaluates the effectiveness of the strategy. To perform these diagnoses, the diagnostic department analyzes the output results of the AI ​​in detail and detects the presence or absence of bias using statistical methods and machine learning algorithms. Furthermore, the diagnostic department can evaluate the performance of the AI ​​by comparing it with past data and benchmark data and identify room for improvement. This allows the diagnostic department to quickly and accurately evaluate the performance of the AI ​​and clarify the necessary improvements.

[0032] The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. Specifically, if the AI ​​responds in a biased manner in customer service operations, training data can be input to correct the response. For example, if the AI ​​consistently responds differently to a specific customer group, a balanced data set is prepared and the AI ​​is made to relearn in order to correct the response. In addition, the correction unit can also input training data to correct the inspection results when the AI ​​produces biased inspection results in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, a data set with appropriate standards is prepared and the AI ​​is made to relearn in order to correct the standards. Furthermore, the correction unit can also input training data to correct a biased marketing strategy when the AI ​​formulates a marketing strategy in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, a balanced data set is prepared and the AI ​​is made to relearn in order to correct the strategy. In order to perform these correction operations efficiently, the correction unit selects an appropriate data set and executes an appropriate learning process for the AI. This allows the corrections department to optimize the AI's performance and provide unbiased and fair results.

[0033] The fork section reconstructs the AI ​​algorithm corrected by the correction section. Specifically, if the AI ​​shows significant bias in customer service operations, the algorithm can be reconstructed. For example, if the AI ​​consistently responds differently to a specific customer group, the algorithm is reviewed and redesigned to respond more fairly. The fork section can also reconstruct the algorithm if the AI ​​shows significant bias in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, the algorithm is reviewed and redesigned to have appropriate standards. Furthermore, the fork section can also reconstruct the algorithm if the AI ​​shows significant bias in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, the algorithm is reviewed and redesigned to create a more balanced strategy. In order to perform these reconstruction operations efficiently, the fork section uses an appropriate algorithm design method to optimize the performance of the AI. As a result, the fork section can reconstruct the AI ​​algorithm and provide unbiased and fair results.

[0034] The management unit manages the diagnostic results and the correction data. Specifically, the diagnostic results and the correction data can be stored on the cloud so that they can be accessed as needed. For example, the diagnostic results and the correction data can be stored in cloud storage so that the diagnostic unit and the correction unit can access them at any time. The management unit can also encrypt and store the diagnostic results and the correction data to ensure security. This can prevent unauthorized access and leakage of data. Furthermore, the management unit can set the authority to access the diagnostic results and the correction data and perform access control. For example, access rights can be granted only to specific users or departments to ensure the safety of the data. The management unit introduces appropriate data management tools and security measures to efficiently perform these management tasks. This allows the management unit to safely and efficiently manage the diagnostic results and the correction data, thereby improving the reliability and safety of the entire system.

[0035] The collection unit can collect data generated when the AI ​​engages in business. The collection unit can collect, for example, 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 also collect marketing data generated when the AI ​​engages in marketing work. In this way, by collecting data generated when the AI ​​engages in business, the performance of the AI ​​can be accurately diagnosed. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input data generated when the AI ​​engages in business into the AI ​​and cause the AI ​​to collect the data.

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

[0037] The correction unit can input teacher data generated based on past data. For example, when the AI ​​responds in a biased manner in customer service operations, the correction unit can input teacher data to correct the response. In addition, when the AI ​​produces biased inspection results in product inspection operations, the correction unit can also input teacher data to correct the inspection results. Furthermore, when the AI ​​formulates a biased marketing strategy in marketing operations, the correction unit can also input teacher data to correct the strategy. In this way, the bias of the AI ​​can be corrected by inputting teacher data generated based on past data. Part or all of the above-mentioned 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 cause the AI ​​to generate and input teacher data.

[0038] The fork unit can perform a hard fork when the AI ​​shows a large bias. For example, when the AI ​​shows a significant bias in customer service work, the fork unit can perform a hard fork to reconstruct the algorithm. In addition, when the AI ​​shows a significant bias in product inspection work, the fork unit can also perform a hard fork to reconstruct the algorithm. In addition, when the AI ​​shows a significant bias in marketing work, the fork unit can also perform a hard fork to reconstruct the algorithm. In this way, by performing a hard fork when the AI ​​shows a significant bias, the performance of the AI ​​can be optimized. Part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the algorithm of the AI ​​to the AI ​​and leave the execution of the hard fork to the AI.

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

[0040] The collection unit can analyze the past performance data of the AI ​​and select an efficient data collection method. The collection unit can, for example, concentrate data collection in 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 an optimal data collection method based on the past performance data. A part 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 past performance data into the AI ​​and cause the AI ​​to select a data collection method.

[0041] The collection unit can perform filtering based on the current business status and field of interest of the AI ​​when collecting data. The collection unit can, for example, collect only data related to the business that the AI ​​is currently working on. The collection unit can also preferentially collect highly relevant data based on the field of interest of the AI. Furthermore, the collection unit can filter unnecessary data according to the business status of the AI ​​and perform efficient data collection. As a result, efficient data collection is possible by filtering data based on the business status and field of interest of the AI. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input data on the business status and field of interest of the AI ​​to the AI ​​and have the AI ​​perform data filtering.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the AI ​​when collecting data. For example, when the AI ​​is performing work in a specific area, the collection unit can preferentially collect data related to that area. The collection unit can also filter and collect highly relevant data based on the geographical location information of the AI. Furthermore, when the AI ​​is moving, the collection unit can also collect data related to the current location in real time. As a result, highly relevant data can be efficiently collected by collecting data in consideration of the geographical location information of the AI. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to collect data.

[0043] The collection unit can analyze the social media activity of the AI ​​and collect relevant data when collecting data. The collection unit can, for example, collect data related to topics in which the AI ​​has shown interest on social media. The collection unit can also preferentially collect highly relevant data based on the social media activity of the AI. Furthermore, the collection unit can also collect data related to accounts that the AI ​​follows on social media. In this way, highly relevant data can be efficiently collected by analyzing the social media activity of the AI ​​and collecting data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input the social media activity data of the AI ​​to the AI ​​and cause the AI ​​to 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. The diagnostic unit can perform a detailed diagnosis for an AI with a high level of importance, for example. The diagnostic unit can also perform a concise diagnosis for an AI with a low level of importance. Furthermore, the diagnostic unit can adjust the frequency and level of detail of the diagnosis according to the importance of the AI. This allows for efficient diagnosis by adjusting the level of detail of the diagnosis based on the importance of the AI. A part or all of the above-mentioned 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 importance data of the AI ​​to 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 category of the AI ​​during diagnosis. For example, the diagnostic unit can apply a diagnostic algorithm that emphasizes customer satisfaction to a customer response 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 allows for appropriate diagnosis by applying a diagnostic algorithm depending on the category of the AI. Some or all of the above-mentioned 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 AI category data to the AI ​​and cause the AI ​​to apply the diagnostic algorithm.

[0046] The diagnostic unit can determine the priority of diagnosis based on the operation time of the AI ​​at the time of diagnosis. For example, the diagnostic unit can give priority to diagnosis of a newly introduced AI. In addition, the diagnostic unit can also periodically diagnose an 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 time. As a result, efficient diagnosis is possible by determining the priority of diagnosis based on the operation time of the AI. A part or all of the above-mentioned 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 operation time data of the AI ​​to the AI ​​and cause the AI ​​to execute the determination of the priority of diagnosis.

[0047] The diagnostic unit can adjust the order of diagnosis based on the relevance of the AI ​​during diagnosis. For example, the diagnostic unit can give priority to diagnosis of AI related to important tasks. Also, the diagnostic unit can postpone diagnosis of AI related to tasks with low relevance. Furthermore, the diagnostic unit can adjust the order and frequency of diagnosis according to the relevance of the AI. As a result, efficient diagnosis is possible by adjusting the order of diagnosis based on the relevance of the AI. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the diagnostic unit can input relevance data of the AI ​​to the AI ​​and cause the AI ​​to adjust the order of diagnosis.

[0048] The correction unit can select an efficient correction method by analyzing the past learning data of the AI ​​at the time of correction. The correction unit can select an effective correction method for a specific task based on, for example, 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. A part or all of the above-mentioned 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 learning data to the AI ​​and cause the AI ​​to select the correction method.

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

[0050] The correction unit can select the optimal correction method in consideration of the geographical location information of the AI ​​at the time of correction. For example, when the AI ​​is working 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 geographical location information of the AI. Furthermore, when the AI ​​is moving, the correction unit can also provide a correction method related to the current location. As a result, by selecting a correction method in consideration of the geographical location information of the AI, highly relevant correction is possible. A part or all of the above-mentioned processing in the correction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the correction unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to select a correction method.

[0051] At the time of correction, the correction unit can analyze the AI's social media activity and suggest a means of correction. The correction unit can provide, for example, a correction method related to a topic in which the AI ​​showed interest on social media. The correction unit can also suggest a highly relevant correction method based on the AI's social media activity. Furthermore, the correction unit can provide a correction method related to an account that the AI ​​follows on social media. This enables highly relevant correction by analyzing the AI's social media activity and suggesting a means of correction. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the correction unit can input the AI's social media activity data into the AI ​​and cause the AI ​​to execute the proposed correction means.

[0052] The fork unit can select the optimal fork method at the time of forking by analyzing the past performance data of the AI. The fork unit can select an effective fork method for a specific task based on, for example, past performance data. The fork unit can also analyze past performance data and adjust the fork method based on a specific pattern. Furthermore, the fork unit can optimize the frequency and timing of forking based on past performance data. This enables efficient forking by selecting the optimal fork method based on past performance data. A part or all of the above-mentioned processing in the fork unit may be performed using, for example, AI, or may be performed without using AI. For example, the fork unit can input past performance data into the AI ​​and have the AI ​​select the fork method.

[0053] The fork unit can customize the fork means based on the current business situation of the AI ​​at the time of forking. The fork unit can provide, for example, a fork method related to the business that the AI ​​is currently working on. The fork unit can also customize the fork means according to the business situation of the AI. Furthermore, the fork unit can also determine the priority of the fork based on the business situation of the AI. This enables efficient forking by customizing the fork means based on the business situation of the AI. A part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the business situation data of the AI ​​to the AI ​​and cause the AI ​​to execute the customization of the fork means.

[0054] The fork unit can select the optimal fork method in consideration of the geographical location information of the AI ​​when forking. For example, when the AI ​​is working in a specific area, the fork unit can select a fork method related to that area. The fork unit can also select a highly relevant fork method based on the geographical location information of the AI. Furthermore, when the AI ​​is moving, the fork unit can also provide a fork method related to the current location. As a result, by selecting a fork method in consideration of the geographical location information of the AI, a highly relevant fork becomes possible. A part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to select the fork method.

[0055] At the time of forking, the forking unit can analyze the social media activity of the AI ​​and suggest a forking means. The forking unit can provide, for example, 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 social media activity of the AI. Furthermore, the forking unit can provide a forking method related to an account that the AI ​​follows on social media. This enables highly relevant forking by analyzing the social media activity of the AI ​​and suggesting a forking means. Some or all of the above-mentioned processing in the forking unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the forking unit can input the social media activity data of the AI ​​to the AI ​​and cause the AI ​​to execute the suggestion of the forking means.

[0056] The management unit can optimize the management algorithm by referring to past management data during management. The management unit can select an effective management algorithm for a specific task based on, for example, past management data. The management unit can also analyze 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 past management data. As a result, efficient management is possible by optimizing the management algorithm based on past management data. Some or all of the above-mentioned 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 cause AI to optimize the management algorithm.

[0057] The management unit can weight the management data based on the operation time of the AI ​​during management. For example, the management unit can preferentially weight the management data for a newly introduced AI. The management unit can also periodically weight the management data for an 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 time. As a result, efficient management is possible by weighting the management data based on the operation time of the AI. A part or all of the above-mentioned processing in the management unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the management unit can input the operation time data of the AI ​​to the AI ​​and have the AI ​​execute the 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 may further include a prediction unit. The prediction unit may predict the future performance of the AI ​​based on the data collected by the collection unit. For example, the prediction unit may predict how the AI ​​will respond in future customer service operations. The prediction unit may also predict what kind of test results the AI ​​will produce in future product inspection operations. Furthermore, the prediction unit may also predict what kind of marketing strategy the AI ​​will develop in future marketing operations. In this way, the prediction unit may 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 data with low reliability. 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 improve the accuracy of AI performance diagnosis by collecting only highly reliable data.

[0061] The diagnostic unit can have an anomaly detection function when diagnosing the performance of the AI. For example, the diagnostic unit can detect an anomaly when the AI ​​deviates from normal performance. The diagnostic unit can also issue an alert when the AI ​​shows abnormal performance in a specific task. Furthermore, the diagnostic unit can perform a detailed analysis of the AI's performance based on the results of the anomaly detection. This enables the diagnostic unit to detect anomalies in the AI ​​early and take measures promptly.

[0062] The correction unit can have a real-time feedback function when correcting the AI's performance. For example, the correction unit can provide feedback in real time when the AI ​​performs a task, and perform correction immediately. The correction unit can also receive feedback in real time when the AI ​​is working with customers, and correct the response. Furthermore, the correction unit can receive feedback in real time when the AI ​​is working in product inspection, and correct the inspection results. In this way, the correction unit can quickly correct the AI's performance by providing feedback in real time.

[0063] The fork unit can have a version management function when reconstructing the AI ​​algorithm. For example, the fork unit can manage different versions of the AI ​​algorithm and revert to a previous version as necessary. 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. As a result, the fork unit can maintain stable performance by managing the versions of the AI ​​algorithm.

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

[0065] The process flow of the first embodiment will be briefly described 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 performance of the AI ​​based on the data collected by the collection department. For example, it diagnoses whether the AI ​​is responding biasedly in customer service tasks, producing biased test results in product inspection tasks, or formulating biased marketing strategies 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 ​​responds biasedly in customer service operations, training data is input to correct that response; if it produces biased inspection results in product inspection operations, training data is input to correct those inspection results; and if it has formulated a biased marketing strategy in marketing operations, training data is input to correct that strategy. Step 4: The forking department reconstructs the AI ​​algorithm that was corrected by the correction department. For example, if the AI ​​shows significant bias in customer service tasks, product inspection tasks, or marketing tasks, the algorithm will be reconstructed. Step 5: The management department manages the diagnostic results and correction data. For example, the management department stores the diagnostic results and correction data on the cloud and makes them accessible as needed. The management department also encrypts and stores the diagnostic results and correction data to ensure security. Furthermore, the management department sets permissions to access the diagnostic results and correction data and performs access control.

[0067] (Example 2) The AI ​​diagnosis and treatment system according to the embodiment of the present invention is a system for preventing the possibility that a generation AI or a general-purpose AI may become biased due to learning of teacher data that becomes noise when engaged in work. This system "treats AI" by diagnosing AI and, if necessary, hard forking or inputting corrective teacher data. For example, data generated when an AI engages in work is collected, and the performance of the AI ​​is diagnosed based on the collected data. This diagnosis is realized by the AI ​​having a function such as a self-diagnosis program or an antivirus. If the AI ​​is learning in a biased manner based on the diagnosis result, teacher data for correction is input. Also, if necessary, a hard fork is performed to optimize the performance of the AI. For example, if an AI makes biased judgments in a specific task, data related to that task is collected and the AI ​​is diagnosed. If it is determined that the AI ​​is learning in a biased manner based on the diagnosis result, teacher data for correction is input and the learning of the AI ​​is corrected. Also, if necessary, a hard fork is performed to optimize the performance of the AI. This mechanism prevents biased learning when an AI engages in work, and always performs optimal performance. For example, if an AI engages in customer service work, customer inquiry data is collected and the AI ​​is diagnosed. If the AI ​​responds in a biased manner based on the diagnosis results, corrective training data is input to correct the AI's response. In addition, a hard fork is performed if necessary to optimize the AI's performance. In this way, by commercializing an AI that diagnoses and treats other AI, the AI's performance can always be kept at an optimal state. This allows the AI ​​diagnosis and treatment system to always keep 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 fork unit, and a management unit. The collection unit collects data generated when the AI ​​engages in work. The collection unit can collect, for example, 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. The diagnosis unit diagnoses the performance of the AI ​​based on the data collected by the collection unit. The diagnosis unit can diagnose, for example, whether the AI ​​responds biasedly in customer service work. The diagnosis unit can also diagnose whether the AI ​​produces biased inspection results in product inspection work. Furthermore, the diagnosis unit can diagnose whether the AI ​​has established a biased marketing strategy in marketing work. The correction unit inputs teacher data to be corrected based on the results diagnosed by the diagnosis unit. For example, if the AI ​​responds biasedly in customer service work, the correction unit can input teacher data to correct the response. In addition, the correction unit can input teacher data to correct the inspection results when the AI ​​produces biased inspection results in product inspection work. Furthermore, in the case where the AI ​​has formulated a biased marketing strategy in marketing work, the correction unit can input teacher data to correct the strategy. The fork unit reconstructs the algorithm of the AI ​​corrected by the correction unit. For example, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in customer service work. In addition, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in product inspection work. Furthermore, the fork unit can reconstruct the algorithm when the AI ​​shows a significant bias in marketing work. The management unit manages the diagnosis results and the correction data. For example, the management unit can store the diagnosis results and the correction data on the cloud and make them accessible as needed.The management unit can also encrypt and store diagnostic results and correction data to ensure security. Furthermore, the management unit can set the authority to access 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 unit collects data generated when the AI ​​is engaged in work. Specifically, it can collect customer inquiry data generated when the AI ​​is engaged in customer support work. For example, the data includes text data and voice data when a customer makes an inquiry through the AI ​​chatbot, as well as metadata such as the response content and response time. The collection unit can also collect inspection data generated when the AI ​​is engaged in product inspection work. In product inspection work, when the AI ​​uses image recognition technology to perform an appearance inspection of a product, image data of the inspection results and log data during the inspection process are collected. In addition, the collection unit can collect marketing data generated when the AI ​​is engaged in marketing work. Marketing data includes customer purchase history and website browsing history analyzed by the AI, as well as customer response data on social media. These data are collected in real time and sent to a central database. The collection unit can centrally manage these data and link them with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the diagnosis department and correction department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0070] The diagnostic department diagnoses the performance of the AI ​​based on the data collected by the collection department. Specifically, it can diagnose whether the AI ​​is responding biasedly in customer service operations. For example, if the AI ​​consistently responds differently to a specific customer group, it identifies the cause and evaluates the fairness of the response. The diagnostic department can also diagnose whether the AI ​​is producing biased inspection results in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, it evaluates the appropriateness of the standards. Furthermore, the diagnostic department can diagnose whether the AI ​​is formulating a biased marketing strategy in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, it evaluates the effectiveness of the strategy. To perform these diagnoses, the diagnostic department analyzes the output results of the AI ​​in detail and detects the presence or absence of bias using statistical methods and machine learning algorithms. Furthermore, the diagnostic department can evaluate the performance of the AI ​​by comparing it with past data and benchmark data and identify room for improvement. This allows the diagnostic department to quickly and accurately evaluate the performance of the AI ​​and clarify the necessary improvements.

[0071] The correction unit inputs training data to be corrected based on the results of the diagnosis by the diagnosis unit. Specifically, if the AI ​​responds in a biased manner in customer service operations, training data can be input to correct the response. For example, if the AI ​​consistently responds differently to a specific customer group, a balanced data set is prepared and the AI ​​is made to relearn in order to correct the response. In addition, the correction unit can also input training data to correct the inspection results when the AI ​​produces biased inspection results in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, a data set with appropriate standards is prepared and the AI ​​is made to relearn in order to correct the standards. Furthermore, the correction unit can also input training data to correct a biased marketing strategy when the AI ​​formulates a marketing strategy in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, a balanced data set is prepared and the AI ​​is made to relearn in order to correct the strategy. In order to perform these correction operations efficiently, the correction unit selects an appropriate data set and executes an appropriate learning process for the AI. This allows the corrections department to optimize the AI's performance and provide unbiased and fair results.

[0072] The fork section reconstructs the AI ​​algorithm corrected by the correction section. Specifically, if the AI ​​shows significant bias in customer service operations, the algorithm can be reconstructed. For example, if the AI ​​consistently responds differently to a specific customer group, the algorithm is reviewed and redesigned to respond more fairly. The fork section can also reconstruct the algorithm if the AI ​​shows significant bias in product inspection operations. In product inspection operations, if the AI ​​applies excessively strict standards to a specific product category, the algorithm is reviewed and redesigned to have appropriate standards. Furthermore, the fork section can also reconstruct the algorithm if the AI ​​shows significant bias in marketing operations. In marketing operations, if the AI ​​allocates excessive resources to a specific customer group, the algorithm is reviewed and redesigned to create a more balanced strategy. In order to perform these reconstruction operations efficiently, the fork section uses an appropriate algorithm design method to optimize the performance of the AI. As a result, the fork section can reconstruct the AI ​​algorithm and provide unbiased and fair results.

[0073] The management unit manages the diagnostic results and the correction data. Specifically, the diagnostic results and the correction data can be stored on the cloud so that they can be accessed as needed. For example, the diagnostic results and the correction data can be stored in cloud storage so that the diagnostic unit and the correction unit can access them at any time. The management unit can also encrypt and store the diagnostic results and the correction data to ensure security. This can prevent unauthorized access and leakage of data. Furthermore, the management unit can set the authority to access the diagnostic results and the correction data and perform access control. For example, access rights can be granted only to specific users or departments to ensure the safety of the data. The management unit introduces appropriate data management tools and security measures to efficiently perform these management tasks. This allows the management unit to safely and efficiently manage the diagnostic results and the correction data, thereby improving the reliability and safety of the entire system.

[0074] The collection unit can collect data generated when the AI ​​engages in business. The collection unit can collect, for example, 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 also collect marketing data generated when the AI ​​engages in marketing work. In this way, by collecting data generated when the AI ​​engages in business, the performance of the AI ​​can be accurately diagnosed. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input data generated when the AI ​​engages in business into the AI ​​and cause the AI ​​to collect the data.

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

[0076] The correction unit can input teacher data generated based on past data. For example, when the AI ​​responds in a biased manner in customer service operations, the correction unit can input teacher data to correct the response. In addition, when the AI ​​produces biased inspection results in product inspection operations, the correction unit can also input teacher data to correct the inspection results. Furthermore, when the AI ​​formulates a biased marketing strategy in marketing operations, the correction unit can also input teacher data to correct the strategy. In this way, the bias of the AI ​​can be corrected by inputting teacher data generated based on past data. Part or all of the above-mentioned 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 cause the AI ​​to generate and input teacher data.

[0077] The fork unit can perform a hard fork when the AI ​​shows a large bias. For example, when the AI ​​shows a significant bias in customer service work, the fork unit can perform a hard fork to reconstruct the algorithm. In addition, when the AI ​​shows a significant bias in product inspection work, the fork unit can also perform a hard fork to reconstruct the algorithm. In addition, when the AI ​​shows a significant bias in marketing work, the fork unit can also perform a hard fork to reconstruct the algorithm. In this way, by performing a hard fork when the AI ​​shows a significant bias, the performance of the AI ​​can be optimized. Part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the algorithm of the AI ​​to the AI ​​and leave the execution of the hard fork to the AI.

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

[0079] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also increase the frequency of data collection and collect detailed data. Furthermore, when the user is in a hurry, the collection unit can shorten the timing of data collection and quickly collect data. This can reduce the burden on the user by adjusting the timing of data collection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to adjust the timing of data collection.

[0080] The collection unit can analyze the past performance data of the AI ​​and select an efficient data collection method. The collection unit can, for example, concentrate data collection in 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 an optimal data collection method based on the past performance data. A part 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 past performance data into the AI ​​and cause the AI ​​to select a data collection method.

[0081] The collection unit can perform filtering based on the current business status and field of interest of the AI ​​when collecting data. The collection unit can, for example, collect only data related to the business that the AI ​​is currently working on. The collection unit can also preferentially collect highly relevant data based on the field of interest of the AI. Furthermore, the collection unit can filter unnecessary data according to the business status of the AI ​​and perform efficient data collection. As a result, efficient data collection is possible by filtering data based on the business status and field of interest of the AI. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input data on the business status and field of interest of the AI ​​to the AI ​​and have the AI ​​perform data filtering.

[0082] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can postpone the collection of data with low importance. In addition, when the user is relaxed, the collection unit can also prioritize the collection of detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize the collection of data with high importance. In this way, important data can be collected preferentially by determining the priority of data based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of the data.

[0083] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the AI ​​when collecting data. For example, when the AI ​​is performing work in a specific area, the collection unit can preferentially collect data related to that area. The collection unit can also filter and collect highly relevant data based on the geographical location information of the AI. Furthermore, when the AI ​​is moving, the collection unit can also collect data related to the current location in real time. As a result, highly relevant data can be efficiently collected by collecting data in consideration of the geographical location information of the AI. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the collection unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to collect data.

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

[0085] The diagnostic unit can estimate the user's emotion and adjust the way of expressing the diagnosis based on the estimated user's emotion. For example, when the user is nervous, the diagnostic unit can provide a simple and highly visible diagnostic result. In addition, when the user is relaxed, the diagnostic unit can also provide a detailed diagnostic result. Furthermore, when the user is in a hurry, the diagnostic unit can also provide a concise diagnostic result that covers the main points. In this way, by adjusting the way of expressing the diagnosis based on the user's emotion, it is possible to provide a diagnostic result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the AI ​​and cause the AI ​​to adjust the way of expressing the diagnosis.

[0086] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the AI ​​during diagnosis. The diagnostic unit can perform a detailed diagnosis for an AI with a high level of importance, for example. The diagnostic unit can also perform a concise diagnosis for an AI with a low level of importance. Furthermore, the diagnostic unit can adjust the frequency and level of detail of the diagnosis according to the importance of the AI. This allows for efficient diagnosis by adjusting the level of detail of the diagnosis based on the importance of the AI. A part or all of the above-mentioned 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 importance data of the AI ​​to 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 category of the AI ​​during diagnosis. For example, the diagnostic unit can apply a diagnostic algorithm that emphasizes customer satisfaction to a customer response 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 allows for appropriate diagnosis by applying a diagnostic algorithm depending on the category of the AI. Some or all of the above-mentioned 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 AI category data to the AI ​​and cause the AI ​​to apply the diagnostic algorithm.

[0088] The diagnosis unit can estimate the user's emotion and adjust the length of the diagnosis based on the estimated user's emotion. For example, when the user is in a hurry, the diagnosis unit can perform a short diagnosis that focuses on the main points. In addition, when the user is relaxed, the diagnosis unit can perform a longer diagnosis that includes detailed explanations. Furthermore, when the user is excited, the diagnosis unit can perform a diagnosis that adds visually stimulating effects. This allows the diagnosis result appropriate for the user to be provided by adjusting the length of the diagnosis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the length of the diagnosis.

[0089] The diagnostic unit can determine the priority of diagnosis based on the operation time of the AI ​​at the time of diagnosis. For example, the diagnostic unit can give priority to diagnosis of a newly introduced AI. In addition, the diagnostic unit can also periodically diagnose an 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 time. As a result, efficient diagnosis is possible by determining the priority of diagnosis based on the operation time of the AI. A part or all of the above-mentioned 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 operation time data of the AI ​​to the AI ​​and cause the AI ​​to execute the determination of the priority of diagnosis.

[0090] The diagnostic unit can adjust the order of diagnosis based on the relevance of the AI ​​during diagnosis. For example, the diagnostic unit can give priority to diagnosis of AI related to important tasks. Also, the diagnostic unit can postpone diagnosis of AI related to tasks with low relevance. Furthermore, the diagnostic unit can adjust the order and frequency of diagnosis according to the relevance of the AI. As a result, efficient diagnosis is possible by adjusting the order of diagnosis based on the relevance of the AI. A part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the diagnostic unit can input relevance data of the AI ​​to the AI ​​and cause the AI ​​to adjust the order of diagnosis.

[0091] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, when the user is nervous, the correction unit can provide a simple and highly visible correction method. In addition, when the user is relaxed, the correction unit can also provide a detailed correction method. Furthermore, when the user is in a hurry, the correction unit can also provide a concise correction method that focuses on the main points. In this way, by adjusting the correction method based on the user's emotion, a correction method appropriate for the user can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the AI ​​and cause the AI ​​to adjust the correction method.

[0092] The correction unit can select an efficient correction method by analyzing the past learning data of the AI ​​at the time of correction. The correction unit can select an effective correction method for a specific task based on, for example, 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. A part or all of the above-mentioned 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 learning data to the AI ​​and cause the AI ​​to select the correction method.

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

[0094] The correction unit can estimate the user's emotion and determine the priority of correction based on the estimated user's emotion. For example, when the user is stressed, the correction unit can postpone correction with low importance. In addition, when the user is relaxed, the correction unit can also prioritize detailed correction. Furthermore, when the user is in a hurry, the correction unit can prioritize correction with high importance. In this way, important correction can be prioritized by determining the priority of correction based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to an AI and cause the AI ​​to execute the determination of the priority of correction.

[0095] The correction unit can select the optimal correction method in consideration of the geographical location information of the AI ​​at the time of correction. For example, when the AI ​​is working 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 geographical location information of the AI. Furthermore, when the AI ​​is moving, the correction unit can also provide a correction method related to the current location. As a result, by selecting a correction method in consideration of the geographical location information of the AI, highly relevant correction is possible. A part or all of the above-mentioned processing in the correction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the correction unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to select a correction method.

[0096] At the time of correction, the correction unit can analyze the AI's social media activity and suggest a means of correction. The correction unit can provide, for example, a correction method related to a topic in which the AI ​​showed interest on social media. The correction unit can also suggest a highly relevant correction method based on the AI's social media activity. Furthermore, the correction unit can provide a correction method related to an account that the AI ​​follows on social media. This enables highly relevant correction by analyzing the AI's social media activity and suggesting a means of correction. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the correction unit can input the AI's social media activity data into the AI ​​and cause the AI ​​to execute the proposed correction means.

[0097] The forking unit can estimate the user's emotions and adjust the forking method based on the estimated user's emotions. For example, when the user is nervous, the forking unit can provide a simple and highly visible forking method. Also, when the user is relaxed, the forking unit can provide a detailed forking method. Furthermore, when the user is in a hurry, the forking unit can provide a concise forking method that focuses on the main points. In this way, by adjusting the forking method based on the user's emotions, a forking method appropriate for the user can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the forking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the forking unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the forking method.

[0098] The fork unit can select the optimal fork method at the time of forking by analyzing the past performance data of the AI. The fork unit can select an effective fork method for a specific task based on, for example, past performance data. The fork unit can also analyze past performance data and adjust the fork method based on a specific pattern. Furthermore, the fork unit can optimize the frequency and timing of forking based on past performance data. This enables efficient forking by selecting the optimal fork method based on past performance data. A part or all of the above-mentioned processing in the fork unit may be performed using, for example, AI, or may be performed without using AI. For example, the fork unit can input past performance data into the AI ​​and have the AI ​​select the fork method.

[0099] The fork unit can customize the fork means based on the current business situation of the AI ​​at the time of forking. The fork unit can provide, for example, a fork method related to the business that the AI ​​is currently working on. The fork unit can also customize the fork means according to the business situation of the AI. Furthermore, the fork unit can also determine the priority of the fork based on the business situation of the AI. This enables efficient forking by customizing the fork means based on the business situation of the AI. A part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the business situation data of the AI ​​to the AI ​​and cause the AI ​​to execute the customization of the fork means.

[0100] The fork unit can estimate the user's emotion and determine the priority of the forks based on the estimated user's emotion. For example, when the user is stressed, the fork unit can postpone forks with low importance. In addition, when the user is relaxed, the fork unit can also prioritize detailed forks. Furthermore, when the user is in a hurry, the fork unit can also prioritize forks with high importance. In this way, by determining the priority of forks based on the user's emotion, important forks can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the fork unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the fork unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the forks.

[0101] The fork unit can select the optimal fork method in consideration of the geographical location information of the AI ​​when forking. For example, when the AI ​​is working in a specific area, the fork unit can select a fork method related to that area. The fork unit can also select a highly relevant fork method based on the geographical location information of the AI. Furthermore, when the AI ​​is moving, the fork unit can also provide a fork method related to the current location. As a result, by selecting a fork method in consideration of the geographical location information of the AI, a highly relevant fork becomes possible. A part or all of the above-mentioned processing in the fork unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the fork unit can input the geographical location information of the AI ​​to the AI ​​and cause the AI ​​to select the fork method.

[0102] At the time of forking, the forking unit can analyze the social media activity of the AI ​​and suggest a forking means. The forking unit can provide, for example, 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 social media activity of the AI. Furthermore, the forking unit can provide a forking method related to an account that the AI ​​follows on social media. This enables highly relevant forking by analyzing the social media activity of the AI ​​and suggesting a forking means. Some or all of the above-mentioned processing in the forking unit may be performed, for example, using the AI, or may be performed without using the AI. For example, the forking unit can input the social media activity data of the AI ​​to the AI ​​and cause the AI ​​to execute the suggestion of the forking means.

[0103] The management unit can estimate the user's emotion and select management data based on the estimated user's emotion. For example, when the user is nervous, the management unit can provide simple and highly visible management data. In addition, when the user is relaxed, the management unit can provide detailed management data. Furthermore, when the user is in a hurry, the management unit can provide concise management data that focuses on the main points. In this way, by selecting management data based on the user's emotion, it is possible to provide management data appropriate for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the user's emotion data to an AI and cause the AI ​​to select management data.

[0104] The management unit can optimize the management algorithm by referring to past management data during management. The management unit can select an effective management algorithm for a specific task based on, for example, past management data. The management unit can also analyze 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 past management data. As a result, efficient management is possible by optimizing the management algorithm based on past management data. Some or all of the above-mentioned 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 cause AI to optimize the management algorithm.

[0105] The management unit can estimate the user's emotion and adjust the frequency of management based on the estimated user's emotion. For example, when the user is feeling stressed, the management unit can reduce the frequency of management to reduce the burden on the user. In addition, when the user is relaxed, the management unit can increase the frequency of management and collect detailed data. Furthermore, when the user is in a hurry, the management unit can shorten the frequency of management and quickly collect data. This can reduce the burden on the user by adjusting the frequency of management based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the frequency of management.

[0106] The management unit can weight the management data based on the operation time of the AI ​​during management. For example, the management unit can preferentially weight the management data for a newly introduced AI. The management unit can also periodically weight the management data for an 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 time. As a result, efficient management is possible by weighting the management data based on the operation time of the AI. A part or all of the above-mentioned processing in the management unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the management unit can input the operation time data of the AI ​​to the AI ​​and have the AI ​​execute the 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 may further include a prediction unit. The prediction unit may predict the future performance of the AI ​​based on the data collected by the collection unit. For example, the prediction unit may predict how the AI ​​will respond in future customer service operations. The prediction unit may also predict what kind of test results the AI ​​will produce in future product inspection operations. Furthermore, the prediction unit may also predict what kind of marketing strategy the AI ​​will develop in future marketing operations. In this way, the prediction unit may 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 data with low reliability. 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 improve the accuracy of AI performance diagnosis by collecting only highly reliable data.

[0110] The diagnostic unit can have an anomaly detection function when diagnosing the performance of the AI. For example, the diagnostic unit can detect an anomaly when the AI ​​deviates from normal performance. The diagnostic unit can also issue an alert when the AI ​​shows abnormal performance in a specific task. Furthermore, the diagnostic unit can perform a detailed analysis of the AI's performance based on the results of the anomaly detection. This enables the diagnostic unit to detect anomalies in the AI ​​early and take measures promptly.

[0111] The correction unit can have a real-time feedback function when correcting the AI's performance. For example, the correction unit can provide feedback in real time when the AI ​​performs a task, and perform correction immediately. The correction unit can also receive feedback in real time when the AI ​​is working with customers, and correct the response. Furthermore, the correction unit can receive feedback in real time when the AI ​​is working in product inspection, and correct the inspection results. In this way, the correction unit can quickly correct the AI's performance by providing feedback in real time.

[0112] The fork unit can have a version management function when reconstructing the AI ​​algorithm. For example, the fork unit can manage different versions of the AI ​​algorithm and revert to a previous version as necessary. 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. As a result, the fork unit can maintain stable performance by managing the versions of the AI ​​algorithm.

[0113] The management unit can have a data visualization function when managing the diagnosis results and correction data. For example, the management unit can display the diagnosis results and correction data in graphs and charts to make them visually easy to understand. The management unit can also visualize trends and patterns of data to grasp fluctuations in AI performance. Furthermore, the management unit can provide a data filtering and sorting function to quickly obtain required 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, when the user is feeling stressed, the collection unit can simplify the data collection method to reduce the burden on the user. Also, when the user is relaxed, the collection unit can adopt a detailed data collection method to collect more information. Furthermore, when the user is in a hurry, the collection unit can select a method for quickly collecting data. 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 realizing efficient data collection.

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

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

[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, when the user is feeling stressed, the forking unit can delay the timing of forking to reduce the burden on the user. Also, when the user is relaxed, the forking unit can advance the timing of forking to perform the forking quickly. Furthermore, when 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 process flow of the second embodiment will be briefly described 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 performance of the AI ​​based on the data collected by the collection department. For example, it diagnoses whether the AI ​​is responding biasedly in customer service tasks, producing biased test results in product inspection tasks, or formulating biased marketing strategies 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 ​​responds biasedly in customer service operations, training data is input to correct that response; if it produces biased inspection results in product inspection operations, training data is input to correct those inspection results; and if it has formulated a biased marketing strategy in marketing operations, training data is input to correct that strategy. Step 4: The forking department reconstructs the AI ​​algorithm that was corrected by the correction department. For example, if the AI ​​shows significant bias in customer service tasks, product inspection tasks, or marketing tasks, the algorithm will be reconstructed. Step 5: The management department manages the diagnostic results and correction data. For example, the management department stores the diagnostic results and correction data on the cloud and makes them accessible as needed. The management department also encrypts and stores the diagnostic results and correction data to ensure security. Furthermore, the management department sets permissions to access the diagnostic results and correction data and performs access control.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice 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<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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such 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 above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements including the above-mentioned collection unit, diagnosis unit, correction unit, fork 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 data collected by the control unit 46A to the data processing device 12. 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 teacher data to be corrected based on the diagnosis result. 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 result and correction data. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0125] 3, the data processing system 210 includes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0131] Fig. 4 shows an example of 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements including the above-mentioned 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 data collected by the control unit 46A to the data processing device 12. 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 teacher data to be corrected based on the diagnosis result. 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 result and the correction data. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes 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 type 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements including the above-mentioned 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 data collected by the control unit 46A to the data processing device 12. 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 teacher data to be corrected based on the diagnosis result. 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 result and correction data. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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. In addition, 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 the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of 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 performed in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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 above-mentioned collection unit, diagnosis unit, correction unit, fork unit, and management unit 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 data collected by the control unit 46A to the data processing device 12. 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 teacher data to be corrected based on the diagnosis result. The fork unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reconstructs the corrected algorithm of the AI. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages the diagnosis result and correction data. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0174] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in 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 of emotion map 400 and usually 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 inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

[0179] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," 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 Universal Serial Bus (USB) 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] In addition, 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 upon 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] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0185] The hardware resource that executes the specific process may be one of these various processors, or may be 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 configuration using one 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 configuration using a processor that realizes the functions of the 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, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0189] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and 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 an AI based on the data collected by the collection unit; a correction unit that inputs teacher data to be corrected based on the result of diagnosis by the diagnosis unit; A fork unit that reconstructs the AI ​​algorithm corrected by the correction unit; A management unit for managing the diagnosis results or correction data. A system characterized by: (Appendix 2) The collecting unit includes: Collect data generated by AI as it works 2. The system of claim 1, (Appendix 3) The diagnosis unit includes: Have a self-diagnosis program and check your performance regularly 2. The system of claim 1, (Appendix 4) The correction unit includes: 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 results or correction data on the cloud and make them accessible when needed 2. The system of claim 1, (Appendix 7) The collecting unit includes: 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 includes: Analyze past AI performance data and select an efficient data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: As data is collected, it is filtered based on the AI’s current work status and areas of interest. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, AI takes geolocation into account to prioritize the collection of relevant data 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, AI analyzes social media activity and collects relevant data 2. The system of claim 1, (Appendix 13) The diagnosis unit includes: 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 diagnosis unit includes: During diagnosis, adjust diagnostic detail based on AI importance 2. The system of claim 1, (Appendix 15) The diagnosis unit includes: During diagnosis, different diagnostic algorithms are applied depending on the AI ​​category. 2. The system of claim 1, (Appendix 16) The diagnosis unit includes: To estimate a user's emotion and adjust the length of a diagnosis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The diagnosis unit includes: During diagnosis, prioritize diagnoses based on when the AI ​​is operational 2. The system of claim 1, (Appendix 18) The diagnosis unit includes: 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 includes: To estimate a user's emotion and adjust a correction method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The correction unit includes: When correcting teeth, the AI's past learning data is analyzed to select the most efficient correction method. 2. The system of claim 1, (Appendix 21) The correction unit includes: During correction, customize the correction measures based on the AI's current work situation 2. The system of claim 1, (Appendix 22) The correction unit includes: Estimating a user's emotion and determining a priority of correction based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The correction unit includes: 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 includes: During correction, AI analyzes social media activity to suggest correction measures 2. The system of claim 1, (Appendix 25) The fork portion is Infer user sentiment and adjust forking strategy based on the inferred 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 At the time of forking, customize the forking method based on the AI's current working situation 2. The system of claim 1, (Appendix 28) The fork portion is Infer user sentiment and prioritize forks based on the inferred 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 a method for forking. 2. The system of claim 1, (Appendix 31) The management unit A user's emotion is estimated, and management data is selected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The management unit During management, the management algorithm is optimized by referencing past management data. 2. The system of claim 1, (Appendix 33) The management unit To estimate a user's emotion and adjust a frequency of management based on the estimated user's emotion. 2. The system of claim 1, (Appendix 34) The management unit During management, the management data is weighted based on when the AI ​​was in operation. 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 teacher data to be corrected based on the result of 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. A system characterized by:

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

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

4. The correction unit includes: 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 correction data on the cloud and make it accessible when needed 2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

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

9. The collecting unit includes: When collecting data, filter it based on the AI’s current business context and areas of interest.

2. The system of claim 1 .

10. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

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