system

Abstract

The system according to the embodiment aims to detect an erroneous input by a user and provide appropriate feedback. [Solution] A system according to an embodiment includes a collection unit, a learning unit, a determination unit, and a providing unit. The collection unit collects data from a user. The learning unit learns past erroneous input patterns based on the data collected by the collection unit. The determination unit determines input data based on the erroneous input patterns learned by the learning unit. The providing unit provides feedback to the user based on the results determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been able to efficiently detect user input errors and provide appropriate feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to detect an erroneous input by a user and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a determination unit, and a providing unit. The collection unit collects data from a user. The learning unit learns past erroneous input patterns based on the data collected by the collection unit. The determination unit determines input data based on the erroneous input patterns learned by the learning unit. The providing unit provides feedback to the user based on the result determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect an erroneous input by the user and provide appropriate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The confirmation AI system according to an embodiment of the present invention is a system for preventing human input errors, such as in My Number registration. This confirmation AI system collects data entered by users, learns past input error patterns, determines whether the entered data is likely to be incorrect, and issues a notice to the user indicating that the data is incorrect. This prevents input errors and improves data accuracy. For example, personal information entered by users, such as My Number, address, and name, is collected and entered into the confirmation AI system. The confirmation AI system then learns past input error patterns. The AI ​​analyzes past input error data and learns the patterns that cause input errors. For example, it learns various input error patterns, such as input errors for numbers and characters. The confirmation AI system then judges the entered data based on the learned input error patterns. For example, if the My Number entered by the user matches a past input error pattern, the AI ​​determines that the data is likely to be incorrect. Based on the judgment result, the confirmation AI system issues a notice to the user indicating that the data is likely to be incorrect. For example, if there is a possibility that the My Number entered by the user is incorrect, the system will display a message saying, "The My Number you entered may be incorrect. Please check again." This will prevent input errors and improve data accuracy. For example, if an input error occurs when registering a My Number, the confirmation AI system will detect the input error and prompt the user to recheck, allowing accurate data to be registered. Similarly, input errors can be prevented when entering personal information such as addresses and names. This will prevent input errors and improve data accuracy.

[0029] The verification AI system according to the embodiment includes a collection unit, a learning unit, a determination unit, and a providing unit. The collection unit collects data from a user. The data from the user includes, but is not limited to, personal information such as a user's My Number, address, and name. The collection unit can collect data, for example, through a web form or an application. The collection unit can also acquire data from a database or collect data through an API. For example, the collection unit collects data entered by a user into a web form in real time. The collection unit can also periodically collect data entered by a user into an application. The collection unit can also acquire data from an external database or collect data through an API. The learning unit analyzes past input error data and learns input error patterns. The learning unit analyzes past input error data and identifies input error patterns, for example, using a machine learning algorithm. For example, the learning unit learns patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit learns erroneous input patterns in the My Number field and the address field. The determination unit determines the input data based on the erroneous input patterns learned by the learning unit. The determination unit, for example, determines whether the data entered by the user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in the My Number field. The providing unit provides feedback to the user based on the result determined by the determining unit. For example, the providing unit issues a notice to the user that "there may be an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit displays a message saying, "The My Number you entered may be incorrect. Please check again."The providing unit can also diversify the method of providing feedback to the user. For example, the providing unit can provide feedback by a method such as a text message, an alert, or a notification. This allows the confirmation AI system according to the embodiment to efficiently collect, learn, judge, and provide feedback to the user.

[0030] The learning unit can analyze past input error data and identify input error patterns. The learning unit can analyze past input error data and identify input error patterns, for example, using a machine learning algorithm. For example, the learning unit can learn patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit can learn input error patterns in the My Number field and the address field. This makes it possible to identify input error patterns by analyzing past input error data. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input past input error data into the generation AI and cause the generation AI to identify input error patterns.

[0031] The determination unit can determine input data based on an erroneous input pattern in a specific field. The determination unit, for example, determines whether data entered by a user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in a My Number field. This improves the accuracy of the determination by making a determination based on an erroneous input pattern in a specific field. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input an erroneous input pattern in a specific field to a generation AI and have the generation AI perform a determination of the input data.

[0032] The providing unit can issue a notice to the user that "there is a possibility that there is an error." For example, the providing unit issues a notice to the user that "there is a possibility that there is an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit displays a message saying, "The My Number you entered may be incorrect. Please check again." By issuing a notice to the user in this way, incorrect input can be prevented in advance. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit may cause the generation AI to issue a notice to the user.

[0033] The providing unit can diversify the methods of providing feedback to the user. The providing unit provides feedback by methods such as text messages, alerts, and notifications. For example, the providing unit provides feedback by text messages in response to data entered by the user. The providing unit can also provide feedback using alerts and notifications. This diversifies the methods of feedback, making it possible to provide appropriate feedback to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause the generation AI to execute the method of providing feedback to the user.

[0034] The collection unit can analyze the user's past data input history and select the optimal collection method. For example, the collection unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The collection unit can also select the optimal collection method for a specific time period from the user's past input history. Furthermore, the collection unit can identify areas where the user made input errors in the past and improve the input method for those areas. In this way, the optimal collection method can be selected by analyzing the past data input history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data input history into a generation AI and have the generation AI select the optimal collection method.

[0035] The collection unit can filter data based on the user's current situation and areas of interest when collecting data. For example, the collection unit collects only necessary data depending on the user's current situation. The collection unit can also prioritize collection of highly relevant data based on the user's areas of interest. Furthermore, the collection unit can filter unnecessary data based on the user's current situation and areas of interest. This makes it possible to collect only necessary data by filtering data based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is moving, the collection unit can collect optimal data based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0037] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's social media activity history and collect optimal data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0038] During learning, the learning unit can adjust the learning algorithm by referring to past erroneous input data. The learning unit, for example, adjusts the parameters of the learning algorithm based on past erroneous input data. The learning unit can also analyze past erroneous input data and strengthen learning for specific patterns. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past erroneous input data. In this way, the accuracy of the learning algorithm is improved by referring to past erroneous input data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past erroneous input data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0039] During learning, the learning unit can apply different learning methods to different types of incorrect input patterns. For example, the learning unit applies a specific learning method to incorrect input patterns of numbers. The learning unit can also apply different learning methods to incorrect input patterns of characters. Furthermore, the learning unit can also apply an optimal learning method to incorrect input patterns in a specific field. This improves the accuracy of learning by applying different learning methods to different types of incorrect input patterns. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can cause the generation AI to execute different learning methods for different types of incorrect input patterns.

[0040] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit can assign a higher weight to recently submitted data. The learning unit can also assign a lower weight to older data. Furthermore, the learning unit can dynamically adjust the weighting of the learning data based on the time of submission. This improves the accuracy of learning by weighting the learning data based on the time of data submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and cause the generation AI to weight the learning data.

[0041] During learning, the learning unit can integrate and learn erroneous input data from different data sources. For example, the learning unit integrates and learns erroneous input data collected from different systems. The learning unit can also integrate and learn erroneous input data collected from different devices. Furthermore, the learning unit can also integrate and learn erroneous input data collected from different users. In this way, integrating erroneous input data from different data sources improves the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input erroneous input data from different data sources to the generation AI and cause the generation AI to integrate and learn the data.

[0042] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of input data when making a judgment. The judgment unit, for example, analyzes the interrelationships of input data and determines the possibility of erroneous input. The judgment unit can also optimize the judgment algorithm by taking into account the interrelationships of input data. Furthermore, the judgment unit can also improve the accuracy of the judgment based on the interrelationships of input data. In this way, the accuracy of the judgment is improved by taking into account the interrelationships of input data. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the interrelationships of input data to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0043] When making a judgment, the judgment unit can make a judgment based on an erroneous input pattern in a specific field. The judgment unit makes a judgment based on an erroneous input pattern in the My Number field, for example. The judgment unit can also make a judgment based on an erroneous input pattern in the address field. Furthermore, the judgment unit can also make a judgment based on an erroneous input pattern in the name field. By making a judgment based on an erroneous input pattern in a specific field, the accuracy of the judgment is improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input an erroneous input pattern in a specific field to a generation AI and have the generation AI perform the judgment.

[0044] The determination unit can make a determination taking into account the geographical distribution of the data. For example, the determination unit makes a determination taking into account erroneous input patterns in a specific region. The determination unit can also determine the possibility of erroneous input based on the geographical distribution. Furthermore, the determination unit can also optimize the determination algorithm taking into account the geographical distribution. This improves the accuracy of the determination by taking into account the geographical distribution of the data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data to a generation AI and have the generation AI perform the determination.

[0045] The judgment unit can improve the accuracy of the judgment by referring to related literature during judgment. The judgment unit, for example, refers to related literature to judge the possibility of erroneous input. The judgment unit can also optimize the judgment algorithm based on the related literature. Furthermore, the judgment unit can also improve the accuracy of the judgment by referring to related literature. As a result, the accuracy of the judgment is improved by referring to related literature. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, or may be performed without using AI. For example, the judgment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0046] When providing feedback, the providing unit can select an appropriate feedback method by referring to the user's past operation history. For example, the providing unit can preferentially provide feedback methods that the user has used favorably in the past. The providing unit can also select an optimal feedback method from the user's past operation history. Furthermore, the providing unit can customize the content of the feedback based on the user's past operation history. In this way, the optimal feedback method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generating AI and cause the generating AI to select a feedback method.

[0047] When providing feedback, the providing unit can customize the content of the feedback based on the user's current situation. The providing unit, for example, provides appropriate feedback according to the user's current situation. The providing unit can also adjust the content of the feedback based on the user's current situation. Furthermore, the providing unit can also select a feedback method taking the user's current situation into consideration. This enables efficient feedback by customizing the content of the feedback based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's current situation to a generating AI and cause the generating AI to customize the feedback content.

[0048] When providing feedback, the providing unit can select an appropriate feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Furthermore, if the user is moving, the providing unit can provide optimal feedback based on the user's current location. Furthermore, if the user is in a specific location, the providing unit can also provide feedback related to that location. In this way, the optimal feedback method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select a feedback method.

[0049] When providing feedback, the providing unit can analyze the user's social media activity and suggest feedback content. The providing unit can provide relevant feedback based on, for example, information shared by the user on social media. The providing unit can also provide feedback related to topics of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal feedback. In this way, optimal feedback content can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback content.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The confirmation AI system can further include a speed analysis unit that analyzes the user's input speed. The speed analysis unit analyzes the speed at which the user inputs data and can determine that an input speed that is abnormally fast or slow is likely to be an input error. For example, if the user inputs data faster than normal, the speed analysis unit can determine that the data is likely to be incorrect and prompt the user to recheck. Also, if the user inputs data very slowly, the speed analysis unit can determine that the user may be having difficulty inputting data and provide input assistance. Furthermore, the speed analysis unit can learn fluctuation patterns in the user's input speed and issue a warning if there is an abnormal fluctuation. This can improve the accuracy of input error detection through input speed analysis.

[0052] The confirmation AI system can further include a voice confirmation unit that confirms the user's input by voice. The voice confirmation unit can read out the data entered by the user and prompt the user to confirm. For example, after a user enters their My Number, the voice confirmation unit reads out the number by voice and asks the user, "Is this number correct?" The voice confirmation unit can also confirm personal information entered by the user by voice, such as their address and name. Furthermore, the voice confirmation unit can record the content confirmed by the user by voice so that it can be referenced later. This makes it possible to prevent incorrect input through voice confirmation.

[0053] The confirmation AI system can also be equipped with a translation unit that translates the user's input in real time. The translation unit can translate the data entered by the user into other languages ​​and prompt the user to confirm. For example, it can translate the My Number or address entered by the user in Japanese into English and ask the user, "Is this correct?" The translation unit can also translate the data entered by the user into multiple languages ​​and confirm in different languages. Furthermore, the translation unit can record the results of the user's confirmation of the translated content so that they can be referenced later. This makes it possible to prevent input errors by confirming in different languages.

[0054] The confirmation AI system can further include a visual highlighting unit that visually highlights the user's input. The visual highlighting unit can visually highlight the data entered by the user and prompt the user to confirm it. For example, the My Number or address entered by the user can be displayed in bold or colored, and the unit can ask the user to confirm, "Is this correct?" The visual highlighting unit can also highlight important parts of the data entered by the user, highlighting parts that are likely to be incorrect. Furthermore, the visual highlighting unit can record the results of the user's confirmation of the visually highlighted content so that they can be referenced later. This can help prevent incorrect input through visual highlighting.

[0055] The confirmation AI system can also be equipped with a completion unit that automatically completes the user's input. The completion unit can automatically complete data that the user has started to enter and prompt the user to confirm. For example, if the user enters only part of their My Number, the completion unit can automatically complete the remaining number and ask, "Is this correct?" The completion unit can also automatically complete the remaining part based on part of the address or name entered by the user. Furthermore, the completion unit can record the results of the user's confirmation of the automatically completed content so that they can be referenced later. This can help prevent input errors through automatic completion.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects data from the user. The data from the user includes, but is not limited to, personal information such as My Number, address, and name. The collection unit can collect data, for example, through a web form or an application. The collection unit can also acquire data from a database or collect data through an API. For example, the collection unit collects data entered by the user into a web form in real time. The collection unit can also periodically collect data entered by the user into an application. The collection unit can also acquire data from an external database or collect data through an API. Step 2: The learning unit analyzes past input error data and learns input error patterns. The learning unit analyzes past input error data and identifies input error patterns, for example, using a machine learning algorithm. For example, the learning unit learns patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit learns input error patterns in the My Number field and input error patterns in the address field. Step 3: The determination unit determines the input data based on the erroneous input patterns learned by the learning unit. The determination unit, for example, determines whether the data entered by the user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in the My Number field. Step 4: The providing unit provides feedback to the user based on the result determined by the determining unit. The providing unit may, for example, issue a notice to the user that "there may be an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit may display a message saying, "The My Number you entered may be incorrect. Please check again." The providing unit may also diversify the methods of providing feedback to the user. For example, the providing unit may provide feedback by methods such as a text message, an alert, or a notification.

[0058] (Example 2) The confirmation AI system according to an embodiment of the present invention is a system for preventing human input errors, such as in My Number registration. This confirmation AI system collects data entered by users, learns past input error patterns, determines whether the entered data is likely to be incorrect, and issues a notice to the user indicating that the data is incorrect. This prevents input errors and improves data accuracy. For example, personal information entered by users, such as My Number, address, and name, is collected and entered into the confirmation AI system. The confirmation AI system then learns past input error patterns. The AI ​​analyzes past input error data and learns the patterns that cause input errors. For example, it learns various input error patterns, such as input errors for numbers and characters. The confirmation AI system then judges the entered data based on the learned input error patterns. For example, if the My Number entered by the user matches a past input error pattern, the AI ​​determines that the data is likely to be incorrect. Based on the judgment result, the confirmation AI system issues a notice to the user indicating that the data is likely to be incorrect. For example, if there is a possibility that the My Number entered by the user is incorrect, the system will display a message saying, "The My Number you entered may be incorrect. Please check again." This will prevent input errors and improve data accuracy. For example, if an input error occurs when registering a My Number, the confirmation AI system will detect the input error and prompt the user to recheck, allowing accurate data to be registered. Similarly, input errors can be prevented when entering personal information such as addresses and names. This will prevent input errors and improve data accuracy.

[0059] The verification AI system according to the embodiment includes a collection unit, a learning unit, a determination unit, and a providing unit. The collection unit collects data from a user. The data from the user includes, but is not limited to, personal information such as a user's My Number, address, and name. The collection unit can collect data, for example, through a web form or an application. The collection unit can also acquire data from a database or collect data through an API. For example, the collection unit collects data entered by a user into a web form in real time. The collection unit can also periodically collect data entered by a user into an application. The collection unit can also acquire data from an external database or collect data through an API. The learning unit analyzes past input error data and learns input error patterns. The learning unit analyzes past input error data and identifies input error patterns, for example, using a machine learning algorithm. For example, the learning unit learns patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit learns erroneous input patterns in the My Number field and the address field. The determination unit determines the input data based on the erroneous input patterns learned by the learning unit. The determination unit, for example, determines whether the data entered by the user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in the My Number field. The providing unit provides feedback to the user based on the result determined by the determining unit. For example, the providing unit issues a notice to the user that "there may be an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit displays a message saying, "The My Number you entered may be incorrect. Please check again."The providing unit can also diversify the method of providing feedback to the user. For example, the providing unit can provide feedback by a method such as a text message, an alert, or a notification. This allows the confirmation AI system according to the embodiment to efficiently collect, learn, judge, and provide feedback to the user.

[0060] The learning unit can analyze past input error data and identify input error patterns. The learning unit can analyze past input error data and identify input error patterns, for example, using a machine learning algorithm. For example, the learning unit can learn patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit can learn input error patterns in the My Number field and the address field. This makes it possible to identify input error patterns by analyzing past input error data. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input past input error data into the generation AI and cause the generation AI to identify input error patterns.

[0061] The determination unit can determine input data based on an erroneous input pattern in a specific field. The determination unit, for example, determines whether data entered by a user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in a My Number field. This improves the accuracy of the determination by making a determination based on an erroneous input pattern in a specific field. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input an erroneous input pattern in a specific field to a generation AI and have the generation AI perform a determination of the input data.

[0062] The providing unit can issue a notice to the user that "there is a possibility that there is an error." For example, the providing unit issues a notice to the user that "there is a possibility that there is an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit displays a message saying, "The My Number you entered may be incorrect. Please check again." By issuing a notice to the user in this way, incorrect input can be prevented in advance. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit may cause the generation AI to issue a notice to the user.

[0063] The providing unit can diversify the methods of providing feedback to the user. The providing unit provides feedback by methods such as text messages, alerts, and notifications. For example, the providing unit provides feedback by text messages in response to data entered by the user. The providing unit can also provide feedback using alerts and notifications. This diversifies the methods of feedback, making it possible to provide appropriate feedback to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause the generation AI to execute the method of providing feedback to the user.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to allow the user to enter data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can collect data immediately and efficiently proceed with processing. Furthermore, if the user is in a hurry, the collection unit can also advance the collection timing to quickly collect data. This enables efficient data collection by adjusting the data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-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 into the generation AI and have the generation AI adjust the timing of data collection.

[0065] The collection unit can analyze the user's past data input history and select the optimal collection method. For example, the collection unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The collection unit can also select the optimal collection method for a specific time period from the user's past input history. Furthermore, the collection unit can identify areas where the user made input errors in the past and improve the input method for those areas. In this way, the optimal collection method can be selected by analyzing the past data input history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data input history into a generation AI and have the generation AI select the optimal collection method.

[0066] The collection unit can filter data based on the user's current situation and areas of interest when collecting data. For example, the collection unit collects only necessary data depending on the user's current situation. The collection unit can also prioritize collection of highly relevant data based on the user's areas of interest. Furthermore, the collection unit can filter unnecessary data based on the user's current situation and areas of interest. This makes it possible to collect only necessary data by filtering data based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can postpone collecting less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting more important data. Furthermore, if the user is in a hurry, the collection unit can prioritize data that can be collected quickly. This enables efficient data collection by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is moving, the collection unit can collect optimal data based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0069] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's social media activity history and collect optimal data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0070] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is feeling stressed, the learning unit can select simplified training data. Furthermore, if the user is in a hurry, the learning unit can select data that can be quickly learned. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0071] During learning, the learning unit can adjust the learning algorithm by referring to past erroneous input data. The learning unit, for example, adjusts the parameters of the learning algorithm based on past erroneous input data. The learning unit can also analyze past erroneous input data and strengthen learning for specific patterns. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to past erroneous input data. In this way, the accuracy of the learning algorithm is improved by referring to past erroneous input data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past erroneous input data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0072] During learning, the learning unit can apply different learning methods to different types of incorrect input patterns. For example, the learning unit applies a specific learning method to incorrect input patterns of numbers. The learning unit can also apply different learning methods to incorrect input patterns of characters. Furthermore, the learning unit can also apply an optimal learning method to incorrect input patterns in a specific field. This improves the accuracy of learning by applying different learning methods to different types of incorrect input patterns. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can cause the generation AI to execute different learning methods for different types of incorrect input patterns.

[0073] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. Furthermore, the learning unit can decrease the frequency of learning when the user is stressed. Furthermore, the learning unit can adjust the frequency of learning when the user is in a hurry to efficiently progress with learning. This enables efficient learning by adjusting the frequency of learning according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of learning.

[0074] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit can assign a higher weight to recently submitted data. The learning unit can also assign a lower weight to older data. Furthermore, the learning unit can dynamically adjust the weighting of the learning data based on the time of submission. This improves the accuracy of learning by weighting the learning data based on the time of data submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and cause the generation AI to weight the learning data.

[0075] During learning, the learning unit can integrate and learn erroneous input data from different data sources. For example, the learning unit integrates and learns erroneous input data collected from different systems. The learning unit can also integrate and learn erroneous input data collected from different devices. Furthermore, the learning unit can also integrate and learn erroneous input data collected from different users. In this way, integrating erroneous input data from different data sources improves the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input erroneous input data from different data sources to the generation AI and cause the generation AI to integrate and learn the data.

[0076] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, the determination unit can apply strict criteria when the user is relaxed. Furthermore, the determination unit can apply flexible criteria when the user is stressed. Furthermore, the determination unit can also apply criteria that allow for quick determination when the user is in a hurry. This allows for efficient determination by adjusting the criteria for determination according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI adjust the criteria for determination.

[0077] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of input data when making a judgment. The judgment unit, for example, analyzes the interrelationships of input data and determines the possibility of erroneous input. The judgment unit can also optimize the judgment algorithm by taking into account the interrelationships of input data. Furthermore, the judgment unit can also improve the accuracy of the judgment based on the interrelationships of input data. In this way, the accuracy of the judgment is improved by taking into account the interrelationships of input data. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the interrelationships of input data to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0078] When making a judgment, the judgment unit can make a judgment based on an erroneous input pattern in a specific field. The judgment unit makes a judgment based on an erroneous input pattern in the My Number field, for example. The judgment unit can also make a judgment based on an erroneous input pattern in the address field. Furthermore, the judgment unit can also make a judgment based on an erroneous input pattern in the name field. By making a judgment based on an erroneous input pattern in a specific field, the accuracy of the judgment is improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input an erroneous input pattern in a specific field to a generation AI and have the generation AI perform the judgment.

[0079] The determination unit can estimate the user's emotions and adjust the display method of the determination result based on the estimated user's emotions. For example, if the user is nervous, the determination unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the determination unit can provide a display method that focuses on the main points. This enables efficient feedback by adjusting the display method of the determination result according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the determination result.

[0080] The determination unit can make a determination taking into account the geographical distribution of the data. For example, the determination unit makes a determination taking into account erroneous input patterns in a specific region. The determination unit can also determine the possibility of erroneous input based on the geographical distribution. Furthermore, the determination unit can also optimize the determination algorithm taking into account the geographical distribution. This improves the accuracy of the determination by taking into account the geographical distribution of the data. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data to a generation AI and have the generation AI perform the determination.

[0081] The judgment unit can improve the accuracy of the judgment by referring to related literature during judgment. The judgment unit, for example, refers to related literature to judge the possibility of erroneous input. The judgment unit can also optimize the judgment algorithm based on the related literature. Furthermore, the judgment unit can also improve the accuracy of the judgment by referring to related literature. As a result, the accuracy of the judgment is improved by referring to related literature. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, or may be performed without using AI. For example, the judgment unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0082] The providing unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide feedback in a calm voice. Furthermore, if the user is relaxed, the providing unit can provide feedback in a cheerful voice. Furthermore, if the user is in a hurry, the providing unit can provide quick and concise feedback. This enables efficient feedback by adjusting the feedback method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback method.

[0083] When providing feedback, the providing unit can select an appropriate feedback method by referring to the user's past operation history. For example, the providing unit can preferentially provide feedback methods that the user has used favorably in the past. The providing unit can also select an optimal feedback method from the user's past operation history. Furthermore, the providing unit can customize the content of the feedback based on the user's past operation history. In this way, the optimal feedback method can be selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generating AI and cause the generating AI to select a feedback method.

[0084] When providing feedback, the providing unit can customize the content of the feedback based on the user's current situation. The providing unit, for example, provides appropriate feedback according to the user's current situation. The providing unit can also adjust the content of the feedback based on the user's current situation. Furthermore, the providing unit can also select a feedback method taking the user's current situation into consideration. This enables efficient feedback by customizing the content of the feedback based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's current situation to a generating AI and cause the generating AI to customize the feedback content.

[0085] The providing unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can postpone providing less important feedback. Furthermore, if the user is relaxed, the providing unit can prioritize providing more important feedback. Furthermore, if the user is in a hurry, the providing unit can prioritize feedback that can be provided quickly. This enables efficient feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback.

[0086] When providing feedback, the providing unit can select an appropriate feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Furthermore, if the user is moving, the providing unit can provide optimal feedback based on the user's current location. Furthermore, if the user is in a specific location, the providing unit can also provide feedback related to that location. In this way, the optimal feedback method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select a feedback method.

[0087] When providing feedback, the providing unit can analyze the user's social media activity and suggest feedback content. The providing unit can provide relevant feedback based on, for example, information shared by the user on social media. The providing unit can also provide feedback related to topics of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal feedback. In this way, optimal feedback content can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback content. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, learning unit, determination unit, and provision unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data from the user through the reception device 38 of the smart device 14. The learning unit, for example, analyzes past erroneous input data using the identification processing unit 290 of the data processing device 12 and learns erroneous input patterns. For example, the determination unit determines input data based on the erroneous input patterns learned by the identification processing unit 290 of the data processing device 12. The provision unit provides feedback to the user through the output device 40 of the smart device 14, for example. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, learning unit, determination unit, and providing unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data from the user through the microphone 238 of the smart glasses 214. The learning unit, for example, analyzes past erroneous input data using the identification processing unit 290 of the data processing device 12 and learns erroneous input patterns. The determination unit, for example, determines input data based on the erroneous input patterns learned by the identification processing unit 290 of the data processing device 12. The providing unit provides feedback to the user through the speaker 240 of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, learning unit, determination unit, and providing unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect data from the user through the microphone 238 of the headset type terminal 314. The learning unit, for example, analyzes past erroneous input data using the identification processing unit 290 of the data processing device 12 and learns erroneous input patterns. For example, the determination unit determines input data based on the erroneous input patterns learned by the identification processing unit 290 of the data processing device 12. The providing unit provides feedback to the user through the speaker 240 of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, learning unit, determination unit, and provision unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data from the user through the microphone 238 of the robot 414. The learning unit, for example, analyzes past erroneous input data using the identification processing unit 290 of the data processing device 12 and learns erroneous input patterns. The determination unit, for example, determines input data based on the erroneous input patterns learned by the identification processing unit 290 of the data processing device 12. The provision unit provides feedback to the user through the speaker 240 of the robot 414, for example.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The confirmation AI system can further include a speed analysis unit that analyzes the user's input speed. The speed analysis unit analyzes the speed at which the user inputs data and can determine that an input speed that is abnormally fast or slow is likely to be an input error. For example, if the user inputs data faster than normal, the speed analysis unit can determine that the data is likely to be incorrect and prompt the user to recheck. Also, if the user inputs data very slowly, the speed analysis unit can determine that the user may be having difficulty inputting data and provide input assistance. Furthermore, the speed analysis unit can learn fluctuation patterns in the user's input speed and issue a warning if there is an abnormal fluctuation. This can improve the accuracy of input error detection through input speed analysis.

[0090] The confirmation AI system can further include a voice confirmation unit that confirms the user's input by voice. The voice confirmation unit can read out the data entered by the user and prompt the user to confirm. For example, after a user enters their My Number, the voice confirmation unit reads out the number by voice and asks the user, "Is this number correct?" The voice confirmation unit can also confirm personal information entered by the user by voice, such as their address and name. Furthermore, the voice confirmation unit can record the content confirmed by the user by voice so that it can be referenced later. This makes it possible to prevent incorrect input through voice confirmation.

[0091] The confirmation AI system can also be equipped with a translation unit that translates the user's input in real time. The translation unit can translate the data entered by the user into other languages ​​and prompt the user to confirm. For example, it can translate the My Number or address entered by the user in Japanese into English and ask the user, "Is this correct?" The translation unit can also translate the data entered by the user into multiple languages ​​and confirm in different languages. Furthermore, the translation unit can record the results of the user's confirmation of the translated content so that they can be referenced later. This makes it possible to prevent input errors by confirming in different languages.

[0092] The confirmation AI system can further include a visual highlighting unit that visually highlights the user's input. The visual highlighting unit can visually highlight the data entered by the user and prompt the user to confirm it. For example, the My Number or address entered by the user can be displayed in bold or colored, and the unit can ask the user to confirm, "Is this correct?" The visual highlighting unit can also highlight important parts of the data entered by the user, highlighting parts that are likely to be incorrect. Furthermore, the visual highlighting unit can record the results of the user's confirmation of the visually highlighted content so that they can be referenced later. This can help prevent incorrect input through visual highlighting.

[0093] The confirmation AI system can also be equipped with a completion unit that automatically completes the user's input. The completion unit can automatically complete data that the user has started to enter and prompt the user to confirm. For example, if the user enters only part of their My Number, the completion unit can automatically complete the remaining number and ask, "Is this correct?" The completion unit can also automatically complete the remaining part based on part of the address or name entered by the user. Furthermore, the completion unit can record the results of the user's confirmation of the automatically completed content so that they can be referenced later. This can help prevent input errors through automatic completion.

[0094] The confirmation AI system may further include a support unit that estimates the user's emotions and provides input support based on the estimated user emotions. The support unit may provide support to simplify input when the user is feeling stressed. For example, when the user is feeling stressed, the support unit may reduce the number of input fields and prompt the user to enter only the minimum amount of information necessary. When the user is relaxed, the support unit may prompt the user to enter more detailed information, thereby increasing the accuracy of the data. Furthermore, when the user is in a hurry, the support unit may provide shortcuts to speed up input. This enables efficient data input through input support that corresponds to the user's emotions.

[0095] The confirmation AI system can further include a confirmation adjustment unit that estimates the user's emotions and adjusts the confirmation method for the input content based on the estimated user emotions. If the user is nervous, the confirmation adjustment unit can provide a simple, highly visible confirmation method. For example, if the user is nervous, the confirmation adjustment unit can display the input content in a large font to prompt confirmation. If the user is relaxed, the confirmation adjustment unit can provide a confirmation method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation adjustment unit can provide a concise confirmation method that focuses on the main points. This enables efficient data confirmation through a confirmation method that suits the user's emotions.

[0096] The confirmation AI system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated user emotions. If the user is feeling stressed, the feedback adjustment unit may provide gentle feedback. For example, if the user is feeling stressed, the feedback adjustment unit may display a message such as "Please take a short break and check again." If the user is relaxed, detailed feedback may be provided, improving the accuracy of the data. Furthermore, if the user is in a hurry, quick and concise feedback may be provided. This enables efficient data confirmation through feedback tailored to the user's emotions.

[0097] The confirmation AI system may further include an order adjustment unit that estimates the user's emotions and adjusts the data input order based on the estimated user emotions. If the user is feeling stressed, the order adjustment unit can postpone less important data. For example, if the user is feeling stressed, the order adjustment unit can have the user input data in order from the easiest input fields, gradually increasing the difficulty level. Also, if the user is relaxed, the order adjustment unit can prioritize input of more important data. Furthermore, if the user is in a hurry, the order adjustment unit can prioritize data that can be input quickly. This enables efficient data input through an input order that corresponds to the user's emotions.

[0098] The confirmation AI system may further include a method adjustment unit that estimates the user's emotions and adjusts the data input method based on the estimated user emotions. If the user is feeling stressed, the method adjustment unit may provide voice input or a simplified input method. For example, if the user is feeling stressed, the method adjustment unit may recommend voice input, and if the user is relaxed, the method adjustment unit may encourage detailed text input. If the user is in a hurry, the method adjustment unit may also provide shortcuts or templates to assist with quick input. This enables efficient data input through an input method that suits the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects data from the user. The data from the user includes, but is not limited to, personal information such as My Number, address, and name. The collection unit can collect data, for example, through a web form or an application. The collection unit can also acquire data from a database or collect data through an API. For example, the collection unit collects data entered by the user into a web form in real time. The collection unit can also periodically collect data entered by the user into an application. The collection unit can also acquire data from an external database or collect data through an API. Step 2: The learning unit analyzes past input error data and learns input error patterns. The learning unit analyzes past input error data and identifies input error patterns, for example, using a machine learning algorithm. For example, the learning unit learns patterns such as input errors for numbers and characters based on past input error data. The learning unit can also learn input error patterns in specific fields. For example, the learning unit learns input error patterns in the My Number field and input error patterns in the address field. Step 3: The determination unit determines the input data based on the erroneous input patterns learned by the learning unit. The determination unit, for example, determines whether the data entered by the user matches a past erroneous input pattern. For example, if the My Number entered by the user matches a past erroneous input pattern, the determination unit determines that the data is likely to be incorrect. The determination unit can also make a determination based on an erroneous input pattern in a specific field. For example, the determination unit makes a determination based on an erroneous input pattern in the My Number field. Step 4: The providing unit provides feedback to the user based on the result determined by the determining unit. The providing unit may, for example, issue a notice to the user that "there may be an error." For example, if the My Number entered by the user is likely to be incorrect, the providing unit may display a message saying, "The My Number you entered may be incorrect. Please check again." The providing unit may also diversify the methods of providing feedback to the user. For example, the providing unit may provide feedback by methods such as a text message, an alert, or a notification.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] 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.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 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 from users; a learning unit that learns past erroneous input patterns based on the data collected by the collecting unit; a determination unit that determines input data based on the erroneous input patterns learned by the learning unit; a providing unit that provides feedback to a user based on the result determined by the determining unit; Equipped with A system characterized by:

2. The learning unit Analyze past input errors and identify input error patterns 2. The system of claim 1.

3. The determination unit Identify input data based on incorrect input patterns in specific fields 2. The system of claim 1.

4. The providing unit Diversify the ways you provide feedback to users 2. The system of claim 1.

5. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions 2. The system of claim 1.

6. The collecting unit Analyze the user's past data entry history and select the appropriate collection method 2. The system of claim 1.

7. The collecting unit As data is collected, it is filtered based on the user's current situation and interests.

2. The system of claim 1.

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