system

Abstract

The system according to the embodiment aims to make accurate judgments by dealing with variations in notation on personal identification documents. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The determination unit makes a determination based on the analysis result obtained by the analysis unit. The provision unit provides the result obtained 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] With conventional technology, variations in the notation on personal identification documents can cause the system to mistakenly identify them as inconsistent, resulting in registration errors.

[0005] The system according to the embodiment aims to make accurate judgments by dealing with variations in notation on personal identification documents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The determination unit makes a determination based on the analysis result obtained by the analysis unit. The provision unit provides the result obtained by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can handle variations in notation on personal identification documents and make accurate judgments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The screening system of an embodiment of the present invention addresses the different spelling variations for each type of identification document, improving the CCOC's screening system, which considers anything other than an exact match to be a "difference." This screening system automatically determines document writing patterns and address registration patterns by storing and analyzing them in AI data. For example, it corrects registration errors that are considered to be inconsistent between "1-chome 1-banchi 1-go" and "1-1-1," thereby reducing the number of screening transitions required by humans. This system makes the accumulated database more useful, reduces manual screening workload, and reduces in-store response time associated with erroneous returns. For example, AI analyzes the different spelling variations for each type of identification document. For example, AI learns the spelling variations for addresses and names on documents such as driver's licenses and passports and stores them in a database. This allows the system to automatically recognize different spellings and register them as accurate information. AI then analyzes the accumulated data, and the system automatically determines the difference. For example, it can recognize different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address, preventing registration errors. This reduces the number of manual screening steps and significantly reduces screening man-hours. It also reduces in-store response man-hours due to erroneous returns. For example, if the system automatically recognizes spelling variations and registers them as accurate information, erroneous returns will decrease and in-store responses will become smoother. This improves customer satisfaction and operational efficiency. In this way, a system that uses AI to handle spelling variations in identification documents will make the accumulated database more useful, reduce the number of manual screening man-hours, and reduce in-store response man-hours due to erroneous returns. This allows the screening system to handle spelling variations on identification documents and register accurate information.

[0029] The screening system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data manually. For example, the collection unit collects data input by a user. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and machine learning. The analysis unit can also analyze data trends using, for example, statistical analysis. The analysis unit can also analyze data patterns using machine learning. For example, the analysis unit can cluster the data to identify data groups. The determination unit makes a determination based on the analysis results obtained by the analysis unit. Examples of the determination include, but are not limited to, a method of determining by setting a threshold and a rule-based determination. For example, the determination unit can set a threshold to detect anomalies in the data. The determination unit can also make a rule-based determination to evaluate the suitability of the data. For example, the judgment unit checks the consistency of the data and detects errors. The provision unit provides the results obtained by the judgment unit. Providing the results includes, but is not limited to, methods such as notifying the user and storing the results in a database. For example, the provision unit notifies the user by email. The provision unit can also store the results in a database. For example, the provision unit displays the results in a web application. This allows the screening system according to the embodiment to efficiently collect, analyze, judge, and provide data. Some or all of the above-described processes in the collection unit, analysis unit, judgment unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit may input data acquired by a sensor to the generation AI and cause the generation AI to collect the data. The analysis unit may input the collected data to the generation AI and cause the generation AI to analyze the data. The judgment unit may input the analysis results to the generation AI and cause the generation AI to make a judgment. The provision unit may input the judgment results to the generation AI and cause the generation AI to provide the results.

[0030] The screening system includes a collection unit that collects different spelling variations for each type of identification document. The collection unit collects different spelling variations for each type of identification document, such as a driver's license, passport, and My Number card. For example, the collection unit collects spelling variations for addresses written on driver's licenses. The collection unit can also collect spelling variations for names written on passports. For example, the collection unit collects spelling variations such as the difference between kanji and hiragana, and the difference between full-width and half-width characters. This allows the collection unit to efficiently collect spelling variations for identification documents. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data of the identification document to a generation AI and have the generation AI collect spelling variations.

[0031] The analysis unit can analyze the collected data and recognize different spellings as the same information. For example, the analysis unit can analyze the collected data and recognize different spellings as the same information. For example, the analysis unit can analyze the difference between kanji and hiragana in a name and recognize them as the same name. The analysis unit can also analyze the difference between an abbreviation and a formal name in an address and recognize them as the same address. For example, the analysis unit can recognize different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address. This allows the analysis unit to recognize different spellings as the same information, thereby preventing registration errors. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI recognize different spellings.

[0032] The judgment unit can make a judgment to prevent registration errors based on the analysis results. The judgment unit makes a judgment to prevent registration errors based on, for example, the analysis results. For example, the judgment unit checks the consistency of the data and detects errors. The judgment unit can also evaluate the suitability of the data and make a judgment to prevent errors. For example, the judgment unit detects data duplication or format inconsistencies and prevents registration errors. This enables the judgment unit to make a judgment to prevent registration errors. 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 analysis results to a generation AI and have the generation AI execute a registration error judgment.

[0033] The providing unit can provide accurate information based on the judgment result. The providing unit provides accurate information based on the judgment result, for example. For example, the providing unit notifies the user by email. The providing unit can also store the result in a database. For example, the providing unit displays the result in a web application. This allows the providing unit to provide accurate information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the judgment result to a generation AI and have the generation AI provide the information.

[0034] The analysis unit can analyze spelling variations of addresses or names written on a driver's license or passport. The analysis unit, for example, analyzes spelling variations of addresses written on a driver's license. For example, the analysis unit recognizes different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address. The analysis unit can also analyze spelling variations of names written on a passport. For example, the analysis unit analyzes the difference between kanji and hiragana and recognizes them as the same name. In this way, the analysis unit can analyze spelling variations of addresses and names and register accurate information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from a driver's license or passport into a generation AI and have the generation AI analyze spelling variations.

[0035] The collection unit can analyze the user's past submission history and select an appropriate collection method when collecting identification documents. For example, when collecting identification documents, the collection unit analyzes the user's past submission history and selects the optimal collection method. For example, the collection unit analyzes the format of documents the user has previously submitted and encourages the user to submit them in the same format. The collection unit can also analyze the time period in which the user previously submitted documents and collect them during the same time period. For example, the collection unit analyzes the submission methods (online, mail, etc.) of documents the user has previously submitted and suggests the optimal method. In this way, the collection unit can select the optimal collection method by analyzing the past submission history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past submission history data into a generation AI and have the generation AI select a collection method.

[0036] The collection unit can filter the identification documents based on the user's current living situation and areas of interest when collecting the identification documents. For example, when collecting the identification documents, the collection unit filters the identification documents based on the user's current living situation and areas of interest. For example, if the user is planning to move, the collection unit can prioritize collecting documents related to the new address. Also, if the user is interested in a particular industry, the collection unit can prioritize collecting documents related to that industry. For example, if the user is planning to attend a particular event, the collection unit can prioritize collecting documents related to that event. In this way, the collection unit can collect highly relevant documents by filtering based on the user's living situation and areas of interest. Some or all of the above-mentioned 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 living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0037] When collecting identification documents, the collection unit can prioritize collecting highly relevant documents by taking into account the user's geographical location information. For example, when collecting identification documents, the collection unit prioritizes collecting highly relevant documents by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting documents related to that area. Also, if the user is traveling, the collection unit can prioritize collecting documents related to the user's travel destination. For example, if the user is planning to move, the collection unit prioritizes collecting documents related to the user's new address. In this way, the collection unit can prioritize collecting highly relevant documents 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 into the generation AI and cause the generation AI to collect documents.

[0038] The collection unit may analyze the user's social media activity and collect related documents when collecting identification documents. For example, the collection unit may analyze the user's social media activity and collect related documents when collecting identification documents. For example, if the user indicates on social media that they will be attending a specific event, the collection unit may collect documents related to the event. Furthermore, if the user indicates interest in a specific region on social media, the collection unit may collect documents related to that region. For example, if the user indicates interest in a specific industry on social media, the collection unit may collect documents related to that industry. This allows the collection unit to efficiently collect related documents by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to collect documents.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the identification document during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the identification document during analysis. For example, the analysis unit performs a detailed analysis on documents of high importance. The analysis unit can also perform a simplified analysis on documents of low importance. For example, the analysis unit determines the priority of the analysis according to the importance. As a result, the analysis unit adjusts the level of detail of the analysis based on the importance of the identification document, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the identification document to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the identification document during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the identification document during analysis. For example, the analysis unit applies a specific algorithm to a driver's license to perform analysis. The analysis unit can also apply a different algorithm to a passport to perform analysis. For example, the analysis unit applies an algorithm depending on the category to other documents to perform analysis. In this way, the analysis unit can perform highly accurate analysis by applying an analysis algorithm depending on the category of the identification document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the identification document to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the submission date of the identification document during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of the identification document during analysis. For example, the analysis unit prioritizes analysis of recently submitted documents. The analysis unit can also postpone analysis of documents that were submitted earlier. For example, the analysis unit adjusts the analysis priority according to the submission date. This enables efficient analysis by the analysis unit determining the analysis priority based on the submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the identification document into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the identification documents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the identification documents during analysis. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. For example, the analysis unit adjusts the order of analysis according to the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the identification documents to the generation AI and have the generation AI adjust the order of analysis.

[0043] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of the analysis results when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by taking into account the interrelationships of the analysis results when making a judgment. For example, the judgment unit integrates multiple analysis results and makes a comprehensive judgment. The judgment unit can also analyze the interrelationships of the analysis results and improve the accuracy of the judgment. For example, the judgment unit detects inconsistencies in the analysis results and improves the accuracy of the judgment. In this way, the judgment unit improves the accuracy of the judgment by taking into account the interrelationships of the analysis results. 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 interrelationship data of the analysis results to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0044] The judgment unit can make a judgment taking into consideration the attribute information of the person who submitted the identification document when making a judgment. The judgment unit, for example, makes a judgment taking into consideration the attribute information of the person who submitted the identification document when making a judgment. For example, the judgment unit makes a judgment taking into consideration the age and gender of the person who submitted the identification document. The judgment unit can also make a judgment taking into consideration the occupation and place of residence of the person who submitted the document. For example, the judgment unit makes a judgment taking into consideration the submitter's past submission history. This enables the judgment unit to make a more appropriate judgment by taking into consideration the attribute information of the person who submitted the document. 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 the attribute information data of the person who submitted the document into a generation AI and have the generation AI make the judgment.

[0045] The judgment unit can make a judgment taking into account the geographical distribution of the identification documents when making a judgment. For example, the judgment unit makes a judgment taking into account the geographical distribution of the identification documents when making a judgment. For example, the judgment unit makes a judgment based on the submitter's place of residence. The judgment unit can also make a judgment taking into account the characteristics of the submitter's place of residence. For example, the judgment unit makes a judgment taking into account the laws and regulations of the submitter's place of residence. This enables the judgment unit to make a more appropriate judgment by taking into account the geographical distribution. 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 geographical distribution data of the identification documents into the generation AI and have the generation AI make the judgment.

[0046] The judgment unit can improve the accuracy of the judgment by referring to related literature of the identification document when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by referring to related literature of the identification document when making a judgment. For example, the judgment unit refers to related literature to improve the accuracy of the judgment. The judgment unit can also integrate information from related literature to make a comprehensive judgment. For example, the judgment unit detects inconsistencies in the related literature to improve the accuracy of the judgment. As a result, the judgment unit improves the accuracy of the judgment by referring to the related literature. 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 related literature data into a generation AI and have the generation AI improve the accuracy of the judgment.

[0047] The providing unit can adjust the level of detail of the information to be provided based on the importance of the analysis result when providing the information. The providing unit, for example, adjusts the level of detail of the information to be provided based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. For example, the providing unit adjusts the level of detail of the information to be provided according to the importance. This enables the providing unit to adjust the level of detail of the information based on the importance of the analysis result, thereby enabling efficient information provision. Some or all of the above-mentioned 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 importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0048] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the data. For example, the providing unit can apply different providing algorithms depending on the category of the analysis results when providing the data. For example, the providing unit can provide information by applying a specific algorithm to the analysis results related to a driver's license. The providing unit can also provide information by applying a different algorithm to the analysis results related to a passport. For example, the providing unit can also provide information by applying an algorithm depending on the category to the analysis results related to other documents. This enables the providing unit to provide highly accurate information by applying a providing algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input category data of the analysis results to the generation AI and cause the generation AI to apply the providing algorithm.

[0049] The providing unit can determine the priority of the information to be provided based on the submission time of the analysis results at the time of provision. The providing unit, for example, determines the priority of the information to be provided based on the submission time of the analysis results at the time of provision. For example, the providing unit provides the most recent analysis results preferentially. The providing unit can also postpone analysis results that were submitted earlier. For example, the providing unit adjusts the priority of the information to be provided depending on the submission time. This enables the providing unit to determine the priority of the information based on the submission time, thereby enabling efficient information provision. Some or all of the above-mentioned 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 data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the information.

[0050] The providing unit can adjust the order of information to be provided based on the relevance of the analysis results when providing the information. The providing unit, for example, adjusts the order of information to be provided based on the relevance of the analysis results when providing the information. For example, the providing unit prioritizes providing highly relevant analysis results. The providing unit can also postpone analysis results with low relevance. For example, the providing unit adjusts the order of information to be provided based on the relevance. This enables the providing unit to provide information efficiently by adjusting the order of information based on the relevance. 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 relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of the information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, the collection unit can analyze what device the user used to input data in the past and encourage the user to use the same device. The collection unit can also analyze what time of day the user input data in the past and collect data during the same time period. Furthermore, the collection unit can analyze what format the user used to input data in the past and encourage the user to input data in the same format. This allows the collection unit to select the optimal data collection method based on the user's past behavioral history, thereby improving the efficiency of data collection.

[0053] The analysis unit can evaluate the reliability of collected data and filter out unreliable data. For example, the analysis unit can analyze the source of the data and the collection method, and exclude unreliable data. The analysis unit can also check the consistency and integrity of the data and detect abnormal data. Furthermore, the analysis unit can detect duplicate or missing data and supplement it as necessary. In this way, the analysis unit can improve the accuracy of the analysis results by filtering out unreliable data.

[0054] The determination unit can predict the user's behavioral patterns based on the analysis results and suggest appropriate actions. For example, the determination unit can analyze the user's past behaviors and predict the behavior in similar situations. The determination unit can also suggest optimal actions taking into account the user's current situation and environment. Furthermore, the determination unit can suggest long-term action plans based on the user's goals and objectives. In this way, the determination unit can predict the user's behavioral patterns and suggest appropriate actions to help the user achieve their goals.

[0055] The analysis unit can analyze collected data in real time and provide immediate feedback. For example, the analysis unit can check the consistency of data and detect errors in real time as the user enters the data. The analysis unit can also analyze trends and patterns in the data in real time and provide feedback to the user. Furthermore, the analysis unit can detect abnormalities in the data in real time and issue an alert immediately. In this way, the analysis unit can improve the quality of data by analyzing data in real time and providing immediate feedback.

[0056] The collection unit can customize the content of data collection based on the user's current living situation and areas of interest. For example, if the user is planning to move, the collection unit can prioritize collecting data related to the new address. Also, if the user is interested in a particular industry, the collection unit can prioritize collecting data related to that industry. Furthermore, if the user is planning to attend a particular event, the collection unit can prioritize collecting data related to that event. In this way, the collection unit can collect highly relevant data by customizing the content of data collection based on the user's living situation and areas of interest.

[0057] The analysis unit can perform multidimensional analysis of the collected data to provide insights from multiple perspectives. For example, the analysis unit can analyze temporal changes in the data to identify trends. The analysis unit can also analyze the geographic distribution of the data to identify regional characteristics. Furthermore, the analysis unit can analyze correlations in the data to identify causal relationships. In this way, the analysis unit can provide deeper insights by performing multidimensional analysis.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects data. Examples of data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data manually. For example, the collection unit collects data entered by a user. Step 2: The analysis unit analyzes the data collected by the collection unit. Examples of analysis include, but are not limited to, statistical analysis and machine learning. For example, the analysis unit analyzes trends in the data using statistical analysis. The analysis unit can also analyze patterns in the data using machine learning. For example, the analysis unit clusters the data to identify groups of data. Step 3: The judgment unit makes a judgment based on the analysis results obtained by the analysis unit. The judgment includes, for example, a method of making a judgment by setting a threshold value and a rule-based judgment, but is not limited to these examples. For example, the judgment unit sets a threshold value to detect anomalies in the data. The judgment unit can also make a rule-based judgment to evaluate the suitability of the data. For example, the judgment unit checks the consistency of the data and detects errors. Step 4: The providing unit provides the results obtained by the determining unit. Providing includes, but is not limited to, methods such as notifying the user and storing the results in a database. For example, the providing unit notifies the user by email. The providing unit can also store the results in a database. For example, the providing unit displays the results in a web application.

[0060] (Example 2) The screening system of an embodiment of the present invention addresses the different spelling variations for each type of identification document, improving the CCOC's screening system, which considers anything other than an exact match to be a "difference." This screening system automatically determines document writing patterns and address registration patterns by storing and analyzing them in AI data. For example, it corrects registration errors that are considered to be inconsistent between "1-chome 1-banchi 1-go" and "1-1-1," thereby reducing the number of screening transitions required by humans. This system makes the accumulated database more useful, reduces manual screening workload, and reduces in-store response time associated with erroneous returns. For example, AI analyzes the different spelling variations for each type of identification document. For example, AI learns the spelling variations for addresses and names on documents such as driver's licenses and passports and stores them in a database. This allows the system to automatically recognize different spellings and register them as accurate information. AI then analyzes the accumulated data, and the system automatically determines the difference. For example, it can recognize different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address, preventing registration errors. This reduces the number of manual screening steps and significantly reduces screening man-hours. It also reduces in-store response man-hours due to erroneous returns. For example, if the system automatically recognizes spelling variations and registers them as accurate information, erroneous returns will decrease and in-store responses will become smoother. This improves customer satisfaction and operational efficiency. In this way, a system that uses AI to handle spelling variations in identification documents will make the accumulated database more useful, reduce the number of manual screening man-hours, and reduce in-store response man-hours due to erroneous returns. This allows the screening system to handle spelling variations on identification documents and register accurate information.

[0061] The screening system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data manually. For example, the collection unit collects data input by a user. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and machine learning. The analysis unit can also analyze data trends using, for example, statistical analysis. The analysis unit can also analyze data patterns using machine learning. For example, the analysis unit can cluster the data to identify data groups. The determination unit makes a determination based on the analysis results obtained by the analysis unit. Examples of the determination include, but are not limited to, a method of determining by setting a threshold and a rule-based determination. For example, the determination unit can set a threshold to detect anomalies in the data. The determination unit can also make a rule-based determination to evaluate the suitability of the data. For example, the judgment unit checks the consistency of the data and detects errors. The provision unit provides the results obtained by the judgment unit. Providing the results includes, but is not limited to, methods such as notifying the user and storing the results in a database. For example, the provision unit notifies the user by email. The provision unit can also store the results in a database. For example, the provision unit displays the results in a web application. This allows the screening system according to the embodiment to efficiently collect, analyze, judge, and provide data. Some or all of the above-described processes in the collection unit, analysis unit, judgment unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit may input data acquired by a sensor to the generation AI and cause the generation AI to collect the data. The analysis unit may input the collected data to the generation AI and cause the generation AI to analyze the data. The judgment unit may input the analysis results to the generation AI and cause the generation AI to make a judgment. The provision unit may input the judgment results to the generation AI and cause the generation AI to provide the results.

[0062] The screening system includes a collection unit that collects different spelling variations for each type of identification document. The collection unit collects different spelling variations for each type of identification document, such as a driver's license, passport, and My Number card. For example, the collection unit collects spelling variations for addresses written on driver's licenses. The collection unit can also collect spelling variations for names written on passports. For example, the collection unit collects spelling variations such as the difference between kanji and hiragana, and the difference between full-width and half-width characters. This allows the collection unit to efficiently collect spelling variations for identification documents. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data of the identification document to a generation AI and have the generation AI collect spelling variations.

[0063] The analysis unit can analyze the collected data and recognize different spellings as the same information. For example, the analysis unit can analyze the collected data and recognize different spellings as the same information. For example, the analysis unit can analyze the difference between kanji and hiragana in a name and recognize them as the same name. The analysis unit can also analyze the difference between an abbreviation and a formal name in an address and recognize them as the same address. For example, the analysis unit can recognize different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address. This allows the analysis unit to recognize different spellings as the same information, thereby preventing registration errors. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI recognize different spellings.

[0064] The judgment unit can make a judgment to prevent registration errors based on the analysis results. The judgment unit makes a judgment to prevent registration errors based on, for example, the analysis results. For example, the judgment unit checks the consistency of the data and detects errors. The judgment unit can also evaluate the suitability of the data and make a judgment to prevent errors. For example, the judgment unit detects data duplication or format inconsistencies and prevents registration errors. This enables the judgment unit to make a judgment to prevent registration errors. 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 analysis results to a generation AI and have the generation AI execute a registration error judgment.

[0065] The providing unit can provide accurate information based on the judgment result. The providing unit provides accurate information based on the judgment result, for example. For example, the providing unit notifies the user by email. The providing unit can also store the result in a database. For example, the providing unit displays the result in a web application. This allows the providing unit to provide accurate information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the judgment result to a generation AI and have the generation AI provide the information.

[0066] The analysis unit can analyze spelling variations of addresses or names written on a driver's license or passport. The analysis unit, for example, analyzes spelling variations of addresses written on a driver's license. For example, the analysis unit recognizes different spellings such as "1-chome 1-banchi 1-go" and "1-1-1" as the same address. The analysis unit can also analyze spelling variations of names written on a passport. For example, the analysis unit analyzes the difference between kanji and hiragana and recognizes them as the same name. In this way, the analysis unit can analyze spelling variations of addresses and names and register accurate information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from a driver's license or passport into a generation AI and have the generation AI analyze spelling variations.

[0067] The collection unit can estimate the user's emotions and adjust the timing of collecting identification documents based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting identification documents based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects the documents at a time when the user is relaxed. The collection unit can also collect identification documents immediately when the user is relaxed. For example, if the user is in a hurry, the collection unit quickly collects the identification documents. This allows the collection unit to adjust the collection timing according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0068] The collection unit can analyze the user's past submission history and select an appropriate collection method when collecting identification documents. For example, when collecting identification documents, the collection unit analyzes the user's past submission history and selects the optimal collection method. For example, the collection unit analyzes the format of documents the user has previously submitted and encourages the user to submit them in the same format. The collection unit can also analyze the time period in which the user previously submitted documents and collect them during the same time period. For example, the collection unit analyzes the submission methods (online, mail, etc.) of documents the user has previously submitted and suggests the optimal method. In this way, the collection unit can select the optimal collection method by analyzing the past submission history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past submission history data into a generation AI and have the generation AI select a collection method.

[0069] The collection unit can filter the identification documents based on the user's current living situation and areas of interest when collecting the identification documents. For example, when collecting the identification documents, the collection unit filters the identification documents based on the user's current living situation and areas of interest. For example, if the user is planning to move, the collection unit can prioritize collecting documents related to the new address. Also, if the user is interested in a particular industry, the collection unit can prioritize collecting documents related to that industry. For example, if the user is planning to attend a particular event, the collection unit can prioritize collecting documents related to that event. In this way, the collection unit can collect highly relevant documents by filtering based on the user's living situation and areas of interest. Some or all of the above-mentioned 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 living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0070] The collection unit can estimate the user's emotions and determine the priority of the identification documents to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the identification documents to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit postpones documents of lower importance. The collection unit can also prioritize collecting documents of higher importance when the user is relaxed. For example, if the user is in a hurry, the collection unit prioritizes collecting documents that can be collected quickly. This enables the collection unit to prioritize documents to be collected according to the user's emotions, thereby enabling efficient collection. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0071] When collecting identification documents, the collection unit can prioritize collecting highly relevant documents by taking into account the user's geographical location information. For example, when collecting identification documents, the collection unit prioritizes collecting highly relevant documents by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting documents related to that area. Also, if the user is traveling, the collection unit can prioritize collecting documents related to the user's travel destination. For example, if the user is planning to move, the collection unit prioritizes collecting documents related to the user's new address. In this way, the collection unit can prioritize collecting highly relevant documents 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 into the generation AI and cause the generation AI to collect documents.

[0072] The collection unit may analyze the user's social media activity and collect related documents when collecting identification documents. For example, the collection unit may analyze the user's social media activity and collect related documents when collecting identification documents. For example, if the user indicates on social media that they will be attending a specific event, the collection unit may collect documents related to the event. Furthermore, if the user indicates interest in a specific region on social media, the collection unit may collect documents related to that region. For example, if the user indicates interest in a specific industry on social media, the collection unit may collect documents related to that industry. This allows the collection unit to efficiently collect related documents by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to collect documents.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, the analysis unit provides analysis results that focus on the main points. By adjusting the presentation method of the analysis according to the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the identification document during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the identification document during analysis. For example, the analysis unit performs a detailed analysis on documents of high importance. The analysis unit can also perform a simplified analysis on documents of low importance. For example, the analysis unit determines the priority of the analysis according to the importance. As a result, the analysis unit adjusts the level of detail of the analysis based on the importance of the identification document, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the identification document to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the identification document during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the identification document during analysis. For example, the analysis unit applies a specific algorithm to a driver's license to perform analysis. The analysis unit can also apply a different algorithm to a passport to perform analysis. For example, the analysis unit applies an algorithm depending on the category to other documents to perform analysis. In this way, the analysis unit can perform highly accurate analysis by applying an analysis algorithm depending on the category of the identification document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the identification document to the generation AI and cause the generation AI to apply the analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide an appropriate analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] The analysis unit can determine the analysis priority based on the submission date of the identification document during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of the identification document during analysis. For example, the analysis unit prioritizes analysis of recently submitted documents. The analysis unit can also postpone analysis of documents that were submitted earlier. For example, the analysis unit adjusts the analysis priority according to the submission date. This enables efficient analysis by the analysis unit determining the analysis priority based on the submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the identification document into the generation AI and have the generation AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the identification documents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the identification documents during analysis. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. For example, the analysis unit adjusts the order of analysis according to the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the identification documents to the generation AI and have the generation AI adjust the order of analysis.

[0079] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, the judgment unit estimates the user's emotions and adjusts the judgment criteria based on the estimated user emotions. For example, the judgment unit relaxes strict criteria when the user is nervous. The judgment unit can also apply normal criteria when the user is relaxed. For example, the judgment unit applies criteria for making a quick decision when the user is in a hurry. This enables the judgment unit to adjust the judgment criteria according to the user's emotions and make an appropriate decision. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the judgment unit may be performed using an AI, for example, or without an AI. For example, the judgment unit can input the user's emotion data into the generation AI and have the generation AI adjust the judgment criteria.

[0080] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of the analysis results when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by taking into account the interrelationships of the analysis results when making a judgment. For example, the judgment unit integrates multiple analysis results and makes a comprehensive judgment. The judgment unit can also analyze the interrelationships of the analysis results and improve the accuracy of the judgment. For example, the judgment unit detects inconsistencies in the analysis results and improves the accuracy of the judgment. In this way, the judgment unit improves the accuracy of the judgment by taking into account the interrelationships of the analysis results. 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 interrelationship data of the analysis results to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0081] The judgment unit can make a judgment taking into consideration the attribute information of the person who submitted the identification document when making a judgment. The judgment unit, for example, makes a judgment taking into consideration the attribute information of the person who submitted the identification document when making a judgment. For example, the judgment unit makes a judgment taking into consideration the age and gender of the person who submitted the identification document. The judgment unit can also make a judgment taking into consideration the occupation and place of residence of the person who submitted the document. For example, the judgment unit makes a judgment taking into consideration the submitter's past submission history. This enables the judgment unit to make a more appropriate judgment by taking into consideration the attribute information of the person who submitted the document. 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 the attribute information data of the person who submitted the document into a generation AI and have the generation AI make the judgment.

[0082] The determination unit can estimate the user's emotions and adjust the order in which the determination results are displayed based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the order in which the determination results are displayed based on the estimated user emotions. For example, if the user is nervous, the determination unit may display important results first. The determination unit may also sequentially display detailed results if the user is relaxed. For example, if the user is in a hurry, the determination unit may display results that highlight the main points first. In this way, the determination unit can adjust the display order of the results according to the user's emotions, thereby providing results that are easy for the user to understand. 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. For example, the determination unit may input user emotion data into the generation AI and cause the generation AI to adjust the display order of the results.

[0083] The judgment unit can make a judgment taking into account the geographical distribution of the identification documents when making a judgment. For example, the judgment unit makes a judgment taking into account the geographical distribution of the identification documents when making a judgment. For example, the judgment unit makes a judgment based on the submitter's place of residence. The judgment unit can also make a judgment taking into account the characteristics of the submitter's place of residence. For example, the judgment unit makes a judgment taking into account the laws and regulations of the submitter's place of residence. This enables the judgment unit to make a more appropriate judgment by taking into account the geographical distribution. 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 geographical distribution data of the identification documents into the generation AI and have the generation AI make the judgment.

[0084] The judgment unit can improve the accuracy of the judgment by referring to related literature of the identification document when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by referring to related literature of the identification document when making a judgment. For example, the judgment unit refers to related literature to improve the accuracy of the judgment. The judgment unit can also integrate information from related literature to make a comprehensive judgment. For example, the judgment unit detects inconsistencies in the related literature to improve the accuracy of the judgment. As a result, the judgment unit improves the accuracy of the judgment by referring to the related literature. 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 related literature data into a generation AI and have the generation AI improve the accuracy of the judgment.

[0085] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. For example, if the user is in a hurry, the providing unit can provide information that is easy for the user to understand by adjusting the presentation method of the information 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 can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without 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 presentation method of the information.

[0086] The providing unit can adjust the level of detail of the information to be provided based on the importance of the analysis result when providing the information. The providing unit, for example, adjusts the level of detail of the information to be provided based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. For example, the providing unit adjusts the level of detail of the information to be provided according to the importance. This enables the providing unit to adjust the level of detail of the information based on the importance of the analysis result, thereby enabling efficient information provision. Some or all of the above-mentioned 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 importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0087] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the data. For example, the providing unit can apply different providing algorithms depending on the category of the analysis results when providing the data. For example, the providing unit can provide information by applying a specific algorithm to the analysis results related to a driver's license. The providing unit can also provide information by applying a different algorithm to the analysis results related to a passport. For example, the providing unit can also provide information by applying an algorithm depending on the category to the analysis results related to other documents. This enables the providing unit to provide highly accurate information by applying a providing algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input category data of the analysis results to the generation AI and cause the generation AI to apply the providing algorithm.

[0088] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. For example, if the user is in a hurry, the providing unit can provide short, concise information. The providing unit can also provide detailed information if the user is relaxed. For example, if the user is excited, the providing unit can provide visually stimulating information. This allows the providing unit to adjust the length of the information according to the user's emotion, thereby providing appropriate information for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without 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 length of the information.

[0089] The providing unit can determine the priority of the information to be provided based on the submission time of the analysis results at the time of provision. The providing unit, for example, determines the priority of the information to be provided based on the submission time of the analysis results at the time of provision. For example, the providing unit provides the most recent analysis results preferentially. The providing unit can also postpone analysis results that were submitted earlier. For example, the providing unit adjusts the priority of the information to be provided depending on the submission time. This enables the providing unit to determine the priority of the information based on the submission time, thereby enabling efficient information provision. Some or all of the above-mentioned 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 data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the information.

[0090] The providing unit can adjust the order of information to be provided based on the relevance of the analysis results when providing the information. The providing unit, for example, adjusts the order of information to be provided based on the relevance of the analysis results when providing the information. For example, the providing unit prioritizes providing highly relevant analysis results. The providing unit can also postpone analysis results with low relevance. For example, the providing unit adjusts the order of information to be provided based on the relevance. This enables the providing unit to provide information efficiently by adjusting the order of information based on the relevance. 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 relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of the information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects image data of identification documents using the camera 42 of the smart device 14 or the reception device 38, and collects spelling variations using the specification processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the specification processing unit 290 of the data processing device 12 and identifies spelling variation patterns. For example, the determination unit makes a determination based on the analysis results using the specification processing unit 290 of the data processing device 12 and recognizes the spelling variations as identical information. The provision unit notifies the user of the result using, for example, the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects image data of the identification document using the camera 42 and microphone 238 of the smart glasses 214, and collects spelling variations using the identification processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the identification processing unit 290 of the data processing device 12 and identifies spelling variation patterns. For example, the determination unit makes a determination based on the analysis results using the identification processing unit 290 of the data processing device 12 and recognizes the spelling variations as identical information. The provision unit notifies the user of the result using, for example, the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects image data of the identification document using the camera 42 and microphone 238 of the headset terminal 314, and collects spelling variations using the specification processing unit 290 of the data processing device 12. The analysis unit, for example, analyzes the collected data using the specification processing unit 290 of the data processing device 12 and identifies spelling variation patterns. The determination unit, for example, makes a determination based on the analysis results using the specification processing unit 290 of the data processing device 12, and recognizes the spelling variations as identical information. The provision unit notifies the user of the result using, for example, the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects image data of the identification document using the camera 42 and microphone 238 of the robot 414, and collects spelling variations using the specification processing unit 290 of the data processing device 12. The analysis unit, for example, analyzes the collected data using the specification processing unit 290 of the data processing device 12 and identifies spelling variation patterns. The determination unit, for example, makes a determination based on the analysis results using the specification processing unit 290 of the data processing device 12, and recognizes the spelling variations as identical information. The provision unit notifies the user of the result using, for example, the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, the collection unit can analyze what device the user used to input data in the past and encourage the user to use the same device. The collection unit can also analyze what time of day the user input data in the past and collect data during the same time period. Furthermore, the collection unit can analyze what format the user used to input data in the past and encourage the user to input data in the same format. This allows the collection unit to select the optimal data collection method based on the user's past behavioral history, thereby improving the efficiency of data collection.

[0093] The collection unit can estimate the user's emotions and adjust the data collection interface based on the estimated user's emotions. For example, the collection unit can provide a simple and intuitive interface when the user is feeling stressed. Alternatively, the collection unit can provide an interface including detailed options when the user is relaxed. Furthermore, the collection unit can provide an interface that allows the user to quickly enter data when the user is in a hurry. In this way, the collection unit can reduce the burden on the user and improve the efficiency of data collection by adjusting the interface according to the user's emotions.

[0094] The analysis unit can evaluate the reliability of collected data and filter out unreliable data. For example, the analysis unit can analyze the source of the data and the collection method, and exclude unreliable data. The analysis unit can also check the consistency and integrity of the data and detect abnormal data. Furthermore, the analysis unit can detect duplicate or missing data and supplement it as necessary. In this way, the analysis unit can improve the accuracy of the analysis results by filtering out unreliable data.

[0095] The determination unit can predict the user's behavioral patterns based on the analysis results and suggest appropriate actions. For example, the determination unit can analyze the user's past behaviors and predict the behavior in similar situations. The determination unit can also suggest optimal actions taking into account the user's current situation and environment. Furthermore, the determination unit can suggest long-term action plans based on the user's goals and objectives. In this way, the determination unit can predict the user's behavioral patterns and suggest appropriate actions to help the user achieve their goals.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that is concise. In this way, the providing unit can provide information that is easy for the user to understand by adjusting the method of providing information according to the user's emotions.

[0097] The analysis unit can analyze collected data in real time and provide immediate feedback. For example, the analysis unit can check the consistency of data and detect errors in real time as the user enters the data. The analysis unit can also analyze trends and patterns in the data in real time and provide feedback to the user. Furthermore, the analysis unit can detect abnormalities in the data in real time and issue an alert immediately. In this way, the analysis unit can improve the quality of data by analyzing data in real time and providing immediate feedback.

[0098] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed. The collection unit can also increase the frequency of data collection when the user is relaxed. Furthermore, the collection unit can collect data quickly when the user is in a hurry. In this way, the collection unit can reduce the burden on the user and improve the efficiency of data collection by adjusting the frequency of data collection according to the user's emotions.

[0099] The collection unit can customize the content of data collection based on the user's current living situation and areas of interest. For example, if the user is planning to move, the collection unit can prioritize collecting data related to the new address. Also, if the user is interested in a particular industry, the collection unit can prioritize collecting data related to that industry. Furthermore, if the user is planning to attend a particular event, the collection unit can prioritize collecting data related to that event. In this way, the collection unit can collect highly relevant data by customizing the content of data collection based on the user's living situation and areas of interest.

[0100] The analysis unit can perform multidimensional analysis of the collected data to provide insights from multiple perspectives. For example, the analysis unit can analyze temporal changes in the data to identify trends. The analysis unit can also analyze the geographic distribution of the data to identify regional characteristics. Furthermore, the analysis unit can analyze correlations in the data to identify causal relationships. In this way, the analysis unit can provide deeper insights by performing multidimensional analysis.

[0101] The determination unit can estimate the user's emotions and adjust the timing of the determination based on the estimated user's emotions. For example, the determination unit delays the timing of the determination when the user is feeling stressed. The determination unit can also make an immediate determination when the user is relaxed. Furthermore, the determination unit makes a quick determination when the user is in a hurry. In this way, the determination unit can reduce the burden on the user and make an appropriate determination by adjusting the timing of the determination according to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects data. Examples of data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data using, for example, a sensor. The collection unit can also collect data manually. For example, the collection unit collects data entered by a user. Step 2: The analysis unit analyzes the data collected by the collection unit. Examples of analysis include, but are not limited to, statistical analysis and machine learning. For example, the analysis unit analyzes trends in the data using statistical analysis. The analysis unit can also analyze patterns in the data using machine learning. For example, the analysis unit clusters the data to identify groups of data. Step 3: The judgment unit makes a judgment based on the analysis results obtained by the analysis unit. The judgment includes, for example, a method of making a judgment by setting a threshold value and a rule-based judgment, but is not limited to these examples. For example, the judgment unit sets a threshold value to detect anomalies in the data. The judgment unit can also make a rule-based judgment to evaluate the suitability of the data. For example, the judgment unit checks the consistency of the data and detects errors. Step 4: The providing unit provides the results obtained by the determining unit. Providing includes, but is not limited to, methods such as notifying the user and storing the results in a database. For example, the providing unit notifies the user by email. The providing unit can also store the results in a database. For example, the providing unit displays the results in a web application.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

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

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

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

[0127] The 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.

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

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

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

[0131] Fig. 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.

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

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

[0134] In the 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.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, 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.

[0138] 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.

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

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0143] The 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.

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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; an analysis unit that analyzes the data collected by the collection unit; A determination unit that makes a decision based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the results obtained by the aforementioned determination unit, Equipped with A system characterized by:

2. The collecting unit Collect variations in spelling across different types of identity verification documents. The system of claim 1 .

3. The analysis unit The collected data is analyzed, and different representations are recognized as the same information. The system of claim 1 .

4. The determination unit Based on the analysis results, decisions will be made to prevent registration errors. The system of claim 1 .

5. The providing unit Provide accurate information based on the judgment results. The system of claim 1 .

6. The analysis unit Analyze variations in the spelling of addresses or names as they appear on a driver's license or passport. The system of claim 1 .

7. The collecting unit We estimate the user's emotions and adjust the timing of collecting identity verification documents based on those estimated emotions. The system of claim 1 .

8. The collecting unit When collecting identity verification documents, we analyze the user's past submission history and select the appropriate collection method. The system of claim 1 .

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