system

Abstract

The system according to this embodiment aims to prevent the incorrect registration or misdelivery of information. [Solution] The system according to the embodiment comprises a collection unit, a checking unit, and an alert unit. The collection unit collects information. The checking unit checks the consistency based on the information collected by the collection unit. The alert unit issues an alert based on the error detected by the checking unit.

Description

Technical Field

[0006] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for preventing misregistration and mistransmission of information is not sufficiently developed, and there is room for improvement.

[0005] The system according to an embodiment aims to prevent misregistration and mistransmission of information.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a check unit, and an alert unit. The collection unit collects information. The check unit checks the consistency based on the information collected by the collection unit. The alert unit issues an alert based on the error detected by the check unit.

Effects of the Invention

[0007] The system according to this embodiment can prevent incorrect registration and misdelivery of information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The erroneous registration and misdelivery prevention system according to an embodiment of the present invention is a system that uses generating AI to prevent erroneous registration and misdelivery of emails. This system uses the SB registration system and browser extensions to provide a mechanism for detecting errors on the system side and issuing alerts. This prevents the misdelivery of attachments when sending emails and erroneous registration in the registration system. For example, when a user tries to send an Excel file attached to an email, the generating AI checks the consistency between the company name written in the attachment and the email recipient. For example, it checks whether the name of the contract holder in the recipient or telephone number list of the quotation matches the name of the recipient company. In this case, it checks by comparing it with the recipient's signature listed in the domain and email history. If the file contains information with a mismatched name, the generating AI issues an alert and warns, "The attachment contains information with a different name. Do you want to send it as is?" If there are no problems with the content of the attachment, the generating AI automatically creates the email body and automatically retrieves the company name and sales representative name. The user can send the email simply by pressing the confirmation button. Next, in the SB registration system, the generating AI checks the consistency of the subscriber name and billing name. For example, if there are lines with different names mixed in, the generating AI will issue an alert warning, "There are lines that are suspected to be with different names with a probability of more than 90%. Do you wish to proceed with processing?" This prevents mis-registered contracts due to incorrect registration. This system is intended for internal use and was developed to solve the problem of incidents occurring due to user error that cannot be controlled by the system. By utilizing the generating AI, it is possible to detect lines with different names and deter erroneous mailings, and the system can implement incident countermeasures that may occur. In this way, the erroneous registration and erroneous mailing prevention system can prevent erroneous registrations and erroneous mailings and implement incident countermeasures that may occur on the system side.

[0029] The system for preventing incorrect registration and misdelivery according to the embodiment comprises a collection unit, a checking unit, and an alert unit. The collection unit collects information. The collection unit collects information such as the recipient of an email, the contents of attachments, the name of the subscriber, and the name of the billing party. For example, the collection unit obtains the recipient information of an email and analyzes the contents of attachments. The collection unit can also collect information such as the name of the subscriber and the billing party. For example, the collection unit obtains the recipient information of an email and analyzes the contents of attachments. The checking unit checks the consistency based on the information collected by the collection unit. For example, the checking unit verifies the consistency and accuracy of the collected information. For example, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. The alert unit issues alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on the detected errors. The alert unit can, for example, issue an alert based on detected errors to warn the user. The alert unit can also issue an alert based on detected errors to warn the user. For example, the alert unit can issue an alert based on detected errors to warn the user. This enables the erroneous registration and erroneous delivery prevention system according to the embodiment to collect information, perform consistency checks, and issue alerts.

[0030] The collection unit collects information. For example, it collects information such as email recipients, attachment contents, subscriber names, and billing names. Specifically, the collection unit can use natural language processing and image recognition technologies to obtain email recipient information from mail servers and databases and to analyze the contents of attachments. For example, email recipient information includes the email addresses of the sender and recipient, as well as addresses included in CC and BCC. Since the contents of attachments vary widely, including document files, image files, and spreadsheets, analysis algorithms corresponding to these file formats are used. Subscriber names and billing names are obtained from customer management systems and billing systems, and this information is efficiently collected using database queries. The collection unit can centrally manage this information and update it in real time. Furthermore, the collection unit can flexibly set the frequency and timing of information collection, and can collect information in response to specific events or triggers. For example, by collecting recipient information and attachment contents immediately before an email is sent, and periodically updating subscriber names and billing names, the latest information can always be maintained. This allows the collection unit to reduce the risk of incorrect registration or misdelivery, and improve the overall reliability of the system.

[0031] The checking unit checks for consistency based on the information collected by the collection unit. For example, the checking unit verifies the consistency and accuracy of the collected information. Specifically, the checking unit compares the collected email recipient information with the subscriber name and billing name information to check if they match. For example, it checks whether the email recipient matches the subscriber's email address and whether the billing name is correct. It also checks whether the content of attached files is appropriate. For example, it checks whether the content of the contract or invoice included in the attached file matches the subscriber name and billing name. The checking unit can use database queries and regular expressions to compare this information. Furthermore, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. For example, if email recipient information spans multiple databases, it verifies whether that information matches. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, if the same subscriber name or billing name exists in multiple records, it verifies whether that information is duplicated. In this way, the checking unit can improve the consistency of the collected information and reduce the risk of incorrect registration or misdelivery.

[0032] The alert unit issues alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on detected errors. Specifically, the alert unit notifies the user of information regarding data inconsistencies, omissions, or duplicates detected by the checking unit. For example, if the recipient information of an email does not match the subscriber name, a warning message is displayed to the user. Also, if the content of an attached file does not match the subscriber name or billing name, the user is warned. The alert unit issues these warnings in real time, enabling users to respond quickly. For example, warning messages are sent to the user in the form of pop-up notifications, email notifications, or SMS notifications. Furthermore, the alert unit can include specific instructions and recommended actions in the warning messages. For example, if an error is detected, it guides the user on how to correct it and the verification steps. The alert unit can also save a history of warning messages for later reference. This allows users to review past warning messages and take measures to prevent recurrence. The alert unit can collect user feedback and continuously improve the content and delivery methods of warning messages. This allows the alerting unit to provide users with prompt and appropriate warnings, minimizing the risk of incorrect registration or misdelivery.

[0033] The automated creation section uses a generation AI to automatically create the email body. For example, the automated creation section uses a generation AI to automatically create the email body, saving the user time. The automated creation section uses a generation AI to automatically create the email body, saving the user time. Furthermore, the automated creation section can also use a generation AI to automatically create the email body, saving the user time. For example, the automated creation section uses a generation AI to automatically create the email body, saving the user time. This allows the generation AI to automatically create the email body, thus saving the user time.

[0034] The name checking unit detects incorrect names. For example, the name checking unit detects incorrect names to prevent erroneous registration. The name checking unit can also detect incorrect names to prevent erroneous registration. For example, the name checking unit detects incorrect names to prevent erroneous registration. This allows for the prevention of erroneous registration by detecting incorrect names.

[0035] The collection unit can collect information such as email recipients, attachment contents, subscriber names, and billing names. For example, the collection unit can obtain email recipient information and analyze the contents of attachments. The collection unit can also collect information such as subscriber names and billing names. For example, the collection unit can obtain email recipient information and analyze the contents of attachments. The collection unit can also collect information such as subscriber names and billing names. By collecting information such as email recipients, attachment contents, subscriber names, and billing names, it is possible to prevent misdelivery and incorrect registration.

[0036] The checking unit can check for consistency based on the collected information. For example, the checking unit can verify the consistency and accuracy of the collected information. For example, the checking unit can verify the consistency of the collected information and detect data inconsistencies or omissions. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, the checking unit can verify the consistency of the collected information and detect data inconsistencies or omissions. In this way, errors can be detected by checking for consistency based on the collected information.

[0037] The alert unit can issue alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on the detected errors. The alert unit can issue alerts based on detected errors and warn the user. The alert unit can also issue alerts based on detected errors and warn the user. For example, the alert unit can issue alerts based on detected errors and warn the user. This allows the user to be warned by issuing alerts based on errors detected by the checking unit.

[0038] The data collection unit can analyze the user's past email sending history and select the optimal information collection method. For example, the data collection unit can analyze patterns of emails the user has frequently sent in the past and collect information based on similar patterns. For example, the data collection unit can analyze the content of emails the user has sent in the past and prioritize the collection of relevant information. The data collection unit can also identify trends in emails sent during specific time periods from the user's past email sending history and collect information during those time periods. For example, the data collection unit can analyze patterns of emails the user has frequently sent in the past and collect information based on similar patterns. This allows the system to select the optimal information collection method by analyzing the user's past email sending history.

[0039] The data collection unit can filter information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. The data collection unit can also filter and collect information based on keywords set by the user. For example, the data collection unit can prioritize collecting information related to the user's current projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can prioritize the collection of information related to places the user has visited in the past. The data collection unit can also prioritize the collection of relevant information based on a geographical range set by the user. For example, the data collection unit can prioritize the collection of information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location.

[0041] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the unit can collect relevant information based on information shared by the user on social media. For example, it can collect relevant information based on the content of posts from accounts the user follows. The unit can also collect relevant information based on groups and events the user participates in. For example, the unit can collect relevant information based on information shared by the user on social media. This allows the system to collect relevant information by analyzing the user's social media activity.

[0042] The checking unit can improve the accuracy of its checks by considering the interrelationships of information during consistency checks. For example, the checking unit checks the interrelationship between the recipient of an email and the content of an attachment to confirm consistency. For example, the checking unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. The checking unit can also verify consistency by comparing it with the recipient's signature listed in the domain and email history. For example, the checking unit checks the interrelationship between the recipient of an email and the content of an attachment to confirm consistency. In this way, the accuracy of the checks can be improved by considering the interrelationships of information.

[0043] The checking unit can perform consistency checks while considering the attribute information of the information submitter. For example, the checking unit adjusts the consistency check criteria based on the information submitter's job title or department. The checking unit also performs consistency checks while considering the information submitter's past submission history. Furthermore, the checking unit can evaluate the reliability of the information submitter and improve the accuracy of the consistency check. For example, the checking unit adjusts the consistency check criteria based on the information submitter's job title or department. This allows for improved accuracy of the check by considering the attribute information of the information submitter.

[0044] The checking unit can perform consistency checks while considering the geographical distribution of the information. For example, the checking unit can perform consistency checks based on the location of the information submitter. For example, the checking unit can perform consistency checks based on the location of the information recipient. Furthermore, the checking unit can also perform consistency checks while considering the geographical relevance of the information. For example, the checking unit can perform consistency checks based on the location of the information submitter. This improves the accuracy of the checks by considering the geographical distribution of the information.

[0045] The checking unit can improve the accuracy of its checks by referring to relevant literature during consistency checks. For example, the checking unit adjusts the consistency check criteria by referring to relevant literature. The checking unit improves the accuracy of its consistency checks based on relevant literature. Furthermore, the checking unit can supplement the results of its consistency checks by referring to relevant literature. For example, the checking unit adjusts the consistency check criteria by referring to relevant literature. This allows for improved accuracy of the checks by referring to relevant literature.

[0046] The alert unit can optimize the current alert by referring to past alert data when issuing an alert. For example, the alert unit analyzes past alert data and optimizes the alert based on similar situations. For example, the alert unit analyzes user responses from past alert data and issues the most appropriate alert. Furthermore, the alert unit can adjust how alerts are displayed based on past alert data. For example, the alert unit analyzes past alert data and optimizes the alert based on similar situations. This allows the current alert to be optimized by referring to past alert data.

[0047] The alert unit can apply different alerting methods depending on the category of information when issuing an alert. For example, the alert unit can distinguish and issue alerts regarding misdelivered emails and incorrect registrations in the registration system. For example, the alert unit can apply different alerting methods depending on the importance of the information. The alert unit can also select the optimal alerting method based on the type of information. For example, the alert unit can distinguish and issue alerts regarding misdelivered emails and incorrect registrations in the registration system. By applying different alerting methods for each category of information, the system can issue the most appropriate alerts.

[0048] The alerting unit can determine the priority of alerts based on when the information was submitted. For example, it will prioritize alerts if the information is recent, and lower the priority of alerts if the information is old. The alerting unit can also adjust the display order of alerts based on when the information was submitted. For example, it will prioritize alerts if the information is recent. This allows users to check the latest information first by prioritizing alerts based on when the information was submitted.

[0049] The alert unit can optimize alerts by referring to relevant market data when an alert is issued. For example, the alert unit optimizes the content of the alert based on relevant market data. For example, the alert unit adjusts how the alert is displayed by referring to relevant market data. Furthermore, the alert unit can also adjust the importance of the alert based on relevant market data. For example, the alert unit optimizes the content of the alert based on relevant market data. This allows for the optimization of alert content by referring to relevant market data.

[0050] The automated generation unit can adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information, and concise information for less important information. The automated generation unit can also adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information. This allows for detailed generation of important information by adjusting the level of detail based on its importance.

[0051] The automated generation unit can apply different generation algorithms depending on the information category during automated generation. For example, the automated generation unit applies a specific algorithm to information about misdelivered emails for automated generation. For example, the automated generation unit applies a different algorithm to information about incorrect registrations in a registration system for automated generation. Furthermore, the automated generation unit can select the optimal generation algorithm depending on the information category. For example, the automated generation unit applies a specific algorithm to information about misdelivered emails for automated generation. This enables optimal automated generation by applying different generation algorithms depending on the information category.

[0052] The automated generation unit can determine the priority of data creation based on the information submission date. For example, it will prioritize the creation of data with a more recent submission date. For example, it will lower the priority of data with an older submission date. The automated generation unit can also adjust the order of data creation based on the information submission date. For example, it will prioritize the creation of data with a more recent submission date. This allows for the creation of the most up-to-date information by determining the priority of data creation based on the submission date.

[0053] The automated generation unit can adjust the order of creation based on the relevance of the information during the automated generation process. For example, if the information is highly relevant, the automated generation unit will prioritize its creation. For example, if the information is not highly relevant, the automated generation unit will postpone its creation. The automated generation unit can also adjust the order of creation based on the relevance of the information. For example, if the information is highly relevant, the automated generation unit will prioritize its creation. In this way, by adjusting the order of creation based on the relevance of the information, highly relevant information can be created preferentially.

[0054] The name verification unit can improve the accuracy of its checks by considering the interrelationships of information during the name verification process. For example, the name verification unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. For example, if there are multiple lines with different names, the name verification unit checks while considering the interrelationships. Furthermore, the name verification unit can improve the accuracy of the name verification based on the interrelationships of information. For example, the name verification unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. In this way, the accuracy of the check can be improved by considering the interrelationships of information.

[0055] The name verification unit can perform name verification while considering the attribute information of the information submitter. For example, the name verification unit adjusts the criteria for name verification based on the information submitter's job title or department. The name verification unit also performs name verification while considering the information submitter's past submission history. Furthermore, the name verification unit can evaluate the reliability of the information submitter and improve the accuracy of the name verification. For example, the name verification unit adjusts the criteria for name verification based on the information submitter's job title or department. This allows for improved verification accuracy by considering the attribute information of the information submitter.

[0056] The name verification unit can perform name verification while considering the geographical distribution of the information. For example, the name verification unit can perform name verification based on the location of the information submitter. For example, the name verification unit can perform name verification based on the location of the information recipient. Furthermore, the name verification unit can also perform name verification while considering the geographical relevance of the information. For example, the name verification unit can perform name verification based on the location of the information submitter. This improves the accuracy of the verification by considering the geographical distribution of the information.

[0057] The name verification unit can improve the accuracy of its name verification by referring to relevant literature. For example, the name verification unit can adjust the criteria for name verification by referring to relevant literature. The name verification unit can improve the accuracy of name verification based on relevant literature. Furthermore, the name verification unit can supplement the results of name verification by referring to relevant literature. For example, the name verification unit can adjust the criteria for name verification by referring to relevant literature. This allows for improved verification accuracy by referring to relevant literature.

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

[0059] The data collection unit can analyze a user's past behavior history and select the most suitable information collection method. For example, it can analyze actions and choices that a user has frequently performed in the past and collect information based on similar patterns. Furthermore, the data collection unit can identify trends in actions performed during specific time periods based on the user's past behavior history and collect information during those times. This allows for the selection of the most optimal information collection method by analyzing the user's past behavior history.

[0060] The data collection unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current projects. It can also filter and collect relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest.

[0061] The checking unit can improve the accuracy of its checks by considering the interrelationships between information during consistency checks. For example, it can check the relationship between the recipient of an email and the content of an attached file to confirm consistency. It can also check the relationship between the subscriber name and the billing name to confirm consistency. In this way, the accuracy of the checks can be improved by considering the interrelationships between information.

[0062] The alerting unit can apply different alerting methods depending on the category of information when issuing an alert. For example, it can distinguish between alerts regarding misdelivered emails and alerts regarding incorrect registrations in the registration system. It can also apply different alerting methods depending on the importance of the information. By applying different alerting methods to each category of information, the system can issue the most appropriate alerts.

[0063] The automated generation unit can adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information, and concise information for less important information. By adjusting the level of detail based on the importance of the information, it is possible to generate detailed information for important information.

[0064] The name verification unit can perform name verification while considering the attribute information of the information submitter. For example, it can adjust the name verification criteria based on the information submitter's job title or department. It can also perform name verification while considering the information submitter's past submission history. By considering the attribute information of the information submitter, the accuracy of the verification can be improved.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects information. For example, it collects information such as the recipient of the email, the contents of the attachment, the subscriber's name, and the billing name. The collection unit obtains the recipient information of the email and analyzes the contents of the attachment. It can also collect the subscriber's name and the billing name. Step 2: The checking unit checks the consistency of the information collected by the collection unit. For example, it verifies the consistency and accuracy of the collected information and detects data inconsistencies, omissions, and duplicates. Step 3: The alert unit issues an alert based on the errors detected by the checking unit. For example, it warns the user based on the detected errors.

[0067] (Example of form 2) The erroneous registration and misdelivery prevention system according to an embodiment of the present invention is a system that uses generating AI to prevent erroneous registration and misdelivery of emails. This system uses the SB registration system and browser extensions to provide a mechanism for detecting errors on the system side and issuing alerts. This prevents the misdelivery of attachments when sending emails and erroneous registration in the registration system. For example, when a user tries to send an Excel file attached to an email, the generating AI checks the consistency between the company name written in the attachment and the email recipient. For example, it checks whether the name of the contract holder in the recipient or telephone number list of the quotation matches the name of the recipient company. In this case, it checks by comparing it with the recipient's signature listed in the domain and email history. If the file contains information with a mismatched name, the generating AI issues an alert and warns, "The attachment contains information with a different name. Do you want to send it as is?" If there are no problems with the content of the attachment, the generating AI automatically creates the email body and automatically retrieves the company name and sales representative name. The user can send the email simply by pressing the confirmation button. Next, in the SB registration system, the generating AI checks the consistency of the subscriber name and billing name. For example, if there are lines with different names mixed in, the generating AI will issue an alert warning, "There are lines that are suspected to be with different names with a probability of more than 90%. Do you wish to proceed with processing?" This prevents mis-registered contracts due to incorrect registration. This system is intended for internal use and was developed to solve the problem of incidents occurring due to user error that cannot be controlled by the system. By utilizing the generating AI, it is possible to detect lines with different names and deter erroneous mailings, and the system can implement incident countermeasures that may occur. In this way, the erroneous registration and erroneous mailing prevention system can prevent erroneous registrations and erroneous mailings and implement incident countermeasures that may occur on the system side.

[0068] The system for preventing incorrect registration and misdelivery according to the embodiment comprises a collection unit, a checking unit, and an alert unit. The collection unit collects information. The collection unit collects information such as the recipient of an email, the contents of attachments, the name of the subscriber, and the name of the billing party. For example, the collection unit obtains the recipient information of an email and analyzes the contents of attachments. The collection unit can also collect information such as the name of the subscriber and the billing party. For example, the collection unit obtains the recipient information of an email and analyzes the contents of attachments. The checking unit checks the consistency based on the information collected by the collection unit. For example, the checking unit verifies the consistency and accuracy of the collected information. For example, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. The alert unit issues alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on the detected errors. The alert unit can, for example, issue an alert based on detected errors to warn the user. The alert unit can also issue an alert based on detected errors to warn the user. For example, the alert unit can issue an alert based on detected errors to warn the user. This enables the erroneous registration and erroneous delivery prevention system according to the embodiment to collect information, perform consistency checks, and issue alerts.

[0069] The collection unit collects information. For example, it collects information such as email recipients, attachment contents, subscriber names, and billing names. Specifically, the collection unit can use natural language processing and image recognition technologies to obtain email recipient information from mail servers and databases and to analyze the contents of attachments. For example, email recipient information includes the email addresses of the sender and recipient, as well as addresses included in CC and BCC. Since the contents of attachments vary widely, including document files, image files, and spreadsheets, analysis algorithms corresponding to these file formats are used. Subscriber names and billing names are obtained from customer management systems and billing systems, and this information is efficiently collected using database queries. The collection unit can centrally manage this information and update it in real time. Furthermore, the collection unit can flexibly set the frequency and timing of information collection, and can collect information in response to specific events or triggers. For example, by collecting recipient information and attachment contents immediately before an email is sent, and periodically updating subscriber names and billing names, the latest information can always be maintained. This allows the collection unit to reduce the risk of incorrect registration or misdelivery, and improve the overall reliability of the system.

[0070] The checking unit checks for consistency based on the information collected by the collection unit. For example, the checking unit verifies the consistency and accuracy of the collected information. Specifically, the checking unit compares the collected email recipient information with the subscriber name and billing name information to check if they match. For example, it checks whether the email recipient matches the subscriber's email address and whether the billing name is correct. It also checks whether the content of attached files is appropriate. For example, it checks whether the content of the contract or invoice included in the attached file matches the subscriber name and billing name. The checking unit can use database queries and regular expressions to compare this information. Furthermore, the checking unit verifies the consistency of the collected information and detects data inconsistencies and omissions. For example, if email recipient information spans multiple databases, it verifies whether that information matches. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, if the same subscriber name or billing name exists in multiple records, it verifies whether that information is duplicated. In this way, the checking unit can improve the consistency of the collected information and reduce the risk of incorrect registration or misdelivery.

[0071] The alert unit issues alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on detected errors. Specifically, the alert unit notifies the user of information regarding data inconsistencies, omissions, or duplicates detected by the checking unit. For example, if the recipient information of an email does not match the subscriber name, a warning message is displayed to the user. Also, if the content of an attached file does not match the subscriber name or billing name, the user is warned. The alert unit issues these warnings in real time, enabling users to respond quickly. For example, warning messages are sent to the user in the form of pop-up notifications, email notifications, or SMS notifications. Furthermore, the alert unit can include specific instructions and recommended actions in the warning messages. For example, if an error is detected, it guides the user on how to correct it and the verification steps. The alert unit can also save a history of warning messages for later reference. This allows users to review past warning messages and take measures to prevent recurrence. The alert unit can collect user feedback and continuously improve the content and delivery methods of warning messages. This allows the alerting unit to provide users with prompt and appropriate warnings, minimizing the risk of incorrect registration or misdelivery.

[0072] The automated creation section uses a generation AI to automatically create the email body. For example, the automated creation section uses a generation AI to automatically create the email body, saving the user time. The automated creation section uses a generation AI to automatically create the email body, saving the user time. Furthermore, the automated creation section can also use a generation AI to automatically create the email body, saving the user time. For example, the automated creation section uses a generation AI to automatically create the email body, saving the user time. This allows the generation AI to automatically create the email body, thus saving the user time.

[0073] The name checking unit detects incorrect names. For example, the name checking unit detects incorrect names to prevent erroneous registration. The name checking unit can also detect incorrect names to prevent erroneous registration. For example, the name checking unit detects incorrect names to prevent erroneous registration. This allows for the prevention of erroneous registration by detecting incorrect names.

[0074] The collection unit can collect information such as email recipients, attachment contents, subscriber names, and billing names. For example, the collection unit can obtain email recipient information and analyze the contents of attachments. The collection unit can also collect information such as subscriber names and billing names. For example, the collection unit can obtain email recipient information and analyze the contents of attachments. The collection unit can also collect information such as subscriber names and billing names. By collecting information such as email recipients, attachment contents, subscriber names, and billing names, it is possible to prevent misdelivery and incorrect registration.

[0075] The checking unit can check for consistency based on the collected information. For example, the checking unit can verify the consistency and accuracy of the collected information. For example, the checking unit can verify the consistency of the collected information and detect data inconsistencies or omissions. The checking unit can also verify the accuracy of the collected information and detect data duplication. For example, the checking unit can verify the consistency of the collected information and detect data inconsistencies or omissions. In this way, errors can be detected by checking for consistency based on the collected information.

[0076] The alert unit can issue alerts based on errors detected by the checking unit. For example, the alert unit warns the user based on the detected errors. The alert unit can issue alerts based on detected errors and warn the user. The alert unit can also issue alerts based on detected errors and warn the user. For example, the alert unit can issue alerts based on detected errors and warn the user. This allows the user to be warned by issuing alerts based on errors detected by the checking unit.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection to collect more detailed data. Also, if the user is in a hurry, the data collection unit can speed up the timing of information collection and gather necessary information urgently. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to lessen the user's burden. In this way, the user's burden can be reduced by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can analyze the user's past email sending history and select the optimal information collection method. For example, the data collection unit can analyze patterns of emails the user has frequently sent in the past and collect information based on similar patterns. For example, the data collection unit can analyze the content of emails the user has sent in the past and prioritize the collection of relevant information. The data collection unit can also identify trends in emails sent during specific time periods from the user's past email sending history and collect information during those time periods. For example, the data collection unit can analyze patterns of emails the user has frequently sent in the past and collect information based on similar patterns. This allows the system to select the optimal information collection method by analyzing the user's past email sending history.

[0079] The data collection unit can filter information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect relevant information based on the user's areas of interest. The data collection unit can also filter and collect information based on keywords set by the user. For example, the data collection unit can prioritize collecting information related to the user's current projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed information. The data collection unit can also prioritize collecting information that can be quickly retrieved if the user is in a hurry. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. This allows for the priority collection of important information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can prioritize the collection of information related to places the user has visited in the past. The data collection unit can also prioritize the collection of relevant information based on a geographical range set by the user. For example, the data collection unit can prioritize the collection of information related to the user's current location. This allows for the priority collection of highly relevant information by considering the user's geographical location.

[0082] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the unit can collect relevant information based on information shared by the user on social media. For example, it can collect relevant information based on the content of posts from accounts the user follows. The unit can also collect relevant information based on groups and events the user participates in. For example, the unit can collect relevant information based on information shared by the user on social media. This allows the system to collect relevant information by analyzing the user's social media activity.

[0083] The checking unit can estimate the user's emotions and adjust the consistency check criteria based on the estimated emotions. For example, if the user is stressed, the checking unit can relax the consistency check criteria and perform the check quickly. For example, if the user is relaxed, the checking unit can tighten the consistency check criteria and perform a detailed check. The checking unit can also adjust the consistency check criteria and perform a quick and accurate check if the user is in a hurry. For example, if the user is stressed, the checking unit can relax the consistency check criteria and perform the check quickly. This allows for quick and accurate checks by adjusting the consistency check criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The checking unit can improve the accuracy of its checks by considering the interrelationships of information during consistency checks. For example, the checking unit checks the interrelationship between the recipient of an email and the content of an attachment to confirm consistency. For example, the checking unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. The checking unit can also verify consistency by comparing it with the recipient's signature listed in the domain and email history. For example, the checking unit checks the interrelationship between the recipient of an email and the content of an attachment to confirm consistency. In this way, the accuracy of the checks can be improved by considering the interrelationships of information.

[0085] The checking unit can perform consistency checks while considering the attribute information of the information submitter. For example, the checking unit adjusts the consistency check criteria based on the information submitter's job title or department. The checking unit also performs consistency checks while considering the information submitter's past submission history. Furthermore, the checking unit can evaluate the reliability of the information submitter and improve the accuracy of the consistency check. For example, the checking unit adjusts the consistency check criteria based on the information submitter's job title or department. This allows for improved accuracy of the check by considering the attribute information of the information submitter.

[0086] The checking unit can estimate the user's emotions and adjust the display order of the check results based on the estimated emotions. For example, if the user is feeling stressed, the checking unit will prioritize displaying important check results. For example, if the user is relaxed, the checking unit will display detailed check results. The checking unit can also display check results in a way that allows for quick review if the user is in a hurry. For example, if the user is feeling stressed, the checking unit will prioritize displaying important check results. This allows for prioritizing the display order of check results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The checking unit can perform consistency checks while considering the geographical distribution of the information. For example, the checking unit can perform consistency checks based on the location of the information submitter. For example, the checking unit can perform consistency checks based on the location of the information recipient. Furthermore, the checking unit can also perform consistency checks while considering the geographical relevance of the information. For example, the checking unit can perform consistency checks based on the location of the information submitter. This improves the accuracy of the checks by considering the geographical distribution of the information.

[0088] The checking unit can improve the accuracy of its checks by referring to relevant literature during consistency checks. For example, the checking unit adjusts the consistency check criteria by referring to relevant literature. The checking unit improves the accuracy of its consistency checks based on relevant literature. Furthermore, the checking unit can supplement the results of its consistency checks by referring to relevant literature. For example, the checking unit adjusts the consistency check criteria by referring to relevant literature. This allows for improved accuracy of the checks by referring to relevant literature.

[0089] The alert unit can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, if the user is stressed, the alert unit will display a simple, highly visible alert. If the user is relaxed, the alert unit will display an alert with more detailed information. The alert unit can also display an alert for quick confirmation if the user is in a hurry. For example, if the user is stressed, the alert unit will display a simple, highly visible alert. This allows for the display of highly visible alerts by adjusting how alerts are displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The alert unit can optimize the current alert by referring to past alert data when issuing an alert. For example, the alert unit analyzes past alert data and optimizes the alert based on similar situations. For example, the alert unit analyzes user responses from past alert data and issues the most appropriate alert. Furthermore, the alert unit can adjust how alerts are displayed based on past alert data. For example, the alert unit analyzes past alert data and optimizes the alert based on similar situations. This allows the current alert to be optimized by referring to past alert data.

[0091] The alert unit can apply different alerting methods depending on the category of information when issuing an alert. For example, the alert unit can distinguish and issue alerts regarding misdelivered emails and incorrect registrations in the registration system. For example, the alert unit can apply different alerting methods depending on the importance of the information. The alert unit can also select the optimal alerting method based on the type of information. For example, the alert unit can distinguish and issue alerts regarding misdelivered emails and incorrect registrations in the registration system. By applying different alerting methods for each category of information, the system can issue the most appropriate alerts.

[0092] The alerting unit can estimate the user's emotions and adjust the importance of alerts based on those emotions. For example, if the user is stressed, the alerting unit will prioritize displaying high-priority alerts. For example, if the user is relaxed, the alerting unit will display alerts containing more detailed information. The alerting unit can also adjust the importance of alerts to allow for quicker review if the user is in a hurry. For example, if the user is stressed, the alerting unit will prioritize displaying high-priority alerts. This allows for prioritizing the display of important alerts by adjusting their importance based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The alerting unit can determine the priority of alerts based on when the information was submitted. For example, it will prioritize alerts if the information is recent, and lower the priority of alerts if the information is old. The alerting unit can also adjust the display order of alerts based on when the information was submitted. For example, it will prioritize alerts if the information is recent. This allows users to check the latest information first by prioritizing alerts based on when the information was submitted.

[0094] The alert unit can optimize alerts by referring to relevant market data when an alert is issued. For example, the alert unit optimizes the content of the alert based on relevant market data. For example, the alert unit adjusts how the alert is displayed by referring to relevant market data. Furthermore, the alert unit can also adjust the importance of the alert based on relevant market data. For example, the alert unit optimizes the content of the alert based on relevant market data. This allows for the optimization of alert content by referring to relevant market data.

[0095] The automated generation unit can estimate the user's emotions and adjust the generated expression based on the estimated emotions. For example, if the user is stressed, the automated generation unit will adopt a simple and easy-to-understand expression. For example, if the user is relaxed, the automated generation unit will adopt an expression that includes detailed information. Also, if the user is in a hurry, the automated generation unit can adopt a concise expression that allows for quick review. For example, if the user is stressed, the automated generation unit will adopt a simple and easy-to-understand expression. In this way, by adjusting the generated expression based on the user's emotions, an appropriate expression can be adopted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The automated generation unit can adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information, and concise information for less important information. The automated generation unit can also adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information. This allows for detailed generation of important information by adjusting the level of detail based on its importance.

[0097] The automated generation unit can apply different generation algorithms depending on the information category during automated generation. For example, the automated generation unit applies a specific algorithm to information about misdelivered emails for automated generation. For example, the automated generation unit applies a different algorithm to information about incorrect registrations in a registration system for automated generation. Furthermore, the automated generation unit can select the optimal generation algorithm depending on the information category. For example, the automated generation unit applies a specific algorithm to information about misdelivered emails for automated generation. This enables optimal automated generation by applying different generation algorithms depending on the information category.

[0098] The automated generation unit can estimate the user's emotions and adjust the length of the generated text based on the estimated emotions. For example, if the user is stressed, the automated generation unit will produce a short, concise text. If the user is relaxed, for example, the automated generation unit will produce a longer text with detailed explanations. Furthermore, if the user is in a hurry, the automated generation unit can produce a short text for quick review. This allows for the creation of text of appropriate length by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The automated generation unit can determine the priority of data creation based on the information submission date. For example, it will prioritize the creation of data with a more recent submission date. For example, it will lower the priority of data with an older submission date. The automated generation unit can also adjust the order of data creation based on the information submission date. For example, it will prioritize the creation of data with a more recent submission date. This allows for the creation of the most up-to-date information by determining the priority of data creation based on the submission date.

[0100] The automated generation unit can adjust the order of creation based on the relevance of the information during the automated generation process. For example, if the information is highly relevant, the automated generation unit will prioritize its creation. For example, if the information is not highly relevant, the automated generation unit will postpone its creation. The automated generation unit can also adjust the order of creation based on the relevance of the information. For example, if the information is highly relevant, the automated generation unit will prioritize its creation. In this way, by adjusting the order of creation based on the relevance of the information, highly relevant information can be created preferentially.

[0101] The name verification unit can estimate the user's emotions and adjust the name verification criteria based on the estimated emotions. For example, if the user is stressed, the name verification unit can relax the name verification criteria and perform a quick check. For example, if the user is relaxed, the name verification unit can make the name verification criteria stricter and perform a detailed check. The name verification unit can also adjust the name verification criteria to perform a quick and accurate check if the user is in a hurry. For example, if the user is stressed, the name verification unit can relax the name verification criteria and perform a quick check. This allows for quick and accurate checks by adjusting the name verification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The name verification unit can improve the accuracy of its checks by considering the interrelationships of information during the name verification process. For example, the name verification unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. For example, if there are multiple lines with different names, the name verification unit checks while considering the interrelationships. Furthermore, the name verification unit can improve the accuracy of the name verification based on the interrelationships of information. For example, the name verification unit checks the interrelationship between the subscriber name and the billing name to confirm consistency. In this way, the accuracy of the check can be improved by considering the interrelationships of information.

[0103] The name verification unit can perform name verification while considering the attribute information of the information submitter. For example, the name verification unit adjusts the criteria for name verification based on the information submitter's job title or department. The name verification unit also performs name verification while considering the information submitter's past submission history. Furthermore, the name verification unit can evaluate the reliability of the information submitter and improve the accuracy of the name verification. For example, the name verification unit adjusts the criteria for name verification based on the information submitter's job title or department. This allows for improved verification accuracy by considering the attribute information of the information submitter.

[0104] The name checking unit can estimate the user's emotions and adjust the order in which the name checking results are displayed based on the estimated emotions. For example, if the user is feeling stressed, the name checking unit will prioritize displaying important name checking results. For example, if the user is relaxed, the name checking unit will display detailed name checking results. The name checking unit can also display name checking results for quick review if the user is in a hurry. For example, if the user is feeling stressed, the name checking unit will prioritize displaying important name checking results. This allows for prioritizing the display of important results by adjusting the order in which the name checking results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The name verification unit can perform name verification while considering the geographical distribution of the information. For example, the name verification unit can perform name verification based on the location of the information submitter. For example, the name verification unit can perform name verification based on the location of the information recipient. Furthermore, the name verification unit can also perform name verification while considering the geographical relevance of the information. For example, the name verification unit can perform name verification based on the location of the information submitter. This improves the accuracy of the verification by considering the geographical distribution of the information.

[0106] The name verification unit can improve the accuracy of its name verification by referring to relevant literature. For example, the name verification unit can adjust the criteria for name verification by referring to relevant literature. The name verification unit can improve the accuracy of name verification based on relevant literature. Furthermore, the name verification unit can supplement the results of name verification by referring to relevant literature. For example, the name verification unit can adjust the criteria for name verification by referring to relevant literature. This allows for improved verification accuracy by referring to relevant literature.

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

[0108] The data collection unit can analyze a user's past behavior history and select the most suitable information collection method. For example, it can analyze actions and choices that a user has frequently performed in the past and collect information based on similar patterns. Furthermore, the data collection unit can identify trends in actions performed during specific time periods based on the user's past behavior history and collect information during those times. This allows for the selection of the most optimal information collection method by analyzing the user's past behavior history.

[0109] The checking unit can estimate the user's emotions during information integrity checks and adjust the rigor of the check based on those emotions. For example, if the user is stressed, the rigor of the check can be reduced, and the check can be performed quickly. Conversely, if the user is relaxed, the rigor of the check can be increased, and a more detailed check can be performed. By adjusting the rigor of the check based on the user's emotions, quick and accurate checks become possible.

[0110] The alert unit can estimate the user's emotions when an alert is issued and adjust the way the alert is displayed based on that estimated emotion. For example, if the user is stressed, a simple and highly visible alert is displayed. Conversely, if the user is relaxed, an alert containing more detailed information can be displayed. This allows for the display of highly visible alerts by adjusting the alert display method based on the user's emotions.

[0111] The automated generation unit can estimate the user's emotions and adjust the generated expression based on those emotions. For example, if the user is stressed, it will use a simple and easy-to-understand expression. Conversely, if the user is relaxed, it can use an expression that includes detailed information. By adjusting the generated expression based on the user's emotions, the system can select the most appropriate expression.

[0112] The name verification unit can estimate the user's emotions and adjust the name verification criteria based on those emotions. For example, if the user is stressed, the name verification criteria can be relaxed and the check can be performed quickly. Conversely, if the user is relaxed, the name verification criteria can be made stricter and a more detailed check can be performed. This allows for quick and accurate verification by adjusting the name verification criteria based on the user's emotions.

[0113] The data collection unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current projects. It can also filter and collect relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest.

[0114] The checking unit can improve the accuracy of its checks by considering the interrelationships between information during consistency checks. For example, it can check the relationship between the recipient of an email and the content of an attached file to confirm consistency. It can also check the relationship between the subscriber name and the billing name to confirm consistency. In this way, the accuracy of the checks can be improved by considering the interrelationships between information.

[0115] The alerting unit can apply different alerting methods depending on the category of information when issuing an alert. For example, it can distinguish between alerts regarding misdelivered emails and alerts regarding incorrect registrations in the registration system. It can also apply different alerting methods depending on the importance of the information. By applying different alerting methods to each category of information, the system can issue the most appropriate alerts.

[0116] The automated generation unit can adjust the level of detail in the generated information based on its importance. For example, it can generate detailed information for highly important information, and concise information for less important information. By adjusting the level of detail based on the importance of the information, it is possible to generate detailed information for important information.

[0117] The name verification unit can perform name verification while considering the attribute information of the information submitter. For example, it can adjust the name verification criteria based on the information submitter's job title or department. It can also perform name verification while considering the information submitter's past submission history. By considering the attribute information of the information submitter, the accuracy of the verification can be improved.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit collects information. For example, it collects information such as the recipient of the email, the contents of the attachment, the subscriber's name, and the billing name. The collection unit obtains the recipient information of the email and analyzes the contents of the attachment. It can also collect the subscriber's name and the billing name. Step 2: The checking unit checks the consistency of the information collected by the collection unit. For example, it verifies the consistency and accuracy of the collected information and detects data inconsistencies, omissions, and duplicates. Step 3: The alert unit issues an alert based on the errors detected by the checking unit. For example, it warns the user based on the detected errors.

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

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0123] Each of the multiple elements described above, including the collection unit, check unit, alert unit, automatic creation unit, and name check unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects email recipient information and the contents of attachments. The check unit is implemented by the identification processing unit 290 of the data processing device 12 and verifies the integrity of the collected information. The alert unit is implemented by the control unit 46A of the smart device 14 and warns the user based on detected errors. The automatic creation unit is implemented by the identification processing unit 290 of the data processing device 12 and the generation AI automatically creates the email body. The name check unit is implemented by the identification processing unit 290 of the data processing device 12 and detects incorrect names. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0139] Each of the multiple elements described above, including the collection unit, checking unit, alert unit, automatic creation unit, and name checking unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects email recipient information and the contents of attachments. The checking unit is implemented by the identification processing unit 290 of the data processing device 12 and verifies the integrity of the collected information. The alert unit is implemented by the control unit 46A of the smart glasses 214 and warns the user based on detected errors. The automatic creation unit is implemented by the identification processing unit 290 of the data processing device 12 and the generation AI automatically creates the email body. The name checking unit is implemented by the identification processing unit 290 of the data processing device 12 and detects name discrepancies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

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

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0155] Each of the multiple elements described above, including the collection unit, check unit, alert unit, automatic creation unit, and name check unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects email recipient information and the contents of attachments. The check unit is implemented by the identification processing unit 290 of the data processing device 12 and verifies the integrity of the collected information. The alert unit is implemented by the control unit 46A of the headset terminal 314 and warns the user based on detected errors. The automatic creation unit is implemented by the identification processing unit 290 of the data processing device 12 and the generation AI automatically creates the email body. The name check unit is implemented by the identification processing unit 290 of the data processing device 12 and detects incorrect names. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0172] Each of the multiple elements described above, including the collection unit, checking unit, alert unit, automatic creation unit, and name checking unit, is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects email recipient information and the contents of attachments. The checking unit is implemented by, for example, the identification processing unit 290 of the data processing device 12 and verifies the integrity of the collected information. The alert unit is implemented by, for example, the control unit 46A of the robot 414 and warns the user based on detected errors. The automatic creation unit is implemented by, for example, the identification processing unit 290 of the data processing device 12 and the generating AI automatically creates the email body. The name checking unit is implemented by, for example, the identification processing unit 290 of the data processing device 12 and detects incorrect names. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

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

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) The information collection unit, A checking unit that checks for consistency based on the information collected by the aforementioned collection unit, The system includes an alert unit that issues an alert based on an error detected by the aforementioned checking unit. A system characterized by the following features. (Note 2) It features an automated generation section where a generation AI automatically creates the email body. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a name checking unit that detects different names. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect information such as the recipient of the email, the contents of the attachment, the name of the contract holder, and the name of the billing address holder. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned checking unit is Check the consistency based on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The alert unit is, The system issues alerts based on errors detected by the checking unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past email sending history and select the most suitable method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned checking unit is We estimate the user's emotions and adjust the consistency check criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is When performing consistency checks, consider the interrelationships between pieces of information to improve the accuracy of the checks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is During consistency checks, the attribute information of the information submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is The system estimates the user's emotions and adjusts the display order of the check results based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is During consistency checks, the geographical distribution of information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned checking unit is During consistency checks, we improve the accuracy of the checks by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, When an alert is issued, past alert data is referenced to optimize the current alert. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, When issuing an alert, different alert methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, It estimates the user's emotions and adjusts the importance of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, When an alert is issued, the priority of the alert is determined based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When an alert is issued, the relevant market data is referenced to optimize the alert. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automatic creation unit, It estimates the user's emotions and adjusts the automatically generated expressions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned automatic creation unit, When automatically generating data, adjust the level of detail based on the importance of the information. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned automatic creation unit, When automatically generating information, different generation algorithms are applied depending on the category of the information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned automatic creation unit, It estimates the user's emotions and adjusts the length of the automatically generated animation based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned automatic creation unit, During automatic creation, the priority of creation is determined based on when the information was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned automatic creation unit, During automatic creation, the creation order is adjusted based on the relevance of the information. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned name checking unit is, The system estimates the user's emotions and adjusts the criteria for name verification based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned name checking unit is, When checking the identity of a person, we improve the accuracy of the check by considering the interrelationships between the information. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned name checking unit is, When checking the name, the attribute information of the person submitting the information is taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned name checking unit is, The system estimates the user's emotions and adjusts the order in which the results of the name check are displayed based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned name checking unit is, When checking the name of the person, the geographical distribution of the information is taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned name checking unit is, When checking the name of the person, we improve the accuracy of the check by referring to relevant literature. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The information collection unit, A checking unit that checks for consistency based on the information collected by the aforementioned collection unit, The system includes an alert unit that issues an alert based on an error detected by the aforementioned checking unit. A system characterized by the following features.

2. It features an automated generation section where a generation AI automatically creates the email body. The system according to feature 1.

3. It is equipped with a name checking unit that detects different names. The system according to feature 1.

4. The aforementioned collection unit is Collect information such as the recipient of the email, the contents of the attachment, the name of the contract holder, and the name of the billing address holder. The system according to feature 1.

5. The aforementioned checking unit is Check the consistency based on the collected information. The system according to feature 1.

6. The alert unit is, The system issues alerts based on errors detected by the checking unit. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past email sending history and select the most suitable method for collecting information. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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