system

Abstract

The system according to the embodiment aims to efficiently carry out reminders after a withdrawal has failed and to monitor the status of delinquency. [Solution] A system according to an embodiment includes a detection unit, a transmission unit, a monitoring unit, and an evaluation unit. The detection unit detects a failed withdrawal. The transmission unit sends a reminder based on information detected by the detection unit. The monitoring unit monitors the delinquency status after the reminder is sent by the transmission unit. The evaluation unit evaluates the risk of default based on the delinquency status monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that sending reminders after failed withdrawals and monitoring delinquency status required time and money, and was not done efficiently.

[0005] The system according to the embodiment aims to efficiently carry out reminders after a withdrawal has failed and to monitor the status of delinquency. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a transmission unit, a monitoring unit, and an evaluation unit. The detection unit detects a failed withdrawal. The transmission unit sends a reminder based on information detected by the detection unit. The monitoring unit monitors the delinquency status after the reminder is sent by the transmission unit. The evaluation unit evaluates the risk of default based on the delinquency status monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently send reminders after a withdrawal has failed and monitor the status of delinquency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dunning system according to an embodiment of the present invention sends voice and email reminders to subscribers' contracted lines and contact telephone numbers when direct debits or credit card payments are unsuccessful. This system automatically sends voice and email reminders to subscribers' contracted lines and contact telephone numbers when a payment is unsuccessful. For example, a notification of the unsuccessful payment is sent to the subscriber's mobile phone or registered email address. The voice and email messages are automatically generated by the system. The system then continuously monitors the status of delinquency and periodically sends reminders. For example, reminders are sent one week or one month after the date of the unsuccessful payment. This allows for periodic reminders even if the subscriber forgets to pay. Furthermore, if the delinquency persists, the system evaluates the risk of default and takes additional measures as necessary. For example, the system can directly contact the subscriber by phone or consider legal action. This system reduces expenses by sending reminders before sending invoices. Furthermore, by continuously monitoring the status of delinquency and periodically sending reminders, the risk of default can be reduced. For example, if a withdrawal fails, immediate reminders can be sent, increasing the likelihood that the customer will make early payments and reducing the risk of bad debts. This allows the reminder system to detect failed withdrawals, send reminders, monitor delinquency status, and evaluate the risk of bad debts, thereby reducing expenses and the risk of bad debts.

[0029] A dunning system according to an embodiment includes a detection unit, a transmission unit, a monitoring unit, and an evaluation unit. The detection unit detects a failed debit attempt. Examples of failed debit attempts include, but are not limited to, insufficient funds in a bank account or technical errors. The transmission unit sends a dunning message based on the information detected by the detection unit. The transmission unit sends a dunning message via voice or email to the subscriber's contracted line and contact phone number. For example, the transmission unit notifies the subscriber of the failed debit attempt via their mobile phone or registered email address. The transmission unit automatically generates the content of the voice message or email. For example, it generates an appropriate message using template-based generation or natural language generation technology. The monitoring unit monitors the delinquency status after the dunning message sent by the transmission unit. The monitoring unit sends dunning messages at regular intervals from the date of the failed debit attempt. For example, the monitoring unit sends another dunning message one week later, one month later, or the like. The evaluation unit evaluates the default risk based on the delinquency status monitored by the monitoring unit. If the delinquency status persists, the evaluation unit evaluates the default risk and takes additional measures as necessary. For example, the system may contact the contract holder directly by telephone, consider legal procedures, etc. As a result, the dunning system according to the embodiment detects failed withdrawals, sends dunning reminders, monitors the status of delinquency, and evaluates the risk of default, thereby enabling cost reduction and reduction of the risk of default.

[0030] The sending unit can send a reminder by voice or email to the contracted line and contact phone number. Contracted lines include, but are not limited to, mobile phone lines and landline lines. Contact phone numbers include numbers provided at the time of contract and emergency contact numbers. The sending unit can, for example, send a voice message to the subscriber's mobile phone. The system automatically generates the voice message and notifies the subscriber that the withdrawal failed. The sending unit can also send an email to the subscriber's registered email address. The system also automatically generates the content of the email and notifies the subscriber that the withdrawal failed. This increases the effectiveness of the reminder by sending a reminder by voice or email to the contracted line and contact phone number. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without AI. For example, the sending unit can input the content of the voice message or email into a generation AI, which can then generate an appropriate message.

[0031] The monitoring unit can send a reminder specifying a specific period from the date on which the withdrawal could not be made. Specific periods include, but are not limited to, one week, one month, etc. For example, the monitoring unit can send another reminder one week after the date on which the withdrawal could not be made. The monitoring unit can also send another reminder one month after the date on which the withdrawal could not be made. By sending reminders at regular intervals, the delinquency status can be continuously monitored and payment can be encouraged. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input monitoring data on the delinquency status into a generation AI, which can then calculate the appropriate timing for the reminder.

[0032] The evaluation unit can evaluate the risk of default if the delinquency continues and specify specific measures to take additional measures. Specific measures include, but are not limited to, additional reminders and the initiation of legal proceedings. For example, the evaluation unit can evaluate the risk of default if the delinquency continues and contact the contract holder directly by telephone. The evaluation unit can also consider legal proceedings if the delinquency continues. In this way, the risk of default can be evaluated if the delinquency continues and additional measures can be taken to reduce the risk of default. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input data on the delinquency status into a generation AI and have the generation AI evaluate the risk of default.

[0033] The sending unit can automatically generate the content of the voice message or email. Specific methods for automatic generation include, but are not limited to, template-based generation and the use of natural language generation technology. For example, the sending unit can generate the voice message using template-based generation. The sending unit can also generate the content of the email using natural language generation technology. This allows for efficient dunning by automatically generating the content of the voice message or email. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the content of the voice message or email into a generation AI, which can then generate an appropriate message.

[0034] The evaluation unit can contact the contractor by telephone, specifying a specific contact method. Specific contact methods include, but are not limited to, telephone, email, and visit. For example, the evaluation unit can contact the contractor directly by telephone. The evaluation unit can also contact the contractor by email. This makes it possible to more effectively encourage payment by contacting the contractor directly by telephone. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the contractor's contact information into the generation AI, causing the generation AI to select an appropriate contact method.

[0035] The detection unit can perform a detailed analysis of the cause of the withdrawal failure and clearly indicate and propose specific countermeasures. Examples of causes of the withdrawal failure include, but are not limited to, insufficient funds in the bank account, the expiration of the credit card, and a bank system error. For example, if the cause is insufficient funds, the detection unit can send a message urging the user to make a deposit. If the cause is an expired credit card, the detection unit can send a message urging the user to register new card information. Furthermore, if the cause is a bank system error, the detection unit can notify the user that another withdrawal attempt will be made. This allows for early resolution of the problem by proposing countermeasures according to the cause of the withdrawal failure. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input data on the cause of the withdrawal failure into a generation AI, causing the generation AI to propose appropriate countermeasures.

[0036] The detection unit can record the frequency of withdrawal failures and specify a specific evaluation method to perform risk assessment. Specific evaluation methods include, but are not limited to, methods for calculating frequency and methods for calculating risk scores. For example, the detection unit can record the number of withdrawal failures over the past three months and perform risk assessment. It can also propose special measures to users who frequently experience withdrawal failures. Furthermore, if the frequency of withdrawal failures is high, it can also evaluate the risk of bad debt as high. Thus, by performing risk assessment based on the frequency of withdrawal failures, it becomes easier to identify high-risk users. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input frequency data of withdrawal failures into a generation AI and have the generation AI perform risk assessment.

[0037] When detecting a failed withdrawal, the detection unit can change the notification content by referring to the user's past payment history and specifying a specific customization method. Specific customization methods include, but are not limited to, changing the message based on the user's past payment history. For example, the detection unit can send a gentle notification if the user has never failed a withdrawal in the past. Alternatively, the detection unit can send a harsh notification if the user has multiple failed withdrawals in the past. Furthermore, the detection unit can send a notification suggesting appropriate measures based on the user's past payment history. This allows for more appropriate notifications by customizing the notification content based on the user's past payment history. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's past payment history data into a generation AI, which can then generate appropriate notification content.

[0038] When detecting a withdrawal failure, the detection unit can select a notification method by specifying the user's geographical location information. Specific methods include, but are not limited to, regional communication method selection criteria. For example, the detection unit can prioritize notification by email when the user is overseas. Furthermore, the detection unit can prioritize notification by voice message when the user is domestically. Furthermore, the detection unit can select the optimal notification method based on the user's geographical location information. This enables more effective notification by selecting a notification method based on the user's geographical location information. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's geographical location information data into a generation AI, causing the generation AI to select an appropriate notification method.

[0039] When sending a reminder message, the transmission unit can generate message content by specifying a specific generation method with reference to the user's past reaction data. Specific generation methods include, but are not limited to, a generation algorithm based on past reaction data. For example, the transmission unit can reuse message content to which the user responded quickly in the past. It can also avoid message content that the user ignored in the past. Furthermore, it can generate optimal message content based on the user's past reaction data. This enables more effective reminders by generating optimal message content based on the user's past reaction data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the user's past reaction data into a generation AI and have the generation AI generate appropriate message content.

[0040] When sending a reminder message, the sending unit can specify and apply a specific template depending on the user's contract details. Specific templates include, but are not limited to, types of templates depending on the contract details. For example, if the user has a high-price contract, the sending unit can apply a polite message template. Also, if the user has a low-price contract, the sending unit can apply a concise message template. Furthermore, an appropriate message template can be applied depending on the user's contract details. This enables more appropriate reminders by applying a message template depending on the user's contract details. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the user's contract details data into a generation AI and have the generation AI select an appropriate message template.

[0041] When sending a reminder message, the sending unit can select the optimal sending method by taking into account the user's device information. For example, if the user is using a smartphone, the sending unit can send a push notification. Also, if the user is using a PC, the sending unit can send an email. Furthermore, the sending unit can select the optimal sending method based on the user's device information. This enables more effective reminders by selecting the optimal sending method based on the user's device information. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the user's device information data into a generation AI and have the generation AI select an appropriate sending method.

[0042] When sending a reminder message, the sending unit can analyze the user's social media activity and send a relevant message. For example, if the user is frequently active on social media, the sending unit can send the reminder message through social media. Also, if the user uses a specific social media platform, the sending unit can send the message through that platform. Furthermore, optimal message content can be generated based on the user's social media activity. This enables more effective reminders by sending relevant messages based on the user's social media activity. Some or all of the above-described processing in the sending unit may be performed using, or without, AI. For example, the sending unit can input the user's social media activity data into a generation AI and have the generation AI generate appropriate message content.

[0043] When monitoring the delinquency status, the monitoring unit can refer to past delinquency data and specify a specific optimization method to adjust the monitoring algorithm. Specific optimization methods include, but are not limited to, methods for adjusting the algorithm based on past delinquency data. The monitoring unit can, for example, adjust the monitoring algorithm based on past delinquency data. It can also apply a special monitoring algorithm to users who frequently fall into delinquency. It can also analyze past delinquency data and introduce an optimal monitoring algorithm. This enables more effective monitoring by optimizing the monitoring algorithm based on past delinquency data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input past delinquency data into a generation AI, which can then generate an optimal monitoring algorithm.

[0044] When monitoring the delinquency status, the monitoring unit can analyze the user's payment pattern and specify a specific detection method to detect anomalies. Specific detection methods include, but are not limited to, an algorithm for detecting abnormal values ​​in payment patterns. The monitoring unit can, for example, detect abnormal delinquencies based on the user's payment pattern. It can also detect anomalies when the user deviates from their normal payment pattern. Furthermore, it can analyze the user's payment pattern to detect abnormal delinquencies early. By detecting anomalies based on the user's payment pattern, problems can be discovered early. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's payment pattern data into a generation AI and have the generation AI detect anomalies.

[0045] When monitoring the delinquency status, the monitoring unit can select a monitoring method by specifying the user's geographical location information. Specific methods include, but are not limited to, selection criteria for monitoring methods for each region. For example, the monitoring unit can adjust the monitoring method when the user is overseas. Furthermore, the monitoring unit can apply a standard monitoring method when the user is domestic. Furthermore, the monitoring unit can select an optimal monitoring method based on the user's geographical location information. Thus, more effective monitoring is possible by selecting a monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's geographical location information data into a generation AI, causing the generation AI to select an appropriate monitoring method.

[0046] When monitoring the delinquency status, the monitoring unit can refer to the user's related contract information and specify specific improvement methods to improve the accuracy of the monitoring. Specific improvement methods include, but are not limited to, methods for adjusting the monitoring algorithm based on the contract information. The monitoring unit can improve the accuracy of the monitoring based on, for example, the user's contract information. If the user has multiple contracts, the monitoring unit can also perform monitoring by referring to all of the contract information. Furthermore, the monitoring unit can select the optimal monitoring method based on the user's contract information. This improves the accuracy of monitoring based on the user's related contract information, enabling more effective monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's contract information data into a generation AI, which can then generate an optimal monitoring method.

[0047] When assessing default risk, the evaluation unit can adjust the evaluation algorithm by referring to past risk data and specifying a specific optimization method. Specific optimization methods include, but are not limited to, methods for adjusting the algorithm based on past risk data. The evaluation unit can, for example, adjust the evaluation algorithm based on past risk data. It can also apply a special evaluation algorithm to users with a high default risk. It can also analyze past risk data and introduce an optimal evaluation algorithm. This enables more accurate risk assessment by optimizing the evaluation algorithm based on past risk data. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input past risk data into a generation AI and have the generation AI generate an optimal evaluation algorithm.

[0048] When assessing default risk, the evaluation unit can analyze the user's repayment ability in detail and specify a specific evaluation method to evaluate the risk. Specific evaluation methods include, but are not limited to, methods of analyzing income, expenses, assets, etc. The evaluation unit can, for example, evaluate the repayment ability based on the user's income data. It can also evaluate the repayment ability based on the user's expense data. It can also evaluate the repayment ability based on the user's asset data. This enables more accurate risk assessment by evaluating risk based on the user's repayment ability. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's repayment ability data into a generation AI and have the generation AI perform an appropriate risk assessment.

[0049] When assessing default risk, the evaluation unit can perform risk assessment by specifying a specific method for the user's geographical location information. Specific methods include, but are not limited to, regional risk assessment standards, for example. For example, the evaluation unit can perform a careful risk assessment when the user is overseas. Furthermore, the evaluation unit can perform a standard risk assessment when the user is domestic. Furthermore, the evaluation unit can perform an optimal risk assessment based on the user's geographical location information. This enables more accurate risk assessment by performing risk assessment based on the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's geographical location information data into a generation AI, causing the generation AI to perform an appropriate risk assessment.

[0050] When assessing default risk, the evaluation unit can refer to the user's relevant financial information to identify specific improvement methods and improve the accuracy of the evaluation. Specific improvement methods include, but are not limited to, methods for adjusting the evaluation algorithm based on financial information. The evaluation unit can, for example, perform risk evaluation based on the user's bank account information. It can also perform risk evaluation based on the user's credit card information. It can also perform risk evaluation based on the user's loan information. This enables more accurate risk evaluation by improving the accuracy of the evaluation based on the user's relevant financial information. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's financial information data into a generation AI, which can then generate an optimal evaluation algorithm.

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

[0052] The dunning system may further include a history analysis unit that analyzes the user's payment history. The history analysis unit may evaluate the user's payment tendency based on the past payment history and optimize the timing and content of dunning messages. For example, a dunning message may be sent earlier to a user who has tended to make late payments in the past. Alternatively, a dunning message with a softer tone may be sent to a user who has never made a late payment. Furthermore, the history analysis unit may recommend a specific payment method based on the user's payment history. This may improve the effectiveness of dunning messages based on the user's payment history.

[0053] The sending unit can acquire the user's device information and select the optimal notification method. For example, if the user is using a smartphone, a push notification can be sent. If the user is using a PC, an email can be sent. Furthermore, if the user is using a specific application, a notification can be sent through that application. This allows for more effective reminders by selecting the optimal notification method based on the user's device information.

[0054] The monitoring unit can acquire the user's geographical location information and select the optimal monitoring method. For example, if the user is overseas, the monitoring frequency can be reduced. If the user is domestically, the normal monitoring frequency can be applied. Furthermore, if the user is in a specific region, the monitoring method can be selected according to the characteristics of that region. This allows for more effective monitoring by optimizing the monitoring method based on the user's geographical location information.

[0055] The evaluation unit can analyze a user's social media activity and evaluate the risk of default. For example, if a user is frequently active on social media, a careful risk evaluation can be performed. Alternatively, if a user is not very active on a particular social media platform, a normal risk evaluation can be performed. Furthermore, the risk evaluation criteria can be adjusted based on the user's social media activity. This allows for more accurate risk evaluation by performing risk evaluation based on the user's social media activity.

[0056] The sending unit can refer to the user's past reaction data and generate optimal message content. For example, it can reuse message content to which the user responded quickly in the past. It can also avoid message content that the user ignored in the past. Furthermore, it can generate optimal message content based on the user's past reaction data. This allows for more effective reminders by generating optimal message content based on the user's past reaction data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The detector detects a failed debit attempt, which may include, but is not limited to, insufficient funds in the bank account or a technical error. Step 2: The sending unit sends a reminder based on the information detected by the detection unit. The sending unit sends a reminder by voice or email to the contracted line and contact phone number. For example, it may notify the subscriber's mobile phone or registered email address that the withdrawal could not be made. The sending unit automatically generates the content of the voice message or email. For example, it uses template-based generation or natural language generation technology to generate an appropriate message. Step 3: The monitoring unit monitors the status of delinquency after the reminder sent by the sending unit. The monitoring unit sends reminders at regular intervals from the date on which the withdrawal was not successful. For example, the monitoring unit sends reminders again one week later, one month later, etc. Step 4: The evaluation department assesses the risk of default based on the delinquency status monitored by the monitoring department. If the delinquency status continues, the evaluation department assesses the risk of default and takes additional measures as necessary. For example, they may contact the contract holder directly by phone or consider legal action.

[0059] (Example 2) A dunning system according to an embodiment of the present invention sends voice and email reminders to subscribers' contracted lines and contact telephone numbers when direct debits or credit card payments are unsuccessful. This system automatically sends voice and email reminders to subscribers' contracted lines and contact telephone numbers when a payment is unsuccessful. For example, a notification of the unsuccessful payment is sent to the subscriber's mobile phone or registered email address. The voice and email messages are automatically generated by the system. The system then continuously monitors the status of delinquency and periodically sends reminders. For example, reminders are sent one week or one month after the date of the unsuccessful payment. This allows for periodic reminders even if the subscriber forgets to pay. Furthermore, if the delinquency persists, the system evaluates the risk of default and takes additional measures as necessary. For example, the system can directly contact the subscriber by phone or consider legal action. This system reduces expenses by sending reminders before sending invoices. Furthermore, by continuously monitoring the status of delinquency and periodically sending reminders, the risk of default can be reduced. For example, if a withdrawal fails, immediate reminders can be sent, increasing the likelihood that the customer will make early payments and reducing the risk of bad debts. This allows the reminder system to detect failed withdrawals, send reminders, monitor delinquency status, and evaluate the risk of bad debts, thereby reducing expenses and the risk of bad debts.

[0060] A dunning system according to an embodiment includes a detection unit, a transmission unit, a monitoring unit, and an evaluation unit. The detection unit detects a failed debit attempt. Examples of failed debit attempts include, but are not limited to, insufficient funds in a bank account or technical errors. The transmission unit sends a dunning message based on the information detected by the detection unit. The transmission unit sends a dunning message via voice or email to the subscriber's contracted line and contact phone number. For example, the transmission unit notifies the subscriber of the failed debit attempt via their mobile phone or registered email address. The transmission unit automatically generates the content of the voice message or email. For example, it generates an appropriate message using template-based generation or natural language generation technology. The monitoring unit monitors the delinquency status after the dunning message sent by the transmission unit. The monitoring unit sends dunning messages at regular intervals from the date of the failed debit attempt. For example, the monitoring unit sends another dunning message one week later, one month later, or the like. The evaluation unit evaluates the default risk based on the delinquency status monitored by the monitoring unit. If the delinquency status persists, the evaluation unit evaluates the default risk and takes additional measures as necessary. For example, the system may contact the contract holder directly by telephone, consider legal procedures, etc. As a result, the dunning system according to the embodiment detects failed withdrawals, sends dunning reminders, monitors the status of delinquency, and evaluates the risk of default, thereby enabling cost reduction and reduction of the risk of default.

[0061] The sending unit can send a reminder by voice or email to the contracted line and contact phone number. Contracted lines include, but are not limited to, mobile phone lines and landline lines. Contact phone numbers include numbers provided at the time of contract and emergency contact numbers. The sending unit can, for example, send a voice message to the subscriber's mobile phone. The system automatically generates the voice message and notifies the subscriber that the withdrawal failed. The sending unit can also send an email to the subscriber's registered email address. The system also automatically generates the content of the email and notifies the subscriber that the withdrawal failed. This increases the effectiveness of the reminder by sending a reminder by voice or email to the contracted line and contact phone number. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without AI. For example, the sending unit can input the content of the voice message or email into a generation AI, which can then generate an appropriate message.

[0062] The monitoring unit can send a reminder specifying a specific period from the date on which the withdrawal could not be made. Specific periods include, but are not limited to, one week, one month, etc. For example, the monitoring unit can send another reminder one week after the date on which the withdrawal could not be made. The monitoring unit can also send another reminder one month after the date on which the withdrawal could not be made. By sending reminders at regular intervals, the delinquency status can be continuously monitored and payment can be encouraged. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input monitoring data on the delinquency status into a generation AI, which can then calculate the appropriate timing for the reminder.

[0063] The evaluation unit can evaluate the risk of default if the delinquency continues and specify specific measures to take additional measures. Specific measures include, but are not limited to, additional reminders and the initiation of legal proceedings. For example, the evaluation unit can evaluate the risk of default if the delinquency continues and contact the contract holder directly by telephone. The evaluation unit can also consider legal proceedings if the delinquency continues. In this way, the risk of default can be evaluated if the delinquency continues and additional measures can be taken to reduce the risk of default. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input data on the delinquency status into a generation AI and have the generation AI evaluate the risk of default.

[0064] The sending unit can automatically generate the content of the voice message or email. Specific methods for automatic generation include, but are not limited to, template-based generation and the use of natural language generation technology. For example, the sending unit can generate the voice message using template-based generation. The sending unit can also generate the content of the email using natural language generation technology. This allows for efficient dunning by automatically generating the content of the voice message or email. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the content of the voice message or email into a generation AI, which can then generate an appropriate message.

[0065] The evaluation unit can contact the contractor by telephone, specifying a specific contact method. Specific contact methods include, but are not limited to, telephone, email, and visit. For example, the evaluation unit can contact the contractor directly by telephone. The evaluation unit can also contact the contractor by email. This makes it possible to more effectively encourage payment by contacting the contractor directly by telephone. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the contractor's contact information into the generation AI, causing the generation AI to select an appropriate contact method.

[0066] The detection unit can estimate the user's emotions and adjust the notification method for the withdrawal failure based on the estimated user emotions. The detection unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, a gentle notification message can be sent. If the user is relaxed, a notification message including a detailed explanation can be sent. Furthermore, if the user is in a hurry, a concise and quick notification message can be sent. This allows for more appropriate notification by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate notification method.

[0067] The detection unit can perform a detailed analysis of the cause of the withdrawal failure and clearly indicate and propose specific countermeasures. Examples of causes of the withdrawal failure include, but are not limited to, insufficient funds in the bank account, the expiration of the credit card, and a bank system error. For example, if the cause is insufficient funds, the detection unit can send a message urging the user to make a deposit. If the cause is an expired credit card, the detection unit can send a message urging the user to register new card information. Furthermore, if the cause is a bank system error, the detection unit can notify the user that another withdrawal attempt will be made. This allows for early resolution of the problem by proposing countermeasures according to the cause of the withdrawal failure. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input data on the cause of the withdrawal failure into a generation AI, causing the generation AI to propose appropriate countermeasures.

[0068] The detection unit can record the frequency of withdrawal failures and specify a specific evaluation method to perform risk assessment. Specific evaluation methods include, but are not limited to, methods for calculating frequency and risk scores. For example, the detection unit can record the number of withdrawal failures over the past three months and perform risk assessment. It can also propose special measures to users who frequently experience withdrawal failures. Furthermore, if the frequency of withdrawal failures is high, it can also evaluate the risk of bad debt as high. Thus, by performing risk assessment based on the frequency of withdrawal failures, it becomes easier to identify high-risk users. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input frequency data of withdrawal failures into a generation AI and have the generation AI perform risk assessment.

[0069] The detection unit can estimate the user's emotions and adjust the timing of the withdrawal failure notification based on the estimated user emotions. The detection unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the notification can be delayed. Alternatively, if the user is relaxed, the notification can be sent immediately. Furthermore, if the user is in a hurry, the notification can be sent quickly. This allows for more effective notifications by adjusting the notification timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input the user's emotion data into the generation AI, causing the generation AI to select an appropriate notification timing.

[0070] When detecting a failed withdrawal, the detection unit can change the notification content by referring to the user's past payment history and specifying a specific customization method. Specific customization methods include, but are not limited to, changing the message based on the user's past payment history. For example, the detection unit can send a gentle notification if the user has never failed a withdrawal in the past. Alternatively, the detection unit can send a harsh notification if the user has multiple failed withdrawals in the past. Furthermore, the detection unit can send a notification suggesting appropriate measures based on the user's past payment history. This allows for more appropriate notifications by customizing the notification content based on the user's past payment history. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's past payment history data into a generation AI, which can then generate appropriate notification content.

[0071] When detecting a withdrawal failure, the detection unit can select a notification method by specifying the user's geographical location information. Specific methods include, but are not limited to, regional communication method selection criteria. For example, the detection unit can prioritize notification by email when the user is overseas. Furthermore, the detection unit can prioritize notification by voice message when the user is domestically. Furthermore, the detection unit can select the optimal notification method based on the user's geographical location information. This enables more effective notification by selecting a notification method based on the user's geographical location information. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's geographical location information data into a generation AI, causing the generation AI to select an appropriate notification method.

[0072] The transmission unit can estimate the user's emotions and adjust the tone of the reminder message based on the estimated user emotions. The transmission unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the transmission unit can send a reminder message with a gentle tone. Also, if the user is relaxed, the transmission unit can send a reminder message with detailed explanations. Furthermore, if the user is in a hurry, the transmission unit can send a concise and quick reminder message. This enables more effective reminders by adjusting the tone of the reminder message according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit can be performed using AI, for example, or without AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate message tone.

[0073] When sending a reminder message, the transmission unit can generate message content by specifying a specific generation method with reference to the user's past reaction data. Specific generation methods include, but are not limited to, a generation algorithm based on past reaction data. For example, the transmission unit can reuse message content to which the user responded quickly in the past. It can also avoid message content that the user ignored in the past. Furthermore, it can generate optimal message content based on the user's past reaction data. This enables more effective reminders by generating optimal message content based on the user's past reaction data. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input the user's past reaction data into a generation AI and have the generation AI generate appropriate message content.

[0074] When sending a reminder message, the sending unit can specify and apply a specific template depending on the user's contract details. Specific templates include, but are not limited to, types of templates depending on the contract details. For example, if the user has a high-price contract, the sending unit can apply a polite message template. Also, if the user has a low-price contract, the sending unit can apply a concise message template. Furthermore, an appropriate message template can be applied depending on the user's contract details. This enables more appropriate reminders by applying a message template depending on the user's contract details. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the user's contract details data into a generation AI and have the generation AI select an appropriate message template.

[0075] The transmission unit can estimate the user's emotions and adjust the frequency of sending reminder messages based on the estimated user emotions. The transmission unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the transmission frequency can be reduced. Furthermore, if the user is relaxed, the transmission frequency can be increased. Furthermore, if the user is in a hurry, multiple reminder messages can be sent quickly. This enables more effective reminders by adjusting the frequency of sending reminder messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit can be performed using AI, for example, or without AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate transmission frequency.

[0076] When sending a reminder message, the sending unit can select the optimal sending method by taking into account the user's device information. For example, if the user is using a smartphone, the sending unit can send a push notification. Also, if the user is using a PC, the sending unit can send an email. Furthermore, the sending unit can select the optimal sending method based on the user's device information. This enables more effective reminders by selecting the optimal sending method based on the user's device information. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input the user's device information data into a generation AI and have the generation AI select an appropriate sending method.

[0077] When sending a reminder message, the sending unit can analyze the user's social media activity and send a relevant message. For example, if the user is frequently active on social media, the sending unit can send the reminder message through social media. Also, if the user uses a specific social media platform, the sending unit can send the message through that platform. Furthermore, optimal message content can be generated based on the user's social media activity. This enables more effective reminders by sending relevant messages based on the user's social media activity. Some or all of the above-described processing in the sending unit may be performed using, or without, AI. For example, the sending unit can input the user's social media activity data into a generation AI and have the generation AI generate appropriate message content.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring method for the delinquency status based on the estimated user emotions. The monitoring unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the monitoring frequency can be reduced. Also, if the user is relaxed, the monitoring frequency can be increased. Furthermore, if the user is in a hurry, monitoring can be performed quickly. This enables more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate monitoring method.

[0079] When monitoring the delinquency status, the monitoring unit can refer to past delinquency data and specify a specific optimization method to adjust the monitoring algorithm. Specific optimization methods include, but are not limited to, methods for adjusting the algorithm based on past delinquency data. The monitoring unit can, for example, adjust the monitoring algorithm based on past delinquency data. It can also apply a special monitoring algorithm to users who frequently fall into delinquency. It can also analyze past delinquency data and introduce an optimal monitoring algorithm. This enables more effective monitoring by optimizing the monitoring algorithm based on past delinquency data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input past delinquency data into a generation AI, which can then generate an optimal monitoring algorithm.

[0080] When monitoring the delinquency status, the monitoring unit can analyze the user's payment pattern and specify a specific detection method to detect anomalies. Specific detection methods include, but are not limited to, an algorithm for detecting abnormal values ​​in payment patterns. The monitoring unit can, for example, detect abnormal delinquencies based on the user's payment pattern. It can also detect anomalies when the user deviates from their normal payment pattern. Furthermore, it can analyze the user's payment pattern to detect abnormal delinquencies early. By detecting anomalies based on the user's payment pattern, problems can be discovered early. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's payment pattern data into a generation AI and have the generation AI detect anomalies.

[0081] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency of the delinquency status based on the estimated user emotions. The monitoring unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the monitoring frequency can be reduced. Also, if the user is relaxed, the monitoring frequency can be increased. Furthermore, if the user is in a hurry, monitoring can be performed quickly. This enables more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate monitoring frequency.

[0082] When monitoring the delinquency status, the monitoring unit can select a monitoring method by specifying the user's geographical location information. Specific methods include, but are not limited to, selection criteria for monitoring methods for each region. For example, the monitoring unit can adjust the monitoring method when the user is overseas. Furthermore, the monitoring unit can apply a standard monitoring method when the user is domestic. Furthermore, the monitoring unit can select an optimal monitoring method based on the user's geographical location information. Thus, more effective monitoring is possible by selecting a monitoring method based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's geographical location information data into a generation AI, causing the generation AI to select an appropriate monitoring method.

[0083] When monitoring the delinquency status, the monitoring unit can refer to the user's related contract information and specify specific improvement methods to improve the accuracy of the monitoring. Specific improvement methods include, but are not limited to, methods for adjusting the monitoring algorithm based on the contract information. The monitoring unit can improve the accuracy of the monitoring based on, for example, the user's contract information. If the user has multiple contracts, the monitoring unit can also perform monitoring by referring to all of the contract information. Furthermore, the monitoring unit can select the optimal monitoring method based on the user's contract information. This improves the accuracy of monitoring based on the user's related contract information, enabling more effective monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's contract information data into a generation AI, which can then generate an optimal monitoring method.

[0084] The evaluation unit can estimate the user's emotions and adjust the default risk assessment criteria based on the estimated user emotions. The evaluation unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the default risk can be assessed as low. Also, if the user is relaxed, the default risk can be assessed as high. Furthermore, if the user is in a hurry, the default risk can be assessed quickly. This enables more appropriate risk assessment by adjusting the default risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI select appropriate evaluation criteria.

[0085] When assessing default risk, the evaluation unit can adjust the evaluation algorithm by referring to past risk data and specifying a specific optimization method. Specific optimization methods include, but are not limited to, methods for adjusting the algorithm based on past risk data. The evaluation unit can, for example, adjust the evaluation algorithm based on past risk data. It can also apply a special evaluation algorithm to users with a high default risk. It can also analyze past risk data and introduce an optimal evaluation algorithm. This enables more accurate risk assessment by optimizing the evaluation algorithm based on past risk data. Some or all of the above-described processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input past risk data into a generation AI and have the generation AI generate an optimal evaluation algorithm.

[0086] When assessing default risk, the evaluation unit can analyze the user's repayment ability in detail and specify a specific evaluation method to evaluate the risk. Specific evaluation methods include, but are not limited to, methods of analyzing income, expenses, assets, etc. The evaluation unit can, for example, evaluate the repayment ability based on the user's income data. It can also evaluate the repayment ability based on the user's expense data. It can also evaluate the repayment ability based on the user's asset data. This enables more accurate risk assessment by evaluating risk based on the user's repayment ability. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's repayment ability data into a generation AI and have the generation AI perform an appropriate risk assessment.

[0087] The evaluation unit can estimate the user's emotions and adjust the frequency of bad debt risk assessments based on the estimated user emotions. The evaluation unit can estimate the user's emotions using technologies such as voice analysis, text analysis, and facial expression recognition. For example, if the user is stressed, the evaluation frequency can be reduced. Also, if the user is relaxed, the evaluation frequency can be increased. Furthermore, if the user is in a hurry, the evaluation can be performed quickly. This enables more appropriate risk assessment by adjusting the evaluation frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI select an appropriate evaluation frequency.

[0088] When assessing default risk, the evaluation unit can perform risk assessment by specifying a specific method for the user's geographical location information. Specific methods include, but are not limited to, regional risk assessment standards, for example. For example, the evaluation unit can perform a careful risk assessment when the user is overseas. Furthermore, the evaluation unit can perform a standard risk assessment when the user is domestic. Furthermore, the evaluation unit can perform an optimal risk assessment based on the user's geographical location information. This enables more accurate risk assessment by performing risk assessment based on the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's geographical location information data into a generation AI, causing the generation AI to perform an appropriate risk assessment.

[0089] When assessing default risk, the evaluation unit can refer to the user's relevant financial information to identify specific improvement methods and improve the accuracy of the evaluation. Specific improvement methods include, but are not limited to, methods for adjusting the evaluation algorithm based on financial information. The evaluation unit can, for example, perform risk evaluation based on the user's bank account information. It can also perform risk evaluation based on the user's credit card information. It can also perform risk evaluation based on the user's loan information. This enables more accurate risk evaluation by improving the accuracy of the evaluation based on the user's relevant financial information. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's financial information data into a generation AI, which can then generate an optimal evaluation algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned detection unit, transmission unit, monitoring unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects a failed withdrawal. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and sends a reminder by voice or email to the contracted line and contact phone number. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the delinquency status. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of default. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned detection unit, transmission unit, monitoring unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects a failed withdrawal. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends a reminder by voice or email to the contract line and contact phone number. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the delinquency status. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of default. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, transmission unit, monitoring unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects a failed withdrawal. The transmission unit is realized, for example, by the control unit 46A of the headset type terminal 314 and sends a reminder by voice or email to the contracted line and contact phone number. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the delinquency status. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of default. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, transmission unit, monitoring unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects a failed withdrawal. The transmission unit is realized, for example, by the control unit 46A of the robot 414 and sends a reminder by voice or email to the contracted line and contact phone number. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the delinquency status. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of default.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The dunning system may further include a history analysis unit that analyzes the user's payment history. The history analysis unit may evaluate the user's payment tendency based on the past payment history and optimize the timing and content of dunning messages. For example, a dunning message may be sent earlier to a user who has tended to make late payments in the past. Alternatively, a dunning message with a softer tone may be sent to a user who has never made a late payment. Furthermore, the history analysis unit may recommend a specific payment method based on the user's payment history. This may improve the effectiveness of dunning messages based on the user's payment history.

[0092] The sending unit can acquire the user's device information and select the optimal notification method. For example, if the user is using a smartphone, a push notification can be sent. If the user is using a PC, an email can be sent. Furthermore, if the user is using a specific application, a notification can be sent through that application. This allows for more effective reminders by selecting the optimal notification method based on the user's device information.

[0093] The monitoring unit can acquire the user's geographical location information and select the optimal monitoring method. For example, if the user is overseas, the monitoring frequency can be reduced. If the user is domestically, the normal monitoring frequency can be applied. Furthermore, if the user is in a specific region, the monitoring method can be selected according to the characteristics of that region. This allows for more effective monitoring by optimizing the monitoring method based on the user's geographical location information.

[0094] The evaluation unit can analyze a user's social media activity and evaluate the risk of default. For example, if a user is frequently active on social media, a careful risk evaluation can be performed. Alternatively, if a user is not very active on a particular social media platform, a normal risk evaluation can be performed. Furthermore, the risk evaluation criteria can be adjusted based on the user's social media activity. This allows for more accurate risk evaluation by performing risk evaluation based on the user's social media activity.

[0095] The sending unit can refer to the user's past reaction data and generate optimal message content. For example, it can reuse message content to which the user responded quickly in the past. It can also avoid message content that the user ignored in the past. Furthermore, it can generate optimal message content based on the user's past reaction data. This allows for more effective reminders by generating optimal message content based on the user's past reaction data.

[0096] The detection unit can estimate the user's emotions and adjust the notification method for a failed withdrawal based on the estimated user emotions. For example, if the user is feeling stressed, a notification message in a gentle tone can be sent. Alternatively, if the user is relaxed, a notification message including a detailed explanation can be sent. Furthermore, if the user is in a hurry, a concise and quick notification message can be sent. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions.

[0097] The sending unit can estimate the user's emotions and adjust the tone of the reminder message based on the estimated user's emotions. For example, if the user is feeling stressed, the sending unit can send a reminder message with a gentle tone. If the user is relaxed, the sending unit can send a reminder message with detailed explanations. Furthermore, if the user is in a hurry, the sending unit can send a concise and quick reminder message. This allows for more effective reminders by adjusting the tone of the reminder message according to the user's emotions.

[0098] The monitoring unit can estimate the user's emotions and adjust the monitoring method for the delinquency state based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced. Also, if the user is relaxed, the monitoring frequency can be increased. Furthermore, if the user is in a hurry, monitoring can be performed quickly. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions.

[0099] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for bad debt risk based on the estimated user's emotions. For example, if the user is feeling stressed, the bad debt risk can be evaluated low. Also, if the user is relaxed, the bad debt risk can be evaluated high. Furthermore, if the user is in a hurry, the bad debt risk can be evaluated quickly. In this way, by adjusting the evaluation criteria for bad debt risk according to the user's emotions, more appropriate risk evaluation is possible.

[0100] The sending unit can estimate the user's emotions and adjust the frequency of sending reminder messages based on the estimated user emotions. For example, if the user is feeling stressed, the sending frequency can be reduced. Alternatively, if the user is relaxed, the sending frequency can be increased. Furthermore, if the user is in a hurry, multiple reminder messages can be sent quickly. This allows for more effective reminders by adjusting the sending frequency of reminder messages according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The detector detects a failed debit attempt, which may include, but is not limited to, insufficient funds in the bank account or a technical error. Step 2: The sending unit sends a reminder based on the information detected by the detection unit. The sending unit sends a reminder by voice or email to the contracted line and contact phone number. For example, it may notify the subscriber's mobile phone or registered email address that the withdrawal could not be made. The sending unit automatically generates the content of the voice message or email. For example, it uses template-based generation or natural language generation technology to generate an appropriate message. Step 3: The monitoring unit monitors the status of delinquency after the reminder sent by the sending unit. The monitoring unit sends reminders at regular intervals from the date on which the withdrawal was not successful. For example, the monitoring unit sends reminders again one week later, one month later, etc. Step 4: The evaluation department assesses the risk of default based on the delinquency status monitored by the monitoring department. If the delinquency status continues, the evaluation department assesses the risk of default and takes additional measures as necessary. For example, they may contact the contract holder directly by phone or consider legal action.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a detection unit that detects a withdrawal failure; a transmitting unit that transmits a reminder based on the information detected by the detecting unit; a monitoring unit that monitors the status of delinquency after the reminder sent by the sending unit; an evaluation unit that evaluates a default risk based on the delinquency status monitored by the monitoring unit; Equipped with A system characterized by:

2. The transmission unit Send voice or email reminders to your contracted lines and contact numbers 2. The system of claim 1.

3. The monitoring unit Send a reminder specifying the specific period from the date the withdrawal could not be made 2. The system of claim 1.

4. The evaluation unit Evaluate the risk of bad debt if delinquency persists, and take additional measures by specifying specific countermeasures.

2. The system of claim 1.

5. The transmission unit Automatically generate voice messages or email content 2. The system of claim 1.

6. The evaluation unit Contact the contractor by phone, clearly indicating the specific contact method.

2. The system of claim 1.

7. The detection unit To estimate a user's emotion and adjust a notification method of a withdrawal failure based on the estimated user's emotion.

2. The system of claim 1.

8. The detection unit Conduct a detailed analysis of the causes of withdrawal failures and propose specific countermeasures.

2. The system of claim 1.

9. The detection unit Record the frequency of failed withdrawals and perform risk assessment by clearly indicating the specific assessment method.

2. The system of claim 1.

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