system

Abstract

The system according to this embodiment aims to detect anomalies in transaction logs in real time and to perform smooth multilingual translation. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, an alert unit, an input unit, a translation unit, and an output unit. The monitoring unit monitors the transaction log in real time. The detection unit detects anomalies from the transaction log monitored by the monitoring unit. The alert unit issues an alert based on the anomaly detected by the detection unit. The input unit receives audio input. The translation unit translates the audio input by the input unit. The output unit outputs the audio translated by the translation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to simultaneously detect abnormalities in transaction logs and perform translations corresponding to multiple languages, and there is room for improvement in achieving both security and convenience.

[0005] The system according to the embodiment aims to detect abnormalities in transaction logs in real time and smoothly perform translations corresponding to multiple languages.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a monitoring unit, a detection unit, an alert unit, an input unit, a translation unit, and an output unit. The monitoring unit monitors the transaction log in real time. The detection unit detects anomalies from the transaction log monitored by the monitoring unit. The alert unit issues an alert based on the anomaly detected by the detection unit. The input unit receives audio input. The translation unit translates the audio input by the input unit. The output unit outputs the audio translated by the translation unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect anomalies in transaction logs in real time and perform smooth multilingual translation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The security enhancement system according to an embodiment of the present invention is a system that provides a payment monitoring system for enhancing the security of personal asset management and a high-precision translation microphone set using LLM. This security enhancement system monitors the logs of each transaction in real time and uses an algorithm to detect abnormal patterns. For example, it issues an alert when a large transaction deviates from the normal transaction pattern or when multiple transactions are made in a short period of time. This allows for the early detection of fraudulent transactions and minimizes damage. The high-precision translation microphone set using LLM aims to eliminate language barriers by translating speech in real time. It instantly translates the language spoken by the user and outputs it in another language. For example, when an English-speaking user converses with a Japanese-speaking person, the English speech can be translated into Japanese and conveyed to the other person. This eliminates the need for an interpreter and enables smooth communication in a global field. Thus, the security enhancement system enhances the security of personal asset management and facilitates smooth global communication.

[0029] The security enhancement system according to the embodiment comprises a monitoring unit, a detection unit, an alert unit, an input unit, a translation unit, and an output unit. The monitoring unit monitors the transaction log in real time. For example, the monitoring unit monitors the log of each transaction in seconds and detects abnormal patterns. The monitoring unit can also monitor in milliseconds, minimizing transaction delays. The detection unit detects anomalies from the transaction log monitored by the monitoring unit. For example, the detection unit detects large-scale transactions that deviate from normal transaction patterns. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit sets upper limits on transaction amounts and thresholds for transaction frequency, and detects transactions exceeding these as anomalies. The alert unit issues alerts based on the anomalies detected by the detection unit. For example, the alert unit issues an alert immediately when an anomaly is detected. The alert unit can also adjust how the alert is displayed. For example, the alert unit issues alerts using pop-up displays or notification sounds. The input unit receives audio in the language spoken by the user. The input unit inputs audio, taking into consideration, for example, the audio format and audio quality. The translation unit translates the audio input by the input unit into another language. The translation unit performs the translation, taking into consideration, for example, the translation algorithm used and the types of languages ​​it corresponds to. The output unit outputs the audio translated by the translation unit. The output unit outputs the audio, taking into consideration, for example, the audio format and audio quality. As a result, the security enhancement system according to the embodiment can consistently perform transaction log monitoring, anomaly detection, alert generation, audio input, translation, and output.

[0030] The monitoring unit can monitor the logs of each transaction in real time. For example, the monitoring unit can monitor the logs of each transaction in seconds. The monitoring unit can also monitor in milliseconds to minimize transaction delays. By monitoring the transaction logs in real time, the monitoring unit can quickly detect anomalies. This allows for the rapid detection of anomalies by monitoring the logs of each transaction in real time.

[0031] The detection unit can detect large-scale transactions or multiple transactions in a short period of time that deviate from normal trading patterns. For example, the detection unit can detect large-scale transactions that deviate from normal trading patterns. The detection unit sets upper limits on transaction amounts and the number of transactions, and detects transactions exceeding these limits as abnormal. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit sets a threshold for transaction frequency, and detects transactions exceeding this threshold as abnormal. This allows for the rapid detection of abnormal transactions, enabling the early detection of fraudulent transactions.

[0032] The alert unit can issue alerts based on detected anomalies. For example, the alert unit can issue an alert immediately when an anomaly is detected. The alert unit can also adjust how alerts are displayed. For example, the alert unit can issue alerts using pop-up displays or notification sounds. This allows for a quick response by issuing alerts immediately when an anomaly is detected.

[0033] The input unit can input the voice of the language the user speaks. The input unit takes into account factors such as the audio format and audio quality when inputting the voice. The input unit can accurately input the voice of the language the user speaks. This ensures accurate input of the voice of the language the user speaks.

[0034] The translation unit can translate input audio into other languages. The translation unit performs translations considering factors such as the translation algorithm used and the types of languages ​​it supports. By translating input audio into other languages, the translation unit can eliminate language barriers. This allows for the elimination of language barriers through the translation of input audio into other languages.

[0035] The output unit can output the translated audio. The output unit outputs the audio considering factors such as audio format and audio quality. The output unit can accurately output the translated audio. This ensures that the translated audio is output accurately.

[0036] The monitoring unit can improve the accuracy of anomaly detection by referring to past transaction data when monitoring transaction logs. For example, the monitoring unit can learn abnormal patterns based on past transaction data and reflect them in real-time monitoring. The monitoring unit can also predict anomalies that are likely to occur during specific time periods from past transaction data and strengthen monitoring. Furthermore, the monitoring unit can analyze past transaction data to improve the accuracy of anomaly detection for specific transaction patterns. In this way, the accuracy of anomaly detection is improved by referring to past transaction data.

[0037] The monitoring unit can apply different monitoring algorithms depending on the type of transaction during monitoring. For example, it can apply a more rigorous monitoring algorithm to large-scale transactions, and a faster monitoring algorithm to small-scale transactions. Furthermore, it can apply industry-specific monitoring algorithms to transactions related to particular industries. This improves the accuracy of monitoring by applying the appropriate monitoring algorithm for each type of transaction.

[0038] The monitoring unit can improve the accuracy of its monitoring by considering the geographical distribution of transactions. For example, it can prioritize monitoring abnormal transactions that frequently occur in a specific region. It can also detect consecutive transactions in geographically distant locations as abnormal. Furthermore, the monitoring unit can learn patterns of abnormal transactions based on geographical distribution and incorporate them into its monitoring. This improves the accuracy of monitoring by considering the geographical distribution of transactions.

[0039] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on the transaction during monitoring. For example, the monitoring unit can refer to the latest research papers related to the transaction and update its monitoring algorithms. It can also refer to industry reports related to the transaction and adjust the criteria for anomaly detection. Furthermore, the monitoring unit can improve the accuracy of its monitoring by referring to relevant laws and regulations on the transaction. Thus, referring to relevant literature on the transaction improves the accuracy of monitoring.

[0040] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships between transactions when detection occurs. For example, the detection unit analyzes the interrelationships of consecutive transactions and detects anomalies. The detection unit can also improve the accuracy of anomaly detection by considering the interrelationships between multiple transactions. Furthermore, the detection unit can learn the interrelationships between transactions and reflect them in the anomaly detection algorithm. As a result, the accuracy of anomaly detection is improved by considering the interrelationships between transactions.

[0041] The detection unit can perform anomaly detection by considering the attribute information of the transaction submitter at the time of detection. For example, the detection unit can detect anomalies based on the submitter's past transaction history. The detection unit can also improve the accuracy of anomaly detection by considering the submitter's attribute information (age, occupation, etc.). Furthermore, the detection unit can learn the submitter's attribute information and reflect it in the anomaly detection algorithm. As a result, the accuracy of anomaly detection is improved by considering the attribute information of the transaction submitter.

[0042] The detection unit can perform anomaly detection by considering the geographical distribution of transactions at the time of detection. For example, the detection unit can prioritize the detection of anomaly transactions that frequently occur in a specific region. The detection unit can also detect consecutive transactions in geographically distant locations as anomalies. Furthermore, the detection unit can learn patterns of anomaly transactions based on geographical distribution and incorporate them into anomaly detection. This improves the accuracy of anomaly detection by considering the geographical distribution of transactions.

[0043] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the transaction when detection occurs. For example, the detection unit can update its anomaly detection algorithm by referring to the latest research papers related to the transaction. The detection unit can also adjust the criteria for anomaly detection by referring to industry reports related to the transaction. Furthermore, the detection unit can improve the accuracy of anomaly detection by referring to laws and regulations related to the transaction. In this way, the accuracy of anomaly detection is improved by referring to relevant literature on the transaction.

[0044] The alerting unit can improve the accuracy of alerts by referring to past alert data when an alert occurs. For example, the alerting unit can adjust the frequency of alerts based on past alert data. Furthermore, the alerting unit can improve the accuracy of alerts for specific patterns based on past alert data. In addition, the alerting unit can analyze past alert data and optimize how alerts are displayed. This improves the accuracy of alerts by referring to past alert data.

[0045] The alerting function can apply different alerting methods depending on the transaction category when an alert occurs. For example, it can display alerts in a prominent color for large transactions, and in a concise message for small transactions. Furthermore, it can apply industry-specific alerting methods to transactions related to a particular industry. This improves the accuracy of alerts by applying the appropriate alerting method for each transaction category.

[0046] The alerting section can prioritize alerts based on when the trade was submitted. For example, it can prioritize alerts for recent trades. It can also prioritize alerts for trades that occurred during a specific time period. Furthermore, the alerting section can adjust the priority of alerts based on the submission date. This allows important alerts to be displayed preferentially by prioritizing them based on when the trade was submitted.

[0047] The alerting unit can issue alerts when an alert occurs, by referring to relevant market data for the transaction. For example, the alerting unit can issue alerts for abnormal transactions based on market data. Furthermore, the alerting unit can improve the accuracy of alerts for specific patterns based on market data. In addition, the alerting unit can optimize how alerts are displayed by referring to market data. This improves the accuracy of alerts by referencing relevant market data for the transaction.

[0048] The input unit can select the optimal input method by referring to the user's past voice data during voice input. For example, the input unit may prioritize suggesting voice input methods the user has used in the past. Furthermore, the input unit can select the optimal voice input method from the user's past voice data. In addition, the input unit can analyze the user's past voice data to improve the accuracy of voice input. Thus, by referring to the user's past voice data, the accuracy of voice input is improved.

[0049] The input unit can filter voice input based on the user's current situation. For example, if the user is in a noisy environment, the input unit will apply noise cancellation. If the user is in a quiet environment, the input unit can also apply the normal voice input method. Furthermore, the input unit can select the optimal voice input method based on the user's current situation. This improves the accuracy of voice input by filtering based on the user's current situation.

[0050] The input unit can prioritize inputting highly relevant speech by considering the user's geographical location during voice input. For example, if the user is in a specific region, the input unit will prioritize inputting speech related to that region. Furthermore, the input unit can suggest highly relevant speech based on the user's geographical location. In addition, the input unit can select the optimal voice input method based on the user's current location. This allows for the priority input of highly relevant speech by considering the user's geographical location.

[0051] The input unit can analyze the user's social media activity during voice input and input relevant audio. For example, the input unit prioritizes inputting relevant audio based on the user's social media activity. Furthermore, the input unit can analyze the user's social media activity and suggest the optimal voice input method. In addition, the input unit can refer to the user's social media activity to improve the accuracy of voice input. This allows for the priority input of relevant audio by analyzing the user's social media activity.

[0052] The translation unit can adjust the level of detail in the translation based on the importance of the audio. For example, it can provide a detailed translation for important audio. It can also provide a standard translation for general audio. Furthermore, it can provide a quickly understandable translation for urgent audio. This allows for more detailed translations of important audio by adjusting the level of detail based on the importance of the audio.

[0053] The translation unit can apply different translation algorithms depending on the category of the audio during translation. For example, it can apply a translation algorithm that includes technical terms to business-related audio. It can also apply a casual translation algorithm to everyday conversational audio. Furthermore, it can apply a translation algorithm that includes technical terms to technical audio. This improves translation accuracy by applying the appropriate translation algorithm according to the category of the audio.

[0054] The translation team can prioritize translations based on when the audio was submitted. For example, they might prioritize translating the most recent audio. They can also prioritize audio submitted within a specific time frame. Furthermore, they can adjust the translation priority based on the submission date. This allows them to prioritize important audio by determining translation priorities based on the submission date.

[0055] The translation unit can adjust the order of translations based on the relevance of the audio during the translation process. For example, the translation unit will prioritize translating highly relevant audio. It can also postpone translating less relevant audio. Furthermore, the translation unit can adjust the order of translations based on the relevance of the audio. This allows for the prioritization of important audio by adjusting the order of translations based on the relevance of the audio.

[0056] The output unit can select the optimal output method by referring to the user's past voice data when outputting audio. For example, the output unit may prioritize suggesting audio output methods that the user has preferred in the past. Furthermore, the output unit can select the optimal audio output method from the user's past voice data. In addition, the output unit can analyze the user's past voice data to improve the accuracy of audio output. Thus, by referring to the user's past voice data, the accuracy of audio output is improved.

[0057] The output unit can customize the output method based on the user's current situation when outputting audio. For example, if the user is in a noisy environment, the output unit will increase the volume. Conversely, if the user is in a quiet environment, the output unit can output audio at a normal volume. Furthermore, the output unit can select the optimal audio output method based on the user's current situation. This improves the accuracy of audio output by customizing the output method based on the user's current situation.

[0058] The output unit can select the optimal audio output method when outputting audio, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit will prioritize outputting audio related to that region. Furthermore, the output unit can suggest highly relevant audio based on the user's geographical location information. In addition, the output unit can select the optimal audio output method based on the user's current location. This allows for the priority output of highly relevant audio by considering the user's geographical location information.

[0059] The output unit can analyze the user's social media activity and suggest methods for outputting audio during audio output. For example, the output unit can prioritize outputting relevant audio based on the user's social media activity. Furthermore, the output unit can analyze the user's social media activity and suggest the optimal audio output method. In addition, the output unit can refer to the user's social media activity to improve the accuracy of audio output. This allows for the priority output of relevant audio by analyzing the user's social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The enhanced security system can also include a learning unit that learns user behavior patterns to improve the accuracy of anomaly detection. For example, the learning unit analyzes the user's past transaction history and behavior patterns to model normal transaction patterns. Furthermore, the learning unit can dynamically adjust the anomaly detection algorithm based on the user's behavior patterns. In addition, the learning unit can detect changes in user behavior patterns in real time and update the anomaly detection criteria. This allows for improved anomaly detection accuracy and more appropriate security measures by learning user behavior patterns.

[0062] The monitoring unit can dynamically adjust the anomaly detection criteria based on the transaction content when monitoring transaction logs. For example, the monitoring unit can perform strict monitoring for transactions of specific products and normal monitoring for transactions of general products. Furthermore, the monitoring unit can change the anomaly detection algorithm depending on the transaction content. In addition, the monitoring unit can dynamically set the anomaly detection threshold based on the transaction content. This allows for more accurate monitoring by adjusting the anomaly detection criteria based on the transaction content.

[0063] The detection unit can also adjust its anomaly detection criteria by considering the time of day when detecting anomalies. For example, it can apply strict criteria to transactions at night and normal criteria to transactions during regular business hours. Furthermore, the detection unit can predict anomaly transactions that are likely to occur during specific time periods and strengthen monitoring during those times. In addition, the detection unit can dynamically adjust its anomaly detection algorithm based on the time of day. This improves the accuracy of anomaly detection by considering the time of day.

[0064] The alerting unit can also adjust how alerts are displayed by referring to the user's past alert response history when an anomaly is detected. For example, the alerting unit will prioritize displaying alerts that the user has responded to quickly in the past. It can also change how alerts that the user has ignored in the past are displayed. Furthermore, the alerting unit can analyze the user's past alert response history and suggest the optimal alert display method. This optimizes the alert display method by referring to the user's past alert response history, enabling quicker responses.

[0065] The input unit can also improve the accuracy of voice input by taking into account the user's speaking speed. For example, if the user speaks quickly, the input unit adjusts the speech recognition algorithm to input accurately. Furthermore, if the user speaks slowly, the input unit can increase the sensitivity of speech recognition. In addition, the input unit can dynamically adjust the timing of voice input based on the user's speaking speed. This improves the accuracy of voice input by considering the user's speaking speed.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The monitoring unit monitors the transaction logs in real time. For example, the monitoring unit monitors the logs of each transaction in seconds and detects abnormal patterns. The monitoring unit can also monitor in milliseconds, minimizing transaction delays. Step 2: The detection unit detects anomalies from the transaction logs monitored by the monitoring unit. The detection unit can, for example, detect large-scale transactions that deviate from normal transaction patterns. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit can set upper limits on transaction amounts or thresholds for transaction frequency and detect transactions exceeding these limits as anomalies. Step 3: The alert unit issues an alert based on the anomaly detected by the detection unit. For example, the alert unit issues an alert immediately when an anomaly is detected. The alert unit can also adjust how the alert is displayed. For example, the alert unit can issue an alert using a pop-up display or a notification sound. Step 4: The input section receives audio in the language the user speaks. The input section takes into account factors such as the audio format and audio quality when receiving the audio. Step 5: The translation unit translates the audio input by the input unit into another language. The translation unit performs the translation considering, for example, the translation algorithm to be used and the types of languages ​​to be translated. Step 6: The output unit outputs the audio translated by the translation unit. The output unit outputs the audio taking into consideration, for example, the audio format and audio quality.

[0068] (Example of form 2) The security enhancement system according to an embodiment of the present invention is a system that provides a payment monitoring system for enhancing the security of personal asset management and a high-precision translation microphone set using LLM. This security enhancement system monitors the logs of each transaction in real time and uses an algorithm to detect abnormal patterns. For example, it issues an alert when a large transaction deviates from the normal transaction pattern or when multiple transactions are made in a short period of time. This allows for the early detection of fraudulent transactions and minimizes damage. The high-precision translation microphone set using LLM aims to eliminate language barriers by translating speech in real time. It instantly translates the language spoken by the user and outputs it in another language. For example, when an English-speaking user converses with a Japanese-speaking person, the English speech can be translated into Japanese and conveyed to the other person. This eliminates the need for an interpreter and enables smooth communication in a global field. Thus, the security enhancement system enhances the security of personal asset management and facilitates smooth global communication.

[0069] The security enhancement system according to the embodiment comprises a monitoring unit, a detection unit, an alert unit, an input unit, a translation unit, and an output unit. The monitoring unit monitors the transaction log in real time. For example, the monitoring unit monitors the log of each transaction in seconds and detects abnormal patterns. The monitoring unit can also monitor in milliseconds, minimizing transaction delays. The detection unit detects anomalies from the transaction log monitored by the monitoring unit. For example, the detection unit detects large-scale transactions that deviate from normal transaction patterns. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit sets upper limits on transaction amounts and thresholds for transaction frequency, and detects transactions exceeding these as anomalies. The alert unit issues alerts based on the anomalies detected by the detection unit. For example, the alert unit issues an alert immediately when an anomaly is detected. The alert unit can also adjust how the alert is displayed. For example, the alert unit issues alerts using pop-up displays or notification sounds. The input unit receives audio in the language spoken by the user. The input unit inputs audio, taking into consideration, for example, the audio format and audio quality. The translation unit translates the audio input by the input unit into another language. The translation unit performs the translation, taking into consideration, for example, the translation algorithm used and the types of languages ​​it corresponds to. The output unit outputs the audio translated by the translation unit. The output unit outputs the audio, taking into consideration, for example, the audio format and audio quality. As a result, the security enhancement system according to the embodiment can consistently perform transaction log monitoring, anomaly detection, alert generation, audio input, translation, and output.

[0070] The monitoring unit can monitor the logs of each transaction in real time. For example, the monitoring unit can monitor the logs of each transaction in seconds. The monitoring unit can also monitor in milliseconds to minimize transaction delays. By monitoring the transaction logs in real time, the monitoring unit can quickly detect anomalies. This allows for the rapid detection of anomalies by monitoring the logs of each transaction in real time.

[0071] The detection unit can detect large-scale transactions or multiple transactions in a short period of time that deviate from normal trading patterns. For example, the detection unit can detect large-scale transactions that deviate from normal trading patterns. The detection unit sets upper limits on transaction amounts and the number of transactions, and detects transactions exceeding these limits as abnormal. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit sets a threshold for transaction frequency, and detects transactions exceeding this threshold as abnormal. This allows for the rapid detection of abnormal transactions, enabling the early detection of fraudulent transactions.

[0072] The alert unit can issue alerts based on detected anomalies. For example, the alert unit can issue an alert immediately when an anomaly is detected. The alert unit can also adjust how alerts are displayed. For example, the alert unit can issue alerts using pop-up displays or notification sounds. This allows for a quick response by issuing alerts immediately when an anomaly is detected.

[0073] The input unit can input the voice of the language the user speaks. The input unit takes into account factors such as the audio format and audio quality when inputting the voice. The input unit can accurately input the voice of the language the user speaks. This ensures accurate input of the voice of the language the user speaks.

[0074] The translation unit can translate input audio into other languages. The translation unit performs translations considering factors such as the translation algorithm used and the types of languages ​​it supports. By translating input audio into other languages, the translation unit can eliminate language barriers. This allows for the elimination of language barriers through the translation of input audio into other languages.

[0075] The output unit can output the translated audio. The output unit outputs the audio considering factors such as audio format and audio quality. The output unit can accurately output the translated audio. This ensures that the translated audio is output accurately.

[0076] Furthermore, the enhanced security system includes a monitoring unit that estimates the user's emotions and adjusts the monitoring frequency based on those emotions. For example, if the user is feeling anxious, the monitoring unit will increase the monitoring frequency. It can also return the monitoring frequency to normal if the user is relaxed. Additionally, if the user is in a hurry, the monitoring unit can prioritize monitoring only important transactions. This allows for more effective monitoring by adjusting the monitoring frequency according to the user's emotions.

[0077] The monitoring unit can improve the accuracy of anomaly detection by referring to past transaction data when monitoring transaction logs. For example, the monitoring unit can learn abnormal patterns based on past transaction data and reflect them in real-time monitoring. The monitoring unit can also predict anomalies that are likely to occur during specific time periods from past transaction data and strengthen monitoring. Furthermore, the monitoring unit can analyze past transaction data to improve the accuracy of anomaly detection for specific transaction patterns. In this way, the accuracy of anomaly detection is improved by referring to past transaction data.

[0078] The monitoring unit can apply different monitoring algorithms depending on the type of transaction during monitoring. For example, it can apply a more rigorous monitoring algorithm to large-scale transactions, and a faster monitoring algorithm to small-scale transactions. Furthermore, it can apply industry-specific monitoring algorithms to transactions related to particular industries. This improves the accuracy of monitoring by applying the appropriate monitoring algorithm for each type of transaction.

[0079] The monitoring unit can estimate the user's emotions and determine the priority of transactions to monitor based on those emotions. For example, if the user is feeling anxious, the monitoring unit will prioritize monitoring important transactions. Conversely, if the user is relaxed, the monitoring unit can monitor transactions with normal priorities. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring transactions that require quick processing. This allows for the priority of monitoring important transactions by determining the priority of transactions according to the user's emotions.

[0080] The monitoring unit can improve the accuracy of its monitoring by considering the geographical distribution of transactions. For example, it can prioritize monitoring abnormal transactions that frequently occur in a specific region. It can also detect consecutive transactions in geographically distant locations as abnormal. Furthermore, the monitoring unit can learn patterns of abnormal transactions based on geographical distribution and incorporate them into its monitoring. This improves the accuracy of monitoring by considering the geographical distribution of transactions.

[0081] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on the transaction during monitoring. For example, the monitoring unit can refer to the latest research papers related to the transaction and update its monitoring algorithms. It can also refer to industry reports related to the transaction and adjust the criteria for anomaly detection. Furthermore, the monitoring unit can improve the accuracy of its monitoring by referring to relevant laws and regulations on the transaction. Thus, referring to relevant literature on the transaction improves the accuracy of monitoring.

[0082] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on those emotions. For example, if the user is feeling anxious, the detection unit will tighten the anomaly detection criteria. Conversely, if the user is relaxed, the detection unit can return the anomaly detection criteria to normal. Furthermore, if the user is in a hurry, the detection unit can set criteria to detect only important anomalies. By adjusting the anomaly detection criteria according to the user's emotions, more accurate anomaly detection becomes possible.

[0083] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships between transactions when detection occurs. For example, the detection unit analyzes the interrelationships of consecutive transactions and detects anomalies. The detection unit can also improve the accuracy of anomaly detection by considering the interrelationships between multiple transactions. Furthermore, the detection unit can learn the interrelationships between transactions and reflect them in the anomaly detection algorithm. As a result, the accuracy of anomaly detection is improved by considering the interrelationships between transactions.

[0084] The detection unit can perform anomaly detection by considering the attribute information of the transaction submitter at the time of detection. For example, the detection unit can detect anomalies based on the submitter's past transaction history. The detection unit can also improve the accuracy of anomaly detection by considering the submitter's attribute information (age, occupation, etc.). Furthermore, the detection unit can learn the submitter's attribute information and reflect it in the anomaly detection algorithm. As a result, the accuracy of anomaly detection is improved by considering the attribute information of the transaction submitter.

[0085] The detection unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the detection unit will prioritize displaying important anomalies. Conversely, if the user is relaxed, the detection unit can display anomalies in the normal order. Furthermore, if the user is in a hurry, the detection unit can prioritize displaying anomalies that require immediate attention. In this way, by adjusting the order in which anomaly detection results are displayed according to the user's emotions, important anomalies can be prioritized.

[0086] The detection unit can perform anomaly detection by considering the geographical distribution of transactions at the time of detection. For example, the detection unit can prioritize the detection of anomaly transactions that frequently occur in a specific region. The detection unit can also detect consecutive transactions in geographically distant locations as anomalies. Furthermore, the detection unit can learn patterns of anomaly transactions based on geographical distribution and incorporate them into anomaly detection. This improves the accuracy of anomaly detection by considering the geographical distribution of transactions.

[0087] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on the transaction when detection occurs. For example, the detection unit can update its anomaly detection algorithm by referring to the latest research papers related to the transaction. The detection unit can also adjust the criteria for anomaly detection by referring to industry reports related to the transaction. Furthermore, the detection unit can improve the accuracy of anomaly detection by referring to laws and regulations related to the transaction. In this way, the accuracy of anomaly detection is improved by referring to relevant literature on the transaction.

[0088] The alert function can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, if the user is feeling anxious, the alert function will display an alert in a more prominent color. Conversely, if the user is relaxed, the alert function can display an alert in a normal color. Furthermore, if the user is in a hurry, the alert function can display a concise message. This allows for more appropriate alert displays by adjusting how alerts are displayed according to the user's emotions.

[0089] The alerting unit can improve the accuracy of alerts by referring to past alert data when an alert occurs. For example, the alerting unit can adjust the frequency of alerts based on past alert data. Furthermore, the alerting unit can improve the accuracy of alerts for specific patterns based on past alert data. In addition, the alerting unit can analyze past alert data and optimize how alerts are displayed. This improves the accuracy of alerts by referring to past alert data.

[0090] The alerting function can apply different alerting methods depending on the transaction category when an alert occurs. For example, it can display alerts in a prominent color for large transactions, and in a concise message for small transactions. Furthermore, it can apply industry-specific alerting methods to transactions related to a particular industry. This improves the accuracy of alerts by applying the appropriate alerting method for each transaction category.

[0091] The alert system can estimate the user's emotions and adjust the importance of alerts based on those emotions. For example, if the user is feeling anxious, the alert system will prioritize displaying important alerts. Conversely, if the user is relaxed, the alert system can display alerts of normal importance. Furthermore, if the user is in a hurry, the alert system can prioritize displaying alerts requiring immediate attention. This allows for the prioritization of important alerts by adjusting their importance according to the user's emotions.

[0092] The alerting section can prioritize alerts based on when the trade was submitted. For example, it can prioritize alerts for recent trades. It can also prioritize alerts for trades that occurred during a specific time period. Furthermore, the alerting section can adjust the priority of alerts based on the submission date. This allows important alerts to be displayed preferentially by prioritizing them based on when the trade was submitted.

[0093] The alerting unit can issue alerts when an alert occurs, by referring to relevant market data for the transaction. For example, the alerting unit can issue alerts for abnormal transactions based on market data. Furthermore, the alerting unit can improve the accuracy of alerts for specific patterns based on market data. In addition, the alerting unit can optimize how alerts are displayed by referring to market data. This improves the accuracy of alerts by referencing relevant market data for the transaction.

[0094] The input unit can estimate the user's emotions and adjust the timing of voice input based on those emotions. For example, if the user is relaxed, the input unit will set the timing of voice input to normal. If the user is tense, the input unit can delay the timing of voice input. Furthermore, if the user is in a hurry, the input unit can speed up the timing of voice input. This allows for more appropriate voice input by adjusting the timing of voice input according to the user's emotions.

[0095] The input unit can select the optimal input method by referring to the user's past voice data during voice input. For example, the input unit may prioritize suggesting voice input methods the user has used in the past. Furthermore, the input unit can select the optimal voice input method from the user's past voice data. In addition, the input unit can analyze the user's past voice data to improve the accuracy of voice input. Thus, by referring to the user's past voice data, the accuracy of voice input is improved.

[0096] The input unit can filter voice input based on the user's current situation. For example, if the user is in a noisy environment, the input unit will apply noise cancellation. If the user is in a quiet environment, the input unit can also apply the normal voice input method. Furthermore, the input unit can select the optimal voice input method based on the user's current situation. This improves the accuracy of voice input by filtering based on the user's current situation.

[0097] The input unit can estimate the user's emotions and determine the priority of the audio input based on those emotions. For example, if the user is feeling anxious, the input unit will prioritize inputting important audio. If the user is relaxed, the input unit can input audio with normal priority. Furthermore, if the user is in a hurry, the input unit can prioritize inputting audio that needs to be processed quickly. In this way, by determining the priority of the audio input according to the user's emotions, important audio can be prioritized.

[0098] The input unit can prioritize inputting highly relevant speech by considering the user's geographical location during voice input. For example, if the user is in a specific region, the input unit will prioritize inputting speech related to that region. Furthermore, the input unit can suggest highly relevant speech based on the user's geographical location. In addition, the input unit can select the optimal voice input method based on the user's current location. This allows for the priority input of highly relevant speech by considering the user's geographical location.

[0099] The input unit can analyze the user's social media activity during voice input and input relevant audio. For example, the input unit prioritizes inputting relevant audio based on the user's social media activity. Furthermore, the input unit can analyze the user's social media activity and suggest the optimal voice input method. In addition, the input unit can refer to the user's social media activity to improve the accuracy of voice input. This allows for the priority input of relevant audio by analyzing the user's social media activity.

[0100] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is nervous, the unit will use concise and easy-to-understand language. If the user is relaxed, the unit may use more detailed language. Furthermore, if the user is in a hurry, the unit may use language that can be quickly understood. By adjusting the translation's expression according to the user's emotions, a more appropriate translation becomes possible.

[0101] The translation unit can adjust the level of detail in the translation based on the importance of the audio. For example, it can provide a detailed translation for important audio. It can also provide a standard translation for general audio. Furthermore, it can provide a quickly understandable translation for urgent audio. This allows for more detailed translations of important audio by adjusting the level of detail based on the importance of the audio.

[0102] The translation unit can apply different translation algorithms depending on the category of the audio during translation. For example, it can apply a translation algorithm that includes technical terms to business-related audio. It can also apply a casual translation algorithm to everyday conversational audio. Furthermore, it can apply a translation algorithm that includes technical terms to technical audio. This improves translation accuracy by applying the appropriate translation algorithm according to the category of the audio.

[0103] The translation unit can estimate the user's emotions and adjust the translation length based on that estimation. For example, if the user is in a hurry, the unit will provide a short, concise translation. If the user is relaxed, the unit can provide a detailed translation. Furthermore, if the user is stressed, the unit can provide a simple and easy-to-understand translation. By adjusting the translation length according to the user's emotions, a more appropriate translation becomes possible.

[0104] The translation team can prioritize translations based on when the audio was submitted. For example, they might prioritize translating the most recent audio. They can also prioritize audio submitted within a specific time frame. Furthermore, they can adjust the translation priority based on the submission date. This allows them to prioritize important audio by determining translation priorities based on the submission date.

[0105] The translation unit can adjust the order of translations based on the relevance of the audio during the translation process. For example, the translation unit will prioritize translating highly relevant audio. It can also postpone translating less relevant audio. Furthermore, the translation unit can adjust the order of translations based on the relevance of the audio. This allows for the prioritization of important audio by adjusting the order of translations based on the relevance of the audio.

[0106] The output unit can estimate the user's emotions and adjust the voice output method based on the estimated emotions. For example, if the user is nervous, the output unit will output voice in a calm voice. Conversely, if the user is relaxed, the output unit can output voice in a cheerful voice. Furthermore, if the user is in a hurry, the output unit can output voice in a quick and concise manner. This allows for more appropriate voice output by adjusting the voice output method according to the user's emotions.

[0107] The output unit can select the optimal output method by referring to the user's past voice data when outputting audio. For example, the output unit may prioritize suggesting audio output methods that the user has preferred in the past. Furthermore, the output unit can select the optimal audio output method from the user's past voice data. In addition, the output unit can analyze the user's past voice data to improve the accuracy of audio output. Thus, by referring to the user's past voice data, the accuracy of audio output is improved.

[0108] The output unit can customize the output method based on the user's current situation when outputting audio. For example, if the user is in a noisy environment, the output unit will increase the volume. Conversely, if the user is in a quiet environment, the output unit can output audio at a normal volume. Furthermore, the output unit can select the optimal audio output method based on the user's current situation. This improves the accuracy of audio output by customizing the output method based on the user's current situation.

[0109] The output unit can estimate the user's emotions and determine the priority of audio output based on those emotions. For example, if the user is feeling anxious, the output unit will prioritize outputting important audio. Conversely, if the user is relaxed, the output unit can output audio with normal priority. Furthermore, if the user is in a hurry, the output unit can prioritize outputting audio that requires quick processing. In this way, by determining the priority of audio output according to the user's emotions, important audio can be prioritized.

[0110] The output unit can select the optimal audio output method when outputting audio, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit will prioritize outputting audio related to that region. Furthermore, the output unit can suggest highly relevant audio based on the user's geographical location information. In addition, the output unit can select the optimal audio output method based on the user's current location. This allows for the priority output of highly relevant audio by considering the user's geographical location information.

[0111] The output unit can analyze the user's social media activity and suggest methods for outputting audio during audio output. For example, the output unit can prioritize outputting relevant audio based on the user's social media activity. Furthermore, the output unit can analyze the user's social media activity and suggest the optimal audio output method. In addition, the output unit can refer to the user's social media activity to improve the accuracy of audio output. This allows for the priority output of relevant audio by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, input unit, translation unit, and output unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the transaction log in real time using the control unit 46A of the smart device 14 and detects abnormal patterns using the identification processing unit 290 of the data processing unit 12. The detection unit detects large-scale transactions that deviate from normal transaction patterns using the identification processing unit 290 of the data processing unit 12. The alert unit immediately issues an alert when an anomaly is detected using the control unit 46A of the smart device 14. The input unit inputs the voice of the user in the language they speak using the microphone 38B of the smart device 14. The translation unit translates the input voice into another language using the identification processing unit 290 of the data processing unit 12. The output unit outputs the translated voice using the speaker 40B of the smart device 14. Furthermore, the monitoring unit estimates the user's emotions using, for example, the specific processing unit 290 of the data processing device 12, and adjusts the monitoring frequency based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, input unit, translation unit, and output unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the transaction log in real time using the control unit 46A of the smart glasses 214 and detects abnormal patterns using the identification processing unit 290 of the data processing unit 12. The detection unit detects large-scale transactions that deviate from normal transaction patterns using the identification processing unit 290 of the data processing unit 12. The alert unit immediately issues an alert when an anomaly is detected using the control unit 46A of the smart glasses 214. The input unit inputs the voice of the language spoken by the user using the microphone 238 of the smart glasses 214. The translation unit translates the input voice into another language using the identification processing unit 290 of the data processing unit 12. The output unit outputs the translated voice using the speaker 240 of the smart glasses 214. Furthermore, the monitoring unit estimates the user's emotions using, for example, the specific processing unit 290 of the data processing device 12, and adjusts the monitoring frequency based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, input unit, translation unit, and output unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the transaction log in real time using the control unit 46A of the headset terminal 314 and detects abnormal patterns using the specific processing unit 290 of the data processing unit 12. The detection unit detects large-scale transactions that deviate from normal transaction patterns using the specific processing unit 290 of the data processing unit 12. The alert unit immediately issues an alert when an anomaly is detected using the control unit 46A of the headset terminal 314. The input unit inputs the voice of the language spoken by the user using the microphone 238 of the headset terminal 314. The translation unit translates the input voice into another language using the specific processing unit 290 of the data processing unit 12. The output unit outputs the translated voice using the speaker 240 of the headset terminal 314. Furthermore, the monitoring unit estimates the user's emotions using, for example, the specific processing unit 290 of the data processing device 12, and adjusts the monitoring frequency based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, input unit, translation unit, and output unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the transaction log in real time by the control unit 46A of the robot 414 and detects abnormal patterns by the specific processing unit 290 of the data processing unit 12. The detection unit detects large-scale transactions that deviate from normal transaction patterns by the specific processing unit 290 of the data processing unit 12. The alert unit immediately issues an alert when an abnormality is detected by the control unit 46A of the robot 414. The input unit inputs the voice of the user in the language spoken by the microphone 238 of the robot 414. The translation unit translates the input voice into another language by the specific processing unit 290 of the data processing unit 12. The output unit outputs the translated voice by the speaker 240 of the robot 414. Furthermore, the monitoring unit estimates the user's emotions using, for example, the specific processing unit 290 of the data processing device 12, and adjusts the monitoring frequency based on the estimated emotions.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The enhanced security system can also include a learning unit that learns user behavior patterns to improve the accuracy of anomaly detection. For example, the learning unit analyzes the user's past transaction history and behavior patterns to model normal transaction patterns. Furthermore, the learning unit can dynamically adjust the anomaly detection algorithm based on the user's behavior patterns. In addition, the learning unit can detect changes in user behavior patterns in real time and update the anomaly detection criteria. This allows for improved anomaly detection accuracy and more appropriate security measures by learning user behavior patterns.

[0114] The monitoring unit can dynamically adjust the anomaly detection criteria based on the transaction content when monitoring transaction logs. For example, the monitoring unit can perform strict monitoring for transactions of specific products and normal monitoring for transactions of general products. Furthermore, the monitoring unit can change the anomaly detection algorithm depending on the transaction content. In addition, the monitoring unit can dynamically set the anomaly detection threshold based on the transaction content. This allows for more accurate monitoring by adjusting the anomaly detection criteria based on the transaction content.

[0115] The detection unit can also adjust its anomaly detection criteria by considering the time of day when detecting anomalies. For example, it can apply strict criteria to transactions at night and normal criteria to transactions during regular business hours. Furthermore, the detection unit can predict anomaly transactions that are likely to occur during specific time periods and strengthen monitoring during those times. In addition, the detection unit can dynamically adjust its anomaly detection algorithm based on the time of day. This improves the accuracy of anomaly detection by considering the time of day.

[0116] The alerting unit can also adjust how alerts are displayed by referring to the user's past alert response history when an anomaly is detected. For example, the alerting unit will prioritize displaying alerts that the user has responded to quickly in the past. It can also change how alerts that the user has ignored in the past are displayed. Furthermore, the alerting unit can analyze the user's past alert response history and suggest the optimal alert display method. This optimizes the alert display method by referring to the user's past alert response history, enabling quicker responses.

[0117] The input unit can also improve the accuracy of voice input by taking into account the user's speaking speed. For example, if the user speaks quickly, the input unit adjusts the speech recognition algorithm to input accurately. Furthermore, if the user speaks slowly, the input unit can increase the sensitivity of speech recognition. In addition, the input unit can dynamically adjust the timing of voice input based on the user's speaking speed. This improves the accuracy of voice input by considering the user's speaking speed.

[0118] The translation unit can estimate the user's emotions during translation and adjust the tone of the translation based on that estimation. For example, if the user is angry, the translation will be done in a calm and composed tone. If the user is happy, the translation will be done in a bright and positive tone. Furthermore, if the user is sad, the translation will be done in a gentle tone. By adjusting the tone of the translation according to the user's emotions, more appropriate communication becomes possible.

[0119] The output unit can also estimate the user's emotions during voice output and adjust the voice speed based on the estimated emotions. For example, if the user is nervous, the voice will be output at a slower speed. If the user is relaxed, the voice will be output at a normal speed. Furthermore, if the user is in a hurry, the voice will be output at a faster speed. By adjusting the voice speed according to the user's emotions, more appropriate voice output becomes possible.

[0120] The monitoring unit can also estimate the user's emotions while monitoring transaction logs and adjust its monitoring focus based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize monitoring important transactions. If the user is relaxed, it can perform normal monitoring. Furthermore, if the user is in a hurry, it can prioritize monitoring transactions that require quick processing. This allows for more appropriate monitoring by adjusting the monitoring focus according to the user's emotions.

[0121] The detection unit can also estimate the user's emotions when detecting an anomaly and adjust how the anomaly detection results are displayed based on the estimated emotions. For example, if the user is feeling anxious, important anomalies can be displayed in a more prominent way. If the user is relaxed, anomalies can be displayed using the normal display method. Furthermore, if the user is in a hurry, anomalies can be displayed with a concise message. By adjusting how the anomaly detection results are displayed according to the user's emotions, a more appropriate response becomes possible.

[0122] The alerting unit can also estimate the user's emotions when an alert occurs and adjust the notification method based on those emotions. For example, if the user is feeling anxious, an immediate notification can be sent. If the user is relaxed, the normal notification method can be used. Furthermore, if the user is in a hurry, a concise notification message can be sent. This allows for more appropriate alert notifications by adjusting the notification method according to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The monitoring unit monitors the transaction logs in real time. For example, the monitoring unit monitors the logs of each transaction in seconds and detects abnormal patterns. The monitoring unit can also monitor in milliseconds, minimizing transaction delays. Step 2: The detection unit detects anomalies from the transaction logs monitored by the monitoring unit. The detection unit can, for example, detect large-scale transactions that deviate from normal transaction patterns. The detection unit can also detect multiple transactions in a short period of time. For example, the detection unit can set upper limits on transaction amounts or thresholds for transaction frequency and detect transactions exceeding these limits as anomalies. Step 3: The alert unit issues an alert based on the anomaly detected by the detection unit. For example, the alert unit issues an alert immediately when an anomaly is detected. The alert unit can also adjust how the alert is displayed. For example, the alert unit can issue an alert using a pop-up display or a notification sound. Step 4: The input section receives audio in the language the user speaks. The input section takes into account factors such as the audio format and audio quality when receiving the audio. Step 5: The translation unit translates the audio input by the input unit into another language. The translation unit performs the translation considering, for example, the translation algorithm to be used and the types of languages ​​to be translated. Step 6: The output unit outputs the audio translated by the translation unit. The output unit outputs the audio taking into consideration, for example, the audio format and audio quality.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors transaction logs in real time, A detection unit that detects anomalies from the transaction logs monitored by the aforementioned monitoring unit, An alert unit that issues an alert based on an abnormality detected by the aforementioned detection unit, The input section for voice input, A translation unit that translates the audio input by the aforementioned input unit, An output unit that outputs the audio translated by the translation unit, Equipped with A system characterized by the following features.

2. The aforementioned monitoring unit, Monitor the logs of each transaction in real time. The system according to feature 1.

3. The detection unit, Detects large-scale transactions or multiple transactions in a short period of time that deviate from normal trading patterns. The system according to feature 1.

4. The alert unit is, An alert is issued based on the detected anomaly. The system according to feature 1.

5. The aforementioned input unit is The user inputs the voice of the language they speak. The system according to feature 1.

6. The aforementioned translation department, Translate the input audio into another language. The system according to feature 1.

7. The output unit is, Output translated audio The system according to feature 1.

8. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

9. The aforementioned monitoring unit, When monitoring transaction logs, we improve the accuracy of anomaly detection by referring to past transaction data. The system according to feature 1.

10. The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the type of transaction. The system according to feature 1.

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