System

Abstract

An object of a system according to an embodiment is to analyze behavior data of an employee, detect a sign of a fraudulent act, and take an action.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and an association unit. The collection part collects action data of employees. The analysis unit analyzes the data collected by the collection unit. The detection unit detects a sign of a fraud based on the data analyzed by the analysis unit. The responder responds on the basis of the sign detected by the detector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to prevent employee misconduct, and there is room for improvement to prevent damage to corporate value.

[0005] The system according to the embodiment aims to analyze employee behavioral data, detect signs of fraudulent activity, and take appropriate action. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a response unit. The collection unit collects employee behavioral data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects signs of fraudulent activity based on the data analyzed by the analysis unit. The response unit takes action based on the signs detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze employee behavioral data to detect and respond to signs of fraudulent activity. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI checker system according to an embodiment of the present invention is a system that prevents quality-related fraudulent activities in companies. The AI ​​checker system collects employee behavioral data, continuously analyzes it using AI, detects signs of fraudulent activities, and takes appropriate measures. For example, the AI ​​checker system collects employee behavioral data, such as employee attendance data, work progress, and quota achievement status. The AI ​​checker system then continuously analyzes the collected data using AI. The AI ​​analyzes the collected data and detects signs of fraudulent activities. For example, it can identify situations in which fraudulent activities are likely to occur, such as when employees feel excessive pressure or when they are having difficulty achieving their quotas. The AI ​​checker system then allows companies to take appropriate measures based on the signs of fraudulent activities detected by the AI. For example, fraudulent activities can be prevented by providing employee counseling, reviewing work, adjusting quotas, etc. This allows the AI ​​checker system to prevent corporate fraud, prevent damage to corporate value, and achieve sustainable corporate growth. In this way, the AI ​​checker system can prevent corporate misconduct, prevent damage to corporate value, and achieve sustainable corporate growth.

[0029] The AI ​​checker system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a response unit. The collection unit collects employee behavioral data. The employee behavioral data includes, but is not limited to, working hours, work content, and break times. The collection unit collects, for example, employee attendance data. The collection unit can also collect work progress information. The collection unit can also collect quota achievement status information. For example, the collection unit records employees' arrival times, departure times, and break times. The collection unit can also record task completion status and project progress. The collection unit can also record the degree of achievement of sales targets and business goals. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can extract patterns from the collected data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also use a machine learning algorithm to build a predictive model for the data. The detection unit detects signs of fraudulent activity based on the data analyzed by the analysis unit. Signs of fraudulent activity include, but are not limited to, abnormal working hours and inconsistencies in work content. For example, the detection unit detects abnormal working hours. The detection unit can also detect inconsistencies in work content. The detection unit can also detect abnormal values ​​in the data. The response unit takes appropriate action based on the signs detected by the detection unit. Examples of responses include, but are not limited to, warnings, counseling, and work review. For example, the response unit issues a warning to the employee. The response unit can also provide counseling to the employee. The response unit can also review work. As a result, the AI ​​checker system according to the embodiment can prevent corporate fraud, prevent damage to corporate value, and achieve sustainable corporate growth.

[0030] The collection unit can collect data on employee attendance data, work progress, and quota achievement status. The attendance data includes, for example, arrival time, departure time, break time, etc., but is not limited to these examples. The collection unit, for example, records employee arrival time. The collection unit can also record employee departure time. The collection unit can also record employee break time. The work progress includes, for example, task completion status, project progress, etc., but is not limited to these examples. The collection unit, for example, records task completion status. The collection unit can also record project progress. The quota achievement status includes, for example, sales target achievement status, business target achievement status, etc., but is not limited to these examples. The collection unit, for example, records sales target achievement status. The collection unit can also record business target achievement status. By collecting detailed employee behavioral data, signs of fraudulent activity can be more accurately detected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input employee attendance data into AI, which then analyzes the data.

[0031] The analysis unit analyzes the collected data and can identify situations in which employees feel excessive pressure, have difficulty achieving their quotas, or are prone to fraudulent behavior. Examples of excessive pressure include, but are not limited to, long working hours and excessive workloads. The analysis unit, for example, identifies employees' long working hours. The analysis unit can also identify employees' excessive workloads. Examples of situations in which it is difficult to achieve quotas include, but are not limited to, a lack of resources and an excessive workload. The analysis unit, for example, identifies a lack of resources. The analysis unit can also identify an excessive workload. By identifying situations in which fraudulent behavior is likely to occur, countermeasures can be taken early. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then analyzes the data.

[0032] The detection unit can detect signs of fraudulent activity based on the analyzed data. Signs of fraudulent activity include, but are not limited to, abnormal working hours, inconsistencies in work content, and the like. The detection unit can, for example, detect abnormal working hours. The detection unit can also detect inconsistencies in work content. The detection unit can also detect abnormal values ​​in the data. This makes it possible to prevent corporate fraud by detecting signs of fraudulent activity. Some or all of the above-described processing in the detection unit can be performed using, or without, AI, for example. For example, the detection unit can input the analyzed data into AI, which can then detect signs of fraudulent activity.

[0033] The response unit can provide counseling to employees, review their work, and adjust their quotas based on the detected signs. Examples of counseling include, but are not limited to, interviews and psychological tests. For example, the response unit can conduct interviews with employees. The response unit can also conduct psychological tests on employees. Examples of reviewing work include, but are not limited to, changing the content of work and adjusting the workload. For example, the response unit can change the content of employees' work. The response unit can also adjust the workload of employees. Examples of quota adjustment include, but are not limited to, changing target values ​​and extending deadlines. For example, the response unit can change employees' target values. The response unit can also extend deadlines for employees. By taking appropriate measures, fraudulent activity can be prevented. Some or all of the above-described processing in the response unit can be performed using, or without, AI. For example, the response unit can input the detected signs into AI, which can then suggest appropriate responses.

[0034] The collection unit can analyze the employee's past behavioral data and select a data collection method. For example, the collection unit can prioritize the use of a data collection method (e.g., voice input or text input) that the employee has previously preferred. The collection unit can also analyze the employee's past behavioral patterns and select the most efficient timing for data collection. The collection unit can also suggest the optimal data collection means based on the employee's past data collection history. In this way, the optimal data collection method can be selected by analyzing the employee's past behavioral data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's past behavioral data into AI, which can select the optimal data collection method.

[0035] When collecting data, the collection unit can filter the data based on the employee's current work situation and areas of interest. For example, the collection unit collects only data related to the project the employee is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the employee's areas of interest. The collection unit can also avoid collecting unnecessary data depending on the employee's work situation. In this way, highly relevant data can be collected by filtering data based on the employee's current work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's current work situation and areas of interest into AI, which then filters the data.

[0036] When collecting data, the collection unit can select a collection means according to the employee's input method. For example, if the employee prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the employee prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the employee prefers image input, the collection unit can also prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means according to the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's input method into AI, which can select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. For example, if the employee is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the employee is traveling, the collection unit can also collect data related to the employee's destination. Furthermore, if the employee stays in a specific location for a long time, the collection unit can also collect data related to that location. In this way, by taking the employee's geographical location information into account, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0038] The collection unit can analyze employees' social media activities and collect relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by employees on social media. The collection unit can also analyze employees' social media activity patterns and select the optimal timing for data collection. The collection unit can also collect relevant data based on the content of employees' social media posts. In this way, highly relevant data can be collected by analyzing employees' social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input employees' social media activities into AI, which can collect relevant data.

[0039] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by employees in the past. The collection unit can also adjust the timing of data collection by reflecting employees' past feedback. The collection unit can also customize the means of data collection based on employees' past feedback. In this way, the optimal data collection method can be selected by reflecting employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input employees' past feedback into AI, which can customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to attendance data. The analysis unit can also apply a different analysis algorithm to work progress data. The analysis unit can also apply yet another analysis algorithm to quota achievement status data. In this way, by applying different analysis algorithms depending on the data category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, corrects the current analysis result based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the employee's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the employee's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis results into AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with high levels of expertise. The analysis unit can also provide analysis results that are explained in simpler terms to employees with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the employee's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI, which can then adjust the use of technical terminology in the analysis.

[0046] The detection unit can improve the accuracy of detection by taking into account the interrelationships between data during detection. The detection unit can detect signs of fraudulent activity, for example, by taking into account the interrelationships between attendance data and work progress data. The detection unit can also detect signs of fraudulent activity by taking into account the interrelationships between quota achievement status and pressure data. The detection unit can also analyze the interrelationships between data to improve the accuracy of detection. In this way, the accuracy of detection can be improved by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the interrelationships between data into AI, which can improve the accuracy of detection.

[0047] The detection unit can perform detection by taking into account employee attribute information. The detection unit detects signs of fraudulent activity by taking into account, for example, the employee's age and gender. The detection unit can also detect signs of fraudulent activity by taking into account the employee's job title and position. The detection unit can also detect signs of fraudulent activity by taking into account the employee's years of service and experience. This allows for more appropriate detection by taking into account the employee's attribute information. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input employee attribute information into AI, which then performs detection.

[0048] The detection unit can weight detection based on the frequency of data submission during detection. For example, the detection unit can weight data that is frequently submitted higher. The detection unit can also weight data that is less frequently submitted lower. The detection unit can also adjust the weight of detection based on the frequency of data submission. This allows for efficient detection by weighting detection based on the frequency of data submission. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the frequency of data submission into AI, which then weights the detection.

[0049] The detection unit can perform detection by taking into account the geographical distribution of data. For example, the detection unit can prioritize detecting signs of fraudulent activity in a specific region. The detection unit can also analyze data covering a wide geographical area and detect signs of fraudulent activity for each region. The detection unit can also improve the accuracy of detection based on the geographical distribution. This enables more appropriate detection by taking the geographical distribution of data into consideration. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the geographical distribution of data into AI, which then performs detection.

[0050] During detection, the detection unit can improve the accuracy of detection by referring to related literature. For example, the detection unit can improve the detection algorithm by referring to related academic papers. The detection unit can also improve the accuracy of detection by referring to industry best practices. The detection unit can also improve the accuracy of detection by referring to past cases. In this way, by referring to related literature, the accuracy of detection can be improved. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input related literature into AI, which can improve the accuracy of detection.

[0051] The detection unit can perform detection taking into account the market value of the data. For example, the detection unit can assign a higher detection weight to data with a high market value. The detection unit can also assign a lower detection weight to data with a low market value. The detection unit can also determine the priority of detection based on the market value of the data. This allows for more appropriate detection by taking the market value of the data into consideration. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the market value of the data into AI, and the AI ​​can perform detection.

[0052] When responding, the response unit can analyze the employee's past behavior and select the optimal response method. For example, the response unit analyzes the employee's past behavioral patterns and selects the optimal counseling method. The response unit can also review the employee's work based on the employee's past work history. The response unit can also adjust quotas by referring to the employee's past quota achievement status. In this way, the optimal response method can be selected by analyzing the employee's past behavior. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's past behavioral data into AI, which can select the optimal response method.

[0053] The response unit can customize the response measures based on the employee's current work situation when responding. The response unit can adjust the content of the counseling depending on, for example, the project the employee is currently working on. The response unit can also review the employee's work taking into account the employee's current work situation. The response unit can also adjust the employee's quota based on the employee's current quota achievement status. This allows for more appropriate response by customizing the response measures based on the employee's current work situation. Some or all of the above-mentioned processing in the response unit can be performed using, for example, AI, or without AI. For example, the response unit can input the employee's current work situation into AI, which can then customize the response measures.

[0054] The response department can improve the response method by reflecting employee feedback when responding. For example, the response department can improve the counseling method based on employee feedback. The response department can also review work by reflecting employee feedback. The response department can also adjust quotas based on employee feedback. In this way, the response method can be improved by reflecting employee feedback. Some or all of the above-mentioned processing in the response department may be performed using AI, for example, or may be performed without using AI. For example, the response department can input employee feedback into AI, which can improve the response method.

[0055] When responding, the response unit can select the optimal response method by taking into account the employee's geographical location information. For example, if the employee is in a specific area, the response unit selects a counseling method appropriate for that area. Furthermore, if the employee is traveling, the response unit can select a response method appropriate for the employee's destination. Furthermore, if the employee is staying in a specific location for a long period of time, the response unit can select a response method appropriate for that location. In this way, the optimal response method can be selected by taking into account the employee's geographical location information. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's geographical location information into AI, which can select the optimal response method.

[0056] When responding, the response department can analyze the employee's social media activity and suggest a response method. For example, the response department can suggest an optimal counseling method based on the employee's social media activity. The response department can also analyze the employee's social media posts and suggest a work review. The response department can also refer to the activity of the employee's friends on social media and suggest an adjustment to quotas. In this way, by analyzing the employee's social media activity, the optimal response method can be suggested. Some or all of the above-mentioned processing in the response department may be performed using, for example, AI, or may be performed without using AI. For example, the response department can input the employee's social media activity into AI, which then suggests a response method.

[0057] The response unit can customize the response method by reflecting the employee's past feedback when responding. For example, the response unit customizes the counseling method based on the employee's past feedback. The response unit can also customize the work review by reflecting the employee's past feedback. The response unit can also customize the quota adjustment based on the employee's past feedback. In this way, the optimal response method can be customized by reflecting the employee's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's past feedback into AI, which can then customize the response method.

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

[0059] In addition to employee behavioral data, the collection unit can also collect employee health data. For example, by collecting data such as an employee's heart rate, blood pressure, and sleep patterns, the employee's health status can be understood. The analysis unit can analyze the collected health data and evaluate the impact of the employee's health status on work. For example, it can identify the impact of an elevated heart rate or lack of sleep on work performance. The detection unit can detect signs of fraudulent activity based on the health data. For example, it can detect signs that an employee with a deteriorating health status is at increased risk of committing fraud. The response unit can take appropriate action based on the health data. For example, it can provide leave or reduce the workload of an employee with a deteriorating health status.

[0060] In addition to employee behavioral data, the collection unit can also collect employee communication data. For example, by collecting the contents of emails and chats between employees, as well as the content of comments made in meetings, it is possible to understand employee communication patterns. The analysis unit analyzes the collected communication data and evaluates the relationships and quality of communication between employees. For example, it can identify cases where communication between specific employees is lacking or where there are a lot of negative comments. The detection unit can detect signs of misconduct based on the communication data. For example, it can detect cases where there is an increase in confidential exchanges between specific employees or where there is a lot of exchange of content unrelated to work. The response unit can take appropriate action based on the communication data. For example, it can provide team-building opportunities to employees who are lacking in communication.

[0061] The analysis unit can analyze employee performance data in addition to employee behavioral data. For example, by analyzing employees' work results and evaluation data, it is possible to understand fluctuations in employee performance. The analysis unit can combine and analyze performance data and behavioral data to evaluate whether a decline in performance is a sign of fraudulent activity. For example, if an employee whose work performance is rapidly declining is at high risk of committing fraud, it can identify such signs. The detection unit can detect signs of fraudulent activity based on the performance data. For example, if an employee whose work performance is declining exhibits a specific behavioral pattern, it can detect such signs. The response unit can take appropriate action based on the performance data. For example, it can provide training or support to employees whose performance is declining.

[0062] The collection unit can collect data on employees' hobbies and interests in addition to employee behavioral data. For example, by understanding what hobbies and interests employees have, data collection can be tailored to each employee's individual needs. The analysis unit can analyze data on hobbies and interests to evaluate the impact on employees' motivation and stress levels. For example, it can identify the positive impact hobbies and interests have on work. The detection unit can detect signs of fraudulent activity based on data on hobbies and interests. For example, it can detect signs that hobbies and interests are negatively impacting work. The response unit can take appropriate action based on data on hobbies and interests. For example, it can assign tasks that utilize hobbies and interests or suggest activities for relaxation.

[0063] The collection unit can collect data on employees' life events in addition to employee behavioral data. For example, by collecting data on life events such as marriage, childbirth, and moving, it is possible to understand changes in employees' living environments. The analysis unit can analyze the data on life events and evaluate the impact on employees' work performance and stress levels. For example, it can identify the impact of life events on work. The detection unit can detect signs of fraudulent activity based on the data on life events. For example, it can detect signs of disruption to work due to life events. The response unit can take appropriate action based on the data on life events. For example, it can provide flexible work systems in response to life events and strengthen support systems.

[0064] The collection unit can collect employee learning data in addition to employee behavioral data. For example, by collecting data on the training that employees have taken and the qualifications they have obtained, it is possible to understand the progress of improvement in employees' skills and knowledge. The analysis unit can analyze the learning data and evaluate the impact that employees' skills and knowledge have on their work. For example, it can identify the positive impact that specific skills and knowledge have on work performance. The detection unit can detect signs of fraudulent activity based on the learning data. For example, it can detect signs that an employee who lacks skills or knowledge is at high risk of committing fraud. The response unit can take appropriate action based on the learning data. For example, it can provide additional training or support to employees who lack skills or knowledge.

[0065] The collection unit can collect employee feedback data in addition to employee behavioral data. For example, by collecting data on opinions and suggestions provided by employees, it is possible to understand employee awareness and thinking. The analysis unit can analyze the feedback data and evaluate the impact of employee awareness and thinking on work. For example, it can identify the positive impact that employee opinions and suggestions have on work improvement. The detection unit can detect signs of misconduct based on the feedback data. For example, it can detect signs that an employee who receives a lot of negative feedback is at high risk of committing misconduct. The response unit can take appropriate action based on the feedback data. For example, it can improve employee motivation by making work improvements that reflect employees' opinions and suggestions.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects employee behavioral data. Employee behavioral data includes working hours, work content, break times, arrival and departure times, task completion status, project progress, sales target achievement, and business target achievement. The collection department records and collects this data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit uses these techniques to extract patterns from the collected data, analyze data trends, and build predictive models. Step 3: The detection unit detects signs of fraudulent activity based on the data analyzed by the analysis unit. Signs of fraudulent activity include abnormal working hours, inconsistencies in work content, and abnormal values ​​in the data. The detection unit detects these signs and identifies abnormal working hours and inconsistencies in work content. Step 4: The response department takes appropriate action based on the symptoms detected by the detection department. Responses include issuing warnings, counseling, and reviewing work practices. The response department issues warnings to employees, provides counseling, and reviews their work practices.

[0068] (Example 2) An AI checker system according to an embodiment of the present invention is a system that prevents quality-related fraudulent activities in companies. The AI ​​checker system collects employee behavioral data, continuously analyzes it using AI, detects signs of fraudulent activities, and takes appropriate measures. For example, the AI ​​checker system collects employee behavioral data, such as employee attendance data, work progress, and quota achievement status. The AI ​​checker system then continuously analyzes the collected data using AI. The AI ​​analyzes the collected data and detects signs of fraudulent activities. For example, it can identify situations in which fraudulent activities are likely to occur, such as when employees feel excessive pressure or when they are having difficulty achieving their quotas. The AI ​​checker system then allows companies to take appropriate measures based on the signs of fraudulent activities detected by the AI. For example, fraudulent activities can be prevented by providing employee counseling, reviewing work, adjusting quotas, etc. This allows the AI ​​checker system to prevent corporate fraud, prevent damage to corporate value, and achieve sustainable corporate growth. In this way, the AI ​​checker system can prevent corporate misconduct, prevent damage to corporate value, and achieve sustainable corporate growth.

[0069] The AI ​​checker system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a response unit. The collection unit collects employee behavioral data. The employee behavioral data includes, but is not limited to, working hours, work content, and break times. The collection unit collects, for example, employee attendance data. The collection unit can also collect work progress information. The collection unit can also collect quota achievement status information. For example, the collection unit records employees' arrival times, departure times, and break times. The collection unit can also record task completion status and project progress. The collection unit can also record the degree of achievement of sales targets and business goals. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can extract patterns from the collected data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis techniques. The analysis unit can also use a machine learning algorithm to build a predictive model for the data. The detection unit detects signs of fraudulent activity based on the data analyzed by the analysis unit. Signs of fraudulent activity include, but are not limited to, abnormal working hours and inconsistencies in work content. For example, the detection unit detects abnormal working hours. The detection unit can also detect inconsistencies in work content. The detection unit can also detect abnormal values ​​in the data. The response unit takes appropriate action based on the signs detected by the detection unit. Examples of responses include, but are not limited to, warnings, counseling, and work review. For example, the response unit issues a warning to the employee. The response unit can also provide counseling to the employee. The response unit can also review work. As a result, the AI ​​checker system according to the embodiment can prevent corporate fraud, prevent damage to corporate value, and achieve sustainable corporate growth.

[0070] The collection unit can collect data on employee attendance data, work progress, and quota achievement status. The attendance data includes, for example, arrival time, departure time, break time, etc., but is not limited to these examples. The collection unit, for example, records employee arrival time. The collection unit can also record employee departure time. The collection unit can also record employee break time. The work progress includes, for example, task completion status, project progress, etc., but is not limited to these examples. The collection unit, for example, records task completion status. The collection unit can also record project progress. The quota achievement status includes, for example, sales target achievement status, business target achievement status, etc., but is not limited to these examples. The collection unit, for example, records sales target achievement status. The collection unit can also record business target achievement status. By collecting detailed employee behavioral data, signs of fraudulent activity can be more accurately detected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input employee attendance data into AI, which then analyzes the data.

[0071] The analysis unit analyzes the collected data and can identify situations in which employees feel excessive pressure, have difficulty achieving their quotas, or are prone to fraudulent behavior. Examples of excessive pressure include, but are not limited to, long working hours and excessive workloads. The analysis unit, for example, identifies employees' long working hours. The analysis unit can also identify employees' excessive workloads. Examples of situations in which it is difficult to achieve quotas include, but are not limited to, a lack of resources and an excessive workload. The analysis unit, for example, identifies a lack of resources. The analysis unit can also identify an excessive workload. By identifying situations in which fraudulent behavior is likely to occur, countermeasures can be taken early. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which then analyzes the data.

[0072] The detection unit can detect signs of fraudulent activity based on the analyzed data. Signs of fraudulent activity include, but are not limited to, abnormal working hours, inconsistencies in work content, and the like. The detection unit can, for example, detect abnormal working hours. The detection unit can also detect inconsistencies in work content. The detection unit can also detect abnormal values ​​in the data. This makes it possible to prevent corporate fraud by detecting signs of fraudulent activity. Some or all of the above-described processing in the detection unit can be performed using, or without, AI, for example. For example, the detection unit can input the analyzed data into AI, which can then detect signs of fraudulent activity.

[0073] The response unit can provide counseling to employees, review their work, and adjust their quotas based on the detected signs. Examples of counseling include, but are not limited to, interviews and psychological tests. For example, the response unit can conduct interviews with employees. The response unit can also conduct psychological tests on employees. Examples of reviewing work include, but are not limited to, changing the content of work and adjusting the workload. For example, the response unit can change the content of employees' work. The response unit can also adjust the workload of employees. Examples of quota adjustment include, but are not limited to, changing target values ​​and extending deadlines. For example, the response unit can change employees' target values. The response unit can also extend deadlines for employees. By taking appropriate measures, fraudulent activity can be prevented. Some or all of the above-described processing in the response unit can be performed using, or without, AI. For example, the response unit can input the detected signs into AI, which can then suggest appropriate responses.

[0074] The collection unit can estimate an employee's emotions and adjust the timing of data collection based on the estimated employee emotions. For example, if an employee is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the employee is relaxed. The collection unit can also collect detailed data when the employee is concentrating. The collection unit can also temporarily stop data collection when the employee is tired and resume it after the employee has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the employee's emotions. The estimation of an employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input employee emotion data into a generation AI, which can then adjust the timing of data collection.

[0075] The collection unit can analyze the employee's past behavioral data and select a data collection method. For example, the collection unit can prioritize the use of a data collection method (e.g., voice input or text input) that the employee has previously preferred. The collection unit can also analyze the employee's past behavioral patterns and select the most efficient timing for data collection. The collection unit can also suggest the optimal data collection means based on the employee's past data collection history. In this way, the optimal data collection method can be selected by analyzing the employee's past behavioral data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's past behavioral data into AI, which can select the optimal data collection method.

[0076] When collecting data, the collection unit can filter the data based on the employee's current work situation and areas of interest. For example, the collection unit collects only data related to the project the employee is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the employee's areas of interest. The collection unit can also avoid collecting unnecessary data depending on the employee's work situation. In this way, highly relevant data can be collected by filtering data based on the employee's current work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's current work situation and areas of interest into AI, which then filters the data.

[0077] When collecting data, the collection unit can select a collection means according to the employee's input method. For example, if the employee prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the employee prefers text input, the collection unit can also prioritize collecting text data. Furthermore, if the employee prefers image input, the collection unit can also prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means according to the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's input method into AI, which can select the optimal collection means.

[0078] The collection unit can estimate the employee's emotions and determine the priority of data to be collected based on the estimated employee's emotions. For example, if the employee is feeling stressed, the collection unit can postpone collection of less important data. Furthermore, if the employee is relaxed, the collection unit can also prioritize collection of more important data. Furthermore, if the employee is concentrating, the collection unit can prioritize collection of detailed data. Thus, by determining the priority of data to be collected according to the employee's emotions, important data can be collected preferentially. The estimation of the employee's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input employee emotion data into the generation AI and determine the priority of data to be collected by the generation AI.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. For example, if the employee is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the employee is traveling, the collection unit can also collect data related to the employee's destination. Furthermore, if the employee stays in a specific location for a long time, the collection unit can also collect data related to that location. In this way, by taking the employee's geographical location information into account, highly relevant data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0080] The collection unit can analyze employees' social media activities and collect relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by employees on social media. The collection unit can also analyze employees' social media activity patterns and select the optimal timing for data collection. The collection unit can also collect relevant data based on the content of employees' social media posts. In this way, highly relevant data can be collected by analyzing employees' social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input employees' social media activities into AI, which can collect relevant data.

[0081] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by employees in the past. The collection unit can also adjust the timing of data collection by reflecting employees' past feedback. The collection unit can also customize the means of data collection based on employees' past feedback. In this way, the optimal data collection method can be selected by reflecting employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input employees' past feedback into AI, which can customize the collection method.

[0082] The analysis unit can estimate the employee's emotions and adjust the presentation method of the analysis based on the estimated employee's emotions. For example, if the employee is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the employee is relaxed. The analysis unit can also provide a complex analysis result if the employee is concentrating. By adjusting the presentation method of the analysis according to the employee's emotions, more appropriate analysis results can be provided. The estimation of the employee's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's emotion data into a generation AI, which can then adjust the presentation method of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the importance of the data to AI, which can then adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to attendance data. The analysis unit can also apply a different analysis algorithm to work progress data. The analysis unit can also apply yet another analysis algorithm to quota achievement status data. In this way, by applying different analysis algorithms depending on the data category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into AI, which then applies an appropriate analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, corrects the current analysis result based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the employee's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the employee's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis results into AI, which can improve the accuracy of the analysis.

[0086] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit can provide a short, concise analysis result. If the employee is relaxed, the analysis unit can also provide a detailed analysis result. If the employee is concentrating, the analysis unit can also provide a long, detailed analysis result. By adjusting the length of the analysis according to the employee's emotions, more appropriate analysis results can be provided. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the employee's emotion data into the generation AI, which can then adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission into AI, and the AI ​​can determine the priority of analysis.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with high levels of expertise. The analysis unit can also provide analysis results that are explained in simpler terms to employees with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the employee's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI, which can then adjust the use of technical terminology in the analysis.

[0090] The detection unit can estimate the employee's emotions and adjust the criteria for detecting signs of misconduct based on the estimated employee's emotions. For example, the detection unit can relax the detection criteria when the employee is stressed. The detection unit can also tighten the detection criteria when the employee is relaxed. The detection unit can also adjust the detection criteria when the employee is focused. This allows for more accurate detection by adjusting the criteria for detecting signs of misconduct according to the employee's emotions. The employee's emotions are estimated using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input employee emotion data into a generative AI, which can then adjust the criteria for detecting signs of misconduct.

[0091] The detection unit can improve the accuracy of detection by taking into account the interrelationships between data during detection. The detection unit can detect signs of fraudulent activity, for example, by taking into account the interrelationships between attendance data and work progress data. The detection unit can also detect signs of fraudulent activity by taking into account the interrelationships between quota achievement status and pressure data. The detection unit can also analyze the interrelationships between data to improve the accuracy of detection. In this way, the accuracy of detection can be improved by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the interrelationships between data into AI, which can improve the accuracy of detection.

[0092] The detection unit can perform detection by taking into account employee attribute information. The detection unit detects signs of fraudulent activity by taking into account, for example, the employee's age and gender. The detection unit can also detect signs of fraudulent activity by taking into account the employee's job title and position. The detection unit can also detect signs of fraudulent activity by taking into account the employee's years of service and experience. This allows for more appropriate detection by taking into account the employee's attribute information. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input employee attribute information into AI, which then performs detection.

[0093] The detection unit can weight detection based on the frequency of data submission during detection. For example, the detection unit can weight data that is frequently submitted higher. The detection unit can also weight data that is less frequently submitted lower. The detection unit can also adjust the weight of detection based on the frequency of data submission. This allows for efficient detection by weighting detection based on the frequency of data submission. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the frequency of data submission into AI, which then weights the detection.

[0094] The detection unit can estimate the employee's emotion and adjust the display order of the detection results based on the estimated employee's emotion. For example, if the employee is feeling stressed, the detection unit can prioritize displaying detection results with high importance. Furthermore, if the employee is relaxed, the detection unit can also display detailed detection results. Furthermore, if the employee is concentrating, the detection unit can also display the detection results in chronological order. This allows for more appropriate information provision by adjusting the display order of the detection results according to the employee's emotion. The estimation of the employee's emotion is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the detection unit can input employee emotion data into a generation AI, which can then adjust the display order of the detection results.

[0095] The detection unit can perform detection by taking into account the geographical distribution of data. For example, the detection unit can prioritize detecting signs of fraudulent activity in a specific region. The detection unit can also analyze data covering a wide geographical area and detect signs of fraudulent activity for each region. The detection unit can also improve the accuracy of detection based on the geographical distribution. This enables more appropriate detection by taking the geographical distribution of data into consideration. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the geographical distribution of data into AI, which then performs detection.

[0096] During detection, the detection unit can improve the accuracy of detection by referring to related literature. For example, the detection unit can improve the detection algorithm by referring to related academic papers. The detection unit can also improve the accuracy of detection by referring to industry best practices. The detection unit can also improve the accuracy of detection by referring to past cases. In this way, by referring to related literature, the accuracy of detection can be improved. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input related literature into AI, which can improve the accuracy of detection.

[0097] The detection unit can perform detection taking into account the market value of the data. For example, the detection unit can assign a higher detection weight to data with a high market value. The detection unit can also assign a lower detection weight to data with a low market value. The detection unit can also determine the priority of detection based on the market value of the data. This allows for more appropriate detection by taking the market value of the data into consideration. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or can be performed without using AI. For example, the detection unit can input the market value of the data into AI, and the AI ​​can perform detection.

[0098] The response unit can estimate the employee's emotions and adjust the response method based on the estimated employee emotions. For example, if the employee is feeling stressed, the response unit can prioritize providing counseling. The response unit can also review the employee's work if the employee is relaxed. The response unit can also adjust the employee's quota if the employee is concentrating. This allows for more appropriate response by adjusting the response method according to the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response unit can be performed using AI, for example, or without AI. For example, the response unit can input the employee's emotion data into the generation AI, which can then adjust the response method.

[0099] When responding, the response unit can analyze the employee's past behavior and select the optimal response method. For example, the response unit analyzes the employee's past behavioral patterns and selects the optimal counseling method. The response unit can also review the employee's work based on the employee's past work history. The response unit can also adjust quotas by referring to the employee's past quota achievement status. In this way, the optimal response method can be selected by analyzing the employee's past behavior. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's past behavioral data into AI, which can select the optimal response method.

[0100] The response unit can customize the response measures based on the employee's current work situation when responding. The response unit can adjust the content of the counseling depending on, for example, the project the employee is currently working on. The response unit can also review the employee's work taking into account the employee's current work situation. The response unit can also adjust the employee's quota based on the employee's current quota achievement status. This allows for more appropriate response by customizing the response measures based on the employee's current work situation. Some or all of the above-mentioned processing in the response unit can be performed using, for example, AI, or without AI. For example, the response unit can input the employee's current work situation into AI, which can then customize the response measures.

[0101] The response department can improve the response method by reflecting employee feedback when responding. For example, the response department can improve the counseling method based on employee feedback. The response department can also review work by reflecting employee feedback. The response department can also adjust quotas based on employee feedback. In this way, the response method can be improved by reflecting employee feedback. Some or all of the above-mentioned processing in the response department may be performed using AI, for example, or may be performed without using AI. For example, the response department can input employee feedback into AI, which can improve the response method.

[0102] The response unit can estimate the employee's emotions and determine response priorities based on the estimated employee emotions. For example, if the employee is feeling stressed, the response unit can prioritize counseling. Furthermore, if the employee is relaxed, the response unit can prioritize reviewing the employee's work. Furthermore, if the employee is concentrating, the response unit can prioritize adjusting the employee's quota. This enables more appropriate response by determining response priorities based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit can be performed using, for example, AI, or without AI. For example, the response unit can input the employee's emotion data into a generation AI, which can then determine response priorities.

[0103] When responding, the response unit can select the optimal response method by taking into account the employee's geographical location information. For example, if the employee is in a specific area, the response unit selects a counseling method appropriate for that area. Furthermore, if the employee is traveling, the response unit can select a response method appropriate for the employee's destination. Furthermore, if the employee is staying in a specific location for a long period of time, the response unit can select a response method appropriate for that location. In this way, the optimal response method can be selected by taking into account the employee's geographical location information. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's geographical location information into AI, which can select the optimal response method.

[0104] When responding, the response department can analyze the employee's social media activity and suggest a response method. For example, the response department can suggest an optimal counseling method based on the employee's social media activity. The response department can also analyze the employee's social media posts and suggest a work review. The response department can also refer to the activity of the employee's friends on social media and suggest an adjustment to quotas. In this way, by analyzing the employee's social media activity, the optimal response method can be suggested. Some or all of the above-mentioned processing in the response department may be performed using, for example, AI, or may be performed without using AI. For example, the response department can input the employee's social media activity into AI, which then suggests a response method.

[0105] The response unit can customize the response method by reflecting the employee's past feedback when responding. For example, the response unit customizes the counseling method based on the employee's past feedback. The response unit can also customize the work review by reflecting the employee's past feedback. The response unit can also customize the quota adjustment based on the employee's past feedback. In this way, the optimal response method can be customized by reflecting the employee's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the employee's past feedback into AI, which can then customize the response method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and response unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect employee behavioral data using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect employee attendance data and work progress status. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects signs of fraudulent activity based on the analyzed data. The response unit, for example, is realized by the control unit 46A of the smart device 14 and can take appropriate action based on the detected signs. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect employee behavioral data using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect employee attendance data and work progress status. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects signs of fraudulent activity based on the analyzed data. The response unit, for example, is realized by the control unit 46A of the smart glasses 214 and can take appropriate action based on the detected signs. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and response unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect employee behavioral data using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect employee attendance data and work progress status. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects signs of fraudulent activity based on the analyzed data. The response unit, for example, is realized by the control unit 46A of the headset terminal 314 and can take appropriate action based on the detected signs. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and response unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect employee behavioral data using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect employee attendance data and work progress status. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects signs of fraudulent activity based on the analyzed data. The response unit, for example, is realized by the control unit 46A of the robot 414 and can take appropriate action based on the detected signs.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] In addition to employee behavioral data, the collection unit can also collect employee health data. For example, by collecting data such as an employee's heart rate, blood pressure, and sleep patterns, the employee's health status can be understood. The analysis unit can analyze the collected health data and evaluate the impact of the employee's health status on work. For example, it can identify the impact of an elevated heart rate or lack of sleep on work performance. The detection unit can detect signs of fraudulent activity based on the health data. For example, it can detect signs that an employee with a deteriorating health status is at increased risk of committing fraud. The response unit can take appropriate action based on the health data. For example, it can provide leave or reduce the workload of an employee with a deteriorating health status.

[0108] In addition to employee behavioral data, the collection unit can also collect employee communication data. For example, by collecting the contents of emails and chats between employees, as well as the content of comments made in meetings, it is possible to understand employee communication patterns. The analysis unit analyzes the collected communication data and evaluates the relationships and quality of communication between employees. For example, it can identify cases where communication between specific employees is lacking or where there are a lot of negative comments. The detection unit can detect signs of misconduct based on the communication data. For example, it can detect cases where there is an increase in confidential exchanges between specific employees or where there is a lot of exchange of content unrelated to work. The response unit can take appropriate action based on the communication data. For example, it can provide team-building opportunities to employees who are lacking in communication.

[0109] The analysis unit can analyze employee performance data in addition to employee behavioral data. For example, by analyzing employees' work results and evaluation data, it is possible to understand fluctuations in employee performance. The analysis unit can combine and analyze performance data and behavioral data to evaluate whether a decline in performance is a sign of fraudulent activity. For example, if an employee whose work performance is rapidly declining is at high risk of committing fraud, it can identify such signs. The detection unit can detect signs of fraudulent activity based on the performance data. For example, if an employee whose work performance is declining exhibits a specific behavioral pattern, it can detect such signs. The response unit can take appropriate action based on the performance data. For example, it can provide training or support to employees whose performance is declining.

[0110] The detection unit can estimate an employee's stress level in addition to employee behavioral data and detect signs of fraudulent activity based on the estimated stress level. For example, by estimating the stress level from employee behavioral data, the detection unit can detect signs of fraudulent activity if an employee with high stress levels is at high risk of committing fraud. The detection unit can detect signs of fraudulent activity based on stress levels with higher accuracy. For example, the detection unit can detect signs of fraudulent activity if an employee with high stress levels is working abnormal hours. The response unit can take appropriate action based on the stress level. For example, the response unit can provide counseling or stress management support to employees with high stress levels.

[0111] The response unit can collect employee motivation data in addition to employee behavioral data and adjust response methods based on the estimated motivation. For example, it can estimate motivation from employee behavioral data and take measures to improve the motivation of low-motivated employees. The response unit can improve employee performance by taking appropriate measures based on the motivation data. For example, it can review goal setting and improve the reward system for low-motivated employees. It can also maintain the motivation of highly motivated employees by providing them with additional challenges.

[0112] The collection unit can collect data on employees' hobbies and interests in addition to employee behavioral data. For example, by understanding what hobbies and interests employees have, data collection can be tailored to each employee's individual needs. The analysis unit can analyze data on hobbies and interests to evaluate the impact on employees' motivation and stress levels. For example, it can identify the positive impact hobbies and interests have on work. The detection unit can detect signs of fraudulent activity based on data on hobbies and interests. For example, it can detect signs that hobbies and interests are negatively impacting work. The response unit can take appropriate action based on data on hobbies and interests. For example, it can assign tasks that utilize hobbies and interests or suggest activities for relaxation.

[0113] The collection unit can collect data on employees' life events in addition to employee behavioral data. For example, by collecting data on life events such as marriage, childbirth, and moving, it is possible to understand changes in employees' living environments. The analysis unit can analyze the data on life events and evaluate the impact on employees' work performance and stress levels. For example, it can identify the impact of life events on work. The detection unit can detect signs of fraudulent activity based on the data on life events. For example, it can detect signs of disruption to work due to life events. The response unit can take appropriate action based on the data on life events. For example, it can provide flexible work systems in response to life events and strengthen support systems.

[0114] The collection unit can collect employee learning data in addition to employee behavioral data. For example, by collecting data on the training that employees have taken and the qualifications they have obtained, it is possible to understand the progress of improvement in employees' skills and knowledge. The analysis unit can analyze the learning data and evaluate the impact that employees' skills and knowledge have on their work. For example, it can identify the positive impact that specific skills and knowledge have on work performance. The detection unit can detect signs of fraudulent activity based on the learning data. For example, it can detect signs that an employee who lacks skills or knowledge is at high risk of committing fraud. The response unit can take appropriate action based on the learning data. For example, it can provide additional training or support to employees who lack skills or knowledge.

[0115] The collection unit can collect employee feedback data in addition to employee behavioral data. For example, by collecting data on opinions and suggestions provided by employees, it is possible to understand employee awareness and thinking. The analysis unit can analyze the feedback data and evaluate the impact of employee awareness and thinking on work. For example, it can identify the positive impact that employee opinions and suggestions have on work improvement. The detection unit can detect signs of misconduct based on the feedback data. For example, it can detect signs that an employee who receives a lot of negative feedback is at high risk of committing misconduct. The response unit can take appropriate action based on the feedback data. For example, it can improve employee motivation by making work improvements that reflect employees' opinions and suggestions.

[0116] The collection unit can collect employee emotion data in addition to employee behavioral data and adjust the data collection method based on the estimated emotion. For example, if an employee is feeling stressed, data collection can be reduced in frequency and collected when the employee is relaxed. Also, if an employee is concentrating, detailed data can be collected at that time. Also, if an employee is tired, data collection can be temporarily stopped and resumed after the employee has rested. This allows for more appropriate data to be collected by adjusting the data collection method according to the employee's emotion. The estimation of an employee's emotion is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the employee's emotion data into a generative AI, which can then adjust the data collection method.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects employee behavioral data. Employee behavioral data includes working hours, work content, break times, arrival and departure times, task completion status, project progress, sales target achievement, and business target achievement. The collection department records and collects this data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit uses these techniques to extract patterns from the collected data, analyze data trends, and build predictive models. Step 3: The detection unit detects signs of fraudulent activity based on the data analyzed by the analysis unit. Signs of fraudulent activity include abnormal working hours, inconsistencies in work content, and abnormal values ​​in the data. The detection unit detects these signs and identifies abnormal working hours and inconsistencies in work content. Step 4: The response department takes appropriate action based on the symptoms detected by the detection department. Responses include issuing warnings, counseling, and reviewing work practices. The response department issues warnings to employees, provides counseling, and reviews their work practices.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects employee behavioral data; an analysis unit that analyzes the data collected by the collection unit; a detection unit that detects signs of fraudulent activity based on the data analyzed by the analysis unit; a response unit that takes action based on the symptom detected by the detection unit. A system characterized by:

2. The collecting unit Collect employee attendance data, work progress, and quota achievement data The system of claim 1 .

3. The analysis unit Analyze the collected data to identify situations where employees feel excessive pressure, are struggling to meet quotas, or are prone to fraud. The system of claim 1 .

4. The detection unit Detect signs of fraud based on analyzed data The system of claim 1 .

5. The corresponding part is Counseling employees, reviewing their work, and adjusting quotas based on the detected symptoms The system of claim 1 .

6. The collecting unit Estimate employee sentiment and adjust data collection timing based on estimated employee sentiment The system of claim 1 .

7. The collecting unit Analyze employee past behavioral data and select data collection methods The system of claim 1 .

8. The collecting unit When collecting data, filter it based on employees' current work situations and areas of interest The system of claim 1 .

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