System

Abstract

An object of the system according to the embodiment is to detect signs of abuse behavior and to address abuse prevention in the entire society.SOLUTION: A system includes an abuse detection part, a financial assistance management part, and a point giving part. The abuse detector analyzes audio or video data to detect a sign of abuse. The financial assistance management unit manages financial assistance from each company. The point awarding unit awards points when no abuse behavior is detected by the abuse 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 has had the problem of making it difficult to detect signs of abuse early and take appropriate measures.

[0005] The system according to the embodiment aims to detect signs of abusive behavior and to promote society-wide efforts to prevent abuse. [Means for solving the problem]

[0006] The system according to the embodiment includes an abuse detection unit, a sponsorship money management unit, and a points allocation unit. The abuse detection unit analyzes audio and video data to detect signs of abuse. The sponsorship money management unit manages sponsorship money from each company. The points allocation unit allocates points when no abuse is detected by the abuse detection unit. [Effects of the Invention]

[0007] The system according to the embodiment detects signs of abusive behavior and enables society as a whole to work together to prevent abuse. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The abuse prevention system according to an embodiment of the present invention utilizes smart technology and generative AI to prevent abuse that can occur in any industry. Furthermore, to present the system as a society-wide initiative, a system is constructed in which sponsorship money is collected from each company and points are awarded for enduring abuse based on the sponsorship money. This allows the abuse prevention system to contribute to society-wide abuse prevention through the detection and prevention of abusive behavior, the management of sponsorship money, and the awarding of points.

[0029] The abuse prevention system according to the embodiment includes an abuse detection unit, a sponsorship money management unit, and a point assignment unit. The abuse detection unit analyzes audio and video data to detect signs of abuse. For example, the generation AI analyzes audio data to detect specific audio patterns. The generation AI can also analyze video data to detect specific movements and facial expressions. The generation AI can also integrate and analyze audio and video data to comprehensively detect signs of abuse. The sponsorship money management unit manages sponsorship money from each company. For example, the sponsorship money management unit receives sponsorship money such as cash, goods, and services from companies and manages it appropriately. The sponsorship money management unit can also record the use of sponsorship money to ensure transparency. The point assignment unit assigns points if the abuse detection unit does not detect any abuse. For example, if the generation AI does not detect any abuse, the point assignment unit assigns points to people in that environment. The point assignment unit can also make the awarded points exchangeable for products and services from sponsoring companies. As a result, the abuse prevention system according to the embodiment can contribute to the prevention of abuse throughout society through the detection and prevention of abuse, management of sponsorship money, and awarding of points.

[0030] The abuse detection unit can analyze text messages and social media posts to detect signs of abuse. For example, the abuse detection unit uses generative AI to analyze text messages and detect offensive language or threatening content. For example, it identifies messages that show signs of bullying or power harassment. The abuse detection unit also monitors social media posts and analyzes negative comments and hashtags. For example, it detects defamatory comments against specific individuals. The abuse detection unit also integrates and analyzes text data, audio, and video data, and comprehensively evaluates signs of abuse from multiple data sources. For example, if text messages and audio data match, the likelihood of abusive behavior is increased. This makes it possible to detect signs of abuse from text messages and social media posts.

[0031] The abuse detection unit can compare detected signs of abusive behavior with past data to assess the risk of recurrence. For example, the abuse detection unit may build a database of past abusive behavior and compare it with newly detected signs by the generation AI. For example, if the same person has engaged in abusive behavior in the past, the risk of recurrence may be assessed as high. The abuse detection unit may also develop a recurrence risk assessment algorithm and integrate and analyze past data with new data. For example, if there is a match with past behavioral patterns, the risk of recurrence may be set high. The abuse detection unit may also build a system to notify administrators and relevant parties based on the results of the recurrence risk assessment. For example, if the risk of recurrence is high, a notification may be sent requesting that countermeasures be taken immediately. This allows the risk of recurrence to be assessed by comparing with past data.

[0032] The abuse detection unit can not only detect signs of abusive behavior but also provide preventative advice. For example, when the generation AI detects signs of abusive behavior, the abuse detection unit provides preventative advice. For example, it may suggest methods for stress management or improving communication. The abuse detection unit also builds a system in which the generation AI provides preventative advice on a regular basis. For example, it may conduct regular stress checks and provide advice based on the results. The abuse detection unit also provides preventative advice tailored to individual situations. For example, it may suggest specific measures tailored to specific workplace or home situations. In this way, by providing preventative advice, it is possible to prevent abusive behavior from occurring in the first place.

[0033] The abuse detection unit can analyze biometric data and detect signs of abuse from physical stress responses. The abuse detection unit, for example, analyzes heart rate data to detect abnormal stress responses. For example, a sudden increase in heart rate is detected as a sign of abuse. The abuse detection unit also analyzes electrodermal activity data to evaluate stress levels. For example, increased electrodermal activity suggests the possibility of abuse. The abuse detection unit also integrates and analyzes biometric data and audio / video data to perform a comprehensive stress assessment. For example, a match between changes in heart rate and voice tone is detected as a sign of abuse. This makes it possible to detect signs of abuse from physical stress responses as well.

[0034] The sponsorship management department can evaluate a company's level of social contribution and adjust the point award rate based on that evaluation. For example, the sponsorship management department sets an index for evaluating a company's level of social contribution and adjusts the point award rate based on that index. For example, it evaluates the company's performance in environmental protection activities and community contribution activities. The sponsorship management department also creates a system that increases the point award rate for companies with a high level of social contribution. For example, it awards more points than usual to companies with a high level of social contribution. The sponsorship management department also regularly evaluates a company's level of social contribution and adjusts the point award rate based on the results. For example, it evaluates based on annual reports and CSR activity performance. This makes it possible to adjust the point award rate based on a company's level of social contribution.

[0035] The sponsorship money management department can make the use of sponsorship money transparent and make it public in real time. For example, the sponsorship money management department builds a system that makes the use of sponsorship money public in real time. For example, the use of sponsorship money and the status of expenditures are made public online. Furthermore, in order to make the use of sponsorship money transparent, the sponsorship money management department creates regular reports and makes them public to companies and the general public. For example, it reports on use on a quarterly basis. Furthermore, the sponsorship money management department builds a system that audits the use of sponsorship money and makes the results public. For example, it conducts an audit by a third-party organization and makes the results public. This makes the use of sponsorship money transparent and improves the company's credibility.

[0036] The point-assigning unit can also be applied to other social contribution activities, allowing corporate sponsorship money to be used in a variety of ways. The point-assigning unit, for example, applies the point-assigning system to environmental protection activities, and uses corporate sponsorship money for environmental protection projects. For example, points are assigned for tree-planting activities and recycling programs. The point-assigning unit can also apply the point-assigning system to educational support activities, and uses corporate sponsorship money for educational programs. For example, points are assigned for scholarship programs and the development of educational facilities. The point-assigning unit can also apply the point-assigning system to other social contribution activities, and uses corporate sponsorship money in a variety of ways. For example, points are assigned for welfare activities and cultural promotion activities in the local community. In this way, corporate sponsorship money can be used in a variety of ways, and applied to other social contribution activities.

[0037] The point allocating unit can make it possible to use points not only for products and services of sponsoring companies, but also for events and activities in the local community. The point allocating unit, for example, builds a system that makes it possible to use points for events and activities in the local community. For example, it makes it possible to use points at local festivals and sporting events. The point allocating unit also makes it possible to use points not only for products and services of sponsoring companies, but also for public services in the local community. For example, points can be used to pay for library and park fees. The point allocating unit also aims to revitalize the local community by making points available for activities in the local community. For example, points can be used for local volunteer activities and use of community centers. This makes it possible to use points for events and activities in the local community.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The abuse prevention system may further include a prediction unit that analyzes a user's behavioral history and predicts abusive behavior. The prediction unit, for example, evaluates the likelihood that a specific behavioral pattern will lead to abusive behavior based on past behavioral data. For example, if behavior at a specific time or location is frequently associated with abusive behavior, the prediction unit monitors behavior at that time or location. The prediction unit may also integrate and analyze behavioral data and emotional data to make more accurate predictions. For example, if a behavioral pattern that increases stress coincides with changes in emotions, the prediction unit may determine that there is a high risk of abusive behavior. This allows the prediction unit to take measures to prevent abusive behavior from occurring.

[0040] The abuse prevention system may further include a health monitoring unit that monitors the user's health condition and detects signs of abusive behavior based on the health data. The health monitoring unit, for example, analyzes the user's sleep data to assess whether lack of sleep increases the risk of abusive behavior. For example, if sleep deprivation continues, stress increases, increasing the risk of abusive behavior. The health monitoring unit may also analyze the user's dietary data to assess whether malnutrition leads to emotional instability. For example, if malnutrition continues, it becomes difficult to control emotions, increasing the risk of abusive behavior. In this way, the health monitoring unit can detect signs of abusive behavior based on the user's health condition and take preventive measures.

[0041] The abuse prevention system may further include a social connection analysis unit that analyzes the user's social connections and assesses the risk that feelings of isolation may lead to abusive behavior. The social connection analysis unit may, for example, analyze the user's social media activity to assess whether feelings of isolation are increasing. For example, if interactions with friends and family decrease, feelings of isolation increase, increasing the risk of abusive behavior. The social connection analysis unit may also analyze the user's participation in community activities to assess whether social support is lacking. For example, if participation in community activities decreases, social support is lacking and the risk of abusive behavior increases. In this way, the social connection analysis unit can assess the risk of abusive behavior based on the user's social connections and take preventive measures.

[0042] The abuse prevention system may further include an environmental monitoring unit that monitors the user's living environment and detects signs of abusive behavior based on environmental data. The environmental monitoring unit, for example, analyzes the noise level in the user's living environment to assess whether the noise causes stress. For example, if the noise level is high, stress increases, increasing the risk of abusive behavior. The environmental monitoring unit may also analyze the temperature and humidity of the user's living environment to assess whether comfort affects emotional stability. For example, if the temperature or humidity is at an uncomfortable level, it becomes difficult to control emotions, increasing the risk of abusive behavior. In this way, the environmental monitoring unit can detect signs of abusive behavior based on the user's living environment and take preventive measures.

[0043] The abuse prevention system can further include an economic situation analysis unit that monitors the user's financial situation and assesses the risk that financial stress will lead to abusive behavior. The economic situation analysis unit, for example, analyzes the user's income and expenditure data to assess whether financial stress is increasing. For example, if income is decreasing and expenditures are increasing, financial stress will increase and the risk of abusive behavior will increase. The economic situation analysis unit also analyzes the user's debt situation to assess whether financial pressure will lead to emotional instability. For example, if debt is increasing, it will become more difficult to control emotions, increasing the risk of abusive behavior. In this way, the economic situation analysis unit can assess the risk of abusive behavior based on the user's financial situation and take preventative measures.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The abuse detection unit analyzes audio and video data to detect signs of abusive behavior. For example, the generation AI analyzes audio data to detect specific audio patterns. The generation AI can also analyze video data to detect specific movements and facial expressions. Furthermore, the generation AI can also integrate and analyze audio and video data to comprehensively detect signs of abusive behavior. Step 2: The sponsorship management department manages the sponsorship money from each company. For example, the sponsorship management department receives sponsorship money such as cash, goods, and services from companies and manages it appropriately. The sponsorship management department can also record how sponsorship money is used to ensure transparency. Step 3: The point assigning unit assigns points if the abuse detection unit does not detect any abusive behavior. For example, if the generation AI does not detect any abusive behavior, the point assigning unit assigns points to people in that environment. The point assigning unit can also make the assigned points exchangeable for products or services from participating companies.

[0046] (Example 2) The abuse prevention system according to an embodiment of the present invention utilizes smart technology and generative AI to prevent abuse that can occur in any industry. Furthermore, to present the system as a society-wide initiative, a system is constructed in which sponsorship money is collected from each company and points are awarded for enduring abuse based on the sponsorship money. This allows the abuse prevention system to contribute to society-wide abuse prevention through the detection and prevention of abusive behavior, the management of sponsorship money, and the awarding of points.

[0047] The abuse prevention system according to the embodiment includes an abuse detection unit, a sponsorship money management unit, and a point assignment unit. The abuse detection unit analyzes audio and video data to detect signs of abuse. For example, the generation AI analyzes audio data to detect specific audio patterns. The generation AI can also analyze video data to detect specific movements and facial expressions. The generation AI can also integrate and analyze audio and video data to comprehensively detect signs of abuse. The sponsorship money management unit manages sponsorship money from each company. For example, the sponsorship money management unit receives sponsorship money such as cash, goods, and services from companies and manages it appropriately. The sponsorship money management unit can also record the use of sponsorship money to ensure transparency. The point assignment unit assigns points if the abuse detection unit does not detect any abuse. For example, if the generation AI does not detect any abuse, the point assignment unit assigns points to people in that environment. The point assignment unit can also make the awarded points exchangeable for products and services from sponsoring companies. As a result, the abuse prevention system according to the embodiment can contribute to the prevention of abuse throughout society through the detection and prevention of abuse, management of sponsorship money, and awarding of points.

[0048] The abuse detection unit can also detect signs of abusive behavior from emotional changes using an emotion estimation function. For example, the abuse detection unit uses a generative AI to analyze audio data and estimate the speaker's emotional state. For example, strong emotions of anger or fear are detected as signs of abusive behavior. The abuse detection unit also analyzes video data and estimates emotions from the subject's facial expressions and movements. For example, if the subject frequently shows a frightened expression, this suggests the possibility of abuse. The abuse detection unit also integrates and analyzes audio and video data to comprehensively evaluate changes in emotions. For example, if changes in voice tone and facial expression match, this is detected as a sign of abusive behavior. This makes it possible to detect signs of abusive behavior from emotional changes as well.

[0049] The abuse detection unit can analyze text messages and social media posts to detect signs of abuse. For example, the abuse detection unit uses generative AI to analyze text messages and detect offensive language or threatening content. For example, it identifies messages that show signs of bullying or power harassment. The abuse detection unit also monitors social media posts and analyzes negative comments and hashtags. For example, it detects defamatory comments against specific individuals. The abuse detection unit also integrates and analyzes text data, audio, and video data, and comprehensively evaluates signs of abuse from multiple data sources. For example, if text messages and audio data match, the likelihood of abusive behavior is increased. This makes it possible to detect signs of abuse from text messages and social media posts.

[0050] The abuse detection unit can compare detected signs of abusive behavior with past data to assess the risk of recurrence. For example, the abuse detection unit may build a database of past abusive behavior and compare it with newly detected signs by the generation AI. For example, if the same person has engaged in abusive behavior in the past, the risk of recurrence may be assessed as high. The abuse detection unit may also develop a recurrence risk assessment algorithm and integrate and analyze past data with new data. For example, if there is a match with past behavioral patterns, the risk of recurrence may be set high. The abuse detection unit may also build a system to notify administrators and relevant parties based on the results of the recurrence risk assessment. For example, if the risk of recurrence is high, a notification may be sent requesting that countermeasures be taken immediately. This allows the risk of recurrence to be assessed by comparing with past data.

[0051] The abuse detection unit can not only detect signs of abusive behavior but also provide preventative advice. For example, when the generation AI detects signs of abusive behavior, the abuse detection unit provides preventative advice. For example, it may suggest methods for stress management or improving communication. The abuse detection unit also builds a system in which the generation AI provides preventative advice on a regular basis. For example, it may conduct regular stress checks and provide advice based on the results. The abuse detection unit also provides preventative advice tailored to individual situations. For example, it may suggest specific measures tailored to specific workplace or home situations. In this way, by providing preventative advice, it is possible to prevent abusive behavior from occurring in the first place.

[0052] The abuse detection unit can analyze biometric data and detect signs of abuse from physical stress responses. The abuse detection unit, for example, analyzes heart rate data to detect abnormal stress responses. For example, a sudden increase in heart rate is detected as a sign of abuse. The abuse detection unit also analyzes electrodermal activity data to evaluate stress levels. For example, increased electrodermal activity suggests the possibility of abuse. The abuse detection unit also integrates and analyzes biometric data and audio / video data to perform a comprehensive stress assessment. For example, a match between changes in heart rate and voice tone is detected as a sign of abuse. This makes it possible to detect signs of abuse from physical stress responses as well.

[0053] The abuse detection unit uses the emotion estimation function to monitor the emotional state of the victim in real time and provide the necessary support. For example, the abuse detection unit monitors the emotional state of the victim in real time and detects abnormal emotional changes. For example, if sudden emotions of fear or sadness are detected, support is provided immediately. The abuse detection unit also builds a system that provides appropriate support to the victim based on the emotion estimation data. For example, psychological counseling and emergency contact information are provided. The abuse detection unit also continuously monitors the emotional state of the victim and provides long-term support. For example, regular follow-up and support programs are provided. This makes it possible to monitor the emotional state of the victim in real time and provide the necessary support.

[0054] The sponsorship management department can evaluate a company's level of social contribution and adjust the point award rate based on that evaluation. For example, the sponsorship management department sets an index for evaluating a company's level of social contribution and adjusts the point award rate based on that index. For example, it evaluates the company's performance in environmental protection activities and community contribution activities. The sponsorship management department also creates a system that increases the point award rate for companies with a high level of social contribution. For example, it awards more points than usual to companies with a high level of social contribution. The sponsorship management department also regularly evaluates a company's level of social contribution and adjusts the point award rate based on the results. For example, it evaluates based on annual reports and CSR activity performance. This makes it possible to adjust the point award rate based on a company's level of social contribution.

[0055] The point assigning unit can use the emotion estimation function to adjust the timing and amount of point assignment taking into account the user's emotional state. The point assigning unit, for example, analyzes the user's emotional state in real time and assigns more points when the user has a strong positive emotion. For example, it increases the number of points when the user shows joy or satisfaction. The point assigning unit also builds a system that adjusts the timing of point assignment based on the emotion estimation data. For example, it assigns points when the user is feeling stressed, thereby eliciting positive emotions. The point assigning unit also continuously monitors the user's emotional state and adjusts the amount of point assignment according to changes in emotion. For example, it assigns more points when the emotion score is high. This makes it possible to adjust the timing and amount of point assignment taking into account the user's emotional state.

[0056] The sponsorship money management department can make the use of sponsorship money transparent and make it public in real time. For example, the sponsorship money management department builds a system that makes the use of sponsorship money public in real time. For example, the use of sponsorship money and the status of expenditures are made public online. Furthermore, in order to make the use of sponsorship money transparent, the sponsorship money management department creates regular reports and makes them public to companies and the general public. For example, it reports on use on a quarterly basis. Furthermore, the sponsorship money management department builds a system that audits the use of sponsorship money and makes the results public. For example, it conducts an audit by a third-party organization and makes the results public. This makes the use of sponsorship money transparent and improves the company's credibility.

[0057] The point-assigning unit can also be applied to other social contribution activities, allowing corporate sponsorship money to be used in a variety of ways. The point-assigning unit, for example, applies the point-assigning system to environmental protection activities, and uses corporate sponsorship money for environmental protection projects. For example, points are assigned for tree-planting activities and recycling programs. The point-assigning unit can also apply the point-assigning system to educational support activities, and uses corporate sponsorship money for educational programs. For example, points are assigned for scholarship programs and the development of educational facilities. The point-assigning unit can also apply the point-assigning system to other social contribution activities, and uses corporate sponsorship money in a variety of ways. For example, points are assigned for welfare activities and cultural promotion activities in the local community. In this way, corporate sponsorship money can be used in a variety of ways, and applied to other social contribution activities.

[0058] The point allocating unit can make it possible to use points not only for products and services of sponsoring companies, but also for events and activities in the local community. The point allocating unit, for example, builds a system that makes it possible to use points for events and activities in the local community. For example, it makes it possible to use points at local festivals and sporting events. The point allocating unit also makes it possible to use points not only for products and services of sponsoring companies, but also for public services in the local community. For example, points can be used to pay for library and park fees. The point allocating unit also aims to revitalize the local community by making points available for activities in the local community. For example, points can be used for local volunteer activities and use of community centers. This makes it possible to use points for events and activities in the local community.

[0059] The point assigning unit can use the emotion estimation function to monitor the user's emotional state when assigning points and provide incentives to elicit positive emotions. The point assigning unit, for example, monitors the user's emotional state in real time when assigning points and provides incentives to elicit positive emotions. For example, additional points are awarded if the emotion score is high. The point assigning unit also builds a system that provides incentives to encourage users to feel positive emotions based on the emotion estimation data. For example, a special benefit is provided if the emotion score is high. The point assigning unit also continuously monitors the user's emotional state and provides incentives to elicit positive emotions. For example, a discount on a limited edition product is provided if the emotion score is high. In this way, the user's emotional state can be monitored and incentives to elicit positive emotions can be provided.

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

[0061] The abuse prevention system may further include a prediction unit that analyzes a user's behavioral history and predicts abusive behavior. The prediction unit, for example, evaluates the likelihood that a specific behavioral pattern will lead to abusive behavior based on past behavioral data. For example, if behavior at a specific time or location is frequently associated with abusive behavior, the prediction unit monitors behavior at that time or location. The prediction unit may also integrate and analyze behavioral data and emotional data to make more accurate predictions. For example, if a behavioral pattern that increases stress coincides with changes in emotions, the prediction unit may determine that there is a high risk of abusive behavior. This allows the prediction unit to take measures to prevent abusive behavior from occurring.

[0062] The abuse prevention system may further include a health monitoring unit that monitors the user's health condition and detects signs of abusive behavior based on the health data. The health monitoring unit, for example, analyzes the user's sleep data to assess whether lack of sleep increases the risk of abusive behavior. For example, if sleep deprivation continues, stress increases, increasing the risk of abusive behavior. The health monitoring unit may also analyze the user's dietary data to assess whether malnutrition leads to emotional instability. For example, if malnutrition continues, it becomes difficult to control emotions, increasing the risk of abusive behavior. In this way, the health monitoring unit can detect signs of abusive behavior based on the user's health condition and take preventive measures.

[0063] The abuse prevention system may further include a social connection analysis unit that analyzes the user's social connections and assesses the risk that feelings of isolation may lead to abusive behavior. The social connection analysis unit may, for example, analyze the user's social media activity to assess whether feelings of isolation are increasing. For example, if interactions with friends and family decrease, feelings of isolation increase, increasing the risk of abusive behavior. The social connection analysis unit may also analyze the user's participation in community activities to assess whether social support is lacking. For example, if participation in community activities decreases, social support is lacking and the risk of abusive behavior increases. In this way, the social connection analysis unit can assess the risk of abusive behavior based on the user's social connections and take preventive measures.

[0064] The abuse prevention system may further include an environmental monitoring unit that monitors the user's living environment and detects signs of abusive behavior based on environmental data. The environmental monitoring unit, for example, analyzes the noise level in the user's living environment to assess whether the noise causes stress. For example, if the noise level is high, stress increases, increasing the risk of abusive behavior. The environmental monitoring unit may also analyze the temperature and humidity of the user's living environment to assess whether comfort affects emotional stability. For example, if the temperature or humidity is at an uncomfortable level, it becomes difficult to control emotions, increasing the risk of abusive behavior. In this way, the environmental monitoring unit can detect signs of abusive behavior based on the user's living environment and take preventive measures.

[0065] The abuse prevention system can further include an economic situation analysis unit that monitors the user's financial situation and assesses the risk that financial stress will lead to abusive behavior. The economic situation analysis unit, for example, analyzes the user's income and expenditure data to assess whether financial stress is increasing. For example, if income is decreasing and expenditures are increasing, financial stress will increase and the risk of abusive behavior will increase. The economic situation analysis unit also analyzes the user's debt situation to assess whether financial pressure will lead to emotional instability. For example, if debt is increasing, it will become more difficult to control emotions, increasing the risk of abusive behavior. In this way, the economic situation analysis unit can assess the risk of abusive behavior based on the user's financial situation and take preventative measures.

[0066] The abuse prevention system can further include a relaxation suggestion unit that suggests appropriate relaxation methods based on the user's emotional state. The relaxation suggestion unit, for example, analyzes the user's emotional state in real time and suggests relaxation methods when stress is high. For example, it suggests relaxation methods such as deep breathing, meditation, or light exercise. The relaxation suggestion unit can also create an individual relaxation plan based on the user's emotional data. For example, it can suggest relaxation methods according to the user's preferences. In this way, the relaxation suggestion unit can suggest appropriate relaxation methods based on the user's emotional state, thereby reducing stress.

[0067] The abuse prevention system can further include a communication suggestion unit that suggests an appropriate communication method based on the user's emotional state. The communication suggestion unit, for example, analyzes the user's emotional state in real time and suggests an appropriate communication method when the user's emotions are unstable. For example, it may suggest speaking calmly or adopting an empathetic attitude. The communication suggestion unit can also create an individual communication plan based on the user's emotional data. For example, it may suggest a communication method that suits the user's personality and situation. In this way, the communication suggestion unit can suggest an appropriate communication method based on the user's emotional state, thereby stabilizing emotions.

[0068] The abuse prevention system can further include a feedback providing unit that provides appropriate feedback based on the user's emotional state. The feedback providing unit, for example, analyzes the user's emotional state in real time and provides praising feedback when the user has strong positive emotions. For example, it provides feedback such as "well done" when the user responds calmly. The feedback providing unit can also create an individual feedback plan based on the user's emotional data. For example, it provides feedback according to the user's personality and situation. In this way, the feedback providing unit can provide appropriate feedback based on the user's emotional state and promote positive behavior.

[0069] The abuse prevention system can further include a support providing unit that provides appropriate support based on the user's emotional state. The support providing unit, for example, analyzes the user's emotional state in real time and provides appropriate support when the user's emotions are unstable. For example, it provides psychological counseling or stress management advice. The support providing unit can also create an individual support plan based on the user's emotional data. For example, it provides specific support according to the user's situation. In this way, the support providing unit can provide appropriate support based on the user's emotional state and promote emotional stability.

[0070] The abuse prevention system can further include a reward provider that provides appropriate rewards based on the user's emotional state. The reward provider, for example, analyzes the user's emotional state in real time and provides rewards when the user has strong positive emotions. For example, it provides benefits or points when the user responds calmly. The reward provider can also create an individual reward plan based on the user's emotional data. For example, it provides rewards according to the user's preferences. In this way, the reward provider can provide appropriate rewards based on the user's emotional state and encourage positive behavior.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The abuse detection unit analyzes audio and video data to detect signs of abusive behavior. For example, the generation AI analyzes audio data to detect specific audio patterns. The generation AI can also analyze video data to detect specific movements and facial expressions. Furthermore, the generation AI can also integrate and analyze audio and video data to comprehensively detect signs of abusive behavior. Step 2: The sponsorship management department manages the sponsorship money from each company. For example, the sponsorship management department receives sponsorship money such as cash, goods, and services from companies and manages it appropriately. The sponsorship management department can also record how sponsorship money is used to ensure transparency. Step 3: The point assigning unit assigns points if the abuse detection unit does not detect any abusive behavior. For example, if the generation AI does not detect any abusive behavior, the point assigning unit assigns points to people in that environment. The point assigning unit can also make the assigned points exchangeable for products or services from participating companies.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0099] 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.

[0100] 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.

[0101] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

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

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

[0117] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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, in order to avoid confusion and to 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.

[0139] 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. [Explanation of symbols]

[0140] 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. an abuse detection unit that analyzes audio and video data to detect signs of abuse; The sponsorship management department manages sponsorship money from each company, a point granting unit that grants points when no abuse is detected by the abuse detection unit. A system characterized by:

2. The abuse detection unit Detecting signs of abusive behavior through emotional changes 2. The system of claim 1.

3. The abuse detection unit Analyzing text messages and social media posts to detect signs of abuse 2. The system of claim 1.

4. The abuse detection unit Compare detected signs of abuse with historical data to assess the risk of recurrence 2. The system of claim 1.

5. The abuse detection unit Not only detect signs of abusive behavior, but also provide preventative advice 2. The system of claim 1.

6. The abuse detection unit Analyzing biometric data to detect signs of abuse through physical stress responses 2. The system of claim 1.

Images