System

Abstract

An object of a system according to an embodiment is to monitor a behavior in real time, detect an abnormality, and immediately cope with the abnormality.SOLUTION: A system includes a monitoring unit, a detection unit, an identification unit, a collection unit, and a notification unit. The monitoring unit monitors a trend of the victim in real time. The detection unit detects an abnormality from the movement of the victim monitored by the monitoring unit. The identification unit identifies a behavior from the abnormality detected by the detection unit. The collection unit collects evidence of the behavior specified by the specification unit. The notification unit notifies the person concerned of the evidence collected by the collection unit.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 monitor stalking behavior in real time, detect abnormalities, and respond immediately.

[0005] The system according to the embodiment aims to monitor stalking behavior in real time, detect abnormalities, and take immediate action. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, an identification unit, a collection unit, and a notification unit. The monitoring unit monitors the victim's movements in real time. The detection unit detects abnormalities from the victim's movements monitored by the monitoring unit. The identification unit identifies stalking behavior from the abnormalities detected by the detection unit. The collection unit collects evidence of the stalking behavior identified by the identification unit. The notification unit notifies relevant parties of the evidence collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor stalking behavior in real time, detect abnormalities, and take immediate action. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A stalking prevention system according to an embodiment of the present invention is a system that monitors the movements of a victim in real time, detects abnormalities, identifies stalking behavior, collects evidence, and notifies relevant parties. As a result, the stalking prevention system can prevent stalking behavior by monitoring the movements of a victim in real time, detecting abnormalities, identifying stalking behavior, collecting evidence, and notifying relevant parties.

[0029] A stalking prevention system according to an embodiment includes a monitoring unit, a detection unit, an identification unit, a collection unit, and a notification unit. The monitoring unit monitors the victim's movements in real time. For example, location information and activity data are collected from the victim's smartphone or wearable device, and the generation AI analyzes the collected data. The generation AI detects abnormalities based on the victim's location information and activity data. The detection unit detects abnormalities from the victim's movements monitored by the monitoring unit. For example, an abnormality is detected when the victim stays in a place they normally do not visit for a long period of time or when a specific person frequently appears around the victim. The identification unit identifies stalking behavior from the abnormality detected by the detection unit. For example, the identification unit analyzes footage from surveillance cameras installed around the victim and call histories and messages recorded on the victim's smartphone. The collection unit collects evidence of stalking behavior identified by the identification unit. For example, the collection unit collects surveillance camera footage, call histories, and messages. The notification unit notifies relevant parties of the evidence collected by the collection unit. For example, the identification unit contacts the police or a security company. As a result, the stalking prevention system of the embodiment can prevent stalking behavior by monitoring the victim's movements in real time, detecting abnormalities, identifying stalking behavior, collecting evidence, and notifying relevant parties.

[0030] The monitoring unit collects location information and activity data from the victim's smartphone or wearable device, and the generating AI analyzes it. For example, to learn the victim's behavioral patterns, the generating AI analyzes the victim's daily travel routes and time spent there, and models their usual behavioral patterns. This allows for more accurate monitoring of the victim's movements.

[0031] The detection unit can detect abnormalities, for example, when a victim stays in a place they normally don't go to for a long time, or when a specific person frequently appears around the victim. For example, to analyze the environmental sounds around the victim, the generation AI collects audio data using the microphone on a smartphone or wearable device and detects abnormal sounds. This allows for early detection of stalking behavior.

[0032] The collection unit can analyze footage from surveillance cameras installed around the victim, as well as call histories and messages recorded on the victim's smartphone. For example, the collection unit analyzes footage from surveillance cameras installed around the victim, as well as call histories and messages recorded on the victim's smartphone. For example, the collection unit uses an emotion estimation function to analyze the victim's facial expressions and voice tone and monitor their emotional state in real time. This allows for the collection of evidence of stalking behavior.

[0033] The notification department can provide the information necessary for victims to file a police report or organize and provide evidence to lawyers. For example, the notification department can collect location and activity data from a wearable device attached to the victim's pet, and the generation AI can analyze it. This can support legal procedures.

[0034] The monitoring unit can learn the victim's behavioral patterns and generate a customized model for detecting anomalies. For example, to learn the victim's behavioral patterns, the generating AI analyzes the victim's daily travel routes and stay times and models their normal behavioral patterns. This can improve the accuracy of anomaly detection.

[0035] The monitoring unit can analyze the environmental and background sounds around the victim and detect any abnormal sounds. For example, to analyze the environmental sounds around the victim, the generation AI collects audio data using the microphone on a smartphone or wearable device and detects any abnormal sounds. This allows for early detection of abnormal situations.

[0036] The monitoring unit can simultaneously monitor the movements of the victim's pet and detect any abnormal behavior. For example, the monitoring unit collects location and activity data from a wearable device attached to the victim's pet, which the AI ​​analyzes. This further ensures the victim's safety.

[0037] The monitoring unit can simultaneously monitor the victim's vehicle movements and detect abnormal driving patterns. For example, the monitoring unit collects location information and driving data from a GPS device attached to the victim's vehicle, which the AI ​​analyzes. This further ensures the victim's safety.

[0038] The detection unit can refer to the victim's past behavioral history and identify abnormal patterns with greater accuracy. For example, the detection unit collects the victim's past behavioral history, which the generation AI analyzes. This improves the accuracy of anomaly detection.

[0039] The detection unit analyzes environmental data such as the temperature and humidity around the victim and can detect abnormal environmental changes. The detection unit collects environmental data such as the temperature and humidity around the victim, and the generation AI analyzes it. This allows for early detection of abnormal situations.

[0040] The detection unit can simultaneously monitor the behavior of other people around the victim and detect abnormal behavior. For example, the detection unit uses the generative AI to analyze surveillance camera footage to monitor the behavior of other people around the victim. This further ensures the safety of the victim.

[0041] The detection unit can work in conjunction with the security systems at the victim's home or workplace to detect abnormal intrusions.The detection unit can work in conjunction with the security systems at the victim's home or workplace to detect abnormal intrusions.This further ensures the safety of the victim.

[0042] The identification unit can analyze surveillance camera footage of the victim's surroundings and learn the behavioral patterns of specific individuals. For example, the identification unit collects surveillance camera footage installed around the victim, and the generation AI analyzes it. This allows for early identification of stalking behavior.

[0043] The identification unit can analyze the communication history of the victim's smartphone and detect abnormal communication patterns. For example, the identification unit collects the call history and message history of the victim's smartphone, which the generation AI analyzes. This allows for early detection of stalking behavior.

[0044] The identification unit can also analyze data from other devices around the victim. For example, the identification unit collects data from smart home devices (such as smart speakers and security cameras) installed around the victim, and the generation AI analyzes it. This allows for the collection of more evidence of stalking behavior.

[0045] The identification unit can monitor the victim's social media activity and detect abnormal contact. For example, the identification unit monitors the victim's social media account activity, and the generation AI detects abnormal contact. This allows for early identification of stalking behavior.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The anti-stalking system can also be equipped with a light detector that monitors the lighting environment around the victim. The light detector collects the brightness and color temperature of the victim's surroundings, which the AI ​​analyzes. For example, if the victim spends a long time in a dark place they normally don't visit, or if there is a sudden change in lighting in a specific location, the system can detect an abnormality. Furthermore, if the victim experiences an unnatural change in lighting in a specific location, the system can determine that the location may be affected by stalking. This further ensures the victim's safety.

[0048] The stalking prevention system can also be equipped with an odor detection unit that monitors the odor environment around the victim. The odor detection unit collects chemicals in the air around the victim, which the generative AI analyzes. For example, if the victim senses an unusual odor in a specific location, it can determine that the location may be affected by stalking. Also, if a specific odor is detected when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0049] The stalking prevention system can further include an electromagnetic wave detection unit that monitors the electromagnetic wave environment around the victim. The electromagnetic wave detection unit collects the electromagnetic wave intensity around the victim, which the generating AI analyzes. For example, if the victim senses abnormally high electromagnetic waves in a specific location, it can determine that that location may be affected by stalking. Also, if the electromagnetic wave intensity increases abnormally when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0050] The stalking prevention system can also be equipped with a vibration detection unit that monitors the vibration environment around the victim. The vibration detection unit collects vibration data around the victim, which the generating AI analyzes. For example, if the victim feels abnormal vibrations in a specific location, it can determine that the location may be affected by stalking. Also, if the vibration data increases abnormally when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0051] The stalking prevention system can further include a power detection unit that monitors power consumption around the victim. The power detection unit collects power consumption data around the victim, which the generating AI analyzes. For example, if abnormally high power consumption is detected in a place the victim does not normally visit, it can be determined that the place may be affected by stalking. Furthermore, if power consumption increases abnormally when the victim comes into contact with a specific person, it can be identified that that person may be the stalker. This further ensures the safety of the victim.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The monitoring unit monitors the victim's movements in real time. For example, location and activity data is collected from the victim's smartphone or wearable device, and the generating AI analyzes it. Step 2: The detection unit detects abnormalities in the victim's movements monitored by the monitoring unit. For example, if the victim stays in a place they normally don't go to for a long time, or if a specific person frequently appears around the victim, this is detected as an abnormality. Step 3: The identification unit identifies stalking behavior based on the abnormalities detected by the detection unit, for example by analyzing footage from surveillance cameras installed around the victim and call history and messages recorded on the victim's smartphone. Step 4: The collection department collects evidence of the stalking behavior identified by the identification department, such as surveillance camera footage, call history, and messages. Step 5: The Notification Department notifies the relevant parties of the evidence collected by the Collection Department, for example by contacting the police or security companies.

[0054] (Example 2) A stalking prevention system according to an embodiment of the present invention is a system that monitors the movements of a victim in real time, detects abnormalities, identifies stalking behavior, collects evidence, and notifies relevant parties. As a result, the stalking prevention system can prevent stalking behavior by monitoring the movements of a victim in real time, detecting abnormalities, identifying stalking behavior, collecting evidence, and notifying relevant parties.

[0055] A stalking prevention system according to an embodiment includes a monitoring unit, a detection unit, an identification unit, a collection unit, and a notification unit. The monitoring unit monitors the victim's movements in real time. For example, location information and activity data are collected from the victim's smartphone or wearable device, and the generation AI analyzes the collected data. The generation AI detects abnormalities based on the victim's location information and activity data. The detection unit detects abnormalities from the victim's movements monitored by the monitoring unit. For example, an abnormality is detected when the victim stays in a place they normally do not visit for a long period of time or when a specific person frequently appears around the victim. The identification unit identifies stalking behavior from the abnormality detected by the detection unit. For example, the identification unit analyzes footage from surveillance cameras installed around the victim and call histories and messages recorded on the victim's smartphone. The collection unit collects evidence of stalking behavior identified by the identification unit. For example, the collection unit collects surveillance camera footage, call histories, and messages. The notification unit notifies relevant parties of the evidence collected by the collection unit. For example, the identification unit contacts the police or a security company. As a result, the stalking prevention system of the embodiment can prevent stalking behavior by monitoring the victim's movements in real time, detecting abnormalities, identifying stalking behavior, collecting evidence, and notifying relevant parties.

[0056] The monitoring unit collects location information and activity data from the victim's smartphone or wearable device, and the generating AI analyzes it. For example, to learn the victim's behavioral patterns, the generating AI analyzes the victim's daily travel routes and time spent there, and models their usual behavioral patterns. This allows for more accurate monitoring of the victim's movements.

[0057] The detection unit can detect abnormalities, for example, when a victim stays in a place they normally don't go to for a long time, or when a specific person frequently appears around the victim. For example, to analyze the environmental sounds around the victim, the generation AI collects audio data using the microphone on a smartphone or wearable device and detects abnormal sounds. This allows for early detection of stalking behavior.

[0058] The collection unit can analyze footage from surveillance cameras installed around the victim, as well as call histories and messages recorded on the victim's smartphone. For example, the collection unit analyzes footage from surveillance cameras installed around the victim, as well as call histories and messages recorded on the victim's smartphone. For example, the collection unit uses an emotion estimation function to analyze the victim's facial expressions and voice tone and monitor their emotional state in real time. This allows for the collection of evidence of stalking behavior.

[0059] The notification department can provide the information necessary for victims to file a police report or organize and provide evidence to lawyers. For example, the notification department can collect location and activity data from a wearable device attached to the victim's pet, and the generation AI can analyze it. This can support legal procedures.

[0060] The monitoring unit can learn the victim's behavioral patterns and generate a customized model for detecting anomalies. For example, to learn the victim's behavioral patterns, the generating AI analyzes the victim's daily travel routes and stay times and models their normal behavioral patterns. This can improve the accuracy of anomaly detection.

[0061] The monitoring unit can analyze the environmental and background sounds around the victim and detect any abnormal sounds. For example, to analyze the environmental sounds around the victim, the generation AI collects audio data using the microphone on a smartphone or wearable device and detects any abnormal sounds. This allows for early detection of abnormal situations.

[0062] The monitoring unit uses the emotion estimation function to monitor the victim's emotional state in real time and detect abnormal emotional changes. For example, the monitoring unit uses the emotion estimation function to analyze the victim's facial expressions and tone of voice and monitor the emotional state in real time. This allows for early detection of psychological abnormalities in the victim.

[0063] The monitoring unit can simultaneously monitor the movements of the victim's pet and detect any abnormal behavior. For example, the monitoring unit collects location and activity data from a wearable device attached to the victim's pet, which the AI ​​analyzes. This further ensures the victim's safety.

[0064] The monitoring unit can simultaneously monitor the victim's vehicle movements and detect abnormal driving patterns. For example, the monitoring unit collects location information and driving data from a GPS device attached to the victim's vehicle, which the AI ​​analyzes. This further ensures the victim's safety.

[0065] The monitoring unit can simultaneously monitor the emotional state of the victim's family and friends and provide information to ensure the victim's safety. For example, the monitoring unit uses generative AI to analyze facial expressions and voice tones to monitor the emotional state of the victim's family and friends. This further ensures the victim's safety.

[0066] The detection unit can refer to the victim's past behavioral history and identify abnormal patterns with greater accuracy. For example, the detection unit collects the victim's past behavioral history, which the generation AI analyzes. This improves the accuracy of anomaly detection.

[0067] The detection unit analyzes environmental data such as the temperature and humidity around the victim and can detect abnormal environmental changes. The detection unit collects environmental data such as the temperature and humidity around the victim, and the generation AI analyzes it. This allows for early detection of abnormal situations.

[0068] The detection unit uses the emotion estimation function to detect an abnormality when the victim's emotional state suddenly changes, and can respond immediately. For example, the detection unit uses the emotion estimation function to analyze the victim's facial expression and tone of voice, and detects an abnormality when the victim's emotional state suddenly changes. This allows the victim's safety to be quickly ensured.

[0069] The detection unit can simultaneously monitor the behavior of other people around the victim and detect abnormal behavior. For example, the detection unit uses the generative AI to analyze surveillance camera footage to monitor the behavior of other people around the victim. This further ensures the safety of the victim.

[0070] The detection unit can work in conjunction with the security systems at the victim's home or workplace to detect abnormal intrusions.The detection unit can work in conjunction with the security systems at the victim's home or workplace to detect abnormal intrusions.This further ensures the safety of the victim.

[0071] The detection unit can detect abnormal changes in the emotional state of the victim's friends and family and respond immediately. For example, the generative AI analyzes facial expressions and voice tones to monitor the emotional state of the victim's friends and family. This allows the victim's safety to be quickly ensured.

[0072] The identification unit can analyze surveillance camera footage of the victim's surroundings and learn the behavioral patterns of specific individuals. For example, the identification unit collects surveillance camera footage installed around the victim, and the generation AI analyzes it. This allows for early identification of stalking behavior.

[0073] The identification unit can analyze the communication history of the victim's smartphone and detect abnormal communication patterns. For example, the identification unit collects the call history and message history of the victim's smartphone, which the generation AI analyzes. This allows for early detection of stalking behavior.

[0074] The identification unit can use the emotion estimation function to collect evidence when the victim's emotional state changes due to stalking behavior. For example, the identification unit uses the emotion estimation function to analyze the victim's facial expressions and voice tone, and collects evidence when the victim's emotional state changes due to stalking behavior. This can support legal proceedings.

[0075] The identification unit can also analyze data from other devices around the victim. For example, the identification unit collects data from smart home devices (such as smart speakers and security cameras) installed around the victim, and the generation AI analyzes it. This allows for the collection of more evidence of stalking behavior.

[0076] The identification unit can monitor the victim's social media activity and detect abnormal contact. For example, the identification unit monitors the victim's social media account activity, and the generation AI detects abnormal contact. This allows for early identification of stalking behavior.

[0077] The identification unit can use the emotion estimation function to collect evidence of changes in the emotional state of the victim's friends and family due to stalking behavior. For example, the identification unit can use the emotion estimation function to analyze the facial expressions and voice tones of the victim's friends and family to collect evidence of changes in the emotional state due to stalking behavior, thereby supporting legal proceedings.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The anti-stalking system can also be equipped with a stress detection unit that monitors the victim's psychological stress level. The stress detection unit collects biometric data such as the victim's heart rate and galvanic skin response, which the generative AI analyzes. For example, if the victim's heart rate spikes when they are in a particular location, it can be determined that the location may be affected by stalking. Similarly, if the victim's galvanic skin response increases abnormally when they come into contact with a specific person, it can be determined that the person is likely to be a stalker. This allows for real-time monitoring of the victim's psychological health and enables early detection and countermeasures against stalking behavior.

[0080] The anti-stalking system can also be equipped with a light detector that monitors the lighting environment around the victim. The light detector collects the brightness and color temperature of the victim's surroundings, which the AI ​​analyzes. For example, if the victim spends a long time in a dark place they normally don't visit, or if there is a sudden change in lighting in a specific location, the system can detect an abnormality. Furthermore, if the victim experiences an unnatural change in lighting in a specific location, the system can determine that the location may be affected by stalking. This further ensures the victim's safety.

[0081] The stalking prevention system can also be equipped with an odor detection unit that monitors the odor environment around the victim. The odor detection unit collects chemicals in the air around the victim, which the generative AI analyzes. For example, if the victim senses an unusual odor in a specific location, it can determine that the location may be affected by stalking. Also, if a specific odor is detected when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0082] The stalking prevention system can further include an electromagnetic wave detection unit that monitors the electromagnetic wave environment around the victim. The electromagnetic wave detection unit collects the electromagnetic wave intensity around the victim, which the generating AI analyzes. For example, if the victim senses abnormally high electromagnetic waves in a specific location, it can determine that that location may be affected by stalking. Also, if the electromagnetic wave intensity increases abnormally when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0083] The stalking prevention system can also be equipped with a vibration detection unit that monitors the vibration environment around the victim. The vibration detection unit collects vibration data around the victim, which the generating AI analyzes. For example, if the victim feels abnormal vibrations in a specific location, it can determine that the location may be affected by stalking. Also, if the vibration data increases abnormally when the victim comes into contact with a specific person, it can identify that person as a possible stalker. This further ensures the victim's safety.

[0084] The stalking prevention system can further include a support unit that estimates the victim's emotional state and provides psychological support to the victim based on the estimated emotions. The support unit analyzes the victim's facial expressions and vocal tone to monitor the victim's emotional state in real time. For example, if the victim feels stressed or anxious, it can provide relaxing music or a meditation guide. If the victim feels fear, it can immediately contact the police or a security company. This supports the victim's psychological health and reduces the impact of stalking.

[0085] The stalking prevention system can further include a prediction unit that estimates the victim's emotional state and predicts the victim's behavior based on the estimated emotion. The prediction unit analyzes the victim's facial expressions and vocal tone to monitor the victim's emotional state in real time. For example, if the victim is feeling anxious, the prediction unit can predict what behavior that anxiety will lead to and take appropriate measures. Also, if the victim is feeling fear, the prediction unit can predict what behavior that fear will lead to and immediately contact the police or a security company. This further ensures the victim's safety.

[0086] The stalking prevention system may further include a guidance unit that estimates the emotional state of the victim and guides the victim's behavior based on the estimated emotion. The guidance unit analyzes the victim's facial expressions and vocal tone to monitor the emotional state in real time. For example, if the victim feels anxious, the system can guide the victim to take action to reduce that anxiety. Also, if the victim feels fear, the system can guide the victim to take action to reduce that fear. This supports the victim's psychological health and reduces the impact of stalking.

[0087] The stalking prevention system may further include an environmental adjustment unit that estimates the victim's emotional state and adjusts the victim's surrounding environment based on the estimated emotion. The environmental adjustment unit analyzes the victim's facial expressions and vocal tone to monitor the victim's emotional state in real time. For example, if the victim feels stressed, the ambient lighting may be adjusted to provide a relaxing environment. Also, if the victim feels anxious, the ambient music may be adjusted to provide a sense of security. This supports the victim's psychological health and reduces the impact of stalking.

[0088] The stalking prevention system can further include a power detection unit that monitors power consumption around the victim. The power detection unit collects power consumption data around the victim, which the generating AI analyzes. For example, if abnormally high power consumption is detected in a place the victim does not normally visit, it can be determined that the place may be affected by stalking. Furthermore, if power consumption increases abnormally when the victim comes into contact with a specific person, it can be identified that that person may be the stalker. This further ensures the safety of the victim.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The monitoring unit monitors the victim's movements in real time. For example, location and activity data is collected from the victim's smartphone or wearable device, and the generating AI analyzes it. Step 2: The detection unit detects abnormalities in the victim's movements monitored by the monitoring unit. For example, if the victim stays in a place they normally don't go to for a long time, or if a specific person frequently appears around the victim, this is detected as an abnormality. Step 3: The identification unit identifies stalking behavior based on the abnormalities detected by the detection unit, for example by analyzing footage from surveillance cameras installed around the victim and call history and messages recorded on the victim's smartphone. Step 4: The collection department collects evidence of the stalking behavior identified by the identification department, such as surveillance camera footage, call history, and messages. Step 5: The Notification Department notifies the relevant parties of the evidence collected by the Collection Department, for example by contacting the police or security companies.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 monitoring department that monitors the victim's movements in real time; a detection unit that detects abnormalities from the movements of the victim monitored by the monitoring unit; an identification unit that identifies stalking behavior from the abnormality detected by the detection unit; a collection unit that collects evidence of the stalking behavior identified by the identification unit; a notification unit that notifies relevant parties of the evidence collected by the collection unit. A system characterized by:

2. The detection unit The abnormality is detected when the victim stays in a place where the victim does not usually go for a long time or when the specific person frequently appears around the victim.

2. The system of claim 1.

3. The collecting unit Analyzing the footage from surveillance cameras installed around the victim, as well as call history and messages recorded on the victim's smartphone 2. The system of claim 1.

4. The notification unit Providing necessary information for the victim to file a police report, and organizing and providing the evidence to the lawyer 2. The system of claim 1.

5. The detection unit By referring to the victim's past behavioral history, the abnormal pattern is identified with higher accuracy.

2. The system of claim 1.

6. The identification unit Analyzing surveillance camera footage of the victim's surroundings and learning the behavioral patterns of the specific person 2. The system of claim 1.

7. The monitoring unit Using an emotion estimation function, the emotional state of the victim is monitored in real time to detect the abnormal emotional changes.

2. The system of claim 1.

8. The detection unit Using emotion estimation capabilities, if the victim's emotional state suddenly changes, the anomaly is detected and an immediate response is taken.

2. The system of claim 1.

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