System

Abstract

An object of a system according to an embodiment is to grasp individual needs of inmates and provide support in real time.SOLUTION: A system according to an embodiment includes an individual needs grasping unit and a real-time support providing unit. The individual needs grasping unit analyzes data such as a past behavior history, a psychological state, and a living environment of the inmate, and grasps individual needs. The real-time support providing unit provides support in real time based on the need grasped by the individual need grasping 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 lacks the ability to address the individual needs of prisoners and provide real-time support, resulting in insufficient support for preventing recidivism and reintegrating into society.

[0005] The system of the embodiment aims to understand the individual needs of prisoners and provide support in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an individual needs assessment unit and a real-time support provision unit. The individual needs assessment unit analyzes data such as the inmate's past behavioral history, psychological state, and living environment to assess individual needs. The real-time support provision unit provides support in real time based on the needs assessed by the individual needs assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the individual needs of prisoners and provide support in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) The rehabilitation support system according to an embodiment of the present invention uses a generative AI to understand the individual needs of inmates and provide support in real time, thereby helping inmates to reintegrate into society without repeating crimes.

[0029] The rehabilitation support system according to the embodiment includes an individual needs assessment unit and a real-time support provision unit. The individual needs assessment unit analyzes data such as the inmate's past behavioral history, psychological state, and living environment to assess the inmate's individual needs. For example, the individual needs assessment unit analyzes the inmate's criminal history and daily behavioral patterns using data mining technology. The individual needs assessment unit can also evaluate the inmate's psychological state based on psychological test results and counseling records. The individual needs assessment unit can also collect and analyze living environment data such as the inmate's housing situation, family composition, and economic situation. The real-time support provision unit provides support in real time based on the needs assessed by the individual needs assessment unit. For example, the real-time support provision unit can suggest relaxation methods when the inmate feels stressed. The real-time support provision unit can also provide an appropriate learning program when the inmate wants to learn a new skill. The real-time support provision unit can also provide emergency response through online counseling and reminder notifications. As a result, the rehabilitation support system according to the embodiment can assess the inmate's individual needs and provide support in real time, thereby reducing the risk of recidivism.

[0030] The Individual Needs Identification Unit can analyze an inmate's social media activity and online behavior to identify potential needs and risks. For example, the Individual Needs Identification Unit uses generative AI to analyze an inmate's social media activity to identify potential needs and risks. For example, it analyzes the content and comments that an inmate frequently posts to understand their psychological state. The Individual Needs Identification Unit can also analyze an inmate's online behavior to identify potential risks. For example, it analyzes website browsing history and online shopping history to assess the risk of recidivism. In this way, potential needs and risks can be identified by analyzing an inmate's social media activity and online behavior.

[0031] The individual needs assessment unit can analyze the physical health data of an inmate and identify needs based on their health condition. For example, the individual needs assessment unit uses generative AI to analyze the inmate's heart rate data and identify needs based on their health condition. For example, it monitors heart rate fluctuations and detects signs of stress or anxiety. The individual needs assessment unit can also analyze the inmate's sleep patterns and assess their health condition. For example, it can use data from a fitness tracker to evaluate sleep quality and provide appropriate support. In this way, by analyzing the inmate's physical health data, it is possible to identify needs based on their health condition.

[0032] The real-time support provider can analyze inmate behavioral data in real time, predict high-risk behavior, and issue warnings. For example, the real-time support provider uses a generation AI to analyze inmate behavioral data in real time, predict high-risk behavior, and issue warnings. For example, if an inmate frequently enters and leaves a specific location, the real-time support provider can issue a warning that the behavior is risky. The real-time support provider can also analyze inmate behavior patterns and detect abnormal behavior. For example, it can use a predictive model based on past data to predict high-risk behavior. This reduces the risk of recidivism by predicting high-risk behavior and issuing warnings.

[0033] The real-time support provider can monitor an inmate's health data in real time and provide support according to their health condition. For example, the generative AI can monitor an inmate's health data in real time and provide support according to their health condition. For example, it can monitor fluctuations in heart rate and blood pressure and suggest medical support if abnormalities are detected. The real-time support provider can also analyze data from an inmate's fitness tracker to evaluate their health condition. For example, it can provide exercise programs and nutritional guidance. In this way, by monitoring an inmate's health data in real time, it can provide appropriate support according to their health condition.

[0034] The real-time support provider can analyze an inmate's communication history and suggest appropriate communication skills. For example, the real-time support provider uses a generation AI to analyze an inmate's communication history and suggest appropriate communication skills. For example, it can analyze past conversations and email exchanges and suggest areas for improvement. The real-time support provider can also analyze an inmate's non-verbal communication and suggest appropriate skills. For example, it can analyze facial expressions and gestures and offer advice on improving assertiveness. In this way, appropriate communication skills can be suggested by analyzing an inmate's communication history.

[0035] The real-time support provider can analyze the inmate's learning history and provide support according to their learning progress. For example, the generative AI analyzes the inmate's learning history and provides support according to their learning progress. For example, it can suggest an appropriate learning program based on past learning achievements and test results. The real-time support provider can also analyze the inmate's learning style and provide individualized instruction. For example, it can suggest adjustments to the learning plan and measures to improve motivation. In this way, by analyzing the inmate's learning history, it can provide appropriate support according to their learning progress.

[0036] The real-time support provider can analyze the behavioral patterns of inmates and provide individualized support to encourage behavioral change. For example, the real-time support provider uses a generative AI to analyze the behavioral patterns of inmates and provide individualized support to encourage behavioral change. For example, it can identify high-risk behaviors based on past behavioral history and propose appropriate countermeasures. The real-time support provider can also analyze the behavioral patterns of inmates and provide measures to improve motivation. For example, it can encourage behavioral change using feedback and reward systems. In this way, by analyzing the behavioral patterns of inmates, it can provide individualized support to encourage behavioral change.

[0037] The real-time support provider can analyze the inmate's learning history and provide individualized support according to their learning progress. For example, the real-time support provider uses a generation AI to analyze the inmate's learning history and provide individualized support according to their learning progress. For example, it can suggest an appropriate learning program based on past learning achievements and test results. The real-time support provider can also analyze the inmate's learning style and provide individualized guidance. For example, it can suggest adjustments to the learning plan and measures to improve motivation. In this way, by analyzing the inmate's learning history, it can provide individualized support according to their learning progress.

[0038] The real-time support provider can analyze the health data of inmates and provide individualized support based on their health condition. For example, the generative AI analyzes the health data of inmates and provides individualized support based on their health condition. For example, it monitors fluctuations in heart rate and blood pressure and suggests medical support if abnormalities are detected. The real-time support provider can also analyze data from inmates' fitness trackers and evaluate their health condition. For example, it can provide exercise programs and nutritional guidance. In this way, by analyzing the health data of inmates, it is possible to provide individualized support based on their health condition.

[0039] The real-time support provider can analyze the occupational aptitude of an inmate and provide individualized support based on their career path. For example, the real-time support provider can use a generation AI to analyze the occupational aptitude of an inmate and provide individualized support based on their career path. For example, it can evaluate aptitude based on past work experience and skills and suggest appropriate occupations. The real-time support provider can also provide vocational training and career counseling based on the inmate's career goals. This makes it possible to provide individualized support based on their career path by analyzing the inmate's occupational aptitude.

[0040] The real-time support provision unit can continuously monitor the inmate's progress and provide feedback as needed. For example, the generation AI can continuously monitor the inmate's progress and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. In this way, by continuously monitoring the inmate's progress, it can provide feedback as needed.

[0041] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0042] The real-time support provision unit can continuously monitor the inmate's progress and provide feedback as needed. For example, the generation AI can continuously monitor the inmate's progress and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. In this way, by continuously monitoring the inmate's progress, it can provide feedback as needed.

[0043] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0044] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0045] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[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 rehabilitation support system can also be equipped with a hobby analysis unit that analyzes the hobbies and interests of inmates. The hobby analysis unit, for example, analyzes the activities an inmate has participated in in the past and the areas in which he or she is interested, and suggests appropriate hobbies and activities. For example, if an inmate is interested in music, it can suggest music-related workshops and events. The hobby analysis unit can also suggest stress relief methods based on the inmate's hobbies. For example, it can promote relaxation through hobbies such as art and sports. This can reduce the risk of recidivism by providing support based on the inmate's hobbies and interests.

[0048] The rehabilitation support system can also include a network analysis unit that analyzes the inmate's social network. The network analysis unit, for example, analyzes the types of people the inmate is associated with and identifies relationships that require support. For example, if the inmate frequently comes into contact with people who may be involved in crime, it will warn that the relationship is a risk. The network analysis unit can also make suggestions to strengthen the inmate's relationships with people who may have a positive influence on them. For example, it could provide counseling to improve relationships with family and friends. In this way, analyzing the inmate's social network can provide appropriate support.

[0049] The rehabilitation support system can also be equipped with a vocational aptitude analysis unit that analyzes the vocational aptitude of an inmate. The vocational aptitude analysis unit, for example, analyzes the inmate's past work experience and skills and suggests appropriate occupations. For example, if the inmate has previously worked in a technical job, it can suggest a technical-related vocational training program. The vocational aptitude analysis unit can also provide vocational training and career counseling based on the inmate's career goals. In this way, by analyzing the inmate's vocational aptitude, it is possible to provide individual support based on their career path.

[0050] The rehabilitation support system can also be equipped with a learning style analysis unit that analyzes the inmate's learning style. The learning style analysis unit can, for example, analyze the inmate's preferred learning method and suggest an appropriate learning program. For example, if the inmate prefers visual learning, it can provide a learning program using visual materials. The learning style analysis unit can also monitor the inmate's learning progress and adjust the learning plan as necessary. This makes it possible to provide individual support based on the inmate's learning style.

[0051] The rehabilitation support system can also be equipped with a health analysis unit that analyzes the inmate's health data. The health analysis unit, for example, monitors fluctuations in the inmate's heart rate and blood pressure and provides support based on their health condition. For example, if an abnormality is detected, it can suggest medical support. The health analysis unit can also analyze data from the inmate's fitness tracker and provide exercise programs and nutritional guidance. This makes it possible to provide individual support based on the inmate's health condition by analyzing their health data.

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

[0053] Step 1: The Individual Needs Assessment Unit analyzes data on inmates' past behavioral history, psychological state, living environment, and other factors to understand their individual needs. For example, the Individual Needs Assessment Unit uses data mining technology to analyze inmates' criminal histories and daily behavioral patterns. It also evaluates their psychological state based on psychological test results and counseling records, and collects and analyzes data on their living environment, such as their housing situation, family structure, and economic situation. Step 2: The Real-Time Support Unit provides real-time support based on the needs identified by the Individual Needs Assessment Unit. For example, if an inmate feels stressed, they can suggest relaxation techniques, or if they want to learn a new skill, they can provide an appropriate learning program. They can also provide emergency support through online counseling and reminder notifications.

[0054] (Example 2) The rehabilitation support system according to an embodiment of the present invention uses a generative AI to understand the individual needs of inmates and provide support in real time, thereby helping inmates to reintegrate into society without repeating crimes.

[0055] The rehabilitation support system according to the embodiment includes an individual needs assessment unit and a real-time support provision unit. The individual needs assessment unit analyzes data such as the inmate's past behavioral history, psychological state, and living environment to assess the inmate's individual needs. For example, the individual needs assessment unit analyzes the inmate's criminal history and daily behavioral patterns using data mining technology. The individual needs assessment unit can also evaluate the inmate's psychological state based on psychological test results and counseling records. The individual needs assessment unit can also collect and analyze living environment data such as the inmate's housing situation, family composition, and economic situation. The real-time support provision unit provides support in real time based on the needs assessed by the individual needs assessment unit. For example, the real-time support provision unit can suggest relaxation methods when the inmate feels stressed. The real-time support provision unit can also provide an appropriate learning program when the inmate wants to learn a new skill. The real-time support provision unit can also provide emergency response through online counseling and reminder notifications. As a result, the rehabilitation support system according to the embodiment can assess the inmate's individual needs and provide support in real time, thereby reducing the risk of recidivism.

[0056] The individual needs assessment unit can estimate the emotions of an inmate in real time and dynamically update their needs in response to changes in their emotions. For example, the individual needs assessment unit uses a generative AI to analyze the inmate's facial expressions and voice to estimate their emotions in real time. For example, it uses a camera or microphone to collect emotional data on the inmate and dynamically updates their individual needs in response to changes in their emotions. The individual needs assessment unit can also estimate the inmate's emotions using biometric data. For example, it can monitor their heart rate and electrodermal activity to detect changes in their emotions. This allows the individual needs to be dynamically updated in response to changes in the inmate's emotions, making it possible to provide more appropriate support.

[0057] The Individual Needs Identification Unit can analyze an inmate's social media activity and online behavior to identify potential needs and risks. For example, the Individual Needs Identification Unit uses generative AI to analyze an inmate's social media activity to identify potential needs and risks. For example, it analyzes the content and comments that an inmate frequently posts to understand their psychological state. The Individual Needs Identification Unit can also analyze an inmate's online behavior to identify potential risks. For example, it analyzes website browsing history and online shopping history to assess the risk of recidivism. In this way, potential needs and risks can be identified by analyzing an inmate's social media activity and online behavior.

[0058] The individual needs assessment unit can analyze the physical health data of an inmate and identify needs based on their health condition. For example, the individual needs assessment unit uses generative AI to analyze the inmate's heart rate data and identify needs based on their health condition. For example, it monitors heart rate fluctuations and detects signs of stress or anxiety. The individual needs assessment unit can also analyze the inmate's sleep patterns and assess their health condition. For example, it can use data from a fitness tracker to evaluate sleep quality and provide appropriate support. In this way, by analyzing the inmate's physical health data, it is possible to identify needs based on their health condition.

[0059] The real-time support provider can monitor the emotions of inmates in real time and provide support according to changes in their emotions. For example, the generative AI monitors the inmate's facial expressions and voice in real time and provides support according to changes in their emotions. For example, if an inmate feels stressed, it will suggest relaxation methods. The real-time support provider can also monitor the inmate's biometric data and detect changes in their emotions. For example, it can monitor heart rate and electrodermal activity and provide support according to changes in their emotions. This allows for more appropriate support by providing support according to changes in the inmate's emotions.

[0060] The real-time support provider can analyze inmate behavioral data in real time, predict high-risk behavior, and issue warnings. For example, the real-time support provider uses a generation AI to analyze inmate behavioral data in real time, predict high-risk behavior, and issue warnings. For example, if an inmate frequently enters and leaves a specific location, the real-time support provider can issue a warning that the behavior is risky. The real-time support provider can also analyze inmate behavior patterns and detect abnormal behavior. For example, it can use a predictive model based on past data to predict high-risk behavior. This reduces the risk of recidivism by predicting high-risk behavior and issuing warnings.

[0061] The real-time support provider can monitor an inmate's health data in real time and provide support according to their health condition. For example, the generative AI can monitor an inmate's health data in real time and provide support according to their health condition. For example, it can monitor fluctuations in heart rate and blood pressure and suggest medical support if abnormalities are detected. The real-time support provider can also analyze data from an inmate's fitness tracker to evaluate their health condition. For example, it can provide exercise programs and nutritional guidance. In this way, by monitoring an inmate's health data in real time, it can provide appropriate support according to their health condition.

[0062] The real-time support provider can analyze an inmate's communication history and suggest appropriate communication skills. For example, the real-time support provider uses a generation AI to analyze an inmate's communication history and suggest appropriate communication skills. For example, it can analyze past conversations and email exchanges and suggest areas for improvement. The real-time support provider can also analyze an inmate's non-verbal communication and suggest appropriate skills. For example, it can analyze facial expressions and gestures and offer advice on improving assertiveness. In this way, appropriate communication skills can be suggested by analyzing an inmate's communication history.

[0063] The real-time support provider can analyze the inmate's learning history and provide support according to their learning progress. For example, the generative AI analyzes the inmate's learning history and provides support according to their learning progress. For example, it can suggest an appropriate learning program based on past learning achievements and test results. The real-time support provider can also analyze the inmate's learning style and provide individualized instruction. For example, it can suggest adjustments to the learning plan and measures to improve motivation. In this way, by analyzing the inmate's learning history, it can provide appropriate support according to their learning progress.

[0064] The real-time support providing unit can use the emotion estimation function to provide real-time support based on the emotions of the inmate. The real-time support providing unit can use the emotion estimation function to provide real-time support based on the emotions of the inmate. For example, it can analyze the facial expressions and voice of the inmate and provide support according to changes in emotions. The real-time support providing unit can also use the emotion estimation function to take emergency measures based on the emotions of the inmate. For example, it can provide counseling when stress or anxiety increases. In this way, by using the emotion estimation function, it is possible to provide real-time support based on the emotions of the inmate.

[0065] The real-time support provision unit can analyze the emotions of inmates in real time and provide individual support based on those emotions. For example, the real-time support provision unit uses a generation AI to analyze the emotions of inmates in real time and provide individual support based on those emotions. For example, it analyzes the facial expressions and voice of inmates and provides support according to changes in emotions. The real-time support provision unit can also provide counseling and relaxation techniques according to the emotions of inmates based on the emotion analysis data. This makes it possible to provide individual support based on emotions by analyzing the emotions of inmates in real time.

[0066] The real-time support provider can analyze the behavioral patterns of inmates and provide individualized support to encourage behavioral change. For example, the real-time support provider uses a generative AI to analyze the behavioral patterns of inmates and provide individualized support to encourage behavioral change. For example, it can identify high-risk behaviors based on past behavioral history and propose appropriate countermeasures. The real-time support provider can also analyze the behavioral patterns of inmates and provide measures to improve motivation. For example, it can encourage behavioral change using feedback and reward systems. In this way, by analyzing the behavioral patterns of inmates, it can provide individualized support to encourage behavioral change.

[0067] The real-time support provider can analyze the inmate's learning history and provide individualized support according to their learning progress. For example, the real-time support provider uses a generation AI to analyze the inmate's learning history and provide individualized support according to their learning progress. For example, it can suggest an appropriate learning program based on past learning achievements and test results. The real-time support provider can also analyze the inmate's learning style and provide individualized guidance. For example, it can suggest adjustments to the learning plan and measures to improve motivation. In this way, by analyzing the inmate's learning history, it can provide individualized support according to their learning progress.

[0068] The real-time support provider can analyze the health data of inmates and provide individualized support based on their health condition. For example, the generative AI analyzes the health data of inmates and provides individualized support based on their health condition. For example, it monitors fluctuations in heart rate and blood pressure and suggests medical support if abnormalities are detected. The real-time support provider can also analyze data from inmates' fitness trackers and evaluate their health condition. For example, it can provide exercise programs and nutritional guidance. In this way, by analyzing the health data of inmates, it is possible to provide individualized support based on their health condition.

[0069] The real-time support provider can analyze the occupational aptitude of an inmate and provide individualized support based on their career path. For example, the real-time support provider can use a generation AI to analyze the occupational aptitude of an inmate and provide individualized support based on their career path. For example, it can evaluate aptitude based on past work experience and skills and suggest appropriate occupations. The real-time support provider can also provide vocational training and career counseling based on the inmate's career goals. This makes it possible to provide individualized support based on their career path by analyzing the inmate's occupational aptitude.

[0070] The real-time support providing unit can use the emotion estimation function to provide individual support based on the emotions of the inmate. The real-time support providing unit can use the emotion estimation function to provide individual support based on the emotions of the inmate. For example, it can analyze the facial expressions and voice of the inmate and provide support according to changes in emotions. The real-time support providing unit can also use the emotion estimation function to take emergency measures based on the emotions of the inmate. For example, it can provide counseling when stress or anxiety increases. In this way, by using the emotion estimation function, individual support can be provided based on the emotions of the inmate.

[0071] The real-time support provision unit can continuously monitor the inmate's progress and provide feedback as needed. For example, the generation AI can continuously monitor the inmate's progress and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. In this way, by continuously monitoring the inmate's progress, it can provide feedback as needed.

[0072] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0073] The real-time support provision unit can continuously monitor the inmate's progress and provide feedback as needed. For example, the generation AI can continuously monitor the inmate's progress and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. In this way, by continuously monitoring the inmate's progress, it can provide feedback as needed.

[0074] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0075] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0076] The real-time support provision unit can monitor the inmate's progress in real time and provide feedback as needed. For example, the generation AI can monitor the inmate's progress in real time and provide feedback as needed. For example, it can evaluate the extent to which the inmate has achieved the goals they have set and set new goals based on the level of achievement. The real-time support provision unit can also provide appropriate support when the inmate faces difficulties. For example, it can provide improvement suggestions and motivation-boosting measures based on the inmate's progress. This makes it possible to monitor the inmate's progress in real time and provide feedback as needed.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The rehabilitation support system can also be equipped with a hobby analysis unit that analyzes the hobbies and interests of inmates. The hobby analysis unit, for example, analyzes the activities an inmate has participated in in the past and the areas in which he or she is interested, and suggests appropriate hobbies and activities. For example, if an inmate is interested in music, it can suggest music-related workshops and events. The hobby analysis unit can also suggest stress relief methods based on the inmate's hobbies. For example, it can promote relaxation through hobbies such as art and sports. This can reduce the risk of recidivism by providing support based on the inmate's hobbies and interests.

[0079] The rehabilitation support system can also include a network analysis unit that analyzes the inmate's social network. The network analysis unit, for example, analyzes the types of people the inmate is associated with and identifies relationships that require support. For example, if the inmate frequently comes into contact with people who may be involved in crime, it will warn that the relationship is a risk. The network analysis unit can also make suggestions to strengthen the inmate's relationships with people who may have a positive influence on them. For example, it could provide counseling to improve relationships with family and friends. In this way, analyzing the inmate's social network can provide appropriate support.

[0080] The rehabilitation support system can also be equipped with a vocational aptitude analysis unit that analyzes the vocational aptitude of an inmate. The vocational aptitude analysis unit, for example, analyzes the inmate's past work experience and skills and suggests appropriate occupations. For example, if the inmate has previously worked in a technical job, it can suggest a technical-related vocational training program. The vocational aptitude analysis unit can also provide vocational training and career counseling based on the inmate's career goals. In this way, by analyzing the inmate's vocational aptitude, it is possible to provide individual support based on their career path.

[0081] The rehabilitation support system can also be equipped with a learning style analysis unit that analyzes the inmate's learning style. The learning style analysis unit can, for example, analyze the inmate's preferred learning method and suggest an appropriate learning program. For example, if the inmate prefers visual learning, it can provide a learning program using visual materials. The learning style analysis unit can also monitor the inmate's learning progress and adjust the learning plan as necessary. This makes it possible to provide individual support based on the inmate's learning style.

[0082] The rehabilitation support system can also be equipped with a health analysis unit that analyzes the inmate's health data. The health analysis unit, for example, monitors fluctuations in the inmate's heart rate and blood pressure and provides support based on their health condition. For example, if an abnormality is detected, it can suggest medical support. The health analysis unit can also analyze data from the inmate's fitness tracker and provide exercise programs and nutritional guidance. This makes it possible to provide individual support based on the inmate's health condition by analyzing their health data.

[0083] The playback support system can also include an emotion estimation unit that estimates the emotions of the inmate and provides support based on those emotions. The emotion estimation unit, for example, analyzes the inmate's facial expressions and voice and provides support according to changes in emotions. For example, if the inmate feels stressed, it suggests relaxation methods. The emotion estimation unit can also monitor the inmate's biometric data to detect changes in emotions. For example, it can monitor heart rate and electrodermal activity and provide support according to changes in emotions. This enables more appropriate support to be provided by providing support according to changes in the inmate's emotions.

[0084] The rehabilitative support system can further include an emergency response unit that estimates the emotions of the inmate and provides emergency response based on the emotions. The emergency response unit, for example, analyzes the inmate's facial expressions and voice and provides emergency response based on changes in emotions. For example, it provides counseling when stress or anxiety increases. The emergency response unit can also monitor the inmate's biometric data to detect changes in emotions. For example, it can monitor heart rate and electrodermal activity and provide emergency response based on changes in emotions. In this way, the emotion estimation function can be used to provide emergency response based on the inmate's emotions.

[0085] The playback support system can also be equipped with a learning support unit that estimates the inmate's emotions and provides learning support based on those emotions. The learning support unit, for example, analyzes the inmate's facial expressions and voice and provides learning support in response to changes in emotions. For example, if the inmate loses motivation to study, it suggests ways to improve motivation. The learning support unit can also monitor the inmate's biometric data to detect changes in emotions. For example, it can monitor heart rate and electrodermal activity and provide learning support in response to changes in emotions. This can improve the effectiveness of learning by providing learning support in response to changes in the inmate's emotions.

[0086] The playback assistance system can also include a communication support unit that estimates the inmate's emotions and provides communication support based on those emotions. The communication support unit, for example, analyzes the inmate's facial expressions and voice and provides communication support in response to changes in emotions. For example, if the inmate is feeling stressed, it can suggest relaxation methods. The communication support unit can also monitor the inmate's biometric data to detect changes in emotions. For example, it can monitor heart rate and electrodermal activity and provide communication support in response to changes in emotions. This allows for more appropriate support by providing communication support in response to changes in the inmate's emotions.

[0087] The rehabilitation support system can also include a health support unit that estimates the inmate's emotions and provides health support based on those emotions. The health support unit, for example, analyzes the inmate's facial expressions and voice and provides health support in response to changes in emotions. For example, if the inmate feels stressed, it can suggest relaxation methods. The health support unit can also monitor the inmate's biometric data and detect changes in emotions. For example, it can monitor heart rate and electrodermal activity and provide health support in response to changes in emotions. This allows for more appropriate support by providing health support in response to changes in the inmate's emotions.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The Individual Needs Assessment Unit analyzes data on inmates' past behavioral history, psychological state, living environment, and other factors to understand their individual needs. For example, the Individual Needs Assessment Unit uses data mining technology to analyze inmates' criminal histories and daily behavioral patterns. It also evaluates their psychological state based on psychological test results and counseling records, and collects and analyzes data on their living environment, such as their housing situation, family structure, and economic situation. Step 2: The Real-Time Support Unit provides real-time support based on the needs identified by the Individual Needs Assessment Unit. For example, if an inmate feels stressed, they can suggest relaxation techniques, or if they want to learn a new skill, they can provide an appropriate learning program. They can also provide emergency support through online counseling and reminder notifications.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0126] The 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.

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

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] 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]

[0157] 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. The Individual Needs Assessment Department analyzes data such as the prisoner's past behavioral history, psychological state, and living environment to understand their individual needs; a real-time support providing unit that provides support in real time based on the needs identified by the individual needs identifying unit. A system characterized by:

2. The individual needs understanding unit Estimating the inmate's emotions in real time and dynamically updating the needs in response to changes in the emotions.

2. The system of claim 1.

3. The individual needs understanding unit Analyze the inmates' social media activity and online behavior to identify potential needs and risks 2. The system of claim 1.

4. The real-time support providing unit The behavioral data of the inmates is analyzed in real time, and high-risk behavior is predicted and a warning issued.

2. The system of claim 1.

5. The real-time support providing unit Continually monitor the inmate's progress and provide feedback as needed 2. The system of claim 1.

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