System

Abstract

An object of a system according to an embodiment is to improve motivation for daily work.SOLUTION: A system includes an image recognition part, an action analysis part, and a story release part. The image recognition unit monitors the work progress using the camera of the smartphone. The behavior analysis unit determines the work progress monitored by the image recognition unit. The story releasing unit releases a new chapter of the story in accordance with the work progress determined by the behavior analyzing 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 techniques make it difficult to maintain motivation for daily work, which can lead to reduced productivity and lower satisfaction.

[0005] The system according to the embodiment aims to improve motivation for daily tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, a behavior analysis unit, and a story release unit. The image recognition unit monitors work progress using a smartphone camera. The behavior analysis unit determines the work progress monitored by the image recognition unit. The story release unit releases a new chapter of the story according to the work progress determined by the behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve motivation for daily tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The ProgressTales system according to an embodiment of the present invention is a system that unlocks new chapters of a story according to the progress of work, thereby increasing motivation and productivity.

[0029] The ProgressTales system according to the embodiment includes an image recognition unit, a behavior analysis unit, and a story release unit. The image recognition unit monitors work progress using a smartphone camera. For example, the image recognition unit captures a user studying at a desk with a camera and collects video data. The image recognition unit also uses AI-based image analysis technology to determine whether the user is working. For example, the image recognition unit analyzes the user's posture and movements to determine whether the user is working. To protect the user's privacy, the image recognition unit encrypts the video data and immediately deletes it after analysis. The behavior analysis unit determines the work progress monitored by the image recognition unit. For example, the behavior analysis unit evaluates the user's work progress using AI-based behavior analysis technology. The behavior analysis unit also monitors the user's work progress in real time and quantifies the progress. For example, the behavior analysis unit calculates the progress based on the number of tasks and the time it takes the user to complete them. The behavior analysis unit also stores the user's work history in a database and compares it with past data to evaluate the progress. The story release unit releases new chapters in the story according to the work progress determined by the behavior analysis unit. For example, the story release unit releases the next chapter of the story when the user reaches a certain level of progress. The story release unit can also customize the content of the story according to the user's progress. For example, the story release unit introduces themes and characters related to the user's work content into the story. Furthermore, the story release unit can provide interactive quizzes or mini-games when the user releases a new chapter of the story. In this way, the ProgressTales system can increase the user's motivation by releasing new chapters in the story according to the work progress. For example, a new chapter of the story is released each time the user completes a daily task. This allows the user to enjoy the progression of the story as they work.

[0030] The behavior analysis unit can combine the user's past work history and performance data to perform an individually optimized progress evaluation. For example, the behavior analysis unit stores the user's past work history in a database, and the AI ​​evaluates progress based on that data. For example, the current progress is evaluated based on the time it took to complete similar tasks in the past. The behavior analysis unit also analyzes the user's performance data to evaluate progress. For example, progress can be evaluated based on the user's work speed and error rate. The behavior analysis unit also sets individual evaluation criteria to perform an optimized progress evaluation for each user. For example, the evaluation criteria can be adjusted according to the user's skill level and the difficulty of the task. This enables an optimized progress evaluation for each user.

[0031] The behavior analysis unit can also evaluate progress based on the content and tone of the user's speech by combining it with voice recognition technology. The behavior analysis unit, for example, uses voice recognition technology to analyze the content of the user's speech and evaluate the progress of the work. For example, when the user explains the work content aloud, the content is analyzed to determine the progress. The behavior analysis unit also analyzes the tone of the user's speech and evaluates the progress of the work. For example, if the user speaks in a calm tone, it can be determined that progress is going well. The behavior analysis unit can also analyze the content and tone of the user's speech in real time using voice recognition technology. For example, it can monitor the content and tone of the user's speech while working and evaluate the progress. In this way, analyzing the content and tone of the user's speech enables more accurate progress evaluation.

[0032] The behavior analysis unit can take into account the user's surrounding environment and evaluate the impact of the environment on work. For example, the behavior analysis unit can monitor the user's surrounding environment and analyze lighting brightness and volume to evaluate work progress. For example, if work is progressing in an appropriate lighting and quiet environment, it can determine that progress is good. The behavior analysis unit can also use environmental sensors to evaluate progress taking into account the user's surrounding environment. For example, it can collect and analyze environmental data using an illuminance sensor or a volume sensor. The behavior analysis unit can also analyze a combination of environmental data and work data to evaluate the impact of the user's surrounding environment on work. For example, it can evaluate the impact of changes in lighting brightness and volume on work efficiency. This can improve work efficiency by taking the surrounding environment into account.

[0033] When unlocking a new chapter in the story, the story release unit can introduce themes and characters related to the user's work. For example, the story release unit introduces themes and characters related to the user's work in the story. For example, if the user is studying, characters related to academics will appear in the story. The story release unit can also change the theme of the story depending on the user's work. For example, if the user is exercising, the story theme can be changed to one related to sports. The story release unit can also customize character settings to introduce characters related to the user's work in the story. For example, the role and personality of the character can be changed depending on the user's work. This can increase the user's sense of immersion in the story by introducing themes and characters related to the user's work.

[0034] The story release unit can provide the user with interactive quizzes and mini-games according to the progress of the story, thereby further promoting the progress of the work. The story release unit, for example, provides the user with interactive quizzes according to the progress of the story. For example, a quiz related to the content of the story is presented, and the next chapter is unlocked by answering it correctly. The story release unit can also provide the user with mini-games to promote the progress of the work. For example, when the user reaches a certain level of progress, the story release unit can provide the user with an opportunity to play a mini-game. The story release unit can also analyze the user's progress data to provide interactive quizzes and mini-games. For example, the difficulty of the quizzes and mini-games can be adjusted according to the user's progress. In this way, the provision of interactive quizzes and mini-games can further promote the progress of the user's work.

[0035] The story release unit can introduce a multiple-ending system that changes the story's branching and ending according to the user's progress when unlocking a new chapter in the story. The story release unit, for example, sets the story's branching according to the user's work progress. For example, a different ending can be reached if the user progresses quickly. The story release unit can also change the ending according to the user's progress. For example, a happy ending can be reached if the user completes all tasks. The story release unit can also change the story scenario according to the user's progress. For example, a new character can appear in the story when the user completes a specific task. This can increase the user's sense of immersion in the story by changing the story's branching and ending according to the user's progress.

[0036] The story release unit may add a social function that allows a user to share their progress with other users and encourage competition and cooperation when unlocking a new chapter in the story. The story release unit may add, for example, a social function that allows a user to share their progress with other users. For example, the user may share their progress with friends and encourage competition and cooperation. The story release unit may also use a social network to share their progress with other users. For example, the user may post their progress through an SNS and exchange comments with friends. The story release unit may also provide a function that allows a user to compete and cooperate with other users. For example, the story release unit may display a ranking based on the user's progress and allow the user to compete with friends. This allows the user to share their progress with other users and encourage competition and cooperation, thereby increasing motivation for the work.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The behavior analysis unit can monitor the temperature and humidity of the user's work environment and make suggestions to maintain a comfortable work environment. For example, if the temperature is too high, it can recommend air conditioning, and if the humidity is too low, it can suggest the use of a humidifier. The behavior analysis unit can also monitor the volume of the user's work environment and evaluate the impact of noise on work. For example, if the noise is too loud, it can recommend the use of noise-canceling headphones. Furthermore, the behavior analysis unit can monitor the lighting in the user's work environment and suggest appropriate lighting conditions. For example, if the lighting is too dim, it can recommend the use of a desk lamp. This can optimize the user's work environment and improve work efficiency.

[0039] The behavioral analysis unit can analyze a user's work patterns and suggest an optimal work schedule. For example, if a user has a high concentration in the morning, it can suggest scheduling important tasks in the morning. Also, if a user tends to get tired in the afternoon, it can suggest taking more breaks. Furthermore, the behavioral analysis unit can analyze a user's work patterns and provide advice to maximize work efficiency. For example, if a user works more efficiently by concentrating for short periods of time, it can suggest the Pomodoro Technique. This allows the system to suggest an optimal work schedule based on the user's work patterns and improve work efficiency.

[0040] The behavior analysis unit can monitor the user's posture while working and provide advice to maintain proper posture. For example, if the user has been working in the same posture for a long time, it can suggest stretching. Also, if the user is working in an inappropriate posture, it can advise the user to adopt a correct posture. Furthermore, the behavior analysis unit can monitor the user's posture and suggest improvements to the work environment. For example, it can suggest adjusting the chair height or desk position. This can improve the user's posture and increase work efficiency.

[0041] The behavior analysis unit can monitor the user's ergonomics while working and provide advice to maintain appropriate ergonomics. For example, if the user works in the same position for a long time, it can suggest ergonomic stretching. Also, if the user works with inappropriate ergonomics, it can advise the user to adopt correct ergonomics. Furthermore, the behavior analysis unit can monitor the user's ergonomics and suggest improvements to the work environment. For example, it can suggest adjusting the chair height or desk position. This can improve the user's ergonomics and increase work efficiency.

[0042] The story release unit can increase the number of options that the user can select in the story according to the user's work progress. For example, when the user reaches a certain level of progress, new options are released. The story release unit can also change the outcome of options in the story according to the user's progress. For example, when the user completes a specific task, the outcome of an option becomes positive. Furthermore, the story release unit can increase the number of options in the story according to the user's progress. For example, when the user completes many tasks, the number of options increases. In this way, by increasing the options in the story according to the user's work progress, it is possible to enhance the sense of immersion in the story.

[0043] The story release unit can release items and skills that the user can select in the story according to the user's work progress. For example, when the user reaches a certain level of progress, new items and skills are released. The story release unit can also change the effects of items and skills in the story according to the user's progress. For example, when the user completes a specific task, the effects of the items and skills are strengthened. Furthermore, the story release unit can increase the number of items and skills in the story according to the user's progress. For example, when the user completes many tasks, the number of items and skills increases. In this way, by releasing items and skills in the story according to the user's work progress, the sense of immersion in the story can be enhanced.

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

[0045] Step 1: The image recognition unit monitors the progress of work using the smartphone's camera. For example, the image recognition unit captures the user studying at a desk with the camera and collects video data. The image recognition unit also uses AI image analysis technology to determine whether the user is working. For example, the image recognition unit analyzes the user's posture and movements to determine whether they are working. Furthermore, to protect the user's privacy, the image recognition unit encrypts the video data and deletes it immediately after analysis. Step 2: The behavior analysis unit determines the work progress monitored by the image recognition unit. For example, the behavior analysis unit evaluates the user's work progress using AI behavior analysis technology. The behavior analysis unit also monitors the user's work progress in real time and quantifies the progress. For example, the behavior analysis unit calculates the progress based on the number of tasks and time the user completed. Furthermore, the behavior analysis unit saves the user's work history in a database and compares it with past data to evaluate the progress. Step 3: The story release unit releases a new chapter of the story according to the work progress determined by the behavior analysis unit. For example, the story release unit releases the next chapter of the story when the user reaches a certain level of progress. The story release unit can also customize the content of the story according to the user's progress. For example, the story release unit can introduce themes and characters related to the user's work content into the story. Furthermore, the story release unit can provide an interactive quiz or mini-game when the user releases a new chapter of the story.

[0046] (Example 2) The ProgressTales system according to an embodiment of the present invention is a system that unlocks new chapters of a story according to the progress of work, thereby increasing motivation and productivity.

[0047] The ProgressTales system according to the embodiment includes an image recognition unit, a behavior analysis unit, and a story release unit. The image recognition unit monitors work progress using a smartphone camera. For example, the image recognition unit captures a user studying at a desk with a camera and collects video data. The image recognition unit also uses AI-based image analysis technology to determine whether the user is working. For example, the image recognition unit analyzes the user's posture and movements to determine whether the user is working. To protect the user's privacy, the image recognition unit encrypts the video data and immediately deletes it after analysis. The behavior analysis unit determines the work progress monitored by the image recognition unit. For example, the behavior analysis unit evaluates the user's work progress using AI-based behavior analysis technology. The behavior analysis unit also monitors the user's work progress in real time and quantifies the progress. For example, the behavior analysis unit calculates the progress based on the number of tasks and the time it takes the user to complete them. The behavior analysis unit also stores the user's work history in a database and compares it with past data to evaluate the progress. The story release unit releases new chapters in the story according to the work progress determined by the behavior analysis unit. For example, the story release unit releases the next chapter of the story when the user reaches a certain level of progress. The story release unit can also customize the content of the story according to the user's progress. For example, the story release unit introduces themes and characters related to the user's work content into the story. Furthermore, the story release unit can provide interactive quizzes or mini-games when the user releases a new chapter of the story. In this way, the ProgressTales system can increase the user's motivation by releasing new chapters in the story according to the work progress. For example, a new chapter of the story is released each time the user completes a daily task. This allows the user to enjoy the progression of the story as they work.

[0048] The behavior analysis unit can analyze the user's facial expressions and body movements and evaluate progress taking into account their emotional state. For example, the behavior analysis unit evaluates work progress by using AI to analyze the user's facial expressions in real time and detect smiling or concentrated expressions. For example, if the user continues working with a smile, it can determine that progress is going well. The behavior analysis unit also analyzes the user's body movements to evaluate work progress. For example, if the user is frequently moving their body, it can determine that their concentration is declining. The behavior analysis unit also evaluates progress taking into account the user's emotional state. For example, if the user is feeling stressed, it can determine that progress is slowing. This allows for more accurate progress evaluation by taking the user's emotional state into account.

[0049] The behavior analysis unit can combine the user's past work history and performance data to perform an individually optimized progress evaluation. For example, the behavior analysis unit stores the user's past work history in a database, and the AI ​​evaluates progress based on that data. For example, the current progress is evaluated based on the time it took to complete similar tasks in the past. The behavior analysis unit also analyzes the user's performance data to evaluate progress. For example, progress can be evaluated based on the user's work speed and error rate. The behavior analysis unit also sets individual evaluation criteria to perform an optimized progress evaluation for each user. For example, the evaluation criteria can be adjusted according to the user's skill level and the difficulty of the task. This enables an optimized progress evaluation for each user.

[0050] The behavior analysis unit can use the emotion estimation function to detect stress and fatigue felt by the user while working in real time and suggest appropriate timing for breaks. For example, the behavior analysis unit uses AI to analyze the user's facial expressions and body movements to detect signs of stress and fatigue. For example, it can detect facial expressions such as furrowing brows and frequent body movements. The behavior analysis unit also uses the emotion estimation function to detect the user's stress and fatigue in real time. For example, it can collect heart rate fluctuations and electrodermal activity using a sensor and analyze them using an emotion estimation algorithm. Furthermore, if the behavior analysis unit detects the user's stress or fatigue, it can suggest appropriate timing for breaks. For example, it can notify the user to take a break. This makes it possible to improve work efficiency by detecting the user's stress and fatigue and suggesting appropriate breaks.

[0051] The behavior analysis unit can also evaluate progress based on the content and tone of the user's speech by combining it with voice recognition technology. The behavior analysis unit, for example, uses voice recognition technology to analyze the content of the user's speech and evaluate the progress of the work. For example, when the user explains the work content aloud, the content is analyzed to determine the progress. The behavior analysis unit also analyzes the tone of the user's speech and evaluates the progress of the work. For example, if the user speaks in a calm tone, it can be determined that progress is going well. The behavior analysis unit can also analyze the content and tone of the user's speech in real time using voice recognition technology. For example, it can monitor the content and tone of the user's speech while working and evaluate the progress. In this way, analyzing the content and tone of the user's speech enables more accurate progress evaluation.

[0052] The behavior analysis unit can take into account the user's surrounding environment and evaluate the impact of the environment on work. For example, the behavior analysis unit can monitor the user's surrounding environment and analyze lighting brightness and volume to evaluate work progress. For example, if work is progressing in an appropriate lighting and quiet environment, it can determine that progress is good. The behavior analysis unit can also use environmental sensors to evaluate progress taking into account the user's surrounding environment. For example, it can collect and analyze environmental data using an illuminance sensor or a volume sensor. The behavior analysis unit can also analyze a combination of environmental data and work data to evaluate the impact of the user's surrounding environment on work. For example, it can evaluate the impact of changes in lighting brightness and volume on work efficiency. This can improve work efficiency by taking the surrounding environment into account.

[0053] The behavior analysis unit can use the emotion estimation function to analyze the emotional state of the user before starting work and suggest the optimal timing to start work. For example, the behavior analysis unit can use the emotion estimation function to analyze the emotional state of the user before starting work in real time. For example, the behavior analysis unit can suggest starting work if the user is relaxed. The behavior analysis unit can also collect emotion data to analyze the user's emotional state and suggest the optimal timing to start work. For example, the behavior analysis unit can analyze the user's facial expressions and voice data to evaluate the emotional state. The behavior analysis unit can also set individual evaluation criteria to suggest the optimal timing to start work, taking the user's emotional state into consideration. For example, the behavior analysis unit can suggest the optimal time to start work depending on the user's emotional state. In this way, by analyzing the user's emotional state and suggesting the optimal timing to start work, work efficiency can be improved.

[0054] When unlocking a new chapter in the story, the story release unit can introduce themes and characters related to the user's work. For example, the story release unit introduces themes and characters related to the user's work in the story. For example, if the user is studying, characters related to academics will appear in the story. The story release unit can also change the theme of the story depending on the user's work. For example, if the user is exercising, the story theme can be changed to one related to sports. The story release unit can also customize character settings to introduce characters related to the user's work in the story. For example, the role and personality of the character can be changed depending on the user's work. This can increase the user's sense of immersion in the story by introducing themes and characters related to the user's work.

[0055] The story release unit can provide the user with interactive quizzes and mini-games according to the progress of the story, thereby further promoting the progress of the work. The story release unit, for example, provides the user with interactive quizzes according to the progress of the story. For example, a quiz related to the content of the story is presented, and the next chapter is unlocked by answering it correctly. The story release unit can also provide the user with mini-games to promote the progress of the work. For example, when the user reaches a certain level of progress, the story release unit can provide the user with an opportunity to play a mini-game. The story release unit can also analyze the user's progress data to provide interactive quizzes and mini-games. For example, the difficulty of the quizzes and mini-games can be adjusted according to the user's progress. In this way, the provision of interactive quizzes and mini-games can further promote the progress of the user's work.

[0056] The story release unit can introduce a multiple-ending system that changes the story's branching and ending according to the user's progress when unlocking a new chapter in the story. The story release unit, for example, sets the story's branching according to the user's work progress. For example, a different ending can be reached if the user progresses quickly. The story release unit can also change the ending according to the user's progress. For example, a happy ending can be reached if the user completes all tasks. The story release unit can also change the story scenario according to the user's progress. For example, a new character can appear in the story when the user completes a specific task. This can increase the user's sense of immersion in the story by changing the story's branching and ending according to the user's progress.

[0057] The story release unit may add a social function that allows a user to share their progress with other users and encourage competition and cooperation when unlocking a new chapter in the story. The story release unit may add, for example, a social function that allows a user to share their progress with other users. For example, the user may share their progress with friends and encourage competition and cooperation. The story release unit may also use a social network to share their progress with other users. For example, the user may post their progress through an SNS and exchange comments with friends. The story release unit may also provide a function that allows a user to compete and cooperate with other users. For example, the story release unit may display a ranking based on the user's progress and allow the user to compete with friends. This allows the user to share their progress with other users and encourage competition and cooperation, thereby increasing motivation for the work.

[0058] The story release unit can use the emotion estimation function to provide a function for a user to share emotions when unlocking a new chapter in a story and compare the emotions with those of other users. For example, the story release unit can use the emotion estimation function to analyze the emotions of a user when unlocking a new chapter in a story in real time and share the data with other users. For example, if a user shows a happy expression, the emotion data is shared. The story release unit can also collect emotion data to share the user's emotions and compare it with the emotional reactions of other users. For example, the user's emotion data can be stored in the cloud and compared with the data of other users. The story release unit can also visualize the emotion data to compare the user's emotional reactions. For example, the emotion data can be displayed using graphs or charts to clearly show differences from other users. This allows the user to share their emotions and compare them with the emotional reactions of other users, encouraging emotional empathy.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The behavior analysis unit can monitor the temperature and humidity of the user's work environment and make suggestions to maintain a comfortable work environment. For example, if the temperature is too high, it can recommend air conditioning, and if the humidity is too low, it can suggest the use of a humidifier. The behavior analysis unit can also monitor the volume of the user's work environment and evaluate the impact of noise on work. For example, if the noise is too loud, it can recommend the use of noise-canceling headphones. Furthermore, the behavior analysis unit can monitor the lighting in the user's work environment and suggest appropriate lighting conditions. For example, if the lighting is too dim, it can recommend the use of a desk lamp. This can optimize the user's work environment and improve work efficiency.

[0061] The behavior analysis unit can estimate the user's emotions and evaluate the progress of the work based on the estimated emotions. For example, if the user has a concentrated expression, it can be determined that progress is going well. On the other hand, if the user has a tired expression, it can be determined that progress is slow. Furthermore, the behavior analysis unit can provide appropriate feedback taking into account the user's emotional state. For example, if the user is feeling stressed, it can provide advice on how to relax. This makes it possible to evaluate progress taking into account the user's emotional state, and to provide more accurate feedback.

[0062] The behavioral analysis unit can analyze a user's work patterns and suggest an optimal work schedule. For example, if a user has a high concentration in the morning, it can suggest scheduling important tasks in the morning. Also, if a user tends to get tired in the afternoon, it can suggest taking more breaks. Furthermore, the behavioral analysis unit can analyze a user's work patterns and provide advice to maximize work efficiency. For example, if a user works more efficiently by concentrating for short periods of time, it can suggest the Pomodoro Technique. This allows the system to suggest an optimal work schedule based on the user's work patterns and improve work efficiency.

[0063] The behavior analysis unit can use the emotion estimation function to detect fluctuations in the user's motivation while working in real time and suggest appropriate measures to maintain motivation. For example, if the user is losing motivation, an encouraging message can be displayed. Also, if the user is maintaining high motivation, a suggestion can be made to challenge themselves further. Furthermore, the behavior analysis unit can detect fluctuations in the user's motivation and suggest appropriate times to take a break. For example, if the user is losing motivation, a suggestion can be made to take a short break. This can maintain the user's motivation and improve work efficiency.

[0064] The behavior analysis unit can monitor the user's posture while working and provide advice to maintain proper posture. For example, if the user has been working in the same posture for a long time, it can suggest stretching. Also, if the user is working in an inappropriate posture, it can advise the user to adopt a correct posture. Furthermore, the behavior analysis unit can monitor the user's posture and suggest improvements to the work environment. For example, it can suggest adjusting the chair height or desk position. This can improve the user's posture and increase work efficiency.

[0065] The behavior analysis unit can use the emotion estimation function to detect frustration felt by the user while working and suggest appropriate countermeasures. For example, if the user feels frustrated, it can suggest a relaxation activity. Also, if the user feels frustrated, it can advise the user to reconsider how they are proceeding with their work. Furthermore, the behavior analysis unit can detect the user's frustration and provide appropriate feedback. For example, if the user feels frustrated, it can display an encouraging message. This can reduce the user's frustration and improve work efficiency.

[0066] The behavior analysis unit can monitor the user's ergonomics while working and provide advice to maintain appropriate ergonomics. For example, if the user works in the same position for a long time, it can suggest ergonomic stretching. Also, if the user works with inappropriate ergonomics, it can advise the user to adopt correct ergonomics. Furthermore, the behavior analysis unit can monitor the user's ergonomics and suggest improvements to the work environment. For example, it can suggest adjusting the chair height or desk position. This can improve the user's ergonomics and increase work efficiency.

[0067] The story release unit can increase the number of options that the user can select in the story according to the user's work progress. For example, when the user reaches a certain level of progress, new options are released. The story release unit can also change the outcome of options in the story according to the user's progress. For example, when the user completes a specific task, the outcome of an option becomes positive. Furthermore, the story release unit can increase the number of options in the story according to the user's progress. For example, when the user completes many tasks, the number of options increases. In this way, by increasing the options in the story according to the user's work progress, it is possible to enhance the sense of immersion in the story.

[0068] The story release unit can use the emotion estimation function to analyze the emotions of the user when unlocking a new chapter in the story and customize the content of the story based on those emotions. For example, if the user shows a happy expression, the story content can be changed to something more positive. Also, if the user shows a surprised expression, the story content can be changed to something with more surprise elements. Furthermore, the story release unit can analyze the user's emotions and change the reaction of the story character based on those emotions. For example, if the user shows a sad expression, the character can react in an encouraging way to the user. In this way, customizing the content of the story based on the user's emotions can increase the user's sense of immersion in the story.

[0069] The story release unit can release items and skills that the user can select in the story according to the user's work progress. For example, when the user reaches a certain level of progress, new items and skills are released. The story release unit can also change the effects of items and skills in the story according to the user's progress. For example, when the user completes a specific task, the effects of the items and skills are strengthened. Furthermore, the story release unit can increase the number of items and skills in the story according to the user's progress. For example, when the user completes many tasks, the number of items and skills increases. In this way, by releasing items and skills in the story according to the user's work progress, the sense of immersion in the story can be enhanced.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The image recognition unit monitors the progress of work using the smartphone's camera. For example, the image recognition unit captures the user studying at a desk with the camera and collects video data. The image recognition unit also uses AI image analysis technology to determine whether the user is working. For example, the image recognition unit analyzes the user's posture and movements to determine whether they are working. Furthermore, to protect the user's privacy, the image recognition unit encrypts the video data and deletes it immediately after analysis. Step 2: The behavior analysis unit determines the work progress monitored by the image recognition unit. For example, the behavior analysis unit evaluates the user's work progress using AI behavior analysis technology. The behavior analysis unit also monitors the user's work progress in real time and quantifies the progress. For example, the behavior analysis unit calculates the progress based on the number of tasks and time the user completed. Furthermore, the behavior analysis unit saves the user's work history in a database and compares it with past data to evaluate the progress. Step 3: The story release unit releases a new chapter of the story according to the work progress determined by the behavior analysis unit. For example, the story release unit releases the next chapter of the story when the user reaches a certain level of progress. The story release unit can also customize the content of the story according to the user's progress. For example, the story release unit can introduce themes and characters related to the user's work content into the story. Furthermore, the story release unit can provide an interactive quiz or mini-game when the user releases a new chapter of the story.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image recognition unit that monitors the progress of work using a smartphone camera; a behavior analysis unit that determines the progress of work monitored by the image recognition unit; a story release unit that releases a new chapter of the story in accordance with the work progress determined by the behavior analysis unit. A system characterized by:

2. The behavior analysis unit Analyzes the user's facial expressions and body movements to evaluate progress taking into account their emotional state 2. The system of claim 1.

3. The behavior analysis unit Combined with voice recognition technology, progress is assessed based on the content and tone of the user's speech.

2. The system of claim 1.

4. The story release section Unlocking new chapters in the story will feature themes and characters related to the user's work.

2. The system of claim 1.

5. The story release section Analyzing the user's emotional response when unlocking a new chapter of the story and customizing the content of the next chapter 2. The system of claim 1.

6. The behavior analysis unit Combines users' past work history and performance data to provide individually optimized progress assessments 2. The system of claim 1.

7. The behavior analysis unit Consider the user's surroundings and evaluate the impact of the environment on the work 2. The system of claim 1.

8. The story release section When unlocking new chapters of the story, a multi-ending system will be introduced that changes the branching and ending of the story depending on the user's progress.

2. The system of claim 1.

Images