system

Abstract

The system according to this embodiment aims to make the act of taking out the trash enjoyable. [Solution] The system according to the embodiment comprises a sensor unit, a reaction unit, and a playful unit. The sensor unit detects whether trash has been placed in the trash can. The reaction unit generates an AI agent that reacts based on the information detected by the sensor unit. The playful unit makes the act of throwing away trash more enjoyable based on the reaction performed by the reaction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the act of throwing away garbage is monotonous and there is little feeling of enjoyment.

[0005] The system according to the embodiment aims to make the act of throwing away garbage enjoyable.

Means for Solving the Problems

[0006] The system according to the embodiment includes a sensor unit, a reaction unit, and a playfulness unit. The sensor unit detects whether garbage has entered the trash can. The reaction unit generates an AI agent to react based on the information detected by the sensor unit. The playfulness unit makes the act of throwing away garbage more enjoyable based on the reaction performed by the reaction unit. [Effects of the Invention]

[0007] The system according to this embodiment can make the act of taking out the trash enjoyable. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The garbage disposal support system according to an embodiment of the present invention is a system that provides enjoyment when throwing garbage into a garbage can in a room by equipping the garbage can with a generating AI agent. This garbage disposal support system is equipped with a generating AI agent in the garbage can, which reacts when garbage is thrown in. For example, when the garbage goes in, it will exclaim with joy, such as "Nice shot!", and when the garbage misses, it will react with "Almost!". This makes the act of garbage disposal fun and livens up the experience of throwing garbage in a room. For example, the user throws garbage into the garbage can. The generating AI agent detects with a sensor whether the garbage has gone into the garbage can. If the garbage goes in, the generating AI agent gives a joyful reaction. For example, it will exclaim with "Nice shot!" or "You did it!". If the garbage misses, the generating AI agent will react with "Almost!" or "Just a little more!". In addition, the generating AI agent will occasionally give playful reactions to make the act of garbage disposal even more enjoyable. For example, it will exclaim with phrases such as "Try harder next time!" or "Try again!" to motivate the user. This system transforms the act of taking out the trash from a chore into something enjoyable. Users can enjoy the reactions of the generated AI agent each time they throw in trash, making trash disposal in their rooms more engaging. Furthermore, by taking out the trash together with family and friends, they can share joys and sorrows, using it as a form of communication. In this way, the trash disposal support system can make the act of taking out the trash fun and liven up the process of taking out trash in one's room.

[0029] The garbage disposal support system according to this embodiment comprises a sensor unit, a reaction unit, and a playful unit. The sensor unit detects whether garbage has entered the trash can. The sensor unit detects the presence or absence of garbage using, for example, an infrared sensor. The sensor unit can also measure the distance to the garbage using an ultrasonic sensor. Furthermore, the sensor unit can analyze an image of the garbage using a camera to determine whether garbage has entered the trash can. For example, the sensor unit uses an infrared sensor to detect the moment garbage enters the trash can. The ultrasonic sensor measures the distance to the garbage and determines whether the garbage is approaching the trash can. The camera analyzes an image of the garbage and determines whether garbage has entered the trash can. The reaction unit uses generative AI to react based on the information detected by the sensor unit. For example, the reaction unit makes joyful sounds such as "Nice shot!" or "You did it!" when garbage enters the trash can. The reaction unit can also show reactions such as "Almost!" or "Almost there!" when garbage does not enter the trash can. Furthermore, the reaction unit can add visual effects such as flashing lights when garbage enters the trash can. For example, the reaction unit uses a generation AI to say "Nice shot!" when trash is caught. The reaction unit says "Almost!" when the trash is missed. The reaction unit flashes a light when trash is caught. The playful unit makes the act of throwing away trash even more fun based on the reactions made by the reaction unit. The playful unit says things like, for example, "Try harder next time!" or "Try again!" The playful unit can also gamify the act of throwing away trash and display scores and rankings. Furthermore, the playful unit can award badges and titles for throwing away trash. For example, the playful unit says, "Try harder next time!" The playful unit gamifies the act of throwing away trash and displays a score. The playful unit awards the title of "Trash Disposal Master" for throwing away trash. In this way, the trash disposal support system according to the embodiment can make the act of throwing away trash fun and liven up the act of throwing away trash in the room.

[0030] The sensor unit detects whether trash has entered the trash can. For example, the sensor unit uses an infrared sensor to detect the presence or absence of trash. The infrared sensor is placed at the opening of the trash can and detects the presence of trash by blocking infrared rays as trash passes through. This allows for accurate determination of the moment trash enters the trash can. The sensor unit can also measure the distance to trash using an ultrasonic sensor. The ultrasonic sensor is placed inside the trash can and measures the distance by emitting ultrasonic waves and receiving the reflected waves from the trash. This allows for determination of whether trash is approaching the trash can. Furthermore, the sensor unit can also analyze images of trash using a camera to determine whether trash has entered. The camera is placed on top of the trash can and captures images of the trash. The captured images are processed by an image analysis algorithm to determine whether trash has entered the trash can. For example, the camera recognizes the shape and color of the trash to confirm whether trash is present inside the trash can. In this way, the sensor unit can combine multiple sensors to detect the presence of trash with high accuracy.

[0031] The reaction unit uses generative AI to react based on information detected by the sensor unit. The generative AI analyzes data from the sensor unit in real time and generates appropriate responses. For example, if trash goes into the trash can, the generative AI generates a joyful voice such as "Nice shot!" or "You did it!". This allows the user to feel the success of throwing away trash and gain a sense of accomplishment. If the trash does not go in, the generative AI generates a response such as "Almost!" or "Almost there!". This helps the user maintain motivation for the next attempt. Furthermore, the reaction unit can also add visual effects, such as flashing lights when trash goes into the can. For example, the lights flash the moment the trash goes into the can, creating a visual celebration of success. This allows the user to visually experience the success of throwing away trash. The reaction unit uses generative AI to generate appropriate responses to the user's actions, making the act of throwing away trash more enjoyable.

[0032] The playful element makes the act of taking out the trash more enjoyable based on the reactions of the reaction element. For example, the playful element might say things like, "Try harder next time!" or "Try again!" This can motivate users to take out the trash next time. The playful element can also gamify the act of taking out the trash, displaying scores and rankings. For example, points could be added each time trash goes into the bin, and a special title could be awarded when a certain score is reached. This allows users to enjoy taking out the trash in a game-like way. Furthermore, the playful element can award badges and titles for taking out the trash. For example, the title of "Trash Master" could be awarded after taking out trash a certain number of times. This allows users to feel a sense of accomplishment for taking out the trash. Based on the reactions of the reaction element, the playful element can implement various measures to make taking out the trash more enjoyable and increase user motivation.

[0033] The sensor unit can detect whether trash has entered the trash can. The sensor unit can detect the presence or absence of trash using, for example, an infrared sensor. The sensor unit can detect the moment trash enters the trash can using, for example, an infrared sensor. The sensor unit can also measure the distance to the trash using an ultrasonic sensor. The sensor unit can determine, for example, whether trash is approaching the trash can using an ultrasonic sensor. Furthermore, the sensor unit can analyze an image of the trash using a camera to determine whether trash has entered the trash can. The sensor unit can analyze an image of the trash using a camera to determine whether trash has entered the trash can. This allows the sensor unit to accurately detect whether trash has entered the trash can. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input trash image data into a generating AI and have the generating AI perform a determination of whether trash has entered the trash can.

[0034] The reaction unit can exclaim "Nice shot!" or "You did it!" when trash is caught. For example, the reaction unit will exclaim "Nice shot!" when trash is caught. For example, the reaction unit will exclaim "You did it!" when trash is caught. For example, the reaction unit can exclaim "Nice shot!" when trash is caught. In this way, the reaction unit increases user satisfaction by exclaiming joy when trash is caught. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to exclaim "Nice shot!" when trash is caught.

[0035] The reaction unit can react with phrases like "Almost!" or "Almost!" if the trash doesn't go in. For example, the reaction unit might say "Almost!" if the trash doesn't go in. For example, the reaction unit might say "Almost!" if the trash doesn't go in. For example, the reaction unit can say "Almost!" if the trash doesn't go in. This allows the reaction unit to motivate the user by reacting when the trash doesn't go in. Some or all of the above processing in the reaction unit is done using generative AI. For example, the reaction unit uses generative AI to say "Almost!" if the trash doesn't go in.

[0036] The playful part can say things like, "Try harder next time!" or "Try again!". For example, the playful part can say, "Try harder next time!". For example, the playful part can say, "Try again!". For example, the playful part can say, "Try harder next time!". In this way, the playful part makes the act of taking out the trash more enjoyable by saying playful things. Some or all of the above processing in the playful part is done using generative AI. For example, the playful part uses generative AI to say, "Try harder next time!".

[0037] The sensor unit can identify the type of waste and react accordingly. For example, if paper waste is placed in the sensor unit, it will respond with "Thank you for recycling!". If plastic waste is placed in the sensor unit, it will respond with "Let's recycle plastic!". If metal waste is placed in the sensor unit, it will respond with "Metal is a resource!". In this way, the sensor unit raises recycling awareness by reacting according to the type of waste. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to identify the type of waste and react accordingly.

[0038] The sensor unit can detect the angle and speed at which the trash is thrown and evaluate the throwing technique. For example, if the trash is thrown quickly and accurately, the sensor unit might evaluate it as "Professional level technique!". If the trash is thrown slowly and slowly, the sensor unit might evaluate it as "Careful throw!". If the throw is unsuccessful due to a bad angle, the sensor unit might advise, "Be more accurate next time!". In this way, the sensor unit encourages the user to improve their throwing technique by evaluating it. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to detect the angle and speed at which the trash is thrown and evaluate the throwing technique.

[0039] The sensor unit can detect the environment around the trash can and react appropriately. For example, in a bright environment, the sensor unit will react with a cheerful voice. In a dark environment, the sensor unit will react with a quiet voice. In a noisy environment, the sensor unit will react with a loud voice. In this way, the sensor unit can provide a more appropriate response by reacting according to the surrounding environment. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to detect the environment around the trash can and react appropriately.

[0040] The sensor unit can automatically adjust the position of the trash can to make it easier for trash to enter. For example, if the trash can is far away, the sensor unit will automatically move it closer. For example, if the trash can is too high, the sensor unit will automatically lower it. For example, if the trash can is tilted, the sensor unit will automatically level it. In this way, the sensor unit automatically adjusts the position of the trash can to make it easier for trash to enter. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to automatically adjust the position of the trash can to make it easier for trash to enter.

[0041] The reaction unit can add visual effects to its reaction when trash enters the device. For example, the reaction unit might flash a light the moment trash enters. For example, the reaction unit might light up colorful lights after trash enters. For example, the reaction unit might display a message on an LED display when trash enters. In this way, the reaction unit makes the reaction more enjoyable by adding visual effects. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to add visual effects to its reaction when trash enters the device.

[0042] The reaction section can add music and sound effects to the reaction when the trash doesn't go in. For example, the reaction section might play the music "Don't worry!" when the trash doesn't go in. For example, the reaction section might play the sound effect "Try again!" when the trash doesn't go in. For example, the reaction section might play the music "Try harder next time!" when the trash doesn't go in. In this way, the reaction section makes the reaction to failure more fun by adding music and sound effects. Some or all of the above processing in the reaction section is performed using generative AI. For example, the reaction section uses generative AI to add music and sound effects to the reaction when the trash doesn't go in.

[0043] The reaction unit can personalize the content of its reactions based on the user's past behavior history. For example, the reaction unit can change its reactions depending on how many times the user has succeeded in the past. For example, the reaction unit can change its reactions depending on how many times the user has failed in the past. For example, the reaction unit can prioritize reactions that the user has enjoyed in the past. In this way, the reaction unit can obtain more appropriate responses by personalizing reactions based on the user's past behavior history. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to personalize the content of its reactions based on the user's past behavior history.

[0044] The reaction unit can customize the content of its reactions according to the user's age and gender. For example, the reaction unit will give bright and energetic reactions to children, calm reactions to adults, and gentle reactions to the elderly. This allows the reaction unit to provide more appropriate responses by tailoring its reactions to the user's age and gender. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to customize the content of its reactions according to the user's age and gender.

[0045] The "Playful" section can gamify the act of taking out the trash and display scores and rankings. For example, the "Playful" section can add points each time trash is placed in the trash can. For example, the "Playful" section can display scores within a certain period in a ranking format. For example, the "Playful" section can award badges based on the success rate of trash disposal. In this way, the "Playful" section motivates users by gamifying the act of taking out the trash. Some or all of the above processes in the "Playful" section are performed using generative AI. For example, the "Playful" section uses generative AI to gamify the act of taking out the trash and display scores and rankings.

[0046] The "Playful" section can award badges and titles for the act of taking out the trash. For example, the Playful section can award the title of "Trash Master" after a certain number of successful attempts. For example, the Playful section can award the "Eco Hero" badge after clearing certain conditions. For example, the Playful section can award the title of "Monthly Champion" to the top scorer each month. In this way, the Playful section motivates users by awarding badges and titles for the act of taking out the trash. Some or all of the above processes in the Playful section are performed using a generative AI. For example, the Playful section uses a generative AI to award badges and titles for the act of taking out the trash.

[0047] The playful element can transform the act of taking out the trash into a competitive format with family and friends. For example, the playful element allows users to compete for scores with family and friends. For example, the playful element can display a ranking of the success rate of trash disposal. For example, the playful element can reward users based on the results of the competition. In this way, the playful element motivates users by making the act of taking out the trash a competitive one. Some or all of the above processing in the playful element is performed using generative AI. For example, the playful element uses generative AI to transform the act of taking out the trash into a competitive format with family and friends.

[0048] The "Playful" section can reward users for the act of taking out the trash if certain conditions are met. For example, the Playful section can reward users for successfully taking out the trash a certain number of times. For example, the Playful section can award badges for meeting certain conditions. For example, the Playful section can reward the top score each month. In this way, the Playful section motivates users by rewarding them for the act of taking out the trash. Some or all of the above processes in the Playful section are performed using generative AI. For example, the Playful section uses generative AI to reward users for taking out the trash if certain conditions are met.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The sensor unit can identify the type of waste and provide corresponding recycling information. For example, if paper waste is placed inside, it will display "This paper is recyclable," and if plastic waste is placed inside, it will display "Put plastic in the recycling box." Furthermore, if metal waste is placed inside, it can display "Please separate metal as recyclable waste." This allows users to learn the appropriate recycling method for each type of waste, thereby increasing their recycling awareness.

[0051] The playful feature allows users to customize the trash disposal process based on their age and gender. For example, it can say "That's great!" in a bright, cheerful voice to children, and "Nice shot!" in a calm voice to adults. It can also say "Well done!" in a gentle voice to elderly users. This allows users to receive reactions that suit them, making the trash disposal process more enjoyable.

[0052] The sensor unit can detect the angle and speed at which the trash is thrown, and evaluate the throwing technique. For example, if the trash is thrown quickly and accurately, it will be evaluated as "Professional level!", and if it is thrown slowly and carefully, it will be evaluated as "Careful throw!". If the throw is unsuccessful due to a poor angle, it can also be advised as "Be more accurate next time!". This allows users to receive feedback to improve their skills.

[0053] The reaction section allows for the addition of music and sound effects when the trash is not collected. For example, when the trash is not collected, music such as "Don't worry!" or a sound effect such as "Try again!" can be played. Music such as "Try harder next time!" can also be played. This allows users to maintain a positive attitude even after failure and motivates them to try again.

[0054] The playful aspect allows users to compete with family and friends in the act of taking out the trash. For example, they can compete for scores with family and friends, or display a ranking of their trash disposal success rates. Rewards can also be given based on the results of the competition. This allows users to have fun competing with family and friends through the act of taking out the trash, and it can also be used as a form of communication.

[0055] The "Playful" section allows users to earn rewards for completing specific tasks like taking out the trash. For example, rewards could be given for successfully completing a certain number of tasks, or badges could be earned for meeting certain conditions. Rewards can also be given to the top scorers each month. This motivates users to take out the trash and makes the task more enjoyable by providing them with rewards.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The sensor unit detects whether trash has entered the trash can. The sensor unit uses infrared sensors, ultrasonic sensors, cameras, etc., to analyze the presence, distance, and image of the trash and determine whether trash has entered the trash can. For example, the infrared sensor detects the moment the trash enters the trash can, the ultrasonic sensor measures the distance to the trash, and the camera analyzes the image of the trash. Step 2: The reaction unit uses a generation AI to react based on the information detected by the sensor unit. For example, if trash is caught, it will exclaim with joyful phrases like "Nice shot!" or "You did it!", and if the trash is missed, it will react with phrases like "Almost!" or "Almost there!". Visual effects, such as flashing lights, can also be added if trash is caught. Step 3: The playful element makes the act of taking out the trash more fun based on the reactions provided by the reaction element. For example, it might say things like, "Try harder next time!" or "Try again!" It can also gamify the act of taking out the trash, displaying scores and rankings. Furthermore, it can award badges or titles for taking out the trash.

[0058] (Example of form 2) The garbage disposal support system according to an embodiment of the present invention is a system that provides enjoyment when throwing garbage into a garbage can in a room by equipping the garbage can with a generating AI agent. This garbage disposal support system is equipped with a generating AI agent in the garbage can, which reacts when garbage is thrown in. For example, when the garbage goes in, it will exclaim with joy, such as "Nice shot!", and when the garbage misses, it will react with "Almost!". This makes the act of garbage disposal fun and livens up the experience of throwing garbage in a room. For example, the user throws garbage into the garbage can. The generating AI agent detects with a sensor whether the garbage has gone into the garbage can. If the garbage goes in, the generating AI agent gives a joyful reaction. For example, it will exclaim with "Nice shot!" or "You did it!". If the garbage misses, the generating AI agent will react with "Almost!" or "Just a little more!". In addition, the generating AI agent will occasionally give playful reactions to make the act of garbage disposal even more enjoyable. For example, it will exclaim with phrases such as "Try harder next time!" or "Try again!" to motivate the user. This system transforms the act of taking out the trash from a chore into something enjoyable. Users can enjoy the reactions of the generated AI agent each time they throw in trash, making trash disposal in their rooms more engaging. Furthermore, by taking out the trash together with family and friends, they can share joys and sorrows, using it as a form of communication. In this way, the trash disposal support system can make the act of taking out the trash fun and liven up the process of taking out trash in one's room.

[0059] The garbage disposal support system according to this embodiment comprises a sensor unit, a reaction unit, and a playful unit. The sensor unit detects whether garbage has entered the trash can. The sensor unit detects the presence or absence of garbage using, for example, an infrared sensor. The sensor unit can also measure the distance to the garbage using an ultrasonic sensor. Furthermore, the sensor unit can analyze an image of the garbage using a camera to determine whether garbage has entered the trash can. For example, the sensor unit uses an infrared sensor to detect the moment garbage enters the trash can. The ultrasonic sensor measures the distance to the garbage and determines whether the garbage is approaching the trash can. The camera analyzes an image of the garbage and determines whether garbage has entered the trash can. The reaction unit uses generative AI to react based on the information detected by the sensor unit. For example, the reaction unit makes joyful sounds such as "Nice shot!" or "You did it!" when garbage enters the trash can. The reaction unit can also show reactions such as "Almost!" or "Almost there!" when garbage does not enter the trash can. Furthermore, the reaction unit can add visual effects such as flashing lights when garbage enters the trash can. For example, the reaction unit uses a generation AI to say "Nice shot!" when trash is caught. The reaction unit says "Almost!" when the trash is missed. The reaction unit flashes a light when trash is caught. The playful unit makes the act of throwing away trash even more fun based on the reactions made by the reaction unit. The playful unit says things like, for example, "Try harder next time!" or "Try again!" The playful unit can also gamify the act of throwing away trash and display scores and rankings. Furthermore, the playful unit can award badges and titles for throwing away trash. For example, the playful unit says, "Try harder next time!" The playful unit gamifies the act of throwing away trash and displays a score. The playful unit awards the title of "Trash Disposal Master" for throwing away trash. In this way, the trash disposal support system according to the embodiment can make the act of throwing away trash fun and liven up the act of throwing away trash in the room.

[0060] The sensor unit detects whether trash has entered the trash can. For example, the sensor unit uses an infrared sensor to detect the presence or absence of trash. The infrared sensor is placed at the opening of the trash can and detects the presence of trash by blocking infrared rays as trash passes through. This allows for accurate determination of the moment trash enters the trash can. The sensor unit can also measure the distance to trash using an ultrasonic sensor. The ultrasonic sensor is placed inside the trash can and measures the distance by emitting ultrasonic waves and receiving the reflected waves from the trash. This allows for determination of whether trash is approaching the trash can. Furthermore, the sensor unit can also analyze images of trash using a camera to determine whether trash has entered. The camera is placed on top of the trash can and captures images of the trash. The captured images are processed by an image analysis algorithm to determine whether trash has entered the trash can. For example, the camera recognizes the shape and color of the trash to confirm whether trash is present inside the trash can. In this way, the sensor unit can combine multiple sensors to detect the presence of trash with high accuracy.

[0061] The reaction unit uses generative AI to react based on information detected by the sensor unit. The generative AI analyzes data from the sensor unit in real time and generates appropriate responses. For example, if trash goes into the trash can, the generative AI generates a joyful voice such as "Nice shot!" or "You did it!". This allows the user to feel the success of throwing away trash and gain a sense of accomplishment. If the trash does not go in, the generative AI generates a response such as "Almost!" or "Almost there!". This helps the user maintain motivation for the next attempt. Furthermore, the reaction unit can also add visual effects, such as flashing lights when trash goes into the can. For example, the lights flash the moment the trash goes into the can, creating a visual celebration of success. This allows the user to visually experience the success of throwing away trash. The reaction unit uses generative AI to generate appropriate responses to the user's actions, making the act of throwing away trash more enjoyable.

[0062] The playful element makes the act of taking out the trash more enjoyable based on the reactions of the reaction element. For example, the playful element might say things like, "Try harder next time!" or "Try again!" This can motivate users to take out the trash next time. The playful element can also gamify the act of taking out the trash, displaying scores and rankings. For example, points could be added each time trash goes into the bin, and a special title could be awarded when a certain score is reached. This allows users to enjoy taking out the trash in a game-like way. Furthermore, the playful element can award badges and titles for taking out the trash. For example, the title of "Trash Master" could be awarded after taking out trash a certain number of times. This allows users to feel a sense of accomplishment for taking out the trash. Based on the reactions of the reaction element, the playful element can implement various measures to make taking out the trash more enjoyable and increase user motivation.

[0063] The sensor unit can detect whether trash has entered the trash can. The sensor unit can detect the presence or absence of trash using, for example, an infrared sensor. The sensor unit can detect the moment trash enters the trash can using, for example, an infrared sensor. The sensor unit can also measure the distance to the trash using an ultrasonic sensor. The sensor unit can determine, for example, whether trash is approaching the trash can using an ultrasonic sensor. Furthermore, the sensor unit can analyze an image of the trash using a camera to determine whether trash has entered the trash can. The sensor unit can analyze an image of the trash using a camera to determine whether trash has entered the trash can. This allows the sensor unit to accurately detect whether trash has entered the trash can. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input trash image data into a generating AI and have the generating AI perform a determination of whether trash has entered the trash can.

[0064] The reaction unit can exclaim "Nice shot!" or "You did it!" when trash is caught. For example, the reaction unit will exclaim "Nice shot!" when trash is caught. For example, the reaction unit will exclaim "You did it!" when trash is caught. For example, the reaction unit can exclaim "Nice shot!" when trash is caught. In this way, the reaction unit increases user satisfaction by exclaiming joy when trash is caught. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to exclaim "Nice shot!" when trash is caught.

[0065] The reaction unit can react with phrases like "Almost!" or "Almost!" if the trash doesn't go in. For example, the reaction unit might say "Almost!" if the trash doesn't go in. For example, the reaction unit might say "Almost!" if the trash doesn't go in. For example, the reaction unit can say "Almost!" if the trash doesn't go in. This allows the reaction unit to motivate the user by reacting when the trash doesn't go in. Some or all of the above processing in the reaction unit is done using generative AI. For example, the reaction unit uses generative AI to say "Almost!" if the trash doesn't go in.

[0066] The playful part can say things like, "Try harder next time!" or "Try again!". For example, the playful part can say, "Try harder next time!". For example, the playful part can say, "Try again!". For example, the playful part can say, "Try harder next time!". In this way, the playful part makes the act of taking out the trash more enjoyable by saying playful things. Some or all of the above processing in the playful part is done using generative AI. For example, the playful part uses generative AI to say, "Try harder next time!".

[0067] The sensor unit can estimate the user's emotions and adjust the detection accuracy of whether or not trash is present based on the estimated user emotions. For example, if the user is having fun, the sensor unit increases the detection accuracy to make an accurate judgment. For example, if the user is feeling stressed, the sensor unit loosens the detection accuracy to increase the success rate. For example, if the user is tired, the sensor unit maintains a moderate detection accuracy, providing a reasonable balance of successes and failures. In this way, the sensor unit can make more appropriate judgments by adjusting the detection accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The sensor unit can identify the type of waste and react accordingly. For example, if paper waste is placed in the sensor unit, it will respond with "Thank you for recycling!". If plastic waste is placed in the sensor unit, it will respond with "Let's recycle plastic!". If metal waste is placed in the sensor unit, it will respond with "Metal is a resource!". In this way, the sensor unit raises recycling awareness by reacting according to the type of waste. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to identify the type of waste and react accordingly.

[0069] The sensor unit can detect the angle and speed at which the trash is thrown and evaluate the throwing technique. For example, if the trash is thrown quickly and accurately, the sensor unit might evaluate it as "Professional level technique!". If the trash is thrown slowly and slowly, the sensor unit might evaluate it as "Careful throw!". If the throw is unsuccessful due to a bad angle, the sensor unit might advise, "Be more accurate next time!". In this way, the sensor unit encourages the user to improve their throwing technique by evaluating it. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to detect the angle and speed at which the trash is thrown and evaluate the throwing technique.

[0070] The sensor unit can estimate the user's emotions and adjust the timing of detection of whether or not trash has entered the container based on the estimated emotions. For example, if the user is having fun, the sensor unit can detect and react immediately. For example, if the user is feeling stressed, the sensor unit can detect and react with a slight delay. For example, if the user is tired, the sensor unit can detect and react at an appropriate time. In this way, the sensor unit can provide a more appropriate reaction by adjusting the detection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI or not using AI. For example, the sensor unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The sensor unit can detect the environment around the trash can and react appropriately. For example, in a bright environment, the sensor unit will react with a cheerful voice. In a dark environment, the sensor unit will react with a quiet voice. In a noisy environment, the sensor unit will react with a loud voice. In this way, the sensor unit can provide a more appropriate response by reacting according to the surrounding environment. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to detect the environment around the trash can and react appropriately.

[0072] The sensor unit can automatically adjust the position of the trash can to make it easier for trash to enter. For example, if the trash can is far away, the sensor unit will automatically move it closer. For example, if the trash can is too high, the sensor unit will automatically lower it. For example, if the trash can is tilted, the sensor unit will automatically level it. In this way, the sensor unit automatically adjusts the position of the trash can to make it easier for trash to enter. Some or all of the above processing in the sensor unit is performed using generative AI. For example, the sensor unit uses generative AI to automatically adjust the position of the trash can to make it easier for trash to enter.

[0073] The reaction unit can estimate the user's emotions and adjust the content of its reactions based on those emotions. For example, if the user is having fun, the reaction unit will react in an energetic voice. If the user is stressed, the reaction unit will react in a calm voice. If the user is tired, the reaction unit will react in a gentle voice. In this way, the reaction unit can provide more appropriate responses by reacting according to the user's emotions. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to estimate the user's emotions and adjust the content of its reactions based on those emotions.

[0074] The reaction unit can add visual effects to its reaction when trash enters the device. For example, the reaction unit might flash a light the moment trash enters. For example, the reaction unit might light up colorful lights after trash enters. For example, the reaction unit might display a message on an LED display when trash enters. In this way, the reaction unit makes the reaction more enjoyable by adding visual effects. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to add visual effects to its reaction when trash enters the device.

[0075] The reaction section can add music and sound effects to the reaction when the trash doesn't go in. For example, the reaction section might play the music "Don't worry!" when the trash doesn't go in. For example, the reaction section might play the sound effect "Try again!" when the trash doesn't go in. For example, the reaction section might play the music "Try harder next time!" when the trash doesn't go in. In this way, the reaction section makes the reaction to failure more fun by adding music and sound effects. Some or all of the above processing in the reaction section is performed using generative AI. For example, the reaction section uses generative AI to add music and sound effects to the reaction when the trash doesn't go in.

[0076] The reaction unit can estimate the user's emotions and adjust the intensity of its reaction based on those emotions. For example, if the user is having fun, the reaction unit will give a strong reaction. If the user is stressed, the reaction unit will give a gentle reaction. If the user is tired, the reaction unit will give a mild reaction. In this way, the reaction unit can provide a more appropriate response by adjusting the intensity of its reaction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to estimate the user's emotions and adjust the intensity of its reaction based on those emotions.

[0077] The reaction unit can personalize the content of its reactions based on the user's past behavior history. For example, the reaction unit can change its reactions depending on how many times the user has succeeded in the past. For example, the reaction unit can change its reactions depending on how many times the user has failed in the past. For example, the reaction unit can prioritize reactions that the user has enjoyed in the past. In this way, the reaction unit can obtain more appropriate responses by personalizing reactions based on the user's past behavior history. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to personalize the content of its reactions based on the user's past behavior history.

[0078] The reaction unit can customize the content of its reactions according to the user's age and gender. For example, the reaction unit will give bright and energetic reactions to children, calm reactions to adults, and gentle reactions to the elderly. This allows the reaction unit to provide more appropriate responses by tailoring its reactions to the user's age and gender. Some or all of the above processing in the reaction unit is performed using generative AI. For example, the reaction unit uses generative AI to customize the content of its reactions according to the user's age and gender.

[0079] The playful unit can estimate the user's emotions and adjust the content of its playful reactions based on the estimated emotions. For example, if the user is having fun, the playful unit will give a humorous reaction. For example, if the user is stressed, the playful unit will give an encouraging reaction. For example, if the user is tired, the playful unit will give a comforting reaction. In this way, the playful unit can provide more appropriate responses by giving playful reactions that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playful unit may be performed using AI or not. For example, the playful unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The "Playful" section can gamify the act of taking out the trash and display scores and rankings. For example, the "Playful" section can add points each time trash is placed in the trash can. For example, the "Playful" section can display scores within a certain period in a ranking format. For example, the "Playful" section can award badges based on the success rate of trash disposal. In this way, the "Playful" section motivates users by gamifying the act of taking out the trash. Some or all of the above processes in the "Playful" section are performed using generative AI. For example, the "Playful" section uses generative AI to gamify the act of taking out the trash and display scores and rankings.

[0081] The "Playful" section can award badges and titles for the act of taking out the trash. For example, the Playful section can award the title of "Trash Master" after a certain number of successful attempts. For example, the Playful section can award the "Eco Hero" badge after clearing certain conditions. For example, the Playful section can award the title of "Monthly Champion" to the top scorer each month. In this way, the Playful section motivates users by awarding badges and titles for the act of taking out the trash. Some or all of the above processes in the Playful section are performed using a generative AI. For example, the Playful section uses a generative AI to award badges and titles for the act of taking out the trash.

[0082] The playful unit can estimate the user's emotions and adjust the frequency of playful reactions based on the estimated emotions. For example, if the user is having fun, the playful unit will give playful reactions frequently. If the user is stressed, the playful unit will give playful reactions at a moderate frequency. If the user is tired, the playful unit will give playful reactions sparingly. In this way, the playful unit can provide more appropriate responses by adjusting the frequency of playful reactions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playful unit may be performed using AI or not. For example, the playful unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The playful element can transform the act of taking out the trash into a competitive format with family and friends. For example, the playful element allows users to compete for scores with family and friends. For example, the playful element can display a ranking of the success rate of trash disposal. For example, the playful element can reward users based on the results of the competition. In this way, the playful element motivates users by making the act of taking out the trash a competitive one. Some or all of the above processing in the playful element is performed using generative AI. For example, the playful element uses generative AI to transform the act of taking out the trash into a competitive format with family and friends.

[0084] The "Playful" section can reward users for the act of taking out the trash if certain conditions are met. For example, the Playful section can reward users for successfully taking out the trash a certain number of times. For example, the Playful section can award badges for meeting certain conditions. For example, the Playful section can reward the top score each month. In this way, the Playful section motivates users by rewarding them for the act of taking out the trash. Some or all of the above processes in the Playful section are performed using generative AI. For example, the Playful section uses generative AI to reward users for taking out the trash if certain conditions are met.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The sensor unit can identify the type of waste and provide corresponding recycling information. For example, if paper waste is placed inside, it will display "This paper is recyclable," and if plastic waste is placed inside, it will display "Put plastic in the recycling box." Furthermore, if metal waste is placed inside, it can display "Please separate metal as recyclable waste." This allows users to learn the appropriate recycling method for each type of waste, thereby increasing their recycling awareness.

[0087] The reaction section can change the content of its reaction when trash is dropped in, based on the user's past success rate. For example, it can praise users with a high success rate in the past with phrases like "Impressive!", and encourage users with a low success rate with phrases like "Well done!". It can also adjust the intensity of the reaction according to past success rates. This allows users to feel a sense of progress and increases their motivation to take out the trash.

[0088] The playful feature allows users to customize the trash disposal process based on their age and gender. For example, it can say "That's great!" in a bright, cheerful voice to children, and "Nice shot!" in a calm voice to adults. It can also say "Well done!" in a gentle voice to elderly users. This allows users to receive reactions that suit them, making the trash disposal process more enjoyable.

[0089] The sensor unit can detect the angle and speed at which the trash is thrown, and evaluate the throwing technique. For example, if the trash is thrown quickly and accurately, it will be evaluated as "Professional level!", and if it is thrown slowly and carefully, it will be evaluated as "Careful throw!". If the throw is unsuccessful due to a poor angle, it can also be advised as "Be more accurate next time!". This allows users to receive feedback to improve their skills.

[0090] The reaction section allows for the addition of music and sound effects when the trash is not collected. For example, when the trash is not collected, music such as "Don't worry!" or a sound effect such as "Try again!" can be played. Music such as "Try harder next time!" can also be played. This allows users to maintain a positive attitude even after failure and motivates them to try again.

[0091] The sensor unit can estimate the user's emotions and adjust the detection accuracy of whether or not trash is present based on the estimated emotions. For example, if the user is enjoying themselves, the detection accuracy is increased to make an accurate judgment, and if the user is feeling stressed, the detection accuracy is relaxed to increase the number of successful judgments. Also, if the user is tired, the detection accuracy can be kept at a moderate level to provide a reasonable balance of success and failure. This allows for appropriate judgments to be made according to the user's emotions.

[0092] The reaction unit can estimate the user's emotions and adjust the content of the reaction based on those emotions. For example, if the user is having fun, it will react in an energetic voice; if the user is stressed, it will react in a calm voice; and if the user is tired, it will react in a gentle voice. This ensures that appropriate responses are obtained according to the user's emotions.

[0093] The playful aspect allows users to compete with family and friends in the act of taking out the trash. For example, they can compete for scores with family and friends, or display a ranking of their trash disposal success rates. Rewards can also be given based on the results of the competition. This allows users to have fun competing with family and friends through the act of taking out the trash, and it can also be used as a form of communication.

[0094] The playful element can estimate the user's emotions and adjust the frequency of playful reactions based on those estimates. For example, it can frequently use playful reactions when the user is having fun, and use them at a moderate frequency when the user is stressed. It can also use playful reactions sparingly when the user is tired. This ensures that appropriate responses are obtained in accordance with the user's emotions.

[0095] The "Playful" section allows users to earn rewards for completing specific tasks like taking out the trash. For example, rewards could be given for successfully completing a certain number of tasks, or badges could be earned for meeting certain conditions. Rewards can also be given to the top scorers each month. This motivates users to take out the trash and makes the task more enjoyable by providing them with rewards.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The sensor unit detects whether trash has entered the trash can. The sensor unit uses infrared sensors, ultrasonic sensors, cameras, etc., to analyze the presence, distance, and image of the trash and determine whether trash has entered the trash can. For example, the infrared sensor detects the moment the trash enters the trash can, the ultrasonic sensor measures the distance to the trash, and the camera analyzes the image of the trash. Step 2: The reaction unit uses a generation AI to react based on the information detected by the sensor unit. For example, if trash is caught, it will exclaim with joyful phrases like "Nice shot!" or "You did it!", and if the trash is missed, it will react with phrases like "Almost!" or "Almost there!". Visual effects, such as flashing lights, can also be added if trash is caught. Step 3: The playful element makes the act of taking out the trash more fun based on the reactions provided by the reaction element. For example, it might say things like, "Try harder next time!" or "Try again!" It can also gamify the act of taking out the trash, displaying scores and rankings. Furthermore, it can award badges or titles for taking out the trash.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the sensor unit, reaction unit, and playful unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit detects the presence or absence of trash using the camera 42 or infrared sensor of the smart device 14. The reaction unit, controlled by the control unit 46A of the smart device 14, says "Nice shot!" when trash is found. The playful unit, controlled by the control unit 46A of the smart device 14, gamifies the act of throwing away trash and displays a score and ranking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 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.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the sensor unit, reaction unit, and playful unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit uses the camera 42 or infrared sensor of the smart glasses 214 to detect the presence or absence of trash. The reaction unit, controlled by the control unit 46A of the smart glasses 214, says "Nice shot!" when trash is found. The playful unit, controlled by the control unit 46A of the smart glasses 214, gamifies the act of throwing away trash and displays a score and ranking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the sensor unit, reaction unit, and playful unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit uses the camera 42 and infrared sensor of the headset terminal 314 to detect the presence or absence of trash. The reaction unit, controlled by the control unit 46A of the headset terminal 314, says "Nice shot!" when trash is found. The playful unit, controlled by the control unit 46A of the headset terminal 314, gamifies the act of throwing away trash and displays a score and ranking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the sensor unit, reaction unit, and playful unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sensor unit uses the camera 42 or infrared sensor of the robot 414 to detect the presence or absence of trash. The reaction unit, controlled by the control unit 46A of the robot 414, exclaims "Nice shot!" when trash is found. The playful unit, controlled by the control unit 46A of the robot 414, gamifies the act of throwing away trash and displays a score and ranking. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A sensor unit that detects whether or not trash has been placed in the trash can, A reaction unit in which a generating AI agent reacts based on the information detected by the sensor unit, The device comprises a playful section that makes the act of throwing away trash even more enjoyable based on the reaction performed by the reaction section. A system characterized by the following features. (Note 2) The aforementioned sensor unit is Detects whether trash has been placed in the trash can. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reaction unit is, If trash gets caught in the shot, it will exclaim with joy, "Nice shot!" or "You did it!" The system described in Appendix 1, characterized by the features described herein. (Note 4) The reaction unit is, If the trash doesn't go in, it will react with phrases like "Almost!" or "Almost there!" The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned playful element is, They say things like, "Try harder next time!" or "Try again!" The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the accuracy of detecting whether trash is present based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is Identify the type of waste and take appropriate action. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor unit is The system detects the angle and speed at which the trash is thrown and evaluates the throwing technique. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the timing of detecting whether trash has entered the container based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor unit is It detects the environment around the trash can and takes the optimal action. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor unit is The trash can's position is automatically adjusted to make it easier to put trash in. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reaction unit is, It estimates the user's emotions and adjusts the content of the reaction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The reaction unit is, Add visual effects to the reaction when trash gets into the device. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reaction unit is, Add music or sound effects to the reaction when the trash doesn't go in. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reaction unit is, It estimates the user's emotions and adjusts the intensity of the reaction based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reaction unit is, Personalize the content of reactions based on the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reaction unit is, Customize the content of reactions according to the user's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned playful element is, It estimates the user's emotions and adjusts the content of playful reactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned playful element is, This game gamifies the act of taking out the trash, displaying scores and rankings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned playful element is, Award badges or titles for the act of littering. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned playful element is, It estimates the user's emotions and adjusts the frequency of playful reactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned playful element is, Turn the act of taking out the trash into a competition between family and friends. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned playful element is, Rewards will be given for fulfilling certain conditions when disposing of trash. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A sensor unit that detects whether or not trash has been placed in the trash can, A reaction unit in which a generating AI agent reacts based on the information detected by the sensor unit, The device comprises a playful section that makes the act of throwing away trash even more enjoyable based on the reaction performed by the reaction section. A system characterized by the following features.

2. The reaction unit is, They let out a cry of joy when trash gets in. The system according to feature 1.

3. The aforementioned sensor unit is The system estimates the user's emotions and adjusts the accuracy of detecting whether trash is present based on those estimated emotions. The system according to feature 1.

4. The aforementioned sensor unit is Identify the type of waste and take appropriate action. The system according to feature 1.

5. The aforementioned sensor unit is The system detects the angle and speed at which the trash is thrown and evaluates the throwing technique. The system according to feature 1.

6. The aforementioned sensor unit is The system estimates the user's emotions and adjusts the timing of detecting whether trash has entered the container based on those estimated emotions. The system according to feature 1.

7. The aforementioned sensor unit is It detects the environment around the trash can and takes the optimal action. The system according to feature 1.

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