system

Abstract

The system according to this embodiment aims to identify waste containing lithium-ion batteries and reduce the risk of fire. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and a sorting unit. The acquisition unit acquires images of the waste. The analysis unit analyzes the images acquired by the acquisition unit and identifies waste containing lithium-ion batteries. The sorting unit sorts the waste identified by the analysis unit to a separate collection route.

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 prior art, there is a risk of ignition of lithium-ion batteries during garbage collection or non-combustible garbage disposal, which may cause a fire.

[0005] The system according to the embodiment aims to identify garbage containing lithium-ion batteries and reduce the risk of ignition.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a separation unit. The acquisition unit acquires an image of garbage. The analysis unit analyzes the image acquired by the acquisition unit to identify garbage containing lithium-ion batteries. The separation unit separates the garbage identified by the analysis unit into a different collection route. [Effects of the Invention]

[0007] The system according to this embodiment can identify waste containing lithium-ion batteries and reduce the risk of fire. [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 waste sorting system according to an embodiment of the present invention is a system for solving the problem of lithium-ion batteries igniting during waste collection or non-combustible waste processing, which can cause fires. This waste sorting system acquires images of waste, and AI analyzes them to identify products and waste that may contain lithium-ion batteries, and sorts the identified waste to a separate collection route. This reduces the risk of ignition during waste collection or non-combustible waste processing and improves the recovery rate of recyclable materials. For example, the waste sorting system installs cameras to acquire images of waste. The cameras are installed on garbage trucks or waste processing facilities. Next, the acquired images are analyzed by AI. The AI ​​uses image recognition technology to identify products and waste that may contain lithium-ion batteries. The waste identified by the AI ​​is sorted to a separate collection route. For example, waste that may contain lithium-ion batteries is moved to a dedicated collection box. The sorted waste is processed safely. Waste containing lithium-ion batteries is recycled in an appropriate manner. This system can reduce the risk of ignition during waste collection or non-combustible waste processing and improve the recovery rate of recyclable materials. This allows waste sorting systems to reduce the risk of fire during waste collection and non-combustible waste processing, and to improve the recovery rate of recyclable materials.

[0029] The waste sorting system according to the embodiment comprises an acquisition unit, an analysis unit, and a sorting unit. The acquisition unit acquires images of waste. The acquisition unit can acquire images of waste using, for example, cameras installed in garbage trucks or waste treatment facilities. The acquisition unit can adjust, for example, the resolution and shooting angle of the camera. The acquisition unit can automatically adjust the resolution and shooting angle of the camera according to the type and amount of waste. The analysis unit analyzes the images acquired by the acquisition unit and identifies waste that may contain lithium-ion batteries. The analysis unit can identify waste containing lithium-ion batteries using, for example, image recognition technology. The analysis unit can accurately determine the possibility of lithium-ion battery inclusion based on, for example, the shape, color, and material of the waste. The analysis unit can dynamically optimize the analysis algorithm by referring to past analysis data. The sorting unit sorts the waste identified by the analysis unit to a separate collection route. The sorting unit can, for example, transfer the identified waste to a dedicated collection box. The sorting unit can, for example, recycle the sorted waste in an appropriate manner. The sorting unit can, for example, record tracking information for sorted waste and improve recycling efficiency. As a result, the waste sorting system according to this embodiment can reduce the risk of fire during waste collection and non-combustible waste processing, and improve the recovery rate of recyclable materials.

[0030] The acquisition unit can be equipped with cameras installed in garbage trucks or waste treatment facilities. For example, the acquisition unit can acquire images of garbage using a camera installed in a garbage truck. For instance, the camera continuously acquires images of garbage while the garbage truck is in motion. The acquisition unit can also acquire images of garbage using a camera installed in a waste treatment facility. For example, a camera installed on a conveyor belt in a waste treatment facility can acquire images of garbage. This allows the acquisition unit to efficiently acquire images of garbage.

[0031] The analysis unit can identify waste containing lithium-ion batteries using image recognition technology. For example, the analysis unit can analyze images of waste using deep learning technology. For instance, a deep learning model receives images of waste as input and identifies waste that may contain lithium-ion batteries. The analysis unit can also analyze images of waste using computer vision technology. For example, a computer vision algorithm analyzes the shape and color of the waste to identify waste containing lithium-ion batteries. This allows the analysis unit to identify waste that may contain lithium-ion batteries with high accuracy.

[0032] The sorting unit can transfer identified waste to a dedicated collection box. The sorting unit may, for example, be equipped with machinery for transferring identified waste to a dedicated collection box. For example, the sorting unit may use a robotic arm to identify waste on a conveyor belt and transfer it to a dedicated collection box. The sorting unit may also perform manual operations to transfer identified waste to a dedicated collection box. For example, an employee may manually transfer identified waste to a dedicated collection box. This allows the sorting unit to safely separate waste containing lithium-ion batteries.

[0033] The sorting unit can recycle the sorted waste in an appropriate manner. For example, the sorting unit may have a system for sending the sorted waste to a recycling facility. For example, the sorting unit may arrange trucks to transport the waste to the recycling facility. The sorting unit may also have a system for managing the processing method at the recycling facility. For example, the sorting unit may monitor the waste processing process at the recycling facility to ensure that recycling is carried out in an appropriate manner. This allows the sorting unit to properly recycle waste containing lithium-ion batteries.

[0034] The acquisition unit can automatically adjust the camera resolution and shooting angle according to the type and amount of dust. The acquisition unit is equipped with sensors to identify the type and amount of dust, for example. For example, a weight sensor measures the weight of the dust, and image analysis technology identifies the type of dust. The acquisition unit automatically adjusts the camera resolution and shooting angle according to the type and amount of dust. For example, if there is a large amount of dust, the camera resolution is increased to acquire a detailed image. Also, if there are various types of dust, the camera's shooting angle can be widened to cover the whole area. In this way, the acquisition unit can acquire the optimal image according to the type and amount of dust.

[0035] The acquisition unit can adjust the camera's shooting frequency according to the garbage truck's speed. The acquisition unit includes, for example, a sensor for measuring the garbage truck's speed. For example, GPS data or a speed sensor can be used to measure the garbage truck's speed. The acquisition unit adjusts the camera's shooting frequency according to the garbage truck's speed. For example, when the garbage truck is moving at high speed, the camera's shooting frequency is increased to continuously acquire images. Conversely, when the garbage truck is moving at low speed, the camera's shooting frequency is decreased to efficiently acquire images. This allows the acquisition unit to efficiently acquire images according to the garbage truck's speed.

[0036] The acquisition unit can detect the temperature and humidity of the dust and adjust the shooting conditions accordingly. The acquisition unit is equipped with, for example, a temperature sensor and a humidity sensor. For example, the temperature sensor measures the temperature of the dust, and the humidity sensor measures the humidity of the dust. The acquisition unit adjusts the shooting conditions based on the temperature and humidity of the dust. For example, if the temperature of the dust is high, it adjusts the camera's exposure to acquire an appropriate image. Also, if the humidity of the dust is high, it can adjust the camera's focus to acquire a clear image. In this way, the acquisition unit can acquire the optimal image according to the temperature and humidity of the dust.

[0037] The acquisition unit can prioritize photographing garbage in specific areas and time periods by utilizing the location information of garbage trucks. For example, the acquisition unit acquires the location information of garbage trucks using GPS data or a Geographic Information System (GIS). For example, GPS data provides the current location of the garbage truck, and the GIS identifies specific areas and time periods. The acquisition unit uses the location information of the garbage trucks to prioritize photographing garbage in specific areas and time periods. For example, when a garbage truck reaches a specific area, it prioritizes photographing the garbage in that area. Also, if the garbage truck is moving during a specific time period, it can prioritize photographing the garbage during that time period. This allows the acquisition unit to efficiently photograph garbage in specific areas and time periods.

[0038] The analysis unit can accurately determine the possibility of a lithium-ion battery being present in waste based on its shape, color, and material. For example, the analysis unit uses image analysis technology to identify the shape, color, and material of the waste. For instance, an image analysis algorithm analyzes the shape of the waste and identifies shapes similar to lithium-ion batteries. The image analysis algorithm can also analyze the color of the waste and identify colors similar to lithium-ion batteries. Furthermore, the image analysis algorithm can analyze the material of the waste and identify materials similar to lithium-ion batteries. As a result, the analysis unit can accurately determine the possibility of a lithium-ion battery being present based on the shape, color, and material of the waste.

[0039] The analysis unit can dynamically optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can obtain past analysis data by referring to a database. For example, it can accurately determine the possibility of lithium-ion battery inclusion based on past analysis data stored in the database. The analysis unit can also dynamically adjust the analysis algorithm based on past analysis data. For example, it can adjust the parameters of the analysis algorithm based on past analysis data to improve the reliability of the analysis results. In this way, the analysis unit can optimize the analysis algorithm based on past analysis data and improve the reliability of the analysis results.

[0040] The analysis unit can evaluate the possibility of lithium-ion battery presence by incorporating the weight and volume of the waste into the analysis. The analysis unit measures the weight and volume of the waste using, for example, a weight sensor and a volume measuring device. For example, the weight sensor measures the weight of the waste, and the volume measuring device measures the volume of the waste. The analysis unit evaluates the possibility of lithium-ion battery presence based on the weight and volume of the waste. For example, if the weight of the waste is close to that of a lithium-ion battery, the possibility of presence is evaluated as high. Similarly, if the volume of the waste is close to that of a lithium-ion battery, the possibility of presence can also be evaluated as high. As a result, the analysis unit can evaluate the possibility of lithium-ion battery presence based on the weight and volume of the waste with high accuracy.

[0041] The analysis unit can improve the reliability of its analysis results by utilizing waste component analysis data. For example, the analysis unit identifies the components of waste using chemical analysis results and spectral data. For instance, chemical analysis results indicate the components of the waste, and spectral data identifies the material of the waste. The analysis unit can also accurately determine the possibility of lithium-ion battery content using waste component analysis data. For example, it evaluates the possibility of lithium-ion battery content based on waste component analysis data. Furthermore, the analysis unit can dynamically adjust its analysis algorithm based on the waste component analysis data. This allows the analysis unit to improve the reliability of its analysis results based on the waste component analysis data.

[0042] The sorting unit can dynamically change the sorting method according to the type and amount of waste. For example, the sorting unit is equipped with sensors to identify the type and amount of waste. For instance, a weight sensor measures the weight of the waste, and image analysis technology identifies the type of waste. The sorting unit dynamically changes the sorting method according to the type and amount of waste. For example, if there are many different types of waste, the sorting method is dynamically changed to sort efficiently. Also, if there is a large amount of waste, the sorting method can be optimized to sort quickly. In this way, the sorting unit can provide the optimal sorting method according to the type and amount of waste.

[0043] The sorting unit can record tracking information for sorted waste and improve recycling efficiency. For example, the sorting unit can record tracking information for sorted waste using RFID tags or barcodes. For instance, RFID tags can be attached to sorted waste, and tracking information can be recorded. Alternatively, barcodes can be printed on sorted waste, and tracking information can be recorded. Based on the tracking information for sorted waste, the sorting unit optimizes the recycling process. For example, it analyzes the tracking information to identify areas for improvement to enhance recycling efficiency. This allows the sorting unit to record tracking information for sorted waste and improve recycling efficiency.

[0044] The sorting unit can optimize the processing method after sorting based on the composition and shape of the waste. For example, the sorting unit identifies the composition and shape of the waste using chemical analysis results or image analysis technology. For instance, chemical analysis results indicate the composition of the waste, and image analysis technology identifies the shape of the waste. The sorting unit optimizes the processing method after sorting based on the composition and shape of the waste. For example, if the composition of the waste is similar to that of a lithium-ion battery, an appropriate processing method is selected. Similarly, if the shape of the waste is similar to that of a lithium-ion battery, an appropriate processing method can also be selected. In this way, the sorting unit can provide the optimal processing method based on the composition and shape of the waste.

[0045] The sorting unit can optimize the transport routes of sorted waste, enabling efficient recycling. The sorting unit includes systems for optimizing logistics routes and selecting transport methods, for example. For instance, the sorting unit optimizes the transport routes of sorted waste for efficient recycling. Furthermore, the sorting unit can optimize the recycling process based on the transport routes of sorted waste. For example, the sorting unit analyzes transport routes and identifies areas for improvement to achieve efficient recycling. This allows the sorting unit to optimize the transport routes of sorted waste and achieve efficient recycling.

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

[0047] The acquisition unit can not only acquire images of garbage but also audio data of the garbage. For example, it can acquire audio data of garbage using microphones installed in garbage trucks or waste treatment facilities. The audio data is used by the analysis unit to identify the type and condition of the garbage. For example, by analyzing specific sound patterns such as the sound of glass breaking or the sound of metal clashing, the type of garbage can be identified with high accuracy. The audio data can also be used to determine the condition of the garbage (for example, whether liquid is leaking). As a result, the acquisition unit can combine images and audio data of the garbage to provide more detailed information.

[0048] The analysis unit can not only analyze images of garbage but also analyze odor data associated with the garbage. For example, it can detect the odor of garbage using an odor sensor and analyze that data. Odor data is used to identify the type and condition of the garbage. For instance, by detecting the odor of a specific chemical substance, it is possible to identify garbage that may contain lithium-ion batteries. Odor data can also be used to determine the state of decay of the garbage and whether it contains harmful substances. As a result, the analysis unit can perform more accurate analysis by combining images of garbage and odor data.

[0049] The sorting unit can not only transfer identified waste to dedicated collection boxes, but also compress and crush the waste. For example, the sorting unit can be equipped with a press for compressing waste or a shredder for crushing waste. This reduces the volume of the sorted waste, enabling efficient collection and recycling. Furthermore, compressed and crushed waste is easier to process at recycling facilities, thus improving recycling efficiency. In summary, the sorting unit can achieve efficient waste processing by compressing and crushing waste.

[0050] The acquisition unit can not only acquire images of waste but also radiation data from the waste. For example, it can measure the radiation level of waste using a radiation sensor and acquire that data. The radiation data is used by the analysis unit to identify the type and condition of the waste. For example, by detecting a specific radiation level, it is possible to identify waste that may contain lithium-ion batteries. The radiation data can also be used to evaluate the safety of the waste. As a result, the acquisition unit can combine images of the waste and radiation data to provide more detailed information.

[0051] The analysis unit can not only analyze images of waste but also analyze its magnetic data. For example, it can measure the magnetic properties of waste using a magnetic sensor and analyze that data. Magnetic data is used to identify the type and condition of the waste. For instance, by detecting specific magnetic properties, it is possible to identify waste that may contain lithium-ion batteries. Magnetic data can also be used to determine the material and structure of the waste. As a result, the analysis unit can perform more accurate analysis by combining images of the waste with magnetic data.

[0052] The sorting unit can not only transfer identified waste to designated collection boxes, but also wash and dry the waste. For example, the sorting unit can be equipped with washing devices for washing waste and drying devices for drying waste. This improves the quality of the sorted waste and increases recycling efficiency. Furthermore, washed and dried waste is easier to process at recycling facilities, further improving recycling efficiency. In this way, the sorting unit can achieve efficient waste processing by washing and drying the waste.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The acquisition unit acquires images of the waste. The acquisition unit can acquire images of the waste using, for example, cameras installed in garbage trucks or waste treatment facilities. The acquisition unit can adjust, for example, the camera's resolution and shooting angle. The acquisition unit can automatically adjust the camera's resolution and shooting angle according to the type and amount of waste. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify debris that may contain lithium-ion batteries. The analysis unit can identify debris containing lithium-ion batteries using, for example, image recognition technology. The analysis unit can accurately determine the likelihood of lithium-ion battery inclusion based on, for example, the shape, color, and material of the debris. The analysis unit can dynamically optimize the analysis algorithm by, for example, referring to past analysis data. Step 3: The sorting unit separates the waste identified by the analysis unit to a different collection route. The sorting unit can, for example, move the identified waste to a dedicated collection box. The sorting unit can, for example, recycle the sorted waste in an appropriate manner. The sorting unit can, for example, record tracking information for the sorted waste to improve recycling efficiency.

[0055] (Example of form 2) The waste sorting system according to an embodiment of the present invention is a system for solving the problem of lithium-ion batteries igniting during waste collection or non-combustible waste processing, which can cause fires. This waste sorting system acquires images of waste, and AI analyzes them to identify products and waste that may contain lithium-ion batteries, and sorts the identified waste to a separate collection route. This reduces the risk of ignition during waste collection or non-combustible waste processing and improves the recovery rate of recyclable materials. For example, the waste sorting system installs cameras to acquire images of waste. The cameras are installed on garbage trucks or waste processing facilities. Next, the acquired images are analyzed by AI. The AI ​​uses image recognition technology to identify products and waste that may contain lithium-ion batteries. The waste identified by the AI ​​is sorted to a separate collection route. For example, waste that may contain lithium-ion batteries is moved to a dedicated collection box. The sorted waste is processed safely. Waste containing lithium-ion batteries is recycled in an appropriate manner. This system can reduce the risk of ignition during waste collection or non-combustible waste processing and improve the recovery rate of recyclable materials. This allows waste sorting systems to reduce the risk of fire during waste collection and non-combustible waste processing, and to improve the recovery rate of recyclable materials.

[0056] The waste sorting system according to the embodiment comprises an acquisition unit, an analysis unit, and a sorting unit. The acquisition unit acquires images of waste. The acquisition unit can acquire images of waste using, for example, cameras installed in garbage trucks or waste treatment facilities. The acquisition unit can adjust, for example, the resolution and shooting angle of the camera. The acquisition unit can automatically adjust the resolution and shooting angle of the camera according to the type and amount of waste. The analysis unit analyzes the images acquired by the acquisition unit and identifies waste that may contain lithium-ion batteries. The analysis unit can identify waste containing lithium-ion batteries using, for example, image recognition technology. The analysis unit can accurately determine the possibility of lithium-ion battery inclusion based on, for example, the shape, color, and material of the waste. The analysis unit can dynamically optimize the analysis algorithm by referring to past analysis data. The sorting unit sorts the waste identified by the analysis unit to a separate collection route. The sorting unit can, for example, transfer the identified waste to a dedicated collection box. The sorting unit can, for example, recycle the sorted waste in an appropriate manner. The sorting unit can, for example, record tracking information for sorted waste and improve recycling efficiency. As a result, the waste sorting system according to this embodiment can reduce the risk of fire during waste collection and non-combustible waste processing, and improve the recovery rate of recyclable materials.

[0057] The acquisition unit can be equipped with cameras installed in garbage trucks or waste treatment facilities. For example, the acquisition unit can acquire images of garbage using a camera installed in a garbage truck. For instance, the camera continuously acquires images of garbage while the garbage truck is in motion. The acquisition unit can also acquire images of garbage using a camera installed in a waste treatment facility. For example, a camera installed on a conveyor belt in a waste treatment facility can acquire images of garbage. This allows the acquisition unit to efficiently acquire images of garbage.

[0058] The analysis unit can identify waste containing lithium-ion batteries using image recognition technology. For example, the analysis unit can analyze images of waste using deep learning technology. For instance, a deep learning model receives images of waste as input and identifies waste that may contain lithium-ion batteries. The analysis unit can also analyze images of waste using computer vision technology. For example, a computer vision algorithm analyzes the shape and color of the waste to identify waste containing lithium-ion batteries. This allows the analysis unit to identify waste that may contain lithium-ion batteries with high accuracy.

[0059] The sorting unit can transfer identified waste to a dedicated collection box. The sorting unit may, for example, be equipped with machinery for transferring identified waste to a dedicated collection box. For example, the sorting unit may use a robotic arm to identify waste on a conveyor belt and transfer it to a dedicated collection box. The sorting unit may also perform manual operations to transfer identified waste to a dedicated collection box. For example, an employee may manually transfer identified waste to a dedicated collection box. This allows the sorting unit to safely separate waste containing lithium-ion batteries.

[0060] The sorting unit can recycle the sorted waste in an appropriate manner. For example, the sorting unit may have a system for sending the sorted waste to a recycling facility. For example, the sorting unit may arrange trucks to transport the waste to the recycling facility. The sorting unit may also have a system for managing the processing method at the recycling facility. For example, the sorting unit may monitor the waste processing process at the recycling facility to ensure that recycling is carried out in an appropriate manner. This allows the sorting unit to properly recycle waste containing lithium-ion batteries.

[0061] The acquisition unit can estimate the user's emotions and adjust the camera's shooting timing based on the estimated emotions. For example, the acquisition unit can estimate the user's emotions using facial recognition technology. For instance, the camera captures the user's facial expressions, and the facial recognition algorithm estimates the user's emotions. The acquisition unit can also estimate the user's emotions using voice analysis technology. For example, the microphone records the user's voice, and the voice analysis algorithm estimates the user's emotions. The acquisition unit adjusts the camera's shooting timing based on the estimated emotions. For example, if the user is stressed, the camera's shooting timing can be shortened to quickly capture images of trash. Conversely, if the user is relaxed, the camera's shooting timing can be extended to capture more detailed images. In this way, the acquisition unit can optimize the camera's shooting timing according to the user's emotions.

[0062] The acquisition unit can automatically adjust the camera resolution and shooting angle according to the type and amount of dust. The acquisition unit is equipped with sensors to identify the type and amount of dust, for example. For example, a weight sensor measures the weight of the dust, and image analysis technology identifies the type of dust. The acquisition unit automatically adjusts the camera resolution and shooting angle according to the type and amount of dust. For example, if there is a large amount of dust, the camera resolution is increased to acquire a detailed image. Also, if there are various types of dust, the camera's shooting angle can be widened to cover the whole area. In this way, the acquisition unit can acquire the optimal image according to the type and amount of dust.

[0063] The acquisition unit can adjust the camera's shooting frequency according to the garbage truck's speed. The acquisition unit includes, for example, a sensor for measuring the garbage truck's speed. For example, GPS data or a speed sensor can be used to measure the garbage truck's speed. The acquisition unit adjusts the camera's shooting frequency according to the garbage truck's speed. For example, when the garbage truck is moving at high speed, the camera's shooting frequency is increased to continuously acquire images. Conversely, when the garbage truck is moving at low speed, the camera's shooting frequency is decreased to efficiently acquire images. This allows the acquisition unit to efficiently acquire images according to the garbage truck's speed.

[0064] The acquisition unit can estimate the user's emotions and change the camera's shooting mode based on the estimated emotions. For example, the acquisition unit can estimate the user's emotions using facial recognition technology. For instance, the camera captures the user's facial expressions, and the facial recognition algorithm estimates the user's emotions. The acquisition unit can also estimate the user's emotions using voice analysis technology. For example, the microphone records the user's voice, and the voice analysis algorithm estimates the user's emotions. The acquisition unit changes the camera's shooting mode based on the estimated emotions. For example, if the user is nervous, the camera's shooting mode can be simplified to quickly acquire images. Conversely, if the user is relaxed, the camera's shooting mode can be made more detailed to acquire detailed images. In this way, the acquisition unit can optimize the camera's shooting mode according to the user's emotions.

[0065] The acquisition unit can detect the temperature and humidity of the dust and adjust the shooting conditions accordingly. The acquisition unit is equipped with, for example, a temperature sensor and a humidity sensor. For example, the temperature sensor measures the temperature of the dust, and the humidity sensor measures the humidity of the dust. The acquisition unit adjusts the shooting conditions based on the temperature and humidity of the dust. For example, if the temperature of the dust is high, it adjusts the camera's exposure to acquire an appropriate image. Also, if the humidity of the dust is high, it can adjust the camera's focus to acquire a clear image. In this way, the acquisition unit can acquire the optimal image according to the temperature and humidity of the dust.

[0066] The acquisition unit can prioritize photographing garbage in specific areas and time periods by utilizing the location information of garbage trucks. For example, the acquisition unit acquires the location information of garbage trucks using GPS data or a Geographic Information System (GIS). For example, GPS data provides the current location of the garbage truck, and the GIS identifies specific areas and time periods. The acquisition unit uses the location information of the garbage trucks to prioritize photographing garbage in specific areas and time periods. For example, when a garbage truck reaches a specific area, it prioritizes photographing the garbage in that area. Also, if the garbage truck is moving during a specific time period, it can prioritize photographing the garbage during that time period. This allows the acquisition unit to efficiently photograph garbage in specific areas and time periods.

[0067] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For instance, a camera captures the user's facial expressions, and a facial recognition algorithm estimates the user's emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, a microphone records the user's voice, and a voice analysis algorithm estimates the user's emotions. The analysis unit adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. This allows the analysis unit to optimize the display method of the analysis results according to the user's emotions.

[0068] The analysis unit can accurately determine the possibility of a lithium-ion battery being present in waste based on its shape, color, and material. For example, the analysis unit uses image analysis technology to identify the shape, color, and material of the waste. For instance, an image analysis algorithm analyzes the shape of the waste and identifies shapes similar to lithium-ion batteries. The image analysis algorithm can also analyze the color of the waste and identify colors similar to lithium-ion batteries. Furthermore, the image analysis algorithm can analyze the material of the waste and identify materials similar to lithium-ion batteries. As a result, the analysis unit can accurately determine the possibility of a lithium-ion battery being present based on the shape, color, and material of the waste.

[0069] The analysis unit can dynamically optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can obtain past analysis data by referring to a database. For example, it can accurately determine the possibility of lithium-ion battery inclusion based on past analysis data stored in the database. The analysis unit can also dynamically adjust the analysis algorithm based on past analysis data. For example, it can adjust the parameters of the analysis algorithm based on past analysis data to improve the reliability of the analysis results. In this way, the analysis unit can optimize the analysis algorithm based on past analysis data and improve the reliability of the analysis results.

[0070] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For instance, a camera captures the user's facial expressions, and a facial recognition algorithm estimates the user's emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, a microphone records the user's voice, and a voice analysis algorithm estimates the user's emotions. The analysis unit determines the priority of analysis results based on the estimated emotions. For example, if the user is tense, important analysis results are displayed preferentially. Conversely, if the user is relaxed, detailed analysis results can be displayed. In this way, the analysis unit can optimize the priority of analysis results according to the user's emotions.

[0071] The analysis unit can evaluate the possibility of lithium-ion battery presence by incorporating the weight and volume of the waste into the analysis. The analysis unit measures the weight and volume of the waste using, for example, a weight sensor and a volume measuring device. For example, the weight sensor measures the weight of the waste, and the volume measuring device measures the volume of the waste. The analysis unit evaluates the possibility of lithium-ion battery presence based on the weight and volume of the waste. For example, if the weight of the waste is close to that of a lithium-ion battery, the possibility of presence is evaluated as high. Similarly, if the volume of the waste is close to that of a lithium-ion battery, the possibility of presence can also be evaluated as high. As a result, the analysis unit can evaluate the possibility of lithium-ion battery presence based on the weight and volume of the waste with high accuracy.

[0072] The analysis unit can improve the reliability of its analysis results by utilizing waste component analysis data. For example, the analysis unit identifies the components of waste using chemical analysis results and spectral data. For instance, chemical analysis results indicate the components of the waste, and spectral data identifies the material of the waste. The analysis unit can also accurately determine the possibility of lithium-ion battery content using waste component analysis data. For example, it evaluates the possibility of lithium-ion battery content based on waste component analysis data. Furthermore, the analysis unit can dynamically adjust its analysis algorithm based on the waste component analysis data. This allows the analysis unit to improve the reliability of its analysis results based on the waste component analysis data.

[0073] The sorting unit can estimate the user's emotions and adjust the timing of sorting based on the estimated emotions. For example, the sorting unit can estimate the user's emotions using facial recognition technology. For instance, a camera captures the user's facial expressions, and a facial recognition algorithm estimates the user's emotions. The sorting unit can also estimate the user's emotions using voice analysis technology. For example, a microphone records the user's voice, and a voice analysis algorithm estimates the user's emotions. The sorting unit adjusts the timing of sorting based on the estimated emotions. For example, if the user is tense, sorting can be done quickly. Conversely, if the user is relaxed, detailed sorting can be performed. In this way, the sorting unit can optimize the timing of sorting according to the user's emotions.

[0074] The sorting unit can dynamically change the sorting method according to the type and amount of waste. For example, the sorting unit is equipped with sensors to identify the type and amount of waste. For instance, a weight sensor measures the weight of the waste, and image analysis technology identifies the type of waste. The sorting unit dynamically changes the sorting method according to the type and amount of waste. For example, if there are many different types of waste, the sorting method is dynamically changed to sort efficiently. Also, if there is a large amount of waste, the sorting method can be optimized to sort quickly. In this way, the sorting unit can provide the optimal sorting method according to the type and amount of waste.

[0075] The sorting unit can record tracking information for sorted waste and improve recycling efficiency. For example, the sorting unit can record tracking information for sorted waste using RFID tags or barcodes. For instance, RFID tags can be attached to sorted waste, and tracking information can be recorded. Alternatively, barcodes can be printed on sorted waste, and tracking information can be recorded. Based on the tracking information for sorted waste, the sorting unit optimizes the recycling process. For example, it analyzes the tracking information to identify areas for improvement to enhance recycling efficiency. This allows the sorting unit to record tracking information for sorted waste and improve recycling efficiency.

[0076] The sorting unit can estimate the user's emotions and determine sorting priorities based on those estimated emotions. For example, the sorting unit can estimate the user's emotions using facial recognition technology. For instance, a camera captures the user's facial expressions, and a facial recognition algorithm estimates the user's emotions. The sorting unit can also estimate the user's emotions using voice analysis technology. For example, a microphone records the user's voice, and a voice analysis algorithm estimates the user's emotions. The sorting unit then determines sorting priorities based on the estimated user emotions. For example, if the user is stressed, important waste will be sorted first. Conversely, if the user is relaxed, more detailed sorting may be performed. In this way, the sorting unit can optimize sorting priorities according to the user's emotions.

[0077] The sorting unit can optimize the processing method after sorting based on the composition and shape of the waste. For example, the sorting unit identifies the composition and shape of the waste using chemical analysis results or image analysis technology. For instance, chemical analysis results indicate the composition of the waste, and image analysis technology identifies the shape of the waste. The sorting unit optimizes the processing method after sorting based on the composition and shape of the waste. For example, if the composition of the waste is similar to that of a lithium-ion battery, an appropriate processing method is selected. Similarly, if the shape of the waste is similar to that of a lithium-ion battery, an appropriate processing method can also be selected. In this way, the sorting unit can provide the optimal processing method based on the composition and shape of the waste.

[0078] The sorting unit can optimize the transport routes of sorted waste, enabling efficient recycling. The sorting unit includes systems for optimizing logistics routes and selecting transport methods, for example. For instance, the sorting unit optimizes the transport routes of sorted waste for efficient recycling. Furthermore, the sorting unit can optimize the recycling process based on the transport routes of sorted waste. For example, the sorting unit analyzes transport routes and identifies areas for improvement to achieve efficient recycling. This allows the sorting unit to optimize the transport routes of sorted waste and achieve efficient recycling. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and sorting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies waste containing lithium-ion batteries using image recognition technology. The sorting unit is implemented, for example, by the control unit 46A of the smart device 14, and can transfer the identified waste to a dedicated collection box. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and sorting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses image recognition technology to identify waste containing lithium-ion batteries. The sorting unit is implemented, for example, by the control unit 46A of the smart glasses 214, which can transfer the identified waste to a dedicated collection box. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and sorting unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses image recognition technology to identify waste containing lithium-ion batteries. The sorting unit is implemented, for example, by the control unit 46A of the headset terminal 314, which can transfer the identified waste to a dedicated collection box. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and sorting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses image recognition technology to identify waste containing lithium-ion batteries. The sorting unit is implemented, for example, by the control unit 46A of the robot 414, which can transfer the identified waste to a dedicated collection box.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The acquisition unit can not only acquire images of garbage but also audio data of the garbage. For example, it can acquire audio data of garbage using microphones installed in garbage trucks or waste treatment facilities. The audio data is used by the analysis unit to identify the type and condition of the garbage. For example, by analyzing specific sound patterns such as the sound of glass breaking or the sound of metal clashing, the type of garbage can be identified with high accuracy. The audio data can also be used to determine the condition of the garbage (for example, whether liquid is leaking). As a result, the acquisition unit can combine images and audio data of the garbage to provide more detailed information.

[0081] The analysis unit can not only analyze images of garbage but also analyze odor data associated with the garbage. For example, it can detect the odor of garbage using an odor sensor and analyze that data. Odor data is used to identify the type and condition of the garbage. For instance, by detecting the odor of a specific chemical substance, it is possible to identify garbage that may contain lithium-ion batteries. Odor data can also be used to determine the state of decay of the garbage and whether it contains harmful substances. As a result, the analysis unit can perform more accurate analysis by combining images of garbage and odor data.

[0082] The sorting unit can not only transfer identified waste to dedicated collection boxes, but also compress and crush the waste. For example, the sorting unit can be equipped with a press for compressing waste or a shredder for crushing waste. This reduces the volume of the sorted waste, enabling efficient collection and recycling. Furthermore, compressed and crushed waste is easier to process at recycling facilities, thus improving recycling efficiency. In summary, the sorting unit can achieve efficient waste processing by compressing and crushing waste.

[0083] The image acquisition unit can estimate the user's emotions and change the method of acquiring images of trash based on the estimated emotions. For example, if the user is stressed, the acquisition unit will quickly acquire images of trash to reduce the user's burden. Conversely, if the user is relaxed, the acquisition unit can acquire detailed images and perform more accurate analysis. In this way, the acquisition unit can optimize the method of acquiring images of trash according to the user's emotions.

[0084] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis results can be notified concisely to reduce the user's burden. Conversely, if the user is relaxed, detailed analysis results can be notified to provide the user with sufficient information. In this way, the analysis unit can optimize the notification method of the analysis results according to the user's emotions.

[0085] The sorting unit can estimate the user's emotions and modify its sorting method based on those emotions. For example, if the user is stressed, the sorting unit will sort quickly to reduce the user's burden. Conversely, if the user is relaxed, it can perform more detailed sorting to achieve higher accuracy. In this way, the sorting unit can optimize its sorting method according to the user's emotions.

[0086] The acquisition unit can not only acquire images of waste but also radiation data from the waste. For example, it can measure the radiation level of waste using a radiation sensor and acquire that data. The radiation data is used by the analysis unit to identify the type and condition of the waste. For example, by detecting a specific radiation level, it is possible to identify waste that may contain lithium-ion batteries. The radiation data can also be used to evaluate the safety of the waste. As a result, the acquisition unit can combine images of the waste and radiation data to provide more detailed information.

[0087] The analysis unit can not only analyze images of waste but also analyze its magnetic data. For example, it can measure the magnetic properties of waste using a magnetic sensor and analyze that data. Magnetic data is used to identify the type and condition of the waste. For instance, by detecting specific magnetic properties, it is possible to identify waste that may contain lithium-ion batteries. Magnetic data can also be used to determine the material and structure of the waste. As a result, the analysis unit can perform more accurate analysis by combining images of the waste with magnetic data.

[0088] The sorting unit can not only transfer identified waste to designated collection boxes, but also wash and dry the waste. For example, the sorting unit can be equipped with washing devices for washing waste and drying devices for drying waste. This improves the quality of the sorted waste and increases recycling efficiency. Furthermore, washed and dried waste is easier to process at recycling facilities, further improving recycling efficiency. In this way, the sorting unit can achieve efficient waste processing by washing and drying the waste.

[0089] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis results can be provided concisely to reduce the user's burden. Conversely, if the user is relaxed, detailed analysis results can be provided to give the user sufficient information. In this way, the analysis unit can optimize the feedback method of the analysis results according to the user's emotions.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The acquisition unit acquires images of the waste. The acquisition unit can acquire images of the waste using, for example, cameras installed in garbage trucks or waste treatment facilities. The acquisition unit can adjust, for example, the camera's resolution and shooting angle. The acquisition unit can automatically adjust the camera's resolution and shooting angle according to the type and amount of waste. Step 2: The analysis unit analyzes the images acquired by the acquisition unit to identify debris that may contain lithium-ion batteries. The analysis unit can identify debris containing lithium-ion batteries using, for example, image recognition technology. The analysis unit can accurately determine the likelihood of lithium-ion battery inclusion based on, for example, the shape, color, and material of the debris. The analysis unit can dynamically optimize the analysis algorithm by, for example, referring to past analysis data. Step 3: The sorting unit separates the waste identified by the analysis unit to a different collection route. The sorting unit can, for example, move the identified waste to a dedicated collection box. The sorting unit can, for example, recycle the sorted waste in an appropriate manner. The sorting unit can, for example, record tracking information for the sorted waste to improve recycling efficiency.

[0092] 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.

[0093] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.).

[0108] 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.

[0109] 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. 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.

[0110] 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.

[0111] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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. 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.

[0126] 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.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. 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.

[0143] 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.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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. An acquisition unit that acquires images of garbage, An analysis unit analyzes the image acquired by the acquisition unit and identifies waste containing lithium-ion batteries, A sorting unit separates the waste identified by the analysis unit for a different collection route, Equipped with A system characterized by the following features.

2. The acquisition unit is, Equipped with cameras installed on garbage trucks and waste disposal facilities. The system according to feature 1.

3. The aforementioned analysis unit, Image recognition technology is used to identify waste containing lithium-ion batteries. The system according to feature 1.

4. The aforementioned sorting section is Transfer the identified waste to the designated collection box. The system according to feature 1.

5. The aforementioned sorting section is Recycle sorted waste in an appropriate manner. The system according to feature 1.

6. The acquisition unit is, It estimates the user's emotions and adjusts the camera's shooting timing based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, The camera's resolution and shooting angle are automatically adjusted according to the type and amount of dust. The system according to feature 1.

8. The acquisition unit is, The camera's shooting frequency is adjusted according to the garbage truck's speed. The system according to feature 1.

Images