system

Abstract

The system according to this embodiment aims to efficiently collect unwanted electronic devices and distribute reusable parts and rare metals within Japan. [Solution] A system according to an embodiment includes a collection unit, an assessment unit, and a distribution unit. The collection unit collects unwanted electronic devices from consumers. The assessment unit assesses the electronic devices collected by the collection unit. The distribution unit distributes reusable parts or rare metals assessed by the assessment unit within the country.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies are not efficient in collecting and reusing unwanted electronic devices, and there is room for improvement in the effective utilization of urban mining resources.

[0005] The system according to this embodiment aims to efficiently collect unwanted electronic devices and distribute reusable parts and rare metals within Japan. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an assessment unit, and a distribution unit. The collection unit collects unwanted electronic devices from consumers. The assessment unit assesses the electronic devices collected by the collection unit. The distribution unit distributes reusable parts or rare metals assessed by the assessment unit within the country. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect unwanted electronic devices and distribute reusable parts and rare metals domestically. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A collection system according to an embodiment of the present invention efficiently collects unwanted electronic devices, such as feature phones, smartphones, and PCs, from consumers and promotes appropriate reuse and recycling. This collection system aims to effectively utilize urban mine resources and reduce CO2 emissions. It also aims to promote the domestic distribution of rare metal resources and contribute to Japan's semiconductor and AI industries. For example, we provide a collection system for consumers to collect unwanted electronic devices. This system sets up collection points that are easily accessible to consumers and collects electronic devices. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. Next, we provide an urban mine appraisal system for appropriately appraising collected electronic devices. This system efficiently extracts and appraise reusable parts and rare metals from collected electronic devices. For example, it can use AI to analyze the condition of electronic devices and evaluate the value of reusable parts and rare metals. Furthermore, we provide a mechanism for distributing appraised parts and rare metals domestically. This will secure resources necessary for Japan's semiconductor and AI industries and contribute to the development of domestic industries. This system not only allows consumers to easily dispose of unwanted electronic devices but also contributes to environmental protection. Furthermore, the effective utilization of urban mine resources is expected to reduce CO2 emissions and promote the domestic distribution of rare metal resources, contributing to the development of Japan's semiconductor and AI industries. This will enable the collection system to efficiently collect unwanted electronic devices from consumers, properly assess them, and distribute reusable parts and rare metals domestically.

[0029] The collection system according to the embodiment comprises a collection unit, an assessment unit, and a distribution unit. The collection unit collects unwanted electronic devices from consumers. Electronic devices collected from consumers include, but are not limited to, smartphones, personal computers, and home appliances. The collection unit sets up collection points that consumers can easily access. For example, by setting up collection boxes in familiar places such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. The assessment unit assesses the electronic devices collected by the collection unit. The assessment unit analyzes the condition of the electronic devices using, for example, AI, and evaluates the value of reusable parts and rare metals. The AI ​​is implemented using, for example, technologies such as machine learning, deep learning, and image recognition. The assessment unit performs assessments based on, for example, the condition, year of manufacture, and model of the electronic devices. The distribution unit distributes the reusable parts and rare metals assessed by the assessment unit within Japan. The distribution unit distributes the assessed parts and rare metals within Japan using, for example, distribution channels such as retailers, recyclers, and auctions. As a result, the collection system according to the embodiment can efficiently collect unwanted electronic devices from consumers, properly assess them, and distribute reusable parts and rare metals within the country.

[0030] The collection department can install collection points that are easily accessible to consumers. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. It is desirable that collection points be installed in locations with good transportation access and long operating hours. Furthermore, increasing the number of collection points will make them even more accessible to consumers. This improves the efficiency of collection by installing collection points that are easily accessible to consumers.

[0031] The appraisal unit can use AI to analyze the condition of the electronic device and assess the value of reusable parts and rare metals. The appraisal unit can, for example, use AI to analyze the condition of the electronic device and assess the value of reusable parts and rare metals. AI is realized using technologies such as machine learning, deep learning, and image recognition. For example, AI can analyze the appearance and internal condition of the electronic device using image recognition technology and assess the value of the reusable parts and rare metals. AI can also analyze the operating condition of the electronic device and assess the value of the reusable parts and rare metals. Furthermore, AI can assess the value of the reusable parts and rare metals based on the year and model of the electronic device. As a result, by using AI, the condition of the electronic device can be accurately analyzed and the value of the reusable parts and rare metals can be assessed.

[0032] The distribution department can distribute the assessed parts and rare metals domestically. For example, the distribution department distributes the assessed parts and rare metals domestically. For example, the distribution department can distribute the assessed parts and rare metals domestically using distribution channels such as dealers, recyclers, and auctions. For example, the distribution department can sell the assessed parts and rare metals to dealers and distribute them domestically. The distribution department can also provide the assessed parts and rare metals to recyclers so that they can be reused. Furthermore, the distribution department can put the assessed parts and rare metals up for auction and distribute them domestically. In this way, distributing the assessed parts and rare metals domestically can contribute to the domestic semiconductor and AI businesses.

[0033] The collection unit can install collection boxes in familiar locations in supermarkets or convenience stores. The collection unit installs collection boxes in familiar locations, for example, in supermarkets or convenience stores. For example, by installing collection boxes at the entrance of a supermarket or near the cash register of a convenience store, consumers can easily bring in unwanted electronic devices. It is desirable that the collection boxes be installed in locations that are easily accessible to consumers, for example. Furthermore, by increasing the number of locations where collection boxes are installed, consumers can more easily access the boxes. Thus, by installing collection boxes in familiar locations, consumers can easily bring in unwanted electronic devices.

[0034] The assessment unit can efficiently extract reusable parts and rare metals from the collected electronic devices. The assessment unit, for example, efficiently extracts reusable parts and rare metals from the collected electronic devices. The assessment unit can extract reusable parts and rare metals using methods such as mechanical disassembly, chemical processing, and manual labor. For example, the assessment unit uses mechanical disassembly to disassemble electronic device components and extract reusable parts and rare metals. The assessment unit can also extract rare metals from electronic device components using chemical processing. Furthermore, the assessment unit can also disassemble electronic device components and extract reusable parts and rare metals using manual labor. This enables efficient extraction of reusable parts and rare metals from collected electronic devices, enabling effective utilization of resources.

[0035] The collection unit can analyze the consumer's past collection history and select the optimal collection method. The collection unit, for example, analyzes the consumer's past collection history and selects the optimal collection method. For example, the collection unit can suggest the optimal collection method based on collection methods that the consumer has used in the past. It can also prioritize the most frequently used collection points based on the consumer's past collection history. It can also analyze the consumer's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the consumer's past collection history.

[0036] The collection unit can filter collection points based on the consumer's current living situation and areas of interest. The collection unit filters collection points based on the consumer's current living situation and areas of interest, for example. For example, if the consumer is busy, a nearby collection point can be preferentially suggested. Also, if the consumer is interested in environmental protection, eco-friendly collection points can be suggested. Furthermore, if the consumer is interested in a particular brand, collection points for that brand can be suggested. In this way, by filtering collection points based on the consumer's living situation and areas of interest, the optimal collection point for the consumer can be provided.

[0037] The collection department can prioritize the installation of highly relevant collection points by taking into account the geographical location information of the consumer. The collection department, for example, prioritizes the installation of highly relevant collection points by taking into account the geographical location information of the consumer. For example, a collection point can be installed near the consumer's home. A collection point can also be installed near the consumer's workplace. Furthermore, a collection point can be installed in a place that the consumer frequently visits (such as a supermarket or convenience store). In this way, highly relevant collection points can be installed by taking into account the geographical location information of the consumer.

[0038] The collection department can analyze the social media activity of consumers and set up related collection points. The collection department can, for example, analyze the social media activity of consumers and set up related collection points. For example, collection points can be set up at locations where consumers frequently check in on social media. Collection points can also be set up at stores of brands that consumers follow on social media. Furthermore, collection points can be set up at event venues that consumers attend on social media. In this way, related collection points can be set up by analyzing the social media activity of consumers.

[0039] The appraisal unit can adjust the level of detail of the appraisal based on the importance of the electronic device. The appraisal unit adjusts the level of detail of the appraisal based on the importance of the electronic device, for example. For example, a detailed appraisal is performed for electronic devices with high importance. A simplified appraisal can also be performed for electronic devices with low importance. Furthermore, the number of appraisal items can be adjusted depending on the importance. This allows for efficient appraisal by adjusting the level of detail of the appraisal based on the importance of the electronic device.

[0040] The assessment unit can apply different assessment algorithms depending on the category of the electronic device. The assessment unit applies different assessment algorithms depending on the category of the electronic device. For example, different assessment algorithms can be applied to smartphones and PCs. Also, different assessment algorithms can be applied to feature phones and smartphones. Furthermore, different assessment algorithms can be applied to tablets and PCs. This allows for accurate assessment by applying different assessment algorithms depending on the category of the electronic device.

[0041] The assessment unit can determine the priority of assessments based on the submission time of the electronic devices. The assessment unit determines the priority of assessments based on, for example, the submission time of the electronic devices. For example, the assessment unit gives priority to electronic devices that are submitted earlier. It can also postpone the assessment of electronic devices that are submitted later. Furthermore, it can adjust the order of assessments depending on the submission time. In this way, by determining the priority of assessments based on the submission time of the electronic devices, efficient assessments are possible.

[0042] The appraisal unit can adjust the order of appraisals based on the relevance of the electronic devices. The appraisal unit adjusts the order of appraisals based on, for example, the relevance of the electronic devices. For example, electronic devices in the same category are appraised together. It is also possible to give priority to appraisals of electronic devices that are highly related. It is also possible to postpone appraisals of electronic devices that are less related. In this way, by adjusting the order of appraisals based on the relevance of the electronic devices, efficient appraisals are possible.

[0043] The distribution department can select the optimal distribution method by analyzing the past distribution history of the assessed parts and rare metals. The distribution department, for example, selects the optimal distribution method by analyzing the past distribution history of the assessed parts and rare metals. For example, the optimal distribution route is selected based on the past distribution history. The most efficient distribution method can also be selected from the past distribution history. Furthermore, the past distribution history can be analyzed to select the most cost-effective distribution method. In this way, the optimal distribution method can be selected by analyzing the past distribution history.

[0044] The distribution department can customize the distribution means based on the current market conditions of the assessed parts and rare metals. The distribution department, for example, customizes the distribution means based on the current market conditions of the assessed parts and rare metals. For example, the distribution department selects the optimal distribution means based on the current market conditions. The distribution means can also be customized according to market demand. Furthermore, the distribution means can be adjusted taking into account the market supply situation. In this way, efficient distribution is possible by customizing the distribution means based on the current market conditions.

[0045] The distribution department can select the optimal distribution method in consideration of the geographical distribution of the assessed parts and rare metals. The distribution department selects the optimal distribution method in consideration of the geographical distribution of the assessed parts and rare metals, for example. For example, distribution is given priority to geographically close locations. It is also possible to select efficient distribution means for geographically distant locations. Furthermore, it is also possible to select the optimal distribution route based on the geographical distribution. In this way, it is possible to select the optimal distribution method by taking the geographical distribution into consideration.

[0046] The distribution department can improve the accuracy of distribution by referring to relevant literature on assessed parts and rare metals. For example, the distribution department can improve the accuracy of distribution by referring to relevant literature on assessed parts and rare metals. For instance, it can select the optimal distribution method based on relevant literature. Furthermore, it can learn methods to improve distribution efficiency from relevant literature. In addition, it can improve the accuracy of distribution by referring to relevant literature. Thus, by referring to relevant literature, the accuracy of distribution can be improved.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection department can analyze a consumer's past collection history and select the optimal collection method. For example, it can propose the most suitable collection method based on the collection methods the consumer has used in the past. It can also prioritize suggesting the most frequently used collection points based on the consumer's past collection history. Furthermore, it can analyze a consumer's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing a consumer's past collection history.

[0049] The assessment department can adjust the level of detail in the assessment based on the importance of the electronic equipment. For example, high-importance electronic equipment will undergo a detailed assessment, while lower-importance equipment will undergo a simplified assessment. Furthermore, the number of assessment items can be adjusted according to importance. This allows for more efficient assessments by adjusting the level of detail based on the importance of the electronic equipment.

[0050] The distribution department can analyze the past distribution history of assessed parts and rare metals to select the optimal distribution method. For example, it can select the optimal distribution route based on past distribution history. It can also select the most efficient distribution method from past distribution history. Furthermore, it can analyze past distribution history to select the most cost-effective distribution method. In this way, the optimal distribution method can be selected by analyzing past distribution history.

[0051] The distribution department can customize distribution methods based on the current market conditions for assessed parts and rare metals. For example, it can select the optimal distribution method based on current market conditions. It can also customize distribution methods according to market demand. Furthermore, it can adjust distribution methods considering market supply conditions. By customizing distribution methods based on current market conditions, efficient distribution becomes possible.

[0052] The collection system can prioritize the placement of collection points that are highly relevant to consumers, taking into account their geographical location. For example, a collection point can be placed near a consumer's home. It can also be placed near a consumer's workplace. Furthermore, collection points can be placed in places that consumers frequently visit (such as supermarkets and convenience stores). In this way, by considering consumers' geographical location, the system can prioritize the placement of collection points that are highly relevant to them.

[0053] The appraisal department can apply different appraisal algorithms depending on the category of electronic device. For example, different appraisal algorithms can be applied to smartphones and PCs. Furthermore, different algorithms can be applied to feature phones and smartphones. In addition, different appraisal algorithms can be applied to tablets and PCs. This allows for accurate appraisals by applying different appraisal algorithms according to the category of electronic device.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects unwanted electronic devices from consumers. These include, for example, smartphones, personal computers, and home appliances. The collection unit sets up easily accessible collection points for consumers. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in their unwanted electronic devices. Step 2: The assessment department assesses the electronic devices collected by the collection department. The assessment department uses AI to analyze the condition of the electronic devices and evaluate the value of reusable parts and rare metals. The AI ​​is implemented using technologies such as machine learning, deep learning, and image recognition. The assessment department makes its assessment based on the condition of the electronic devices, year of manufacture, model, etc. Step 3: The Distribution Department distributes the reusable parts and rare metals assessed by the Assessment Department domestically. The Distribution Department distributes the assessed parts and rare metals domestically using distribution channels such as retailers, recyclers, and auctions.

[0056] (Example 2) A collection system according to an embodiment of the present invention efficiently collects unwanted electronic devices, such as feature phones, smartphones, and PCs, from consumers and promotes appropriate reuse and recycling. This collection system aims to effectively utilize urban mine resources and reduce CO2 emissions. It also aims to promote the domestic distribution of rare metal resources and contribute to Japan's semiconductor and AI industries. For example, we provide a collection system for consumers to collect unwanted electronic devices. This system sets up collection points that are easily accessible to consumers and collects electronic devices. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. Next, we provide an urban mine appraisal system for appropriately appraising collected electronic devices. This system efficiently extracts and appraise reusable parts and rare metals from collected electronic devices. For example, it can use AI to analyze the condition of electronic devices and evaluate the value of reusable parts and rare metals. Furthermore, we provide a mechanism for distributing appraised parts and rare metals domestically. This will secure resources necessary for Japan's semiconductor and AI industries and contribute to the development of domestic industries. This system not only allows consumers to easily dispose of unwanted electronic devices but also contributes to environmental protection. Furthermore, the effective utilization of urban mine resources is expected to reduce CO2 emissions and promote the domestic distribution of rare metal resources, contributing to the development of Japan's semiconductor and AI industries. This will enable the collection system to efficiently collect unwanted electronic devices from consumers, properly assess them, and distribute reusable parts and rare metals domestically.

[0057] The collection system according to the embodiment comprises a collection unit, an assessment unit, and a distribution unit. The collection unit collects unwanted electronic devices from consumers. Electronic devices collected from consumers include, but are not limited to, smartphones, personal computers, and home appliances. The collection unit sets up collection points that consumers can easily access. For example, by setting up collection boxes in familiar places such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. The assessment unit assesses the electronic devices collected by the collection unit. The assessment unit analyzes the condition of the electronic devices using, for example, AI, and evaluates the value of reusable parts and rare metals. The AI ​​is implemented using, for example, technologies such as machine learning, deep learning, and image recognition. The assessment unit performs assessments based on, for example, the condition, year of manufacture, and model of the electronic devices. The distribution unit distributes the reusable parts and rare metals assessed by the assessment unit within Japan. The distribution unit distributes the assessed parts and rare metals within Japan using, for example, distribution channels such as retailers, recyclers, and auctions. As a result, the collection system according to the embodiment can efficiently collect unwanted electronic devices from consumers, properly assess them, and distribute reusable parts and rare metals within the country.

[0058] The collection department can install collection points that are easily accessible to consumers. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in unwanted electronic devices. It is desirable that collection points be installed in locations with good transportation access and long operating hours. Furthermore, increasing the number of collection points will make them even more accessible to consumers. This improves the efficiency of collection by installing collection points that are easily accessible to consumers.

[0059] The appraisal unit can use AI to analyze the condition of the electronic device and assess the value of reusable parts and rare metals. The appraisal unit can, for example, use AI to analyze the condition of the electronic device and assess the value of reusable parts and rare metals. AI is realized using technologies such as machine learning, deep learning, and image recognition. For example, AI can analyze the appearance and internal condition of the electronic device using image recognition technology and assess the value of the reusable parts and rare metals. AI can also analyze the operating condition of the electronic device and assess the value of the reusable parts and rare metals. Furthermore, AI can assess the value of the reusable parts and rare metals based on the year and model of the electronic device. As a result, by using AI, the condition of the electronic device can be accurately analyzed and the value of the reusable parts and rare metals can be assessed.

[0060] The distribution department can distribute the assessed parts and rare metals domestically. For example, the distribution department distributes the assessed parts and rare metals domestically. For example, the distribution department can distribute the assessed parts and rare metals domestically using distribution channels such as dealers, recyclers, and auctions. For example, the distribution department can sell the assessed parts and rare metals to dealers and distribute them domestically. The distribution department can also provide the assessed parts and rare metals to recyclers so that they can be reused. Furthermore, the distribution department can put the assessed parts and rare metals up for auction and distribute them domestically. In this way, distributing the assessed parts and rare metals domestically can contribute to the domestic semiconductor and AI businesses.

[0061] The collection unit can install collection boxes in familiar locations in supermarkets or convenience stores. The collection unit installs collection boxes in familiar locations, for example, in supermarkets or convenience stores. For example, by installing collection boxes at the entrance of a supermarket or near the cash register of a convenience store, consumers can easily bring in unwanted electronic devices. It is desirable that the collection boxes be installed in locations that are easily accessible to consumers, for example. Furthermore, by increasing the number of locations where collection boxes are installed, consumers can more easily access the boxes. Thus, by installing collection boxes in familiar locations, consumers can easily bring in unwanted electronic devices.

[0062] The assessment unit can efficiently extract reusable parts and rare metals from the collected electronic devices. The assessment unit, for example, efficiently extracts reusable parts and rare metals from the collected electronic devices. The assessment unit can extract reusable parts and rare metals using methods such as mechanical disassembly, chemical processing, and manual labor. For example, the assessment unit uses mechanical disassembly to disassemble electronic device components and extract reusable parts and rare metals. The assessment unit can also extract rare metals from electronic device components using chemical processing. Furthermore, the assessment unit can also disassemble electronic device components and extract reusable parts and rare metals using manual labor. This enables efficient extraction of reusable parts and rare metals from collected electronic devices, enabling effective utilization of resources.

[0063] The collection unit can estimate the consumer's emotions and adjust the location of the collection point based on the estimated consumer's emotions. The collection unit, for example, estimates the consumer's emotions and adjusts the location of the collection point based on the estimated consumer's emotions. For example, if the consumer is feeling stressed, the collection point can be set up in an easily accessible location. Also, if the consumer is relaxed, the collection point can be set up in a slightly more scenic location. Furthermore, if the consumer is in a hurry, the collection point can be set up in the closest location. In this way, adjusting the location of the collection point based on the consumer's emotions improves consumer convenience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The collection unit can analyze the consumer's past collection history and select the optimal collection method. The collection unit, for example, analyzes the consumer's past collection history and selects the optimal collection method. For example, the collection unit can suggest the optimal collection method based on collection methods that the consumer has used in the past. It can also prioritize the most frequently used collection points based on the consumer's past collection history. It can also analyze the consumer's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the consumer's past collection history.

[0065] The collection unit can filter collection points based on the consumer's current living situation and areas of interest. The collection unit filters collection points based on the consumer's current living situation and areas of interest, for example. For example, if the consumer is busy, a nearby collection point can be preferentially suggested. Also, if the consumer is interested in environmental protection, eco-friendly collection points can be suggested. Furthermore, if the consumer is interested in a particular brand, collection points for that brand can be suggested. In this way, by filtering collection points based on the consumer's living situation and areas of interest, the optimal collection point for the consumer can be provided.

[0066] The retrieval unit can estimate the consumer's emotions and determine the priority of electronic devices to retrieve based on those estimated emotions. For example, if the consumer is in a hurry, the unit will prioritize the electronic devices that are easiest to retrieve. If the consumer is relaxed, multiple electronic devices can be retrieved at once. Furthermore, if the consumer is stressed, the unit can prioritize the electronic devices of the highest value. This improves consumer convenience by prioritizing the electronic devices to retrieve based on the consumer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The collection department can prioritize the installation of highly relevant collection points by taking into account the geographical location information of the consumer. The collection department, for example, prioritizes the installation of highly relevant collection points by taking into account the geographical location information of the consumer. For example, a collection point can be installed near the consumer's home. A collection point can also be installed near the consumer's workplace. Furthermore, a collection point can be installed in a place that the consumer frequently visits (such as a supermarket or convenience store). In this way, highly relevant collection points can be installed by taking into account the geographical location information of the consumer.

[0068] The collection department can analyze the social media activity of consumers and set up related collection points. The collection department can, for example, analyze the social media activity of consumers and set up related collection points. For example, collection points can be set up at locations where consumers frequently check in on social media. Collection points can also be set up at stores of brands that consumers follow on social media. Furthermore, collection points can be set up at event venues that consumers attend on social media. In this way, related collection points can be set up by analyzing the social media activity of consumers.

[0069] The assessment unit can estimate the consumer's emotions and adjust the way the assessment is expressed based on the estimated consumer's emotions. The assessment unit, for example, estimates the consumer's emotions and adjusts the way the assessment is expressed based on the estimated consumer's emotions. For example, if the consumer is nervous, a simple and easy-to-understand way of expression can be provided. If the consumer is relaxed, a way of expression including detailed information can be provided. Furthermore, if the consumer is in a hurry, a way of expression that focuses on the main points can be provided. In this way, by adjusting the way the assessment is expressed based on the consumer's emotions, it is possible to provide an assessment result that is easy for the consumer to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The appraisal unit can adjust the level of detail of the appraisal based on the importance of the electronic device. The appraisal unit adjusts the level of detail of the appraisal based on the importance of the electronic device, for example. For example, a detailed appraisal is performed for electronic devices with high importance. A simplified appraisal can also be performed for electronic devices with low importance. Furthermore, the number of appraisal items can be adjusted depending on the importance. This allows for efficient appraisal by adjusting the level of detail of the appraisal based on the importance of the electronic device.

[0071] The assessment unit can apply different assessment algorithms depending on the category of the electronic device. The assessment unit applies different assessment algorithms depending on the category of the electronic device. For example, different assessment algorithms can be applied to smartphones and PCs. Also, different assessment algorithms can be applied to feature phones and smartphones. Furthermore, different assessment algorithms can be applied to tablets and PCs. This allows for accurate assessment by applying different assessment algorithms depending on the category of the electronic device.

[0072] The assessment unit can estimate the consumer's emotions and adjust the length of the assessment based on those emotions. For example, if the consumer is in a hurry, the assessment can be completed quickly. If the consumer is relaxed, a detailed assessment can be performed. Furthermore, if the consumer is stressed, the assessment can be performed quickly. By adjusting the length of the assessment based on the consumer's emotions, an appropriate assessment time can be provided for the consumer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The assessment unit can determine the priority of assessments based on the submission time of the electronic devices. The assessment unit determines the priority of assessments based on, for example, the submission time of the electronic devices. For example, the assessment unit gives priority to electronic devices that are submitted earlier. It can also postpone the assessment of electronic devices that are submitted later. Furthermore, it can adjust the order of assessments depending on the submission time. In this way, by determining the priority of assessments based on the submission time of the electronic devices, efficient assessments are possible.

[0074] The appraisal unit can adjust the order of appraisals based on the relevance of the electronic devices. The appraisal unit adjusts the order of appraisals based on, for example, the relevance of the electronic devices. For example, electronic devices in the same category are appraised together. It is also possible to give priority to appraisals of electronic devices that are highly related. It is also possible to postpone appraisals of electronic devices that are less related. In this way, by adjusting the order of appraisals based on the relevance of the electronic devices, efficient appraisals are possible.

[0075] The distribution unit can estimate the consumer's emotions and adjust the distribution method based on the estimated consumer's emotions. The distribution unit, for example, estimates the consumer's emotions and adjusts the distribution method based on the estimated consumer's emotions. For example, if the consumer is relaxed, distribution can be performed at a leisurely pace. If the consumer is in a hurry, distribution can be performed quickly. Furthermore, if the consumer is stressed, distribution can be performed efficiently. In this way, by adjusting the distribution method based on the consumer's emotions, distribution that meets the consumer's needs becomes possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The distribution department can select the optimal distribution method by analyzing the past distribution history of the assessed parts and rare metals. The distribution department, for example, selects the optimal distribution method by analyzing the past distribution history of the assessed parts and rare metals. For example, the optimal distribution route is selected based on the past distribution history. The most efficient distribution method can also be selected from the past distribution history. Furthermore, the past distribution history can be analyzed to select the most cost-effective distribution method. In this way, the optimal distribution method can be selected by analyzing the past distribution history.

[0077] The distribution department can customize the distribution means based on the current market conditions of the assessed parts and rare metals. The distribution department, for example, customizes the distribution means based on the current market conditions of the assessed parts and rare metals. For example, the distribution department selects the optimal distribution means based on the current market conditions. The distribution means can also be customized according to market demand. Furthermore, the distribution means can be adjusted taking into account the market supply situation. In this way, efficient distribution is possible by customizing the distribution means based on the current market conditions.

[0078] The distribution department can estimate consumer emotions and determine distribution priorities based on the estimated consumer emotions. For example, the distribution department can estimate consumer emotions and determine distribution priorities based on the estimated consumer emotions. For example, if a consumer is in a hurry, the most important parts or rare metals can be distributed first. Also, if a consumer is relaxed, overall distribution can be carried out evenly. Furthermore, if a consumer is stressed, distribution can be carried out quickly. In this way, by determining distribution priorities based on consumer emotions, distribution that meets consumer needs can be achieved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The distribution department can select the optimal distribution method in consideration of the geographical distribution of the assessed parts and rare metals. The distribution department selects the optimal distribution method in consideration of the geographical distribution of the assessed parts and rare metals, for example. For example, distribution is given priority to geographically close locations. It is also possible to select efficient distribution means for geographically distant locations. Furthermore, it is also possible to select the optimal distribution route based on the geographical distribution. In this way, it is possible to select the optimal distribution method by taking the geographical distribution into consideration.

[0080] The distribution department can improve the accuracy of distribution by referring to relevant literature on assessed parts and rare metals. For example, the distribution department can improve the accuracy of distribution by referring to relevant literature on assessed parts and rare metals. For instance, it can select the optimal distribution method based on relevant literature. Furthermore, it can learn methods to improve distribution efficiency from relevant literature. In addition, it can improve the accuracy of distribution by referring to relevant literature. Thus, by referring to relevant literature, the accuracy of distribution can be improved. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, appraisal unit, and distribution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and allows consumers to access collection points to collect electronic devices. The appraisal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the condition of the electronic devices using AI and evaluates the value of reusable parts and rare metals. The distribution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and distributes the appraised parts and rare metals domestically. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, appraisal unit, and distribution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and allows consumers to access collection points to collect electronic devices. The appraisal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the condition of the electronic devices using AI and evaluates the value of reusable parts and rare metals. The distribution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and distributes the appraised parts and rare metals domestically. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, assessment unit, and distribution unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314, allowing consumers to access collection points and collect electronic devices. The assessment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the condition of electronic devices and evaluate the value of reusable parts and rare metals. The distribution unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which distributes the assessed parts and rare metals domestically. === Hard Collateral 1-4 === Each of the multiple elements described above, including the recovery unit, assessment unit, and distribution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recovery unit is implemented by the control unit 46A of the robot 414, allowing consumers to access a recovery point and recover electronic devices. The assessment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the condition of electronic devices and evaluate the value of reusable parts and rare metals. The distribution unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which distributes the assessed parts and rare metals domestically.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection department can analyze a consumer's past collection history and select the optimal collection method. For example, it can propose the most suitable collection method based on the collection methods the consumer has used in the past. It can also prioritize suggesting the most frequently used collection points based on the consumer's past collection history. Furthermore, it can analyze a consumer's past collection history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing a consumer's past collection history.

[0083] The assessment department can adjust the level of detail in the assessment based on the importance of the electronic equipment. For example, high-importance electronic equipment will undergo a detailed assessment, while lower-importance equipment will undergo a simplified assessment. Furthermore, the number of assessment items can be adjusted according to importance. This allows for more efficient assessments by adjusting the level of detail based on the importance of the electronic equipment.

[0084] The distribution department can analyze the past distribution history of assessed parts and rare metals to select the optimal distribution method. For example, it can select the optimal distribution route based on past distribution history. It can also select the most efficient distribution method from past distribution history. Furthermore, it can analyze past distribution history to select the most cost-effective distribution method. In this way, the optimal distribution method can be selected by analyzing past distribution history.

[0085] The collection unit can estimate the consumer's emotions and adjust the location of the collection point based on the estimated consumer's emotions. For example, if the consumer is feeling stressed, the collection point can be set up in an easily accessible location. If the consumer is feeling relaxed, the collection point can be set up in a scenic location, even if it is a little further away. Furthermore, if the consumer is in a hurry, the collection point can be set up in the nearest location. In this way, by adjusting the location of the collection point based on the consumer's emotions, convenience for the consumer can be improved.

[0086] The assessment unit can estimate the consumer's emotions and adjust the way the assessment is expressed based on the estimated consumer's emotions. For example, if the consumer is nervous, a simple and easy-to-understand way of expression can be provided. If the consumer is relaxed, a way of expression including detailed information can be provided. Furthermore, if the consumer is in a hurry, a way of expression that gets straight to the point can be provided. In this way, by adjusting the way the assessment is expressed based on the consumer's emotions, it is possible to provide an assessment result that is easy for the consumer to understand.

[0087] The distribution department can customize distribution methods based on the current market conditions for assessed parts and rare metals. For example, it can select the optimal distribution method based on current market conditions. It can also customize distribution methods according to market demand. Furthermore, it can adjust distribution methods considering market supply conditions. By customizing distribution methods based on current market conditions, efficient distribution becomes possible.

[0088] The collection system can prioritize the placement of collection points that are highly relevant to consumers, taking into account their geographical location. For example, a collection point can be placed near a consumer's home. It can also be placed near a consumer's workplace. Furthermore, collection points can be placed in places that consumers frequently visit (such as supermarkets and convenience stores). In this way, by considering consumers' geographical location, the system can prioritize the placement of collection points that are highly relevant to them.

[0089] The appraisal department can apply different appraisal algorithms depending on the category of electronic device. For example, different appraisal algorithms can be applied to smartphones and PCs. Furthermore, different algorithms can be applied to feature phones and smartphones. In addition, different appraisal algorithms can be applied to tablets and PCs. This allows for accurate appraisals by applying different appraisal algorithms according to the category of electronic device.

[0090] The distribution department can estimate the consumer's emotions and determine distribution priorities based on the estimated consumer emotions. For example, if the consumer is in a hurry, the most important parts and rare metals can be distributed first. Also, if the consumer is relaxed, overall distribution can be distributed evenly. Furthermore, if the consumer is stressed, distribution can be carried out quickly. In this way, by determining distribution priorities based on the consumer's emotions, distribution that meets the consumer's needs becomes possible.

[0091] The assessment department can estimate the consumer's emotions and adjust the assessment length based on that estimation. For example, if the consumer is in a hurry, the assessment can be completed quickly. Conversely, if the consumer is relaxed, a detailed assessment can be conducted. Furthermore, if the consumer is stressed, the assessment can be conducted quickly. By adjusting the assessment length based on the consumer's emotions, the system can provide consumers with an appropriate assessment time.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects unwanted electronic devices from consumers. These include, for example, smartphones, personal computers, and home appliances. The collection unit sets up easily accessible collection points for consumers. For example, by installing collection boxes in familiar locations such as supermarkets and convenience stores, consumers can easily bring in their unwanted electronic devices. Step 2: The assessment department assesses the electronic devices collected by the collection department. The assessment department uses AI to analyze the condition of the electronic devices and evaluate the value of reusable parts and rare metals. The AI ​​is implemented using technologies such as machine learning, deep learning, and image recognition. The assessment department makes its assessment based on the condition of the electronic devices, year of manufacture, model, etc. Step 3: The Distribution Department distributes the reusable parts and rare metals assessed by the Assessment Department domestically. The Distribution Department distributes the assessed parts and rare metals domestically using distribution channels such as retailers, recyclers, and auctions.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects unwanted electronic devices from consumers, An assessment unit for assessing the electronic devices recovered by the aforementioned recovery unit, The system includes a distribution unit that distributes reusable parts or rare metals assessed by the assessment unit within Japan. A system characterized by:

2. The recovery unit includes: Install collection points that are easily accessible to consumers.

2. The system of claim 1.

3. The assessment unit Using AI to analyze the condition of electronic devices and evaluate the value of reusable parts and rare metals.

2. The system of claim 1.

4. The distribution unit includes: The assessed parts and rare metals will be distributed domestically.

2. The system of claim 1.

5. The recovery unit includes: Collection boxes will be placed in convenient locations such as supermarkets or convenience stores.

2. The system of claim 1.

6. The assessment unit Efficiently extract reusable components and rare metals from recovered electronic devices.

2. The system of claim 1.

7. The recovery unit includes: The system estimates consumer sentiment and adjusts the location of collection points based on that estimated sentiment.

2. The system of claim 1.

8. The recovery unit includes: We analyze consumers' past recall history and select the optimal recall method.

2. The system of claim 1.

Images