System

Abstract

An object of a system according to an embodiment is to accurately compare a state at the time of shipment of a product with a state at the time of return of the product, and to efficiently cope with an illegal return claim.SOLUTION: A system includes a data collection unit, a comparison unit, an analysis unit, and a notification unit. The data collection part records a state at the time of commodity shipment in detail. The comparison unit compares the state of the returned commodity based on the data collected by the data collection unit. The analysis unit analyzes the result obtained by the comparison unit. The notification unit deals with the unjust return claim based on the result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately compare the condition of a product when it was shipped with that when it was returned, making it difficult to deal with unjustified returns.

[0005] The system according to the embodiment aims to accurately compare the condition of a product at the time of shipment with the condition at the time of return, and to efficiently deal with unjustified return claims. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a comparison unit, an analysis unit, and a notification unit. The data collection unit records the condition of the product at the time of shipment in detail. The comparison unit compares the condition of the returned product based on the data collected by the data collection unit. The analysis unit analyzes the results obtained by the comparison unit. The notification unit deals with unjustified return claims based on the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately compare the condition of the product at the time of shipment with the condition at the time of return, and can efficiently deal with unjustified return claims. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The malicious return prevention system according to an embodiment of the present invention is a system that records in detail the condition of the product at the time of shipment, and uses a generation AI to compare and analyze the condition of the returned product, thereby efficiently dealing with unjustified return claims. As a result, the malicious return prevention system can deal with unjustified return claims by comparing in detail the condition of the product at the time of shipment with the condition at the time of return.

[0029] The malicious return prevention system according to the embodiment includes a data collection unit, a comparison unit, an analysis unit, and a notification unit. The data collection unit records the condition of the product at the time of shipment in detail. For example, the data collection unit may photograph the exterior of the product with a high-resolution camera, record the product's odor with an odor sensor, and measure the product's surface texture with a texture sensor. The data collection unit may also scan the product's internal structure with an X-ray or ultrasonic sensor and record the internal condition. The comparison unit compares the condition of the returned product based on the data collected by the data collection unit. For example, the data collection unit may photograph the exterior of the returned product again with a high-resolution camera, record the odor with an odor sensor, and measure the surface texture with the texture sensor. The comparison unit may also scan the internal structure of the returned product with an X-ray or ultrasonic sensor and record the internal condition. The analysis unit analyzes the results obtained by the comparison unit. For example, the analysis unit may compare image data of the product to check for scratches or stains. Furthermore, comparing the odor data and detecting a different odor can indicate problems with the use or storage conditions of the product. Furthermore, comparing the texture data and detecting surface wear or discoloration can prove the product's usage. The notification unit deals with unjustified return claims based on the results obtained by the analysis unit. For example, an algorithm for evaluating the veracity of the reason for return can be developed based on the data analyzed by AI. It is also possible to build a system in which data on returned products is sent to a third-party organization for objective evaluation. This allows the malicious return prevention system according to the embodiment to deal with unjustified return claims by making a detailed comparison between the condition of the product at the time of shipment and the condition at the time of return.

[0030] The data collection unit can photograph the appearance of the product with a high-resolution camera, record the product's smell with an odor sensor, and measure the product's surface texture with a texture sensor. For example, the data collection unit photographs the appearance of the product with a high-resolution camera and saves the image data. The data collection unit also records the product's smell using an odor sensor. For example, the odor sensor detects the product's smell and saves the data. The data collection unit also measures the product's surface texture using a texture sensor. For example, the texture sensor detects the product's surface texture and saves the data. This allows the appearance, smell, and texture of the product to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipment.

[0031] The data collection unit can scan the internal structure of a product with an X-ray or ultrasonic sensor and record the internal condition. The data collection unit, for example, scans the internal structure of a product with an X-ray and records the internal condition in detail. For example, the data collection unit can check the internal wiring and component layout of an electronic device and save the data as reference data at the time of shipment. The data collection unit can also scan the internal structure of a product using an ultrasonic sensor. For example, the ultrasonic sensor can detect the internal structure of the product and save it as data. This allows the internal structure of the product to be recorded in detail and the condition at the time of shipment to be accurately understood.

[0032] The data collection unit can record environmental data on the product's temperature and humidity using a temperature sensor and a humidity sensor. For example, the data collection unit uses a temperature sensor to record the product's temperature when the product is shipped. For example, the temperature of frozen food at the time of shipping is recorded and saved as reference data for the storage environment. The data collection unit can also record the product's humidity using a humidity sensor. For example, the humidity sensor detects the product's humidity and saves it as data. This allows the product's storage environment to be recorded in detail, making it possible to accurately understand the condition at the time of shipping.

[0033] The data collection unit can record the operating sounds of the product and the packaging sounds of the product as audio data. The data collection unit collects the operating sounds, for example, when the product is shipped. For example, the operating sounds of an electronic device are recorded and saved as reference data at the time of shipping. The data collection unit can also collect the packaging sounds of the product. For example, the packaging sounds of the product are recorded and saved as data. This allows the operating sounds and packaging sounds of the product to be recorded in detail, making it possible to accurately understand the condition at the time of shipping.

[0034] The data collection unit can collect three-dimensional shape data of products by 3D scanning. For example, the data collection unit performs a 3D scan of a product when it is shipped to collect the three-dimensional shape data of the product. For example, the data collection unit records 3D scan data of furniture and saves it as reference data at the time of shipping. This allows the three-dimensional shape data of the product to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipping.

[0035] The comparison unit can perform a microbial test on the returned product and record the hygienic condition of the product. The comparison unit can, for example, wipe the surface of the returned product and perform a microbial test. For example, the surface of the food product can be wiped and the type and number of microorganisms can be recorded. The comparison unit can also perform a microbial test on the returned product and record the hygienic condition of the product. For example, the surface of the returned product can be wiped and the type and number of microorganisms can be recorded and compared with the data at the time of shipment. This allows the hygienic condition of the returned product to be recorded in detail and compared with the condition at the time of shipment.

[0036] The comparison unit can precisely measure the weight of the returned product and compare it to the weight at the time of shipment. The comparison unit, for example, precisely measures the weight of the returned product and compares it with the weight at the time of shipment. For example, the comparison unit measures the weight of the product using an electronic scale and compares it with the data at the time of shipment. The comparison unit can also precisely measure the weight of the returned product and compare it to the weight at the time of shipment. For example, the comparison unit can precisely measure the weight of the returned product and compare it to the data at the time of shipment to evaluate the weight difference. This allows the weight of the returned product to be precisely measured and compared to the state at the time of shipment.

[0037] The comparison unit can re-record the operating sounds and packaging sounds of the returned product as audio data. The comparison unit, for example, collects the operating sounds of the returned product and compares them with data at the time of shipment. For example, the comparison unit can record the operating sounds of electronic devices and check for abnormal sounds. The comparison unit can also collect the packaging sounds of the returned product and compare them with data at the time of shipment. For example, the comparison unit can record the packaging sounds of the product, save them as data, and compare them with data at the time of shipment. This allows the operating sounds and packaging sounds of the returned product to be recorded in detail and compared with the state at the time of shipment.

[0038] The comparison unit can recollect three-dimensional shape data of the returned product by 3D scanning. For example, the comparison unit 3D scans the returned product to collect three-dimensional shape data. For example, the comparison unit records 3D scan data of furniture and compares it with the data at the time of shipment. This allows the three-dimensional shape data of the returned product to be recorded in detail and compared with the condition at the time of shipment.

[0039] The analysis unit can develop an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI. The analysis unit, for example, develops an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI. For example, it analyzes image data and scent data of a product to evaluate the validity of the reason for return. The analysis unit can also develop an algorithm to evaluate the credibility of the reason for return and deal with unjustified return claims. For example, it evaluates the credibility of the reason for return based on the data analyzed by AI and prevents unjustified return claims. This makes it possible to develop an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI.

[0040] The analysis unit can construct a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated. The analysis unit, for example, constructs a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated. For example, image data and scent data of the product are sent to receive an expert evaluation. The analysis unit can also deal with unjustified return claims by sending the data to a third-party organization and receiving an objective evaluation. For example, the analysis unit can evaluate the validity of the reason for return based on the evaluation of the third-party organization and prevent unjustified return claims. This makes it possible to construct a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated.

[0041] The analysis unit can automatically classify reasons for returns and identify patterns of improper returns by comparing them with past data. The analysis unit, for example, builds a system that automatically classifies reasons for returns and identifies patterns of improper returns by comparing them with past data. For example, AI analyzes reasons for returns and identifies patterns. The analysis unit can also identify patterns of improper returns based on past data. For example, it analyzes past return data and identifies patterns of improper returns. This makes it possible to automatically classify reasons for returns and identify patterns of improper returns by comparing them with past data.

[0042] The analysis unit can share data on returned products on the cloud and exchange information with other companies, thereby helping to prevent unfair returns. The analysis unit, for example, builds a system for sharing data on returned products on the cloud and exchanging information with other companies. For example, image data and scent data of the products are stored in the cloud and shared with other companies. The analysis unit can also exchange information with other companies, thereby helping to prevent unfair returns. For example, sharing information with other companies helps to prevent unfair returns. In this way, sharing data on returned products on the cloud and exchanging information with other companies helps to prevent unfair returns.

[0043] The notification department can develop an algorithm that uses AI to analyze past return data and predict the behavior patterns of malicious complainers. The notification department, for example, can use AI to analyze past return data and develop an algorithm that predicts the behavior patterns of malicious complainers. For example, it can identify the patterns of customers who frequently return products. The notification department can also develop an algorithm that predicts the behavior patterns of malicious complainers and take countermeasures. For example, it can predict the behavior patterns of malicious complainers based on the data analyzed by AI and take countermeasures. This allows AI to analyze past return data and develop an algorithm that predicts the behavior patterns of malicious complainers.

[0044] The notification unit can take into account the purchase history and review history of the customer when identifying malicious complainers. The notification unit, for example, builds a system that takes into account the purchase history of the customer when identifying malicious complainers. For example, the purchase history of a customer who frequently returns products is analyzed to identify malicious complainers. The notification unit can also take into account the review history of the customer. For example, the review history of the customer is analyzed to identify malicious complainers. This makes it possible to take into account the purchase history and review history of the customer when identifying malicious complainers.

[0045] The notification department can share data on malicious complainers with other companies and strengthen countermeasures across the industry. The notification department, for example, builds a system for sharing data on malicious complainers with other companies and strengthening countermeasures across the industry. For example, the data is shared on the cloud and information is exchanged with other companies. The notification department can also strengthen countermeasures across the industry by sharing data with other companies. For example, information is shared with other companies and countermeasures across the industry are strengthened. This allows data on malicious complainers to be shared with other companies and countermeasures across the industry are strengthened.

[0046] The notification unit can visualize the behavioral patterns of complainers and promote information sharing within the company. The notification unit, for example, visualizes the behavioral patterns of complainers and builds a system that promotes information sharing within the company. For example, the behavioral patterns of complainers are displayed in graphs or charts. The notification unit can also promote information sharing within the company. For example, the behavioral patterns of complainers are visualized and promote information sharing within the company. This makes it possible to visualize the behavioral patterns of complainers and promote information sharing within the company.

[0047] The data collection unit can use blockchain technology to prevent data tampering when storing data. The data collection unit, for example, uses blockchain technology to store data and builds a system to prevent data tampering. For example, product shipping data and return data are recorded on the blockchain. The data collection unit can also use blockchain technology to prevent data tampering. For example, blockchain technology is used to prevent data tampering. In this way, blockchain technology is used to prevent data tampering when storing data.

[0048] The data collection unit uses AI to periodically analyze the stored data and automatically suggest areas for improvement in quality control and customer service. The data collection unit, for example, builds a system in which AI periodically analyzes stored data and automatically suggests areas for improvement in quality control and customer service. For example, it analyzes product shipping data and return data and suggests areas for improvement. The data collection unit can also suggest areas for improvement in quality control and customer service based on the data analyzed by AI. For example, it suggests areas for improvement in quality control and customer service based on the data analyzed by AI. This allows AI to periodically analyze stored data and automatically suggest areas for improvement in quality control and customer service.

[0049] The data collection unit can store and manage data on the cloud, making it accessible from multiple locations. The data collection unit, for example, builds a system that stores and manages data on the cloud and makes it accessible from multiple locations. For example, product shipping data and return data are stored in the cloud and accessed from multiple locations. The data collection unit can also store and manage data on the cloud, making it accessible from multiple locations. For example, data is stored and managed on the cloud and accessed from multiple locations. This allows data to be stored and managed on the cloud and accessed from multiple locations.

[0050] The data collection unit can share the stored data with other companies to strengthen quality control across the industry. The data collection unit, for example, shares the stored data with other companies to build a system that strengthens quality control across the industry. For example, product shipping data and return data is stored in the cloud and shared with other companies. The data collection unit can also strengthen quality control across the industry by sharing data with other companies. For example, information is shared with other companies to strengthen quality control across the industry. In this way, the stored data is shared with other companies to strengthen quality control across the industry.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data collection unit can record vibration data when a product is shipped. For example, a sensor can detect vibrations that occur during product transportation and store the data. The data collection unit can also record impact data when a product is shipped. For example, a sensor can detect impacts that occur when the product is packaged or during transportation and store the data. This allows the effects of vibrations and impacts during product transportation to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipment.

[0053] The data collection unit can record the influence of light at the time of product shipment. For example, it can use a sensor to measure the light intensity in the product storage area and save the data. The data collection unit can also record the influence of sound at the time of product shipment. For example, it can use a sensor to measure the noise level in the product storage area and save the data. This makes it possible to record in detail the influence of light and sound in the product storage environment and accurately understand the condition at the time of shipment.

[0054] The data collection unit can record the effects of electromagnetic waves at the time of product shipment. For example, it can measure the electromagnetic wave levels in the product storage location with a sensor and save the data. The data collection unit can also record the effects of radiation at the time of product shipment. For example, it can measure the radiation levels in the product storage location with a sensor and save the data. This makes it possible to record in detail the effects of electromagnetic waves and radiation in the product storage environment and accurately understand the condition at the time of shipment.

[0055] The data collection unit can record the effects of chemical substances at the time of product shipment. For example, it can use a sensor to measure the concentration of chemical substances in the air where the product is stored and save the data. The data collection unit can also record the effects of humidity at the time of product shipment. For example, it can use a sensor to measure the humidity at the time of product storage and save the data. This allows the effects of chemical substances and humidity in the product's storage environment to be recorded in detail, making it possible to accurately understand the condition at the time of shipment.

[0056] The data collection unit can record the influence of temperature at the time of product shipment. For example, it can measure the temperature of the product storage location with a sensor and save the data. The data collection unit can also record the influence of humidity at the time of product shipment. For example, it can measure the humidity of the product storage location with a sensor and save the data. This makes it possible to record in detail the influence of temperature and humidity in the product storage environment and accurately grasp the condition at the time of shipment.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection unit records the product's condition at the time of shipment in detail. For example, the product's exterior is photographed with a high-resolution camera, its smell is recorded with an odor sensor, and the texture of the product's surface is measured with a texture sensor. The internal structure of the product is also scanned with X-ray and ultrasonic sensors, and the internal condition is also recorded. Step 2: The comparison unit compares the condition of the returned product based on the data collected by the data collection unit. For example, the exterior of the returned product is photographed again with a high-resolution camera, the odor is recorded with an odor sensor, and the surface texture is measured with a texture sensor. The internal structure of the returned product is also scanned with X-ray or ultrasonic sensors, and the internal condition is recorded. Step 3: The analysis unit analyzes the results obtained by the comparison unit. For example, it compares the image data of the products to check for scratches or stains. It also compares the scent data, and if a different scent is detected, it can indicate that there was a problem with the product's use or storage conditions. It also compares the texture data, and if surface wear or discoloration is confirmed, it can prove the product's usage conditions. Step 4: The notification department will deal with unjustified return claims based on the results obtained by the analysis department. For example, they could develop an algorithm to evaluate the veracity of the reason for return based on the data analyzed by AI. They could also build a system to send data on returned products to a third-party organization for objective evaluation.

[0059] (Example 2) The malicious return prevention system according to an embodiment of the present invention is a system that records in detail the condition of the product at the time of shipment, and uses a generation AI to compare and analyze the condition of the returned product, thereby efficiently dealing with unjustified return claims. As a result, the malicious return prevention system can deal with unjustified return claims by comparing in detail the condition of the product at the time of shipment with the condition at the time of return.

[0060] The malicious return prevention system according to the embodiment includes a data collection unit, a comparison unit, an analysis unit, and a notification unit. The data collection unit records the condition of the product at the time of shipment in detail. For example, the data collection unit may photograph the exterior of the product with a high-resolution camera, record the product's odor with an odor sensor, and measure the product's surface texture with a texture sensor. The data collection unit may also scan the product's internal structure with an X-ray or ultrasonic sensor and record the internal condition. The comparison unit compares the condition of the returned product based on the data collected by the data collection unit. For example, the data collection unit may photograph the exterior of the returned product again with a high-resolution camera, record the odor with an odor sensor, and measure the surface texture with the texture sensor. The comparison unit may also scan the internal structure of the returned product with an X-ray or ultrasonic sensor and record the internal condition. The analysis unit analyzes the results obtained by the comparison unit. For example, the analysis unit may compare image data of the product to check for scratches or stains. Furthermore, comparing the odor data and detecting a different odor can indicate problems with the use or storage conditions of the product. Furthermore, comparing the texture data and detecting surface wear or discoloration can prove the product's usage. The notification unit deals with unjustified return claims based on the results obtained by the analysis unit. For example, an algorithm for evaluating the veracity of the reason for return can be developed based on the data analyzed by AI. It is also possible to build a system in which data on returned products is sent to a third-party organization for objective evaluation. This allows the malicious return prevention system according to the embodiment to deal with unjustified return claims by making a detailed comparison between the condition of the product at the time of shipment and the condition at the time of return.

[0061] The data collection unit can photograph the appearance of the product with a high-resolution camera, record the product's smell with an odor sensor, and measure the product's surface texture with a texture sensor. For example, the data collection unit photographs the appearance of the product with a high-resolution camera and saves the image data. The data collection unit also records the product's smell using an odor sensor. For example, the odor sensor detects the product's smell and saves the data. The data collection unit also measures the product's surface texture using a texture sensor. For example, the texture sensor detects the product's surface texture and saves the data. This allows the appearance, smell, and texture of the product to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipment.

[0062] The data collection unit can scan the internal structure of a product with an X-ray or ultrasonic sensor and record the internal condition. The data collection unit, for example, scans the internal structure of a product with an X-ray and records the internal condition in detail. For example, the data collection unit can check the internal wiring and component layout of an electronic device and save the data as reference data at the time of shipment. The data collection unit can also scan the internal structure of a product using an ultrasonic sensor. For example, the ultrasonic sensor can detect the internal structure of the product and save it as data. This allows the internal structure of the product to be recorded in detail and the condition at the time of shipment to be accurately understood.

[0063] The data collection unit can record environmental data on the product's temperature and humidity using a temperature sensor and a humidity sensor. For example, the data collection unit uses a temperature sensor to record the product's temperature when the product is shipped. For example, the temperature of frozen food at the time of shipping is recorded and saved as reference data for the storage environment. The data collection unit can also record the product's humidity using a humidity sensor. For example, the humidity sensor detects the product's humidity and saves it as data. This allows the product's storage environment to be recorded in detail, making it possible to accurately understand the condition at the time of shipping.

[0064] The data collection unit uses the emotion estimation function to record the emotional state of the shipping staff and consider the influence of their attention and concentration during shipping. The data collection unit, for example, uses the emotion estimation function to record the emotional state of the shipping staff. For example, it analyzes facial expressions and voices during shipping work and calculates an emotion score. The data collection unit also records the emotional state of the shipping staff and considers the influence of their attention and concentration during shipping. For example, it evaluates the influence of their attention and concentration during shipping based on their emotional state during shipping work. In this way, it is possible to record the emotional state of the shipping staff and consider the influence of their attention and concentration during shipping.

[0065] The data collection unit can record the operating sounds of the product and the packaging sounds of the product as audio data. The data collection unit collects the operating sounds, for example, when the product is shipped. For example, the operating sounds of an electronic device are recorded and saved as reference data at the time of shipping. The data collection unit can also collect the packaging sounds of the product. For example, the packaging sounds of the product are recorded and saved as data. This allows the operating sounds and packaging sounds of the product to be recorded in detail, making it possible to accurately understand the condition at the time of shipping.

[0066] The data collection unit can collect three-dimensional shape data of products by 3D scanning. For example, the data collection unit performs a 3D scan of a product when it is shipped to collect the three-dimensional shape data of the product. For example, the data collection unit records 3D scan data of furniture and saves it as reference data at the time of shipping. This allows the three-dimensional shape data of the product to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipping.

[0067] The data collection unit can use the emotion estimation function to monitor the emotional state of shipping personnel in real time and optimize the efficiency of shipping operations. The data collection unit, for example, uses the emotion estimation function to monitor the emotional state of shipping personnel in real time. For example, it analyzes facial expressions and voices during shipping operations and calculates an emotion score. The data collection unit can also monitor the emotional state of shipping personnel in real time and optimize the efficiency of shipping operations. For example, it evaluates and optimizes the efficiency of shipping operations based on the emotional state during shipping operations. In this way, the emotional state of shipping personnel can be monitored in real time and the efficiency of shipping operations can be optimized.

[0068] The comparison unit can perform a microbial test on the returned product and record the hygienic condition of the product. The comparison unit can, for example, wipe the surface of the returned product and perform a microbial test. For example, the surface of the food product can be wiped and the type and number of microorganisms can be recorded. The comparison unit can also perform a microbial test on the returned product and record the hygienic condition of the product. For example, the surface of the returned product can be wiped and the type and number of microorganisms can be recorded and compared with the data at the time of shipment. This allows the hygienic condition of the returned product to be recorded in detail and compared with the condition at the time of shipment.

[0069] The comparison unit can precisely measure the weight of the returned product and compare it to the weight at the time of shipment. The comparison unit, for example, precisely measures the weight of the returned product and compares it with the weight at the time of shipment. For example, the comparison unit measures the weight of the product using an electronic scale and compares it with the data at the time of shipment. The comparison unit can also precisely measure the weight of the returned product and compare it to the weight at the time of shipment. For example, the comparison unit can precisely measure the weight of the returned product and compare it to the data at the time of shipment to evaluate the weight difference. This allows the weight of the returned product to be precisely measured and compared to the state at the time of shipment.

[0070] The comparison unit can use the emotion estimation function to record the emotional state of the person in charge of returning goods and evaluate the accuracy of the return processing. The comparison unit, for example, uses the emotion estimation function to record the emotional state of the person in charge of returning goods. For example, the comparison unit analyzes facial expressions and voices during the return processing and calculates an emotion score. The comparison unit can also record the emotional state of the person in charge of returning goods and evaluate the accuracy of the return processing. For example, the accuracy of the return processing is evaluated based on the emotional state during the return processing. In this way, the emotional state of the person in charge of returning goods can be recorded and the accuracy of the return processing can be evaluated.

[0071] The comparison unit can re-record the operating sounds and packaging sounds of the returned product as audio data. The comparison unit, for example, collects the operating sounds of the returned product and compares them with data at the time of shipment. For example, the comparison unit can record the operating sounds of electronic devices and check for abnormal sounds. The comparison unit can also collect the packaging sounds of the returned product and compare them with data at the time of shipment. For example, the comparison unit can record the packaging sounds of the product, save them as data, and compare them with data at the time of shipment. This allows the operating sounds and packaging sounds of the returned product to be recorded in detail and compared with the state at the time of shipment.

[0072] The comparison unit can recollect three-dimensional shape data of the returned product by 3D scanning. For example, the comparison unit 3D scans the returned product to collect three-dimensional shape data. For example, the comparison unit records 3D scan data of furniture and compares it with the data at the time of shipment. This allows the three-dimensional shape data of the returned product to be recorded in detail and compared with the condition at the time of shipment.

[0073] The comparison unit can use the emotion estimation function to monitor the emotional state of the return clerk in real time and optimize the efficiency of return processing. The comparison unit, for example, uses the emotion estimation function to monitor the emotional state of the return clerk in real time. For example, it analyzes facial expressions and voices during return processing and calculates an emotion score. The comparison unit can also monitor the emotional state of the return clerk in real time and optimize the efficiency of return processing. For example, it evaluates and optimizes the efficiency of return processing based on the emotional state during return processing. In this way, the emotional state of the return clerk can be monitored in real time and the efficiency of return processing can be optimized.

[0074] The analysis unit can develop an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI. The analysis unit, for example, develops an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI. For example, it analyzes image data and scent data of a product to evaluate the validity of the reason for return. The analysis unit can also develop an algorithm to evaluate the credibility of the reason for return and deal with unjustified return claims. For example, it evaluates the credibility of the reason for return based on the data analyzed by AI and prevents unjustified return claims. This makes it possible to develop an algorithm to evaluate the credibility of the reason for return based on the data analyzed by AI.

[0075] The analysis unit can construct a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated. The analysis unit, for example, constructs a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated. For example, image data and scent data of the product are sent to receive an expert evaluation. The analysis unit can also deal with unjustified return claims by sending the data to a third-party organization and receiving an objective evaluation. For example, the analysis unit can evaluate the validity of the reason for return based on the evaluation of the third-party organization and prevent unjustified return claims. This makes it possible to construct a system in which data on returned products is sent to a third-party organization and the product is objectively evaluated.

[0076] The analysis unit can use the emotion estimation function to analyze the customer's emotional response to the reason for return and evaluate the possibility of an inappropriate return. The analysis unit, for example, uses the emotion estimation function to analyze the customer's emotional response to the reason for return. For example, the analysis unit analyzes the customer's facial expressions and voice and calculates an emotion score. The analysis unit can also evaluate the possibility of an inappropriate return based on the customer's emotional response. For example, the analysis unit analyzes the customer's emotional response and evaluates the possibility of an inappropriate return. In this way, the analysis unit can analyze the customer's emotional response to the reason for return and evaluate the possibility of an inappropriate return.

[0077] The analysis unit can automatically classify reasons for returns and identify patterns of improper returns by comparing them with past data. The analysis unit, for example, builds a system that automatically classifies reasons for returns and identifies patterns of improper returns by comparing them with past data. For example, AI analyzes reasons for returns and identifies patterns. The analysis unit can also identify patterns of improper returns based on past data. For example, it analyzes past return data and identifies patterns of improper returns. This makes it possible to automatically classify reasons for returns and identify patterns of improper returns by comparing them with past data.

[0078] The analysis unit can share data on returned products on the cloud and exchange information with other companies, thereby helping to prevent unfair returns. The analysis unit, for example, builds a system for sharing data on returned products on the cloud and exchanging information with other companies. For example, image data and scent data of the products are stored in the cloud and shared with other companies. The analysis unit can also exchange information with other companies, thereby helping to prevent unfair returns. For example, sharing information with other companies helps to prevent unfair returns. In this way, sharing data on returned products on the cloud and exchanging information with other companies helps to prevent unfair returns.

[0079] The analysis unit uses the emotion estimation function to monitor the customer's emotional response to the reason for return in real time and can respond quickly. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the customer's emotional response to the reason for return in real time. For example, the analysis unit analyzes the customer's facial expressions and voice and calculates an emotion score. The analysis unit also monitors the customer's emotional response in real time and can respond quickly. For example, the analysis unit monitors the customer's emotional response in real time and can respond quickly. This allows the customer's emotional response to the reason for return to be monitored in real time and can respond quickly.

[0080] The notification department can develop an algorithm that uses AI to analyze past return data and predict the behavior patterns of malicious complainers. The notification department, for example, can use AI to analyze past return data and develop an algorithm that predicts the behavior patterns of malicious complainers. For example, it can identify the patterns of customers who frequently return products. The notification department can also develop an algorithm that predicts the behavior patterns of malicious complainers and take countermeasures. For example, it can predict the behavior patterns of malicious complainers based on the data analyzed by AI and take countermeasures. This allows AI to analyze past return data and develop an algorithm that predicts the behavior patterns of malicious complainers.

[0081] The notification unit can take into account the purchase history and review history of the customer when identifying malicious complainers. The notification unit, for example, builds a system that takes into account the purchase history of the customer when identifying malicious complainers. For example, the purchase history of a customer who frequently returns products is analyzed to identify malicious complainers. The notification unit can also take into account the review history of the customer. For example, the review history of the customer is analyzed to identify malicious complainers. This makes it possible to take into account the purchase history and review history of the customer when identifying malicious complainers.

[0082] The notification unit can use the emotion estimation function to analyze the emotional state of the complainer and propose appropriate countermeasures. The notification unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of the complainer. For example, the notification unit analyzes the facial expressions and voice of the complainer and calculates an emotion score. The notification unit can also propose appropriate countermeasures based on the emotional state of the complainer. For example, the notification unit analyzes the emotional state of the complainer and proposes appropriate countermeasures. This makes it possible to analyze the emotional state of the complainer and propose appropriate countermeasures.

[0083] The notification department can share data on malicious complainers with other companies and strengthen countermeasures across the industry. The notification department, for example, builds a system for sharing data on malicious complainers with other companies and strengthening countermeasures across the industry. For example, the data is shared on the cloud and information is exchanged with other companies. The notification department can also strengthen countermeasures across the industry by sharing data with other companies. For example, information is shared with other companies and countermeasures across the industry are strengthened. This allows data on malicious complainers to be shared with other companies and countermeasures across the industry are strengthened.

[0084] The notification unit can visualize the behavioral patterns of complainers and promote information sharing within the company. The notification unit, for example, visualizes the behavioral patterns of complainers and builds a system that promotes information sharing within the company. For example, the behavioral patterns of complainers are displayed in graphs or charts. The notification unit can also promote information sharing within the company. For example, the behavioral patterns of complainers are visualized and promote information sharing within the company. This makes it possible to visualize the behavioral patterns of complainers and promote information sharing within the company.

[0085] The notification unit uses the emotion estimation function to monitor the emotional state of the complainer in real time and respond quickly. The notification unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the complainer in real time. For example, the notification unit analyzes the facial expressions and voice of the complainer and calculates an emotion score. The notification unit also monitors the emotional state of the complainer in real time and responds quickly. For example, the notification unit monitors the emotional state of the complainer in real time and responds quickly. This makes it possible to monitor the emotional state of the complainer in real time and respond quickly.

[0086] The data collection unit can use blockchain technology to prevent data tampering when storing data. The data collection unit, for example, uses blockchain technology to store data and builds a system to prevent data tampering. For example, product shipping data and return data are recorded on the blockchain. The data collection unit can also use blockchain technology to prevent data tampering. For example, blockchain technology is used to prevent data tampering. In this way, blockchain technology is used to prevent data tampering when storing data.

[0087] The data collection unit uses AI to periodically analyze the stored data and automatically suggest areas for improvement in quality control and customer service. The data collection unit, for example, builds a system in which AI periodically analyzes stored data and automatically suggests areas for improvement in quality control and customer service. For example, it analyzes product shipping data and return data and suggests areas for improvement. The data collection unit can also suggest areas for improvement in quality control and customer service based on the data analyzed by AI. For example, it suggests areas for improvement in quality control and customer service based on the data analyzed by AI. This allows AI to periodically analyze stored data and automatically suggest areas for improvement in quality control and customer service.

[0088] The data collection unit can use the emotion estimation function to record the emotional state of the data manager and evaluate the efficiency of data management. The data collection unit, for example, uses the emotion estimation function to build a system that records the emotional state of the data manager. For example, it analyzes facial expressions and voices during data management work and calculates an emotion score. The data collection unit can also record the emotional state of the data manager and evaluate the efficiency of data management. For example, it evaluates the efficiency of data management based on the emotional state during data management work. In this way, the emotional state of the data manager is recorded and the efficiency of data management is evaluated.

[0089] The data collection unit can store and manage data on the cloud, making it accessible from multiple locations. The data collection unit, for example, builds a system that stores and manages data on the cloud and makes it accessible from multiple locations. For example, product shipping data and return data are stored in the cloud and accessed from multiple locations. The data collection unit can also store and manage data on the cloud, making it accessible from multiple locations. For example, data is stored and managed on the cloud and accessed from multiple locations. This allows data to be stored and managed on the cloud and accessed from multiple locations.

[0090] The data collection unit can share the stored data with other companies to strengthen quality control across the industry. The data collection unit, for example, shares the stored data with other companies to build a system that strengthens quality control across the industry. For example, product shipping data and return data is stored in the cloud and shared with other companies. The data collection unit can also strengthen quality control across the industry by sharing data with other companies. For example, information is shared with other companies to strengthen quality control across the industry. In this way, the stored data is shared with other companies to strengthen quality control across the industry.

[0091] The data collection unit can use the emotion estimation function to monitor the emotional state of the data manager in real time and optimize the efficiency of data management. The data collection unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the data manager in real time. For example, it analyzes facial expressions and voices during data management work and calculates an emotion score. The data collection unit can also monitor the emotional state of the data manager in real time and optimize the efficiency of data management. For example, it evaluates and optimizes the efficiency of data management based on the emotional state during data management work. In this way, the emotional state of the data manager is monitored in real time and the efficiency of data management is optimized.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The data collection unit can record vibration data when a product is shipped. For example, a sensor can detect vibrations that occur during product transportation and store the data. The data collection unit can also record impact data when a product is shipped. For example, a sensor can detect impacts that occur when the product is packaged or during transportation and store the data. This allows the effects of vibrations and impacts during product transportation to be recorded in detail, making it possible to accurately understand the condition of the product at the time of shipment.

[0094] The data collection unit can record the influence of light at the time of product shipment. For example, it can use a sensor to measure the light intensity in the product storage area and save the data. The data collection unit can also record the influence of sound at the time of product shipment. For example, it can use a sensor to measure the noise level in the product storage area and save the data. This makes it possible to record in detail the influence of light and sound in the product storage environment and accurately understand the condition at the time of shipment.

[0095] The data collection unit can record the effects of electromagnetic waves at the time of product shipment. For example, it can measure the electromagnetic wave levels in the product storage location with a sensor and save the data. The data collection unit can also record the effects of radiation at the time of product shipment. For example, it can measure the radiation levels in the product storage location with a sensor and save the data. This makes it possible to record in detail the effects of electromagnetic waves and radiation in the product storage environment and accurately understand the condition at the time of shipment.

[0096] The data collection unit can record the effects of chemical substances at the time of product shipment. For example, it can use a sensor to measure the concentration of chemical substances in the air where the product is stored and save the data. The data collection unit can also record the effects of humidity at the time of product shipment. For example, it can use a sensor to measure the humidity at the time of product storage and save the data. This allows the effects of chemical substances and humidity in the product's storage environment to be recorded in detail, making it possible to accurately understand the condition at the time of shipment.

[0097] The data collection unit can record the influence of temperature at the time of product shipment. For example, it can measure the temperature of the product storage location with a sensor and save the data. The data collection unit can also record the influence of humidity at the time of product shipment. For example, it can measure the humidity of the product storage location with a sensor and save the data. This makes it possible to record in detail the influence of temperature and humidity in the product storage environment and accurately grasp the condition at the time of shipment.

[0098] The data collection unit can use the emotion estimation function to record the emotional state of the customer and evaluate the veracity of the reason for return. For example, it can analyze the facial expressions and voice of the customer when explaining the reason for return and calculate an emotion score. The data collection unit can also record the emotional state of the customer and evaluate the veracity of the reason for return. For example, it can evaluate the veracity of the reason for return based on the emotional state of the customer. In this way, it is possible to record the emotional state of the customer and evaluate the veracity of the reason for return.

[0099] The data collection unit can use the emotion estimation function to record the emotional state of the shipping staff and evaluate the efficiency of the shipping work. For example, it can analyze facial expressions and voices during shipping work and calculate an emotion score. The data collection unit can also record the emotional state of the shipping staff and evaluate the efficiency of the shipping work. For example, it can evaluate the efficiency of the shipping work based on the emotional state during shipping work. In this way, it is possible to record the emotional state of the shipping staff and evaluate the efficiency of the shipping work.

[0100] The data collection unit can use the emotion estimation function to monitor the emotional state of the customer in real time and evaluate the veracity of the reason for return. For example, it can analyze the facial expressions and voice of the customer when explaining the reason for return and calculate an emotion score. The data collection unit can also monitor the emotional state of the customer in real time and evaluate the veracity of the reason for return. For example, it can evaluate the veracity of the reason for return based on the customer's emotional state. This makes it possible to monitor the emotional state of the customer in real time and evaluate the veracity of the reason for return.

[0101] The data collection unit can use the emotion estimation function to monitor the emotional state of shipping personnel in real time and optimize the efficiency of shipping work. For example, it can analyze facial expressions and voices during shipping work and calculate an emotion score. The data collection unit can also monitor the emotional state of shipping personnel in real time and optimize the efficiency of shipping work. For example, it can evaluate and optimize the efficiency of shipping work based on the emotional state during shipping work. In this way, it is possible to monitor the emotional state of shipping personnel in real time and optimize the efficiency of shipping work.

[0102] The data collection unit can use the emotion estimation function to record the emotional state of the customer and evaluate the veracity of the reason for return. For example, it can analyze the facial expressions and voice of the customer when explaining the reason for return and calculate an emotion score. The data collection unit can also record the emotional state of the customer and evaluate the veracity of the reason for return. For example, it can evaluate the veracity of the reason for return based on the emotional state of the customer. In this way, it is possible to record the emotional state of the customer and evaluate the veracity of the reason for return.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The data collection unit records the product's condition at the time of shipment in detail. For example, the product's exterior is photographed with a high-resolution camera, its smell is recorded with an odor sensor, and the texture of the product's surface is measured with a texture sensor. The internal structure of the product is also scanned with X-ray and ultrasonic sensors, and the internal condition is also recorded. Step 2: The comparison unit compares the condition of the returned product based on the data collected by the data collection unit. For example, the exterior of the returned product is photographed again with a high-resolution camera, the odor is recorded with an odor sensor, and the surface texture is measured with a texture sensor. The internal structure of the returned product is also scanned with X-ray or ultrasonic sensors, and the internal condition is recorded. Step 3: The analysis unit analyzes the results obtained by the comparison unit. For example, it compares the image data of the products to check for scratches or stains. It also compares the scent data, and if a different scent is detected, it can indicate that there was a problem with the product's use or storage conditions. It also compares the texture data, and if surface wear or discoloration is confirmed, it can prove the product's usage conditions. Step 4: The notification department will deal with unjustified return claims based on the results obtained by the analysis department. For example, they could develop an algorithm to evaluate the veracity of the reason for return based on the data analyzed by AI. They could also build a system to send data on returned products to a third-party organization for objective evaluation.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 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.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0126] The 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.

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

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

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

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 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 data collection unit that records the product's condition at the time of shipment in detail; a comparison unit that compares the condition of the returned product based on the data collected by the data collection unit; an analysis unit that analyzes the results obtained by the comparison unit; A notification unit is provided to deal with unjustified return claims based on the results obtained by the analysis unit. A system characterized by:

2. The data collection unit The appearance of the product is photographed with a high-resolution camera, the smell of the product is recorded with the odor sensor, and the texture of the surface of the product is measured with a texture sensor.

2. The system of claim 1.

3. The data collection unit The operating sound of the product and the packaging sound of the product are recorded as audio data.

2. The system of claim 1.

4. The comparison unit Conducting a microbial test on the returned product and recording the sanitary condition of the product 2. The system of claim 1.

5. The analysis unit Based on the data analyzed by the AI, an algorithm will be developed to evaluate the veracity of the reason for return.

2. The system of claim 1.

6. The notification unit Using emotion estimation function, analyze the emotional state of the complainer and propose appropriate countermeasures 2. The system of claim 1.

7. The data collection unit Using the emotion estimation function, the emotional state of the data manager is recorded and the efficiency of data management is evaluated.

2. The system of claim 1.

8. The analysis unit Using emotion estimation functionality, analyze the customer's emotional response to the reason for return and evaluate whether the return is unjustified.

2. The system of claim 1.

Images