system

Abstract

An object of a system according to an embodiment is to enable a purchase contemplation person to contemplate purchase after sufficiently understanding a product.SOLUTION: A system includes a moving image photographing unit, a posting unit, and a browsing unit. The moving image capturing unit captures a moving image at a timing such as when the purchaser opens the product, one week after the use, or one month after the use. The posting unit posts the moving image captured by the moving image capturing unit to the EC site. The browsing unit allows another purchase contemplation person to browse the moving image posted by the posting 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] With conventional technology, text-based reviews did not convey enough product details, making it difficult for potential buyers to understand the product.

[0005] The system according to the embodiment aims to enable a potential purchaser to fully understand a product before considering its purchase. [Means for solving the problem]

[0006] The system according to the embodiment includes a video capture unit, a posting unit, and a viewing unit. The video capture unit captures videos when a purchaser opens the product, one week after use, one month after use, and so on. The posting unit posts the videos captured by the video capture unit to an e-commerce site. The viewing unit allows other potential purchasers to view the videos posted by the posting unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a potential purchaser to fully understand a product before considering its purchase. [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 word-of-mouth substitute system according to the embodiment of the present invention is a system in which a customer takes a video when opening a product, one week after using it, one month after using it, etc., and posts the video on an e-commerce site. This allows the customer to fully understand the product before considering a purchase.

[0029] The word-of-mouth proxy system according to the embodiment includes a video capture unit, a posting unit, and a viewing unit. The video capture unit captures videos when a customer opens a product, one week after use, one month after use, and so on. For example, the video capture unit captures high-resolution videos using a smartphone or a camera. The video capture unit can also record detailed footage of a customer using a product. The video capture unit can also capture videos in which the customer reports on the product's usage and effectiveness. The posting unit posts the videos captured by the video capture unit to an e-commerce site. For example, the posting unit uploads the videos to a dedicated page on the e-commerce site. The posting unit can also provide an interface for entering the title and description of the video. The posting unit also provides a video preview function, allowing customers to check the video before posting it. The viewing unit allows other potential purchasers to view the videos posted by the posting unit. For example, the viewing unit provides a function for playing videos on a dedicated page on the e-commerce site. The viewing unit also provides a video search function, allowing potential purchasers to easily find videos related to a specific product. The viewing unit also provides a video rating function, allowing other potential purchasers to comment on and rate the video. This allows the word-of-mouth proxy system according to the embodiment to allow purchasers to fully understand the product before considering a purchase. For example, the purchaser can check the product's condition upon opening, its usability, durability, etc. through the video. The purchaser can also refer to the opinions and ratings of other purchasers. The purchaser can also obtain detailed information about the product through the video.

[0030] The video recording unit can automatically remind the customer when to take a video depending on the product usage status. For example, when the customer is using the product, the video recording unit detects the usage status with a sensor and reminds the customer to take a video at the appropriate time. For example, a reminder notification is sent after a smart home appliance has been used for a certain period of time. The video recording unit can also send reminder notifications to the customer using a smartphone app. For example, the app monitors the product usage status and sends reminder notifications at the appropriate time. The video recording unit can also send regular reminder notifications to the customer using a timer function. For example, a reminder notification is sent at the time set by the timer. This allows the customer to take videos at the appropriate time.

[0031] The video recording unit can customize the timing of video posting based on the lifestyle and frequency of use of the customer. For example, the video recording unit collects lifestyle data of the customer and customizes the timing of video posting based on the frequency of use and time of day. For example, a reminder is sent at night to a user who uses a product at night. The video recording unit can also analyze the usage frequency data of the customer and send reminder notifications according to the frequency of use. For example, reminders are sent frequently to a user who uses a product frequently. The video recording unit can also send reminder notifications based on lifestyle events of the customer. For example, reminders are sent to coincide with specific events or routines. This makes it possible to post videos that match the lifestyle of the customer.

[0032] The video shooting unit can set the timing of video posting to match the season or event of the product, and collect reviews specialized for a specific time of year. For example, the video shooting unit takes into account seasonal product usage and reminds users to post videos tailored to a specific season. For example, encouraging users to post reviews on the use of heating appliances in winter. The video shooting unit can also remind users to post videos tailored to specific events. For example, sending reminders to match events such as Christmas or Halloween. The video shooting unit can also remind users to post videos during specific campaign periods. For example, sending reminders during sales events. This makes it possible to collect reviews specialized for seasons or events.

[0033] The video recording unit can adjust the timing at which a customer shoots a video based on the posting status of other customers, thereby providing a balanced review. The video recording unit, for example, analyzes the video posting status of other customers and reminds the customer to post a new video at a time when there are few posts. For example, the video recording unit can adjust the posting status so that posts are not concentrated in a specific time period. The video recording unit can also monitor the posting data of other customers and send reminder notifications to provide a balanced review. For example, reminders are sent taking into account the balance between positive and negative reviews. The video recording unit can also adjust the frequency of reminder notifications based on the posting status. For example, reminders are sent more frequently during times when there are few posts. This allows the customer to provide a balanced review.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The word-of-mouth substitute system may further include a voice recognition unit. The voice recognition unit can simplify operations when a customer shoots a video by using voice commands. For example, when a customer says "start recording," the video capture unit automatically starts recording. The voice recognition unit can also convert the customer's voice into text and automatically input it into the posting unit when the customer describes the content of the video. For example, when a customer describes their experience using a product, the content is automatically converted into text and posted as the video description. The voice recognition unit can also stop the video recording by using a voice command when the customer finishes shooting the video. For example, when the customer says "stop recording," the video capture unit stops recording. This allows customers to easily shoot and post videos.

[0036] The word-of-mouth substitute system may further include an augmented reality (AR) unit. When a customer is shooting a video, the AR unit can provide AR content that visually shows how to use a product and its effects. For example, when a customer uses cosmetics, the AR unit displays the makeup effect on the customer's face in real time. The AR unit can also display an image of how the product will look in a room when purchasing furniture or interior goods. For example, when a customer purchases a sofa, the AR unit displays an image of the sofa placed in the room. The AR unit can also provide a guide that visually explains how to use a product. For example, when a customer uses a home appliance, the operating procedure is displayed in AR. This allows the customer to more specifically understand how to use the product and its effects.

[0037] The word-of-mouth proxy system may further include a gamification unit. The gamification unit may provide a mechanism for allowing customers to earn points or badges when posting videos. For example, when a customer posts a video for the first time, the customer may earn a welcome badge. The gamification unit may also award additional points when specific conditions are met. For example, a customer may earn bonus points when posting multiple videos within a certain period of time. The gamification unit may also provide a mechanism for allowing customers to receive rewards using the points they have earned. For example, the customer may use the points to obtain a discount coupon. This may increase the customer's motivation to post videos.

[0038] The word-of-mouth proxy system may further include a social sharing unit. The social sharing unit may provide a function for sharing videos taken by shoppers on social media. For example, when a shopper takes a video and posts it on an e-commerce site, the video is automatically shared on social media sites such as Facebook and Twitter. The social sharing unit may also add a customizable message when a shopper shares a video. For example, a shopper may add a short message about their impressions of a product and post it on social media. The social sharing unit may also analyze the number of views and reactions to the shared video and provide feedback to the shopper. For example, the social sharing unit may notify the shopper of how many times the video has been played and what comments have been made. This allows shoppers to widely share the video and interact with other users.

[0039] The review substitute system can further include an AI assistant section. When a customer shoots a video, the AI ​​assistant section can suggest the best shooting method and angle. For example, when a customer shoots a video showing how to use a product, the AI ​​assistant section can suggest the best camera angle and lighting conditions. The AI ​​assistant section can also provide editing advice when the customer edits the video. For example, it can suggest ways to cut out unnecessary parts or add effective transitions. The AI ​​assistant section can also provide feedback to help the customer improve the content of the video. For example, it can provide advice on how to better emphasize the features of the product. This allows the customer to shoot and post high-quality videos.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The videography department takes videos of the customer when they open the product, one week after use, one month after use, etc. For example, they can use a smartphone or camera to take high-resolution videos and record in detail how the customer uses the product, its feel, and its effects. Step 2: The posting unit posts the video captured by the video capture unit to the EC site. For example, the posting unit uploads the video to a dedicated page on the EC site, and provides an interface for inputting the title and description of the video, as well as a preview function. Step 3: The viewing unit allows other potential buyers to view the videos posted by the posting unit. For example, it provides functions for playing videos on a dedicated page on the e-commerce site, as well as video search and rating functions, allowing potential buyers to easily find videos related to a specific product and leave comments and ratings.

[0042] (Example 2) The word-of-mouth substitute system according to the embodiment of the present invention is a system in which a customer takes a video when opening a product, one week after using it, one month after using it, etc., and posts the video on an e-commerce site. This allows the customer to fully understand the product before considering a purchase.

[0043] The word-of-mouth proxy system according to the embodiment includes a video capture unit, a posting unit, and a viewing unit. The video capture unit captures videos when a customer opens a product, one week after use, one month after use, and so on. For example, the video capture unit captures high-resolution videos using a smartphone or a camera. The video capture unit can also record detailed footage of a customer using a product. The video capture unit can also capture videos in which the customer reports on the product's usage and effectiveness. The posting unit posts the videos captured by the video capture unit to an e-commerce site. For example, the posting unit uploads the videos to a dedicated page on the e-commerce site. The posting unit can also provide an interface for entering the title and description of the video. The posting unit also provides a video preview function, allowing customers to check the video before posting it. The viewing unit allows other potential purchasers to view the videos posted by the posting unit. For example, the viewing unit provides a function for playing videos on a dedicated page on the e-commerce site. The viewing unit also provides a video search function, allowing potential purchasers to easily find videos related to a specific product. The viewing unit also provides a video rating function, allowing other potential purchasers to comment on and rate the video. This allows the word-of-mouth proxy system according to the embodiment to allow purchasers to fully understand the product before considering a purchase. For example, the purchaser can check the product's condition upon opening, its usability, durability, etc. through the video. The purchaser can also refer to the opinions and ratings of other purchasers. The purchaser can also obtain detailed information about the product through the video.

[0044] The video recording unit can automatically remind the customer when to take a video depending on the product usage status. For example, when the customer is using the product, the video recording unit detects the usage status with a sensor and reminds the customer to take a video at the appropriate time. For example, a reminder notification is sent after a smart home appliance has been used for a certain period of time. The video recording unit can also send reminder notifications to the customer using a smartphone app. For example, the app monitors the product usage status and sends reminder notifications at the appropriate time. The video recording unit can also send regular reminder notifications to the customer using a timer function. For example, a reminder notification is sent at the time set by the timer. This allows the customer to take videos at the appropriate time.

[0045] The video recording unit can customize the timing of video posting based on the lifestyle and frequency of use of the customer. For example, the video recording unit collects lifestyle data of the customer and customizes the timing of video posting based on the frequency of use and time of day. For example, a reminder is sent at night to a user who uses a product at night. The video recording unit can also analyze the usage frequency data of the customer and send reminder notifications according to the frequency of use. For example, reminders are sent frequently to a user who uses a product frequently. The video recording unit can also send reminder notifications based on lifestyle events of the customer. For example, reminders are sent to coincide with specific events or routines. This makes it possible to post videos that match the lifestyle of the customer.

[0046] The video recording unit can use an emotion estimation function to encourage shoppers to post videos at the timing when they are feeling the most positive emotions. For example, the video recording unit analyzes the shopper's emotional state in real time and reminds them to post videos when they are feeling the most positive emotions. For example, it analyzes emotions using a smartphone camera or microphone. The video recording unit can also collect shopper emotional data and send reminder notifications when they are feeling the most positive emotions. For example, it can send reminders when they are smiling a lot. The video recording unit can also analyze the shopper's emotional state using an emotion estimation algorithm and send reminder notifications at the optimal timing. For example, it can send reminders based on an emotion score. This allows shoppers to post videos when they are feeling the most positive emotions.

[0047] The video shooting unit can set the timing of video posting to match the season or event of the product, and collect reviews specialized for a specific time of year. For example, the video shooting unit takes into account seasonal product usage and reminds users to post videos tailored to a specific season. For example, encouraging users to post reviews on the use of heating appliances in winter. The video shooting unit can also remind users to post videos tailored to specific events. For example, sending reminders to match events such as Christmas or Halloween. The video shooting unit can also remind users to post videos during specific campaign periods. For example, sending reminders during sales events. This makes it possible to collect reviews specialized for seasons or events.

[0048] The video recording unit can adjust the timing at which a customer shoots a video based on the posting status of other customers, thereby providing a balanced review. The video recording unit, for example, analyzes the video posting status of other customers and reminds the customer to post a new video at a time when there are few posts. For example, the video recording unit can adjust the posting status so that posts are not concentrated in a specific time period. The video recording unit can also monitor the posting data of other customers and send reminder notifications to provide a balanced review. For example, reminders are sent taking into account the balance between positive and negative reviews. The video recording unit can also adjust the frequency of reminder notifications based on the posting status. For example, reminders are sent more frequently during times when there are few posts. This allows the customer to provide a balanced review.

[0049] The video recording unit can use the emotion estimation function to analyze the emotions of shoppers when they are recording videos in real time and suggest the optimal timing for posting. For example, the video recording unit can use the emotion estimation function to analyze the emotions of shoppers when they are recording videos in real time and encourage them to post at a time when they are feeling strong positive emotions. For example, the video recording unit can remind shoppers to post at moments when they are smiling a lot. The video recording unit can also analyze the shoppers' emotional data in real time and suggest the optimal timing for posting. For example, the optimal timing can be suggested based on an emotion score. The video recording unit can also use an emotion estimation algorithm to analyze the shoppers' emotional state in real time and send reminder notifications at the optimal timing. For example, the reminder can be sent based on an emotion score. This allows shoppers to post videos at the optimal timing.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The word-of-mouth substitute system may further include a voice recognition unit. The voice recognition unit can simplify operations when a customer shoots a video by using voice commands. For example, when a customer says "start recording," the video capture unit automatically starts recording. The voice recognition unit can also convert the customer's voice into text and automatically input it into the posting unit when the customer describes the content of the video. For example, when a customer describes their experience using a product, the content is automatically converted into text and posted as the video description. The voice recognition unit can also stop the video recording by using a voice command when the customer finishes shooting the video. For example, when the customer says "stop recording," the video capture unit stops recording. This allows customers to easily shoot and post videos.

[0052] The word-of-mouth substitute system may further include an augmented reality (AR) unit. When a customer is shooting a video, the AR unit can provide AR content that visually shows how to use a product and its effects. For example, when a customer uses cosmetics, the AR unit displays the makeup effect on the customer's face in real time. The AR unit can also display an image of how the product will look in a room when purchasing furniture or interior goods. For example, when a customer purchases a sofa, the AR unit displays an image of the sofa placed in the room. The AR unit can also provide a guide that visually explains how to use a product. For example, when a customer uses a home appliance, the operating procedure is displayed in AR. This allows the customer to more specifically understand how to use the product and its effects.

[0053] The word-of-mouth proxy system may further include a gamification unit. The gamification unit may provide a mechanism for allowing customers to earn points or badges when posting videos. For example, when a customer posts a video for the first time, the customer may earn a welcome badge. The gamification unit may also award additional points when specific conditions are met. For example, a customer may earn bonus points when posting multiple videos within a certain period of time. The gamification unit may also provide a mechanism for allowing customers to receive rewards using the points they have earned. For example, the customer may use the points to obtain a discount coupon. This may increase the customer's motivation to post videos.

[0054] The word-of-mouth proxy system may further include a social sharing unit. The social sharing unit may provide a function for sharing videos taken by shoppers on social media. For example, when a shopper takes a video and posts it on an e-commerce site, the video is automatically shared on social media sites such as Facebook and Twitter. The social sharing unit may also add a customizable message when a shopper shares a video. For example, a shopper may add a short message about their impressions of a product and post it on social media. The social sharing unit may also analyze the number of views and reactions to the shared video and provide feedback to the shopper. For example, the social sharing unit may notify the shopper of how many times the video has been played and what comments have been made. This allows shoppers to widely share the video and interact with other users.

[0055] The review substitute system can further include an AI assistant section. When a customer shoots a video, the AI ​​assistant section can suggest the best shooting method and angle. For example, when a customer shoots a video showing how to use a product, the AI ​​assistant section can suggest the best camera angle and lighting conditions. The AI ​​assistant section can also provide editing advice when the customer edits the video. For example, it can suggest ways to cut out unnecessary parts or add effective transitions. The AI ​​assistant section can also provide feedback to help the customer improve the content of the video. For example, it can provide advice on how to better emphasize the features of the product. This allows the customer to shoot and post high-quality videos.

[0056] The review substitute system can also use its emotion estimation function to analyze the emotions customers have when they shoot videos and suggest optimal editing methods. For example, if a customer has positive emotions, the system can suggest editing methods to emphasize those emotions, such as suggesting transitions and effects to emphasize smiling scenes. The emotion estimation function can also be used to suggest editing methods to soften negative emotions, such as adding relaxing music. The emotion estimation function can also be used to automatically generate video titles and descriptions based on the customer's emotions. For example, if the customer has strong positive emotions, the system can suggest a title such as "I love this product!" This allows customers to post optimal videos based on their emotions.

[0057] The word-of-mouth proxy system can further use an emotion estimation function to analyze the emotions felt by shoppers when they watch videos and recommend the most appropriate videos. For example, if a shopper is feeling positive emotions, it can recommend videos that will further enhance those emotions. For example, it can recommend videos that other shoppers have given high ratings. The emotion estimation function can also be used to recommend videos that will ease those emotions if a shopper is feeling negative emotions. For example, it can recommend videos with a relaxing atmosphere. The emotion estimation function can also be used to adjust the playback order of videos based on the shopper's emotions. For example, if a shopper is feeling strongly positive, it can play fun videos first. This allows shoppers to watch the most appropriate videos based on their emotions.

[0058] The review substitute system can also use an emotion estimation function to analyze the emotions felt by shoppers when they post videos and suggest optimal comments and ratings. For example, if a shopper has positive emotions, the system can suggest comments and ratings that reflect those emotions. For example, it can suggest a comment such as, "This product is great!". The emotion estimation function can also be used to suggest comments and ratings that alleviate negative emotions felt by shoppers. For example, it can suggest a comment such as, "There is room for improvement, but overall I am satisfied." The emotion estimation function can also be used to provide comment and rating templates based on the shopper's emotions. For example, if the shopper has strong positive emotions, the system can suggest a template such as, "I would recommend this product to my friends." This allows shoppers to post optimal comments and ratings based on their emotions.

[0059] The review substitute system can also use an emotion estimation function to analyze the emotions of shoppers when they are shooting videos and suggest optimal background music and sound effects. For example, if a shopper is feeling positive emotions, the system can suggest upbeat background music to emphasize those emotions. For example, up-tempo music can be suggested. The emotion estimation function can also be used to suggest relaxing background music to soothe those emotions if a shopper is feeling negative emotions. For example, calm music can be suggested. The emotion estimation function can also be used to add sound effects based on the shopper's emotions. For example, if the shopper is feeling strongly positive, sound effects such as laughter or applause can be suggested. This allows shoppers to add optimal background music and sound effects based on their emotions.

[0060] The word-of-mouth proxy system can further use an emotion estimation function to analyze the emotions of shoppers when they watch videos and display the most appropriate advertisements. For example, if a shopper is feeling positive emotions, it can display advertisements that will further enhance those emotions. For example, it can display advertisements for other popular products or products with special offers. The emotion estimation function can also be used to display advertisements that will ease the emotions of shoppers when they are feeling negative emotions. For example, it can display advertisements for products or services with a relaxing atmosphere. The emotion estimation function can also be used to adjust the display order of advertisements based on the shopper's emotions. For example, if positive emotions are strong, it can display fun advertisements first. This allows shoppers to view the most appropriate advertisements based on their emotions.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: The videography department takes videos of the customer when they open the product, one week after use, one month after use, etc. For example, they can use a smartphone or camera to take high-resolution videos and record in detail how the customer uses the product, its feel, and its effects. Step 2: The posting unit posts the video captured by the video capture unit to the EC site. For example, the posting unit uploads the video to a dedicated page on the EC site, and provides an interface for inputting the title and description of the video, as well as a preview function. Step 3: The viewing unit allows other potential buyers to view the videos posted by the posting unit. For example, it provides functions for playing videos on a dedicated page on the e-commerce site, as well as video search and rating functions, allowing potential buyers to easily find videos related to a specific product and leave comments and ratings.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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."

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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, in order to avoid confusion and to 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.

[0129] 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]

[0130] 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 video recording unit takes videos when the customer opens the product, one week after use, one month after use, etc. a posting unit that posts the video taken by the video taking unit to an EC site; a viewing unit that enables other potential purchasers to view the video posted by the posting unit. A system characterized by:

2. The video shooting unit The timing for the purchaser to take a video is automatically reminded according to the usage status of the product.

2. The system of claim 1.

3. The video shooting unit Timing video postings to coincide with the season or event of the product in question, allowing for time-specific reviews to be collected.

2. The system of claim 1.

4. The video shooting unit The timing of the customer's video recording is adjusted based on the posting status of other customers to provide a balanced review.

2. The system of claim 1.

5. The video shooting unit Using emotion estimation, the company encourages customers to post videos at the time when they are most likely to feel positive emotions.

2. The system of claim 1.

Images