system

Abstract

The system according to the embodiment aims to enable viewers to easily obtain information about items in video content. [Solution] A system according to an embodiment includes an acquisition unit, a recognition unit, a reference unit, and a provision unit. The acquisition unit acquires tap information. The recognition unit recognizes an item based on the tap information acquired by the acquisition unit. The reference unit acquires related information based on the item information recognized by the recognition unit. The provision unit provides the information acquired by the reference unit.

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 techniques have had the problem that it is difficult for viewers to easily obtain information about items in video content.

[0005] The system according to the embodiment aims to enable viewers to easily obtain information about items in video content. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, a recognition unit, a reference unit, and a provision unit. The acquisition unit acquires tap information. The recognition unit recognizes an item based on the tap information acquired by the acquisition unit. The reference unit acquires related information based on the item information recognized by the recognition unit. The provision unit provides the information acquired by the reference unit. [Effects of the Invention]

[0007] A system according to an embodiment can enable viewers to easily obtain information about items in video content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention is a system in which an AI acquires and provides information about an item that appears in video content when the viewer taps on the item on the device. The information provision system acquires information about an item of interest when the viewer watches video content on the device and taps on the item. For example, if the viewer taps on an actress, her profile is displayed, and if the viewer taps on clothing, information about the sales website is provided. Even if the video quality is low, the AI ​​can provide accurate information by referencing e-commerce data. This allows the viewer to easily acquire information about items of interest while watching the video. The information provision system thus allows the viewer to easily acquire information about items of interest while enjoying the video content, thereby increasing purchasing motivation. Furthermore, for businesses, providing product information through video content and directing users to e-commerce sites can be expected to have a positive marketing effect.

[0029] An information provision system according to an embodiment includes an acquisition unit, a recognition unit, a reference unit, and a provision unit. The acquisition unit acquires tap information when a viewer watches video content on a device and taps on an item of interest. For example, the acquisition unit can acquire information such as the position, time, and strength of the tap. The acquisition unit can also estimate a user's emotion and adjust the timing of acquiring the tap information based on the estimated user's emotion. The recognition unit recognizes the tapped item based on the tap information acquired by the acquisition unit. For example, the recognition unit can recognize the tapped item using image recognition technology. The recognition unit can also estimate a user's emotion and adjust the accuracy of item recognition based on the estimated user's emotion. The reference unit acquires related information based on the item information recognized by the recognition unit. For example, the reference unit can reference an e-commerce database or other related databases to acquire detailed product information, reviews, prices, etc. The reference unit can also estimate a user's emotion and select a database to reference based on the estimated user's emotion. The provision unit provides the information acquired by the reference unit to the viewer. For example, the providing unit can provide information by methods such as a pop-up display, a notification, or an email. The providing unit can also estimate a user's emotions and adjust the method of providing information based on the estimated user's emotions. This allows the information providing system according to the embodiment to easily obtain information about items of interest to a viewer while watching video content. For example, if a viewer taps on an actress, her profile is displayed, and if a viewer taps on clothing, information about the sales website is provided. Even if the video has low accuracy, the AI ​​can provide accurate information by referencing e-commerce data. This allows a viewer to easily obtain information about items of interest to them while watching a video.

[0030] The reference unit can reference an EC database. Examples of EC databases include, but are not limited to, databases such as Yahoo! (registered trademark) Shopping and Amazon (registered trademark). The reference unit can, for example, reference the EC database to obtain detailed product information, reviews, prices, etc. This allows accurate acquisition of product information by referencing the EC database. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the EC database into AI and cause the AI ​​to acquire detailed product information, reviews, prices, etc.

[0031] The reference unit can refer to other related databases. Examples of related databases include, but are not limited to, industry-specific databases and open databases. The reference unit can, for example, refer to related databases to acquire a wide range of information. This allows a wide range of information to be acquired by referencing related databases. Some or all of the above-described processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can input the related database into AI and have the AI ​​acquire a wide range of information.

[0032] The recognition unit can recognize the tapped item. The recognition unit can recognize the tapped item using, for example, image recognition technology. For example, the recognition unit can analyze an image of the tapped item and extract and recognize its features. The recognition unit can also recognize the tapped item using location information. For example, the recognition unit can identify the location of the item based on the tapped location information and acquire that information. The recognition unit can also estimate the user's emotions and adjust the accuracy of item recognition based on the estimated user's emotions. This allows for accurate recognition of the tapped item. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input image data of the tapped item to AI and have the AI ​​recognize the item.

[0033] The providing unit can provide information to viewers. The providing unit can provide information by, for example, a pop-up display, a notification, an email, or the like. For example, the providing unit can provide information about the tapped item to viewers by a pop-up display. The providing unit can also provide information about the tapped item to viewers by a notification. The providing unit can also provide information about the tapped item to viewers by email. This allows information to be provided to viewers quickly. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the tapped item to AI and cause the AI ​​to execute the information providing method.

[0034] The acquisition unit can analyze the user's past tap history and select an appropriate acquisition method. For example, the acquisition unit analyzes patterns of items that the user has frequently tapped in the past and prioritizes acquisition of similar items. For example, the acquisition unit analyzes tapping trends during a specific time period from the user's tap history and selects the acquisition method optimal for that time period. The acquisition unit can also customize an acquisition method for a specific content genre based on the user's tap history. This allows the optimal acquisition method to be selected based on the past tap history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's tap history data into AI and have the AI ​​select the optimal acquisition method.

[0035] When acquiring tap information, the acquisition unit can perform filtering based on the user's currently viewed content and areas of interest. The acquisition unit, for example, preferentially acquires tap information of related items based on the genre of content being viewed by the user. For example, the acquisition unit filters and acquires tap information of highly relevant items based on the user's areas of interest. The acquisition unit can also acquire tap information at appropriate timing depending on the scene of the content being viewed by the user. This makes it possible to filter tap information based on the viewed content and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's viewed content and areas of interest into AI and have the AI ​​perform filtering of the tap information.

[0036] When acquiring tap information, the acquisition unit can select an appropriate acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires tap information based on a voice command. For example, if the user is using gesture input, the acquisition unit acquires tap information based on gesture movements. Furthermore, if the user is using text input, the acquisition unit can also acquire tap information based on the input text. This makes it possible to select the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's input method into AI and have the AI ​​select the optimal acquisition means.

[0037] When acquiring tap information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring tap information of items related to that area. For example, when the user is traveling, the acquisition unit prioritizes acquiring tap information of items related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring tap information of items related to stores and services around the user's home. This makes it possible to acquire highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant information.

[0038] When acquiring the tap information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires tap information of items related to places where the user has checked in on social media. For example, the acquisition unit analyzes the content of the user's social media posts and acquires tap information of related items. The acquisition unit can also acquire tap information of related items by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's social media activity into AI and cause the AI ​​to acquire related information.

[0039] When acquiring tap information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, optimizes the acquisition method based on feedback provided by the user in the past. For example, the acquisition unit customizes the acquisition method for a specific item based on the user's past feedback. The acquisition unit can also analyze the user's feedback and reflect improvements to the acquisition method. This allows the acquisition method to be customized based on the past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into AI and have the AI ​​customize the acquisition method.

[0040] The recognition unit can adjust the accuracy of recognition based on the importance of the item during recognition. For example, the recognition unit performs detailed recognition for items with high importance. For example, the recognition unit performs simplified recognition for items with low importance. The recognition unit can also dynamically adjust the accuracy of recognition according to the importance of the item. This allows the accuracy of recognition to be adjusted according to the importance of the item. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input item importance data to AI and have the AI ​​adjust the recognition accuracy.

[0041] During recognition, the recognition unit can apply different recognition algorithms depending on the category of the item. For example, the recognition unit can apply a recognition algorithm dedicated to fashion to a clothing item. For example, the recognition unit can apply a recognition algorithm dedicated to technology to an electronic item. The recognition unit can also apply a recognition algorithm dedicated to food to a food item. This allows the recognition algorithm to be applied depending on the category of the item. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input item category data into AI and have the AI ​​apply the recognition algorithm.

[0042] During recognition, the recognition unit can improve the accuracy of recognition by referring to the user's past recognition results. The recognition unit improves the recognition accuracy, for example, based on data on items that the user has previously recognized. For example, the recognition unit adjusts the recognition accuracy for a specific item based on the user's past recognition results. The recognition unit can also analyze the user's past recognition history and optimize the recognition algorithm. This improves the recognition accuracy based on the past recognition results. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past recognition result data into AI and have the AI ​​improve the recognition accuracy.

[0043] During recognition, the recognition unit can determine the recognition priority based on the time of submission of the item. For example, the recognition unit prioritizes the recognition of recently tapped items. For example, the recognition unit postpones the recognition of items that have not been tapped for a long time. The recognition unit can also dynamically adjust the recognition priority according to the time of submission. This allows the recognition priority to be determined according to the time of submission. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input item submission time data into AI and have the AI ​​determine the recognition priority.

[0044] The recognition unit can adjust the order of recognition based on the relevance of items during recognition. For example, the recognition unit prioritizes recognizing highly relevant items. For example, the recognition unit postpones recognizing less relevant items. The recognition unit can also dynamically adjust the order of recognition according to the relevance of items. This allows the order of recognition to be adjusted according to the relevance of items. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input item relevance data into AI and have the AI ​​adjust the order of recognition.

[0045] During recognition, the recognition unit can adjust the use of technical terminology in recognition according to the user's level of expertise. For example, the recognition unit provides recognition results using detailed technical terminology for a user with high level of expertise. For example, the recognition unit provides recognition results using simple terminology for a user with low level of expertise. The recognition unit can also dynamically adjust the use of technical terminology in the recognition result according to the user's level of expertise. This allows the use of technical terminology in recognition to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0046] The reference unit can adjust the accuracy of the reference based on the importance of the item when referencing. For example, the reference unit references detailed information for an item with high importance. For example, the reference unit references simplified information for an item with low importance. The reference unit can also dynamically adjust the accuracy of the reference according to the importance of the item. This allows the accuracy of the reference to be adjusted according to the importance of the item. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item importance data into AI and have the AI ​​adjust the accuracy of the reference.

[0047] The reference unit can apply different reference algorithms depending on the category of the item during reference. For example, the reference unit can apply a reference algorithm dedicated to fashion to clothing items. For example, the reference unit can apply a reference algorithm dedicated to technology to electronic items. The reference unit can also apply a reference algorithm dedicated to food to food items. This allows the reference algorithm to be applied depending on the category of the item. Some or all of the above-mentioned processing in the reference unit can be performed using AI, for example, or can be performed without using AI. For example, the reference unit can input item category data into AI and have the AI ​​apply the reference algorithm.

[0048] The reference unit can improve the accuracy of the reference by referring to the user's past reference results when performing the reference. The reference unit improves the reference accuracy, for example, based on data on items the user has referenced in the past. For example, the reference unit adjusts the reference accuracy for a specific item based on the user's past reference results. The reference unit can also analyze the user's past reference history and optimize the reference algorithm. This improves the reference accuracy based on the past reference results. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the user's past reference result data into AI and have the AI ​​improve the reference accuracy.

[0049] The reference unit can determine the reference priority based on the submission time of the item at the time of reference. For example, the reference unit preferentially references items that have been tapped recently. For example, the reference unit postpones items that have not been tapped for a long time. The reference unit can also dynamically adjust the reference priority according to the submission time. This makes it possible to determine the reference priority according to the submission time. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item submission time data into AI and have the AI ​​determine the reference priority.

[0050] The reference unit can adjust the order of reference based on the relevance of the items when referencing. For example, the reference unit prioritizes referencing highly relevant items. For example, the reference unit postpones referencing less relevant items. The reference unit can also dynamically adjust the order of reference according to the relevance of the items. This allows the order of reference to be adjusted according to the relevance of the items. Some or all of the above-described processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item relevance data into AI and have the AI ​​adjust the order of reference.

[0051] The reference unit can adjust the use of technical terminology in the reference depending on the user's level of expertise during the reference. For example, the reference unit provides reference results using detailed technical terminology for a user with high level of expertise. For example, the reference unit provides reference results using simple terminology for a user with low level of expertise. The reference unit can also dynamically adjust the use of technical terminology in the reference results depending on the user's level of expertise. This allows the use of technical terminology in the reference depending on the user's level of expertise. Some or all of the above-described processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0052] The providing unit can adjust the accuracy of the provision based on the importance of the information when providing the information. For example, the providing unit provides detailed information for information with high importance. For example, the providing unit provides simplified information for information with low importance. The providing unit can also dynamically adjust the accuracy of the provision according to the importance of the information. This makes it possible to adjust the accuracy of the provision according to the importance of the information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data into AI and cause the AI ​​to adjust the accuracy of the provision.

[0053] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a providing algorithm dedicated to fashion to clothing information. For example, the providing unit applies a providing algorithm dedicated to technology to electronic device information. The providing unit can also apply a providing algorithm dedicated to food to food information. This allows the providing algorithm to be applied depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the information into AI and cause the AI ​​to apply the providing algorithm.

[0054] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results when providing the information. The providing unit improves the accuracy of the information provided by the user, for example, based on data of information provided by the user in the past. For example, the providing unit adjusts the accuracy of the information provided by specific information based on the user's past provision results. The providing unit can also analyze the user's past provision history and optimize the provision algorithm. This improves the accuracy of the information provided by the user based on the past provision results. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into AI and cause the AI ​​to improve the accuracy of the information provided.

[0055] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides information that has been acquired recently with priority. For example, the providing unit postpones information that has not been acquired for a long time. The providing unit can also dynamically adjust the priority of provision according to the time of submission. This makes it possible to determine the priority of provision according to the time of submission. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of provision.

[0056] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information preferentially. For example, the providing unit postpones less relevant information. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the information into AI and have the AI ​​adjust the order of provision.

[0057] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, the providing unit provides information using detailed technical terminology to a user with high level of expertise. For example, the providing unit provides information using simple terminology to a user with low level of expertise. The providing unit can also dynamically adjust the use of technical terminology in the provided information according to the user's level of expertise. This allows the use of technical terminology in the provided information to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The acquisition unit can analyze the user's past purchase history and preferentially acquire information about related items. For example, the acquisition unit preferentially acquires tap information about similar items based on data about items the user has previously purchased. The acquisition unit can also analyze the user's interest in specific brands or categories from the user's purchase history and preferentially acquire that information. Furthermore, the acquisition unit can preferentially acquire information about items according to seasons or trends based on the user's purchase history. This allows for efficient acquisition of related information based on the user's purchase history.

[0060] The recognition unit can use the user's gaze tracking data to prioritize recognizing items on which the user's gaze is focused. For example, the recognition unit prioritizes recognizing items on which the user's gaze remains for a long time. The recognition unit can also analyze patterns of gaze movement to prioritize recognizing items that are likely to be of interest to the user. Furthermore, the recognition unit can adjust the accuracy of recognition according to the degree of gaze concentration, thereby improving the accuracy of recognition of items on which the gaze is focused. This allows the user's gaze data to be utilized to efficiently recognize items of interest.

[0061] The reference unit can analyze a user's social media activity and obtain related information. For example, the reference unit can prioritize obtaining information about brands and influencers that the user follows on social media. The reference unit can also analyze the user's posts and comments to obtain information related to topics of interest. Furthermore, the reference unit can obtain related information by referring to the activities of the user's friends on social media. This makes it possible to efficiently obtain related information based on social media activity.

[0062] The providing unit can adjust the method of providing information based on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide information in a manner suitable for the screen size. Also, if the user is using a tablet, the providing unit can provide information in a manner that makes use of a large screen. Furthermore, if the user is using a desktop, the providing unit can provide information in a manner that makes use of multiple windows. This makes it possible to provide information in an optimal manner depending on the usage status of the user's device.

[0063] The acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring tap information of items related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring tap information of items related to the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring tap information of items related to stores and services around the home. This makes it possible to acquire highly relevant information in consideration of the geographical location information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The acquisition unit acquires tap information when a viewer watches video content on a device and taps on an item of interest. For example, the acquisition unit can acquire information such as the position, time, and strength of the tap. The acquisition unit can also estimate the user's emotion and adjust the timing of acquiring tap information based on the estimated user's emotion. Step 2: The recognition unit recognizes the tapped item based on the tap information acquired by the acquisition unit. For example, the recognition unit can recognize the tapped item using image recognition technology. The recognition unit can also estimate the user's emotion and adjust the accuracy of item recognition based on the estimated user's emotion. Step 3: The reference unit acquires related information based on the item information recognized by the recognition unit. For example, the reference unit can reference an e-commerce database or other related databases to acquire detailed product information, reviews, prices, etc. The reference unit can also estimate the user's emotions and select databases to reference based on the estimated user emotions. Step 4: The providing unit provides the information acquired by the reference unit to the viewer. For example, the providing unit can provide the information by a method such as a pop-up display, a notification, or an email. The providing unit can also estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions.

[0066] (Example 2) An information provision system according to an embodiment of the present invention is a system in which an AI acquires and provides information about an item that appears in video content when the viewer taps on the item on the device. The information provision system acquires information about an item of interest when the viewer watches video content on the device and taps on the item. For example, if the viewer taps on an actress, her profile is displayed, and if the viewer taps on clothing, information about the sales website is provided. Even if the video quality is low, the AI ​​can provide accurate information by referencing e-commerce data. This allows the viewer to easily acquire information about items of interest while watching the video. The information provision system thus allows the viewer to easily acquire information about items of interest while enjoying the video content, thereby increasing purchasing motivation. Furthermore, for businesses, providing product information through video content and directing users to e-commerce sites can be expected to have a positive marketing effect.

[0067] An information provision system according to an embodiment includes an acquisition unit, a recognition unit, a reference unit, and a provision unit. The acquisition unit acquires tap information when a viewer watches video content on a device and taps on an item of interest. For example, the acquisition unit can acquire information such as the position, time, and strength of the tap. The acquisition unit can also estimate a user's emotion and adjust the timing of acquiring the tap information based on the estimated user's emotion. The recognition unit recognizes the tapped item based on the tap information acquired by the acquisition unit. For example, the recognition unit can recognize the tapped item using image recognition technology. The recognition unit can also estimate a user's emotion and adjust the accuracy of item recognition based on the estimated user's emotion. The reference unit acquires related information based on the item information recognized by the recognition unit. For example, the reference unit can reference an e-commerce database or other related databases to acquire detailed product information, reviews, prices, etc. The reference unit can also estimate a user's emotion and select a database to reference based on the estimated user's emotion. The provision unit provides the information acquired by the reference unit to the viewer. For example, the providing unit can provide information by methods such as a pop-up display, a notification, or an email. The providing unit can also estimate a user's emotions and adjust the method of providing information based on the estimated user's emotions. This allows the information providing system according to the embodiment to easily obtain information about items of interest to a viewer while watching video content. For example, if a viewer taps on an actress, her profile is displayed, and if a viewer taps on clothing, information about the sales website is provided. Even if the video has low accuracy, the AI ​​can provide accurate information by referencing e-commerce data. This allows a viewer to easily obtain information about items of interest to them while watching a video.

[0068] The reference unit can reference an EC database. Examples of EC databases include, but are not limited to, databases from Yahoo! Shopping and Amazon. The reference unit can, for example, reference the EC database to obtain detailed product information, reviews, prices, etc. This allows accurate product information to be obtained by referring to the EC database. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the EC database into AI and cause the AI ​​to obtain detailed product information, reviews, prices, etc.

[0069] The reference unit can refer to other related databases. Examples of related databases include, but are not limited to, industry-specific databases and open databases. The reference unit can, for example, refer to related databases to acquire a wide range of information. This allows a wide range of information to be acquired by referencing related databases. Some or all of the above-described processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can input the related database into AI and have the AI ​​acquire a wide range of information.

[0070] The recognition unit can recognize the tapped item. The recognition unit can recognize the tapped item using, for example, image recognition technology. For example, the recognition unit can analyze an image of the tapped item and extract and recognize its features. The recognition unit can also recognize the tapped item using location information. For example, the recognition unit can identify the location of the item based on the tapped location information and acquire that information. The recognition unit can also estimate the user's emotions and adjust the accuracy of item recognition based on the estimated user's emotions. This allows for accurate recognition of the tapped item. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input image data of the tapped item to AI and have the AI ​​recognize the item.

[0071] The providing unit can provide information to viewers. The providing unit can provide information by, for example, a pop-up display, a notification, an email, or the like. For example, the providing unit can provide information about the tapped item to viewers by a pop-up display. The providing unit can also provide information about the tapped item to viewers by a notification. The providing unit can also provide information about the tapped item to viewers by email. This allows information to be provided to viewers quickly. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the tapped item to AI and cause the AI ​​to execute the information providing method.

[0072] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring tap information based on the estimated user's emotion. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This allows the timing of acquiring tap information to be optimized according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0073] The acquisition unit can analyze the user's past tap history and select an appropriate acquisition method. For example, the acquisition unit analyzes patterns of items that the user has frequently tapped in the past and prioritizes acquisition of similar items. For example, the acquisition unit analyzes tapping trends during a specific time period from the user's tap history and selects the acquisition method optimal for that time period. The acquisition unit can also customize an acquisition method for a specific content genre based on the user's tap history. This allows the optimal acquisition method to be selected based on the past tap history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's tap history data into AI and have the AI ​​select the optimal acquisition method.

[0074] When acquiring tap information, the acquisition unit can perform filtering based on the user's currently viewed content and areas of interest. The acquisition unit, for example, preferentially acquires tap information of related items based on the genre of content being viewed by the user. For example, the acquisition unit filters and acquires tap information of highly relevant items based on the user's areas of interest. The acquisition unit can also acquire tap information at appropriate timing depending on the scene of the content being viewed by the user. This makes it possible to filter tap information based on the viewed content and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's viewed content and areas of interest into AI and have the AI ​​perform filtering of the tap information.

[0075] When acquiring tap information, the acquisition unit can select an appropriate acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires tap information based on a voice command. For example, if the user is using gesture input, the acquisition unit acquires tap information based on gesture movements. Furthermore, if the user is using text input, the acquisition unit can also acquire tap information based on the input text. This makes it possible to select the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's input method into AI and have the AI ​​select the optimal acquisition means.

[0076] The acquisition unit can estimate the user's emotions and determine the priority of tap information to be acquired based on the estimated user's emotions. For example, when the user is excited, the acquisition unit prioritizes acquiring tap information of items that interest the user. For example, when the user is relaxed, the acquisition unit prioritizes acquiring tap information of items that have a relaxing effect. Furthermore, when the user is stressed, the acquisition unit can also prioritize acquiring tap information of items that are useful for stress reduction. This allows the priority of tap information to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the tap information.

[0077] When acquiring tap information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring tap information of items related to that area. For example, when the user is traveling, the acquisition unit prioritizes acquiring tap information of items related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring tap information of items related to stores and services around the user's home. This makes it possible to acquire highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant information.

[0078] When acquiring the tap information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires tap information of items related to places where the user has checked in on social media. For example, the acquisition unit analyzes the content of the user's social media posts and acquires tap information of related items. The acquisition unit can also acquire tap information of related items by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's social media activity into AI and cause the AI ​​to acquire related information.

[0079] When acquiring tap information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, optimizes the acquisition method based on feedback provided by the user in the past. For example, the acquisition unit customizes the acquisition method for a specific item based on the user's past feedback. The acquisition unit can also analyze the user's feedback and reflect improvements to the acquisition method. This allows the acquisition method to be customized based on the past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into AI and have the AI ​​customize the acquisition method.

[0080] The recognition unit can estimate the user's emotions and adjust the accuracy of item recognition based on the estimated user emotions. For example, when the user is excited, the recognition unit increases the recognition accuracy and immediately recognizes items. For example, when the user is relaxed, the recognition unit slightly relaxes the recognition accuracy and recognizes items in a natural flow. Furthermore, when the user is stressed, the recognition unit can optimize the recognition accuracy to reduce the user's burden. This allows the accuracy of item recognition to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the accuracy of item recognition.

[0081] The recognition unit can adjust the accuracy of recognition based on the importance of the item during recognition. For example, the recognition unit performs detailed recognition for items with high importance. For example, the recognition unit performs simplified recognition for items with low importance. The recognition unit can also dynamically adjust the accuracy of recognition according to the importance of the item. This allows the accuracy of recognition to be adjusted according to the importance of the item. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input item importance data to AI and have the AI ​​adjust the recognition accuracy.

[0082] During recognition, the recognition unit can apply different recognition algorithms depending on the category of the item. For example, the recognition unit can apply a recognition algorithm dedicated to fashion to a clothing item. For example, the recognition unit can apply a recognition algorithm dedicated to technology to an electronic item. The recognition unit can also apply a recognition algorithm dedicated to food to a food item. This allows the recognition algorithm to be applied depending on the category of the item. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input item category data into AI and have the AI ​​apply the recognition algorithm.

[0083] During recognition, the recognition unit can improve the accuracy of recognition by referring to the user's past recognition results. The recognition unit improves the recognition accuracy, for example, based on data on items that the user has previously recognized. For example, the recognition unit adjusts the recognition accuracy for a specific item based on the user's past recognition results. The recognition unit can also analyze the user's past recognition history and optimize the recognition algorithm. This improves the recognition accuracy based on the past recognition results. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past recognition result data into AI and have the AI ​​improve the recognition accuracy.

[0084] The recognition unit can estimate the user's emotions and determine recognition priorities based on the estimated user emotions. For example, if the user is excited, the recognition unit prioritizes recognition of items that interest the user. For example, if the user is relaxed, the recognition unit prioritizes recognition of items that have a relaxing effect. Furthermore, if the user is stressed, the recognition unit can prioritize recognition of items that help reduce stress. This allows recognition priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's emotion data into the generation AI and have the generation AI determine the recognition priorities.

[0085] During recognition, the recognition unit can determine the recognition priority based on the time of submission of the item. For example, the recognition unit prioritizes the recognition of recently tapped items. For example, the recognition unit postpones the recognition of items that have not been tapped for a long time. The recognition unit can also dynamically adjust the recognition priority according to the time of submission. This allows the recognition priority to be determined according to the time of submission. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input item submission time data into AI and have the AI ​​determine the recognition priority.

[0086] The recognition unit can adjust the order of recognition based on the relevance of items during recognition. For example, the recognition unit prioritizes recognizing highly relevant items. For example, the recognition unit postpones recognizing less relevant items. The recognition unit can also dynamically adjust the order of recognition according to the relevance of items. This allows the order of recognition to be adjusted according to the relevance of items. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input item relevance data into AI and have the AI ​​adjust the order of recognition.

[0087] During recognition, the recognition unit can adjust the use of technical terminology in recognition according to the user's level of expertise. For example, the recognition unit provides recognition results using detailed technical terminology for a user with high level of expertise. For example, the recognition unit provides recognition results using simple terminology for a user with low level of expertise. The recognition unit can also dynamically adjust the use of technical terminology in the recognition result according to the user's level of expertise. This allows the use of technical terminology in recognition to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0088] The reference unit can estimate the user's emotion and select a database to reference based on the estimated user emotion. For example, if the user is excited, the reference unit preferentially references entertainment-related databases. For example, if the user is relaxed, the reference unit preferentially references databases with a relaxing effect. Furthermore, if the user is stressed, the reference unit can preferentially reference databases that are useful for stress reduction. This allows the database to be selected based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reference unit may be performed using AI, or may be performed without AI. For example, the reference unit can input the user's emotion data into the generation AI and have the generation AI select a database.

[0089] The reference unit can adjust the accuracy of the reference based on the importance of the item when referencing. For example, the reference unit references detailed information for an item with high importance. For example, the reference unit references simplified information for an item with low importance. The reference unit can also dynamically adjust the accuracy of the reference according to the importance of the item. This allows the accuracy of the reference to be adjusted according to the importance of the item. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item importance data into AI and have the AI ​​adjust the accuracy of the reference.

[0090] The reference unit can apply different reference algorithms depending on the category of the item during reference. For example, the reference unit can apply a reference algorithm dedicated to fashion to clothing items. For example, the reference unit can apply a reference algorithm dedicated to technology to electronic items. The reference unit can also apply a reference algorithm dedicated to food to food items. This allows the reference algorithm to be applied depending on the category of the item. Some or all of the above-mentioned processing in the reference unit can be performed using AI, for example, or can be performed without using AI. For example, the reference unit can input item category data into AI and have the AI ​​apply the reference algorithm.

[0091] The reference unit can improve the accuracy of the reference by referring to the user's past reference results when performing the reference. The reference unit improves the reference accuracy, for example, based on data on items the user has referenced in the past. For example, the reference unit adjusts the reference accuracy for a specific item based on the user's past reference results. The reference unit can also analyze the user's past reference history and optimize the reference algorithm. This improves the reference accuracy based on the past reference results. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input the user's past reference result data into AI and have the AI ​​improve the reference accuracy.

[0092] The reference unit can estimate the user's emotions and determine a reference priority based on the estimated user's emotions. For example, if the user is excited, the reference unit prioritizes reference to items that interest the user. For example, if the user is relaxed, the reference unit prioritizes reference to items that have a relaxing effect. Furthermore, if the user is stressed, the reference unit can prioritize reference to items that help reduce stress. This allows the reference priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reference unit may be performed using an AI, or may be performed without using an AI. For example, the reference unit can input the user's emotion data into the generation AI and have the generation AI determine the reference priority.

[0093] The reference unit can determine the reference priority based on the submission time of the item at the time of reference. For example, the reference unit preferentially references items that have been tapped recently. For example, the reference unit postpones items that have not been tapped for a long time. The reference unit can also dynamically adjust the reference priority according to the submission time. This makes it possible to determine the reference priority according to the submission time. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item submission time data into AI and have the AI ​​determine the reference priority.

[0094] The reference unit can adjust the order of reference based on the relevance of the items when referencing. For example, the reference unit prioritizes referencing highly relevant items. For example, the reference unit postpones referencing less relevant items. The reference unit can also dynamically adjust the order of reference according to the relevance of the items. This allows the order of reference to be adjusted according to the relevance of the items. Some or all of the above-described processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input item relevance data into AI and have the AI ​​adjust the order of reference.

[0095] The reference unit can adjust the use of technical terminology in the reference depending on the user's level of expertise during the reference. For example, the reference unit provides reference results using detailed technical terminology for a user with high level of expertise. For example, the reference unit provides reference results using simple terminology for a user with low level of expertise. The reference unit can also dynamically adjust the use of technical terminology in the reference results depending on the user's level of expertise. This allows the use of technical terminology in the reference depending on the user's level of expertise. Some or all of the above-described processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information in a visually stimulating manner. For example, if the user is relaxed, the providing unit can provide information in a calming manner. Furthermore, if the user is stressed, the providing unit can also provide information in a simple, highly visible manner. This allows the method of providing information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing information.

[0097] The providing unit can adjust the accuracy of the provision based on the importance of the information when providing the information. For example, the providing unit provides detailed information for information with high importance. For example, the providing unit provides simplified information for information with low importance. The providing unit can also dynamically adjust the accuracy of the provision according to the importance of the information. This makes it possible to adjust the accuracy of the provision according to the importance of the information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information importance data into AI and cause the AI ​​to adjust the accuracy of the provision.

[0098] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit applies a providing algorithm dedicated to fashion to clothing information. For example, the providing unit applies a providing algorithm dedicated to technology to electronic device information. The providing unit can also apply a providing algorithm dedicated to food to food information. This allows the providing algorithm to be applied depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the information into AI and cause the AI ​​to apply the providing algorithm.

[0099] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results when providing the information. The providing unit improves the accuracy of the information provided by the user, for example, based on data of information provided by the user in the past. For example, the providing unit adjusts the accuracy of the information provided by specific information based on the user's past provision results. The providing unit can also analyze the user's past provision history and optimize the provision algorithm. This improves the accuracy of the information provided by the user based on the past provision results. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into AI and cause the AI ​​to improve the accuracy of the information provided.

[0100] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is excited, the providing unit can prioritize providing interesting information. For example, if the user is relaxed, the providing unit can prioritize providing information that has a relaxing effect. Furthermore, if the user is stressed, the providing unit can prioritize providing information that is useful for stress reduction. This allows the priority of information to be provided to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0101] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides information that has been acquired recently with priority. For example, the providing unit postpones information that has not been acquired for a long time. The providing unit can also dynamically adjust the priority of provision according to the time of submission. This makes it possible to determine the priority of provision according to the time of submission. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of provision.

[0102] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information preferentially. For example, the providing unit postpones less relevant information. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the information into AI and have the AI ​​adjust the order of provision.

[0103] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, the providing unit provides information using detailed technical terminology to a user with high level of expertise. For example, the providing unit provides information using simple terminology to a user with low level of expertise. The providing unit can also dynamically adjust the use of technical terminology in the provided information according to the user's level of expertise. This allows the use of technical terminology in the provided information to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into AI and have the AI ​​execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, recognition unit, reference unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires tap information of a viewer using the touch panel 38A of the smart device 14. The recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the tapped item using image recognition technology. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information by referring to the EC database 24. The provision unit provides information to the viewer using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, recognition unit, reference unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires tap information of the viewer using a touch panel of the smart glasses 214. The recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recognizes the tapped item using image recognition technology. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information by referring to the EC database 24. The provision unit provides information to the viewer using, for example, the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, recognition unit, reference unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires tap information of the viewer using a touch panel of the headset terminal 314. The recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recognizes the tapped item using image recognition technology. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires related information by referring to the EC database 24. The provision unit provides information to the viewer using, for example, the display of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, recognition unit, reference unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires tap information of the viewer using a touch panel of the robot 414. The recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recognizes the tapped item using image recognition technology. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and acquires related information by referring to the EC database 24. The provision unit provides information to the viewer using, for example, a display of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The acquisition unit can analyze the user's past purchase history and preferentially acquire information about related items. For example, the acquisition unit preferentially acquires tap information about similar items based on data about items the user has previously purchased. The acquisition unit can also analyze the user's interest in specific brands or categories from the user's purchase history and preferentially acquire that information. Furthermore, the acquisition unit can preferentially acquire information about items according to seasons or trends based on the user's purchase history. This allows for efficient acquisition of related information based on the user's purchase history.

[0106] The recognition unit can use the user's gaze tracking data to prioritize recognizing items on which the user's gaze is focused. For example, the recognition unit prioritizes recognizing items on which the user's gaze remains for a long time. The recognition unit can also analyze patterns of gaze movement to prioritize recognizing items that are likely to be of interest to the user. Furthermore, the recognition unit can adjust the accuracy of recognition according to the degree of gaze concentration, thereby improving the accuracy of recognition of items on which the gaze is focused. This allows the user's gaze data to be utilized to efficiently recognize items of interest.

[0107] The reference unit can analyze a user's social media activity and obtain related information. For example, the reference unit can prioritize obtaining information about brands and influencers that the user follows on social media. The reference unit can also analyze the user's posts and comments to obtain information related to topics of interest. Furthermore, the reference unit can obtain related information by referring to the activities of the user's friends on social media. This makes it possible to efficiently obtain related information based on social media activity.

[0108] The providing unit can adjust the method of providing information based on the usage status of the user's device. For example, if the user is using a smartphone, the providing unit can provide information in a manner suitable for the screen size. Also, if the user is using a tablet, the providing unit can provide information in a manner that makes use of a large screen. Furthermore, if the user is using a desktop, the providing unit can provide information in a manner that makes use of multiple windows. This makes it possible to provide information in an optimal manner depending on the usage status of the user's device.

[0109] The acquisition unit can estimate the user's emotions and determine the type of information to acquire based on the estimated user's emotions. For example, if the user is excited, the acquisition unit can prioritize acquiring entertainment-related information. Also, if the user is relaxed, the acquisition unit can prioritize acquiring information that has a relaxing effect. Furthermore, if the user is feeling stressed, the acquisition unit can prioritize acquiring information that is useful for reducing stress. This makes it possible to optimize the type of information to acquire according to the user's emotions.

[0110] The recognition unit can estimate the user's emotions and determine the recognition priority based on the estimated user's emotions. For example, if the user is excited, the recognition unit can prioritize recognition of items that interest the user. Also, if the user is relaxed, the recognition unit can prioritize recognition of items that have a relaxing effect. Furthermore, if the user is stressed, the recognition unit can prioritize recognition of items that are useful for reducing stress. In this way, the recognition priority can be determined according to the user's emotions.

[0111] The reference unit can estimate the user's emotion and select a database to reference based on the estimated user's emotion. For example, if the user is excited, the reference unit can preferentially refer to an entertainment-related database. Also, if the user is relaxed, the reference unit can preferentially refer to a database with a relaxing effect. Furthermore, if the user is feeling stressed, the reference unit can preferentially refer to a database that helps reduce stress. In this way, a database to reference can be selected according to the user's emotion.

[0112] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information in a visually stimulating manner. If the user is relaxed, the providing unit can also provide information in a calming manner. Furthermore, if the user is feeling stressed, the providing unit can also provide information in a simple, highly visible manner. This makes it possible to adjust the method of providing information according to the user's emotions.

[0113] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information that attracts the user's interest preferentially. Furthermore, if the user is relaxed, the providing unit can provide information that has a relaxing effect preferentially. Furthermore, if the user is feeling stressed, the providing unit can provide information that is useful for reducing stress preferentially. In this way, the priority of information to be provided can be determined according to the user's emotions.

[0114] The acquisition unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring tap information of items related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring tap information of items related to the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring tap information of items related to stores and services around the home. This makes it possible to acquire highly relevant information in consideration of the geographical location information.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The acquisition unit acquires tap information when a viewer watches video content on a device and taps on an item of interest. For example, the acquisition unit can acquire information such as the position, time, and strength of the tap. The acquisition unit can also estimate the user's emotion and adjust the timing of acquiring tap information based on the estimated user's emotion. Step 2: The recognition unit recognizes the tapped item based on the tap information acquired by the acquisition unit. For example, the recognition unit can recognize the tapped item using image recognition technology. The recognition unit can also estimate the user's emotion and adjust the accuracy of item recognition based on the estimated user's emotion. Step 3: The reference unit acquires related information based on the item information recognized by the recognition unit. For example, the reference unit can reference an e-commerce database or other related databases to acquire detailed product information, reviews, prices, etc. The reference unit can also estimate the user's emotions and select databases to reference based on the estimated user emotions. Step 4: The providing unit provides the information acquired by the reference unit to the viewer. For example, the providing unit can provide the information by a method such as a pop-up display, a notification, or an email. The providing unit can also estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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. an acquisition unit that acquires tap information; a recognition unit that recognizes an item based on the tap information acquired by the acquisition unit; a reference unit that acquires related information based on the item information recognized by the recognition unit; a providing unit that provides the information acquired by the reference unit. A system characterized by:

2. The reference portion is Browse the EC database 2. The system of claim 1.

3. The reference portion is See other related databases 2. The system of claim 1.

4. The recognition unit Recognize tapped items 2. The system of claim 1.

5. The providing unit Inform your audience 2. The system of claim 1.

6. The acquisition unit The system estimates the user's emotions and adjusts the timing of tap information acquisition based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze the user's past tap history and select the appropriate acquisition method 2. The system of claim 1.

8. The acquisition unit When capturing tap information, it filters it based on the user's current viewing content and interests.

2. The system of claim 1.

9. The acquisition unit When acquiring tap information, select the appropriate acquisition method depending on the user's input method.

2. The system of claim 1.

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