system

Abstract

The system according to the embodiment aims to enable users to efficiently search for the lowest price and increase profits. [Solution] The system according to the embodiment includes a search reception unit, a search unit, a display unit, and an advertising revenue unit. The search reception unit inputs the product or service for which the user is searching. The search unit prioritizes searching for the company's own content based on the information input by the search reception unit and displays the lowest price. The display unit displays the search results obtained by the search unit. The advertising revenue unit converts advertising fees into revenue when the search results include information about other companies.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, searching for the lowest price required searching by category, which was inefficient.

[0005] The system according to the embodiment aims to enable users to efficiently search for the lowest price and increase profits. [Means for solving the problem]

[0006] The system according to the embodiment includes a search reception unit, a search unit, a display unit, and an advertising revenue unit. The search reception unit inputs the product or service for which the user is searching. The search unit prioritizes searching for the company's own content based on the information input by the search reception unit and displays the lowest price. The display unit displays the search results obtained by the search unit. The advertising revenue unit converts advertising fees into revenue when information about other companies is included. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently search for the lowest price and increase profits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A search system according to an embodiment of the present invention allows users to search primarily for their own company's content, providing a mechanism for increasing revenue for group companies. This search system allows users to efficiently search for the lowest price without having to search by category or item. Furthermore, when other companies' content is included, advertising fees can be converted into revenue, and when brand sites and the like advertise products, video content can also be introduced. First, a user inputs the product or service they are searching for. Next, the search system prioritizes searching for their own company's content and displays the lowest price. This includes information related to group companies, which has the benefit of increasing revenue. When other companies' information is included, a mechanism is introduced that converts advertising fees into revenue. For example, when a brand site advertises a product, video content can also be introduced. This increases advertising revenue. This search system allows users to efficiently search for the lowest price, allowing companies to increase their revenue. This search system allows users to efficiently search for the lowest price, allowing companies to increase their revenue.

[0029] A search system according to an embodiment includes a search reception unit, a search unit, a display unit, and an advertising revenue unit. The search reception unit inputs products or services for which a user is searching. For example, the search reception unit analyzes keywords input by the user and identifies related products or services. The search unit prioritizes searches for the company's own content based on the information input by the search reception unit and displays the lowest prices. For example, the search unit references the company's own database to identify the cheapest products or services. The search unit also includes information related to group companies. For example, the search unit references databases of parent companies, subsidiaries, and affiliated companies to obtain information on related products and services. The display unit displays the search results obtained by the search unit. For example, the display unit displays the search results in a list format or a grid format. The display unit also specifically sets the display order of the search results and the display method of advertisements. For example, the display unit sorts the search results based on criteria such as relevance, popularity, or price. The advertising revenue unit converts advertising fees into revenue when information about other companies is included. For example, the advertising revenue unit can introduce video content when a brand site advertises a product. This allows for increased advertising revenue, and the search system according to the embodiment allows users to efficiently search for the lowest price, thereby enabling companies to increase their revenue.

[0030] The search unit can include information related to group companies. Information related to group companies includes, for example, information on parent companies, subsidiaries, and affiliated companies. For example, the search unit references the parent company's database to obtain information on related products and services. The search unit can also reference the subsidiary's database to obtain information on related products and services. Furthermore, the search unit can reference the affiliated company's database to obtain information on related products and services. This has the advantage of increasing profits by including information related to group companies.

[0031] The advertising revenue unit can introduce video content when a brand site advertises a product. The video content introduction can include, for example, a promotional video for the product or a demonstration of how to use the product. The advertising revenue unit can, for example, display a promotional video provided by the brand site. The advertising revenue unit can also display a demonstration video showing how to use the product. Furthermore, the advertising revenue unit can display video content including product reviews and ratings. In this way, advertising revenue can be increased by introducing video content.

[0032] The display unit can set in detail the display order of search results and the display method of advertisements. The display order of search results includes criteria such as relevance, popularity, and price order, for example. The display unit sorts the search results based on relevance, for example. The display unit can also sort the search results based on popularity. The display unit can also sort the search results based on price order. The display method of advertisements includes, for example, banner advertisements, text advertisements, and video advertisements. The display unit displays, for example, banner advertisements. The display unit can also display text advertisements. The display unit can also display video advertisements. This makes it possible to specifically set the display order of search results and the display method of advertisements in a way that is easy for users to view.

[0033] The search acceptance unit can analyze the user's past search history and automatically complete appropriate search keywords. For example, the search acceptance unit automatically displays keywords that the user has frequently searched for in the past as candidates. The search acceptance unit can also prioritize and suggest search methods (voice, text, etc.) that the user has used in the past. Furthermore, the search acceptance unit can predict and suggest keywords that will be used during specific time periods based on the user's past search history. This improves search efficiency by providing optimal search keywords based on the user's past search history.

[0034] The search reception unit can display search suggestions based on the user's current areas of interest when accepting a search. The search reception unit presents related search suggestions based on, for example, products or services recently viewed by the user. The search reception unit can also present search suggestions based on topics in which the user has shown interest on social media. Furthermore, the search reception unit can present search suggestions related to products recently purchased by the user. This improves search accuracy by presenting search suggestions based on the user's areas of interest.

[0035] When accepting a search, the search acceptance unit can display highly relevant search suggestions based on the user's geographical location information. For example, the search acceptance unit can prioritize displaying stores and services close to the user's current location. If the user is traveling, the search acceptance unit can also present search suggestions related to the user's destination. Furthermore, if the user is interested in a specific area, the search acceptance unit can also present search suggestions related to that area. In this way, by presenting search suggestions that take geographical location information into consideration, it is possible to provide information that is highly relevant to the user.

[0036] The search reception unit can analyze the user's social media activity when accepting a search and display related search suggestions. The search reception unit presents search suggestions based on, for example, products and services that the user has "liked" or commented on on social media. The search reception unit can also present products and services introduced by influencers the user follows as search suggestions. Furthermore, the search reception unit can present products and services that are trending in groups or communities in which the user participates as search suggestions. In this way, by presenting search suggestions based on social media activity, it is possible to provide information that matches the user's interests.

[0037] When searching, the search unit can display appropriate search results taking into consideration product inventory status and delivery time. For example, the search unit can prioritize displaying products with abundant inventory. The search unit can also prioritize displaying products with short delivery times. Furthermore, the search unit can display optimal products by comprehensively considering inventory status and delivery time. In this way, providing search results that take inventory status and delivery time into consideration increases the user's desire to purchase.

[0038] When searching, the search unit can display personalized search results by referring to the user's purchase history. For example, the search unit can prioritize displaying products related to products the user has purchased in the past. The search unit can also analyze the user's preferences and trends from the user's purchase history to suggest optimal products. Furthermore, the search unit can display related products based on reviews and ratings of products the user has purchased in the past. This improves user satisfaction by providing personalized search results based on the user's purchase history.

[0039] When searching, the search unit can display appropriate search results based on the user's geographical location information. For example, the search unit can prioritize displaying stores and services close to the user's current location. If the user is traveling, the search unit can also display search results related to the user's destination. Furthermore, if the user is interested in a specific area, the search unit can also display search results related to that area. This allows the search results to be provided taking geographical location information into consideration, thereby providing information that is highly relevant to the user.

[0040] The search unit can analyze a user's social media activity during a search and present relevant search results. For example, the search unit displays search results based on products and services that the user has "liked" or commented on on social media. The search unit can also display search results for products and services introduced by influencers the user follows. Furthermore, the search unit can display search results for products and services that are trending in groups or communities the user participates in. This allows the system to provide search results based on social media activity, thereby providing information that meets the user's interests.

[0041] When displaying the results, the display unit can select an appropriate display method by referring to the user's past browsing history. The display unit displays related search results based on, for example, products and services that the user has previously viewed. The display unit can also analyze the user's preferences and tendencies from the user's past browsing history and suggest an optimal display method. Furthermore, the display unit can display related search results based on reviews and ratings of products that the user has previously viewed. This improves user satisfaction by providing an optimal display method based on the user's past browsing history.

[0042] The display unit can increase the user's willingness to purchase by displaying detailed product information and reviews during display. The display unit, for example, displays detailed product information to serve as a reference when the user is considering purchasing. The display unit can also display product reviews and ratings, allowing the user to refer to the opinions of other purchasers. Furthermore, the display unit can also display related information and usage examples for the product to increase the user's willingness to purchase. In this way, the display of detailed product information and reviews increases the user's willingness to purchase.

[0043] The display unit can select an appropriate display method by taking into consideration the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This improves user convenience by providing an optimal display method that takes into consideration the device information.

[0044] The display unit can analyze the user's social media activity and display relevant advertisements during display. For example, the display unit displays advertisements based on products or services that the user has "liked" or commented on on social media. The display unit can also display advertisements for products or services introduced by influencers the user follows. Furthermore, the display unit can also display advertisements for products or services that are trending in groups or communities in which the user participates. This improves the effectiveness of advertisements by providing advertisements based on social media activity.

[0045] The advertising revenue unit can display appropriate advertisements by referring to the user's past purchase history when collecting advertising revenue. For example, the advertising revenue unit can prioritize displaying advertisements related to products the user has previously purchased. The advertising revenue unit can also analyze the user's preferences and tendencies from the user's purchase history and suggest optimal advertisements. Furthermore, the advertising revenue unit can display relevant advertisements based on reviews and ratings of products the user has previously purchased. This improves the effectiveness of advertising by providing optimal advertisements based on the user's past purchase history.

[0046] The advertising revenue unit may personalize advertisements based on the user's current areas of interest during advertising revenue. For example, the advertising revenue unit may display relevant advertisements based on products or services recently viewed by the user. The advertising revenue unit may also display advertisements based on topics in which the user has shown interest on social media. Furthermore, the advertising revenue unit may display advertisements related to products recently purchased by the user. This improves advertising effectiveness by providing advertisements based on the user's current areas of interest.

[0047] The advertising revenue unit can display appropriate advertisements taking into consideration the user's geographical location information when generating advertising revenue. For example, the advertising revenue unit can prioritize displaying advertisements for stores and services close to the user's current location. In addition, if the user is traveling, the advertising revenue unit can also display advertisements related to the user's destination. Furthermore, if the user shows interest in a specific area, the advertising revenue unit can also display advertisements related to that area. This improves the effectiveness of advertising by providing advertisements that take into consideration the user's geographical location information.

[0048] The advertising revenue unit can analyze a user's social media activity and display relevant advertisements when generating advertising revenue. For example, the advertising revenue unit displays advertisements based on products or services that the user has "liked" or commented on on social media. The advertising revenue unit can also display advertisements for products or services introduced by influencers the user follows. Furthermore, the advertising revenue unit can also display advertisements for products or services that are trending in groups or communities in which the user participates. This improves the effectiveness of advertising by providing advertisements based on social media activity.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The search unit can display reviews and ratings of related products based on the user's purchase history. For example, it can display reviews of products the user has purchased in the past, allowing the user to refer to the opinions of other users. The search unit can also analyze the user's preferences and trends from the user's purchase history and prioritize displaying reviews of related products. Furthermore, the search unit can display ratings of related products based on reviews of products that the user has given high ratings to in the past. This provides personalized search results that utilize the user's purchase history.

[0051] The display unit can select the optimal display format based on the user's device information. For example, if the user is using a smartphone, a vertical display format that matches the screen size can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, multiple search results can be displayed simultaneously. This improves user convenience by providing the optimal display format that takes device information into consideration.

[0052] The search unit can display relevant local event and promotion information based on the user's geographic location information. For example, if the user is in a specific area, event information held in that area can be displayed. Also, if the user is traveling, promotion information related to the destination can be displayed. Furthermore, if the user shows interest in a specific area, special offers and discount information related to that area can be displayed. This makes it possible to provide personalized information using geographic location information.

[0053] The display unit can display detailed information and reviews of related products based on the user's past browsing history. For example, detailed information related to products the user has previously viewed can be displayed to serve as a reference when considering a purchase. Reviews and ratings of products the user has previously viewed can also be displayed to provide reference for the opinions of other buyers. Furthermore, related information and usage examples of products the user has previously viewed can also be displayed. This makes it possible to provide personalized information based on the user's past browsing history.

[0054] The search unit can display promotional information for related products based on the user's social media activity. For example, it can display promotional information related to products that the user has "liked" or commented on on social media. It can also display promotional information for products introduced by influencers the user follows. It can also display promotional information for products that are trending in groups or communities the user participates in. This provides personalized promotional information based on social media activity.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The search reception unit inputs the product or service that the user wants to search for. For example, the search reception unit analyzes the keywords input by the user and identifies related products and services. Step 2: The search unit prioritizes searching the company's content based on the information entered by the search reception unit and displays the lowest price. For example, the search unit references the company's database to identify the cheapest products or services. The search unit also includes information related to group companies. For example, the search unit references the databases of the parent company, subsidiaries, and affiliated companies to obtain information on related products and services. Step 3: The display unit displays the search results obtained by the search unit. For example, the display unit displays the search results in a list format or a grid format. The display unit also specifically sets the display order of the search results and the display method of advertisements. For example, the display unit sorts the search results based on criteria such as relevance, popularity, or price. Step 4: The advertising revenue department converts advertising fees into revenue when information about other companies is included. For example, when a brand site advertises a product, the advertising revenue department can introduce video content. This can increase advertising revenue.

[0057] (Example 2) A search system according to an embodiment of the present invention allows users to search primarily for their own company's content, providing a mechanism for increasing revenue for group companies. This search system allows users to efficiently search for the lowest price without having to search by category or item. Furthermore, when other companies' content is included, advertising fees can be converted into revenue, and when brand sites and the like advertise products, video content can also be introduced. First, a user inputs the product or service they are searching for. Next, the search system prioritizes searching for their own company's content and displays the lowest price. This includes information related to group companies, which has the benefit of increasing revenue. When other companies' information is included, a mechanism is introduced that converts advertising fees into revenue. For example, when a brand site advertises a product, video content can also be introduced. This increases advertising revenue. This search system allows users to efficiently search for the lowest price, allowing companies to increase their revenue. This search system allows users to efficiently search for the lowest price, allowing companies to increase their revenue.

[0058] A search system according to an embodiment includes a search reception unit, a search unit, a display unit, and an advertising revenue unit. The search reception unit inputs products or services for which a user is searching. For example, the search reception unit analyzes keywords input by the user and identifies related products or services. The search unit prioritizes searches for the company's own content based on the information input by the search reception unit and displays the lowest prices. For example, the search unit references the company's own database to identify the cheapest products or services. The search unit also includes information related to group companies. For example, the search unit references databases of parent companies, subsidiaries, and affiliated companies to obtain information on related products and services. The display unit displays the search results obtained by the search unit. For example, the display unit displays the search results in a list format or a grid format. The display unit also specifically sets the display order of the search results and the display method of advertisements. For example, the display unit sorts the search results based on criteria such as relevance, popularity, or price. The advertising revenue unit converts advertising fees into revenue when information about other companies is included. For example, the advertising revenue unit can introduce video content when a brand site advertises a product. This allows for increased advertising revenue, and the search system according to the embodiment allows users to efficiently search for the lowest price, thereby enabling companies to increase their revenue.

[0059] The search unit can include information related to group companies. Information related to group companies includes, for example, information on parent companies, subsidiaries, and affiliated companies. For example, the search unit references the parent company's database to obtain information on related products and services. The search unit can also reference the subsidiary's database to obtain information on related products and services. Furthermore, the search unit can reference the affiliated company's database to obtain information on related products and services. This has the advantage of increasing profits by including information related to group companies.

[0060] The advertising revenue unit can introduce video content when a brand site advertises a product. The video content introduction can include, for example, a promotional video for the product or a demonstration of how to use the product. The advertising revenue unit can, for example, display a promotional video provided by the brand site. The advertising revenue unit can also display a demonstration video showing how to use the product. Furthermore, the advertising revenue unit can display video content including product reviews and ratings. In this way, advertising revenue can be increased by introducing video content.

[0061] The display unit can set in detail the display order of search results and the display method of advertisements. The display order of search results includes criteria such as relevance, popularity, and price order, for example. The display unit sorts the search results based on relevance, for example. The display unit can also sort the search results based on popularity. The display unit can also sort the search results based on price order. The display method of advertisements includes, for example, banner advertisements, text advertisements, and video advertisements. The display unit displays, for example, banner advertisements. The display unit can also display text advertisements. The display unit can also display video advertisements. This makes it possible to specifically set the display order of search results and the display method of advertisements in a way that is easy for users to view.

[0062] The search reception unit can estimate the user's emotions and change the search reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the search reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the search reception unit can provide detailed search options and suggest a customizable interface. Furthermore, if the user is in a hurry, the search reception unit can prioritize voice input and enable quick input of search keywords. This improves user convenience by providing an interface that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0063] The search acceptance unit can analyze the user's past search history and automatically complete appropriate search keywords. For example, the search acceptance unit automatically displays keywords that the user has frequently searched for in the past as candidates. The search acceptance unit can also prioritize and suggest search methods (voice, text, etc.) that the user has used in the past. Furthermore, the search acceptance unit can predict and suggest keywords that will be used during specific time periods based on the user's past search history. This improves search efficiency by providing optimal search keywords based on the user's past search history.

[0064] The search reception unit can display search suggestions based on the user's current areas of interest when accepting a search. The search reception unit presents related search suggestions based on, for example, products or services recently viewed by the user. The search reception unit can also present search suggestions based on topics in which the user has shown interest on social media. Furthermore, the search reception unit can present search suggestions related to products recently purchased by the user. This improves search accuracy by presenting search suggestions based on the user's areas of interest.

[0065] The search reception unit can estimate the user's emotions and prioritize search keywords based on the estimated user emotions. For example, if the user is excited, the search reception unit can prioritize highly relevant keywords. Furthermore, if the user is relaxed, the search reception unit can also provide a wide range of options. Furthermore, if the user is stressed, the search reception unit can prioritize simple and intuitive keywords. This improves the accuracy of search results by providing a priority order of search keywords according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] When accepting a search, the search acceptance unit can display highly relevant search suggestions based on the user's geographical location information. For example, the search acceptance unit can prioritize displaying stores and services close to the user's current location. If the user is traveling, the search acceptance unit can also present search suggestions related to the user's destination. Furthermore, if the user is interested in a specific area, the search acceptance unit can also present search suggestions related to that area. In this way, by presenting search suggestions that take geographical location information into consideration, it is possible to provide information that is highly relevant to the user.

[0067] The search reception unit can analyze the user's social media activity when accepting a search and display related search suggestions. The search reception unit presents search suggestions based on, for example, products and services that the user has "liked" or commented on on social media. The search reception unit can also present products and services introduced by influencers the user follows as search suggestions. Furthermore, the search reception unit can present products and services that are trending in groups or communities in which the user participates as search suggestions. In this way, by presenting search suggestions based on social media activity, it is possible to provide information that matches the user's interests.

[0068] The search unit can estimate the user's emotions and change the display method of search results based on the estimated user emotions. For example, if the user is relaxed, the search unit can display search results including detailed information. Furthermore, if the user is in a hurry, the search unit can display concise search results that focus on the main points. Furthermore, if the user is excited, the search unit can display visually appealing search results. This improves user convenience by providing a display method of search results that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When searching, the search unit can display appropriate search results taking into consideration product inventory status and delivery time. For example, the search unit can prioritize displaying products with abundant inventory. The search unit can also prioritize displaying products with short delivery times. Furthermore, the search unit can display optimal products by comprehensively considering inventory status and delivery time. In this way, providing search results that take inventory status and delivery time into consideration increases the user's desire to purchase.

[0070] When searching, the search unit can display personalized search results by referring to the user's purchase history. For example, the search unit can prioritize displaying products related to products the user has purchased in the past. The search unit can also analyze the user's preferences and trends from the user's purchase history to suggest optimal products. Furthermore, the search unit can display related products based on reviews and ratings of products the user has purchased in the past. This improves user satisfaction by providing personalized search results based on the user's purchase history.

[0071] The search unit can estimate the user's emotions and prioritize search results based on the estimated user emotions. For example, if the user is excited, the search unit can prioritize highly relevant search results. The search unit can also provide a wide range of options if the user is relaxed. Furthermore, if the user is stressed, the search unit can prioritize simple and intuitive search results. This improves the accuracy of search results by prioritizing search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When searching, the search unit can display appropriate search results based on the user's geographical location information. For example, the search unit can prioritize displaying stores and services close to the user's current location. If the user is traveling, the search unit can also display search results related to the user's destination. Furthermore, if the user is interested in a specific area, the search unit can also display search results related to that area. This allows the search results to be provided taking geographical location information into consideration, thereby providing information that is highly relevant to the user.

[0073] The search unit can analyze a user's social media activity during a search and present relevant search results. For example, the search unit displays search results based on products and services that the user has "liked" or commented on on social media. The search unit can also display search results for products and services introduced by influencers the user follows. Furthermore, the search unit can display search results for products and services that are trending in groups or communities the user participates in. This allows the system to provide search results based on social media activity, thereby providing information that meets the user's interests.

[0074] The display unit can estimate the user's emotions and change the display order of search results based on the estimated user's emotions. For example, when the user is relaxed, the display unit can display search results including detailed information. Furthermore, when the user is in a hurry, the display unit can display concise search results that focus on the main points. Furthermore, when the user is excited, the display unit can display visually appealing search results. This improves user convenience by providing a display order of search results 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When displaying the results, the display unit can select an appropriate display method by referring to the user's past browsing history. The display unit displays related search results based on, for example, products and services that the user has previously viewed. The display unit can also analyze the user's preferences and tendencies from the user's past browsing history and suggest an optimal display method. Furthermore, the display unit can display related search results based on reviews and ratings of products that the user has previously viewed. This improves user satisfaction by providing an optimal display method based on the user's past browsing history.

[0076] The display unit can increase the user's willingness to purchase by displaying detailed product information and reviews during display. The display unit, for example, displays detailed product information to serve as a reference when the user is considering purchasing. The display unit can also display product reviews and ratings, allowing the user to refer to the opinions of other purchasers. Furthermore, the display unit can also display related information and usage examples for the product to increase the user's willingness to purchase. In this way, the display of detailed product information and reviews increases the user's willingness to purchase.

[0077] The display unit can estimate the user's emotions and change the advertisement display method based on the estimated user emotions. For example, when the user is relaxed, the display unit can display an advertisement containing detailed information. When the user is in a hurry, the display unit can also display a concise advertisement that gets to the point. Furthermore, when the user is excited, the display unit can also display a visually appealing advertisement. This improves the effectiveness of the advertisement by providing an advertisement display method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The display unit can select an appropriate display method by taking into consideration the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This improves user convenience by providing an optimal display method that takes into consideration the device information.

[0079] The display unit can analyze the user's social media activity and display relevant advertisements during display. For example, the display unit displays advertisements based on products or services that the user has "liked" or commented on on social media. The display unit can also display advertisements for products or services introduced by influencers the user follows. Furthermore, the display unit can also display advertisements for products or services that are trending in groups or communities in which the user participates. This improves the effectiveness of advertisements by providing advertisements based on social media activity.

[0080] The advertising revenue unit can estimate a user's emotions and change the timing of advertisement display based on the estimated user emotions. For example, if the user is relaxed, the advertising revenue unit can display an advertisement containing detailed information. If the user is in a hurry, the advertising revenue unit can also display a concise advertisement that gets to the point. Furthermore, if the user is excited, the advertising revenue unit can also display a visually appealing advertisement. This improves the effectiveness of advertisements by providing advertisement display timing that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The advertising revenue unit can display appropriate advertisements by referring to the user's past purchase history when collecting advertising revenue. For example, the advertising revenue unit can prioritize displaying advertisements related to products the user has previously purchased. The advertising revenue unit can also analyze the user's preferences and tendencies from the user's purchase history and suggest optimal advertisements. Furthermore, the advertising revenue unit can display relevant advertisements based on reviews and ratings of products the user has previously purchased. This improves the effectiveness of advertising by providing optimal advertisements based on the user's past purchase history.

[0082] The advertising revenue unit may personalize advertisements based on the user's current areas of interest during advertising revenue. For example, the advertising revenue unit may display relevant advertisements based on products or services recently viewed by the user. The advertising revenue unit may also display advertisements based on topics in which the user has shown interest on social media. Furthermore, the advertising revenue unit may display advertisements related to products recently purchased by the user. This improves advertising effectiveness by providing advertisements based on the user's current areas of interest.

[0083] The advertising revenue unit can estimate a user's emotions and prioritize advertisements based on the estimated user emotions. For example, if the user is excited, the advertising revenue unit can prioritize displaying highly relevant advertisements. Furthermore, if the user is relaxed, the advertising revenue unit can also prioritize displaying advertisements that offer a wide range of options. Furthermore, if the user is stressed, the advertising revenue unit can prioritize displaying simple and intuitive advertisements. This improves advertising effectiveness by prioritizing advertisements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The advertising revenue unit can display appropriate advertisements taking into consideration the user's geographical location information when generating advertising revenue. For example, the advertising revenue unit can prioritize displaying advertisements for stores and services close to the user's current location. In addition, if the user is traveling, the advertising revenue unit can also display advertisements related to the user's destination. Furthermore, if the user shows interest in a specific area, the advertising revenue unit can also display advertisements related to that area. This improves the effectiveness of advertising by providing advertisements that take into consideration the user's geographical location information.

[0085] The advertising revenue unit can analyze a user's social media activity and display relevant advertisements when generating advertising revenue. For example, the advertising revenue unit displays advertisements based on products or services that the user has "liked" or commented on on social media. The advertising revenue unit can also display advertisements for products or services introduced by influencers the user follows. Furthermore, the advertising revenue unit can also display advertisements for products or services that are trending in groups or communities in which the user participates. This improves the effectiveness of advertising by providing advertisements based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the search reception unit, search unit, display unit, and advertising revenue unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the search reception unit is realized by the reception device 38 of the smart device 14, and the user inputs the product or service they are searching for. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and prioritizes searching for the company's own content and displays the lowest price. The display unit is realized, for example, by the output device 40 of the smart device 14, and displays the search results. The advertising revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts advertising fees into revenue when information about other companies is included. === Hard Collateral 1-2 === Each of the multiple elements, including the search reception unit, search unit, display unit, and advertising revenue unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the search reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs the product or service they are searching for. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and prioritizes searching for the company's own content and displays the lowest price. The display unit is realized, for example, by the speaker 240 of the smart glasses 214, and displays the search results. The advertising revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts advertising fees into revenue when information about other companies is included. === Hard Collateral 1-3 === Each of the multiple elements including the search reception unit, search unit, display unit, and advertising revenue unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the search reception unit is realized by the microphone 238 of the headset terminal 314, and the user inputs the product or service they are searching for. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and prioritizes searching for the company's own content and displays the lowest price. The display unit is realized, for example, by the display 343 of the headset terminal 314, and displays the search results. The advertising revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts advertising fees into revenue when information about other companies is included. === Hard Collateral 1-4 === Each of the multiple elements including the search reception unit, search unit, display unit, and advertising revenue unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the search reception unit is realized by the microphone 238 of the robot 414, and the user inputs the product or service they are searching for. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and prioritizes searching for the company's own content and displays the lowest price. The display unit is realized, for example, by the speaker 240 of the robot 414, and displays the search results. The advertising revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts advertising fees into revenue when information about other companies is included.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The search reception unit can analyze the user's voice input and automatically generate search keywords using natural language processing technology. For example, if a user voice-inputs, "Find a nearby cafe," the search reception unit converts the voice into text and generates appropriate search keywords. The search reception unit can also analyze the user's voice tone and speed to estimate the user's emotions. For example, if the user is in a hurry, it can generate keywords to quickly display search results. Furthermore, the search reception unit can refer to the user's past voice input history and suggest optimal search keywords. This enables efficient searches using voice input.

[0088] The search unit can display reviews and ratings of related products based on the user's purchase history. For example, it can display reviews of products the user has purchased in the past, allowing the user to refer to the opinions of other users. The search unit can also analyze the user's preferences and trends from the user's purchase history and prioritize displaying reviews of related products. Furthermore, the search unit can display ratings of related products based on reviews of products that the user has given high ratings to in the past. This provides personalized search results that utilize the user's purchase history.

[0089] The advertising revenue unit can estimate the user's emotions and customize the content of advertisements based on the estimated user's emotions. For example, if the user is relaxed, advertisements for products and services that have a relaxing effect can be displayed. Also, if the user is stressed, advertisements for products and services that help relieve stress can be displayed. Furthermore, if the user is excited, advertisements with a high entertainment value can be displayed. In this way, the effectiveness of advertisements can be improved by providing advertisement content that matches the user's emotions.

[0090] The display unit can select the optimal display format based on the user's device information. For example, if the user is using a smartphone, a vertical display format that matches the screen size can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, multiple search results can be displayed simultaneously. This improves user convenience by providing the optimal display format that takes device information into consideration.

[0091] The search acceptance unit can estimate the user's emotions and change the display method of search results based on the estimated user's emotions. For example, if the user is relaxed, search results including detailed information can be displayed. If the user is in a hurry, concise search results that focus on the main points can be displayed. Furthermore, if the user is excited, visually appealing search results can be displayed. This improves user convenience by providing a display method of search results that corresponds to the user's emotions.

[0092] The search unit can display relevant local event and promotion information based on the user's geographic location information. For example, if the user is in a specific area, event information held in that area can be displayed. Also, if the user is traveling, promotion information related to the destination can be displayed. Furthermore, if the user shows interest in a specific area, special offers and discount information related to that area can be displayed. This makes it possible to provide personalized information using geographic location information.

[0093] The search reception unit can estimate the user's emotions and set the priority of search keywords based on the estimated user's emotions. For example, if the user is excited, highly relevant keywords can be displayed with priority. Also, if the user is relaxed, a wide range of options can be provided. Furthermore, if the user is stressed, simple and intuitive keywords can be displayed with priority. This improves the accuracy of search results by providing the priority of search keywords according to the user's emotions.

[0094] The display unit can display detailed information and reviews of related products based on the user's past browsing history. For example, detailed information related to products the user has previously viewed can be displayed to serve as a reference when considering a purchase. Reviews and ratings of products the user has previously viewed can also be displayed to provide reference for the opinions of other buyers. Furthermore, related information and usage examples of products the user has previously viewed can also be displayed. This makes it possible to provide personalized information based on the user's past browsing history.

[0095] The advertising revenue unit can estimate the user's emotions and change the timing of advertisement display based on the estimated user's emotions. For example, if the user is relaxed, an advertisement containing detailed information can be displayed. If the user is in a hurry, a concise advertisement that gets to the point can be displayed. Furthermore, if the user is excited, a visually appealing advertisement can be displayed. This improves the effectiveness of advertisements by providing advertisement display timing that corresponds to the user's emotions.

[0096] The search unit can display promotional information for related products based on the user's social media activity. For example, it can display promotional information related to products that the user has "liked" or commented on on social media. It can also display promotional information for products introduced by influencers the user follows. It can also display promotional information for products that are trending in groups or communities the user participates in. This provides personalized promotional information based on social media activity.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The search reception unit inputs the product or service that the user wants to search for. For example, the search reception unit analyzes the keywords input by the user and identifies related products and services. Step 2: The search unit prioritizes searching the company's content based on the information entered by the search reception unit and displays the lowest price. For example, the search unit references the company's database to identify the cheapest products or services. The search unit also includes information related to group companies. For example, the search unit references the databases of the parent company, subsidiaries, and affiliated companies to obtain information on related products and services. Step 3: The display unit displays the search results obtained by the search unit. For example, the display unit displays the search results in a list format or a grid format. The display unit also specifically sets the display order of the search results and the display method of advertisements. For example, the display unit sorts the search results based on criteria such as relevance, popularity, or price. Step 4: The advertising revenue department converts advertising fees into revenue when information about other companies is included. For example, when a brand site advertises a product, the advertising revenue department can introduce video content. This can increase advertising revenue.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a search reception section where a user inputs products or services they wish to search for; a search unit that searches for company content with priority based on the information input by the search reception unit and displays the lowest price; a display unit that displays search results obtained by the search unit; Equipped with an advertising revenue department that converts advertising fees into revenue when information about other companies is included A system characterized by:

2. The search unit Includes information related to group companies 2. The system of claim 1.

3. The advertising revenue department When brand sites advertise products, they can use video content to introduce them.

2. The system of claim 1.

4. The display unit Configure the order of search results and how ads are displayed 2. The system of claim 1.

5. The search reception unit Estimate the user's emotions and change the search reception interface based on the estimated user emotions.

2. The system of claim 1.

6. The search reception unit Analyzes the user's past search history and auto-completes appropriate search keywords 2. The system of claim 1.

7. The search reception unit When a search is submitted, search suggestions are displayed based on the user's current interests.

2. The system of claim 1.

8. The search reception unit Estimate user sentiment and prioritize search keywords based on the estimated sentiment 2. The system of claim 1.

9. The search reception unit When a search is submitted, relevant search suggestions are displayed based on the user's geographic location.

2. The system of claim 1.

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