system

Abstract

The system according to the embodiment aims to enable a user to find a product that meets the desired purchase conditions in a short time. [Solution] A system according to an embodiment includes a receiving unit, a recommendation unit, and a providing unit. The receiving unit receives desired purchase conditions from a user. The recommendation unit recommends products based on the information received by the receiving unit. The providing unit provides the user with the products recommended by the recommendation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to find products that meet their desired purchasing conditions in a short amount of time.

[0005] The system according to the embodiment aims to enable a user to find a product that meets the desired purchase conditions in a short time. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a recommendation unit, and a providing unit. The receiving unit receives desired purchase conditions from a user. The recommendation unit recommends products based on the information received by the receiving unit. The providing unit provides the user with the products recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to find products that meet their desired purchase criteria in a short amount of time. [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 product purchase support system according to an embodiment of the present invention uses AI to provide a service that enables users to purchase products in a short time. This system allows users to input the category and desired conditions of a product they wish to purchase. The AI ​​analyzes the information and recommends optimal products. The user then reviews the recommended products and makes a purchase decision. For example, if a user inputs information such as "smartphone" or "budget within 50,000 yen," the AI ​​recommends the optimal smartphone based on that information. The user can quickly purchase the product by checking the details of the recommended smartphone and clicking the purchase button. This system saves users the trouble of searching for products themselves and enables reliable recommendations based on past purchase history and other users' ratings. For example, if a user desires a product in the same category as a product they have previously purchased, the AI ​​takes that information into account when making recommendations. In this way, users can quickly purchase products. As a result, the product purchase support system recommends optimal products based on the user's desired conditions, allowing them to purchase products in a short time.

[0029] A product purchase support system according to an embodiment includes a receiving unit, a recommendation unit, and a providing unit. The receiving unit receives desired purchase conditions from a user. The desired purchase conditions include, but are not limited to, categories, budgets, brands, and functions. The receiving unit receives information input by the user, such as "smartphone" and "budget within 50,000 yen." The receiving unit can also provide multiple input methods, such as voice input and text input. The recommendation unit recommends optimal products based on the information received by the receiving unit. The recommendation unit selects products based on, for example, past purchase history and ratings from other users. For example, if a user desires a "smartphone" and has a budget within 50,000 yen, the recommendation unit recommends a smartphone that meets those conditions. The recommendation unit can also make recommendations based on the product's rating score, number of reviews, price range, and the like. The providing unit provides the user with the products recommended by the recommendation unit. The providing unit provides the user with detailed information about the recommended product. For example, the providing unit can provide a display method for the product detail page and a layout of the purchase button. As a result, the product purchase support system according to the embodiment can recommend optimal products based on the user's desired conditions and enable the user to purchase the products in a short time.

[0030] The reception unit can accept desired purchase conditions, such as category, budget, brand, and function. For example, the reception unit accepts input of a category of a product the user wishes to purchase. Examples of categories include, but are not limited to, electronic devices, clothing, and food. The reception unit also accepts input of a budget for the product the user wishes to purchase. Examples of budgets include, but are not limited to, a price range and a currency unit. The reception unit also accepts input of a desired brand by the user. Examples of brands include, but are not limited to, a specific brand name or a brand category. Finally, the reception unit accepts input of a desired product function by the user. Examples of functions include, but are not limited to, technical functions and intended use. This allows the reception unit to accept detailed desired purchase conditions from the user.

[0031] The recommendation unit can select products based on past purchase history or ratings of other users. The recommendation unit selects products based on, for example, the user's past purchase history. Past purchase history includes, for example, purchase date and time, purchased products, purchase frequency, etc., but is not limited to these examples. The recommendation unit can also select products based on ratings of other users. Ratings of other users include, for example, rating scores, review content, rating criteria, etc., but is not limited to these examples. This enables the recommendation unit to make highly reliable product recommendations.

[0032] The service provider can provide users with detailed information about recommended products. For example, the service provider can provide users with detailed information about recommended products. This detailed information may include, but is not limited to, product specifications, usage instructions, and pricing information. The service provider can also provide, for example, how the product details page is displayed. This display method may include, but is not limited to, layout, font size, and color. The service provider can also provide the placement of the purchase button. This placement may include, but is not limited to, the top of the page, the bottom, or the sidebar. This allows the service provider to enable users to view detailed product information.

[0033] The recommendation system can recommend products based on their rating, number of reviews, and price range. For example, it can recommend products based on their rating. Ratings include, but are not limited to, a 5-point scale or a 10-point scale. The recommendation system can also recommend products based on the number of reviews. The number of reviews includes, but is not limited to, the total number of reviews or the number of reviews within a specific period. Furthermore, the recommendation system can also recommend products based on their price range. Price ranges include, but are not limited to, low-priced, mid-priced, and high-priced items. This allows the recommendation system to recommend the most suitable products for the user.

[0034] The provider can provide the display method for the product details page or the placement of the purchase button. For example, the provider can provide the display method for the product details page. The display method includes, but is not limited to, layout, font size, and color. The provider can also provide the placement of the purchase button. The placement of the purchase button includes, but is not limited to, the top of the page, the bottom, or the sidebar. This allows the provider to easily purchase products.

[0035] The reception desk can analyze the user's past purchase preferences and suggest input methods. For example, it can automatically display as suggestions the purchase preferences the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest preferences to be used during specific time periods based on the user's past purchase preferences. This allows the reception desk to suggest the optimal input method based on the user's past purchase preferences.

[0036] The reception system can filter the user's current purchasing behavior or areas of interest when they enter their purchase preferences. For example, the reception system can prioritize displaying relevant purchase preferences based on the product categories the user has recently searched for. It can also prioritize displaying products in the same category as products the user has previously purchased. Furthermore, the reception system can suggest relevant purchase preferences based on the user's areas of interest. This allows the reception system to suggest the most suitable purchase preferences based on the user's current purchasing behavior and areas of interest.

[0037] The reception system can prioritize the input of purchase preferences based on the user's geographical location. For example, it can prioritize displaying products from stores close to the user's current location. Furthermore, if the user is in a specific region, the reception system can prioritize displaying purchase preferences for products available in that region. Additionally, if the user is traveling, the reception system can prioritize displaying purchase preferences for products available at their travel destination. This allows the reception system to suggest the most suitable purchase preferences based on the user's geographical location.

[0038] The reception unit can analyze the user's social media activity and suggest conditions when the user inputs desired purchase conditions. For example, the reception unit can suggest related desired purchase conditions based on product categories mentioned by the user on social media. The reception unit can also suggest related desired purchase conditions based on brands the user follows on social media. Furthermore, the reception unit can also suggest related desired purchase conditions based on interests shared by the user on social media. This allows the reception unit to suggest optimal desired purchase conditions based on the user's social media activity.

[0039] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the product. For example, in the case of an expensive product, the recommendation unit provides a recommendation including a detailed description and reviews. In addition, in the case of a product used daily, the recommendation unit can also provide a recommendation including a concise description and basic information. Furthermore, in the case of a new product or a limited-edition product, the recommendation unit can also provide a recommendation including special promotion information. In this way, the recommendation unit can provide a level of detail of the recommendation according to the importance of the product.

[0040] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the product category. For example, in the case of electronic devices, the recommendation unit provides recommendations that emphasize technical specifications and user reviews. In addition, in the case of fashion items, the recommendation unit can also provide recommendations that emphasize trend information and styling suggestions. Furthermore, in the case of food, the recommendation unit can also provide recommendations that emphasize nutritional information and recipe suggestions. This allows the recommendation unit to provide optimal recommendations according to the product category.

[0041] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of product submission. For example, the recommendation unit prioritizes recommendations for new products and limited-edition products. The recommendation unit can also prioritize recommendations for products on sale or discounted products. Furthermore, the recommendation unit can also prioritize recommendations for seasonal products or limited-time products. This allows the recommendation unit to provide optimal recommendations according to the time of product submission.

[0042] The recommendation unit can adjust the order of recommendations based on the relevance of the products when making recommendations. For example, the recommendation unit preferentially recommends products that best match the user's desired purchase conditions. The recommendation unit can also preferentially recommend highly relevant products based on the user's past purchase history. Furthermore, the recommendation unit can also preferentially recommend products that have received high ratings from other users. This allows the recommendation unit to provide optimal recommendations based on the relevance of the products.

[0043] When providing detailed product information, the providing unit can analyze the user's past purchasing behavior and select a display method. For example, if the user has previously preferred to view detailed information, the providing unit can provide a display method including detailed information. Furthermore, if the user has previously preferred to view concise information, the providing unit can also provide a display method including concise information. Furthermore, if the user has previously preferred to view products of a specific brand, the providing unit can also preferentially display products of that brand. This allows the providing unit to provide an optimal display method based on the user's past purchasing behavior.

[0044] When providing detailed product information, the providing unit can customize the means for displaying information based on the user's current lifestyle. For example, if the user is busy, the providing unit can provide concise and to-the-point information. Alternatively, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is participating in a specific event, the providing unit can provide information related to the event. This allows the providing unit to provide the optimal means for displaying information according to the user's current lifestyle.

[0045] When providing detailed product information, the providing unit can provide the information based on the user's geographical location information. For example, the providing unit can prioritize displaying product information from stores close to the user's current location. Furthermore, when the user is in a specific area, the providing unit can also prioritize displaying information about products available in that area. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information about products available at the user's travel destination. This allows the providing unit to provide optimal information based on the user's geographical location information.

[0046] When providing detailed product information, the providing unit can analyze the user's social media activity and suggest a means of displaying the information. For example, the providing unit can display related information based on product categories mentioned by the user on social media. The providing unit can also display related information based on brands the user follows on social media. Furthermore, the providing unit can display related information based on interests and concerns shared by the user on social media. This allows the providing unit to provide an optimal means of displaying information based on the user's social media activity.

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

[0048] The reception desk can analyze a user's purchase history and automatically suggest new products related to items previously purchased. For example, if a user has previously purchased a smartphone of a specific brand, it can suggest new models and related accessories from that brand. The reception desk can also analyze the usage history of previously purchased items and suggest consumables and items that need replenishing. Furthermore, the reception desk can learn the user's purchasing patterns and provide a function to remind users of items they should purchase regularly. This allows the reception desk to provide optimal product suggestions based on the user's purchase history.

[0049] The recommendation system can recommend products by considering not only the user's purchase history but also their browsing history. For example, if a user frequently views a particular product page, it will prioritize recommending that product and related products. The recommendation system can also select highly reliable products based on reviews and ratings of products the user has viewed. Furthermore, the recommendation system can track price fluctuations of products the user has viewed and suggest the optimal purchase timing. As a result, the recommendation system can recommend the most suitable products based on the user's browsing history.

[0050] The service provider can provide personalized product information based on the user's purchase and browsing history. For example, it can provide usage and maintenance information for products the user has previously purchased. The service provider can also provide detailed information and comparison information for products the user has viewed. Furthermore, it can provide the latest information and promotional information for products the user is interested in. This allows the service provider to provide optimal product information based on the user's purchase and browsing history.

[0051] The reception unit can suggest local promotions and special offers based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can provide discount coupons and special offers that are only available in that area. If the user is traveling, the reception unit can also provide special promotion information that can be used at the user's travel destination. Furthermore, the reception unit can also provide inventory status and special sale information at nearby stores based on the user's current location. This allows the reception unit to suggest optimal promotions and special offers based on the user's geographical location information.

[0052] The recommendation unit can analyze the user's social media activity and make recommendations based on products and trends that the user is interested in. For example, it can prioritize recommendations of product categories that the user frequently mentions on social media. The recommendation unit can also recommend the latest products from influencers or brands that the user follows. Furthermore, the recommendation unit can recommend related products based on the interests and concerns that the user has shared on social media. This allows the recommendation unit to recommend optimal products based on the user's social media activity.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives the desired purchase conditions from the user. The desired purchase conditions include category, budget, brand, and features. For example, the reception unit receives information such as "smartphone" and "budget within 50,000 yen" from the user. The reception unit can also provide multiple input methods, such as voice input and text input. Step 2: The recommendation unit recommends the most suitable product based on the information received by the reception unit. For example, it selects products based on past purchase history and ratings from other users. If a user desires a "smartphone" and has a budget of 50,000 yen or less, it will recommend a smartphone that meets those conditions. It can also make recommendations based on the product's rating score, number of reviews, price range, etc. Step 3: The providing unit provides the user with the product recommended by the recommendation unit. For example, the providing unit provides the user with detailed information about the recommended product. The providing unit can provide the user with information about how to display the product detail page and the placement of the purchase button.

[0055] (Example 2) A product purchase support system according to an embodiment of the present invention uses AI to provide a service that enables users to purchase products in a short time. This system allows users to input the category and desired conditions of a product they wish to purchase. The AI ​​analyzes the information and recommends optimal products. The user then reviews the recommended products and makes a purchase decision. For example, if a user inputs information such as "smartphone" or "budget within 50,000 yen," the AI ​​recommends the optimal smartphone based on that information. The user can quickly purchase the product by checking the details of the recommended smartphone and clicking the purchase button. This system saves users the trouble of searching for products themselves and enables reliable recommendations based on past purchase history and other users' ratings. For example, if a user desires a product in the same category as a product they have previously purchased, the AI ​​takes that information into account when making recommendations. In this way, users can quickly purchase products. As a result, the product purchase support system recommends optimal products based on the user's desired conditions, allowing them to purchase products in a short time.

[0056] A product purchase support system according to an embodiment includes a receiving unit, a recommendation unit, and a providing unit. The receiving unit receives desired purchase conditions from a user. The desired purchase conditions include, but are not limited to, categories, budgets, brands, and functions. The receiving unit receives information input by the user, such as "smartphone" and "budget within 50,000 yen." The receiving unit can also provide multiple input methods, such as voice input and text input. The recommendation unit recommends optimal products based on the information received by the receiving unit. The recommendation unit selects products based on, for example, past purchase history and ratings from other users. For example, if a user desires a "smartphone" and has a budget within 50,000 yen, the recommendation unit recommends a smartphone that meets those conditions. The recommendation unit can also make recommendations based on the product's rating score, number of reviews, price range, and the like. The providing unit provides the user with the products recommended by the recommendation unit. The providing unit provides the user with detailed information about the recommended product. For example, the providing unit can provide a display method for the product detail page and a layout of the purchase button. As a result, the product purchase support system according to the embodiment can recommend optimal products based on the user's desired conditions and enable the user to purchase the products in a short time.

[0057] The reception unit can accept desired purchase conditions, such as category, budget, brand, and function. For example, the reception unit accepts input of a category of a product the user wishes to purchase. Examples of categories include, but are not limited to, electronic devices, clothing, and food. The reception unit also accepts input of a budget for the product the user wishes to purchase. Examples of budgets include, but are not limited to, a price range and a currency unit. The reception unit also accepts input of a desired brand by the user. Examples of brands include, but are not limited to, a specific brand name or a brand category. Finally, the reception unit accepts input of a desired product function by the user. Examples of functions include, but are not limited to, technical functions and intended use. This allows the reception unit to accept detailed desired purchase conditions from the user.

[0058] The recommendation unit can select products based on past purchase history or ratings of other users. The recommendation unit selects products based on, for example, the user's past purchase history. Past purchase history includes, for example, purchase date and time, purchased products, purchase frequency, etc., but is not limited to these examples. The recommendation unit can also select products based on ratings of other users. Ratings of other users include, for example, rating scores, review content, rating criteria, etc., but is not limited to these examples. This enables the recommendation unit to make highly reliable product recommendations.

[0059] The service provider can provide users with detailed information about recommended products. For example, the service provider can provide users with detailed information about recommended products. This detailed information may include, but is not limited to, product specifications, usage instructions, and pricing information. The service provider can also provide, for example, how the product details page is displayed. This display method may include, but is not limited to, layout, font size, and color. The service provider can also provide the placement of the purchase button. This placement may include, but is not limited to, the top of the page, the bottom, or the sidebar. This allows the service provider to enable users to view detailed product information.

[0060] The recommendation system can recommend products based on their rating, number of reviews, and price range. For example, it can recommend products based on their rating. Ratings include, but are not limited to, a 5-point scale or a 10-point scale. The recommendation system can also recommend products based on the number of reviews. The number of reviews includes, but is not limited to, the total number of reviews or the number of reviews within a specific period. Furthermore, the recommendation system can also recommend products based on their price range. Price ranges include, but are not limited to, low-priced, mid-priced, and high-priced items. This allows the recommendation system to recommend the most suitable products for the user.

[0061] The provider can provide the display method for the product details page or the placement of the purchase button. For example, the provider can provide the display method for the product details page. The display method includes, but is not limited to, layout, font size, and color. The provider can also provide the placement of the purchase button. The placement of the purchase button includes, but is not limited to, the top of the page, the bottom, or the sidebar. This allows the provider to easily purchase products.

[0062] The reception desk can estimate the user's emotions and customize the input interface for purchase requests based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of purchase requests. In this way, the reception desk can provide an input interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0063] The reception desk can analyze the user's past purchase preferences and suggest input methods. For example, it can automatically display as suggestions the purchase preferences the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest preferences to be used during specific time periods based on the user's past purchase preferences. This allows the reception desk to suggest the optimal input method based on the user's past purchase preferences.

[0064] The reception system can filter the user's current purchasing behavior or areas of interest when they enter their purchase preferences. For example, the reception system can prioritize displaying relevant purchase preferences based on the product categories the user has recently searched for. It can also prioritize displaying products in the same category as products the user has previously purchased. Furthermore, the reception system can suggest relevant purchase preferences based on the user's areas of interest. This allows the reception system to suggest the most suitable purchase preferences based on the user's current purchasing behavior and areas of interest.

[0065] The reception desk can estimate the user's emotions and prioritize the entered purchase preferences based on those emotions. For example, if the user is stressed, the reception desk will prioritize displaying important purchase preferences. If the user is relaxed, the reception desk can also prioritize displaying detailed purchase preferences. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying purchase preferences that can be entered quickly. This allows the reception desk to prioritize purchase preferences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] The reception system can prioritize the input of purchase preferences based on the user's geographical location. For example, it can prioritize displaying products from stores close to the user's current location. Furthermore, if the user is in a specific region, the reception system can prioritize displaying purchase preferences for products available in that region. Additionally, if the user is traveling, the reception system can prioritize displaying purchase preferences for products available at their travel destination. This allows the reception system to suggest the most suitable purchase preferences based on the user's geographical location.

[0067] The reception unit can analyze the user's social media activity and suggest conditions when the user inputs desired purchase conditions. For example, the reception unit can suggest related desired purchase conditions based on product categories mentioned by the user on social media. The reception unit can also suggest related desired purchase conditions based on brands the user follows on social media. Furthermore, the reception unit can also suggest related desired purchase conditions based on interests shared by the user on social media. This allows the reception unit to suggest optimal desired purchase conditions based on the user's social media activity.

[0068] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can provide a recommendation that includes a detailed explanation. If the user is in a hurry, the recommendation unit can also provide a concise and to-the-point recommendation. Furthermore, if the user is excited, the recommendation unit can also provide a visually appealing recommendation. This allows the recommendation unit to provide a way of presenting recommendations that corresponds 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the product. For example, in the case of an expensive product, the recommendation unit provides a recommendation including a detailed description and reviews. In addition, in the case of a product used daily, the recommendation unit can also provide a recommendation including a concise description and basic information. Furthermore, in the case of a new product or a limited-edition product, the recommendation unit can also provide a recommendation including special promotion information. In this way, the recommendation unit can provide a level of detail of the recommendation according to the importance of the product.

[0070] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the product category. For example, in the case of electronic devices, the recommendation unit provides recommendations that emphasize technical specifications and user reviews. In addition, in the case of fashion items, the recommendation unit can also provide recommendations that emphasize trend information and styling suggestions. Furthermore, in the case of food, the recommendation unit can also provide recommendations that emphasize nutritional information and recipe suggestions. This allows the recommendation unit to provide optimal recommendations according to the product category.

[0071] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. For example, if the user is in a hurry, the recommendation unit can provide a short, to-the-point recommendation. If the user is relaxed, the recommendation unit can also provide a longer recommendation with detailed explanations. Furthermore, if the user is excited, the recommendation unit can also provide a recommendation with a visually stimulating effect. This allows the recommendation unit to provide the length of the recommendation 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of product submission. For example, the recommendation unit prioritizes recommendations for new products and limited-edition products. The recommendation unit can also prioritize recommendations for products on sale or discounted products. Furthermore, the recommendation unit can also prioritize recommendations for seasonal products or limited-time products. This allows the recommendation unit to provide optimal recommendations according to the time of product submission.

[0073] The recommendation unit can adjust the order of recommendations based on the relevance of the products when making recommendations. For example, the recommendation unit preferentially recommends products that best match the user's desired purchase conditions. The recommendation unit can also preferentially recommend highly relevant products based on the user's past purchase history. Furthermore, the recommendation unit can also preferentially recommend products that have received high ratings from other users. This allows the recommendation unit to provide optimal recommendations based on the relevance of the products.

[0074] The service provider can estimate the user's emotions and adjust how product details are displayed based on those emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display. In this way, the service provider can provide a display method for product details that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] When providing detailed product information, the providing unit can analyze the user's past purchasing behavior and select a display method. For example, if the user has previously preferred to view detailed information, the providing unit can provide a display method including detailed information. Furthermore, if the user has previously preferred to view concise information, the providing unit can also provide a display method including concise information. Furthermore, if the user has previously preferred to view products of a specific brand, the providing unit can also preferentially display products of that brand. This allows the providing unit to provide an optimal display method based on the user's past purchasing behavior.

[0076] When providing detailed product information, the providing unit can customize the means for displaying information based on the user's current lifestyle. For example, if the user is busy, the providing unit can provide concise and to-the-point information. Alternatively, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is participating in a specific event, the providing unit can provide information related to the event. This allows the providing unit to provide the optimal means for displaying information according to the user's current lifestyle.

[0077] The service provider can estimate the user's emotions and prioritize product details based on those emotions. For example, if the user is stressed, the service provider will prioritize displaying important information. It can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize displaying information that can be quickly viewed. This allows the service provider to provide product details with prioritization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] When providing detailed product information, the providing unit can provide the information based on the user's geographical location information. For example, the providing unit can prioritize displaying product information from stores close to the user's current location. Furthermore, when the user is in a specific area, the providing unit can also prioritize displaying information about products available in that area. Furthermore, when the user is traveling, the providing unit can also prioritize displaying information about products available at the user's travel destination. This allows the providing unit to provide optimal information based on the user's geographical location information.

[0079] When providing detailed product information, the providing unit can analyze the user's social media activity and suggest a means of displaying the information. For example, the providing unit can display related information based on product categories mentioned by the user on social media. The providing unit can also display related information based on brands the user follows on social media. Furthermore, the providing unit can display related information based on interests and concerns shared by the user on social media. This allows the providing unit to provide an optimal means of displaying information based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives user input information such as "smartphone" and "budget within 50,000 yen." The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products based on past purchase history and ratings by other users. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides detailed information about the recommended products to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user, such as "smartphone" or "budget within 50,000 yen." The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products based on past purchase history and ratings from other users. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides detailed information about the recommended products to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, recommendation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives voice input from the user of information such as "smartphone" or "budget within 50,000 yen." The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products based on past purchase history and ratings from other users. The provision unit is realized, for example, by the display 343 of the headset terminal 314 and provides the user with detailed information about the recommended products. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, recommendation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user of information such as "smartphone" or "budget within 50,000 yen." The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal products based on past purchase history and ratings from other users. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides detailed information about the recommended products to the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception desk can analyze a user's purchase history and automatically suggest new products related to items previously purchased. For example, if a user has previously purchased a smartphone of a specific brand, it can suggest new models and related accessories from that brand. The reception desk can also analyze the usage history of previously purchased items and suggest consumables and items that need replenishing. Furthermore, the reception desk can learn the user's purchasing patterns and provide a function to remind users of items they should purchase regularly. This allows the reception desk to provide optimal product suggestions based on the user's purchase history.

[0082] The recommendation unit can recommend products taking into consideration not only the user's purchase history but also the user's browsing history. For example, if the user frequently views a specific product page, that product and related products will be recommended preferentially. The recommendation unit can also select highly reliable products based on reviews and ratings of products viewed by the user. Furthermore, the recommendation unit can track price fluctuations of products viewed by the user and suggest the optimal timing for purchase. This allows the recommendation unit to recommend optimal products based on the user's browsing history.

[0083] The service provider can provide personalized product information based on the user's purchase and browsing history. For example, it can provide usage and maintenance information for products the user has previously purchased. The service provider can also provide detailed information and comparison information for products the user has viewed. Furthermore, it can provide the latest information and promotional information for products the user is interested in. This allows the service provider to provide optimal product information based on the user's purchase and browsing history.

[0084] The reception unit can estimate the user's emotions and customize the input interface for desired purchase conditions based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface is provided to minimize input steps. In addition, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input desired purchase conditions. In this way, the reception unit can provide an input interface according to the user's emotions.

[0085] The recommendation unit can estimate the user's emotions and adjust the way in which recommendations are expressed based on the estimated user's emotions. For example, if the user is relaxed, the recommendation unit can provide a recommendation that includes detailed explanations. If the user is in a hurry, the recommendation unit can also provide a concise and to-the-point recommendation. Furthermore, if the user is excited, the recommendation unit can also provide a visually appealing recommendation. This allows the recommendation unit to provide a way to express recommendations that corresponds to the user's emotions.

[0086] The providing unit can estimate the user's emotions and adjust the display method of detailed product information based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, the providing unit can provide a display method of detailed product information according to the user's emotions.

[0087] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user's emotions. For example, if the user is in a hurry, the recommendation unit can provide a short, to-the-point recommendation. If the user is relaxed, the recommendation unit can also provide a longer recommendation with detailed explanations. Furthermore, if the user is excited, the recommendation unit can also provide a recommendation with a visually stimulating effect. This allows the recommendation unit to provide the length of the recommendation according to the user's emotions.

[0088] The providing unit can estimate the user's emotions and determine the priority of detailed product information based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed with priority. The providing unit can also display detailed information with priority when the user is relaxed. Furthermore, if the user is in a hurry, the providing unit can display information that can be checked quickly with priority. In this way, the providing unit can provide the priority of detailed product information according to the user's emotions.

[0089] The reception unit can suggest local promotions and special offers based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can provide discount coupons and special offers that are only available in that area. If the user is traveling, the reception unit can also provide special promotion information that can be used at the user's travel destination. Furthermore, the reception unit can also provide inventory status and special sale information at nearby stores based on the user's current location. This allows the reception unit to suggest optimal promotions and special offers based on the user's geographical location information.

[0090] The recommendation unit can analyze the user's social media activity and make recommendations based on products and trends that the user is interested in. For example, it can prioritize recommendations of product categories that the user frequently mentions on social media. The recommendation unit can also recommend the latest products from influencers or brands that the user follows. Furthermore, the recommendation unit can recommend related products based on the interests and concerns that the user has shared on social media. This allows the recommendation unit to recommend optimal products based on the user's social media activity.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives the desired purchase conditions from the user. The desired purchase conditions include category, budget, brand, and features. For example, the reception unit receives information such as "smartphone" and "budget within 50,000 yen" from the user. The reception unit can also provide multiple input methods, such as voice input and text input. Step 2: The recommendation unit recommends the most suitable product based on the information received by the reception unit. For example, it selects products based on past purchase history and ratings from other users. If a user desires a "smartphone" and has a budget of 50,000 yen or less, it will recommend a smartphone that meets those conditions. It can also make recommendations based on the product's rating score, number of reviews, price range, etc. Step 3: The providing unit provides the user with the product recommended by the recommendation unit. For example, the providing unit provides the user with detailed information about the recommended product. The providing unit can provide the user with information about how to display the product detail page and the placement of the purchase button.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0107] 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.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 reception unit that receives desired purchase conditions from a user; a recommendation unit that recommends products based on the information received by the reception unit; a providing unit that provides the product recommended by the recommendation unit to the user. A system characterized by:

2. The reception unit Accept purchase criteria such as category, budget, brand, and function 2. The system of claim 1.

3. The recommendation unit Select products based on past purchase history or other users' ratings 2. The system of claim 1.

4. The providing unit Providing users with detailed information about recommended products 2. The system of claim 1.

5. The recommendation unit Recommend products based on rating, number of reviews, and price range 2. The system of claim 1.

6. The providing unit Provide product detail page display or buy button placement 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and customizes the input interface for purchase conditions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past purchase preferences and suggest input methods 2. The system of claim 1.

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