System

Abstract

An object of a system according to an embodiment is to enable a user to grasp an optimal purchase timing of a purchase-desired product.SOLUTION: A system includes a commodity registration part, an information collection part, an analysis part, and a notification part. The commodity registration unit registers a purchase desired commodity of a user. The information collection part collects sale information or campaign information on the Internet. The analysis unit analyzes the information collected by the information collection unit and proposes an optimal purchase timing. The notification unit notifies the user of the optimal purchase timing proposed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to determine the optimal timing to purchase a desired product.

[0005] The system according to the embodiment aims to enable a user to know the optimal timing for purchasing a desired product. [Means for solving the problem]

[0006] The system according to the embodiment includes a product registration unit, an information collection unit, an analysis unit, and a notification unit. The product registration unit registers products that a user wishes to purchase. The information collection unit collects sales information or campaign information on the Internet. The analysis unit analyzes the information collected by the information collection unit and suggests the optimal timing for purchase. The notification unit notifies the user of the optimal timing for purchase suggested by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to know the optimal timing to purchase a desired product. [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) The purchase timing suggestion system according to an embodiment of the present invention is a system in which a user registers the product they want to purchase, and a generation AI collects and analyzes sales and campaign information, and notifies the user of the optimal purchase timing. As a result, the purchase timing suggestion system allows the user to purchase the product at the optimal timing.

[0029] A purchase timing suggestion system according to an embodiment includes a product registration unit, an information collection unit, an analysis unit, and a notification unit. The product registration unit registers a product desired by a user. For example, the user can input a specific product name, such as a smartphone or laptop. The product registration unit can also register information such as a desired price and category. The information collection unit collects online sales and campaign information. For example, the generation AI collects information about specific sales events, such as Black Friday and Cyber ​​Monday. The information collection unit can automatically collect information from online stores and websites. The analysis unit analyzes the collected information and suggests the optimal purchase timing. For example, the generation AI analyzes past sales data and price fluctuation patterns to predict when a specific product will be cheapest. The analysis unit can also suggest the optimal purchase timing for an individual user, taking into account the user's purchase history and preferences. The notification unit notifies the user of the optimal purchase timing. For example, the generation AI notifies the user via smartphone push notification, email, or a messenger app. The notification unit can also analyze the user's emotions in real time and provide feedback to elicit positive emotions. As a result, the purchase timing suggestion system according to the embodiment allows users to purchase products at the optimal timing. For example, users can purchase expensive electronic devices at the lowest price, thereby enjoying savings. In addition, since the generation AI automatically collects and analyzes information, users can easily obtain the latest sales information.

[0030] The information collection unit can prioritize collecting information about specific sales events. The information collection unit prioritizes collecting information about specific sales events, such as Black Friday and Cyber ​​Monday. For example, the generation AI collects discount information and limited-time offers related to these events. The information collection unit can also collect news articles and social media posts about specific sales events. By prioritizing the collection of information about specific sales events, users can obtain important sales information without missing out.

[0031] The analysis unit can analyze past sales data and price fluctuation patterns. For example, the analysis unit collects past sales data and analyzes price fluctuation patterns. For example, the generation AI analyzes price history from past sales periods and predicts when a particular product will be cheapest. The analysis unit can also analyze price fluctuation patterns using time series analysis and statistical methods. For example, the generation AI can graph price fluctuation trends and present them visually to the user. By analyzing past sales data and price fluctuation patterns, the user can determine the optimal timing for purchases.

[0032] The notification unit can notify the user via push notification, email, or messenger app. For example, the notification unit notifies the user via push notification on a smartphone. For example, the generation AI notifies the user that a registered smartphone will be on sale this weekend. The notification unit can also notify the user via email. For example, the generation AI notifies the user via email that a specific product is on sale. The notification unit can also notify the user via a messenger app. For example, the generation AI notifies the user of sale information via LINE (registered trademark) or WhatsApp. This allows the user to receive sale information through various means.

[0033] The system can manage a user's purchase history and store information about products purchased in the past. For example, the system stores information about products purchased in the past by the user in a database. For example, the generation AI stores information such as the purchase date and time, purchased products, and purchase price. The system can also refer to information about products purchased in the past by the user. For example, the generation AI notifies the user that accessories for a laptop purchased in the past are on sale. This allows the user to refer to their past purchase history and use it when purchasing the same product again.

[0034] The system can analyze a user's past purchase history and search history and automatically suggest related products. For example, the system can analyze a user's past purchase history and automatically suggest related products. For example, the generation AI can suggest the latest compatible products based on smartphone cases and chargers purchased in the past. The system can also analyze a user's past search history and automatically suggest related products. For example, the generation AI can suggest the latest compatible products based on office software and external hard drives searched for in the past. This allows users to find related products efficiently.

[0035] The system can analyze a user's preferences and lifestyle and recommend the most suitable product category and brand. For example, the generation AI can suggest a lightweight, easy-to-carry camera to a user who enjoys traveling. The system can also analyze a user's lifestyle and recommend the most suitable shoe brand and model. For example, the generation AI can suggest long-distance shoes to a user who is planning to run a marathon. This allows users to find products that suit their lifestyle.

[0036] The system can analyze the user's voice input and automatically register product information using voice recognition technology. For example, the system registers product information using the user's voice input. For example, when the user says, "Register the latest iPhone," the generation AI analyzes the voice and automatically registers the corresponding product information. The system can also analyze the voice and automatically register the corresponding product information when the user says, "Register a gaming laptop." This allows users to register product information without any hassle.

[0037] The system can evaluate the reliability of registered products by referring to reviews and ratings from other users. The system can evaluate the reliability of registered products by referring to reviews and ratings from other users. For example, the generation AI will preferentially suggest products with high average review ratings. The system can also recommend highly rated products. For example, the generation AI will preferentially suggest products with a large number of reviews and high ratings. This allows users to select highly reliable products.

[0038] The system can prioritize collecting sales information for specific regions and stores and provide optimal sales information based on the user's location information. For example, the system prioritizes collecting sales information for nearby stores and regions based on the user's location information. For example, if the user is in Tokyo, the generation AI will collect sales information for major electronics retailers in Tokyo. The system can also prioritize collecting sales information for online stores in a specific region. For example, if the user is in Osaka, the generation AI will collect sales information for online stores that can deliver to Osaka. This allows the user to obtain optimal sales information based on their location information.

[0039] The system can compare collected sale information with past price data and analyze price fluctuation patterns. For example, the system can compare collected sale information with past price data and analyze price fluctuation patterns. For example, the generation AI can predict when a particular product will be cheapest based on past price history. The system can also suggest the optimal purchase timing to the user by analyzing price fluctuation patterns. For example, the generation AI can graph price fluctuation trends and present them visually to the user. This allows the user to understand price fluctuation patterns and know the optimal purchase timing.

[0040] The system can also collect sales information from social media and news sites to provide a wider range of sales information. The system collects sales information from social media, for example. For example, the generation AI obtains the latest sales information from posts on Twitter (registered trademark) and Facebook (registered trademark). The system can also collect sales information from news sites. For example, the generation AI obtains the latest sales information from IT-related news sites. This allows the user to obtain a wider range of sales information.

[0041] The system can filter collected sale information based on the user's purchasing history and preferences to provide the most appropriate information. For example, the system filters collected sale information based on the user's past purchasing history. For example, the generation AI may prioritize displaying sale information for brands that the user has purchased in the past. The system can also filter sale information based on the user's preferences. For example, the generation AI may prioritize displaying sale information for specifications and designs that the user prefers. This allows the user to obtain the most appropriate sale information based on their preferences and past purchasing history.

[0042] The system can predict the optimal timing for a purchase by taking into account past purchase data and market trends. For example, the system analyzes past purchase data and market trends to predict the optimal timing for a purchase. For example, the generative AI can analyze past smartphone purchase data and market trends to identify a tendency for prices to fall at specific times. The system can also analyze past laptop purchase data and market trends to predict that prices will fall during specific sales events. This allows users to know the optimal timing for a purchase based on past data and market trends.

[0043] The system analyzes users' purchasing patterns and can customize and suggest the optimal purchase timing for each individual user. For example, the system analyzes a user's past purchasing patterns and can customize and suggest the optimal purchase timing for each individual user. For example, the generative AI predicts the optimal timing based on the timing and frequency of the user's past purchases. The system can also predict the optimal timing based on the user's tendency to prefer specific brands or models. This allows users to know the optimal purchase timing based on their own purchasing patterns.

[0044] The system can make highly reliable suggestions by referring to the purchase data and reviews of other users.The system can make highly reliable suggestions by referring to the purchase data and reviews of other users.For example, the generation AI can make suggestions based on periods with high review ratings or periods when purchases are concentrated.The system can also make suggestions based on periods of sales events when purchases are concentrated.This allows users to receive highly reliable suggestions.

[0045] The system can make special suggestions by taking into account the user's life events. For example, the system can make special suggestions by taking into account the user's birthday or anniversary. For example, the generation AI can suggest birthday discounts and special offers. The system can also suggest sale information and special offers exclusive to anniversaries. This allows users to receive special suggestions tailored to their life events.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The system can provide maintenance information for related products based on the user's purchase history. For example, it can notify the user when maintenance is due for home appliances purchased in the past. It can also notify the user before the warranty period of a product purchased by the user expires. This allows the user to efficiently carry out maintenance after purchase.

[0048] The system can suggest accessories and additional options for related products based on the user's purchasing history. For example, if a user purchases a smartphone, the system can suggest a corresponding case or charger. If a user purchases a laptop, the system can suggest a corresponding bag or mouse. This allows users to find related products efficiently.

[0049] The system can provide reviews and ratings of related products based on a user's purchasing history. For example, it can display reviews of a product the user is considering purchasing. It can also suggest similar products based on ratings of products the user has previously purchased. This allows users to consider purchases based on reliable information.

[0050] The system can prioritize notifications of sales information for related products based on the user's purchasing history. For example, it can prioritize displaying sales information for brands the user has previously purchased. It can also notify users of sales information for accessories or additional options for products the user has previously purchased. This allows users to obtain the most appropriate sales information based on their preferences and past purchasing history.

[0051] The system can provide new product information for related products based on a user's purchasing history. For example, the system can notify the user when a new model of a smartphone that the user previously purchased is released. It can also notify the user when a new version of a home appliance that the user previously purchased is released. This allows the user to efficiently obtain the latest product information.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The product registration unit registers the product that the user wishes to purchase. For example, the user can enter a specific product name such as a smartphone or laptop. The product registration unit can also register information such as the desired price and category. Step 2: The information gathering unit collects sales and campaign information from the Internet. For example, the generation AI gathers information on specific sales events such as Black Friday and Cyber ​​Monday. The information gathering unit can also automatically collect information from online stores and websites. Step 3: The analysis unit analyzes the collected information and suggests the optimal time to purchase. For example, the generation AI analyzes past sales data and price fluctuation patterns to predict when a particular product will be cheapest. The analysis unit can also suggest the optimal time to purchase for each individual user, taking into account the user's purchase history and preferences. Step 4: The notification unit notifies the user of the optimal purchase timing. For example, the generation AI can notify the user via smartphone push notification, email, or messenger app. The notification unit can also analyze the user's emotions in real time and provide feedback to elicit positive emotions.

[0054] (Example 2) The purchase timing suggestion system according to an embodiment of the present invention is a system in which a user registers the product they want to purchase, and a generation AI collects and analyzes sales and campaign information, and notifies the user of the optimal purchase timing. As a result, the purchase timing suggestion system allows the user to purchase the product at the optimal timing.

[0055] A purchase timing suggestion system according to an embodiment includes a product registration unit, an information collection unit, an analysis unit, and a notification unit. The product registration unit registers a product desired by a user. For example, the user can input a specific product name, such as a smartphone or laptop. The product registration unit can also register information such as a desired price and category. The information collection unit collects online sales and campaign information. For example, the generation AI collects information about specific sales events, such as Black Friday and Cyber ​​Monday. The information collection unit can automatically collect information from online stores and websites. The analysis unit analyzes the collected information and suggests the optimal purchase timing. For example, the generation AI analyzes past sales data and price fluctuation patterns to predict when a specific product will be cheapest. The analysis unit can also suggest the optimal purchase timing for an individual user, taking into account the user's purchase history and preferences. The notification unit notifies the user of the optimal purchase timing. For example, the generation AI notifies the user via smartphone push notification, email, or a messenger app. The notification unit can also analyze the user's emotions in real time and provide feedback to elicit positive emotions. As a result, the purchase timing suggestion system according to the embodiment allows users to purchase products at the optimal timing. For example, users can purchase expensive electronic devices at the lowest price, thereby enjoying savings. In addition, since the generation AI automatically collects and analyzes information, users can easily obtain the latest sales information.

[0056] The information collection unit can prioritize collecting information about specific sales events. The information collection unit prioritizes collecting information about specific sales events, such as Black Friday and Cyber ​​Monday. For example, the generation AI collects discount information and limited-time offers related to these events. The information collection unit can also collect news articles and social media posts about specific sales events. By prioritizing the collection of information about specific sales events, users can obtain important sales information without missing out.

[0057] The analysis unit can analyze past sales data and price fluctuation patterns. For example, the analysis unit collects past sales data and analyzes price fluctuation patterns. For example, the generation AI analyzes price history from past sales periods and predicts when a particular product will be cheapest. The analysis unit can also analyze price fluctuation patterns using time series analysis and statistical methods. For example, the generation AI can graph price fluctuation trends and present them visually to the user. By analyzing past sales data and price fluctuation patterns, the user can determine the optimal timing for purchases.

[0058] The notification unit can notify the user via push notification, email, or messenger app. For example, the notification unit notifies the user via push notification on a smartphone. For example, the generation AI notifies the user that a registered smartphone will be on sale this weekend. The notification unit can also notify the user via email. For example, the generation AI notifies the user via email that a specific product is on sale. The notification unit can also notify the user via a messenger app. For example, the generation AI notifies the user of sale information via LINE (registered trademark) or WhatsApp. This allows the user to receive sale information through various means.

[0059] The system can manage a user's purchase history and store information about products purchased in the past. For example, the system stores information about products purchased in the past by the user in a database. For example, the generation AI stores information such as the purchase date and time, purchased products, and purchase price. The system can also refer to information about products purchased in the past by the user. For example, the generation AI notifies the user that accessories for a laptop purchased in the past are on sale. This allows the user to refer to their past purchase history and use it when purchasing the same product again.

[0060] The system can analyze a user's past purchase history and search history and automatically suggest related products. For example, the system can analyze a user's past purchase history and automatically suggest related products. For example, the generation AI can suggest the latest compatible products based on smartphone cases and chargers purchased in the past. The system can also analyze a user's past search history and automatically suggest related products. For example, the generation AI can suggest the latest compatible products based on office software and external hard drives searched for in the past. This allows users to find related products efficiently.

[0061] The system can analyze a user's preferences and lifestyle and recommend the most suitable product category and brand. For example, the generation AI can suggest a lightweight, easy-to-carry camera to a user who enjoys traveling. The system can also analyze a user's lifestyle and recommend the most suitable shoe brand and model. For example, the generation AI can suggest long-distance shoes to a user who is planning to run a marathon. This allows users to find products that suit their lifestyle.

[0062] The system can analyze the user's voice input and automatically register product information using voice recognition technology. For example, the system registers product information using the user's voice input. For example, when the user says, "Register the latest iPhone," the generation AI analyzes the voice and automatically registers the corresponding product information. The system can also analyze the voice and automatically register the corresponding product information when the user says, "Register a gaming laptop." This allows users to register product information without any hassle.

[0063] The system can evaluate the reliability of registered products by referring to reviews and ratings from other users. The system can evaluate the reliability of registered products by referring to reviews and ratings from other users. For example, the generation AI will preferentially suggest products with high average review ratings. The system can also recommend highly rated products. For example, the generation AI will preferentially suggest products with a large number of reviews and high ratings. This allows users to select highly reliable products.

[0064] The system can use the emotion estimation function to analyze the user's emotions in real time when registering a product and provide feedback to make the registration process more comfortable. The system can, for example, use the emotion estimation function to analyze the user's emotions in real time and provide feedback to make the registration process more comfortable. For example, if the user is feeling anxious, the generation AI can display a reassuring message. Also, if the user is excited, the system can display an encouraging message. This allows the user to register a product more comfortably.

[0065] The system can prioritize collecting sales information for specific regions and stores and provide optimal sales information based on the user's location information. For example, the system prioritizes collecting sales information for nearby stores and regions based on the user's location information. For example, if the user is in Tokyo, the generation AI will collect sales information for major electronics retailers in Tokyo. The system can also prioritize collecting sales information for online stores in a specific region. For example, if the user is in Osaka, the generation AI will collect sales information for online stores that can deliver to Osaka. This allows the user to obtain optimal sales information based on their location information.

[0066] The system can compare collected sale information with past price data and analyze price fluctuation patterns. For example, the system can compare collected sale information with past price data and analyze price fluctuation patterns. For example, the generation AI can predict when a particular product will be cheapest based on past price history. The system can also suggest the optimal purchase timing to the user by analyzing price fluctuation patterns. For example, the generation AI can graph price fluctuation trends and present them visually to the user. This allows the user to understand price fluctuation patterns and know the optimal purchase timing.

[0067] The system can also collect sales information from social media and news sites to provide a wider range of sales information. The system collects sales information from social media, for example. For example, the generation AI obtains the latest sales information from posts on Twitter (registered trademark) and Facebook (registered trademark). The system can also collect sales information from news sites. For example, the generation AI obtains the latest sales information from IT-related news sites. This allows the user to obtain a wider range of sales information.

[0068] The system can filter collected sale information based on the user's purchasing history and preferences to provide the most appropriate information. For example, the system filters collected sale information based on the user's past purchasing history. For example, the generation AI may prioritize displaying sale information for brands that the user has purchased in the past. The system can also filter sale information based on the user's preferences. For example, the generation AI may prioritize displaying sale information for specifications and designs that the user prefers. This allows the user to obtain the most appropriate sale information based on their preferences and past purchasing history.

[0069] The system uses the emotion estimation function to analyze the user's emotions in real time when browsing sale information, and can prioritize displaying information that elicits positive emotions. For example, the system uses the emotion estimation function to analyze the user's emotions in real time, and can prioritize displaying information that elicits positive emotions. For example, if the user is excited, the generation AI can display special discounts and limited offers. The system can also display products with high reviews and ratings if the user is excited. This allows the user to prioritize information that elicits positive emotions.

[0070] The system can predict the optimal timing for a purchase by taking into account past purchase data and market trends. For example, the system analyzes past purchase data and market trends to predict the optimal timing for a purchase. For example, the generative AI can analyze past smartphone purchase data and market trends to identify a tendency for prices to fall at specific times. The system can also analyze past laptop purchase data and market trends to predict that prices will fall during specific sales events. This allows users to know the optimal timing for a purchase based on past data and market trends.

[0071] The system analyzes users' purchasing patterns and can customize and suggest the optimal purchase timing for each individual user. For example, the system analyzes a user's past purchasing patterns and can customize and suggest the optimal purchase timing for each individual user. For example, the generative AI predicts the optimal timing based on the timing and frequency of the user's past purchases. The system can also predict the optimal timing based on the user's tendency to prefer specific brands or models. This allows users to know the optimal purchase timing based on their own purchasing patterns.

[0072] The system uses the emotion estimation function to analyze the user's emotions in real time when receiving suggestions for the best time to purchase, and can adjust the suggestions accordingly. For example, the generation AI can suggest special discounts and limited offers if the user is excited. The system can also suggest products with high reviews and ratings if the user is excited. This allows users to receive the best suggestions according to their emotions.

[0073] The system can make highly reliable suggestions by referring to the purchase data and reviews of other users.The system can make highly reliable suggestions by referring to the purchase data and reviews of other users.For example, the generation AI can make suggestions based on periods with high review ratings or periods when purchases are concentrated.The system can also make suggestions based on periods of sales events when purchases are concentrated.This allows users to receive highly reliable suggestions.

[0074] The system can make special suggestions by taking into account the user's life events. For example, the system can make special suggestions by taking into account the user's birthday or anniversary. For example, the generation AI can suggest birthday discounts and special offers. The system can also suggest sale information and special offers exclusive to anniversaries. This allows users to receive special suggestions tailored to their life events.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The system can estimate a user's emotions and tailor purchase suggestions based on the estimated emotions. For example, if a user is feeling stressed, the system can suggest products or services that have a relaxing effect. Or, if a user is excited, the system can suggest special discounts or exclusive offers. This allows users to receive optimal suggestions based on their emotions.

[0077] The system can estimate the user's emotions and adjust the timing and content of notifications based on the estimated emotions. For example, if the user feels busy, the system can suppress notifications or adjust the notifications to be sent later. If the user feels relaxed, the system can provide detailed sales information. This allows users to receive notifications that are optimal for their emotions.

[0078] The system can estimate the user's emotions and provide post-purchase feedback based on the estimated emotions. For example, if the user is satisfied, it can provide information or special offers that will be useful for the next purchase. If the user is dissatisfied, it can also provide support information to solve the problem. This allows the user to have a pleasant experience even after the purchase.

[0079] The system can estimate a user's emotions and provide pre-purchase advice based on the estimated emotions. For example, if a user is feeling anxious, the system can provide information and reviews to reassure the user. If a user is excited, the system can provide special offers and discount information to encourage the purchase. This allows users to receive the most appropriate advice based on their emotions.

[0080] The system can estimate the user's emotions and provide post-purchase support based on the estimated emotions. For example, if the user is satisfied, it can provide information or benefits that will be useful for the next purchase. Also, if the user is dissatisfied, it can provide support information to solve the problem. This allows the user to have a pleasant experience even after purchasing.

[0081] The system can provide maintenance information for related products based on the user's purchase history. For example, it can notify the user when maintenance is due for home appliances purchased in the past. It can also notify the user before the warranty period of a product purchased by the user expires. This allows the user to efficiently carry out maintenance after purchase.

[0082] The system can suggest accessories and additional options for related products based on the user's purchasing history. For example, if a user purchases a smartphone, the system can suggest a corresponding case or charger. If a user purchases a laptop, the system can suggest a corresponding bag or mouse. This allows users to find related products efficiently.

[0083] The system can provide reviews and ratings of related products based on a user's purchasing history. For example, it can display reviews of a product the user is considering purchasing. It can also suggest similar products based on ratings of products the user has previously purchased. This allows users to consider purchases based on reliable information.

[0084] The system can prioritize notifications of sales information for related products based on the user's purchasing history. For example, it can prioritize displaying sales information for brands the user has previously purchased. It can also notify users of sales information for accessories or additional options for products the user has previously purchased. This allows users to obtain the most appropriate sales information based on their preferences and past purchasing history.

[0085] The system can provide new product information for related products based on a user's purchasing history. For example, the system can notify the user when a new model of a smartphone that the user previously purchased is released. It can also notify the user when a new version of a home appliance that the user previously purchased is released. This allows the user to efficiently obtain the latest product information.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The product registration unit registers the product that the user wishes to purchase. For example, the user can enter a specific product name such as a smartphone or laptop. The product registration unit can also register information such as the desired price and category. Step 2: The information gathering unit collects sales and campaign information from the Internet. For example, the generation AI gathers information on specific sales events such as Black Friday and Cyber ​​Monday. The information gathering unit can also automatically collect information from online stores and websites. Step 3: The analysis unit analyzes the collected information and suggests the optimal time to purchase. For example, the generation AI analyzes past sales data and price fluctuation patterns to predict when a particular product will be cheapest. The analysis unit can also suggest the optimal time to purchase for each individual user, taking into account the user's purchase history and preferences. Step 4: The notification unit notifies the user of the optimal purchase timing. For example, the generation AI can notify the user via smartphone push notification, email, or messenger app. The notification unit can also analyze the user's emotions in real time and provide feedback to elicit positive emotions.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] 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.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 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 product registration unit for registering products that users wish to purchase; an information collection unit that collects sales information or campaign information on the Internet; an analysis unit that analyzes the information collected by the information collection unit and suggests the optimal timing for purchasing; a notification unit that notifies the user of the optimal purchase timing suggested by the analysis unit. A system characterized by:

2. The information collecting unit Prioritize collection of information on specific sales events The system of claim 1 .

3. The analysis unit Analyze historical sales data or patterns of price fluctuations The system of claim 1 .

4. The notification unit Notify the user via push notification, email, or messenger app The system of claim 1 .

5. The system comprises: Manage the user's purchase history and store information about products purchased in the past The system of claim 1 .

6. The system comprises: Analyze the user's past purchase history and search history to automatically suggest related products. The system of claim 1 .

7. The system comprises: Analyze the user's preferences and lifestyle to recommend the most suitable product categories and brands The system of claim 1 .

8. The system comprises: Analyze the user's voice input and automatically register product information using voice recognition technology. The system of claim 1 .

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