System

Abstract

An object of a system according to an embodiment is to automate selection and sending of a gift for a special day and reduce a burden on a user.SOLUTION: A system includes a reception unit, an acquisition unit, a selection unit, a notification unit, and a transmission unit. The reception unit receives input of a special day and a budget. The acquisition unit acquires a past purchase history and preference of the user based on the information received by the reception unit. The selection unit selects a gift based on the information acquired by the acquisition unit. The notification unit notifies the user of the gift selected by the selection unit and requests the user to confirm the gift. The sending unit automatically purchases and sends the gift after the user confirms by the notification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, selecting a gift for a special occasion can be time-consuming and laborious, potentially causing stress for users.

[0005] The system according to the embodiment aims to automate the process of selecting and sending gifts for special occasions, thereby reducing the burden on users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a selection unit, a notification unit, and a sending unit. The reception unit accepts input of a special date and a budget. The acquisition unit acquires the user's past purchase history and preferences based on the information accepted by the reception unit. The selection unit selects a gift based on the information acquired by the acquisition unit. The notification unit notifies the user of the gift selected by the selection unit and asks for confirmation. The sending unit automatically purchases and sends the gift after the user confirms via the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the process of selecting and sending gifts for special occasions, thereby reducing the burden on users. [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 gift selection and delivery system according to an embodiment of the present invention automatically selects and delivers an optimal gift by inputting a special date and budget, taking into account the user's past purchase history and preferences. The gift selection and delivery system accepts input of the special date and budget, acquires the user's past purchase history and preferences, selects an optimal gift, notifies the user and asks for confirmation, and automatically purchases and delivers the gift after confirmation. For example, in a gift selection and delivery system, a user specifies a special date (e.g., a birthday or anniversary) and sets a budget. This information is entered into the system. The system then automatically selects an appropriate gift based on the user's specified special date and budget. In doing so, the system takes into account the user's past purchase history and preferences, as well as information about friends and acquaintances. For example, the system refers to gifts the user has purchased in the past and brands and products preferred by friends. The selected gift is then notified to the user and requested for confirmation. Once the user confirms, the system automatically purchases the gift and delivers it to the specified address. In this way, the gift selection and delivery system reduces the time and stress of the user's gift selection process, allowing them to celebrate special occasions without forgetting them. As a result, the gift selection and delivery system reduces the time and stress users spend selecting gifts, allowing them to celebrate special occasions without forgetting them. For example, this service is extremely convenient for busy businessmen and people who have trouble choosing gifts. Furthermore, the system can take into account the latest trends and popular products when selecting gifts, allowing users to always choose the perfect gift. This service not only increases user convenience, but also makes gift selection fun. For example, users can enjoy the fun of choosing their own gifts from those suggested by the system. In this way, the gift selection and delivery system provides an automatic and convenient gift selection and delivery service, enriching users' lives.

[0029] A gift selection and delivery system according to an embodiment includes a reception unit, an acquisition unit, a selection unit, a notification unit, and a delivery unit. The reception unit receives input of a special day and a budget. Examples of special days include, but are not limited to, birthdays, anniversaries, and holidays. Examples of budgets include, but are not limited to, upper and lower limits on amounts and currency units. The acquisition unit acquires the user's past purchase history and preferences based on the information received by the reception unit. Examples of past purchase history include, but are not limited to, purchase history for the past year and purchase history for specific categories. Examples of preferences include, but are not limited to, preferences estimated from the past purchase history and preferences explicitly set by the user. The selection unit selects a gift based on the information acquired by the acquisition unit. Examples of gifts include, but are not limited to, goods, services, digital content, and the like. The selection unit can select the optimal gift by, for example, analyzing trends and popular products using AI. The notification unit notifies the user of the gift selected by the selection unit and requests confirmation. The notification may include, but is not limited to, email, SMS, in-app notification, etc. The sending unit automatically purchases and sends the gift after the user confirms via the notification unit. The sending may include, but is not limited to, delivery by courier, email, download link, etc. In this way, the gift selection and sending system according to the embodiment can automatically select and send a gift based on the user's special day and budget.

[0030] The acquisition unit can acquire a user's past purchase history and preferences by linking data from an e-commerce site or a social networking site. Examples of e-commerce sites include, but are not limited to, Yahoo! (registered trademark), Amazon (registered trademark), Rakuten (registered trademark), and eBay (registered trademark). Examples of social networking sites include, but are not limited to, Facebook (registered trademark), Twitter (registered trademark), and Instagram (registered trademark). The acquisition unit can acquire a user's purchase history by, for example, using an API of the e-commerce site. The acquisition unit can also estimate a user's preferences by analyzing social networking site data. For example, the acquisition unit estimates a user's preferences based on products that the user has "liked" or posts that the user has shared on the social networking site. Furthermore, the acquisition unit can combine the user's past purchase history with data from the social networking site to acquire more accurate preferences. This allows the acquisition unit to acquire a user's past purchase history and preferences more accurately.

[0031] The selection unit can use AI to analyze trends and popular products and select the optimal gift. AI includes, but is not limited to, technologies such as machine learning, deep learning, and natural language processing. Trends include, but are not limited to, recent popular products and popular themes. Popular products include, but are not limited to, sales rankings and review ratings. The selection unit can predict trends from past data using, for example, a machine learning algorithm. The selection unit can also use deep learning to analyze product images and text data to identify popular products. Furthermore, the selection unit can use natural language processing to analyze user reviews and comments and evaluate products. This allows the selection unit to select the optimal gift taking trends and popular products into consideration.

[0032] The notification unit may provide an interface for the user to confirm. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio interface. For example, the notification unit may display the gift selection results to the user through the web interface. The notification unit may also send a notification to the user through the mobile app and request confirmation. Furthermore, the notification unit may notify the user through an audio interface and receive confirmation by audio. This allows the notification unit to provide an interface that is easy for the user to confirm.

[0033] The selection unit may execute an algorithm for gift selection. Examples of the algorithm include, but are not limited to, a recommendation system and a filtering algorithm. For example, the selection unit may use a recommendation system to recommend gifts based on a user's past purchase history and preferences. The selection unit may also use a filtering algorithm to select gifts based on specific criteria. For example, the selection unit may filter gifts based on price ranges or categories. Furthermore, the selection unit may improve the algorithm based on user feedback to increase the accuracy of the selection. As a result, the selection unit may select the optimal gift by executing the gift selection algorithm.

[0034] The sending unit can automatically purchase and send the gift after the user confirms. Examples of sending methods include, but are not limited to, delivery by courier, email, download link, etc. For example, the sending unit can send an item to the user using a courier. The sending unit can also send digital content to the user using email. Furthermore, the sending unit can provide a download link so that the user can download the digital content. This allows the sending unit to automatically purchase and send the gift after the user confirms.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. The input history includes, for example, past input data, input frequency, etc., but is not limited to these examples. For example, the reception unit can automatically display special days and budgets that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest special days and budgets to be used in a specific time period from the user's past input history. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0036] When inputting a special date or budget, the reception unit can present input candidates based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the reception unit can suggest related special dates and budgets based on events or topics in which the user has recently taken an interest. The reception unit can also suggest appropriate special dates and budgets based on the user's current living situation (e.g., moving or a new job). Furthermore, the reception unit can suggest related special dates and budgets based on the user's areas of interest (e.g., hobbies and sports). This allows the reception unit to present appropriate input candidates based on the user's current living situation and areas of interest.

[0037] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting a special date or budget. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user inputs a special date or budget by voice, the reception unit can automatically analyze the input content using voice recognition technology. Furthermore, when a user inputs a special date or budget by text, the reception unit can analyze the input content in real time and present appropriate candidates. Furthermore, when a user inputs a special date or budget by image, the reception unit can analyze the input content using image recognition technology and present appropriate candidates. This allows the reception unit to select the optimal input means depending on the user's input method.

[0038] When inputting special dates or budgets, the reception unit can prioritize relevant input candidates by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the reception unit can prioritize presenting special dates and budgets related to that area. Furthermore, if the user is traveling, the reception unit can prioritize presenting special dates and budgets related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize presenting special dates and budgets related to the event. This allows the reception unit to present relevant input candidates based on the user's geographical location information.

[0039] When inputting a special day or budget, the reception unit can analyze the user's social media activity and present related input candidates. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can present special days and budgets related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and present related special days and budgets. Furthermore, the reception unit can present related special days and budgets by referring to the activities of the user's friends on social media. In this way, the reception unit can present related input candidates based on the user's social media activity.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback when inputting information about special dates or budgets. Examples of feedback include, but are not limited to, survey results and review comments. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also simplify the input procedure based on the user's past feedback. Furthermore, the reception unit can improve the method of presenting input candidates based on the user's past feedback. This allows the reception unit to customize the input interface based on the user's past feedback.

[0041] The acquisition unit can analyze the user's past purchase history and select the optimal acquisition method. Examples of purchase history include, but are not limited to, purchase history for the past year and purchase history for a specific category. Examples of acquisition methods include, but are not limited to, API integration and database query. The acquisition unit can select the optimal acquisition method based on, for example, products frequently purchased by the user in the past. The acquisition unit can also predict products likely to be purchased during a specific time period based on the user's past purchase history and select an acquisition method. Furthermore, the acquisition unit can analyze the user's past purchase history and select the most efficient acquisition method. This allows the acquisition unit to select the optimal acquisition method based on the user's past purchase history.

[0042] When acquiring the purchase history and preferences, the acquisition unit can filter the purchase history and preferences based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. The acquisition unit can filter the purchase history and preferences based on, for example, the user's current living situation (e.g., moving or a new job). The acquisition unit can also filter the purchase history and preferences based on the user's areas of interest (e.g., hobbies and sports). Furthermore, the acquisition unit can acquire optimal purchase history and preferences based on the user's current living situation and areas of interest. This allows the acquisition unit to filter the purchase history and preferences based on the user's current living situation and areas of interest.

[0043] When acquiring purchase history or preferences, the acquisition unit can select the optimal acquisition means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. Acquisition means include, but are not limited to, API integration, database query, etc. For example, when a user inputs purchase history or preferences by voice, the acquisition unit can automatically analyze the acquired content using voice recognition technology. Furthermore, when a user inputs purchase history or preferences by text, the acquisition unit can analyze the input content in real time and present appropriate candidates. Furthermore, when a user inputs purchase history or preferences by image, the acquisition unit can analyze the acquired content using image recognition technology and present appropriate candidates. This allows the acquisition unit to select the optimal acquisition means depending on the user's input method.

[0044] When acquiring purchase histories and preferences, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the acquisition unit can prioritize acquiring purchase histories and preferences related to that area. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring purchase histories and preferences related to the travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring purchase histories and preferences related to the event. This allows the acquisition unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0045] When acquiring the purchase history and preferences, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the acquisition unit can acquire purchase history and preferences related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related purchase history and preferences. Furthermore, the acquisition unit can acquire related purchase history and preferences by referring to the activities of the user's friends on social media. This allows the acquisition unit to acquire related information based on the user's social media activity.

[0046] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the purchase history or preferences. Examples of feedback include, but are not limited to, survey results and review comments. The acquisition unit can adjust the acquisition method, for example, based on feedback previously provided by the user. The acquisition unit can also simplify the acquisition procedure based on feedback previously provided by the user. Furthermore, the acquisition unit can improve the method of presenting acquisition candidates based on feedback previously provided by the user. This allows the acquisition unit to customize the acquisition method based on the user's past feedback.

[0047] The selection unit can adjust the level of detail of the selection based on the importance of the product when selecting a gift. Product importance includes, but is not limited to, for example, price, popularity, and review rating. For example, the selection unit can provide detailed product information for an important gift to increase the accuracy of the selection. Furthermore, the selection unit can provide simple product information for a less important gift to enable quick selection. Furthermore, the selection unit can adjust the level of detail of the selection based on the importance of the product to reduce the burden on the user. This allows the selection unit to adjust the level of detail of the selection based on the importance of the product.

[0048] When selecting a gift, the selection unit can apply different selection algorithms depending on the product category. Product categories include, but are not limited to, electronic devices, books, and clothing. Selection algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, in the case of fashion items, the selection unit can apply a selection algorithm that takes into account trends and popular products. In addition, in the case of electronic devices, the selection unit can apply a selection algorithm that emphasizes performance and price. Furthermore, in the case of food, the selection unit can apply a selection algorithm that takes into account expiration dates and freshness. This allows the selection unit to apply the optimal selection algorithm depending on the product category.

[0049] When selecting a gift, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. Past selection results include, for example, selection results from the past year, selection results in a specific category, etc., but are not limited to these examples. Methods for improving the selection accuracy include, for example, improving the selection algorithm and incorporating feedback, but are not limited to these examples. The selection unit can improve the accuracy of the selection based on, for example, evaluations of gifts selected by the user in the past. The selection unit can also analyze the user's preferences and tendencies from the user's past selection results and suggest optimal gifts. Furthermore, the selection unit can improve the selection algorithm by referring to the user's past selection results. This allows the selection unit to improve the accuracy of the selection based on the user's past selection results.

[0050] When selecting gifts, the selection unit can determine the selection priority based on the submission date of the product. The submission date can be, for example, a release date, a stock date, etc., but is not limited to these examples. Criteria for determining the priority can be, for example, importance, urgency, etc., but is not limited to these examples. For example, the selection unit can prioritize gifts that are due soon to be submitted so that they can be sent quickly. For gifts that are due further in the future, the selection unit can provide more detailed selection options to broaden the range of choices. Furthermore, the selection unit can adjust the selection priority based on the submission date to reduce the burden on the user. This allows the selection unit to determine the selection priority based on the submission date of the product.

[0051] The selection unit can adjust the selection order based on the relevance of products when selecting a gift. Relevance includes, but is not limited to, for example, matching of categories, matching of user preferences, etc. The order can be adjusted based on, but is not limited to, for example, criteria such as high relevance or high importance. For example, the selection unit can prioritize displaying highly relevant products to enable quick selection. The selection unit can also postpone products with low relevance to reduce the effort required for selection. Furthermore, the selection unit can adjust the selection order based on the relevance of products to reduce the burden on the user. This allows the selection unit to adjust the selection order based on the relevance of products.

[0052] The selection unit may adjust the use of technical terms in the selection process according to the user's level of expertise when selecting a gift. Examples of the level of expertise include, but are not limited to, survey results, past selection history, etc. Examples of technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the selection unit may use detailed technical terms to improve the accuracy of the selection. Furthermore, if the user does not have specialized knowledge, the selection unit may use simple terms to reduce the effort required for selection. Furthermore, the selection unit may adjust the use of technical terms in the selection process according to the user's level of expertise, thereby reducing the burden on the user. This allows the selection unit to adjust the use of technical terms in the selection process according to the user's level of expertise.

[0053] The notification unit may adjust the level of detail of the notification based on the importance of the gift when sending the notification. Examples of the importance of the gift include, but are not limited to, price, popularity, and review ratings. Examples of the level of detail of the notification include, but are not limited to, detailed information about the notification content and the frequency of notifications. For example, the notification unit may provide detailed notification content for an important gift to increase the accuracy of confirmation. Furthermore, the notification unit may provide simple notification content for a less important gift to enable quick confirmation. Furthermore, the notification unit may adjust the level of detail of the notification based on the importance of the gift to reduce the burden on the user. This allows the notification unit to adjust the level of detail of the notification based on the importance of the gift.

[0054] The notification unit may apply different notification algorithms depending on the gift category when issuing a notification. Gift categories include, but are not limited to, electronic devices, books, and clothing. Notification algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, in the case of fashion items, the notification unit may apply a notification algorithm that takes into account trends and popular items. In addition, in the case of electronic devices, the notification unit may apply a notification algorithm that emphasizes performance and price. Furthermore, in the case of food, the notification unit may apply a notification algorithm that takes into account expiration dates and freshness. This allows the notification unit to apply the optimal notification algorithm depending on the gift category.

[0055] The notification unit can improve the accuracy of notifications by referring to the user's past notification results. Past notification results include, but are not limited to, notification results from the past year and notification results for specific categories. Methods for improving notification accuracy include, but are not limited to, improving the notification algorithm and incorporating feedback. The notification unit can improve the accuracy of notifications based on, for example, evaluations of notifications previously viewed by the user. The notification unit can also analyze the user's preferences and trends from the user's past notification results and suggest optimal notifications. Furthermore, the notification unit can improve the notification algorithm by referring to the user's past notification results. This allows the notification unit to improve the accuracy of notifications based on the user's past notification results.

[0056] The notification unit may determine the priority of notifications based on the submission date of the gift at the time of notification. The submission date may include, but is not limited to, the release date, the arrival date, etc. The priority may be determined based on, but is not limited to, the importance, the urgency, etc. For example, the notification unit may prioritize notifications of gifts whose submission date is approaching, allowing for quick confirmation. For gifts whose submission date is further away, the notification unit may provide detailed notification options to broaden the scope of confirmation. Furthermore, the notification unit may adjust the priority of notifications according to the submission date to reduce the burden on the user. This allows the notification unit to determine the priority of notifications according to the submission date of the gift.

[0057] The notification unit can adjust the order of notifications based on the relevance of the gifts when notifying the user. Relevance includes, but is not limited to, for example, matching of the category or matching of the user's preferences. The order can be adjusted based on, but is not limited to, criteria such as relevance or importance. For example, the notification unit can prioritize notifying highly relevant gifts to enable quick confirmation. The notification unit can also postpone less relevant gifts to reduce the effort required for confirmation. Furthermore, the notification unit can adjust the order of notifications based on the relevance of the gifts to reduce the burden on the user. This allows the notification unit to adjust the order of notifications based on the relevance of the gifts.

[0058] The notification unit may adjust the use of technical terms in notifications according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results, past selection history, etc. Examples of technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the notification unit may use detailed technical terms to improve the accuracy of the notification. Furthermore, if the user does not have specialized knowledge, the notification unit may use simple terms to reduce the effort required for notification. Furthermore, the notification unit may adjust the use of technical terms in notifications according to the user's level of expertise, thereby reducing the burden on the user. This allows the notification unit to adjust the use of technical terms in notifications according to the user's level of expertise.

[0059] At the time of sending, the sending unit can analyze the user's past sending history to select the optimal sending method. Examples of sending history include, but are not limited to, sending history for the past year and sending history for a specific category. Examples of sending methods include, but are not limited to, courier service, email, and download link. The sending unit can select the optimal sending method based on, for example, sending methods used by the user in the past. The sending unit can also predict and select a sending method for a specific time period based on the user's past sending history. Furthermore, the sending unit can analyze the user's past sending history and select the most efficient sending method. This allows the sending unit to select the optimal sending method based on the user's past sending history.

[0060] The sending unit can customize the sending means based on the user's current living situation at the time of sending. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of sending means include, but are not limited to, courier service, email, and download link. The sending unit can suggest the optimal sending means based on, for example, the user's current living situation (e.g., moving or a new job). The sending unit can also customize the sending means according to the user's current living situation to expand the range of choices. Furthermore, the sending unit can adjust the sending means based on the user's current living situation to reduce the burden on the user. This allows the sending unit to customize the sending means based on the user's current living situation.

[0061] The sending unit can improve the sending method by reflecting user feedback at the time of sending. Examples of feedback include, but are not limited to, survey results and review comments. Examples of sending methods include, but are not limited to, courier service, email, and download links. For example, the sending unit can adjust the sending method based on feedback previously provided by the user. The sending unit can also simplify the sending procedure based on feedback previously provided by the user. Furthermore, the sending unit can improve the method of presenting delivery options based on feedback previously provided by the user. This allows the sending unit to improve the sending method based on user feedback.

[0062] The sending unit can select the optimal sending method by taking into account the user's geographical location information when sending. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of sending methods include, but are not limited to, courier service, email, and download links. For example, if the user lives in a specific area, the sending unit can suggest the optimal sending method for that area. Furthermore, if the user is traveling, the sending unit can suggest the optimal sending method for the user's travel destination. Furthermore, if the user is participating in a specific event, the sending unit can suggest the optimal sending method for that event. This allows the sending unit to select the optimal sending method based on the user's geographical location information.

[0063] At the time of delivery, the delivery unit can analyze the user's social media activity to suggest a delivery method. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. Delivery methods include, for example, but are not limited to, courier service, email, download links, etc. The delivery unit can, for example, suggest a delivery method related to a location where the user has checked in on social media. The delivery unit can also analyze the content of the user's social media posts to suggest a related delivery method. Furthermore, the delivery unit can suggest a related delivery method based on the activity of the user's friends on social media. This allows the delivery unit to suggest a delivery method based on the user's social media activity.

[0064] The sending unit can customize the sending method by reflecting the user's past feedback at the time of sending. Examples of feedback include, but are not limited to, survey results and review comments. Examples of sending methods include, but are not limited to, courier service, email, and download links. The sending unit can adjust the sending method based on, for example, feedback provided by the user in the past. The sending unit can also simplify the sending procedure based on the user's past feedback. Furthermore, the sending unit can improve the method of presenting delivery options based on the user's past feedback. This allows the sending unit to customize the sending method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The acquisition unit may also take into account the user's current health condition when acquiring the user's past purchase history and preferences. For example, if the user uses a fitness tracker or a health app, the acquisition unit may acquire that data and adjust gift selection based on the user's health condition. Furthermore, if the user has set a specific health goal, the acquisition unit may preferentially select gifts related to that goal. This allows the acquisition unit to select the optimal gift based on the user's health condition.

[0067] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, if the user has previously preferred email notifications, the notification unit can prioritize providing email notifications. Also, if the user frequently checks mobile app notifications, the notification unit can prioritize providing in-app notifications. Furthermore, if the user prefers voice notifications, the notification unit can provide notifications through a voice interface. This allows the notification unit to select the optimal notification method based on the user's past notification history.

[0068] The acquisition unit may also take the user's geographical location information into consideration when acquiring the user's past purchase history and preferences. For example, if the user lives in a specific area, the acquisition unit may preferentially acquire products and services that are popular in that area. Also, if the user is traveling, the acquisition unit may suggest gifts that can be used at the user's travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit may acquire gifts related to the event. This allows the acquisition unit to select the optimal gift based on the user's geographical location information.

[0069] The notification unit can customize the notification content taking into account the user's current living situation. For example, if the user has just started a new job, the notification unit can suggest work-related gifts. If the user has just moved, the notification unit can suggest gifts related to the new residence. Furthermore, if the user is attending a specific event, the notification unit can suggest gifts related to the event. In this way, the notification unit can provide optimal notification content based on the user's current living situation.

[0070] The acquisition unit can analyze the user's social media activities when acquiring the user's past purchase history and preferences. For example, the acquisition unit can infer preferences based on products the user has "liked" on social media and posts the user has shared. The acquisition unit can also refer to products and services the user's friends have shared on social media. Furthermore, the acquisition unit can acquire products and services related to places the user has checked in to on social media. This allows the acquisition unit to select the best gift based on the user's social media activities.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts input of special days and budgets. Special days include birthdays, anniversaries, holidays, etc., and budgets include upper and lower limits of amounts, currency units, etc. Step 2: The acquisition unit acquires the user's past purchase history and preferences based on the information received by the reception unit. The past purchase history includes purchase history for the past year and purchase history for specific categories, and the preferences include preferences estimated from the past purchase history and preferences explicitly set by the user. Step 3: The selection unit selects gifts based on the information acquired by the acquisition unit. Gifts can include goods, services, digital content, etc. The selection unit uses AI to analyze trends and popular products and select the most suitable gift. Step 4: The notification unit notifies the user of the gift selected by the selection unit and asks for confirmation. Notifications may include email, SMS, in-app notifications, etc. Step 5: The sending unit automatically purchases and sends the gift after the user confirms it via the notification unit. The sending method may include courier delivery, email, download link, etc.

[0073] (Example 2) A gift selection and delivery system according to an embodiment of the present invention automatically selects and delivers an optimal gift by taking into account a user's past purchase history and preferences when the user inputs a special date and budget. The gift selection and delivery system accepts input of the special date and budget, acquires the user's past purchase history and preferences, selects an optimal gift, notifies the user and asks for confirmation, and automatically purchases and delivers the gift after confirmation. For example, in a gift selection and delivery system, a user specifies a special date (e.g., a birthday or anniversary) and sets a budget. This information is entered into the system. The system then automatically selects an appropriate gift based on the user's specified special date and budget. In doing so, the system takes into account the user's past purchase history and preferences, as well as information about friends and acquaintances. For example, the system refers to gifts the user has purchased in the past and brands and products preferred by friends. The selected gift is then notified to the user and requested for confirmation. Once the user confirms, the system automatically purchases the gift and delivers it to the specified address. In this way, the gift selection and delivery system reduces the time and stress of the user's gift selection process, allowing them to celebrate special occasions without forgetting them. As a result, the gift selection and delivery system reduces the time and stress users spend selecting gifts, allowing them to celebrate special occasions without forgetting them. For example, this service is extremely convenient for busy businessmen and people who have trouble choosing gifts. Furthermore, the system can take into account the latest trends and popular products when selecting gifts, allowing users to always choose the perfect gift. This service not only increases user convenience, but also makes gift selection fun. For example, users can enjoy the fun of choosing their own gifts from those suggested by the system. In this way, the gift selection and delivery system provides an automatic and convenient gift selection and delivery service, enriching users' lives.

[0074] A gift selection and delivery system according to an embodiment includes a reception unit, an acquisition unit, a selection unit, a notification unit, and a delivery unit. The reception unit receives input of a special day and a budget. Examples of special days include, but are not limited to, birthdays, anniversaries, and holidays. Examples of budgets include, but are not limited to, upper and lower limits on amounts and currency units. The acquisition unit acquires the user's past purchase history and preferences based on the information received by the reception unit. Examples of past purchase history include, but are not limited to, purchase history for the past year and purchase history for specific categories. Examples of preferences include, but are not limited to, preferences estimated from the past purchase history and preferences explicitly set by the user. The selection unit selects a gift based on the information acquired by the acquisition unit. Examples of gifts include, but are not limited to, goods, services, digital content, and the like. The selection unit can select the optimal gift by, for example, analyzing trends and popular products using AI. The notification unit notifies the user of the gift selected by the selection unit and requests confirmation. The notification may include, but is not limited to, email, SMS, in-app notification, etc. The sending unit automatically purchases and sends the gift after the user confirms via the notification unit. The sending may include, but is not limited to, delivery by courier, email, download link, etc. In this way, the gift selection and sending system according to the embodiment can automatically select and send a gift based on the user's special day and budget.

[0075] The acquisition unit can acquire a user's past purchase history and preferences by linking data from an e-commerce site or a social networking site. Examples of e-commerce sites include, but are not limited to, Amazon, Rakuten, and eBay. Examples of social networking sites include, but are not limited to, Facebook, Twitter, and Instagram. The acquisition unit can acquire a user's purchase history by, for example, using an API of the e-commerce site. The acquisition unit can also estimate a user's preferences by analyzing social networking site data. For example, the acquisition unit estimates a user's preferences based on products that the user has "liked" or posts that the user has shared on the social networking site. Furthermore, the acquisition unit can combine the user's past purchase history with the social networking site data to acquire more accurate preferences. This allows the acquisition unit to more accurately acquire a user's past purchase history and preferences.

[0076] The selection unit can use AI to analyze trends and popular products and select the optimal gift. AI includes, but is not limited to, technologies such as machine learning, deep learning, and natural language processing. Trends include, but are not limited to, recent popular products and popular themes. Popular products include, but are not limited to, sales rankings and review ratings. The selection unit can predict trends from past data using, for example, a machine learning algorithm. The selection unit can also use deep learning to analyze product images and text data to identify popular products. Furthermore, the selection unit can use natural language processing to analyze user reviews and comments and evaluate products. This allows the selection unit to select the optimal gift taking trends and popular products into consideration.

[0077] The notification unit may provide an interface for the user to confirm. Examples of the interface include, but are not limited to, a web interface, a mobile app, and an audio interface. For example, the notification unit may display the gift selection results to the user through the web interface. The notification unit may also send a notification to the user through the mobile app and request confirmation. Furthermore, the notification unit may notify the user through an audio interface and receive confirmation by audio. This allows the notification unit to provide an interface that is easy for the user to confirm.

[0078] The selection unit may execute an algorithm for gift selection. Examples of the algorithm include, but are not limited to, a recommendation system and a filtering algorithm. For example, the selection unit may use a recommendation system to recommend gifts based on a user's past purchase history and preferences. The selection unit may also use a filtering algorithm to select gifts based on specific criteria. For example, the selection unit may filter gifts based on price ranges or categories. Furthermore, the selection unit may improve the algorithm based on user feedback to increase the accuracy of the selection. As a result, the selection unit may select the optimal gift by executing the gift selection algorithm.

[0079] The sending unit can automatically purchase and send the gift after the user confirms. Examples of sending methods include, but are not limited to, delivery by courier, email, download link, etc. For example, the sending unit can send an item to the user using a courier. The sending unit can also send digital content to the user using email. Furthermore, the sending unit can provide a download link so that the user can download the digital content. This allows the sending unit to automatically purchase and send the gift after the user confirms.

[0080] The reception unit can estimate the user's emotions and adjust the input method for special days and budgets based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, 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 quick input of special days and budgets. This allows the reception unit to adjust the input method for special days and budgets according to the user's emotions.

[0081] The reception unit can analyze the user's past input history and provide an optimal input interface. The input history includes, for example, past input data, input frequency, etc., but is not limited to these examples. For example, the reception unit can automatically display special days and budgets that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest special days and budgets to be used in a specific time period from the user's past input history. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0082] When inputting a special date or budget, the reception unit can present input candidates based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the reception unit can suggest related special dates and budgets based on events or topics in which the user has recently taken an interest. The reception unit can also suggest appropriate special dates and budgets based on the user's current living situation (e.g., moving or a new job). Furthermore, the reception unit can suggest related special dates and budgets based on the user's areas of interest (e.g., hobbies and sports). This allows the reception unit to present appropriate input candidates based on the user's current living situation and areas of interest.

[0083] The reception unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting a special date or budget. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, when a user inputs a special date or budget by voice, the reception unit can automatically analyze the input content using voice recognition technology. Furthermore, when a user inputs a special date or budget by text, the reception unit can analyze the input content in real time and present appropriate candidates. Furthermore, when a user inputs a special date or budget by image, the reception unit can analyze the input content using image recognition technology and present appropriate candidates. This allows the reception unit to select the optimal input means depending on the user's input method.

[0084] The reception unit can estimate the user's emotions and prioritize input contents based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the reception unit can prioritize displaying important input items to enable the user to complete input quickly. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to enable the user to complete input quickly. In this way, the reception unit can prioritize input contents according to the user's emotions.

[0085] When inputting special dates or budgets, the reception unit can prioritize relevant input candidates by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the reception unit can prioritize presenting special dates and budgets related to that area. Furthermore, if the user is traveling, the reception unit can prioritize presenting special dates and budgets related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize presenting special dates and budgets related to the event. This allows the reception unit to present relevant input candidates based on the user's geographical location information.

[0086] When inputting a special day or budget, the reception unit can analyze the user's social media activity and present related input candidates. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can present special days and budgets related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and present related special days and budgets. Furthermore, the reception unit can present related special days and budgets by referring to the activities of the user's friends on social media. In this way, the reception unit can present related input candidates based on the user's social media activity.

[0087] The reception unit can customize the input interface by reflecting the user's past feedback when inputting information about special dates or budgets. Examples of feedback include, but are not limited to, survey results and review comments. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also simplify the input procedure based on the user's past feedback. Furthermore, the reception unit can improve the method of presenting input candidates based on the user's past feedback. This allows the reception unit to customize the input interface based on the user's past feedback.

[0088] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the purchase history and preferences based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the acquisition unit can acquire detailed purchase history and preferences when the user is relaxed. Furthermore, the acquisition unit can prioritize acquiring the most important purchase history and preferences when the user is in a hurry. Furthermore, the acquisition unit can acquire simple purchase history and preferences when the user is stressed. This allows the acquisition unit to adjust the timing of acquiring the purchase history and preferences according to the user's emotions.

[0089] The acquisition unit can analyze the user's past purchase history and select the optimal acquisition method. Examples of purchase history include, but are not limited to, purchase history for the past year and purchase history for a specific category. Examples of acquisition methods include, but are not limited to, API integration and database query. The acquisition unit can select the optimal acquisition method based on, for example, products frequently purchased by the user in the past. The acquisition unit can also predict products likely to be purchased during a specific time period based on the user's past purchase history and select an acquisition method. Furthermore, the acquisition unit can analyze the user's past purchase history and select the most efficient acquisition method. This allows the acquisition unit to select the optimal acquisition method based on the user's past purchase history.

[0090] When acquiring the purchase history and preferences, the acquisition unit can filter the purchase history and preferences based on the user's current living situation and areas of interest. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. The acquisition unit can filter the purchase history and preferences based on, for example, the user's current living situation (e.g., moving or a new job). The acquisition unit can also filter the purchase history and preferences based on the user's areas of interest (e.g., hobbies and sports). Furthermore, the acquisition unit can acquire optimal purchase history and preferences based on the user's current living situation and areas of interest. This allows the acquisition unit to filter the purchase history and preferences based on the user's current living situation and areas of interest.

[0091] When acquiring purchase history or preferences, the acquisition unit can select the optimal acquisition means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. Acquisition means include, but are not limited to, API integration, database query, etc. For example, when a user inputs purchase history or preferences by voice, the acquisition unit can automatically analyze the acquired content using voice recognition technology. Furthermore, when a user inputs purchase history or preferences by text, the acquisition unit can analyze the input content in real time and present appropriate candidates. Furthermore, when a user inputs purchase history or preferences by image, the acquisition unit can analyze the acquired content using image recognition technology and present appropriate candidates. This allows the acquisition unit to select the optimal acquisition means depending on the user's input method.

[0092] The acquisition unit can estimate the user's emotion and determine the priority of information to be acquired based on the estimated user's emotion. Techniques for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring important information and provide it quickly. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring and providing detailed information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most important information and provide it quickly. This allows the acquisition unit to determine the priority of information to be acquired according to the user's emotion.

[0093] When acquiring purchase histories and preferences, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the acquisition unit can prioritize acquiring purchase histories and preferences related to that area. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring purchase histories and preferences related to the travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring purchase histories and preferences related to the event. This allows the acquisition unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0094] When acquiring the purchase history and preferences, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the acquisition unit can acquire purchase history and preferences related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related purchase history and preferences. Furthermore, the acquisition unit can acquire related purchase history and preferences by referring to the activities of the user's friends on social media. This allows the acquisition unit to acquire related information based on the user's social media activity.

[0095] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the purchase history or preferences. Examples of feedback include, but are not limited to, survey results and review comments. The acquisition unit can adjust the acquisition method, for example, based on feedback previously provided by the user. The acquisition unit can also simplify the acquisition procedure based on feedback previously provided by the user. Furthermore, the acquisition unit can improve the method of presenting acquisition candidates based on feedback previously provided by the user. This allows the acquisition unit to customize the acquisition method based on the user's past feedback.

[0096] The selection unit can estimate the user's emotions and adjust the presentation of the gift selection based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is relaxed, the selection unit can provide detailed gift information to widen the range of choices. Furthermore, when the user is in a hurry, the selection unit can prioritize displaying the most important gift information to enable quick selection. Furthermore, when the user is stressed, the selection unit can provide simple gift information to reduce the effort required for selection. This allows the selection unit to adjust the presentation of the gift selection according to the user's emotions.

[0097] The selection unit can adjust the level of detail of the selection based on the importance of the product when selecting a gift. Product importance includes, but is not limited to, for example, price, popularity, and review rating. For example, the selection unit can provide detailed product information for an important gift to increase the accuracy of the selection. Furthermore, the selection unit can provide simple product information for a less important gift to enable quick selection. Furthermore, the selection unit can adjust the level of detail of the selection based on the importance of the product to reduce the burden on the user. This allows the selection unit to adjust the level of detail of the selection based on the importance of the product.

[0098] When selecting a gift, the selection unit can apply different selection algorithms depending on the product category. Product categories include, but are not limited to, electronic devices, books, and clothing. Selection algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, in the case of fashion items, the selection unit can apply a selection algorithm that takes into account trends and popular products. In addition, in the case of electronic devices, the selection unit can apply a selection algorithm that emphasizes performance and price. Furthermore, in the case of food, the selection unit can apply a selection algorithm that takes into account expiration dates and freshness. This allows the selection unit to apply the optimal selection algorithm depending on the product category.

[0099] When selecting a gift, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. Past selection results include, for example, selection results from the past year, selection results in a specific category, etc., but are not limited to these examples. Methods for improving the selection accuracy include, for example, improving the selection algorithm and incorporating feedback, but are not limited to these examples. The selection unit can improve the accuracy of the selection based on, for example, evaluations of gifts selected by the user in the past. The selection unit can also analyze the user's preferences and tendencies from the user's past selection results and suggest optimal gifts. Furthermore, the selection unit can improve the selection algorithm by referring to the user's past selection results. This allows the selection unit to improve the accuracy of the selection based on the user's past selection results.

[0100] The selection unit can estimate the user's emotion and adjust the length of the selection based on the estimated user's emotion. Techniques for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The length of the selection can include, but are not limited to, the number of options and the number of steps in the selection process. For example, if the user is in a hurry, the selection unit can simplify the selection procedure to enable the user to make a quick selection. Furthermore, if the user is relaxed, the selection unit can provide detailed selection options to expand the range of choices. Furthermore, if the user is stressed, the selection unit can provide a simple selection procedure to reduce the effort required for selection. This allows the selection unit to adjust the length of the selection according to the user's emotion.

[0101] When selecting gifts, the selection unit can determine the selection priority based on the submission date of the product. The submission date can be, for example, a release date, a stock date, etc., but is not limited to these examples. Criteria for determining the priority can be, for example, importance, urgency, etc., but is not limited to these examples. For example, the selection unit can prioritize gifts that are due soon to be submitted so that they can be sent quickly. For gifts that are due further in the future, the selection unit can provide more detailed selection options to broaden the range of choices. Furthermore, the selection unit can adjust the selection priority based on the submission date to reduce the burden on the user. This allows the selection unit to determine the selection priority based on the submission date of the product.

[0102] The selection unit can adjust the selection order based on the relevance of products when selecting a gift. Relevance includes, but is not limited to, for example, matching of categories, matching of user preferences, etc. The order can be adjusted based on, but is not limited to, for example, criteria such as high relevance or high importance. For example, the selection unit can prioritize displaying highly relevant products to enable quick selection. The selection unit can also postpone products with low relevance to reduce the effort required for selection. Furthermore, the selection unit can adjust the selection order based on the relevance of products to reduce the burden on the user. This allows the selection unit to adjust the selection order based on the relevance of products.

[0103] The selection unit may adjust the use of technical terms in the selection process according to the user's level of expertise when selecting a gift. Examples of the level of expertise include, but are not limited to, survey results, past selection history, etc. Examples of technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the selection unit may use detailed technical terms to improve the accuracy of the selection. Furthermore, if the user does not have specialized knowledge, the selection unit may use simple terms to reduce the effort required for selection. Furthermore, the selection unit may adjust the use of technical terms in the selection process according to the user's level of expertise, thereby reducing the burden on the user. This allows the selection unit to adjust the use of technical terms in the selection process according to the user's level of expertise.

[0104] The notification unit can estimate the user's emotion and adjust the notification presentation method based on the estimated user's emotion. Techniques for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. Notification presentation methods include, but are not limited to, text, images, and videos. For example, when the user is relaxed, the notification unit can provide detailed notification content to expand the range of options. Furthermore, when the user is in a hurry, the notification unit can prioritize the most important notification content to enable quick confirmation. Furthermore, when the user is stressed, the notification unit can provide simple notification content to reduce the effort required for confirmation. This allows the notification unit to adjust the notification presentation method according to the user's emotion.

[0105] The notification unit may adjust the level of detail of the notification based on the importance of the gift when sending the notification. Examples of the importance of the gift include, but are not limited to, price, popularity, and review ratings. Examples of the level of detail of the notification include, but are not limited to, detailed information about the notification content and the frequency of notifications. For example, the notification unit may provide detailed notification content for an important gift to increase the accuracy of confirmation. Furthermore, the notification unit may provide simple notification content for a less important gift to enable quick confirmation. Furthermore, the notification unit may adjust the level of detail of the notification based on the importance of the gift to reduce the burden on the user. This allows the notification unit to adjust the level of detail of the notification based on the importance of the gift.

[0106] The notification unit may apply different notification algorithms depending on the gift category when issuing a notification. Gift categories include, but are not limited to, electronic devices, books, and clothing. Notification algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, in the case of fashion items, the notification unit may apply a notification algorithm that takes into account trends and popular items. In addition, in the case of electronic devices, the notification unit may apply a notification algorithm that emphasizes performance and price. Furthermore, in the case of food, the notification unit may apply a notification algorithm that takes into account expiration dates and freshness. This allows the notification unit to apply the optimal notification algorithm depending on the gift category.

[0107] The notification unit can improve the accuracy of notifications by referring to the user's past notification results. Past notification results include, but are not limited to, notification results from the past year and notification results for specific categories. Methods for improving notification accuracy include, but are not limited to, improving the notification algorithm and incorporating feedback. The notification unit can improve the accuracy of notifications based on, for example, evaluations of notifications previously viewed by the user. The notification unit can also analyze the user's preferences and trends from the user's past notification results and suggest optimal notifications. Furthermore, the notification unit can improve the notification algorithm by referring to the user's past notification results. This allows the notification unit to improve the accuracy of notifications based on the user's past notification results.

[0108] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated user emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The length of the notification can include, but are not limited to, detailed information about the notification content and the frequency of notifications. For example, if the user is in a hurry, the notification unit can simplify the notification procedure to enable quick confirmation. Furthermore, if the user is relaxed, the notification unit can provide detailed notification options to broaden the scope of confirmation. Furthermore, if the user is stressed, the notification unit can provide a simple notification procedure to reduce the effort required for confirmation. This allows the notification unit to adjust the length of the notification according to the user's emotions.

[0109] The notification unit may determine the priority of notifications based on the submission date of the gift at the time of notification. The submission date may include, but is not limited to, the release date, the arrival date, etc. The priority may be determined based on, but is not limited to, the importance, the urgency, etc. For example, the notification unit may prioritize notifications of gifts whose submission date is approaching, allowing for quick confirmation. For gifts whose submission date is further away, the notification unit may provide detailed notification options to broaden the scope of confirmation. Furthermore, the notification unit may adjust the priority of notifications according to the submission date to reduce the burden on the user. This allows the notification unit to determine the priority of notifications according to the submission date of the gift.

[0110] The notification unit can adjust the order of notifications based on the relevance of the gifts when notifying the user. Relevance includes, but is not limited to, for example, matching of the category or matching of the user's preferences. The order can be adjusted based on, but is not limited to, criteria such as relevance or importance. For example, the notification unit can prioritize notifying highly relevant gifts to enable quick confirmation. The notification unit can also postpone less relevant gifts to reduce the effort required for confirmation. Furthermore, the notification unit can adjust the order of notifications based on the relevance of the gifts to reduce the burden on the user. This allows the notification unit to adjust the order of notifications based on the relevance of the gifts.

[0111] The notification unit may adjust the use of technical terms in notifications according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results, past selection history, etc. Examples of technical terms include, but are not limited to, technical terms, industry jargon, etc. For example, if the user has specialized knowledge, the notification unit may use detailed technical terms to improve the accuracy of the notification. Furthermore, if the user does not have specialized knowledge, the notification unit may use simple terms to reduce the effort required for notification. Furthermore, the notification unit may adjust the use of technical terms in notifications according to the user's level of expertise, thereby reducing the burden on the user. This allows the notification unit to adjust the use of technical terms in notifications according to the user's level of expertise.

[0112] The sending unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. Techniques for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. Delivery methods include, but are not limited to, delivery by courier, email, and download link. For example, if the user is relaxed, the sending unit can provide detailed delivery options to expand the range of choices. Furthermore, if the user is in a hurry, the sending unit can prioritize the quickest delivery method. Furthermore, if the user is stressed, the sending unit can provide simple delivery options to reduce the effort required for selection. This allows the sending unit to adjust the delivery method according to the user's emotions.

[0113] At the time of sending, the sending unit can analyze the user's past sending history to select the optimal sending method. Examples of sending history include, but are not limited to, sending history for the past year and sending history for a specific category. Examples of sending methods include, but are not limited to, courier service, email, and download link. The sending unit can select the optimal sending method based on, for example, sending methods used by the user in the past. The sending unit can also predict and select a sending method for a specific time period based on the user's past sending history. Furthermore, the sending unit can analyze the user's past sending history and select the most efficient sending method. This allows the sending unit to select the optimal sending method based on the user's past sending history.

[0114] The sending unit can customize the sending means based on the user's current living situation at the time of sending. Examples of living situations include, but are not limited to, occupation, family structure, and lifestyle. Examples of sending means include, but are not limited to, courier service, email, and download link. The sending unit can suggest the optimal sending means based on, for example, the user's current living situation (e.g., moving or a new job). The sending unit can also customize the sending means according to the user's current living situation to expand the range of choices. Furthermore, the sending unit can adjust the sending means based on the user's current living situation to reduce the burden on the user. This allows the sending unit to customize the sending means based on the user's current living situation.

[0115] The sending unit can improve the sending method by reflecting user feedback at the time of sending. Examples of feedback include, but are not limited to, survey results and review comments. Examples of sending methods include, but are not limited to, courier service, email, and download links. For example, the sending unit can adjust the sending method based on feedback previously provided by the user. The sending unit can also simplify the sending procedure based on feedback previously provided by the user. Furthermore, the sending unit can improve the method of presenting delivery options based on feedback previously provided by the user. This allows the sending unit to improve the sending method based on user feedback.

[0116] The sending unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. Estimating emotions can be achieved using, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. Determining priorities can be achieved using, but is not limited to, criteria such as importance and urgency. For example, if the user is in a hurry, the sending unit can preferentially suggest the quickest delivery method. Furthermore, if the user is relaxed, the sending unit can provide detailed delivery options to expand the range of choices. Furthermore, if the user is stressed, the sending unit can provide simple delivery options to reduce the effort required for selection. This allows the sending unit to determine delivery priorities according to the user's emotions.

[0117] The sending unit can select the optimal sending method by taking into account the user's geographical location information when sending. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of sending methods include, but are not limited to, courier service, email, and download links. For example, if the user lives in a specific area, the sending unit can suggest the optimal sending method for that area. Furthermore, if the user is traveling, the sending unit can suggest the optimal sending method for the user's travel destination. Furthermore, if the user is participating in a specific event, the sending unit can suggest the optimal sending method for that event. This allows the sending unit to select the optimal sending method based on the user's geographical location information.

[0118] At the time of delivery, the delivery unit can analyze the user's social media activity to suggest a delivery method. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. Delivery methods include, for example, but are not limited to, courier service, email, download links, etc. The delivery unit can, for example, suggest a delivery method related to a location where the user has checked in on social media. The delivery unit can also analyze the content of the user's social media posts to suggest a related delivery method. Furthermore, the delivery unit can suggest a related delivery method based on the activity of the user's friends on social media. This allows the delivery unit to suggest a delivery method based on the user's social media activity.

[0119] The sending unit can customize the sending method by reflecting the user's past feedback at the time of sending. Examples of feedback include, but are not limited to, survey results and review comments. Examples of sending methods include, but are not limited to, courier service, email, and download links. The sending unit can adjust the sending method based on, for example, feedback provided by the user in the past. The sending unit can also simplify the sending procedure based on the user's past feedback. Furthermore, the sending unit can improve the method of presenting delivery options based on the user's past feedback. This allows the sending unit to customize the sending method based on the user's past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, acquisition unit, selection unit, notification unit, and delivery unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input of special dates and budgets. The acquisition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and acquires the user's past purchase history and preferences. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects a gift based on the acquired information. The notification unit is implemented, for example, by the output device 40 of the smart device 14 and notifies the user of the selected gift and requests confirmation. The delivery unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically purchases and delivers the gift after the user confirms. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, selection unit, notification unit, and delivery 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 input of the special date and budget. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the user's past purchase history and preferences. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects a gift based on the acquired information. The notification unit is realized, for example, by the speaker 240 of the smart glasses 214 and notifies the user of the selected gift and requests confirmation. The delivery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically purchases and delivers the gift after the user confirms. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, selection unit, notification unit, and delivery unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input of special dates and budgets. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires the user's past purchase history and preferences. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a gift based on the acquired information. The notification unit is realized by the speaker 240 of the headset-type terminal 314 and notifies the user of the selected gift and requests confirmation. The delivery unit is realized by the specific processing unit 290 of the data processing device 12 and automatically purchases and delivers the gift after the user confirms. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, selection unit, notification unit, and delivery 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 input of the special date and budget. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the user's past purchase history and preferences. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects a gift based on the acquired information. The notification unit is realized, for example, by the speaker 240 of the robot 414 and notifies the user of the selected gift and asks for confirmation. The delivery unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically purchases and delivers the gift after the user confirms.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The acquisition unit may also take into account the user's current health condition when acquiring the user's past purchase history and preferences. For example, if the user uses a fitness tracker or a health app, the acquisition unit may acquire that data and adjust gift selection based on the user's health condition. Furthermore, if the user has set a specific health goal, the acquisition unit may preferentially select gifts related to that goal. This allows the acquisition unit to select the optimal gift based on the user's health condition.

[0122] The selection unit can estimate the user's emotions and adjust the gift selection process based on the estimated user's emotions. For example, the selection unit can provide a simple and quick selection process when the user is stressed. Alternatively, the selection unit can provide detailed selection options and expand the range of choices when the user is relaxed. Furthermore, the selection unit can suggest special offers and limited products when the user is excited. In this way, the selection unit can provide an optimal gift selection process according to the user's emotions.

[0123] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, if the user has previously preferred email notifications, the notification unit can prioritize providing email notifications. Also, if the user frequently checks mobile app notifications, the notification unit can prioritize providing in-app notifications. Furthermore, if the user prefers voice notifications, the notification unit can provide notifications through a voice interface. This allows the notification unit to select the optimal notification method based on the user's past notification history.

[0124] The sending unit can estimate the user's emotions and adjust the timing of sending based on the estimated user's emotions. For example, if the user is feeling stressed, the sending unit can select a quick sending method to deliver the gift quickly. If the user is feeling relaxed, the sending unit can select a regular sending method to reduce costs. Furthermore, if the user is excited, the sending unit can send the gift in special packaging or with a special message. This allows the sending unit to provide the optimal timing of sending based on the user's emotions.

[0125] The acquisition unit may also take the user's geographical location information into consideration when acquiring the user's past purchase history and preferences. For example, if the user lives in a specific area, the acquisition unit may preferentially acquire products and services that are popular in that area. Also, if the user is traveling, the acquisition unit may suggest gifts that can be used at the user's travel destination. Furthermore, if the user is participating in a specific event, the acquisition unit may acquire gifts related to the event. This allows the acquisition unit to select the optimal gift based on the user's geographical location information.

[0126] The selection unit can estimate the user's emotions and adjust the gift selection algorithm based on the estimated user's emotions. For example, if the user is stressed, the selection unit can use a simple and quick algorithm to select gifts. If the user is relaxed, the selection unit can use a detailed algorithm to broaden the selection options. Furthermore, if the user is excited, the selection unit can prioritize special offers and limited-edition products. This allows the selection unit to provide an optimal gift selection algorithm according to the user's emotions.

[0127] The notification unit can customize the notification content taking into account the user's current living situation. For example, if the user has just started a new job, the notification unit can suggest work-related gifts. If the user has just moved, the notification unit can suggest gifts related to the new residence. Furthermore, if the user is attending a specific event, the notification unit can suggest gifts related to the event. In this way, the notification unit can provide optimal notification content based on the user's current living situation.

[0128] The sending unit can estimate the user's emotions and adjust the sending method based on the estimated user's emotions. For example, if the user is feeling stressed, the sending unit can select a quick and simple sending method. If the user is feeling relaxed, the sending unit can provide detailed sending options to expand the range of choices. Furthermore, if the user is excited, the sending unit can send the item in special packaging or with a special message. In this way, the sending unit can provide the optimal sending method according to the user's emotions.

[0129] The acquisition unit can analyze the user's social media activities when acquiring the user's past purchase history and preferences. For example, the acquisition unit can infer preferences based on products the user has "liked" on social media and posts the user has shared. The acquisition unit can also refer to products and services the user's friends have shared on social media. Furthermore, the acquisition unit can acquire products and services related to places the user has checked in to on social media. This allows the acquisition unit to select the best gift based on the user's social media activities.

[0130] The selection unit can estimate the user's emotions and adjust the gift selection process based on the estimated user's emotions. For example, the selection unit can provide a simple and quick selection process when the user is stressed. Alternatively, the selection unit can provide detailed selection options and expand the range of choices when the user is relaxed. Furthermore, the selection unit can suggest special offers and limited products when the user is excited. In this way, the selection unit can provide an optimal gift selection process according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit accepts input of special days and budgets. Special days include birthdays, anniversaries, holidays, etc., and budgets include upper and lower limits of amounts, currency units, etc. Step 2: The acquisition unit acquires the user's past purchase history and preferences based on the information received by the reception unit. The past purchase history includes purchase history for the past year and purchase history for specific categories, and the preferences include preferences estimated from the past purchase history and preferences explicitly set by the user. Step 3: The selection unit selects gifts based on the information acquired by the acquisition unit. Gifts can include goods, services, digital content, etc. The selection unit uses AI to analyze trends and popular products and select the most suitable gift. Step 4: The notification unit notifies the user of the gift selected by the selection unit and asks for confirmation. Notifications may include email, SMS, in-app notifications, etc. Step 5: The sending unit automatically purchases and sends the gift after the user confirms it via the notification unit. The sending method may include courier delivery, email, download link, etc.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 system comprising: a reception unit that receives input of special dates and budgets; an acquisition unit that acquires a user's past purchase history and preferences based on the information received by the reception unit; a selection unit that selects a gift based on the information acquired by the acquisition unit; a notification unit that notifies the user of the gift selected by the selection unit and asks for confirmation; and a sending unit that automatically purchases and sends the gift after the user confirms via the notification unit.

2. The system according to claim 1 , wherein the acquisition unit acquires the user's past purchase history and preferences by linking data from an e-commerce site or a social networking site.

3. The system according to claim 1 , wherein the selection unit uses AI to analyze trends and popular products and select the most suitable gift.

4. The system according to claim 1 , wherein the notification unit provides an interface for a user to confirm.

5. 10. The system of claim 1, wherein the selector executes an algorithm for gift selection.

6. The system according to claim 1 , wherein the sending unit automatically purchases and sends the gift after the user confirms it.

7. The system according to claim 1 , wherein the reception unit estimates a user's emotion and adjusts a method for inputting special days and a budget based on the estimated user's emotion.

8. The system according to claim 1 , wherein the accepting unit analyzes the user's past input history and provides an appropriate input interface.

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