system

Abstract

The system according to the embodiment aims to propose the most suitable gift based on the characteristics of the user and the recipient. [Solution] A system according to an embodiment includes a characteristic determination unit, an information analysis unit, a suggestion unit, and a link provision unit. The characteristic determination unit determines a user's characteristics. The information analysis unit analyzes information about the recipient based on the characteristics determined by the characteristic determination unit. The suggestion unit suggests an appropriate gift based on the information analyzed by the information analysis unit. The link provision unit provides a link for purchasing the gift suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult and time-consuming to select the right gift for the recipient.

[0005] The system according to the embodiment aims to propose the most suitable gift based on the characteristics of the user and the recipient. [Means for solving the problem]

[0006] The system according to the embodiment includes a characteristic determination unit, an information analysis unit, a suggestion unit, and a link provision unit. The characteristic determination unit determines the characteristics of a user. The information analysis unit analyzes information about the recipient based on the characteristics determined by the characteristic determination unit. The suggestion unit suggests an appropriate gift based on the information analyzed by the information analysis unit. The link provision unit provides a link for purchasing the gift suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable gift based on the characteristics of the user and the recipient. [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 recommendation system according to an embodiment of the present invention proposes the best gift for a recipient. This gift recommendation system uses AI to suggest the best gift based on the recipient's input information and image. Furthermore, providing links to shopping sites can increase purchasing opportunities. This system utilizes a generative AI and enables grounding via a cloud service. Grounding is a mechanism that combines in-house data with external data to improve the accuracy of the generative AI. This significantly reduces hallucination and produces highly accurate results. For example, a user inputs the recipient's information and image. The AI ​​then analyzes the information and suggests the best gift. The AI ​​then combines external data with the recipient's data to determine the gift most likely to match the user's characteristics and the recipient's information. Finally, it provides a link to a shopping site where the suggested gift can be purchased. This mechanism allows users to easily find the perfect gift and select a gift that will please the recipient. Furthermore, providing links to shopping sites can increase purchasing opportunities. This allows the gift recommendation system to identify the user's characteristics, suggest the best gift for the recipient, and provide links to increase purchasing opportunities.

[0029] A gift recommendation system according to an embodiment includes a characteristic determination unit, an information analysis unit, a suggestion unit, and a link provision unit. The characteristic determination unit determines a user's characteristics. Examples of the user's characteristics include, but are not limited to, age, gender, hobbies, and purchase history. The characteristic determination unit analyzes, for example, a search history and a web service usage history. The characteristic determination unit can also analyze the user's past purchase history to improve the accuracy of the characteristic determination. The characteristic determination unit can also analyze the user's social media activities and reflect the results in the characteristic determination. For example, the characteristic determination unit analyzes posts that the user has "liked" on social media and reflects the results in the characteristic determination. The information analysis unit analyzes information about the recipient based on the characteristics determined by the characteristic determination unit. The information about the recipient includes, for example, text information, image information, and behavioral history, but is not limited to these examples. The information analysis unit performs, for example, image analysis and text analysis. The information analysis unit can also analyze the recipient's past gift receiving history to improve the accuracy of the information analysis. Furthermore, the information analysis unit can analyze the recipient's social media activity and reflect the results in the information analysis. For example, the information analysis unit analyzes posts that the recipient has "liked" on social media and reflects the results in the information analysis. The suggestion unit suggests an optimal gift based on the information analyzed by the information analysis unit. The suggestion unit, for example, combines external data and the company's own data for analysis. The suggestion unit can also estimate a user's emotions and adjust the way the gift suggestion is presented based on the estimated emotions. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the gift when suggesting the gift. The link providing unit provides a link for purchasing the gift suggested by the suggestion unit. The link providing unit provides, for example, a link to a shopping site. The link providing unit can also estimate a user's emotions and adjust the way the link is provided based on the estimated emotions. Furthermore, the link providing unit can select an optimal link by referring to the user's past click history when providing the link. As a result, the gift recommendation system according to the embodiment can determine a user's characteristics, suggest an optimal gift to the recipient, and provide the link, thereby increasing purchasing opportunities.

[0030] The characteristic determination unit can analyze search history or web service usage history. The search history includes, for example, search keywords, search frequency, search date and time, etc., but is not limited to these examples. The characteristic determination unit can, for example, analyze keywords searched by the user in the past and reflect these in the characteristic determination. The characteristic determination unit can also adjust the characteristic determination criteria taking into account the user's search frequency. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the content searched by the user on a specific date and time. The web service usage history includes, for example, accessed pages, usage time, usage frequency, etc., but is not limited to these examples. The characteristic determination unit can, for example, analyze pages frequently accessed by the user and reflect these in the characteristic determination. The characteristic determination unit can also adjust the characteristic determination criteria taking into account the user's usage time. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on services used by the user with a specific frequency. In this way, by analyzing search history and web service usage history, the accuracy of user characteristic determination is improved.

[0031] The information analysis unit may perform image analysis or text analysis. Examples of image analysis include, but are not limited to, facial recognition, object detection, and scene analysis. For example, the information analysis unit may analyze a facial image of the recipient to estimate the recipient's age and gender. The information analysis unit may also analyze an image of the recipient to identify the recipient's hobbies and interests. Furthermore, the information analysis unit may detect objects included in the recipient's image to understand the recipient's preferences. Examples of text analysis include, but are not limited to, natural language processing, emotion analysis, and keyword extraction. For example, the information analysis unit may analyze the recipient's text message to estimate the recipient's emotions. Furthermore, the information analysis unit may analyze the recipient's written text to identify the recipient's interests. Furthermore, the information analysis unit may extract important keywords from the recipient's text data to understand the recipient's preferences. This allows for more accurate analysis of the recipient's information by performing image analysis or text analysis.

[0032] The suggestion unit can combine and analyze external data and the company's data. Examples of external data include, but are not limited to, public databases and data from partner companies. For example, the suggestion unit suggests optimal gifts based on data acquired from public databases. The suggestion unit can also select gifts based on data provided by partner companies. The suggestion unit can combine and analyze external data and the company's data to make more accurate gift suggestions. Examples of the company's data include, but are not limited to, customer databases, sales histories, and marketing data. For example, the suggestion unit can understand user characteristics based on a customer database and suggest optimal gifts. The suggestion unit can also suggest gifts that were popular in the past based on sales histories. The suggestion unit can also identify user interests and concerns based on marketing data and select gifts. This allows for more accurate gift suggestions by combining and analyzing external data and the company's data.

[0033] The link providing unit can provide a link to a shopping site. Examples of shopping sites include, but are not limited to, Yahoo! Shopping, Amazon, and an independent e-commerce site. For example, the link providing unit can provide a link to the Yahoo! Shopping site. The link providing unit can also provide a link to the Amazon shopping site. The link providing unit can also provide a link to an independent e-commerce site. Examples of link formats include, but are not limited to, a URL link, a two-dimensional code (e.g., a QR code), an in-app link, and the like. For example, the link providing unit can provide a URL link so that a user can access the shopping site by clicking it. The link providing unit can also provide a two-dimensional code so that a user can access the shopping site by scanning it with a smartphone. The link providing unit can also provide an in-app link so that a user can access the shopping site within the app. In this way, by providing a link to a shopping site, the user can easily purchase a suggested gift.

[0034] The information analysis unit can evaluate a gift with the most similar tendency based on the information of the recipient. Examples of the most similar tendency include, but are not limited to, past purchasing patterns and the behavior of similar users. For example, the information analysis unit analyzes the past purchasing patterns of the recipient and evaluates a gift with the most similar tendency. The information analysis unit can also suggest an optimal gift for the recipient based on the behavior of similar users. Furthermore, the information analysis unit can evaluate a gift with the most similar tendency based on the behavioral history of the recipient. In this way, by determining the gift with the most similar tendency based on the information of the recipient, a more appropriate gift can be suggested.

[0035] The characteristic determination unit can analyze the user's past purchase history to improve the accuracy of the characteristic determination. For example, the characteristic determination unit can analyze the categories of products purchased by the user in the past and reflect the results in the characteristic determination. The characteristic determination unit can also adjust the criteria for the characteristic determination, taking into account the user's purchase frequency. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on evaluations of products purchased by the user in the past. The past purchase history includes, for example, purchased products, purchase dates and times, purchase frequency, etc., but is not limited to these examples. For example, the characteristic determination unit can analyze details of products purchased by the user in the past and reflect the results in the characteristic determination. Furthermore, the characteristic determination unit can analyze the user's purchase history in chronological order and adjust the criteria for the characteristic determination. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on evaluations of products purchased by the user in the past. In this way, the accuracy of the characteristic determination is improved by analyzing the user's past purchase history.

[0036] The characteristic determination unit can analyze the user's social media activity and reflect it in the characteristic determination. For example, the characteristic determination unit can analyze posts that the user has "liked" on social media and reflect it in the characteristic determination. The characteristic determination unit can also analyze the trends of the user's followers and the accounts they follow and reflect it in the characteristic determination. Furthermore, the characteristic determination unit can adjust the criteria for the characteristic determination based on the content shared by the user. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the characteristic determination unit can analyze the content posted by the user and reflect it in the characteristic determination. Furthermore, the characteristic determination unit can adjust the criteria for the characteristic determination based on the number of likes the user has. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the number of followers the user has. In this way, the accuracy of the characteristic determination can be improved by analyzing the user's social media activity.

[0037] The characteristic determination unit may perform characteristic determination based on the user's geographical location information. For example, if the user lives in a particular region, the characteristic determination unit may perform characteristic determination taking into account the culture and customs of that region. Furthermore, if the user is traveling, the characteristic determination unit may perform characteristic determination based on information about the travel destination. Furthermore, if the user is participating in a particular event, the characteristic determination unit may perform characteristic determination based on information about the event. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, the characteristic determination unit may perform characteristic determination based on the user's GPS data. Furthermore, the characteristic determination unit may adjust the criteria for characteristic determination based on the user's IP address. Furthermore, the characteristic determination unit may improve the accuracy of the characteristic determination based on the user's location information services. This improves the accuracy of the characteristic determination by taking the user's geographical location information into account.

[0038] The characteristic determination unit can analyze the user's device usage history and reflect the results in the characteristic determination. For example, the characteristic determination unit can analyze the categories of apps frequently used by the user and reflect the results in the characteristic determination. The characteristic determination unit can also adjust the criteria for the characteristic determination by taking into account the user's device usage time. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the types of devices used by the user in the past. The device usage history includes, but is not limited to, the apps used, the usage time, and the device type. For example, the characteristic determination unit can analyze the details of the apps frequently used by the user and reflect the results in the characteristic determination. Furthermore, the characteristic determination unit can analyze the user's device usage time in chronological order and adjust the criteria for the characteristic determination. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the types of devices used by the user in the past. In this way, the accuracy of the characteristic determination can be improved by analyzing the user's device usage history.

[0039] The information analysis unit can analyze the recipient's past gift reception history to improve the accuracy of the information analysis. For example, the information analysis unit can analyze the categories of gifts the recipient has received in the past and reflect this in the information analysis. The information analysis unit can also adjust the criteria for the information analysis, taking into account the recipient's frequency of gift reception. Furthermore, the information analysis unit can also improve the accuracy of the information analysis based on the recipient's evaluations of gifts they have received in the past. The past gift reception history includes, for example, the type of gift received, the date and time of receipt, and the frequency of receipt, but is not limited to these examples. For example, the information analysis unit can analyze details of gifts the recipient has received in the past and reflect this in the information analysis. Furthermore, the information analysis unit can also analyze the recipient's gift reception history in chronological order and adjust the criteria for the information analysis. Furthermore, the information analysis unit can also improve the accuracy of the information analysis based on the recipient's evaluations of gifts they have received in the past. Thus, analyzing the recipient's past gift reception history improves the accuracy of the information analysis.

[0040] The information analysis unit can analyze the social media activity of the recipient and reflect it in the information analysis. For example, the information analysis unit can analyze posts that the recipient has "liked" on social media and reflect it in the information analysis. The information analysis unit can also analyze the trends of the recipient's followers and the accounts they follow and reflect it in the information analysis. Furthermore, the information analysis unit can adjust the criteria for information analysis based on content shared by the recipient. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the information analysis unit can analyze the content posted by the recipient and reflect it in the information analysis. Furthermore, the information analysis unit can adjust the criteria for information analysis based on the number of likes the recipient has. Furthermore, the information analysis unit can improve the accuracy of the information analysis based on the number of followers the recipient has. In this way, by analyzing the recipient's social media activity, the accuracy of the information analysis is improved.

[0041] The information analysis unit can analyze information based on the recipient's geographical location information. For example, if the recipient lives in a specific region, the information analysis unit can analyze the information taking into account the culture and customs of that region. Furthermore, if the recipient is traveling, the information analysis unit can analyze the information based on travel destination information. Furthermore, if the recipient is participating in a specific event, the information analysis unit can analyze the information based on event information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, the information analysis unit can analyze the information based on the recipient's GPS data. Furthermore, the information analysis unit can adjust the criteria for information analysis based on the recipient's IP address. Furthermore, the information analysis unit can improve the accuracy of the information analysis based on the recipient's location information services. Thus, by taking the recipient's geographical location information into account, the accuracy of the information analysis is improved.

[0042] The information analysis unit can analyze the device usage history of the recipient and reflect the results in the information analysis. For example, the information analysis unit can analyze the categories of apps frequently used by the recipient and reflect the results in the information analysis. The information analysis unit can also adjust the criteria for the information analysis, taking into account the recipient's device usage time. The information analysis unit can also improve the accuracy of the information analysis based on the type of device used by the recipient in the past. The device usage history includes, but is not limited to, the apps used, the usage time, and the type of device. For example, the information analysis unit can analyze the details of the apps frequently used by the recipient and reflect the results in the information analysis. The information analysis unit can also analyze the recipient's device usage time in chronological order and adjust the criteria for the information analysis. The information analysis unit can also improve the accuracy of the information analysis based on the type of device used by the recipient in the past. In this way, analyzing the recipient's device usage history improves the accuracy of the information analysis.

[0043] When making a suggestion, the suggestion unit may adjust the level of detail of the suggestion based on the importance of the gift. For example, for an important gift, the suggestion unit may provide a detailed description and multiple options. For a general gift, the suggestion unit may provide a brief description and several options. For a light gift, the suggestion unit may provide a simple description and one option. Examples of importance include, but are not limited to, the price of the gift, the relationship of the recipient, and the importance of the event. The suggestion unit may adjust the level of detail of the suggestion based on, for example, the price of the gift. The suggestion unit may also adjust the criteria for the suggestion based on the relationship of the recipient. Furthermore, the suggestion unit may improve the accuracy of the suggestion based on the importance of the event. As a result, adjusting the level of detail of the suggestion based on the importance of the gift enables more appropriate suggestions.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the gift category. For example, in the case of a food gift, the suggestion unit can make a suggestion taking into account expiration dates and storage methods. Furthermore, in the case of a fashion gift, the suggestion unit can make a suggestion taking into account size and color variations. Furthermore, in the case of an electronic gift, the suggestion unit can make a suggestion taking into account technical specifications and compatibility. Categories include, but are not limited to, food, clothing, electronic devices, etc., for example. For example, the suggestion unit can analyze details of a food gift and make a suggestion taking into account expiration dates and storage methods. Furthermore, the suggestion unit can analyze details of a fashion gift and make a suggestion taking into account size and color variations. Furthermore, the suggestion unit can analyze details of an electronic gift and make a suggestion taking into account technical specifications and compatibility. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the gift category.

[0045] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of gift submission. For example, if an important event is approaching, the suggestion unit can prioritize gifts related to the event. The suggestion unit can also make regular suggestions for a general event. The suggestion unit can also make simple suggestions for a minor event. The submission time includes, but is not limited to, the date and time of the event, the season, the recipient's schedule, and the like. The suggestion unit can determine the priority of the suggestions based on, for example, the date and time of the event. The suggestion unit can also adjust the criteria for the suggestions based on the season. The suggestion unit can also improve the accuracy of the suggestions based on the recipient's schedule. This enables more appropriate suggestions by determining the priority of the suggestions based on the time of gift submission.

[0046] The suggestion unit may adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant gifts based on the user's past purchasing history. The suggestion unit may also prioritize suggesting highly relevant gifts based on the user's current interests. Furthermore, the suggestion unit may also prioritize suggesting highly relevant gifts based on the user's social media activity. Examples of relevance include, but are not limited to, gift theme, recipient interests, and past gift history. The suggestion unit may adjust the order of suggestions based on, for example, the gift theme. The suggestion unit may also adjust the criteria for suggestions based on the recipient's interests. Furthermore, the suggestion unit may improve the accuracy of suggestions based on past gift history. As a result, adjusting the order of suggestions based on gift relevance enables more appropriate suggestions.

[0047] When providing a link, the link providing unit can select an optimal link by referring to the user's past click history. For example, the link providing unit analyzes the categories of links the user has clicked in the past and selects the optimal link. The link providing unit can also select the optimal link by taking into account the user's click frequency. Furthermore, the link providing unit can select the optimal link based on the user's evaluation of links clicked in the past. The click history includes, for example, the clicked link, the click date and time, and the click frequency, but is not limited to these examples. For example, the link providing unit analyzes the details of links clicked in the past by the user and selects the optimal link. The link providing unit can also analyze the user's click frequency in chronological order and select the optimal link. Furthermore, the link providing unit can select the optimal link based on the evaluation of links clicked in the past by the user. In this way, the optimal link can be selected by referring to the user's past click history.

[0048] When providing a link, the link providing unit can customize the link display method based on the user's current purchasing intent. For example, if the user has a high purchasing intent, the link providing unit can display the link in a prominent position. Furthermore, if the user has a low purchasing intent, the link providing unit can display the link in a more discreet position. Furthermore, the link providing unit can adjust the color and font size of the link according to the user's purchasing intent. Purchasing intent includes, for example, past purchasing history, current browsing behavior, survey results, etc., but is not limited to these examples. For example, the link providing unit estimates the user's purchasing intent based on the user's past purchasing history. Furthermore, the link providing unit can estimate the user's purchasing intent based on the user's current browsing behavior. Furthermore, the link providing unit can estimate the user's purchasing intent based on survey results. This allows for more effective link provision by customizing the link display method based on the user's purchasing intent.

[0049] When providing links, the link providing unit can select appropriate links based on the user's geographical location information. For example, if the user lives in a specific area, the link providing unit can provide links to stores and services in that area. Furthermore, if the user is traveling, the link providing unit can provide links to stores and services in the user's travel destination. Furthermore, if the user is participating in a specific event, the link providing unit can provide links related to the event. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. The link providing unit selects appropriate links based on the user's GPS data. Furthermore, the link providing unit can adjust link criteria based on the user's IP address. Furthermore, the link providing unit can improve link accuracy based on the user's location information services. This allows the optimal link to be selected by taking the user's geographical location information into consideration.

[0050] The link providing unit may provide an optimal link by taking into consideration device information of the user when providing a link. For example, if the user is using a smartphone, the link providing unit may provide a mobile-friendly link. Furthermore, if the user is using a tablet, the link providing unit may provide a link optimized for a large screen. Furthermore, if the user is using a desktop, the link providing unit may provide a link including detailed information. Device information may include, but is not limited to, the device type, OS, browser information, etc. The link providing unit may provide an optimal link based on, for example, the user's device type. Furthermore, the link providing unit may adjust link criteria based on the user's OS. Furthermore, the link providing unit may improve link accuracy based on the user's browser information. This allows the optimal link to be provided by taking into consideration the user's device information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The characteristic determination unit can analyze the user's health data and reflect it in the characteristic determination. For example, it can identify the user's health condition and exercise habits based on data obtained from the user's fitness tracker or smartwatch. The characteristic determination unit can also analyze the user's food records and suggest health-conscious gifts. Furthermore, the characteristic determination unit can analyze the user's sleep patterns and suggest relaxation products and sleep aids. This allows for more personalized gift suggestions by taking the user's health data into consideration.

[0053] The information analysis unit can analyze the activity of online communities and forums to identify the recipient's hobbies and interests. For example, it can analyze the content of posts in forums in which the recipient participates to identify specific hobbies and interests. The information analysis unit can also analyze the content of websites frequently visited by the recipient to identify topics of interest. Furthermore, the information analysis unit can analyze the content of newsletters and blogs to which the recipient subscribes to identify areas of interest. This allows for more appropriate gift suggestions by taking the recipient's online activity into consideration.

[0054] The suggestion unit can analyze the user's past gift selection history to improve the accuracy of suggestions. For example, it analyzes the types and price ranges of gifts the user has previously selected and suggests gifts with similar trends. The suggestion unit can also take into account the reactions of recipients of gifts the user has previously selected and reflect the characteristics of successful gifts. Furthermore, the suggestion unit can make suggestions at appropriate times based on the seasons and events for gifts the user has previously selected. This allows for more accurate gift suggestions by taking into account the user's past gift selection history.

[0055] The link providing unit can provide related promotion and discount information based on the user's purchase history. For example, it can provide discount coupons related to products the user has previously purchased. The link providing unit can also provide promotion information for products in which the user has shown interest. Furthermore, the link providing unit can analyze the user's purchase history and provide special offer information for specific brands or categories. This makes it possible to provide links that increase purchasing motivation by taking the user's purchase history into consideration.

[0056] The characteristic determination unit can determine the characteristics by taking into account the user's life events. For example, when the user experiences a life event such as marriage, childbirth, or moving, the characteristics related to that event are reflected. The characteristic determination unit can also adjust the criteria for determining the characteristics by taking into account important dates such as the user's birthday or anniversary. Furthermore, the characteristic determination unit can also take into account changes in the user's life stage, such as a change in the user's career or academic progress, and reflect these changes in the characteristic determination. This allows for more appropriate characteristic determination by taking into account the user's life events.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The characteristic determination unit determines the user's characteristics. User characteristics include age, gender, hobbies, and purchasing history. The characteristic determination unit analyzes search history, web service usage history, past purchasing history, and social media activity to improve the accuracy of characteristic determination. Step 2: The information analysis unit analyzes the recipient's information based on the characteristics determined by the characteristic determination unit. The recipient's information includes text information, image information, and behavioral history. The information analysis unit analyzes images, text information, past gift receipt history, and social media activity to improve the accuracy of the information analysis. Step 3: The suggestion unit suggests optimal gifts based on the information analyzed by the information analysis unit. The suggestion unit combines and analyzes external data and the company's own data, estimates the user's emotions, and adjusts the way gift suggestions are presented. It also adjusts the level of detail in the suggestions based on the importance of the gift. Step 4: The link provider provides links to purchase the gifts suggested by the suggestion unit. The link provider provides links to shopping sites, estimates the user's sentiment, and adjusts the method of providing links. It also selects the optimal link by referring to the user's past click history.

[0059] (Example 2) A gift recommendation system according to an embodiment of the present invention proposes the best gift for a recipient. This gift recommendation system uses AI to suggest the best gift based on the recipient's input information and image. Furthermore, providing links to shopping sites can increase purchasing opportunities. This system utilizes a generative AI and enables grounding via a cloud service. Grounding is a mechanism that combines in-house data with external data to improve the accuracy of the generative AI. This significantly reduces hallucination and produces highly accurate results. For example, a user inputs the recipient's information and image. The AI ​​then analyzes the information and suggests the best gift. The AI ​​then combines external data with the recipient's data to determine the gift most likely to match the user's characteristics and the recipient's information. Finally, it provides a link to a shopping site where the suggested gift can be purchased. This mechanism allows users to easily find the perfect gift and select a gift that will please the recipient. Furthermore, providing links to shopping sites can increase purchasing opportunities. This allows the gift recommendation system to identify the user's characteristics, suggest the best gift for the recipient, and provide links to increase purchasing opportunities.

[0060] A gift recommendation system according to an embodiment includes a characteristic determination unit, an information analysis unit, a suggestion unit, and a link provision unit. The characteristic determination unit determines a user's characteristics. Examples of the user's characteristics include, but are not limited to, age, gender, hobbies, and purchase history. The characteristic determination unit analyzes, for example, a search history and a web service usage history. The characteristic determination unit can also analyze the user's past purchase history to improve the accuracy of the characteristic determination. The characteristic determination unit can also analyze the user's social media activities and reflect the results in the characteristic determination. For example, the characteristic determination unit analyzes posts that the user has "liked" on social media and reflects the results in the characteristic determination. The information analysis unit analyzes information about the recipient based on the characteristics determined by the characteristic determination unit. The information about the recipient includes, for example, text information, image information, and behavioral history, but is not limited to these examples. The information analysis unit performs, for example, image analysis and text analysis. The information analysis unit can also analyze the recipient's past gift receiving history to improve the accuracy of the information analysis. Furthermore, the information analysis unit can analyze the recipient's social media activity and reflect the results in the information analysis. For example, the information analysis unit analyzes posts that the recipient has "liked" on social media and reflects the results in the information analysis. The suggestion unit suggests an optimal gift based on the information analyzed by the information analysis unit. The suggestion unit, for example, combines external data and the company's own data for analysis. The suggestion unit can also estimate a user's emotions and adjust the way the gift suggestion is presented based on the estimated emotions. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the gift when suggesting the gift. The link providing unit provides a link for purchasing the gift suggested by the suggestion unit. The link providing unit provides, for example, a link to a shopping site. The link providing unit can also estimate a user's emotions and adjust the way the link is provided based on the estimated emotions. Furthermore, the link providing unit can select an optimal link by referring to the user's past click history when providing the link. As a result, the gift recommendation system according to the embodiment can determine a user's characteristics, suggest an optimal gift to the recipient, and provide the link, thereby increasing purchasing opportunities.

[0061] The characteristic determination unit can analyze search history or web service usage history. The search history includes, for example, search keywords, search frequency, search date and time, etc., but is not limited to these examples. The characteristic determination unit can, for example, analyze keywords searched by the user in the past and reflect these in the characteristic determination. The characteristic determination unit can also adjust the characteristic determination criteria taking into account the user's search frequency. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the content searched by the user on a specific date and time. The web service usage history includes, for example, accessed pages, usage time, usage frequency, etc., but is not limited to these examples. The characteristic determination unit can, for example, analyze pages frequently accessed by the user and reflect these in the characteristic determination. The characteristic determination unit can also adjust the characteristic determination criteria taking into account the user's usage time. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on services used by the user with a specific frequency. In this way, by analyzing search history and web service usage history, the accuracy of user characteristic determination is improved.

[0062] The information analysis unit may perform image analysis or text analysis. Examples of image analysis include, but are not limited to, facial recognition, object detection, and scene analysis. For example, the information analysis unit may analyze a facial image of the recipient to estimate the recipient's age and gender. The information analysis unit may also analyze an image of the recipient to identify the recipient's hobbies and interests. Furthermore, the information analysis unit may detect objects included in the recipient's image to understand the recipient's preferences. Examples of text analysis include, but are not limited to, natural language processing, emotion analysis, and keyword extraction. For example, the information analysis unit may analyze the recipient's text message to estimate the recipient's emotions. Furthermore, the information analysis unit may analyze the recipient's written text to identify the recipient's interests. Furthermore, the information analysis unit may extract important keywords from the recipient's text data to understand the recipient's preferences. This allows for more accurate analysis of the recipient's information by performing image analysis or text analysis.

[0063] The suggestion unit can combine and analyze external data and the company's data. Examples of external data include, but are not limited to, public databases and data from partner companies. For example, the suggestion unit suggests optimal gifts based on data acquired from public databases. The suggestion unit can also select gifts based on data provided by partner companies. The suggestion unit can combine and analyze external data and the company's data to make more accurate gift suggestions. Examples of the company's data include, but are not limited to, customer databases, sales histories, and marketing data. For example, the suggestion unit can understand user characteristics based on a customer database and suggest optimal gifts. The suggestion unit can also suggest gifts that were popular in the past based on sales histories. The suggestion unit can also identify user interests and concerns based on marketing data and select gifts. This allows for more accurate gift suggestions by combining and analyzing external data and the company's data.

[0064] The link providing unit can provide a link to a shopping site. Examples of shopping sites include, but are not limited to, Yahoo! Shopping, Amazon, and an independent e-commerce site. For example, the link providing unit can provide a link to the Yahoo! Shopping site. The link providing unit can also provide a link to the Amazon shopping site. The link providing unit can also provide a link to an independent e-commerce site. Examples of link formats include, but are not limited to, a URL link, a two-dimensional code (e.g., a QR code), an in-app link, and the like. For example, the link providing unit can provide a URL link that allows a user to access the shopping site by clicking it. The link providing unit can also provide a two-dimensional code that allows a user to access the shopping site by scanning it with a smartphone. The link providing unit can also provide an in-app link that allows a user to access the shopping site within the app. In this way, by providing the link to the shopping site, the user can easily purchase the suggested gift.

[0065] The information analysis unit can evaluate a gift with the most similar tendency based on the information of the recipient. Examples of the most similar tendency include, but are not limited to, past purchasing patterns and the behavior of similar users. For example, the information analysis unit analyzes the past purchasing patterns of the recipient and evaluates a gift with the most similar tendency. The information analysis unit can also suggest an optimal gift for the recipient based on the behavior of similar users. Furthermore, the information analysis unit can evaluate a gift with the most similar tendency based on the behavioral history of the recipient. In this way, by determining the gift with the most similar tendency based on the information of the recipient, a more appropriate gift can be suggested.

[0066] The characteristic determination unit can estimate the user's emotions and adjust the characteristic determination criteria based on the estimated user emotions. For example, when the user is feeling stressed, the characteristic determination unit relaxes the characteristic determination criteria and determines the user's characteristics with simple questions. Furthermore, when the user is relaxed, the characteristic determination unit can ask detailed questions to perform more accurate characteristic determination. Furthermore, when the user is in a hurry, the characteristic determination unit can prioritize past data to perform characteristic determination quickly. Emotion estimation includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the characteristic determination unit can analyze the user's facial expression to estimate the user's emotions. Furthermore, the characteristic determination unit can analyze the user's text messages to estimate the user's emotions. Furthermore, the characteristic determination unit can analyze the user's voice to estimate the user's emotions. This allows for more appropriate characteristic determination by adjusting the characteristic determination criteria based on the user's emotions.

[0067] The characteristic determination unit can analyze the user's past purchase history to improve the accuracy of the characteristic determination. For example, the characteristic determination unit can analyze the categories of products purchased by the user in the past and reflect the results in the characteristic determination. The characteristic determination unit can also adjust the criteria for the characteristic determination, taking into account the user's purchase frequency. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on evaluations of products purchased by the user in the past. The past purchase history includes, for example, purchased products, purchase dates and times, purchase frequency, etc., but is not limited to these examples. For example, the characteristic determination unit can analyze details of products purchased by the user in the past and reflect the results in the characteristic determination. Furthermore, the characteristic determination unit can analyze the user's purchase history in chronological order and adjust the criteria for the characteristic determination. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on evaluations of products purchased by the user in the past. In this way, the accuracy of the characteristic determination is improved by analyzing the user's past purchase history.

[0068] The characteristic determination unit can analyze the user's social media activity and reflect it in the characteristic determination. For example, the characteristic determination unit can analyze posts that the user has "liked" on social media and reflect it in the characteristic determination. The characteristic determination unit can also analyze the trends of the user's followers and the accounts they follow and reflect it in the characteristic determination. Furthermore, the characteristic determination unit can adjust the criteria for the characteristic determination based on the content shared by the user. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the characteristic determination unit can analyze the content posted by the user and reflect it in the characteristic determination. Furthermore, the characteristic determination unit can adjust the criteria for the characteristic determination based on the number of likes the user has. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the number of followers the user has. In this way, the accuracy of the characteristic determination can be improved by analyzing the user's social media activity.

[0069] The characteristic determination unit can estimate the user's emotions and determine a priority for characteristic determination based on the estimated user's emotions. For example, if the user is feeling stressed, the characteristic determination unit can set a low priority for characteristic determination and start with simple questions. Furthermore, if the user is relaxed, the characteristic determination unit can set a high priority for characteristic determination and ask detailed questions. Furthermore, if the user is in a hurry, the characteristic determination unit can set a medium priority for characteristic determination and make a quick determination. Emotion estimation includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the characteristic determination unit can analyze the user's facial expression to estimate the emotion. Furthermore, the characteristic determination unit can analyze the user's text message to estimate the emotion. Furthermore, the characteristic determination unit can analyze the user's voice to estimate the emotion. This allows for more appropriate characteristic determination by determining the priority for characteristic determination based on the user's emotions.

[0070] The characteristic determination unit may perform characteristic determination based on the user's geographical location information. For example, if the user lives in a particular region, the characteristic determination unit may perform characteristic determination taking into account the culture and customs of that region. Furthermore, if the user is traveling, the characteristic determination unit may perform characteristic determination based on information about the travel destination. Furthermore, if the user is participating in a particular event, the characteristic determination unit may perform characteristic determination based on information about the event. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, the characteristic determination unit may perform characteristic determination based on the user's GPS data. Furthermore, the characteristic determination unit may adjust the criteria for characteristic determination based on the user's IP address. Furthermore, the characteristic determination unit may improve the accuracy of the characteristic determination based on the user's location information services. This improves the accuracy of the characteristic determination by taking the user's geographical location information into account.

[0071] The characteristic determination unit can analyze the user's device usage history and reflect the results in the characteristic determination. For example, the characteristic determination unit can analyze the categories of apps frequently used by the user and reflect the results in the characteristic determination. The characteristic determination unit can also adjust the criteria for the characteristic determination by taking into account the user's device usage time. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the types of devices used by the user in the past. The device usage history includes, but is not limited to, the apps used, the usage time, and the device type. For example, the characteristic determination unit can analyze the details of the apps frequently used by the user and reflect the results in the characteristic determination. Furthermore, the characteristic determination unit can analyze the user's device usage time in chronological order and adjust the criteria for the characteristic determination. Furthermore, the characteristic determination unit can improve the accuracy of the characteristic determination based on the types of devices used by the user in the past. In this way, the accuracy of the characteristic determination can be improved by analyzing the user's device usage history.

[0072] The information analysis unit can estimate the emotions of the recipient and adjust the information analysis method based on the estimated emotions. For example, if the recipient is happy, the information analysis unit can prioritize analyzing positive information. Furthermore, if the recipient is sad, the information analysis unit can prioritize analyzing comforting information. Furthermore, if the recipient is excited, the information analysis unit can prioritize analyzing interesting information. Estimation of emotions includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the information analysis unit can analyze the recipient's facial expression to estimate the emotions. Furthermore, the information analysis unit can analyze the recipient's text message to estimate the emotions. Furthermore, the information analysis unit can analyze the recipient's voice to estimate the emotions. This allows for more appropriate information analysis by adjusting the information analysis method based on the recipient's emotions.

[0073] The information analysis unit can analyze the recipient's past gift reception history to improve the accuracy of the information analysis. For example, the information analysis unit can analyze the categories of gifts the recipient has received in the past and reflect this in the information analysis. The information analysis unit can also adjust the criteria for the information analysis, taking into account the recipient's frequency of gift reception. Furthermore, the information analysis unit can also improve the accuracy of the information analysis based on the recipient's evaluations of gifts they have received in the past. The past gift reception history includes, for example, the type of gift received, the date and time of receipt, and the frequency of receipt, but is not limited to these examples. For example, the information analysis unit can analyze details of gifts the recipient has received in the past and reflect this in the information analysis. Furthermore, the information analysis unit can also analyze the recipient's gift reception history in chronological order and adjust the criteria for the information analysis. Furthermore, the information analysis unit can also improve the accuracy of the information analysis based on the recipient's evaluations of gifts they have received in the past. Thus, analyzing the recipient's past gift reception history improves the accuracy of the information analysis.

[0074] The information analysis unit can analyze the social media activity of the recipient and reflect it in the information analysis. For example, the information analysis unit can analyze posts that the recipient has "liked" on social media and reflect it in the information analysis. The information analysis unit can also analyze the trends of the recipient's followers and the accounts they follow and reflect it in the information analysis. Furthermore, the information analysis unit can adjust the criteria for information analysis based on content shared by the recipient. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the information analysis unit can analyze the content posted by the recipient and reflect it in the information analysis. Furthermore, the information analysis unit can adjust the criteria for information analysis based on the number of likes the recipient has. Furthermore, the information analysis unit can improve the accuracy of the information analysis based on the number of followers the recipient has. In this way, by analyzing the recipient's social media activity, the accuracy of the information analysis is improved.

[0075] The information analysis unit can estimate the emotions of the recipient and determine the priority of information analysis based on the estimated emotions. For example, if the recipient is happy, the information analysis unit can prioritize analyzing positive information. Furthermore, if the recipient is sad, the information analysis unit can prioritize analyzing comforting information. Furthermore, if the recipient is excited, the information analysis unit can prioritize analyzing interesting information. Estimation of emotions includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the information analysis unit can analyze the recipient's facial expression to estimate the emotion. Furthermore, the information analysis unit can analyze the recipient's text message to estimate the emotion. Furthermore, the information analysis unit can analyze the recipient's voice to estimate the emotion. This enables more appropriate information analysis by determining the priority of information analysis based on the recipient's emotions.

[0076] The information analysis unit can analyze information based on the recipient's geographical location information. For example, if the recipient lives in a specific region, the information analysis unit can analyze the information taking into account the culture and customs of that region. Furthermore, if the recipient is traveling, the information analysis unit can analyze the information based on travel destination information. Furthermore, if the recipient is participating in a specific event, the information analysis unit can analyze the information based on event information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, the information analysis unit can analyze the information based on the recipient's GPS data. Furthermore, the information analysis unit can adjust the criteria for information analysis based on the recipient's IP address. Furthermore, the information analysis unit can improve the accuracy of the information analysis based on the recipient's location information services. Thus, by taking the recipient's geographical location information into account, the accuracy of the information analysis is improved.

[0077] The information analysis unit can analyze the device usage history of the recipient and reflect the results in the information analysis. For example, the information analysis unit can analyze the categories of apps frequently used by the recipient and reflect the results in the information analysis. The information analysis unit can also adjust the criteria for the information analysis, taking into account the recipient's device usage time. The information analysis unit can also improve the accuracy of the information analysis based on the type of device used by the recipient in the past. The device usage history includes, but is not limited to, the apps used, the usage time, and the type of device. For example, the information analysis unit can analyze the details of the apps frequently used by the recipient and reflect the results in the information analysis. The information analysis unit can also analyze the recipient's device usage time in chronological order and adjust the criteria for the information analysis. The information analysis unit can also improve the accuracy of the information analysis based on the type of device used by the recipient in the past. In this way, analyzing the recipient's device usage history improves the accuracy of the information analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way the gift suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, the suggestion unit can analyze the user's facial expressions to estimate the emotions. The suggestion unit can also analyze the user's text messages to estimate the emotions. The suggestion unit can also analyze the user's voice to estimate the emotions. This allows for more appropriate suggestions to be presented based on the user's emotions.

[0079] When making a suggestion, the suggestion unit may adjust the level of detail of the suggestion based on the importance of the gift. For example, for an important gift, the suggestion unit may provide a detailed description and multiple options. For a general gift, the suggestion unit may provide a brief description and several options. For a light gift, the suggestion unit may provide a simple description and one option. Examples of importance include, but are not limited to, the price of the gift, the relationship of the recipient, and the importance of the event. The suggestion unit may adjust the level of detail of the suggestion based on, for example, the price of the gift. The suggestion unit may also adjust the criteria for the suggestion based on the relationship of the recipient. Furthermore, the suggestion unit may improve the accuracy of the suggestion based on the importance of the event. As a result, adjusting the level of detail of the suggestion based on the importance of the gift enables more appropriate suggestions.

[0080] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the gift category. For example, in the case of a food gift, the suggestion unit can make a suggestion taking into account expiration dates and storage methods. Furthermore, in the case of a fashion gift, the suggestion unit can make a suggestion taking into account size and color variations. Furthermore, in the case of an electronic gift, the suggestion unit can make a suggestion taking into account technical specifications and compatibility. Categories include, but are not limited to, food, clothing, electronic devices, etc., for example. For example, the suggestion unit can analyze details of a food gift and make a suggestion taking into account expiration dates and storage methods. Furthermore, the suggestion unit can analyze details of a fashion gift and make a suggestion taking into account size and color variations. Furthermore, the suggestion unit can analyze details of an electronic gift and make a suggestion taking into account technical specifications and compatibility. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the gift category.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide short, concise, and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, the suggestion unit can analyze the user's facial expressions to estimate the emotions. The suggestion unit can also analyze the user's text messages to estimate the emotions. The suggestion unit can also analyze the user's voice to estimate the emotions. This allows for more appropriate suggestions to be made by adjusting the length of the suggestions based on the user's emotions.

[0082] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of gift submission. For example, if an important event is approaching, the suggestion unit can prioritize gifts related to the event. The suggestion unit can also make regular suggestions for a general event. The suggestion unit can also make simple suggestions for a minor event. The submission time includes, but is not limited to, the date and time of the event, the season, the recipient's schedule, and the like. The suggestion unit can determine the priority of the suggestions based on, for example, the date and time of the event. The suggestion unit can also adjust the criteria for the suggestions based on the season. The suggestion unit can also improve the accuracy of the suggestions based on the recipient's schedule. This enables more appropriate suggestions by determining the priority of the suggestions based on the time of gift submission.

[0083] The suggestion unit may adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit may prioritize suggesting highly relevant gifts based on the user's past purchasing history. The suggestion unit may also prioritize suggesting highly relevant gifts based on the user's current interests. Furthermore, the suggestion unit may also prioritize suggesting highly relevant gifts based on the user's social media activity. Examples of relevance include, but are not limited to, gift theme, recipient interests, and past gift history. The suggestion unit may adjust the order of suggestions based on, for example, the gift theme. The suggestion unit may also adjust the criteria for suggestions based on the recipient's interests. Furthermore, the suggestion unit may improve the accuracy of suggestions based on past gift history. As a result, adjusting the order of suggestions based on gift relevance enables more appropriate suggestions.

[0084] The link providing unit can estimate the user's emotions and adjust the link providing method based on the estimated emotions. For example, if the user is relaxed, the link providing unit can provide a link with a detailed description. Furthermore, if the user is in a hurry, the link providing unit can provide a link with a concise description. Furthermore, if the user is excited, the link providing unit can provide a visually appealing link. Estimation of emotions includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the link providing unit can analyze the user's facial expression to estimate the emotion. Furthermore, the link providing unit can analyze the user's text message to estimate the emotion. Furthermore, the link providing unit can analyze the user's voice to estimate the emotion. This allows for more appropriate link provision by adjusting the link providing method based on the user's emotions.

[0085] When providing a link, the link providing unit can select an optimal link by referring to the user's past click history. For example, the link providing unit analyzes the categories of links the user has clicked in the past and selects the optimal link. The link providing unit can also select the optimal link by taking into account the user's click frequency. Furthermore, the link providing unit can select the optimal link based on the user's evaluation of links clicked in the past. The click history includes, for example, the clicked link, the click date and time, and the click frequency, but is not limited to these examples. For example, the link providing unit analyzes the details of links clicked in the past by the user and selects the optimal link. The link providing unit can also analyze the user's click frequency in chronological order and select the optimal link. Furthermore, the link providing unit can select the optimal link based on the evaluation of links clicked in the past by the user. In this way, the optimal link can be selected by referring to the user's past click history.

[0086] When providing a link, the link providing unit can customize the link display method based on the user's current purchasing intent. For example, if the user has a high purchasing intent, the link providing unit can display the link in a prominent position. Furthermore, if the user has a low purchasing intent, the link providing unit can display the link in a more discreet position. Furthermore, the link providing unit can adjust the color and font size of the link according to the user's purchasing intent. Purchasing intent includes, for example, past purchasing history, current browsing behavior, survey results, etc., but is not limited to these examples. For example, the link providing unit estimates the user's purchasing intent based on the user's past purchasing history. Furthermore, the link providing unit can estimate the user's purchasing intent based on the user's current browsing behavior. Furthermore, the link providing unit can estimate the user's purchasing intent based on survey results. This allows for more effective link provision by customizing the link display method based on the user's purchasing intent.

[0087] The link providing unit can estimate the user's emotions and determine the priority of link provision based on the estimated emotions. For example, if the user is relaxed, the link providing unit can provide a link with a detailed description. Furthermore, if the user is in a hurry, the link providing unit can provide a link with a concise description. Furthermore, if the user is excited, the link providing unit can provide a visually appealing link. Estimation of emotions includes, but is not limited to, facial expression recognition, text analysis, and voice analysis. For example, the link providing unit can analyze the user's facial expression to estimate the emotion. Furthermore, the link providing unit can analyze the user's text message to estimate the emotion. Furthermore, the link providing unit can analyze the user's voice to estimate the emotion. This enables more appropriate link provision by determining the priority of link provision based on the user's emotions.

[0088] When providing links, the link providing unit can select appropriate links based on the user's geographical location information. For example, if the user lives in a specific area, the link providing unit can provide links to stores and services in that area. Furthermore, if the user is traveling, the link providing unit can provide links to stores and services in the user's travel destination. Furthermore, if the user is participating in a specific event, the link providing unit can provide links related to the event. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. The link providing unit selects appropriate links based on the user's GPS data. Furthermore, the link providing unit can adjust link criteria based on the user's IP address. Furthermore, the link providing unit can improve link accuracy based on the user's location information services. This allows the optimal link to be selected by taking the user's geographical location information into consideration.

[0089] The link providing unit may provide an optimal link by taking into consideration device information of the user when providing a link. For example, if the user is using a smartphone, the link providing unit may provide a mobile-friendly link. Furthermore, if the user is using a tablet, the link providing unit may provide a link optimized for a large screen. Furthermore, if the user is using a desktop, the link providing unit may provide a link including detailed information. Device information may include, but is not limited to, the device type, OS, browser information, etc. The link providing unit may provide an optimal link based on, for example, the user's device type. Furthermore, the link providing unit may adjust link criteria based on the user's OS. Furthermore, the link providing unit may improve link accuracy based on the user's browser information. This allows the optimal link to be provided by taking into consideration the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned characteristic determination unit, information analysis unit, suggestion unit, and link providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the characteristic determination unit is realized by the control unit 46A of the smart device 14 and determines the user's characteristics. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information about the recipient. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the most suitable gift. The link providing unit is realized by the control unit 46A of the smart device 14 and provides a link for purchasing the suggested gift. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned characteristic determination unit, information analysis unit, suggestion unit, and link provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the characteristic determination unit is realized by the control unit 46A of the smart glasses 214 and determines the user's characteristics. The information analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information about the recipient. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable gift. The link provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a link for purchasing the suggested gift. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned characteristic determination unit, information analysis unit, suggestion unit, and link provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the characteristic determination unit is realized by the control unit 46A of the headset type terminal 314 and determines the user's characteristics. The information analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information about the recipient. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable gift. The link provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a link for purchasing the suggested gift. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned characteristic determination unit, information analysis unit, suggestion unit, and link provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the characteristic determination unit is realized by the control unit 46A of the robot 414 and determines the characteristics of the user. The information analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information about the recipient. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable gift. The link provision unit is realized, for example, by the control unit 46A of the robot 414 and provides a link for purchasing the suggested gift.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The characteristic determination unit can analyze the user's health data and reflect it in the characteristic determination. For example, it can identify the user's health condition and exercise habits based on data obtained from the user's fitness tracker or smartwatch. The characteristic determination unit can also analyze the user's food records and suggest health-conscious gifts. Furthermore, the characteristic determination unit can analyze the user's sleep patterns and suggest relaxation products and sleep aids. This allows for more personalized gift suggestions by taking the user's health data into consideration.

[0092] The information analysis unit can analyze the activity of online communities and forums to identify the recipient's hobbies and interests. For example, it can analyze the content of posts in forums in which the recipient participates to identify specific hobbies and interests. The information analysis unit can also analyze the content of websites frequently visited by the recipient to identify topics of interest. Furthermore, the information analysis unit can analyze the content of newsletters and blogs to which the recipient subscribes to identify areas of interest. This allows for more appropriate gift suggestions by taking the recipient's online activity into consideration.

[0093] The suggestion unit can analyze the user's past gift selection history to improve the accuracy of suggestions. For example, it analyzes the types and price ranges of gifts the user has previously selected and suggests gifts with similar trends. The suggestion unit can also take into account the reactions of recipients of gifts the user has previously selected and reflect the characteristics of successful gifts. Furthermore, the suggestion unit can make suggestions at appropriate times based on the seasons and events for gifts the user has previously selected. This allows for more accurate gift suggestions by taking into account the user's past gift selection history.

[0094] The link providing unit can provide related promotion and discount information based on the user's purchase history. For example, it can provide discount coupons related to products the user has previously purchased. The link providing unit can also provide promotion information for products in which the user has shown interest. Furthermore, the link providing unit can analyze the user's purchase history and provide special offer information for specific brands or categories. This makes it possible to provide links that increase purchasing motivation by taking the user's purchase history into consideration.

[0095] The characteristic determination unit can determine the characteristics by taking into account the user's life events. For example, when the user experiences a life event such as marriage, childbirth, or moving, the characteristics related to that event are reflected. The characteristic determination unit can also adjust the criteria for determining the characteristics by taking into account important dates such as the user's birthday or anniversary. Furthermore, the characteristic determination unit can also take into account changes in the user's life stage, such as a change in the user's career or academic progress, and reflect these changes in the characteristic determination. This allows for more appropriate characteristic determination by taking into account the user's life events.

[0096] The characteristic determination unit can estimate the user's emotions and adjust the criteria for characteristic determination based on the estimated emotions. For example, if the user is feeling stressed, the criteria for characteristic determination can be relaxed and the characteristics can be determined by simple questions. Furthermore, if the user is relaxed, the characteristic determination unit can ask detailed questions to perform a more accurate characteristic determination. Furthermore, if the user is in a hurry, the characteristic determination unit can prioritize past data and perform a quick characteristic determination. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. As a result, by adjusting the criteria for characteristic determination based on the user's emotions, more appropriate characteristic determination can be performed.

[0097] The information analysis unit can estimate the emotions of the recipient and adjust the information analysis method based on the estimated emotions. For example, if the recipient is happy, positive information can be prioritized in the analysis. Furthermore, if the recipient is sad, the information analysis unit can prioritize analysis of comforting information. Furthermore, if the recipient is excited, the information analysis unit can prioritize analysis of interesting information. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. This allows for more appropriate information analysis by adjusting the information analysis method based on the recipient's emotions.

[0098] The suggestion unit can estimate the user's emotions and adjust the presentation of gift suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. This allows for more appropriate suggestions by adjusting the presentation of gift suggestions based on the user's emotions.

[0099] The link providing unit can estimate the user's emotions and adjust the link providing method based on the estimated emotions. For example, if the user is relaxed, the link providing unit can provide a link with a detailed description. If the user is in a hurry, the link providing unit can provide a link with a concise description. Furthermore, if the user is excited, the link providing unit can provide a visually appealing link. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. This allows for more appropriate link provision by adjusting the link providing method based on the user's emotions.

[0100] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated emotion. For example, if the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is in a hurry, the suggestion unit can provide a short, concise, and to-the-point suggestion. Furthermore, if the user is excited, the suggestion unit can provide a visually appealing suggestion. Examples of emotion estimation include, but are not limited to, facial expression recognition, text analysis, and voice analysis. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The characteristic determination unit determines the user's characteristics. User characteristics include age, gender, hobbies, and purchasing history. The characteristic determination unit analyzes search history, web service usage history, past purchasing history, and social media activity to improve the accuracy of characteristic determination. Step 2: The information analysis unit analyzes the recipient's information based on the characteristics determined by the characteristic determination unit. The recipient's information includes text information, image information, and behavioral history. The information analysis unit analyzes images, text information, past gift receipt history, and social media activity to improve the accuracy of the information analysis. Step 3: The suggestion unit suggests optimal gifts based on the information analyzed by the information analysis unit. The suggestion unit combines and analyzes external data and the company's own data, estimates the user's emotions, and adjusts the way gift suggestions are presented. It also adjusts the level of detail in the suggestions based on the importance of the gift. Step 4: The link provider provides links to purchase the gifts suggested by the suggestion unit. The link provider provides links to shopping sites, estimates the user's sentiment, and adjusts the method of providing links. It also selects the optimal link by referring to the user's past click history.

[0103] 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.

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

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 characteristic determination unit for determining a characteristic of a user; an information analysis unit that analyzes information of a recipient based on the characteristics determined by the characteristic determination unit; a suggestion unit that suggests an appropriate gift based on the information analyzed by the information analysis unit; a link providing unit that provides a link for purchasing the gift suggested by the suggestion unit. A system characterized by:

2. The characteristic determination unit Analyzing search history or web service usage history 2. The system of claim 1.

3. The information analysis unit Perform image or text analysis 2. The system of claim 1.

4. The proposal unit Combine and analyze external and internal data 2. The system of claim 1.

5. The link providing unit Provide links to shopping sites 2. The system of claim 1.

6. The information analysis unit Evaluate the gift that best suits your needs based on the recipient's information 2. The system of claim 1.

7. The characteristic determination unit Estimate the user's emotions and adjust the criteria for determining characteristics based on the estimated user emotions.

2. The system of claim 1.

8. The characteristic determination unit Analyze users' past purchase history to improve the accuracy of their characteristics 2. The system of claim 1.

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