System

Abstract

An object of a system according to an embodiment is to propose an appropriate candidate for a gift without causing a user to worry about selecting a gift.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a link unit. The collection unit collects information input by the user. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests a gift candidate based on the information analyzed by the analysis unit. The link unit links the gift candidate proposed by the proposing unit to the e-commerce site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making appropriate suggestions to users who are struggling to choose a gift.

[0005] The system according to the embodiment aims to suggest suitable gift candidates so that the user does not have to worry about choosing a gift. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a link unit. The collection unit collects information input by a user. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests gift candidates based on the information analyzed by the analysis unit. The link unit links the gift candidates suggested by the suggestion unit to an e-commerce site. [Effects of the Invention]

[0007] The system according to the embodiment can suggest suitable gift candidates to the user without the user having to worry about choosing a gift. [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) In an embodiment of the present invention, a gift selection support system uses a generation AI to propose gift candidates based on information entered by a user and provides a link to an e-commerce site. In the gift selection support system, the user answers questions in a chat format and inputs information about the recipient (age, gender, relationship, budget, preferences, and other free text). The generation AI then analyzes the input information and proposes gift candidates. The proposed gift candidates are then provided with a link to an e-commerce site, and the user can immediately purchase the gift by clicking the link. For example, in the gift selection support system, the user inputs information about the recipient by answering questions such as "How old is the recipient?", "What is their gender?", "What is your relationship with them?", "What is your budget?", and "What are their preferences?" The generation AI then analyzes the input information and proposes gift candidates. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the generation AI will propose fashion items. Specifically, potential candidates include accessories, bags, and clothing. The proposed gift candidates are then provided with a link to an e-commerce site, and the user can immediately purchase the gift by clicking the link. For example, when a user clicks on a link for an accessory suggested by the generation AI, the user is taken to the accessory's page on an e-commerce site, where they can complete the purchase process. This allows the gift selection support system to enable users to easily select a gift and give a present that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. This allows the user to easily select a gift and give a present that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. For example, by being able to select the perfect gift for a special occasion such as a birthday or anniversary, user satisfaction is increased.

[0029] A gift selection support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a link unit. The collection unit collects information input by a user. The information input by a user includes, but is not limited to, age, gender, relationship, budget, preferences, and other free descriptions. The collection unit, for example, displays questions in a chat format and collects information by the user's answers to the questions. The collection unit can also store the information input by the user in a database. For example, the collection unit collects age information by the user answering the question, "How old is the other person?" The collection unit can also collect gender information by the user answering the question, "What is the other person's gender?" The collection unit can also analyze the user's input content and collect information using a generation AI. For example, the collection unit extracts the other person's preferences from the user's free descriptions using the generation AI. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using the generation AI to identify optimal gift candidates for the other person. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the analysis unit identifies fashion items. The analysis unit can also use a generation AI to rank gift candidates based on the collected information. For example, the analysis unit uses the generation AI to score and rank gift candidates based on the recipient's preferences and budget. The suggestion unit suggests gift candidates based on the information analyzed by the analysis unit. The suggestion unit uses, for example, the generation AI to suggest gift candidates. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the suggestion unit suggests accessories, bags, clothes, etc. The suggestion unit can also use the generation AI to present gift candidates to the user. For example, the suggestion unit displays gift candidates suggested by the generation AI on a chat screen. The linking unit links the gift candidates suggested by the suggestion unit to an e-commerce site. For example, the linking unit uses the generation AI to generate links to e-commerce sites corresponding to the suggested gift candidates.For example, the link unit generates a link for an accessory suggested by the generation AI, and when the user clicks on it, the link unit can move to the accessory page on an e-commerce site. The link unit can also use the generation AI to adjust the display method of the link. For example, the link unit uses the generation AI to estimate the user's emotions and provide a simple, highly visible link display. This allows the gift selection support system according to the embodiment to enable the user to easily select a gift and give a gift that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. For example, the gift selection support system collects information entered by the user, analyzes it using the generation AI, suggests gift candidates, and provides a link to the e-commerce site, allowing the user to easily select and purchase a gift.

[0030] The collection unit can analyze the user's past input history and select an appropriate collection method. The collection unit can analyze the user's past input history, for example, using a generation AI. For example, the collection unit can extract patterns from the user's past input history using data mining technology. The collection unit can also analyze the user's past input history using time series analysis technology. For example, the collection unit can select an optimal question format based on information previously input by the user. The collection unit can also prioritize and suggest input methods (such as voice or text) that the user has used in the past. The collection unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows information to be collected efficiently by selecting the optimal collection method based on the user's past input history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past input history data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting information, the collection unit can perform filtering based on the user's current situation and areas of interest. The collection unit, for example, uses a generation AI to identify the user's current situation and areas of interest. For example, the collection unit can identify the user's current situation based on the user's location information. The collection unit can also identify the user's areas of interest based on the user's past search history. For example, the collection unit can prioritize displaying questions related to the user's current area of ​​interest. The collection unit can also collect relevant information based on the user's current situation (e.g., immediately before an event). The collection unit can also omit unnecessary questions based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's location information data to the generation AI and cause the generation AI to identify the current situation.

[0032] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, uses a generation AI to identify the user's input method. For example, the collection unit can use voice recognition technology to identify the user's voice input. The collection unit can also use text analysis technology to identify the user's text input. For example, if the user selects voice input, the collection unit can collect information using voice recognition technology. If the user selects text input, the collection unit can also collect information using text analysis technology. If the user selects image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to the generation AI and have the generation AI perform voice recognition.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to identify the user's geographical location information. For example, the collection unit can identify the user's current location using GPS data. The collection unit can also obtain the user's geographical location information using a location-based service. For example, if the user is in a specific area, the collection unit can prioritize collecting information related to that area. If the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. If the user is at home, the collection unit can prioritize collecting information around the user's home. This allows for more appropriate information to be provided by collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using an AI, for example, or may be performed without using an AI. For example, the collection unit can input the user's GPS data into the generation AI and cause the generation AI to identify highly relevant information.

[0034] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, use a generation AI to analyze the user's social media activities. For example, the collection unit can analyze the content of the user's posts and collect related information. The collection unit can also collect related information based on the user's number of likes and followers. For example, the collection unit can collect information related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0035] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses a generation AI to analyze the user's past feedback. For example, the collection unit customizes the collection method based on the user's ratings and comments. The collection unit can also preferentially suggest specific question formats based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. For example, the collection unit selects an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially suggest specific question formats based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, uses a generation AI to evaluate the importance of the information. For example, the analysis unit sets specific evaluation criteria to evaluate the importance of the information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on general information. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of information. The analysis unit, for example, uses a generation AI to identify the category of information. For example, the analysis unit can classify information into categories using clustering technology. The analysis unit can also apply an appropriate analysis algorithm depending on the category of information. For example, the analysis unit can apply a fashion-specific analysis algorithm to fashion-related information. The analysis unit can also apply a technology-specific analysis algorithm to technology-related information. The analysis unit can also select an optimal analysis algorithm based on the user's interest category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the analysis unit uses a feedback loop to adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. In this way, the analysis accuracy is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time of information submission. The analysis unit, for example, uses a generation AI to identify the time of information submission. For example, the analysis unit sets specific evaluation criteria to evaluate the urgency of the submission time. The analysis unit can also determine the analysis priority based on the time of information submission. For example, the analysis unit prioritizes analysis of the latest information. The analysis unit can also postpone analysis of older information. The analysis unit can also adjust the analysis priority based on the time of user submission. In this way, by determining the analysis priority based on the time of information submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, uses a generation AI to evaluate the relevance of information. For example, the analysis unit sets relevance evaluation criteria and evaluates the relevance of information. The analysis unit can also adjust the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the user's level of interest. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to identify the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's past input history and feedback. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise. The analysis unit can also provide analysis results in simpler language if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the gift when making a suggestion. The suggestion unit, for example, uses a generation AI to evaluate the importance of the gift. For example, the suggestion unit sets specific evaluation criteria to evaluate the importance of the gift. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the gift. For example, the suggestion unit makes detailed suggestions for important gifts. The suggestion unit can also make simplified suggestions for general gifts. The suggestion unit can also adjust the level of detail of the suggestion based on the user's level of interest. In this way, by adjusting the level of detail of the suggestion based on the importance of the gift, suggestions can be made efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input gift importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0043] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the category of the gift. The suggestion unit, for example, uses a generation AI to identify the category of the gift. For example, the suggestion unit can classify the gifts into categories using clustering technology. The suggestion unit can also apply an appropriate suggestion algorithm depending on the category of the gift. For example, the suggestion unit can apply a fashion-specific suggestion algorithm to fashion-related gifts. The suggestion unit can also apply a technology-specific suggestion algorithm to technology-related gifts. The suggestion unit can also select an optimal suggestion algorithm based on the user's interest category. This improves suggestion accuracy by applying the optimal suggestion algorithm depending on the gift category. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input gift category data into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0044] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, uses a generation AI to analyze the user's past suggestion results. For example, the suggestion unit uses a feedback loop to adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract specific patterns from the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results to improve the suggestion method. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract specific patterns from the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results to improve the suggestion method. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the suggestion accuracy.

[0045] The suggestion unit can determine the priority of suggestions based on the timing of gift submission when making suggestions. The suggestion unit, for example, uses a generation AI to identify the timing of gift submission. For example, the suggestion unit sets specific evaluation criteria to evaluate the urgency of the submission timing. The suggestion unit can also determine the priority of suggestions based on the timing of gift submission. For example, the suggestion unit can prioritize suggestions immediately before an important event. The suggestion unit can also postpone suggestions for general events. The suggestion unit can also adjust the priority of suggestions based on the user's submission timing. In this way, by determining the priority of suggestions based on the timing of gift submission, suggestions can be made efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input gift submission timing data into the generation AI and cause the generation AI to determine the priority of suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the presents when suggesting them. The suggestion unit, for example, uses a generation AI to evaluate the relevance of the presents. For example, the suggestion unit sets relevance evaluation criteria and evaluates the relevance of the presents. The suggestion unit can also adjust the order of suggestions based on the relevance of the presents. For example, the suggestion unit prioritizes suggestions for highly relevant presents. The suggestion unit can also postpone suggestions for less relevant presents. The suggestion unit can also adjust the order of suggestions based on the user's level of interest. In this way, suggestions can be made efficiently by adjusting the order of suggestions based on the relevance of the presents. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input relevance data of the presents to the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, uses a generation AI to identify the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise based on the user's past input history and feedback. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can provide a proposal that uses a lot of technical terminology if the user has technical expertise. The suggestion unit can also provide a proposal in simple language if the user does not have technical expertise. The suggestion unit can also adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When displaying a link, the link unit can select an optimal link by referring to the user's past click history. The link unit, for example, uses a generation AI to analyze the user's past click history. For example, the link unit selects an optimal link based on the user's click frequency and click pattern. The link unit can also extract specific patterns from the user's past click history and suggest optimal links. The link unit can also analyze the user's past click history to improve the link display method. For example, the link unit selects an optimal link based on links the user has clicked in the past. The link unit can also extract specific patterns from the user's past click history and suggest optimal links. The link unit can also analyze the user's past click history to improve the link display method. In this way, the optimal link can be provided by referring to the user's past click history. Some or all of the above-described processing in the link unit may be performed using, for example, AI, or may be performed without using AI. For example, the link unit can input the user's click history data into the generation AI and cause the generation AI to select an optimal link.

[0049] When displaying a link, the link unit can customize the link content according to the user's current task. The link unit, for example, uses a generation AI to identify the user's current task. For example, the link unit identifies the current task based on the user's operation history. The link unit can also customize the link content according to the user's current task. For example, if the user is looking for a gift, the link unit can prioritize displaying related links. For example, if the user is in the process of making a purchase, the link unit can prioritize displaying links related to the purchase. The link unit can also customize the link content based on the user's current task. In this way, by customizing the link content according to the user's current task, more appropriate links can be provided. Some or all of the above-described processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input user operation history data into the generation AI and have the generation AI customize the link content.

[0050] The link unit can improve the link display method by reflecting user feedback when displaying links. The link unit can, for example, analyze user feedback using a generation AI. For example, the link unit can improve the link display method based on user ratings and comments. The link unit can also preferentially suggest a specific display format based on user feedback. The link unit can also analyze user feedback and optimize the link display method. For example, the link unit can improve the link display method based on feedback provided by the user. The link unit can also preferentially suggest a specific display format based on user feedback. The link unit can also analyze user feedback and optimize the link display method. In this way, the link display method can be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the link unit can be performed using, for example, AI, or can be performed without using AI. For example, the link unit can input user feedback data into the generation AI and cause the generation AI to improve the link display method.

[0051] When displaying a link, the link unit can select the optimal link by taking into account the user's device information. The link unit, for example, uses a generation AI to identify the user's device information. For example, the link unit selects the optimal link based on the user's device type and screen size. The link unit can also adjust the link display method based on the user's device's OS and browser information. For example, if the user is using a smartphone, the link unit can provide a link tailored to the screen size. If the user is using a tablet, the link unit can provide a link optimized for a large screen. If the user is using a smartwatch, the link unit can provide a concise, highly visible link. This allows the optimal link to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the link unit may be performed using, for example, AI, or may be performed without using AI. For example, the link unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal link.

[0052] When displaying a link, the link unit can make the link content multilingual according to the user's language setting. The link unit, for example, uses a generation AI to identify the user's language setting. For example, the link unit automatically sets the link language based on the language setting of the user's device. The link unit can also provide a language switching function when the user uses multiple languages. The link unit can also provide a link in a specific language when the user selects that language. This makes it possible to provide more appropriate links by making the link content multilingual according to the user's language setting. Some or all of the above-described processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input the user's language setting data into the generation AI and cause the generation AI to set the link content to be multilingual.

[0053] When displaying a link, the link unit can analyze the user's social media activity and provide related links. The link unit can analyze the user's social media activity using, for example, a generation AI. For example, the link unit can analyze the content of the user's posts and provide related links. The link unit can also provide related links based on the number of likes and followers the user has. The link unit can also provide related links based on the activities of the user's friends on social media. For example, the link unit can provide links related to places the user has checked in to on social media. The link unit can also analyze the content of the user's social media posts and provide related links. The link unit can also provide related links based on the activities of the user's friends on social media. In this way, highly relevant links can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the link unit can be performed using, for example, AI, or without AI. For example, the link unit can input the user's social media data into the generation AI and cause the generation AI to provide related links.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The suggestion unit can analyze the user's past purchase history and make new suggestions based on the trends of past purchases of presents. For example, the suggestion unit can analyze the categories and price ranges of presents the user has purchased in the past and suggest presents with similar trends. The suggestion unit can also prioritize suggesting highly rated presents based on ratings of presents the user has purchased in the past. Furthermore, the suggestion unit can analyze the reactions of recipients of presents the user has purchased in the past and suggest new presents that have the characteristics of presents that the recipients enjoyed. This allows for more personalized suggestions to be made based on the user's past purchase history.

[0056] The analysis unit can analyze the user's past search history and suggest gift candidates based on the search history. For example, the analysis unit can suggest related gifts based on keywords the user has searched for in the past. The analysis unit can also suggest gifts in a similar category based on the category of products the user has viewed in the past. Furthermore, the analysis unit can suggest gifts that fit the user's budget based on the price range of products the user has searched for in the past. This makes it possible to suggest more relevant gift candidates based on the user's past search history.

[0057] The link unit can analyze a user's past click history and customize the link display method based on the click history. For example, the link unit can provide links in a similar display format based on the display format of links the user has previously clicked. The link unit can also display links in a similar position based on the position of links the user has previously clicked. Furthermore, the link unit can preferentially display related links based on the content of links the user has previously clicked. This allows for more effective link display based on the user's past click history.

[0058] The analysis unit can suggest regional gift candidates based on the user's current location information. For example, if the user is in a specific region, the analysis unit can suggest local specialties and popular products. If the user is traveling, the analysis unit can also suggest gifts related to souvenirs and tourist attractions at the user's travel destination. Furthermore, if the user is at home, the analysis unit can also suggest gifts that can be purchased at stores near the user's home. This makes it possible to suggest regional gift candidates based on the user's current location information.

[0059] The link unit can display links optimized for the device based on the user's device information. For example, if the user is using a smartphone, the link unit can provide links that fit the screen size. Also, if the user is using a tablet, the link unit can provide links optimized for a large screen. Furthermore, if the user is using a smartwatch, the link unit can provide concise and highly visible links. This makes it possible to display optimal links based on the user's device information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects information entered by the user. The information entered by the user includes age, gender, relationship, budget, preferences, and other free descriptions. The collection unit displays questions in chat format and collects information by the user's answers. The collection unit can also save the information entered by the user in a database. Furthermore, it can use generation AI to extract the other person's preferences from the user's free descriptions. Step 2: The analysis unit analyzes the information collected by the collection unit. Using the generation AI, the collected information is analyzed to identify the best gift candidates for the recipient. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and likes fashion, fashion items will be identified. The generation AI can also be used to rank gift candidates based on the collected information. Step 3: The suggestion unit suggests gift candidates based on the information analyzed by the analysis unit. Using the generation AI, it suggests gift candidates and presents them to the user. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and likes fashion, it will suggest accessories, bags, clothes, etc. Step 4: The linking unit links the gift candidates suggested by the suggestion unit to an e-commerce site. Using the generation AI, the link of the e-commerce site corresponding to the suggested gift candidate is generated, and when the user clicks, they can be taken to the corresponding page of the e-commerce site. The generation AI can also be used to adjust how the link is displayed.

[0062] (Example 2) In an embodiment of the present invention, a gift selection support system uses a generation AI to propose gift candidates based on information entered by a user and provides a link to an e-commerce site. In the gift selection support system, the user answers questions in a chat format and inputs information about the recipient (age, gender, relationship, budget, preferences, and other free text). The generation AI then analyzes the input information and proposes gift candidates. The proposed gift candidates are then provided with a link to an e-commerce site, and the user can immediately purchase the gift by clicking the link. For example, in the gift selection support system, the user inputs information about the recipient by answering questions such as "How old is the recipient?", "What is their gender?", "What is your relationship with them?", "What is your budget?", and "What are their preferences?" The generation AI then analyzes the input information and proposes gift candidates. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the generation AI will propose fashion items. Specifically, potential candidates include accessories, bags, and clothing. The proposed gift candidates are then provided with a link to an e-commerce site, and the user can immediately purchase the gift by clicking the link. For example, when a user clicks on a link for an accessory suggested by the generation AI, the user is taken to the accessory's page on an e-commerce site, where they can complete the purchase process. This allows the gift selection support system to enable users to easily select a gift and give a present that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. This allows the user to easily select a gift and give a present that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. For example, by being able to select the perfect gift for a special occasion such as a birthday or anniversary, user satisfaction is increased.

[0063] A gift selection support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a link unit. The collection unit collects information input by a user. The information input by a user includes, but is not limited to, age, gender, relationship, budget, preferences, and other free descriptions. The collection unit, for example, displays questions in a chat format and collects information by the user's answers to the questions. The collection unit can also store the information input by the user in a database. For example, the collection unit collects age information by the user answering the question, "How old is the other person?" The collection unit can also collect gender information by the user answering the question, "What is the other person's gender?" The collection unit can also analyze the user's input content and collect information using a generation AI. For example, the collection unit extracts the other person's preferences from the user's free descriptions using the generation AI. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using the generation AI to identify optimal gift candidates for the other person. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the analysis unit identifies fashion items. The analysis unit can also use a generation AI to rank gift candidates based on the collected information. For example, the analysis unit uses the generation AI to score and rank gift candidates based on the recipient's preferences and budget. The suggestion unit suggests gift candidates based on the information analyzed by the analysis unit. The suggestion unit uses, for example, the generation AI to suggest gift candidates. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and a preference for fashion, the suggestion unit suggests accessories, bags, clothes, etc. The suggestion unit can also use the generation AI to present gift candidates to the user. For example, the suggestion unit displays gift candidates suggested by the generation AI on a chat screen. The linking unit links the gift candidates suggested by the suggestion unit to an e-commerce site. For example, the linking unit uses the generation AI to generate links to e-commerce sites corresponding to the suggested gift candidates.For example, the link unit generates a link for an accessory suggested by the generation AI, and when the user clicks on it, the link unit can move to the accessory page on an e-commerce site. The link unit can also use the generation AI to adjust the display method of the link. For example, the link unit uses the generation AI to estimate the user's emotions and provide a simple, highly visible link display. This allows the gift selection support system according to the embodiment to enable the user to easily select a gift and give a gift that will please the recipient. Furthermore, by providing a path to the e-commerce site, the purchase process can be carried out smoothly. For example, the gift selection support system collects information entered by the user, analyzes it using the generation AI, suggests gift candidates, and provides a link to the e-commerce site, allowing the user to easily select and purchase a gift.

[0064] The collection unit estimates the user's emotion and adjusts the timing of information collection based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, a generation AI. For example, the collection unit estimates the emotion from the user's facial expression using facial expression recognition technology. The collection unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, if the user is feeling stressed, the collection unit starts collecting information at a timing when the user is able to relax. The collection unit can also start collecting information immediately when the user is relaxed. The collection unit can also collect information quickly when the user is in a hurry. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can analyze the user's past input history and select an appropriate collection method. The collection unit can analyze the user's past input history, for example, using a generation AI. For example, the collection unit can extract patterns from the user's past input history using data mining technology. The collection unit can also analyze the user's past input history using time series analysis technology. For example, the collection unit can select an optimal question format based on information previously input by the user. The collection unit can also prioritize and suggest input methods (such as voice or text) that the user has used in the past. The collection unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This allows information to be collected efficiently by selecting the optimal collection method based on the user's past input history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past input history data into a generation AI and have the generation AI select the optimal collection method.

[0066] When collecting information, the collection unit can perform filtering based on the user's current situation and areas of interest. The collection unit, for example, uses a generation AI to identify the user's current situation and areas of interest. For example, the collection unit can identify the user's current situation based on the user's location information. The collection unit can also identify the user's areas of interest based on the user's past search history. For example, the collection unit can prioritize displaying questions related to the user's current area of ​​interest. The collection unit can also collect relevant information based on the user's current situation (e.g., immediately before an event). The collection unit can also omit unnecessary questions based on the user's areas of interest. In this way, highly relevant information can be collected by filtering information based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's location information data to the generation AI and cause the generation AI to identify the current situation.

[0067] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, uses a generation AI to identify the user's input method. For example, the collection unit can use voice recognition technology to identify the user's voice input. The collection unit can also use text analysis technology to identify the user's text input. For example, if the user selects voice input, the collection unit can collect information using voice recognition technology. If the user selects text input, the collection unit can also collect information using text analysis technology. If the user selects image input, the collection unit can also collect information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to the generation AI and have the generation AI perform voice recognition.

[0068] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, a generation AI. For example, the collection unit can estimate emotions from the user's facial expressions using facial expression recognition technology. The collection unit can also estimate emotions from the tone and speed of the user's voice using voice analysis technology. For example, the collection unit prioritizes collecting important information when the user is stressed. The collection unit can also collect detailed information when the user is relaxed. The collection unit can also prioritize information that can be collected quickly when the user is in a hurry. This allows important information to be collected preferentially by determining the priority of information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a generation AI to identify the user's geographical location information. For example, the collection unit can identify the user's current location using GPS data. The collection unit can also obtain the user's geographical location information using a location-based service. For example, if the user is in a specific area, the collection unit can prioritize collecting information related to that area. If the user is traveling, the collection unit can prioritize collecting information related to the user's travel destination. If the user is at home, the collection unit can prioritize collecting information around the user's home. This allows for more appropriate information to be provided by collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using an AI, for example, or may be performed without using an AI. For example, the collection unit can input the user's GPS data into the generation AI and cause the generation AI to identify highly relevant information.

[0070] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, use a generation AI to analyze the user's social media activities. For example, the collection unit can analyze the content of the user's posts and collect related information. The collection unit can also collect related information based on the user's number of likes and followers. For example, the collection unit can collect information related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0071] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses a generation AI to analyze the user's past feedback. For example, the collection unit customizes the collection method based on the user's ratings and comments. The collection unit can also preferentially suggest specific question formats based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. For example, the collection unit selects an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially suggest specific question formats based on the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0072] The analysis unit estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, a generation AI. For example, the analysis unit estimates the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the analysis unit provides a simple, highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is in a hurry. This allows for adjusting the presentation method of the analysis according to the user's emotion, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, uses a generation AI to evaluate the importance of the information. For example, the analysis unit sets specific evaluation criteria to evaluate the importance of the information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on general information. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of information. The analysis unit, for example, uses a generation AI to identify the category of information. For example, the analysis unit can classify information into categories using clustering technology. The analysis unit can also apply an appropriate analysis algorithm depending on the category of information. For example, the analysis unit can apply a fashion-specific analysis algorithm to fashion-related information. The analysis unit can also apply a technology-specific analysis algorithm to technology-related information. The analysis unit can also select an optimal analysis algorithm based on the user's interest category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the analysis unit uses a feedback loop to adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. In this way, the analysis accuracy is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0076] The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user emotion. The analysis unit estimates the user's emotion using, for example, a generation AI. For example, the analysis unit estimates the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the user's tone and speed of voice using voice analysis technology. For example, the analysis unit provides a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide an analysis result with visually stimulating effects when the user is excited. This allows for adjusting the length of the analysis according to the user's emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0077] During analysis, the analysis unit can determine the analysis priority based on the time of information submission. The analysis unit, for example, uses a generation AI to identify the time of information submission. For example, the analysis unit sets specific evaluation criteria to evaluate the urgency of the submission time. The analysis unit can also determine the analysis priority based on the time of information submission. For example, the analysis unit prioritizes analysis of the latest information. The analysis unit can also postpone analysis of older information. The analysis unit can also adjust the analysis priority based on the time of user submission. In this way, by determining the analysis priority based on the time of information submission, analysis can be performed efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, uses a generation AI to evaluate the relevance of information. For example, the analysis unit sets relevance evaluation criteria and evaluates the relevance of information. The analysis unit can also adjust the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the user's level of interest. In this way, adjusting the order of analysis based on the relevance of information enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to identify the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's past input history and feedback. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms if the user has technical expertise. The analysis unit can also provide analysis results in simpler language if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0080] The suggestion unit estimates the user's emotion and adjusts the way in which suggestions are presented based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, a generation AI. For example, the suggestion unit estimates the emotion from the user's facial expression using facial expression recognition technology. The suggestion unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the suggestion unit provides simple, highly visible suggestions when the user is nervous. The suggestion unit can also provide detailed suggestions when the user is relaxed. The suggestion unit can also provide suggestions that focus on the main points when the user is in a hurry. This allows the suggestion unit to adjust the way suggestions are presented based on the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0081] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the gift when making a suggestion. The suggestion unit, for example, uses a generation AI to evaluate the importance of the gift. For example, the suggestion unit sets specific evaluation criteria to evaluate the importance of the gift. The suggestion unit can also adjust the level of detail of the suggestion based on the importance of the gift. For example, the suggestion unit makes detailed suggestions for important gifts. The suggestion unit can also make simplified suggestions for general gifts. The suggestion unit can also adjust the level of detail of the suggestion based on the user's level of interest. In this way, by adjusting the level of detail of the suggestion based on the importance of the gift, suggestions can be made efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input gift importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0082] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the category of the gift. The suggestion unit, for example, uses a generation AI to identify the category of the gift. For example, the suggestion unit can classify the gifts into categories using clustering technology. The suggestion unit can also apply an appropriate suggestion algorithm depending on the category of the gift. For example, the suggestion unit can apply a fashion-specific suggestion algorithm to fashion-related gifts. The suggestion unit can also apply a technology-specific suggestion algorithm to technology-related gifts. The suggestion unit can also select an optimal suggestion algorithm based on the user's interest category. This improves suggestion accuracy by applying the optimal suggestion algorithm depending on the gift category. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input gift category data into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0083] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, uses a generation AI to analyze the user's past suggestion results. For example, the suggestion unit uses a feedback loop to adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract specific patterns from the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results to improve the suggestion method. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract specific patterns from the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results to improve the suggestion method. In this way, the suggestion accuracy is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the suggestion accuracy.

[0084] The suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, a generation AI. For example, the suggestion unit estimates the emotion from the user's facial expression using facial expression recognition technology. The suggestion unit can also estimate the emotion from the user's tone and speed of voice using voice analysis technology. For example, the suggestion unit can provide short and to-the-point suggestions when the user is in a hurry. The suggestion unit can also provide detailed suggestions when the user is relaxed. The suggestion unit can also provide suggestions with visually stimulating effects when the user is excited. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion, thereby providing more appropriate suggestions. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0085] The suggestion unit can determine the priority of suggestions based on the timing of gift submission when making suggestions. The suggestion unit, for example, uses a generation AI to identify the timing of gift submission. For example, the suggestion unit sets specific evaluation criteria to evaluate the urgency of the submission timing. The suggestion unit can also determine the priority of suggestions based on the timing of gift submission. For example, the suggestion unit can prioritize suggestions immediately before an important event. The suggestion unit can also postpone suggestions for general events. The suggestion unit can also adjust the priority of suggestions based on the user's submission timing. In this way, by determining the priority of suggestions based on the timing of gift submission, suggestions can be made efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input gift submission timing data into the generation AI and cause the generation AI to determine the priority of suggestions.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the presents when suggesting them. The suggestion unit, for example, uses a generation AI to evaluate the relevance of the presents. For example, the suggestion unit sets relevance evaluation criteria and evaluates the relevance of the presents. The suggestion unit can also adjust the order of suggestions based on the relevance of the presents. For example, the suggestion unit prioritizes suggestions for highly relevant presents. The suggestion unit can also postpone suggestions for less relevant presents. The suggestion unit can also adjust the order of suggestions based on the user's level of interest. In this way, suggestions can be made efficiently by adjusting the order of suggestions based on the relevance of the presents. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input relevance data of the presents to the generation AI and cause the generation AI to adjust the order of suggestions.

[0087] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, uses a generation AI to identify the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise based on the user's past input history and feedback. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can provide a proposal that uses a lot of technical terminology if the user has technical expertise. The suggestion unit can also provide a proposal in simple language if the user does not have technical expertise. The suggestion unit can also adjust the use of technical terminology in the proposal based on the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The link unit estimates the user's emotion and adjusts the link display method based on the estimated user's emotion. The link unit estimates the user's emotion using, for example, a generation AI. For example, the link unit estimates the emotion from the user's facial expression using facial expression recognition technology. The link unit can also estimate the emotion from the user's tone and speed of voice using voice analysis technology. For example, the link unit provides a simple, highly visible link display when the user is nervous. The link unit can also provide a detailed link display when the user is relaxed. The link unit can also provide a link display that focuses on the main points when the user is in a hurry. This allows the link display method to be adjusted according to the user's emotion, thereby providing a more appropriate link display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0089] When displaying a link, the link unit can select an optimal link by referring to the user's past click history. The link unit, for example, uses a generation AI to analyze the user's past click history. For example, the link unit selects an optimal link based on the user's click frequency and click pattern. The link unit can also extract specific patterns from the user's past click history and suggest optimal links. The link unit can also analyze the user's past click history to improve the link display method. For example, the link unit selects an optimal link based on links the user has clicked in the past. The link unit can also extract specific patterns from the user's past click history and suggest optimal links. The link unit can also analyze the user's past click history to improve the link display method. In this way, the optimal link can be provided by referring to the user's past click history. Some or all of the above-described processing in the link unit may be performed using, for example, AI, or may be performed without using AI. For example, the link unit can input the user's click history data into the generation AI and cause the generation AI to select an optimal link.

[0090] When displaying a link, the link unit can customize the link content according to the user's current task. The link unit, for example, uses a generation AI to identify the user's current task. For example, the link unit identifies the current task based on the user's operation history. The link unit can also customize the link content according to the user's current task. For example, if the user is looking for a gift, the link unit can prioritize displaying related links. For example, if the user is in the process of making a purchase, the link unit can prioritize displaying links related to the purchase. The link unit can also customize the link content based on the user's current task. In this way, by customizing the link content according to the user's current task, more appropriate links can be provided. Some or all of the above-described processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input user operation history data into the generation AI and have the generation AI customize the link content.

[0091] The link unit can improve the link display method by reflecting user feedback when displaying links. The link unit can, for example, analyze user feedback using a generation AI. For example, the link unit can improve the link display method based on user ratings and comments. The link unit can also preferentially suggest a specific display format based on user feedback. The link unit can also analyze user feedback and optimize the link display method. For example, the link unit can improve the link display method based on feedback provided by the user. The link unit can also preferentially suggest a specific display format based on user feedback. The link unit can also analyze user feedback and optimize the link display method. In this way, the link display method can be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the link unit can be performed using, for example, AI, or can be performed without using AI. For example, the link unit can input user feedback data into the generation AI and cause the generation AI to improve the link display method.

[0092] The link unit estimates the user's emotion and adjusts the link operation procedure based on the estimated user's emotion. The link unit estimates the user's emotion using, for example, a generation AI. For example, the link unit estimates the emotion from the user's facial expression using facial expression recognition technology. The link unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. For example, the link unit provides simple and intuitive operation procedures when the user is nervous. The link unit can also provide detailed operation procedures when the user is relaxed. The link unit can also provide quick operation procedures when the user is in a hurry. This allows the link operation procedure to be adjusted according to the user's emotion, thereby providing more appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0093] When displaying a link, the link unit can select the optimal link by taking into account the user's device information. The link unit, for example, uses a generation AI to identify the user's device information. For example, the link unit selects the optimal link based on the user's device type and screen size. The link unit can also adjust the link display method based on the user's device's OS and browser information. For example, if the user is using a smartphone, the link unit can provide a link tailored to the screen size. If the user is using a tablet, the link unit can provide a link optimized for a large screen. If the user is using a smartwatch, the link unit can provide a concise, highly visible link. This allows the optimal link to be provided by taking the user's device information into consideration. Some or all of the above-described processing in the link unit may be performed using, for example, AI, or may be performed without using AI. For example, the link unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal link.

[0094] When displaying a link, the link unit can make the link content multilingual according to the user's language setting. The link unit, for example, uses a generation AI to identify the user's language setting. For example, the link unit automatically sets the link language based on the language setting of the user's device. The link unit can also provide a language switching function when the user uses multiple languages. The link unit can also provide a link in a specific language when the user selects that language. This makes it possible to provide more appropriate links by making the link content multilingual according to the user's language setting. Some or all of the above-described processing in the link unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the link unit can input the user's language setting data into the generation AI and cause the generation AI to set the link content to be multilingual.

[0095] When displaying a link, the link unit can analyze the user's social media activity and provide related links. The link unit can analyze the user's social media activity using, for example, a generation AI. For example, the link unit can analyze the content of the user's posts and provide related links. The link unit can also provide related links based on the number of likes and followers the user has. The link unit can also provide related links based on the activities of the user's friends on social media. For example, the link unit can provide links related to places the user has checked in to on social media. The link unit can also analyze the content of the user's social media posts and provide related links. The link unit can also provide related links based on the activities of the user's friends on social media. In this way, highly relevant links can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the link unit can be performed using, for example, AI, or without AI. For example, the link unit can input the user's social media data into the generation AI and cause the generation AI to provide related links. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and link unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information entered by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests gift candidates based on the analysis results. The link unit is realized by the control unit 46A of the smart device 14 and generates links to e-commerce sites corresponding to the suggested gift candidates. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and link unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests gift candidates based on the analysis results. The link unit is realized by the control unit 46A of the smart glasses 214 and generates links to e-commerce sites corresponding to the suggested gift candidates. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and link unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests gift candidates based on the analysis results. The link unit is realized by the control unit 46A of the headset type terminal 314 and generates links to e-commerce sites corresponding to the suggested gift candidates. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and link unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests gift candidates based on the analysis results. The link unit is realized by the control unit 46A of the robot 414 and generates links to e-commerce sites corresponding to the suggested gift candidates.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can analyze the user's past purchase history and make new suggestions based on the trends of past purchases of presents. For example, the suggestion unit can analyze the categories and price ranges of presents the user has purchased in the past and suggest presents with similar trends. The suggestion unit can also prioritize suggesting highly rated presents based on ratings of presents the user has purchased in the past. Furthermore, the suggestion unit can analyze the reactions of recipients of presents the user has purchased in the past and suggest new presents that have the characteristics of presents that the recipients enjoyed. This allows for more personalized suggestions to be made based on the user's past purchase history.

[0098] The collection unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can start with simple questions and gradually increase the difficulty. Also, if the user is relaxed, the collection unit can ask detailed questions. Furthermore, if the user is in a hurry, the collection unit can prioritize questions that can be answered quickly. In this way, adjusting the difficulty of questions according to the user's emotions enables smoother information collection.

[0099] The analysis unit can analyze the user's past search history and suggest gift candidates based on the search history. For example, the analysis unit can suggest related gifts based on keywords the user has searched for in the past. The analysis unit can also suggest gifts in a similar category based on the category of products the user has viewed in the past. Furthermore, the analysis unit can suggest gifts that fit the user's budget based on the price range of products the user has searched for in the past. This makes it possible to suggest more relevant gift candidates based on the user's past search history.

[0100] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestion based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make a suggestion immediately. Also, if the user is feeling stressed, the suggestion unit can make a suggestion at a timing that allows the user to relax. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion quickly. In this way, by adjusting the timing of the suggestion according to the user's emotions, the suggestion can be made at a more appropriate timing.

[0101] The link unit can analyze a user's past click history and customize the link display method based on the click history. For example, the link unit can provide links in a similar display format based on the display format of links the user has previously clicked. The link unit can also display links in a similar position based on the position of links the user has previously clicked. Furthermore, the link unit can preferentially display related links based on the content of links the user has previously clicked. This allows for more effective link display based on the user's past click history.

[0102] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important information. Furthermore, when the user is relaxed, the collection unit can also collect detailed information. Furthermore, when the user is in a hurry, the collection unit can also prioritize information that can be collected quickly. In this way, by determining the priority of information to be collected according to the user's emotions, important information can be collected preferentially.

[0103] The analysis unit can suggest regional gift candidates based on the user's current location information. For example, if the user is in a specific region, the analysis unit can suggest local specialties and popular products. If the user is traveling, the analysis unit can also suggest gifts related to souvenirs and tourist attractions at the user's travel destination. Furthermore, if the user is at home, the analysis unit can also suggest gifts that can be purchased at stores near the user's home. This makes it possible to suggest regional gift candidates based on the user's current location information.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided.

[0105] The link unit can display links optimized for the device based on the user's device information. For example, if the user is using a smartphone, the link unit can provide links that fit the screen size. Also, if the user is using a tablet, the link unit can provide links optimized for a large screen. Furthermore, if the user is using a smartwatch, the link unit can provide concise and highly visible links. This makes it possible to display optimal links based on the user's device information.

[0106] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects information entered by the user. The information entered by the user includes age, gender, relationship, budget, preferences, and other free descriptions. The collection unit displays questions in chat format and collects information by the user's answers. The collection unit can also save the information entered by the user in a database. Furthermore, it can use generation AI to extract the other person's preferences from the user's free descriptions. Step 2: The analysis unit analyzes the information collected by the collection unit. Using the generation AI, the collected information is analyzed to identify the best gift candidates for the recipient. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and likes fashion, fashion items will be identified. The generation AI can also be used to rank gift candidates based on the collected information. Step 3: The suggestion unit suggests gift candidates based on the information analyzed by the analysis unit. Using the generation AI, it suggests gift candidates and presents them to the user. For example, if the recipient is a woman in her 20s, has a friendship relationship, a budget of 5,000 yen, and likes fashion, it will suggest accessories, bags, clothes, etc. Step 4: The linking unit links the gift candidates suggested by the suggestion unit to an e-commerce site. Using the generation AI, the link of the e-commerce site corresponding to the suggested gift candidate is generated, and when the user clicks, they can be taken to the corresponding page of the e-commerce site. The generation AI can also be used to adjust how the link is displayed.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

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

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

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

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 collection unit that collects information input by a user; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests gift candidates based on the information analyzed by the analysis unit; a linking unit that links the gift candidates proposed by the proposal unit to an e-commerce site. A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze the user's past input history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit When collecting information, filter it based on the user's current situation and interests.

2. The system of claim 1.

5. The collecting unit When collecting information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting information, prioritize collecting highly relevant information by taking into account the user's geographical location information.

2. The system of claim 1.

8. The collecting unit When collecting information, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

Images