system

Abstract

The system according to the embodiment aims to suggest personalized gifts based on the user's preferences and values ​​at the appropriate time. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, and a suggestion unit. The collection unit collects user chat data. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The suggestion unit makes gift suggestions based on seasonal events or anniversaries.

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 it difficult to select gifts based on users' preferences and values, and suggestions are not made at the appropriate time.

[0005] The system according to the embodiment aims to suggest personalized gifts based on the user's preferences and values ​​at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a suggestion unit. The collection unit collects user chat data. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The suggestion unit makes gift suggestions based on seasonal events or anniversaries. [Effects of the Invention]

[0007] The system according to the embodiment can suggest personalized gifts based on the user's preferences and values ​​at the appropriate time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AI gift concierge system according to an embodiment of the present invention analyzes chats on messaging apps while respecting privacy, extracting the user's preferences, values, and recent interests, and presenting personalized gift suggestions. This system collects user chat data and uses AI to analyze it to identify the user's preferences, values, and recent interests. Furthermore, the system considers seasonal events and anniversaries to provide optimally timed gift suggestions. This allows users to select thoughtful gifts. For example, when collecting user chat data, only the minimum necessary data is collected to protect privacy. Data can be collected based on specific keywords and phrases. The collected data is then analyzed by AI. The AI ​​uses natural language processing technology to analyze the user's chat content and identify the user's preferences, values, and recent interests. For example, if a user has recently been talking a lot about "travel" or "cooking," the AI ​​analyzes this and determines that the user is interested in travel or cooking. Based on the analysis results, the AI ​​presents personalized gift suggestions. The AI ​​considers the user's preferences, values, and recent interests to select optimal gift suggestions. For example, if a user is interested in travel, it can suggest travel-related gifts. The system also takes seasonal events and anniversaries into consideration and makes gift suggestions at the optimal time. The AI ​​references calendar information and makes gift suggestions based on events such as Christmas and birthdays. This allows users to choose thoughtful gifts to coincide with important events. This system allows users to receive personalized gift suggestions while respecting their privacy. The AI ​​gift concierge system supports thoughtful gift selection by taking into consideration the user's preferences, values, and recent interests to suggest the most appropriate gift. This system allows the AI ​​gift concierge system to take into consideration the user's preferences, values, and recent interests to suggest the most appropriate gift.

[0029] The AI ​​gift concierge system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a suggestion unit. The collection unit collects user chat data. The user chat data includes, but is not limited to, text messages, voice data, and chat logs. The collection unit can collect data based on specific keywords or phrases. For example, if a user frequently talks about topics like "travel" or "cooking," the collection unit can collect data based on these keywords. The collection unit can also estimate the user's emotions and adjust the timing of chat data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze the user's chat content and identify preferences, values, and recent interests. For example, the analysis unit can perform a detailed analysis of the user's chat content using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can also estimate the user's emotions and adjust the analysis expression method based on the estimated emotions. For example, if a user is feeling stressed, the system can provide simple and easy-to-understand analysis results. The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The presentation unit selects optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, the presentation unit can present travel-related gift candidates. The presentation unit can also estimate the user's emotions and adjust the presentation method of gift candidates based on the estimated emotions. For example, if a user is feeling stressed, the system can present simple and easy-to-understand gift candidates. The suggestion unit makes gift suggestions based on seasonal events or anniversaries. The suggestion unit makes gift suggestions based on seasonal events or anniversaries based on calendar information. For example, the suggestion unit can make gift suggestions based on events such as Christmas and birthdays. The suggestion unit can also estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions.For example, if a user is feeling stressed, the AI ​​gift concierge system according to the embodiment can suggest a gift that is simple and easy to understand. This allows the AI ​​gift concierge system according to the embodiment to suggest the most suitable gift by taking into account the user's preferences, values, and recent interests.

[0030] The collection unit can collect data based on specific keywords or phrases. For example, if a user frequently talks about "travel" or "cooking," the collection unit can collect data based on these keywords. For example, the collection unit can prioritize collecting chat data in which the user includes the keyword "travel." The collection unit can also prioritize collecting chat data in which the user includes the keyword "cooking." By collecting data based on specific keywords or phrases, only the minimum amount of data necessary can be collected, thereby taking privacy into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input chat data in which the user includes specific keywords or phrases into AI, which then collects the data.

[0031] The analysis unit can analyze the user's conversation content using natural language processing technology to identify preferences, values, and recent interests. The analysis unit can perform a detailed analysis of the user's conversation content using technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, if the user frequently talks about "travel" or "cooking," the analysis unit can analyze this and determine that the user is interested in travel or cooking. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand analysis result can be provided. This allows the use of natural language processing technology to accurately analyze the user's conversation content and identify preferences, values, and recent interests. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's conversation data into AI, which then analyzes the data.

[0032] The suggestion unit can make gift suggestions based on seasonal events or anniversaries based on the calendar information. For example, the suggestion unit can make gift suggestions based on events such as Christmas or birthdays by referring to the calendar information. For example, the suggestion unit can suggest Christmas-related gifts for Christmas. The suggestion unit can also suggest birthday-related gifts for birthdays. In this way, by referring to the calendar information, gift suggestions can be made at appropriate times to match seasonal events or anniversaries. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input calendar information into AI, which then makes gift suggestions.

[0033] The presentation unit can select optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, the presentation unit can present travel-related gift candidates. For example, the presentation unit can present travel guidebooks, travel accessories, etc. as gift candidates. Furthermore, if the user is interested in cooking, the presentation unit can present cooking-related gift candidates. For example, the presentation unit can present cookbooks, kitchen tools, etc. as gift candidates. This allows personalized gift candidates to be presented by taking the user's preferences, values, and recent interests into consideration. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the user's preferences, values, and recent interests into AI, which can then select optimal gift candidates.

[0034] The collection unit can select an optimal collection method based on the user's past chat history. The collection unit can select talk data to be collected based on, for example, keywords frequently used by the user in the past. For example, the collection unit can select talk data to be collected based on keywords frequently used by the user in the past. The collection unit can also concentrate collection on a specific time period from the user's past chat history. For example, the collection unit can concentrate collection on a specific time period from the user's past chat history. The collection unit can also analyze the user's past chat history and prioritize collection of data related to a specific topic. For example, the collection unit can analyze the user's past chat history and prioritize collection of data related to a specific topic. This enables more effective data collection by analyzing the past chat 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 chat history into AI, which can select the optimal collection method.

[0035] When collecting talk data, the collection unit can filter the data based on the user's current areas of interest. For example, if the user has recently shown an interest in "travel," the collection unit can prioritize collecting talk data related to travel. For example, if the user has recently shown an interest in "travel," the collection unit can prioritize collecting talk data related to travel. Furthermore, if the user is interested in "cooking," the collection unit can filter and collect talk data related to cooking. For example, if the user is interested in "cooking," the collection unit can filter and collect talk data related to cooking. Furthermore, if the user is interested in "sports," the collection unit can collect talk data related to sports. For example, if the user is interested in "sports," the collection unit can collect talk data related to sports. In this way, by filtering data based on the user's areas of interest, highly relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current areas of interest into AI, which then filters the data.

[0036] When collecting talk data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting talk data related to that area. For example, when the user is in a specific area, the collection unit can prioritize collecting talk data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting talk data related to the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting talk data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting talk data related to the area around the user's home. For example, when the user is at home, the collection unit can prioritize collecting talk data related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which then collects the data.

[0037] When collecting talk data, the collection unit can collect relevant data based on the user's social media activity. For example, if a user posts frequently about "travel" on social media, the collection unit can collect talk data related to travel. For example, if a user posts frequently about "travel" on social media, the collection unit can collect talk data related to travel. Furthermore, if a user posts frequently about "cooking," the collection unit can collect talk data related to cooking. For example, if a user posts frequently about "cooking," the collection unit can collect talk data related to cooking. Furthermore, if a user posts frequently about "sports," the collection unit can collect talk data related to sports. For example, if a user posts frequently about "sports," the collection unit can collect talk data related to sports. This makes it possible to collect highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, which then collects the data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the talk content. For example, the analysis unit can perform a detailed analysis on important talk content. For example, the analysis unit can perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on general talk content. For example, the analysis unit can perform a simplified analysis on general talk content. The analysis unit can also focus on analyzing talk content related to specific keywords. For example, the analysis unit can focus on analyzing talk content related to specific keywords. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the talk content. Some or all of the above-described 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 the importance of the talk content into AI, which can adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms based on the category of the talk content. For example, the analysis unit can apply a travel-related analysis algorithm to talk content about travel. For example, the analysis unit can apply a travel-related analysis algorithm to talk content about travel. The analysis unit can also apply a cooking-related analysis algorithm to talk content about cooking. For example, the analysis unit can apply a cooking-related analysis algorithm to talk content about cooking. The analysis unit can also apply a sports-related analysis algorithm to talk content about sports. For example, the analysis unit can apply a sports-related analysis algorithm to talk content about sports. This enables more accurate analysis by applying different analysis algorithms depending on the category of the talk content. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the talk content into AI, and the AI ​​can apply different analysis algorithms.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the talk content. The analysis unit can, for example, prioritize analysis of recent talk content. For example, the analysis unit can prioritize analysis of recent talk content. The analysis unit can also prioritize analysis of talk content related to a specific event. For example, the analysis unit can prioritize analysis of talk content related to a specific event. The analysis unit can also prioritize analysis of talk content conducted by a user during a specific time period. For example, the analysis unit can prioritize analysis of talk content conducted by a user during a specific time period. This enables more effective analysis by determining the priority of analysis based on the time of submission of the talk content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the talk content into AI, and the AI ​​can determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the talk content. For example, the analysis unit can prioritize analysis of highly relevant talk content. For example, the analysis unit can prioritize analysis of highly relevant talk content. The analysis unit can also prioritize analysis of talk content related to a specific keyword. For example, the analysis unit can prioritize analysis of talk content related to a specific keyword. The analysis unit can also prioritize analysis of talk content related to a user's interests. For example, the analysis unit can prioritize analysis of talk content related to a user's interests. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the talk content. Some or all of the above-described 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 the relevance of the talk content into AI, which can adjust the order of analysis.

[0042] The presentation unit can adjust the level of detail of the presentation based on the importance of the gift candidate when presenting the gift candidate. For example, the presentation unit can provide detailed information for important gift candidates. For example, the presentation unit can provide detailed information for important gift candidates. The presentation unit can also provide simplified information for general gift candidates. For example, the presentation unit can provide simplified information for general gift candidates. The presentation unit can also provide focused information for gift candidates related to a specific event. For example, the presentation unit can provide focused information for gift candidates related to a specific event. In this way, by adjusting the level of detail of the presentation based on the importance of the gift candidate, more appropriate information can be provided. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the importance of the gift candidate into AI, which can adjust the level of detail of the presentation.

[0043] When presenting gift candidates, the presentation unit can apply different presentation algorithms based on the category of the gift candidates. For example, the presentation unit can apply a travel-related presentation algorithm to travel-related gift candidates. For example, the presentation unit can apply a travel-related presentation algorithm to travel-related gift candidates. The presentation unit can also apply a cooking-related presentation algorithm to cooking-related gift candidates. For example, the presentation unit can apply a cooking-related presentation algorithm to cooking-related gift candidates. The presentation unit can also apply a sports-related presentation algorithm to sports-related gift candidates. In this way, by applying different presentation algorithms depending on the category of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the category of the gift candidates into AI, and the AI ​​can apply different presentation algorithms.

[0044] When presenting gift candidates, the presentation unit can determine a presentation priority based on the time of submission of the gift candidates. The presentation unit can, for example, prioritize presenting recent gift candidates. For example, the presentation unit can prioritize presenting recent gift candidates. The presentation unit can also prioritize presenting gift candidates related to a specific event. For example, the presentation unit can prioritize presenting gift candidates related to a specific event. The presentation unit can also prioritize presenting gift candidates submitted by a user during a specific time period. For example, the presentation unit can prioritize presenting gift candidates submitted by a user during a specific time period. In this way, by determining the presentation priority based on the time of submission of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the time of submission of gift candidates into AI, and the AI ​​can determine the presentation priority.

[0045] The presentation unit can adjust the presentation order based on the relevance of the gift candidates when presenting them. For example, the presentation unit can prioritize presenting highly relevant gift candidates. For example, the presentation unit can prioritize presenting highly relevant gift candidates. The presentation unit can also prioritize presenting gift candidates related to a specific keyword. For example, the presentation unit can prioritize presenting gift candidates related to a specific keyword. The presentation unit can also prioritize presenting gift candidates related to the user's interests. For example, the presentation unit can prioritize presenting gift candidates related to the user's interests. By adjusting the presentation order based on the relevance of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the relevance of the gift candidates into AI, which can then adjust the presentation order.

[0046] When making a suggestion, the suggestion unit can select an optimal suggestion method based on the user's past gift selection history. For example, the suggestion unit can analyze trends in gifts selected by the user in the past and select an optimal suggestion method. For example, the suggestion unit can analyze trends in gifts selected by the user in the past and select an optimal suggestion method. The suggestion unit can also find specific patterns from the user's past gift selection history and adjust the suggestion method. For example, the suggestion unit can find specific patterns from the user's past gift selection history and adjust the suggestion method. The suggestion unit can also select a suggestion method based on categories of gifts selected by the user in the past. For example, the suggestion unit can select a suggestion method based on categories of gifts selected by the user in the past. This allows for more appropriate gift suggestions to be made by analyzing the user's past gift selection history. 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 the user's past gift selection history into AI, which can select an optimal suggestion method.

[0047] When making a suggestion, the suggestion unit can customize the suggestion means based on the user's current living situation. For example, if the user is busy, the suggestion unit can suggest a gift that can be easily purchased. For example, if the user is busy, the suggestion unit can suggest a gift that can be easily purchased. Furthermore, if the user is relaxed, the suggestion unit can suggest a gift that can be selected over time. For example, if the user is relaxed, the suggestion unit can suggest a gift that can be selected over time. Furthermore, if the user is traveling, the suggestion unit can suggest a gift that can be purchased at the travel destination. For example, if the user is traveling, the suggestion unit can suggest a gift that can be purchased at the travel destination. In this way, by customizing the suggestion means based on the user's current living situation, more appropriate gift suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation into AI, which can customize the suggestion means.

[0048] When making a suggestion, the suggestion unit can select an optimal gift suggestion method based on the user's geographical location information. For example, if the user is in a specific area, the suggestion unit can suggest gifts that can be purchased in that area. For example, if the user is in a specific area, the suggestion unit can suggest gifts that can be purchased in that area. Furthermore, if the user is traveling, the suggestion unit can suggest gifts that can be purchased at the travel destination. For example, if the user is traveling, the suggestion unit can suggest gifts that can be purchased at the travel destination. Furthermore, if the user is at home, the suggestion unit can suggest gifts that can be purchased near the user's home. For example, if the user is at home, the suggestion unit can suggest gifts that can be purchased near the user's home. This allows for more appropriate gift suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, which can select the optimal gift suggestion method.

[0049] When making a suggestion, the suggestion unit can suggest a means of suggesting a gift based on the user's social media activity. For example, if the user posts frequently about "travel" on social media, the suggestion unit can suggest travel-related gifts. For example, if the user posts frequently about "travel" on social media, the suggestion unit can suggest travel-related gifts. Furthermore, if the user posts frequently about "cooking," the suggestion unit can suggest cooking-related gifts. For example, if the user posts frequently about "cooking," the suggestion unit can suggest cooking-related gifts. Furthermore, if the user posts frequently about "sports," the suggestion unit can suggest sports-related gifts. For example, if the user posts frequently about "sports," the suggestion unit can suggest sports-related gifts. This allows for more appropriate gift suggestions by analyzing the user's social media activity. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into AI, which then suggests a means of suggesting a gift.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The AI ​​gift concierge system can further include a history analysis unit that analyzes a user's past purchase history. The history analysis unit analyzes trends in gifts purchased by the user in the past, enabling a more accurate understanding of the user's preferences and values. For example, the history analysis unit can analyze the categories and price ranges of gifts purchased by the user in the past and suggest gifts in similar categories and price ranges. The history analysis unit can also consider the ratings of gifts purchased by the user in the past and suggest products similar to highly rated gifts. Furthermore, the history analysis unit can analyze the frequency of gifts purchased by the user in the past and suggest products that the user tends to purchase regularly. This allows for more personalized gift suggestions to be made by utilizing the user's past purchase history.

[0052] The suggestion unit can make gift suggestions taking into account the user's health data. For example, if the user uses a fitness tracker or a smartwatch, that data can be collected to understand the user's health condition and exercise habits. The suggestion unit can suggest health-related gifts based on the user's health data. For example, if the user is not getting enough exercise, fitness-related gifts can be suggested. Also, if the user is feeling stressed, relaxation-related gifts can be suggested. Furthermore, the suggestion unit can suggest healthy foods and supplements taking into account the user's dietary data. This makes it possible to make personalized gift suggestions that take the user's health condition into account.

[0053] The presentation unit can customize the display method of gift candidates based on the user's visual preferences. For example, it can analyze the designs and colors of gifts selected by the user in the past and prioritize displaying gifts with similar designs and colors. The presentation unit can also adjust the display method of gift candidates taking into account the user's preferred fonts and layouts. For example, if the user prefers simple designs, it can display gift candidates in a simple layout. Furthermore, the presentation unit can customize images and videos of gift candidates based on the user's visual preferences. For example, if the user prefers dynamic content, it can prioritize displaying videos of gift candidates. This makes it possible to provide a display method of gift candidates that suits the user's visual preferences.

[0054] The suggestion unit can customize the suggestion means based on the user's current living situation. For example, if the user is busy, it can suggest gifts that can be easily purchased. If the user is relaxed, it can suggest gifts that can be selected over time. Furthermore, if the user is traveling, it can suggest gifts that can be purchased at the user's travel destination. In this way, by customizing the suggestion means based on the user's current living situation, it is possible to make more appropriate gift suggestions.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit collects the user's chat data. The user's chat data includes text messages, voice data, chat logs, etc. The collection unit can collect data based on specific keywords or phrases. For example, if the user frequently talks about "travel" or "cooking," the collection unit can collect data based on these keywords. The collection unit can also estimate the user's emotions and adjust the timing of chat data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed and data can be collected when the user is relaxed. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze the user's conversation content and identify preferences, values, and recent interests. For example, it uses techniques such as morphological analysis, grammatical analysis, and semantic analysis to perform a detailed analysis of the user's conversation content. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, it will provide a simple and easy-to-understand analysis result. Step 3: The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The presentation unit selects optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, travel-related gift candidates will be presented. The presentation unit can also estimate the user's emotions and adjust the way gift candidates are presented based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand gift candidates will be presented. Step 4: The suggestion unit makes gift suggestions based on seasonal events or anniversaries. The suggestion unit makes gift suggestions based on seasonal events or anniversaries based on calendar information. For example, gift suggestions can be made to coincide with events such as Christmas or birthdays. The suggestion unit can also estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand gift suggestions can be made.

[0057] (Example 2) An AI gift concierge system according to an embodiment of the present invention analyzes chats on messaging apps while respecting privacy, extracting the user's preferences, values, and recent interests, and presenting personalized gift suggestions. This system collects user chat data and uses AI to analyze it to identify the user's preferences, values, and recent interests. Furthermore, the system considers seasonal events and anniversaries to provide optimally timed gift suggestions. This allows users to select thoughtful gifts. For example, when collecting user chat data, only the minimum necessary data is collected to protect privacy. Data can be collected based on specific keywords and phrases. The collected data is then analyzed by AI. The AI ​​uses natural language processing technology to analyze the user's chat content and identify the user's preferences, values, and recent interests. For example, if a user has recently been talking a lot about "travel" or "cooking," the AI ​​analyzes this and determines that the user is interested in travel or cooking. Based on the analysis results, the AI ​​presents personalized gift suggestions. The AI ​​considers the user's preferences, values, and recent interests to select optimal gift suggestions. For example, if a user is interested in travel, it can suggest travel-related gifts. The system also takes seasonal events and anniversaries into consideration and makes gift suggestions at the optimal time. The AI ​​references calendar information and makes gift suggestions based on events such as Christmas and birthdays. This allows users to choose thoughtful gifts to coincide with important events. This system allows users to receive personalized gift suggestions while respecting their privacy. The AI ​​gift concierge system supports thoughtful gift selection by taking into consideration the user's preferences, values, and recent interests to suggest the most appropriate gift. This system allows the AI ​​gift concierge system to take into consideration the user's preferences, values, and recent interests to suggest the most appropriate gift.

[0058] The AI ​​gift concierge system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a suggestion unit. The collection unit collects user chat data. The user chat data includes, but is not limited to, text messages, voice data, and chat logs. The collection unit can collect data based on specific keywords or phrases. For example, if a user frequently talks about topics like "travel" or "cooking," the collection unit can collect data based on these keywords. The collection unit can also estimate the user's emotions and adjust the timing of chat data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze the user's chat content and identify preferences, values, and recent interests. For example, the analysis unit can perform a detailed analysis of the user's chat content using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can also estimate the user's emotions and adjust the analysis expression method based on the estimated emotions. For example, if a user is feeling stressed, the system can provide simple and easy-to-understand analysis results. The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The presentation unit selects optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, the presentation unit can present travel-related gift candidates. The presentation unit can also estimate the user's emotions and adjust the presentation method of gift candidates based on the estimated emotions. For example, if a user is feeling stressed, the system can present simple and easy-to-understand gift candidates. The suggestion unit makes gift suggestions based on seasonal events or anniversaries. The suggestion unit makes gift suggestions based on seasonal events or anniversaries based on calendar information. For example, the suggestion unit can make gift suggestions based on events such as Christmas and birthdays. The suggestion unit can also estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions.For example, if a user is feeling stressed, the AI ​​gift concierge system according to the embodiment can suggest a gift that is simple and easy to understand. This allows the AI ​​gift concierge system according to the embodiment to suggest the most suitable gift by taking into account the user's preferences, values, and recent interests.

[0059] The collection unit can collect data based on specific keywords or phrases. For example, if a user frequently talks about "travel" or "cooking," the collection unit can collect data based on these keywords. For example, the collection unit can prioritize collecting chat data in which the user includes the keyword "travel." The collection unit can also prioritize collecting chat data in which the user includes the keyword "cooking." By collecting data based on specific keywords or phrases, only the minimum amount of data necessary can be collected, thereby taking privacy into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input chat data in which the user includes specific keywords or phrases into AI, which then collects the data.

[0060] The analysis unit can analyze the user's conversation content using natural language processing technology to identify preferences, values, and recent interests. The analysis unit can perform a detailed analysis of the user's conversation content using technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, if the user frequently talks about "travel" or "cooking," the analysis unit can analyze this and determine that the user is interested in travel or cooking. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand analysis result can be provided. This allows the use of natural language processing technology to accurately analyze the user's conversation content and identify preferences, values, and recent interests. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's conversation data into AI, which then analyzes the data.

[0061] The suggestion unit can make gift suggestions based on seasonal events or anniversaries based on the calendar information. For example, the suggestion unit can make gift suggestions based on events such as Christmas or birthdays by referring to the calendar information. For example, the suggestion unit can suggest Christmas-related gifts for Christmas. The suggestion unit can also suggest birthday-related gifts for birthdays. In this way, by referring to the calendar information, gift suggestions can be made at appropriate times to match seasonal events or anniversaries. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input calendar information into AI, which then makes gift suggestions.

[0062] The presentation unit can select optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, the presentation unit can present travel-related gift candidates. For example, the presentation unit can present travel guidebooks, travel accessories, etc. as gift candidates. Furthermore, if the user is interested in cooking, the presentation unit can present cooking-related gift candidates. For example, the presentation unit can present cookbooks, kitchen tools, etc. as gift candidates. This allows personalized gift candidates to be presented by taking the user's preferences, values, and recent interests into consideration. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the user's preferences, values, and recent interests into AI, which can then select optimal gift candidates.

[0063] The collection unit can estimate the user's emotions and adjust the timing of collecting talk data based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is feeling stressed, the collection unit can delay the collection timing to collect data when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect talk data and analyze it in real time. For example, if the user is relaxed, the collection unit can immediately collect talk data and analyze it in real time. Furthermore, if the user is excited, the collection unit can adjust the collection timing to collect data after the user has calmed down. For example, if the user is excited, the collection unit can adjust the collection timing to collect data after the user has calmed down. In this way, by adjusting the collection timing according to the user's emotions, data can be collected at a more appropriate timing. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's emotions into AI, and the AI ​​can adjust the collection timing.

[0064] The collection unit can select an optimal collection method based on the user's past chat history. The collection unit can select talk data to be collected based on, for example, keywords frequently used by the user in the past. For example, the collection unit can select talk data to be collected based on keywords frequently used by the user in the past. The collection unit can also concentrate collection on a specific time period from the user's past chat history. For example, the collection unit can concentrate collection on a specific time period from the user's past chat history. The collection unit can also analyze the user's past chat history and prioritize collection of data related to a specific topic. For example, the collection unit can analyze the user's past chat history and prioritize collection of data related to a specific topic. This enables more effective data collection by analyzing the past chat 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 chat history into AI, which can select the optimal collection method.

[0065] When collecting talk data, the collection unit can filter the data based on the user's current areas of interest. For example, if the user has recently shown an interest in "travel," the collection unit can prioritize collecting talk data related to travel. For example, if the user has recently shown an interest in "travel," the collection unit can prioritize collecting talk data related to travel. Furthermore, if the user is interested in "cooking," the collection unit can filter and collect talk data related to cooking. For example, if the user is interested in "cooking," the collection unit can filter and collect talk data related to cooking. Furthermore, if the user is interested in "sports," the collection unit can collect talk data related to sports. For example, if the user is interested in "sports," the collection unit can collect talk data related to sports. In this way, by filtering data based on the user's areas of interest, highly relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current areas of interest into AI, which then filters the data.

[0066] The collection unit can estimate the user's emotions and determine the priority of talk data to be collected based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting talk data related to stress reduction. For example, if the user is feeling stressed, the collection unit can prioritize collecting talk data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting talk data related to relaxation. For example, if the user is relaxed, the collection unit can prioritize collecting talk data related to relaxation. Furthermore, if the user is excited, the collection unit can prioritize collecting talk data related to excitement. For example, if the user is excited, the collection unit can prioritize collecting talk data related to excitement. This allows more appropriate data to be collected by determining the priority of data according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's emotions into AI, which can then prioritize the data.

[0067] When collecting talk data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting talk data related to that area. For example, when the user is in a specific area, the collection unit can prioritize collecting talk data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting talk data related to the travel destination. For example, when the user is traveling, the collection unit can prioritize collecting talk data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting talk data related to the area around the user's home. For example, when the user is at home, the collection unit can prioritize collecting talk data related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which then collects the data.

[0068] When collecting talk data, the collection unit can collect relevant data based on the user's social media activity. For example, if a user posts frequently about "travel" on social media, the collection unit can collect talk data related to travel. For example, if a user posts frequently about "travel" on social media, the collection unit can collect talk data related to travel. Furthermore, if a user posts frequently about "cooking," the collection unit can collect talk data related to cooking. For example, if a user posts frequently about "cooking," the collection unit can collect talk data related to cooking. Furthermore, if a user posts frequently about "sports," the collection unit can collect talk data related to sports. For example, if a user posts frequently about "sports," the collection unit can collect talk data related to sports. This makes it possible to collect highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, which then collects the data.

[0069] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is feeling stressed, the analysis unit can provide a simple and easy-to-understand analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is feeling relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. This allows the analysis to be presented in accordance with the user's emotions, thereby providing a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotions into AI, which can then adjust the way the analysis is presented.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the talk content. For example, the analysis unit can perform a detailed analysis on important talk content. For example, the analysis unit can perform a detailed analysis on important talk content. The analysis unit can also perform a simplified analysis on general talk content. For example, the analysis unit can perform a simplified analysis on general talk content. The analysis unit can also focus on analyzing talk content related to specific keywords. For example, the analysis unit can focus on analyzing talk content related to specific keywords. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the talk content. Some or all of the above-described 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 the importance of the talk content into AI, which can adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms based on the category of the talk content. For example, the analysis unit can apply a travel-related analysis algorithm to talk content about travel. For example, the analysis unit can apply a travel-related analysis algorithm to talk content about travel. The analysis unit can also apply a cooking-related analysis algorithm to talk content about cooking. For example, the analysis unit can apply a cooking-related analysis algorithm to talk content about cooking. The analysis unit can also apply a sports-related analysis algorithm to talk content about sports. For example, the analysis unit can apply a sports-related analysis algorithm to talk content about sports. This enables more accurate analysis by applying different analysis algorithms depending on the category of the talk content. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the talk content into AI, and the AI ​​can apply different analysis algorithms.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's emotions into AI, and the AI ​​can adjust the length of the analysis.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the talk content. The analysis unit can, for example, prioritize analysis of recent talk content. For example, the analysis unit can prioritize analysis of recent talk content. The analysis unit can also prioritize analysis of talk content related to a specific event. For example, the analysis unit can prioritize analysis of talk content related to a specific event. The analysis unit can also prioritize analysis of talk content conducted by a user during a specific time period. For example, the analysis unit can prioritize analysis of talk content conducted by a user during a specific time period. This enables more effective analysis by determining the priority of analysis based on the time of submission of the talk content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the talk content into AI, and the AI ​​can determine the priority of analysis.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the talk content. For example, the analysis unit can prioritize analysis of highly relevant talk content. For example, the analysis unit can prioritize analysis of highly relevant talk content. The analysis unit can also prioritize analysis of talk content related to a specific keyword. For example, the analysis unit can prioritize analysis of talk content related to a specific keyword. The analysis unit can also prioritize analysis of talk content related to a user's interests. For example, the analysis unit can prioritize analysis of talk content related to a user's interests. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the talk content. Some or all of the above-described 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 the relevance of the talk content into AI, which can adjust the order of analysis.

[0075] The presentation unit can estimate the user's emotions and adjust the presentation method of gift candidates based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit can present simple and easy-to-understand gift candidates. For example, if the user is feeling stressed, the presentation unit can present simple and easy-to-understand gift candidates. Furthermore, if the user is relaxed, the presentation unit can present gift candidates including detailed information. For example, if the user is relaxed, the presentation unit can present gift candidates including detailed information. Furthermore, if the user is excited, the presentation unit can present visually appealing gift candidates. For example, if the user is excited, the presentation unit can present visually appealing gift candidates. In this way, by adjusting the presentation method of gift candidates according to the user's emotions, more appropriate gift candidates can be presented. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's emotions into AI, which can adjust the presentation method of gift candidates.

[0076] The presentation unit can adjust the level of detail of the presentation based on the importance of the gift candidate when presenting the gift candidate. For example, the presentation unit can provide detailed information for important gift candidates. For example, the presentation unit can provide detailed information for important gift candidates. The presentation unit can also provide simplified information for general gift candidates. For example, the presentation unit can provide simplified information for general gift candidates. The presentation unit can also provide focused information for gift candidates related to a specific event. For example, the presentation unit can provide focused information for gift candidates related to a specific event. In this way, by adjusting the level of detail of the presentation based on the importance of the gift candidate, more appropriate information can be provided. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the importance of the gift candidate into AI, which can adjust the level of detail of the presentation.

[0077] When presenting gift candidates, the presentation unit can apply different presentation algorithms based on the category of the gift candidates. For example, the presentation unit can apply a travel-related presentation algorithm to travel-related gift candidates. For example, the presentation unit can apply a travel-related presentation algorithm to travel-related gift candidates. The presentation unit can also apply a cooking-related presentation algorithm to cooking-related gift candidates. For example, the presentation unit can apply a cooking-related presentation algorithm to cooking-related gift candidates. The presentation unit can also apply a sports-related presentation algorithm to sports-related gift candidates. In this way, by applying different presentation algorithms depending on the category of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the category of the gift candidates into AI, and the AI ​​can apply different presentation algorithms.

[0078] The presentation unit can estimate the user's emotions and adjust the presentation order of gift candidates based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit can prioritize presenting gift candidates related to stress reduction. For example, if the user is feeling stressed, the presentation unit can prioritize presenting gift candidates related to stress reduction. Furthermore, if the user is feeling relaxed, the presentation unit can prioritize presenting gift candidates related to relaxation. For example, if the user is feeling relaxed, the presentation unit can prioritize presenting gift candidates related to relaxation. Furthermore, if the user is feeling excited, the presentation unit can prioritize presenting gift candidates related to excitement. For example, if the user is excited, the presentation unit can prioritize presenting gift candidates related to excitement. In this way, by adjusting the presentation order of gift candidates according to the user's emotions, more appropriate gift candidates can be presented. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's emotions into AI, which can then adjust the presentation order of gift candidates.

[0079] When presenting gift candidates, the presentation unit can determine a presentation priority based on the time of submission of the gift candidates. The presentation unit can, for example, prioritize presenting recent gift candidates. For example, the presentation unit can prioritize presenting recent gift candidates. The presentation unit can also prioritize presenting gift candidates related to a specific event. For example, the presentation unit can prioritize presenting gift candidates related to a specific event. The presentation unit can also prioritize presenting gift candidates submitted by a user during a specific time period. For example, the presentation unit can prioritize presenting gift candidates submitted by a user during a specific time period. In this way, by determining the presentation priority based on the time of submission of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the time of submission of gift candidates into AI, and the AI ​​can determine the presentation priority.

[0080] The presentation unit can adjust the presentation order based on the relevance of the gift candidates when presenting them. For example, the presentation unit can prioritize presenting highly relevant gift candidates. For example, the presentation unit can prioritize presenting highly relevant gift candidates. The presentation unit can also prioritize presenting gift candidates related to a specific keyword. For example, the presentation unit can prioritize presenting gift candidates related to a specific keyword. The presentation unit can also prioritize presenting gift candidates related to the user's interests. For example, the presentation unit can prioritize presenting gift candidates related to the user's interests. By adjusting the presentation order based on the relevance of the gift candidates, more appropriate gift candidates can be presented. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the relevance of the gift candidates into AI, which can then adjust the presentation order.

[0081] The suggestion unit can estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make a simple and easy-to-understand gift suggestion. For example, if the user is feeling stressed, the suggestion unit can make a simple and easy-to-understand gift suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a gift suggestion that includes detailed information. For example, if the user is relaxed, the suggestion unit can make a gift suggestion that includes detailed information. Furthermore, if the user is excited, the suggestion unit can make a visually appealing gift suggestion. For example, if the user is excited, the suggestion unit can make a visually appealing gift suggestion. This allows for more appropriate gift suggestions to be made by adjusting the gift suggestion method according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotions into AI, which can then adjust the gift suggestion method.

[0082] When making a suggestion, the suggestion unit can select an optimal suggestion method based on the user's past gift selection history. For example, the suggestion unit can analyze trends in gifts selected by the user in the past and select an optimal suggestion method. For example, the suggestion unit can analyze trends in gifts selected by the user in the past and select an optimal suggestion method. The suggestion unit can also find specific patterns from the user's past gift selection history and adjust the suggestion method. For example, the suggestion unit can find specific patterns from the user's past gift selection history and adjust the suggestion method. The suggestion unit can also select a suggestion method based on categories of gifts selected by the user in the past. For example, the suggestion unit can select a suggestion method based on categories of gifts selected by the user in the past. This allows for more appropriate gift suggestions to be made by analyzing the user's past gift selection history. 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 the user's past gift selection history into AI, which can select an optimal suggestion method.

[0083] When making a suggestion, the suggestion unit can customize the suggestion means based on the user's current living situation. For example, if the user is busy, the suggestion unit can suggest a gift that can be easily purchased. For example, if the user is busy, the suggestion unit can suggest a gift that can be easily purchased. Furthermore, if the user is relaxed, the suggestion unit can suggest a gift that can be selected over time. For example, if the user is relaxed, the suggestion unit can suggest a gift that can be selected over time. Furthermore, if the user is traveling, the suggestion unit can suggest a gift that can be purchased at the travel destination. For example, if the user is traveling, the suggestion unit can suggest a gift that can be purchased at the travel destination. In this way, by customizing the suggestion means based on the user's current living situation, more appropriate gift suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's current living situation into AI, which can customize the suggestion means.

[0084] The suggestion unit can estimate the user's emotions and determine the priority of gift suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting gifts related to stress reduction. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting gifts related to stress reduction. Furthermore, if the user is feeling relaxed, the suggestion unit can prioritize suggesting gifts related to relaxation. For example, if the user is feeling relaxed, the suggestion unit can prioritize suggesting gifts related to relaxation. Furthermore, if the user is feeling excited, the suggestion unit can prioritize suggesting gifts related to excitement. For example, if the user is excited, the suggestion unit can prioritize suggesting gifts related to excitement. In this way, by determining the priority of gift suggestions according to the user's emotions, more appropriate gift suggestions can be made. 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 the user's emotions into AI, which can then determine the priority of gift suggestions.

[0085] When making a suggestion, the suggestion unit can select an optimal gift suggestion method based on the user's geographical location information. For example, if the user is in a specific area, the suggestion unit can suggest gifts that can be purchased in that area. For example, if the user is in a specific area, the suggestion unit can suggest gifts that can be purchased in that area. Furthermore, if the user is traveling, the suggestion unit can suggest gifts that can be purchased at the travel destination. For example, if the user is traveling, the suggestion unit can suggest gifts that can be purchased at the travel destination. Furthermore, if the user is at home, the suggestion unit can suggest gifts that can be purchased near the user's home. For example, if the user is at home, the suggestion unit can suggest gifts that can be purchased near the user's home. This allows for more appropriate gift suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into AI, which can select the optimal gift suggestion method.

[0086] When making a suggestion, the suggestion unit can suggest a means of suggesting a gift based on the user's social media activity. For example, if the user posts frequently about "travel" on social media, the suggestion unit can suggest travel-related gifts. For example, if the user posts frequently about "travel" on social media, the suggestion unit can suggest travel-related gifts. Furthermore, if the user posts frequently about "cooking," the suggestion unit can suggest cooking-related gifts. For example, if the user posts frequently about "cooking," the suggestion unit can suggest cooking-related gifts. Furthermore, if the user posts frequently about "sports," the suggestion unit can suggest sports-related gifts. For example, if the user posts frequently about "sports," the suggestion unit can suggest sports-related gifts. This allows for more appropriate gift suggestions by analyzing the user's social media activity. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into AI, which then suggests a means of suggesting a gift. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, and suggestion 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 user chat data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The presentation unit is realized by the control unit 46A of the smart device 14 and presents gift candidates based on the analysis results. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes gift suggestions based on seasonal events or anniversaries. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, and suggestion 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 user chat data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The presentation unit is realized by the control unit 46A of the smart glasses 214 and presents gift candidates based on the analysis results. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and presents gift suggestions based on seasonal events or anniversaries. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, and suggestion 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 user talk data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The presentation unit is realized by the control unit 46A of the headset type terminal 314 and presents gift candidates based on the analysis results. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes gift suggestions based on seasonal events or anniversaries. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, and suggestion 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 user talk data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The presentation unit is realized, for example, by the control unit 46A of the robot 414 and presents gift candidates based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes gift suggestions based on seasonal events or anniversaries.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The AI ​​gift concierge system can further include a history analysis unit that analyzes a user's past purchase history. The history analysis unit analyzes trends in gifts purchased by the user in the past, enabling a more accurate understanding of the user's preferences and values. For example, the history analysis unit can analyze the categories and price ranges of gifts purchased by the user in the past and suggest gifts in similar categories and price ranges. The history analysis unit can also consider the ratings of gifts purchased by the user in the past and suggest products similar to highly rated gifts. Furthermore, the history analysis unit can analyze the frequency of gifts purchased by the user in the past and suggest products that the user tends to purchase regularly. This allows for more personalized gift suggestions to be made by utilizing the user's past purchase history.

[0089] The collection unit can analyze the user's voice data and infer emotions from the voice. For example, by analyzing the tone, speed, and volume of the user's voice when speaking in a voice message, it can infer whether the user is relaxed or stressed. Furthermore, the collection unit can extract specific keywords from the voice data and identify the user's interests. For example, if the user frequently uses keywords such as "travel" or "cooking," the user's interests can be inferred based on this. The collection unit can also convert the voice data into text data and analyze it as text data. This makes it possible to use the voice data to understand the user's emotions and interests from a more multifaceted perspective.

[0090] The analysis unit can analyze images and videos included in a user's chat data. For example, it can analyze images and videos shared by the user in chats and use image recognition technology to identify the user's interests. For example, if a user shares many photos of travel destinations, it can be determined that the user is interested in traveling. The analysis unit can also analyze the audio and text in videos to infer the user's emotions and interests. For example, if a user appears to be enjoying themselves in a video, it can be determined that the user is highly interested in the content. Furthermore, the analysis unit can analyze the metadata of images and videos to understand the user's behavioral patterns based on information such as the location and time of filming. This makes it possible to use images and videos to identify the user's interests and emotions in more detail.

[0091] The suggestion unit can make gift suggestions taking into account the user's health data. For example, if the user uses a fitness tracker or a smartwatch, that data can be collected to understand the user's health condition and exercise habits. The suggestion unit can suggest health-related gifts based on the user's health data. For example, if the user is not getting enough exercise, fitness-related gifts can be suggested. Also, if the user is feeling stressed, relaxation-related gifts can be suggested. Furthermore, the suggestion unit can suggest healthy foods and supplements taking into account the user's dietary data. This makes it possible to make personalized gift suggestions that take the user's health condition into account.

[0092] The presentation unit can customize the display method of gift candidates based on the user's visual preferences. For example, it can analyze the designs and colors of gifts selected by the user in the past and prioritize displaying gifts with similar designs and colors. The presentation unit can also adjust the display method of gift candidates taking into account the user's preferred fonts and layouts. For example, if the user prefers simple designs, it can display gift candidates in a simple layout. Furthermore, the presentation unit can customize images and videos of gift candidates based on the user's visual preferences. For example, if the user prefers dynamic content, it can prioritize displaying videos of gift candidates. This makes it possible to provide a display method of gift candidates that suits the user's visual preferences.

[0093] The collection unit can estimate the user's emotions and adjust the method of collecting talk data based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can collect data when the user is relaxed. Also, if the user is relaxed, the collection unit can immediately collect talk data and analyze it in real time. Furthermore, if the user is excited, the collection unit can adjust the collection timing and collect data after the user's emotions have calmed down. In this way, by adjusting the collection method according to the user's emotions, data can be collected at more appropriate times.

[0094] The analysis unit can analyze emotions contained in the user's chat data and customize the analysis results based on those emotions. For example, if the user is feeling stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by customizing the analysis results according to the user's emotions, more appropriate information can be provided.

[0095] The suggestion unit can estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand gift suggestion can be made. If the user is feeling relaxed, a gift suggestion including detailed information can be made. Furthermore, if the user is excited, a visually appealing gift suggestion can be made. In this way, by adjusting the gift suggestion method according to the user's emotions, more appropriate gift suggestions can be made.

[0096] The presentation unit can estimate the user's emotions and adjust the presentation order of gift candidates based on the estimated emotions. For example, if the user is feeling stressed, gift candidates related to stress reduction can be presented preferentially. Also, if the user is feeling relaxed, gift candidates related to relaxation can be presented preferentially. Furthermore, if the user is excited, gift candidates related to excitement can be presented preferentially. In this way, by adjusting the presentation order of gift candidates according to the user's emotions, more appropriate gift candidates can be presented.

[0097] The suggestion unit can customize the suggestion means based on the user's current living situation. For example, if the user is busy, it can suggest gifts that can be easily purchased. If the user is relaxed, it can suggest gifts that can be selected over time. Furthermore, if the user is traveling, it can suggest gifts that can be purchased at the user's travel destination. In this way, by customizing the suggestion means based on the user's current living situation, it is possible to make more appropriate gift suggestions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection unit collects the user's chat data. The user's chat data includes text messages, voice data, chat logs, etc. The collection unit can collect data based on specific keywords or phrases. For example, if the user frequently talks about "travel" or "cooking," the collection unit can collect data based on these keywords. The collection unit can also estimate the user's emotions and adjust the timing of chat data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection timing can be delayed and data can be collected when the user is relaxed. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses natural language processing technology to analyze the user's conversation content and identify preferences, values, and recent interests. For example, it uses techniques such as morphological analysis, grammatical analysis, and semantic analysis to perform a detailed analysis of the user's conversation content. The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, it will provide a simple and easy-to-understand analysis result. Step 3: The presentation unit presents gift candidates based on the analysis results obtained by the analysis unit. The presentation unit selects optimal gift candidates based on the user's preferences, values, and recent interests. For example, if the user is interested in travel, travel-related gift candidates will be presented. The presentation unit can also estimate the user's emotions and adjust the way gift candidates are presented based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand gift candidates will be presented. Step 4: The suggestion unit makes gift suggestions based on seasonal events or anniversaries. The suggestion unit makes gift suggestions based on seasonal events or anniversaries based on calendar information. For example, gift suggestions can be made to coincide with events such as Christmas or birthdays. The suggestion unit can also estimate the user's emotions and adjust the gift suggestion method based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand gift suggestions can be made.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

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

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

[0147] In the 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.

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

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

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

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 user talk data; an analysis unit that analyzes the data collected by the collection unit; a presentation unit that presents gift candidates based on the analysis results obtained by the analysis unit; A suggestion unit that makes gift suggestions based on seasonal events or anniversaries. A system characterized by:

2. The collecting unit Collect data based on specific keywords or phrases 2. The system of claim 1.

3. The analysis unit Uses natural language processing technology to analyze user conversations to identify preferences, values, and current interests 2. The system of claim 1.

4. The proposal unit Use calendar information to provide gift suggestions for seasonal events or anniversaries 2. The system of claim 1.

5. The presentation unit Select the best gift ideas based on your preferences, values, and current interests 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust the timing of collecting chat data based on those emotions.

2. The system of claim 1.

7. The collecting unit Select the optimal collection method based on the user's past chat history 2. The system of claim 1.

8. The collecting unit When collecting chat data, filter it based on the user's current interests.

2. The system of claim 1.

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