System

Abstract

An object of a system according to an embodiment is to efficiently recommend an optimal destination for a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The receiving unit receives information of a user. The analysis unit analyzes the information input by the reception unit. The recommendation unit recommends a destination based on the information analyzed by the analysis unit. The providing unit provides detailed information of the outing destination recommended by the recommending unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that collecting information to find the best destination for a user is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently recommend the best destinations to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs user information. The analysis unit analyzes the information input by the reception unit. The recommendation unit recommends a destination based on the information analyzed by the analysis unit. The provision unit provides detailed information about the destination recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently recommend the most suitable destinations to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A destination recommendation system according to an embodiment of the present invention is a system in which a user inputs information, and an AI analyzes the information to recommend optimal destinations and provide detailed information. In the destination recommendation system, a user inputs information such as their preferences, children's ages, and interests, and an AI analyzes the information to recommend optimal destinations. For example, a user inputs information such as "I have a 3-year-old child," "I like zoos," and "I want to go on weekends." This information is then input into the AI. The AI ​​then analyzes the input information and recommends optimal destinations based on the user's preferences, children's ages, and interests. For example, it suggests places where children can have fun, such as zoos, parks, and museums. It also considers factors such as the user's current location, weather, and season to make recommendations at optimal times. Furthermore, the AI ​​provides detailed information about the recommended destinations, such as the zoo's opening hours, admission fees, and access information. This allows users to easily understand the details of their destinations. This allows the outing recommendation system to save parents the trouble of researching different places to take their children, and also suggests places that will interest children, making outings more enjoyable.

[0029] The outing destination recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs user information. The user information includes, but is not limited to, personal information, location information, and preference information. The reception unit allows the user to input information such as their own preferences, their children's ages, and interests. The analysis unit uses AI to analyze the information input by the reception unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit analyzes optimal outing destinations based on information such as the user's preferences, their children's ages, and interests. The recommendation unit recommends outing destinations based on the information analyzed by the analysis unit. The recommendation may be performed based on, but is not limited to, a type of algorithm or an evaluation criterion. For example, the recommendation unit suggests places where children can enjoy themselves, such as zoos, parks, and museums. The provision unit provides detailed information about the outing destinations recommended by the recommendation unit. The detailed information includes, but is not limited to, for example, text information, images, links, etc. For example, the providing unit provides information such as the zoo's opening hours, admission fees, and access methods. This allows the destination recommendation system according to the embodiment to efficiently analyze user information, recommend optimal destinations, and provide detailed information.

[0030] The analysis unit can perform analysis based on the user's past behavioral history and ratings. The analysis unit, for example, performs analysis taking into account the user's past behavioral history. Past behavioral history includes, but is not limited to, visit history, search history, and purchase history. For example, the analysis unit analyzes the best destination for a trip based on places the user has visited in the past and keywords searched for by the user. The analysis unit can also perform analysis taking into account the user's ratings. Ratings include, but are not limited to, user feedback, star ratings, and comments. For example, the analysis unit analyzes the best destination for a trip based on places the user has rated in the past and comments. This enables more accurate analysis by taking into account the user's past behavioral history and ratings.

[0031] The recommendation unit can make recommendations based on factors such as the user's current location, weather, and season. The recommendation unit makes recommendations, for example, taking into account the user's current location. The current location includes, for example, GPS information, IP address, etc., but is not limited to these examples. For example, the recommendation unit suggests nearby destinations to visit based on the user's current location. The recommendation unit can also make recommendations taking into account the weather. Weather information includes, for example, weather data provision services, but is not limited to these examples. For example, the recommendation unit suggests outdoor activities on sunny days. The recommendation unit can also make recommendations taking into account the season. The season includes, for example, the four seasons, specific event periods, etc., but is not limited to these examples. For example, the recommendation unit suggests destinations to visit depending on the season. This makes it possible to make recommendations at more appropriate times by taking into account factors such as the user's current location, weather, and season.

[0032] The providing unit can provide detailed information such as opening hours, admission fees, and access methods for the recommended destination. The providing unit, for example, provides opening hours for the recommended destination. Examples of opening hours include, but are not limited to, official websites, APIs, etc. For example, the providing unit provides opening hours for a zoo. The providing unit can also provide admission fees. Examples of admission fees include, but are not limited to, official websites, APIs, etc. For example, the providing unit can provide admission fees for the zoo. The providing unit can also provide access methods. Examples of access methods include, but are not limited to, map information, transportation information, etc. For example, the providing unit provides access methods to the zoo. By providing detailed information about the recommended destination, the user can easily understand the details of the destination.

[0033] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The input history includes, but is not limited to, past input data, input frequency, and the like. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also provide an auto-completion function to reduce the effort required for input based on information previously input by the user. For example, the reception unit auto-completes addresses previously input by the user. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user frequently uses voice input at night, the reception unit suggests voice input at night. In this way, the optimal input method can be selected by analyzing the user's past input history, reducing the effort required for input.

[0034] The reception unit can filter information based on the user's current living situation and areas of interest when inputting information. The reception unit, for example, displays only relevant information on the input screen based on the user's current living situation (work, home, etc.). Examples of living situations include, but are not limited to, occupation, home environment, and health status. For example, if the user is at work, the reception unit displays only information related to work. The reception unit can also filter the information to be input based on the user's areas of interest (hobbies, interests, etc.) to display only highly relevant information. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is interested in sports, the reception unit displays only information related to sports. The reception unit can also determine the priority of the information to be input based on the user's living situation and areas of interest, allowing important information to be input first. For example, the reception unit prompts the user to input information related to their home environment first. In this way, by filtering information based on the user's current living situation and areas of interest, only highly relevant information can be displayed.

[0035] The reception unit can select an optimal input means depending on the user's input method when inputting information. For example, if the user prefers voice input, the reception unit provides voice input preferentially and uses text input as an auxiliary. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit provides voice input preferentially and uses text input as an auxiliary. Furthermore, if the user prefers text input, the reception unit can provide text input preferentially and use voice input as an auxiliary. For example, if the user prefers text input, the reception unit can provide text input preferentially and use voice input as an auxiliary. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially and use text and voice input as an auxiliary. For example, if the user prefers image input, the reception unit can provide image input preferentially and use text and voice input as an auxiliary. In this way, by selecting an optimal input means depending on the user's input method, input efficiency is improved.

[0036] When inputting information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, causes the user to prioritize inputting information about places close to the user's current location. Geographical location information includes, but is not limited to, GPS information, IP address, etc. For example, the reception unit can cause the user to prioritize inputting information about places close to the user's current location. Furthermore, if the user is interested in a particular region, the reception unit can also prioritize inputting information related to the region. For example, if the user is interested in a particular region, the reception unit can also prioritize inputting information related to the region. Furthermore, if the user is traveling, the reception unit can also prioritize inputting information related to the travel destination. For example, if the user is traveling, the reception unit can prioritize inputting information related to the travel destination. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and input related information when inputting information. The reception unit, for example, causes the user to input information about places where the user has checked in on social media. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can cause the user to input information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and input related information. For example, the reception unit can analyze the content of the user's posts on social media and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. For example, the reception unit can input related information by referring to the activities of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit customizes the input method based on, for example, feedback provided by the user in the past, thereby improving usability. Feedback includes, for example, but is not limited to, user ratings, comments, and survey results. For example, the reception unit customizes the input method based on the user's past feedback, thereby improving usability. The reception unit can also preferentially provide a specific input method based on the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit preferentially provides voice input. The reception unit can also analyze the user's past feedback and reflect improvements to the input method. For example, the reception unit analyzes the user's past feedback and reflects improvements to the input method. In this way, the input method is customized by reflecting the user's past feedback, thereby improving usability.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information to deepen understanding. The importance of information includes, but is not limited to, the user's level of interest and the recency of the information. For example, the analysis unit performs a detailed analysis on information in which the user is interested to deepen understanding. The analysis unit can also perform a concise analysis on information of low importance to save time. For example, the analysis unit can perform a concise analysis on information in which the user is not interested to save time. The analysis unit can also gradually adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit combines detailed analysis and concise analysis depending on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. Categories of information include, but are not limited to, text information, image information, and audio information. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. The analysis unit can also apply a meteorological algorithm to weather information for analysis. For example, the analysis unit applies a meteorological algorithm to weather information for analysis. The analysis unit can also apply a traffic algorithm to traffic information for analysis. For example, the analysis unit applies a traffic algorithm to traffic information for analysis. In this way, by applying different analysis algorithms depending on the category of information, more accurate analysis is possible.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results to improve the accuracy. Past analysis results include, but are not limited to, past data, analysis reports, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results to improve the accuracy. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. For example, the analysis unit can extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also analyze the user's past analysis results, identify areas for improvement in the analysis, and reflect them in the analysis. For example, the analysis unit can analyze the user's past analysis results, identify areas for improvement in the analysis, and reflect them in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information and provides results quickly. The time of submission of information includes, but is not limited to, the submission date and time, the submission frequency, etc. For example, the analysis unit prioritizes analysis of the most recent information and provides results quickly. The analysis unit can also postpone analysis of information that was submitted earlier. For example, the analysis unit postpones analysis of information that was submitted earlier. The analysis unit can also gradually adjust the priority of analysis depending on the time of submission. For example, the analysis unit gradually adjusts the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be analyzed quickly.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of highly relevant information and provides the results. Examples of the relevance of the information include, but are not limited to, common topics, related keywords, etc. For example, the analysis unit prioritizes analysis of highly relevant information and provides the results. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. The analysis unit can also gradually adjust the order of analysis depending on the relevance of the information. For example, the analysis unit gradually adjusts the order of analysis depending on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results using a lot of technical terms. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has technical expertise, the analysis unit provides analysis results using a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results avoiding technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results avoiding technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms according to the user's level of expertise. For example, the analysis unit gradually adjusts the use of technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0045] The recommendation unit can improve the accuracy of recommendations by taking into account interrelationships between pieces of information when making recommendations. The recommendation unit makes recommendations by taking into account interrelationships, for example, based on the user's past recommendation results. Interrelationships between pieces of information include, but are not limited to, common topics, related keywords, and the like. For example, the recommendation unit makes recommendations by taking into account interrelationships between pieces of information based on the user's past recommendation results. The recommendation unit can also analyze interrelationships between pieces of information to be recommended and improve accuracy. For example, the recommendation unit can analyze interrelationships between pieces of information to be recommended and improve accuracy. The recommendation unit can also make recommendations by taking into account interrelationships between pieces of information based on the user's past behavioral history. For example, the recommendation unit makes recommendations by taking into account interrelationships between pieces of information based on the user's past behavioral history. In this way, the accuracy of recommendations is improved by taking into account interrelationships between pieces of information.

[0046] The recommendation unit can make recommendations taking into consideration attribute information of the user when making recommendations. The recommendation unit makes optimal recommendations based on, for example, the age and gender of the user. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. For example, the recommendation unit makes optimal recommendations based on the age and gender of the user. The recommendation unit can also make optimal recommendations based on the user's interests and concerns. For example, the recommendation unit makes optimal recommendations based on the user's interests and concerns. The recommendation unit can also make recommendations taking into consideration attribute information based on the user's past behavior history. For example, the recommendation unit makes recommendations taking into consideration attribute information based on the user's past behavior history. This makes it possible to make more appropriate recommendations by taking into consideration the user's attribute information.

[0047] When making a recommendation, the recommendation unit can weight the recommendation based on the frequency of information submission. The recommendation unit, for example, preferentially recommends information that is submitted frequently. The submission frequency includes, for example, the number of submissions, the submission interval, etc., but is not limited to these examples. For example, the recommendation unit preferentially recommends information that is submitted frequently. The recommendation unit can also recommend information that is submitted less frequently at a later date. For example, the recommendation unit recommends information that is submitted less frequently at a later date. The recommendation unit can also gradually adjust the weighting of the recommendation according to the submission frequency. For example, the recommendation unit gradually adjusts the weighting of the recommendation according to the submission frequency. As a result, weighting the recommendation based on the submission frequency of the information enables more appropriate recommendations.

[0048] The recommendation unit can make recommendations taking into account the geographical distribution of information. For example, the recommendation unit prioritizes recommending locations close to the user's current location. Geographical distribution includes, but is not limited to, information distribution by region and geographical relevance. For example, the recommendation unit prioritizes recommending locations close to the user's current location. Furthermore, if the user is interested in a specific region, the recommendation unit can also recommend information related to that region. For example, if the user is interested in a specific region, the recommendation unit can recommend information related to that region. Furthermore, if the user is traveling, the recommendation unit can also recommend information related to the travel destination. For example, if the user is traveling, the recommendation unit recommends information related to the travel destination. This enables more appropriate recommendations by taking into account the geographical distribution of information.

[0049] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to literature related to the information. The recommendation unit improves the accuracy of the recommendation based on, for example, related literature. Related literature includes, for example, academic papers, technical reports, etc., but is not limited to such examples. For example, the recommendation unit improves the accuracy of the recommendation based on related literature. The recommendation unit can also make recommendations by referring to related literature based on a user's past behavior history. For example, the recommendation unit makes recommendations by referring to related literature based on a user's past behavior history. The recommendation unit can also analyze literature related to the information to be recommended and improve the accuracy. For example, the recommendation unit analyzes literature related to the information to be recommended and improves the accuracy. As a result, the accuracy of the recommendation is improved by referring to literature related to the information.

[0050] The recommendation unit can make recommendations taking into consideration the market value of information when making recommendations. For example, the recommendation unit preferentially recommends information with high market value. Market value includes, for example, sales data, market research results, etc., but is not limited to these examples. For example, the recommendation unit preferentially recommends information with high market value. Furthermore, the recommendation unit can also postpone recommending information with low market value. For example, the recommendation unit postpones recommending information with low market value. Furthermore, the recommendation unit can also gradually adjust the weighting of recommendations according to market value. For example, the recommendation unit gradually adjusts the weighting of recommendations according to market value. This enables more appropriate recommendations by taking into consideration the market value of information.

[0051] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. The providing unit, for example, analyzes the interrelationships between the information to be provided and improves the accuracy. The interrelationships between the information to be provided include, but are not limited to, common topics, related keywords, and the like. For example, the providing unit analyzes the interrelationships between the information to be provided and improves the accuracy. The providing unit can also provide information by taking into account the interrelationships based on the user's past behavioral history. For example, the providing unit provides information by taking into account the interrelationships based on the user's past behavioral history. The providing unit can also provide optimal information based on the interrelationships between the information to be provided. For example, the providing unit provides optimal information based on the interrelationships between the information to be provided. This improves the accuracy of the information provided by taking into account the interrelationships between the information to be provided.

[0052] The providing unit can provide information taking into consideration attribute information of the user when providing the information. The providing unit provides optimal information based on, for example, the age and gender of the user. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. For example, the providing unit provides optimal information based on the age and gender of the user. The providing unit can also provide optimal information based on the user's interests and concerns. For example, the providing unit provides optimal information based on the user's interests and concerns. The providing unit can also provide information taking into consideration attribute information based on the user's past behavior history. For example, the providing unit provides information taking into consideration attribute information based on the user's past behavior history. This makes it possible to provide more appropriate information by taking into consideration the user's attribute information.

[0053] The providing unit can weight the provision of information based on the frequency of information submission when providing the information. For example, the providing unit prioritizes the provision of information with a high submission frequency. The submission frequency includes, but is not limited to, for example, the number of submissions and the submission interval. For example, the providing unit prioritizes the provision of information with a high submission frequency. Furthermore, the providing unit can also postpone the provision of information with a low submission frequency. For example, the providing unit postpones the provision of information with a low submission frequency. Furthermore, the providing unit can also gradually adjust the weighting of the provision according to the submission frequency. For example, the providing unit gradually adjusts the weighting of the provision according to the submission frequency. In this way, by weighting the provision based on the frequency of information submission, more appropriate information provision is possible.

[0054] The providing unit can provide information taking into consideration the geographical distribution of the information. For example, the providing unit can provide information about places close to the user's current location with priority. Geographical distribution includes, but is not limited to, information distribution by region and geographical relevance, for example. For example, the providing unit can provide information about places close to the user's current location with priority. Furthermore, if the user is interested in a particular region, the providing unit can provide information related to that region. For example, if the user is interested in a particular region, the providing unit can provide information related to that region. Furthermore, if the user is traveling, the providing unit can provide information related to the travel destination. For example, if the user is traveling, the providing unit provides information related to the travel destination. This makes it possible to provide more appropriate information by taking into consideration the geographical distribution of the information.

[0055] The providing unit can improve the accuracy of the information provided by referring to related literature when providing the information. The providing unit improves the accuracy of the information provided, for example, based on related literature. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the providing unit improves the accuracy of the information provided based on related literature. The providing unit can also provide information by referring to related literature based on a user's past behavior history. For example, the providing unit provides information by referring to related literature based on a user's past behavior history. The providing unit can also analyze related literature of the information to be provided and improve the accuracy. For example, the providing unit analyzes related literature of the information to be provided and improves the accuracy. As a result, the accuracy of the information provided is improved by referring to related literature of the information.

[0056] The providing unit can provide information taking into consideration the market value of the information when providing the information. For example, the providing unit provides information with high market value preferentially. Market value includes, for example, sales data, market research results, etc., but is not limited to such examples. For example, the providing unit provides information with high market value preferentially. Furthermore, the providing unit can also provide information with low market value at a later date. For example, the providing unit provides information with low market value at a later date. Furthermore, the providing unit can also adjust the weighting of the provision in stages according to the market value. For example, the providing unit adjusts the weighting of the provision in stages according to the market value. This makes it possible to provide more appropriate information by taking into consideration the market value of the information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] In addition to the user's past behavioral history, the analysis unit can also use the user's social media activity for analysis. For example, it can analyze places of interest based on the places the user has checked in to on social media and the content of their posts. It can also use places visited and rated by the user's friends for analysis. Furthermore, it can also reflect the frequency of the user's social media activity and topics of interest in the analysis. This enables highly accurate analysis that takes into account the user's social media activity.

[0059] The recommendation unit can also make recommendations taking into account the user's current location, weather, and seasonal factors, as well as the user's means of transportation. For example, if the user uses a car, it can suggest locations with parking. If the user uses public transportation, it can also suggest locations with good access. Furthermore, if the user uses a bicycle, it can also suggest locations within cycling distance. This makes it possible to make recommendations according to the user's means of transportation.

[0060] The providing unit can provide event information related to the user's interests in addition to detailed information about the recommended destination. For example, if a special event is being held at a zoo, that information can be provided. It can also provide information about seasonal events at parks. It can also provide information about special exhibitions and workshops at museums. This allows the user to grasp related event information along with detailed information about the destination.

[0061] The reception unit can analyze the user's device usage history in addition to the user's past input history and select the optimal input method. For example, if the user frequently uses a smartphone, it can suggest an input method optimized for the smartphone. Also, if the user frequently uses a tablet, it can suggest an input method optimized for the tablet. Furthermore, if the user frequently uses a PC, it can suggest an input method optimized for the PC. This makes it possible to select the optimal input method based on the user's device usage history.

[0062] When inputting information, the reception unit can filter the information based on the user's lifestyle in addition to the user's current living situation and areas of interest. For example, if the user is health-conscious, only health-related information can be displayed. If the user is eco-conscious, only environmentally friendly information can be displayed. Furthermore, if the user is outdoorsy, only outdoor-related information can be displayed. This makes it possible to filter information based on the user's lifestyle.

[0063] When inputting information, the reception unit not only selects the optimal input means according to the user's input method, but also adjusts the input means taking into account the user's input speed. For example, if the user inputs quickly, text input can be given priority. Also, if the user inputs slowly, voice input can be given priority. Furthermore, if the user prefers image input, image input can be given priority. In this way, the optimal input means can be selected according to the user's input speed.

[0064] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into consideration the user's movement history in addition to the user's geographical location information. For example, information about places the user has visited in the past can be prioritized. Also, information related to routes the user frequently travels can be prioritized. Furthermore, if the user is traveling, information related to travel destinations can be prioritized. This allows highly relevant information based on the user's movement history to be prioritized.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit inputs user information. The user information includes, for example, personal information, location information, and preference information. The user can input information such as their own preferences, the ages and interests of their children, etc. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, it analyzes the best destinations to go to based on information such as the user's preferences, children's ages, and interests. Step 3: The recommendation unit recommends places to go based on the information analyzed by the analysis unit. Recommendations are made based on the type of algorithm and evaluation criteria. For example, it suggests places that children can enjoy, such as zoos, parks, and museums. Step 4: The provider provides detailed information about the recommended destinations. The detailed information includes text, images, links, etc. For example, the provider provides information about the zoo's opening hours, admission fees, and access methods.

[0067] (Example 2) A destination recommendation system according to an embodiment of the present invention is a system in which a user inputs information, and an AI analyzes the information to recommend optimal destinations and provide detailed information. In the destination recommendation system, a user inputs information such as their preferences, children's ages, and interests, and an AI analyzes the information to recommend optimal destinations. For example, a user inputs information such as "I have a 3-year-old child," "I like zoos," and "I want to go on weekends." This information is then input into the AI. The AI ​​then analyzes the input information and recommends optimal destinations based on the user's preferences, children's ages, and interests. For example, it suggests places where children can have fun, such as zoos, parks, and museums. It also considers factors such as the user's current location, weather, and season to make recommendations at optimal times. Furthermore, the AI ​​provides detailed information about the recommended destinations, such as the zoo's opening hours, admission fees, and access information. This allows users to easily understand the details of their destinations. This allows the outing recommendation system to save parents the trouble of researching different places to take their children, and also suggests places that will interest children, making outings more enjoyable.

[0068] The outing destination recommendation system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and a provision unit. The reception unit inputs user information. The user information includes, but is not limited to, personal information, location information, and preference information. The reception unit allows the user to input information such as their own preferences, their children's ages, and interests. The analysis unit uses AI to analyze the information input by the reception unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit analyzes optimal outing destinations based on information such as the user's preferences, their children's ages, and interests. The recommendation unit recommends outing destinations based on the information analyzed by the analysis unit. The recommendation may be performed based on, but is not limited to, a type of algorithm or an evaluation criterion. For example, the recommendation unit suggests places where children can enjoy themselves, such as zoos, parks, and museums. The provision unit provides detailed information about the outing destinations recommended by the recommendation unit. The detailed information includes, but is not limited to, for example, text information, images, links, etc. For example, the providing unit provides information such as the zoo's opening hours, admission fees, and access methods. This allows the destination recommendation system according to the embodiment to efficiently analyze user information, recommend optimal destinations, and provide detailed information.

[0069] The analysis unit can perform analysis based on the user's past behavioral history and ratings. The analysis unit, for example, performs analysis taking into account the user's past behavioral history. Past behavioral history includes, but is not limited to, visit history, search history, and purchase history. For example, the analysis unit analyzes the best destination for a trip based on places the user has visited in the past and keywords searched for by the user. The analysis unit can also perform analysis taking into account the user's ratings. Ratings include, but are not limited to, user feedback, star ratings, and comments. For example, the analysis unit analyzes the best destination for a trip based on places the user has rated in the past and comments. This enables more accurate analysis by taking into account the user's past behavioral history and ratings.

[0070] The recommendation unit can make recommendations based on factors such as the user's current location, weather, and season. The recommendation unit makes recommendations, for example, taking into account the user's current location. The current location includes, for example, GPS information, IP address, etc., but is not limited to these examples. For example, the recommendation unit suggests nearby destinations to visit based on the user's current location. The recommendation unit can also make recommendations taking into account the weather. Weather information includes, for example, weather data provision services, but is not limited to these examples. For example, the recommendation unit suggests outdoor activities on sunny days. The recommendation unit can also make recommendations taking into account the season. The season includes, for example, the four seasons, specific event periods, etc., but is not limited to these examples. For example, the recommendation unit suggests destinations to visit depending on the season. This makes it possible to make recommendations at more appropriate times by taking into account factors such as the user's current location, weather, and season.

[0071] The providing unit can provide detailed information such as opening hours, admission fees, and access methods for the recommended destination. The providing unit, for example, provides opening hours for the recommended destination. Examples of opening hours include, but are not limited to, official websites, APIs, etc. For example, the providing unit provides opening hours for a zoo. The providing unit can also provide admission fees. Examples of admission fees include, but are not limited to, official websites, APIs, etc. For example, the providing unit can provide admission fees for the zoo. The providing unit can also provide access methods. Examples of access methods include, but are not limited to, map information, transportation information, etc. For example, the providing unit provides access methods to the zoo. By providing detailed information about the recommended destination, the user can easily understand the details of the destination.

[0072] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of prompting input so that the user can input in a relaxed state. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is excited, the reception unit can prompt the user to input quickly and allow the user to enter information while their interest is high. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is tired, the reception unit can adjust the timing of prompting input and prompt the user to input after a break. For example, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. This allows the user to input information at a more appropriate time by adjusting the timing of information input according to the user's emotions.

[0073] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The input history includes, but is not limited to, past input data, input frequency, and the like. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also provide an auto-completion function to reduce the effort required for input based on information previously input by the user. For example, the reception unit auto-completes addresses previously input by the user. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, if the user frequently uses voice input at night, the reception unit suggests voice input at night. In this way, the optimal input method can be selected by analyzing the user's past input history, reducing the effort required for input.

[0074] The reception unit can filter information based on the user's current living situation and areas of interest when inputting information. The reception unit, for example, displays only relevant information on the input screen based on the user's current living situation (work, home, etc.). Examples of living situations include, but are not limited to, occupation, home environment, and health status. For example, if the user is at work, the reception unit displays only information related to work. The reception unit can also filter the information to be input based on the user's areas of interest (hobbies, interests, etc.) to display only highly relevant information. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is interested in sports, the reception unit displays only information related to sports. The reception unit can also determine the priority of the information to be input based on the user's living situation and areas of interest, allowing important information to be input first. For example, the reception unit prompts the user to input information related to their home environment first. In this way, by filtering information based on the user's current living situation and areas of interest, only highly relevant information can be displayed.

[0075] The reception unit can select an optimal input means depending on the user's input method when inputting information. For example, if the user prefers voice input, the reception unit provides voice input preferentially and uses text input as an auxiliary. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit provides voice input preferentially and uses text input as an auxiliary. Furthermore, if the user prefers text input, the reception unit can provide text input preferentially and use voice input as an auxiliary. For example, if the user prefers text input, the reception unit can provide text input preferentially and use voice input as an auxiliary. Furthermore, if the user prefers image input, the reception unit can provide image input preferentially and use text and voice input as an auxiliary. For example, if the user prefers image input, the reception unit can provide image input preferentially and use text and voice input as an auxiliary. In this way, by selecting an optimal input means depending on the user's input method, input efficiency is improved.

[0076] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit may prompt the user to input important information first and postpone detailed information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is relaxed, the reception unit may prompt the user to input detailed information first and postpone important information. For example, the reception unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the reception unit may prompt the user to input only the most important information and omit other information. For example, the reception unit may record the user's voice and estimate the emotion using voice analysis technology. This allows the user to prioritize the input of important information by determining the priority of information to be input based on the user's emotions.

[0077] When inputting information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, causes the user to prioritize inputting information about places close to the user's current location. Geographical location information includes, but is not limited to, GPS information, IP address, etc. For example, the reception unit can cause the user to prioritize inputting information about places close to the user's current location. Furthermore, if the user is interested in a particular region, the reception unit can also prioritize inputting information related to the region. For example, if the user is interested in a particular region, the reception unit can also prioritize inputting information related to the region. Furthermore, if the user is traveling, the reception unit can also prioritize inputting information related to the travel destination. For example, if the user is traveling, the reception unit can prioritize inputting information related to the travel destination. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0078] The reception unit can analyze the user's social media activity and input related information when inputting information. The reception unit, for example, causes the user to input information about places where the user has checked in on social media. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can cause the user to input information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and input related information. For example, the reception unit can analyze the content of the user's posts on social media and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. For example, the reception unit can input related information by referring to the activities of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit customizes the input method based on, for example, feedback provided by the user in the past, thereby improving usability. Feedback includes, for example, but is not limited to, user ratings, comments, and survey results. For example, the reception unit customizes the input method based on the user's past feedback, thereby improving usability. The reception unit can also preferentially provide a specific input method based on the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit preferentially provides voice input. The reception unit can also analyze the user's past feedback and reflect improvements to the input method. For example, the reception unit analyzes the user's past feedback and reflects improvements to the input method. In this way, the input method is customized by reflecting the user's past feedback, thereby improving usability.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results to deepen understanding. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information to deepen understanding. The importance of information includes, but is not limited to, the user's level of interest and the recency of the information. For example, the analysis unit performs a detailed analysis on information in which the user is interested to deepen understanding. The analysis unit can also perform a concise analysis on information of low importance to save time. For example, the analysis unit can perform a concise analysis on information in which the user is not interested to save time. The analysis unit can also gradually adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit combines detailed analysis and concise analysis depending on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. Categories of information include, but are not limited to, text information, image information, and audio information. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. The analysis unit can also apply a meteorological algorithm to weather information for analysis. For example, the analysis unit applies a meteorological algorithm to weather information for analysis. The analysis unit can also apply a traffic algorithm to traffic information for analysis. For example, the analysis unit applies a traffic algorithm to traffic information for analysis. In this way, by applying different analysis algorithms depending on the category of information, more accurate analysis is possible.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results to improve the accuracy. Past analysis results include, but are not limited to, past data, analysis reports, etc. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results to improve the accuracy. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. For example, the analysis unit can extract specific patterns from the user's past analysis results and reflect them in the analysis. The analysis unit can also analyze the user's past analysis results, identify areas for improvement in the analysis, and reflect them in the analysis. For example, the analysis unit can analyze the user's past analysis results, identify areas for improvement in the analysis, and reflect them in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. This allows the analysis length to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information and provides results quickly. The time of submission of information includes, but is not limited to, the submission date and time, the submission frequency, etc. For example, the analysis unit prioritizes analysis of the most recent information and provides results quickly. The analysis unit can also postpone analysis of information that was submitted earlier. For example, the analysis unit postpones analysis of information that was submitted earlier. The analysis unit can also gradually adjust the priority of analysis depending on the time of submission. For example, the analysis unit gradually adjusts the priority of analysis depending on the time of submission. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be analyzed quickly.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of highly relevant information and provides the results. Examples of the relevance of the information include, but are not limited to, common topics, related keywords, etc. For example, the analysis unit prioritizes analysis of highly relevant information and provides the results. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. The analysis unit can also gradually adjust the order of analysis depending on the relevance of the information. For example, the analysis unit gradually adjusts the order of analysis depending on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results using a lot of technical terms. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has technical expertise, the analysis unit provides analysis results using a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results avoiding technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results avoiding technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms according to the user's level of expertise. For example, the analysis unit gradually adjusts the use of technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0088] The recommendation unit can estimate the user's emotions and adjust the recommendation criteria based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations and expand the user's options. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is in a hurry, the recommendation unit can provide concise recommendations that focus on the main points. For example, the recommendation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the recommendation unit can provide recommendations with visually stimulating effects. For example, the recommendation unit can record the user's voice and estimate the emotion using voice analysis technology. This allows the recommendation criteria to be adjusted according to the user's emotions, enabling more appropriate recommendations.

[0089] The recommendation unit can improve the accuracy of recommendations by taking into account interrelationships between pieces of information when making recommendations. The recommendation unit makes recommendations by taking into account interrelationships, for example, based on the user's past recommendation results. Interrelationships between pieces of information include, but are not limited to, common topics, related keywords, and the like. For example, the recommendation unit makes recommendations by taking into account interrelationships between pieces of information based on the user's past recommendation results. The recommendation unit can also analyze interrelationships between pieces of information to be recommended and improve accuracy. For example, the recommendation unit can analyze interrelationships between pieces of information to be recommended and improve accuracy. The recommendation unit can also make recommendations by taking into account interrelationships between pieces of information based on the user's past behavioral history. For example, the recommendation unit makes recommendations by taking into account interrelationships between pieces of information based on the user's past behavioral history. In this way, the accuracy of recommendations is improved by taking into account interrelationships between pieces of information.

[0090] The recommendation unit can make recommendations taking into consideration attribute information of the user when making recommendations. The recommendation unit makes optimal recommendations based on, for example, the age and gender of the user. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. For example, the recommendation unit makes optimal recommendations based on the age and gender of the user. The recommendation unit can also make optimal recommendations based on the user's interests and concerns. For example, the recommendation unit makes optimal recommendations based on the user's interests and concerns. The recommendation unit can also make recommendations taking into consideration attribute information based on the user's past behavior history. For example, the recommendation unit makes recommendations taking into consideration attribute information based on the user's past behavior history. This makes it possible to make more appropriate recommendations by taking into consideration the user's attribute information.

[0091] When making a recommendation, the recommendation unit can weight the recommendation based on the frequency of information submission. The recommendation unit, for example, preferentially recommends information that is submitted frequently. The submission frequency includes, for example, the number of submissions, the submission interval, etc., but is not limited to these examples. For example, the recommendation unit preferentially recommends information that is submitted frequently. The recommendation unit can also recommend information that is submitted less frequently at a later date. For example, the recommendation unit recommends information that is submitted less frequently at a later date. The recommendation unit can also gradually adjust the weighting of the recommendation according to the submission frequency. For example, the recommendation unit gradually adjusts the weighting of the recommendation according to the submission frequency. As a result, weighting the recommendation based on the submission frequency of the information enables more appropriate recommendations.

[0092] The recommendation unit can estimate the user's emotions and adjust the order in which recommendation results are displayed based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can display detailed recommendation results first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is in a hurry, the recommendation unit can display recommendation results that focus on the main points first. For example, the recommendation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the recommendation unit can display visually stimulating recommendation results first. For example, the recommendation unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more appropriate recommendations by adjusting the order in which recommendation results are displayed according to the user's emotions.

[0093] The recommendation unit can make recommendations taking into account the geographical distribution of information. For example, the recommendation unit prioritizes recommending locations close to the user's current location. Geographical distribution includes, but is not limited to, information distribution by region and geographical relevance. For example, the recommendation unit prioritizes recommending locations close to the user's current location. Furthermore, if the user is interested in a specific region, the recommendation unit can also recommend information related to that region. For example, if the user is interested in a specific region, the recommendation unit can recommend information related to that region. Furthermore, if the user is traveling, the recommendation unit can also recommend information related to the travel destination. For example, if the user is traveling, the recommendation unit recommends information related to the travel destination. This enables more appropriate recommendations by taking into account the geographical distribution of information.

[0094] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to literature related to the information. The recommendation unit improves the accuracy of the recommendation based on, for example, related literature. Related literature includes, for example, academic papers, technical reports, etc., but is not limited to such examples. For example, the recommendation unit improves the accuracy of the recommendation based on related literature. The recommendation unit can also make recommendations by referring to related literature based on a user's past behavior history. For example, the recommendation unit makes recommendations by referring to related literature based on a user's past behavior history. The recommendation unit can also analyze literature related to the information to be recommended and improve the accuracy. For example, the recommendation unit analyzes literature related to the information to be recommended and improves the accuracy. As a result, the accuracy of the recommendation is improved by referring to literature related to the information.

[0095] The recommendation unit can make recommendations taking into consideration the market value of information when making recommendations. For example, the recommendation unit preferentially recommends information with high market value. Market value includes, for example, sales data, market research results, etc., but is not limited to these examples. For example, the recommendation unit preferentially recommends information with high market value. Furthermore, the recommendation unit can also postpone recommending information with low market value. For example, the recommendation unit postpones recommending information with low market value. Furthermore, the recommendation unit can also gradually adjust the weighting of recommendations according to market value. For example, the recommendation unit gradually adjusts the weighting of recommendations according to market value. This enables more appropriate recommendations by taking into consideration the market value of information.

[0096] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit provides detailed information first. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points first. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the providing unit can provide visually stimulating information first. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more appropriate information to be provided by determining the priority of information to be provided according to the user's emotions.

[0097] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between the information when providing the information. The providing unit, for example, analyzes the interrelationships between the information to be provided and improves the accuracy. The interrelationships between the information to be provided include, but are not limited to, common topics, related keywords, and the like. For example, the providing unit analyzes the interrelationships between the information to be provided and improves the accuracy. The providing unit can also provide information by taking into account the interrelationships based on the user's past behavioral history. For example, the providing unit provides information by taking into account the interrelationships based on the user's past behavioral history. The providing unit can also provide optimal information based on the interrelationships between the information to be provided. For example, the providing unit provides optimal information based on the interrelationships between the information to be provided. This improves the accuracy of the information provided by taking into account the interrelationships between the information to be provided.

[0098] The providing unit can provide information taking into consideration attribute information of the user when providing the information. The providing unit provides optimal information based on, for example, the age and gender of the user. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. For example, the providing unit provides optimal information based on the age and gender of the user. The providing unit can also provide optimal information based on the user's interests and concerns. For example, the providing unit provides optimal information based on the user's interests and concerns. The providing unit can also provide information taking into consideration attribute information based on the user's past behavior history. For example, the providing unit provides information taking into consideration attribute information based on the user's past behavior history. This makes it possible to provide more appropriate information by taking into consideration the user's attribute information.

[0099] The providing unit can weight the provision of information based on the frequency of information submission when providing the information. For example, the providing unit prioritizes the provision of information with a high submission frequency. The submission frequency includes, but is not limited to, for example, the number of submissions and the submission interval. For example, the providing unit prioritizes the provision of information with a high submission frequency. Furthermore, the providing unit can also postpone the provision of information with a low submission frequency. For example, the providing unit postpones the provision of information with a low submission frequency. Furthermore, the providing unit can also gradually adjust the weighting of the provision according to the submission frequency. For example, the providing unit gradually adjusts the weighting of the provision according to the submission frequency. In this way, by weighting the provision based on the frequency of information submission, more appropriate information provision is possible.

[0100] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit displays detailed information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The providing unit can also display information that focuses on the main points when the user is in a hurry. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit can display visually stimulating information when the user is excited. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. This allows the display method of the information to be adjusted according to the user's emotions, thereby providing more appropriate information.

[0101] The providing unit can provide information taking into consideration the geographical distribution of the information. For example, the providing unit can provide information about places close to the user's current location with priority. Geographical distribution includes, but is not limited to, information distribution by region and geographical relevance, for example. For example, the providing unit can provide information about places close to the user's current location with priority. Furthermore, if the user is interested in a particular region, the providing unit can provide information related to that region. For example, if the user is interested in a particular region, the providing unit can provide information related to that region. Furthermore, if the user is traveling, the providing unit can provide information related to the travel destination. For example, if the user is traveling, the providing unit provides information related to the travel destination. This makes it possible to provide more appropriate information by taking into consideration the geographical distribution of the information.

[0102] The providing unit can improve the accuracy of the information provided by referring to related literature when providing the information. The providing unit improves the accuracy of the information provided, for example, based on related literature. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the providing unit improves the accuracy of the information provided based on related literature. The providing unit can also provide information by referring to related literature based on a user's past behavior history. For example, the providing unit provides information by referring to related literature based on a user's past behavior history. The providing unit can also analyze related literature of the information to be provided and improve the accuracy. For example, the providing unit analyzes related literature of the information to be provided and improves the accuracy. As a result, the accuracy of the information provided is improved by referring to related literature of the information.

[0103] The providing unit can provide information taking into consideration the market value of the information when providing the information. For example, the providing unit provides information with high market value preferentially. Market value includes, for example, sales data, market research results, etc., but is not limited to such examples. For example, the providing unit provides information with high market value preferentially. Furthermore, the providing unit can also provide information with low market value at a later date. For example, the providing unit provides information with low market value at a later date. Furthermore, the providing unit can also adjust the weighting of the provision in stages according to the market value. For example, the providing unit adjusts the weighting of the provision in stages according to the market value. This makes it possible to provide more appropriate information by taking into consideration the market value of the information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs user information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends destinations based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides detailed information about the recommended destinations. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs user information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends destinations based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides detailed information about the recommended destinations. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and inputs user information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends destinations based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset terminal 314 and provides detailed information about the recommended destinations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs user information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using AI. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends destinations based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides detailed information about the recommended destinations.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can also estimate the user's health condition based on the user's input information and make recommendations based on the estimated health condition. For example, if the user is tired, it can suggest places where the user can relax. Also, if the user is active, it can suggest places where the user can exercise. Furthermore, if the user is feeling stressed, it can suggest places that will help relieve stress. This makes it possible to make recommendations based on the user's health condition.

[0106] In addition to the user's past behavioral history, the analysis unit can also use the user's social media activity for analysis. For example, it can analyze places of interest based on the places the user has checked in to on social media and the content of their posts. It can also use places visited and rated by the user's friends for analysis. Furthermore, it can also reflect the frequency of the user's social media activity and topics of interest in the analysis. This enables highly accurate analysis that takes into account the user's social media activity.

[0107] The recommendation unit can also make recommendations taking into account the user's current location, weather, and seasonal factors, as well as the user's means of transportation. For example, if the user uses a car, it can suggest locations with parking. If the user uses public transportation, it can also suggest locations with good access. Furthermore, if the user uses a bicycle, it can also suggest locations within cycling distance. This makes it possible to make recommendations according to the user's means of transportation.

[0108] The providing unit can provide event information related to the user's interests in addition to detailed information about the recommended destination. For example, if a special event is being held at a zoo, that information can be provided. It can also provide information about seasonal events at parks. It can also provide information about special exhibitions and workshops at museums. This allows the user to grasp related event information along with detailed information about the destination.

[0109] The receiving unit can also estimate the user's emotions and adjust the type of information to be input based on the estimated user's emotions. For example, if the user is relaxed, the receiving unit can prompt the user to input detailed information. If the user is in a hurry, the receiving unit can prompt the user to input only the minimum necessary information. Furthermore, if the user is excited, the receiving unit can prompt the user to input information that attracts the user's attention. This makes it possible to input information according to the user's emotions.

[0110] The reception unit can analyze the user's device usage history in addition to the user's past input history and select the optimal input method. For example, if the user frequently uses a smartphone, it can suggest an input method optimized for the smartphone. Also, if the user frequently uses a tablet, it can suggest an input method optimized for the tablet. Furthermore, if the user frequently uses a PC, it can suggest an input method optimized for the PC. This makes it possible to select the optimal input method based on the user's device usage history.

[0111] When inputting information, the reception unit can filter the information based on the user's lifestyle in addition to the user's current living situation and areas of interest. For example, if the user is health-conscious, only health-related information can be displayed. If the user is eco-conscious, only environmentally friendly information can be displayed. Furthermore, if the user is outdoorsy, only outdoor-related information can be displayed. This makes it possible to filter information based on the user's lifestyle.

[0112] When inputting information, the reception unit not only selects the optimal input means according to the user's input method, but also adjusts the input means taking into account the user's input speed. For example, if the user inputs quickly, text input can be given priority. Also, if the user inputs slowly, voice input can be given priority. Furthermore, if the user prefers image input, image input can be given priority. In this way, the optimal input means can be selected according to the user's input speed.

[0113] The reception unit estimates the user's emotions and determines the priority of information to be input based on the estimated user emotions, and can also adjust the amount of information to be input. For example, if the user is feeling stressed, the reception unit can reduce the amount of information to be input and encourage concise input. Also, if the user is relaxed, the reception unit can encourage the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can encourage the user to input only the most important information. In this way, the amount of information to be input can be adjusted according to the user's emotions.

[0114] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into consideration the user's movement history in addition to the user's geographical location information. For example, information about places the user has visited in the past can be prioritized. Also, information related to routes the user frequently travels can be prioritized. Furthermore, if the user is traveling, information related to travel destinations can be prioritized. This allows highly relevant information based on the user's movement history to be prioritized.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit inputs user information. The user information includes, for example, personal information, location information, and preference information. The user can input information such as their own preferences, the ages and interests of their children, etc. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, it analyzes the best destinations to go to based on information such as the user's preferences, children's ages, and interests. Step 3: The recommendation unit recommends places to go based on the information analyzed by the analysis unit. Recommendations are made based on the type of algorithm and evaluation criteria. For example, it suggests places that children can enjoy, such as zoos, parks, and museums. Step 4: The provider provides detailed information about the recommended destinations. The detailed information includes text, images, links, etc. For example, the provider provides information about the zoo's opening hours, admission fees, and access methods.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

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

[0124] The 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.

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

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

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

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0140] The 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.

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

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

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

[0144] Fig. 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.

[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 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.

[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 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.

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

[0151] The data processing system 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.

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

[0154] 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.

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

[0156] The 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.

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

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

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 reception unit for inputting user information; an analysis unit that analyzes the information input by the reception unit; a recommendation unit that recommends a destination based on the information analyzed by the analysis unit; a providing unit that provides detailed information about the destination recommended by the recommendation unit. A system characterized by:

2. The analysis unit Analyze based on the user's past behavior history and ratings 2. The system of claim 1.

3. The recommendation unit Recommendations based on the user's location, weather, and seasonal factors 2. The system of claim 1.

4. The providing unit Providing detailed information on recommended destinations, including opening hours, admission fees, and access methods 2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

7. The reception unit As you enter information, it filters it based on your current life situation and interests.

2. The system of claim 1.

8. The reception unit When entering information, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

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