System

Abstract

A system according to an embodiment is directed to analyzing user feedback and providing personalized information.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects feedback of a user. The analysis unit analyzes the feedback collected by the collection unit. The providing unit provides information based on the feedback analyzed by the analyzing 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 not adequately collected user feedback in real time and provided information based on that feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user feedback and provide personalized information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user feedback. The analysis unit analyzes the feedback collected by the collection unit. The provision unit provides information based on the feedback analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's feedback and provide personalized information. [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 travel experience personalization system according to an embodiment of the present invention is a system that collects and analyzes user feedback in real time and provides information. The travel experience personalization system collects and analyzes user feedback and provides information to provide a personalized travel experience. For example, the travel experience personalization system collects feedback provided by a user during a trip in the form of text, audio, images, etc. Next, the travel experience personalization system uses AI to collect and analyze the feedback in real time. Based on the analyzed feedback, the travel experience personalization system adjusts the information provided about the travel destination and learning content. For example, if a user expresses interest in a specific tourist destination, the travel experience personalization system provides detailed information about the tourist destination. Also, if a user expresses interest in a specific food, the travel experience personalization system provides information about the food and recommended restaurants. In this way, the travel experience personalization system can personalize the travel experience based on the user's interests. In this way, the travel experience personalization system learns from the user's feedback and can provide more accurate information the next time the user travels. For example, the travel experience personalization system can learn about tourist destinations visited by the user and food preferences and suggest a next travel plan based on that information.

[0029] A travel experience personalization system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user feedback. The user feedback may include, but is not limited to, text, audio, and images. For example, the collection unit may input photos taken by the user at tourist spots or text about impressions of restaurants visited by the user. The collection unit may also collect feedback in real time using AI. For example, the collection unit may collect user audio feedback using voice recognition technology. The collection unit may also analyze photos taken by the user using image analysis technology. The analysis unit analyzes the feedback collected by the collection unit. The analysis unit may analyze the user feedback using AI to understand the user's interests. For example, the analysis unit may analyze the user's text feedback using text analysis technology. The analysis unit may also analyze the user's emotions using emotion analysis technology. The analysis unit may also statistically analyze the user's feedback using statistical analysis technology. The provision unit provides information based on the feedback analyzed by the analysis unit. The providing unit uses AI to provide information based on the user's interests. For example, if the user expresses interest in a particular tourist destination, the providing unit provides detailed information about the tourist destination. If the user expresses interest in a particular food, the providing unit can provide information about the food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the providing unit can provide information about the activity and a reservation method. As a result, the travel experience personalization system according to the embodiment can provide a personalized travel experience by collecting and analyzing user feedback in real time and providing information.

[0030] The collection unit can collect feedback in the form of text, audio, and images. For example, the collection unit can collect photos taken by the user at tourist spots. The collection unit can also allow the user to input their impressions of restaurants they visited in text. Furthermore, the collection unit can also allow the user to record their experiences at tourist spots in audio. This makes it possible to meet the diverse needs of users by collecting feedback in various formats. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect the user's audio feedback using voice recognition technology and convert it into text data.

[0031] The analysis unit can analyze the collected feedback and understand the user's interests and concerns. The analysis unit can, for example, analyze the user's text feedback using text analysis technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. Furthermore, the analysis unit can statistically analyze the user's feedback using statistical analysis technology. This makes it possible to understand the user's interests and concerns and provide more appropriate information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze the user's text feedback using text analysis technology and understand the user's interests and concerns.

[0032] The providing unit can provide information about travel destinations or information about recommended restaurants and activities based on the analyzed feedback. For example, if the user expresses interest in a particular tourist destination, the providing unit can provide detailed information about the tourist destination. Furthermore, if the user expresses interest in a particular food, the providing unit can provide information about the food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the providing unit can provide information about the activity and how to make a reservation. This makes it possible to provide information based on the user's interests and concerns. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information about tourist destinations, restaurants, and activities based on the user's interests and concerns.

[0033] The providing unit can learn the user's past feedback and suggest the next travel plan. The providing unit can, for example, learn the user's past visited tourist spots and food preferences and suggest the next travel plan based on that. The providing unit can also analyze the user's past feedback and improve the accuracy of the next travel plan. In this way, by learning the past feedback, the accuracy of the next travel plan improves. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can learn the user's past feedback and suggest the next travel plan.

[0034] The collection unit can analyze the user's past feedback history and select the optimal collection method. For example, if the user has provided feedback in text form in the past, the collection unit can prioritize text input. Furthermore, if the user has provided feedback in voice form in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided feedback in image form in the past, the collection unit can prioritize image input. In this way, by analyzing the past feedback history, the optimal collection method can be provided to the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can analyze the user's past feedback history and select the optimal collection method.

[0035] When collecting feedback, the collection unit can filter the feedback based on the user's current travel situation and areas of interest. For example, if the user is at a tourist spot, the collection unit can prioritize collecting feedback about the tourist spot. Furthermore, if the user is at a restaurant, the collection unit can prioritize collecting feedback about the restaurant. Furthermore, if the user is participating in an activity, the collection unit can prioritize collecting feedback about the activity. In this way, by collecting feedback based on the user's current situation and interests, more relevant information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can filter the feedback based on the user's current travel situation and areas of interest.

[0036] When collecting feedback, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can collect feedback by voice. Furthermore, if the user prefers text input, the collection unit can collect feedback by text. Furthermore, if the user prefers image input, the collection unit can collect feedback by image. This improves user convenience by collecting feedback by the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect feedback using voice recognition technology, text analysis technology, or image analysis technology depending on the user's input method.

[0037] When collecting feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, when the user is at a tourist attraction, the collection unit can prioritize collecting feedback about the tourist attraction. Furthermore, when the user is at a restaurant, the collection unit can prioritize collecting feedback about the restaurant. Furthermore, when the user is participating in an activity, the collection unit can prioritize collecting feedback about the activity. In this way, highly relevant feedback can be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can obtain the user's geographical location information using GPS data or a location information service and collect feedback based on that information.

[0038] When collecting feedback, the collection unit can analyze the user's social media activity and collect related feedback. For example, the collection unit can collect feedback regarding places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related feedback. Furthermore, the collection unit can collect related feedback by referring to the activities of the user's friends on social media. In this way, related feedback can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activity using social media analysis technology and collect related feedback.

[0039] When collecting feedback, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has provided feedback in text form in the past, the collection unit can prioritize text input. Furthermore, if the user has provided feedback in voice form in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided feedback in image form in the past, the collection unit can prioritize image input. In this way, by reflecting past feedback, the optimal collection method can be provided for the user. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can analyze the user's past feedback and customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the feedback. For example, the analysis unit can perform a detailed analysis on important feedback. The analysis unit can also perform a concise analysis on general feedback. Furthermore, the analysis unit can also perform a detailed analysis on feedback that is of great interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the feedback, important information can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the feedback.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of feedback. For example, the analysis unit can apply an analysis algorithm dedicated to tourist destinations to feedback about tourist destinations. Furthermore, the analysis unit can apply an analysis algorithm dedicated to restaurants to feedback about restaurants. Furthermore, the analysis unit can apply an analysis algorithm dedicated to activities to feedback about activities. In this way, by applying an analysis algorithm depending on the category of feedback, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply different analysis algorithms depending on the category of feedback.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of feedback. For example, the analysis unit can prioritize analysis of the most recent feedback. The analysis unit can also prioritize analysis of feedback submitted immediately after an important event. Furthermore, the analysis unit can also prioritize analysis of feedback submitted by a user during a specific time period. In this way, by determining the priority of analysis based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of analysis based on the time of submission of feedback.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the feedback. For example, the analysis unit can prioritize analysis of feedback related to the user's current travel situation. The analysis unit can also prioritize analysis of feedback related to the user's areas of interest. Furthermore, the analysis unit can also prioritize analysis of feedback related to the user's past feedback. In this way, by adjusting the order of analysis based on relevance, important information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the feedback.

[0045] 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 can perform an analysis that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the analysis unit can perform an analysis that uses concise terms. Furthermore, if the user is a beginner, the analysis unit can perform an analysis that uses easy-to-understand terms. This provides analysis results that correspond to the user's level of expertise, thereby deepening the user's understanding. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise.

[0046] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past feedback. For example, if the user has provided feedback in text form in the past, the providing unit can provide the information in text form. Furthermore, if the user has provided feedback in audio form in the past, the providing unit can also provide the information in audio form. Furthermore, if the user has provided feedback in image form in the past, the providing unit can also provide the information in image form. In this way, by analyzing the past feedback, the optimal information providing method can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can analyze the user's past feedback and select the optimal information providing method.

[0047] When providing information, the providing unit can customize the means of providing information based on the user's current travel situation. For example, when the user is in a tourist spot, the providing unit can provide information about the tourist spot. Furthermore, when the user is in a restaurant, the providing unit can provide information about the restaurant. Furthermore, when the user is participating in an activity, the providing unit can provide information about the activity. This makes it possible to provide information based on the current travel situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means of providing information based on the user's current travel situation.

[0048] The providing unit can improve the method of providing information by reflecting user feedback when providing information. For example, if the user has previously provided feedback by text, the providing unit can provide the information by text. Furthermore, if the user has previously provided feedback by voice, the providing unit can also provide the information by voice. Furthermore, if the user has previously provided feedback by image, the providing unit can also provide the information by image. In this way, the method of providing information is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can improve the method of providing information by reflecting user feedback.

[0049] When providing information, the providing unit can select the optimal information providing method by taking into account the user's geographical location information. For example, when the user is in a tourist spot, the providing unit can provide information about the tourist spot. Furthermore, when the user is in a restaurant, the providing unit can provide information about the restaurant. Furthermore, when the user is participating in an activity, the providing unit can provide information about the activity. In this way, by taking the geographical location information into account, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can obtain the user's geographical location information using GPS data or a location information service, and select the optimal information providing method based on that information.

[0050] When providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's social media activity using social media analysis technology and suggest a means of providing information.

[0051] When providing information, the providing unit can customize the method of providing information by reflecting the user's past feedback. For example, if the user has provided feedback in text form in the past, the providing unit can provide the information in text form. Furthermore, if the user has provided feedback in audio form in the past, the providing unit can also provide the information in audio form. Furthermore, if the user has provided feedback in image form in the past, the providing unit can also provide the information in image form. In this way, by reflecting past feedback, the optimal information providing method for the user can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can customize the method of providing information by reflecting the user's past feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When analyzing a user's feedback, the analysis unit can improve the accuracy of the analysis by taking into account the user's past travel history. For example, by referencing data on tourist spots and restaurants visited by the user in the past and comparing it with the user's current feedback, it is possible to more accurately grasp the user's interests. The analysis unit can also analyze trends in the user's past feedback and reflect this in the analysis of the current feedback. Furthermore, the analysis unit can extract information useful for proposing future travel plans based on the user's past travel history. By taking into account the user's past travel history, the analysis accuracy can be improved, making it possible to provide more personalized information.

[0054] When collecting user feedback, the collection unit can adjust the collection method taking into account the user's current activity status. For example, if the user is visiting a tourist attraction, feedback about the tourist attraction can be collected preferentially. Also, if the user is eating at a restaurant, feedback about the restaurant can be collected preferentially. Furthermore, if the user is participating in an activity, feedback about the activity can be collected preferentially. In this way, more relevant information can be obtained by collecting feedback according to the user's current activity status.

[0055] When collecting user feedback, the providing unit can analyze the user's social media activities to collect relevant feedback. For example, feedback regarding places where the user has checked in on social media can be collected. The providing unit can also analyze the content of the user's social media posts to collect relevant feedback. Furthermore, the providing unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, relevant feedback can be collected by analyzing the user's social media activities.

[0056] When analyzing user feedback, the analysis unit can determine the priority of analysis based on the time of submission of the feedback. For example, by prioritizing analysis of the most recent feedback, it is possible to provide information in real time. Furthermore, by prioritizing analysis of feedback submitted immediately after an important event, it is possible to quickly provide information related to the event. Furthermore, by prioritizing analysis of feedback submitted by a user during a specific time period, it is possible to provide information according to the time period. In this way, by determining the priority of analysis based on the time of submission, it is possible to prioritize analysis of the most recent information.

[0057] When collecting user feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, if the user is at a tourist spot, feedback about the tourist spot can be prioritized. Also, if the user is at a restaurant, feedback about the restaurant can be prioritized. Furthermore, if the user is participating in an activity, feedback about the activity can be prioritized. In this way, highly relevant feedback can be collected by taking into account the user's geographical location information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user feedback. User feedback can include text, voice, and images. For example, a user can enter photos taken at tourist spots or their impressions of a restaurant they visited in text. The collection unit can also collect feedback in real time using AI. It can also collect user voice feedback using voice recognition technology and analyze photos taken by users using image analysis technology. Step 2: The analysis unit analyzes the feedback collected by the collection unit. The analysis unit uses AI to analyze the user's feedback and understand the user's interests and concerns. It can also analyze the user's text feedback using text analysis technology and analyze the user's emotions using emotion analysis technology. Furthermore, it can statistically analyze the user's feedback using statistical analysis technology. Step 3: The provision unit provides information based on the feedback analyzed by the analysis unit. The provision unit uses AI to provide information based on the user's interests. For example, if the user expresses interest in a particular tourist destination, the provision unit can provide detailed information about that tourist destination. If the user expresses interest in a particular food, the provision unit can also provide information about that food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the provision unit can provide information about that activity and how to make a reservation.

[0060] (Example 2) A travel experience personalization system according to an embodiment of the present invention is a system that collects and analyzes user feedback in real time and provides information. The travel experience personalization system collects and analyzes user feedback and provides information to provide a personalized travel experience. For example, the travel experience personalization system collects feedback provided by a user during a trip in the form of text, audio, images, etc. Next, the travel experience personalization system uses AI to collect and analyze the feedback in real time. Based on the analyzed feedback, the travel experience personalization system adjusts the information provided about the travel destination and learning content. For example, if a user expresses interest in a specific tourist destination, the travel experience personalization system provides detailed information about the tourist destination. Also, if a user expresses interest in a specific food, the travel experience personalization system provides information about the food and recommended restaurants. In this way, the travel experience personalization system can personalize the travel experience based on the user's interests. In this way, the travel experience personalization system learns from the user's feedback and can provide more accurate information the next time the user travels. For example, the travel experience personalization system can learn about tourist destinations visited by the user and food preferences and suggest a next travel plan based on that information.

[0061] A travel experience personalization system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user feedback. The user feedback may include, but is not limited to, text, audio, and images. For example, the collection unit may input photos taken by the user at tourist spots or text about impressions of restaurants visited by the user. The collection unit may also collect feedback in real time using AI. For example, the collection unit may collect user audio feedback using voice recognition technology. The collection unit may also analyze photos taken by the user using image analysis technology. The analysis unit analyzes the feedback collected by the collection unit. The analysis unit may analyze the user feedback using AI to understand the user's interests. For example, the analysis unit may analyze the user's text feedback using text analysis technology. The analysis unit may also analyze the user's emotions using emotion analysis technology. The analysis unit may also statistically analyze the user's feedback using statistical analysis technology. The provision unit provides information based on the feedback analyzed by the analysis unit. The providing unit uses AI to provide information based on the user's interests. For example, if the user expresses interest in a particular tourist destination, the providing unit provides detailed information about the tourist destination. If the user expresses interest in a particular food, the providing unit can provide information about the food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the providing unit can provide information about the activity and a reservation method. As a result, the travel experience personalization system according to the embodiment can provide a personalized travel experience by collecting and analyzing user feedback in real time and providing information.

[0062] The collection unit can collect feedback in the form of text, audio, and images. For example, the collection unit can collect photos taken by the user at tourist spots. The collection unit can also allow the user to input their impressions of restaurants they visited in text. Furthermore, the collection unit can also allow the user to record their experiences at tourist spots in audio. This makes it possible to meet the diverse needs of users by collecting feedback in various formats. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect the user's audio feedback using voice recognition technology and convert it into text data.

[0063] The analysis unit can analyze the collected feedback and understand the user's interests and concerns. The analysis unit can, for example, analyze the user's text feedback using text analysis technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. Furthermore, the analysis unit can statistically analyze the user's feedback using statistical analysis technology. This makes it possible to understand the user's interests and concerns and provide more appropriate information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze the user's text feedback using text analysis technology and understand the user's interests and concerns.

[0064] The providing unit can provide information about travel destinations or information about recommended restaurants and activities based on the analyzed feedback. For example, if the user expresses interest in a particular tourist destination, the providing unit can provide detailed information about the tourist destination. Furthermore, if the user expresses interest in a particular food, the providing unit can provide information about the food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the providing unit can provide information about the activity and how to make a reservation. This makes it possible to provide information based on the user's interests and concerns. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information about tourist destinations, restaurants, and activities based on the user's interests and concerns.

[0065] The providing unit can learn the user's past feedback and suggest the next travel plan. The providing unit can, for example, learn the user's past visited tourist spots and food preferences and suggest the next travel plan based on that. The providing unit can also analyze the user's past feedback and improve the accuracy of the next travel plan. In this way, by learning the past feedback, the accuracy of the next travel plan improves. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can learn the user's past feedback and suggest the next travel plan.

[0066] The collection unit can estimate the user's emotions and adjust the timing of collecting feedback based on the estimated user emotions. For example, if the user is excited, the collection unit can collect real-time impressions by immediately requesting feedback. Furthermore, if the user is tired, the collection unit can collect accurate impressions by requesting feedback after a break. Furthermore, if the user is relaxed, the collection unit can request feedback at a specific timing during the trip. This allows for more accurate feedback to be obtained by collecting feedback at a timing appropriate to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can estimate the user's emotions and adjust the timing of collecting feedback based on the estimated user emotions.

[0067] The collection unit can analyze the user's past feedback history and select the optimal collection method. For example, if the user has provided feedback in text form in the past, the collection unit can prioritize text input. Furthermore, if the user has provided feedback in voice form in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided feedback in image form in the past, the collection unit can prioritize image input. In this way, by analyzing the past feedback history, the optimal collection method can be provided to the user. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can analyze the user's past feedback history and select the optimal collection method.

[0068] When collecting feedback, the collection unit can filter the feedback based on the user's current travel situation and areas of interest. For example, if the user is at a tourist spot, the collection unit can prioritize collecting feedback about the tourist spot. Furthermore, if the user is at a restaurant, the collection unit can prioritize collecting feedback about the restaurant. Furthermore, if the user is participating in an activity, the collection unit can prioritize collecting feedback about the activity. In this way, by collecting feedback based on the user's current situation and interests, more relevant information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can filter the feedback based on the user's current travel situation and areas of interest.

[0069] When collecting feedback, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can collect feedback by voice. Furthermore, if the user prefers text input, the collection unit can collect feedback by text. Furthermore, if the user prefers image input, the collection unit can collect feedback by image. This improves user convenience by collecting feedback by the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect feedback using voice recognition technology, text analysis technology, or image analysis technology depending on the user's input method.

[0070] The collection unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user's emotions. For example, when the user is excited, the collection unit can prioritize collecting feedback indicating heightened emotions. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed feedback. Furthermore, when the user is tired, the collection unit can prioritize collecting concise feedback. In this way, by prioritizing feedback based on the user's emotions, important feedback can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can estimate the user's emotions and determine the priority of feedback to be collected based on the estimated user's emotions.

[0071] When collecting feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, when the user is at a tourist attraction, the collection unit can prioritize collecting feedback about the tourist attraction. Furthermore, when the user is at a restaurant, the collection unit can prioritize collecting feedback about the restaurant. Furthermore, when the user is participating in an activity, the collection unit can prioritize collecting feedback about the activity. In this way, highly relevant feedback can be collected by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can obtain the user's geographical location information using GPS data or a location information service and collect feedback based on that information.

[0072] When collecting feedback, the collection unit can analyze the user's social media activity and collect related feedback. For example, the collection unit can collect feedback regarding places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related feedback. Furthermore, the collection unit can collect related feedback by referring to the activities of the user's friends on social media. In this way, related feedback can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's social media activity using social media analysis technology and collect related feedback.

[0073] When collecting feedback, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has provided feedback in text form in the past, the collection unit can prioritize text input. Furthermore, if the user has provided feedback in voice form in the past, the collection unit can prioritize voice input. Furthermore, if the user has provided feedback in image form in the past, the collection unit can prioritize image input. In this way, by reflecting past feedback, the optimal collection method can be provided for the user. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can analyze the user's past feedback and customize the collection method.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is tired, the analysis unit can provide a concise analysis result. This allows the analysis result to be presented in a manner appropriate to the user's emotions, thereby deepening the user's understanding. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the feedback. For example, the analysis unit can perform a detailed analysis on important feedback. The analysis unit can also perform a concise analysis on general feedback. Furthermore, the analysis unit can also perform a detailed analysis on feedback that is of great interest to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the feedback, important information can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the feedback.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of feedback. For example, the analysis unit can apply an analysis algorithm dedicated to tourist destinations to feedback about tourist destinations. Furthermore, the analysis unit can apply an analysis algorithm dedicated to restaurants to feedback about restaurants. Furthermore, the analysis unit can apply an analysis algorithm dedicated to activities to feedback about activities. In this way, by applying an analysis algorithm depending on the category of feedback, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can apply different analysis algorithms depending on the category of feedback.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0078] 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 can perform a short, to-the-point analysis. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows the analysis results to be provided at a length appropriate to the user's emotions, thereby deepening the user's understanding. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of feedback. For example, the analysis unit can prioritize analysis of the most recent feedback. The analysis unit can also prioritize analysis of feedback submitted immediately after an important event. Furthermore, the analysis unit can also prioritize analysis of feedback submitted by a user during a specific time period. In this way, by determining the priority of analysis based on the time of submission, the most recent information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority of analysis based on the time of submission of feedback.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the feedback. For example, the analysis unit can prioritize analysis of feedback related to the user's current travel situation. The analysis unit can also prioritize analysis of feedback related to the user's areas of interest. Furthermore, the analysis unit can also prioritize analysis of feedback related to the user's past feedback. In this way, by adjusting the order of analysis based on relevance, important information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the feedback.

[0081] 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 can perform an analysis that makes extensive use of technical terms. Furthermore, if the user has general knowledge, the analysis unit can perform an analysis that uses concise terms. Furthermore, if the user is a beginner, the analysis unit can perform an analysis that uses easy-to-understand terms. This provides analysis results that correspond to the user's level of expertise, thereby deepening the user's understanding. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise.

[0082] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide visually stimulating information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is tired, the providing unit can provide concise information. This improves user satisfaction by providing information in a manner appropriate to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions.

[0083] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past feedback. For example, if the user has provided feedback in text form in the past, the providing unit can provide the information in text form. Furthermore, if the user has provided feedback in audio form in the past, the providing unit can also provide the information in audio form. Furthermore, if the user has provided feedback in image form in the past, the providing unit can also provide the information in image form. In this way, by analyzing the past feedback, the optimal information providing method can be provided to the user. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can analyze the user's past feedback and select the optimal information providing method.

[0084] When providing information, the providing unit can customize the means of providing information based on the user's current travel situation. For example, when the user is in a tourist spot, the providing unit can provide information about the tourist spot. Furthermore, when the user is in a restaurant, the providing unit can provide information about the restaurant. Furthermore, when the user is participating in an activity, the providing unit can provide information about the activity. This makes it possible to provide information based on the current travel situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means of providing information based on the user's current travel situation.

[0085] The providing unit can improve the method of providing information by reflecting user feedback when providing information. For example, if the user has previously provided feedback by text, the providing unit can provide the information by text. Furthermore, if the user has previously provided feedback by voice, the providing unit can also provide the information by voice. Furthermore, if the user has previously provided feedback by image, the providing unit can also provide the information by image. In this way, the method of providing information is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can improve the method of providing information by reflecting user feedback.

[0086] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is excited, the providing unit can prioritize providing information that expresses heightened emotions. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is tired, the providing unit can prioritize providing concise information. This allows important information to be provided preferentially by determining the priority of information provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions.

[0087] When providing information, the providing unit can select the optimal information providing method by taking into account the user's geographical location information. For example, when the user is in a tourist spot, the providing unit can provide information about the tourist spot. Furthermore, when the user is in a restaurant, the providing unit can provide information about the restaurant. Furthermore, when the user is participating in an activity, the providing unit can provide information about the activity. In this way, by taking the geographical location information into account, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can obtain the user's geographical location information using GPS data or a location information service, and select the optimal information providing method based on that information.

[0088] When providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's social media activity using social media analysis technology and suggest a means of providing information.

[0089] When providing information, the providing unit can customize the method of providing information by reflecting the user's past feedback. For example, if the user has provided feedback in text form in the past, the providing unit can provide the information in text form. Furthermore, if the user has provided feedback in audio form in the past, the providing unit can also provide the information in audio form. Furthermore, if the user has provided feedback in image form in the past, the providing unit can also provide the information in image form. In this way, by reflecting past feedback, the optimal information providing method for the user can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can customize the method of providing information by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis 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 collection unit can collect user feedback using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected feedback. For example, the provision unit is realized by the control unit 46A of the smart device 14 and provides information to the user based on the analysis result. For example, the collection unit, analysis unit, and provision unit are also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis 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 collection unit can collect user feedback using the camera 42 or microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected feedback. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 and provides information to the user based on the analysis result. For example, the collection unit, analysis unit, and provision unit are also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect user feedback using the camera 42 or the microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected feedback. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 and provides information to the user based on the analysis result. For example, the collection unit, analysis unit, and provision unit are also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis 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 collection unit can collect user feedback using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected feedback. For example, the provision unit is realized by the control unit 46A of the robot 414 and provides information to the user based on the analysis result. For example, the collection unit, analysis unit, and provision unit are also realized by the specific processing unit 290 of the data processing device 12.

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

[0091] When analyzing a user's feedback, the analysis unit can improve the accuracy of the analysis by taking into account the user's past travel history. For example, by referencing data on tourist spots and restaurants visited by the user in the past and comparing it with the user's current feedback, it is possible to more accurately grasp the user's interests. The analysis unit can also analyze trends in the user's past feedback and reflect this in the analysis of the current feedback. Furthermore, the analysis unit can extract information useful for proposing future travel plans based on the user's past travel history. By taking into account the user's past travel history, the analysis accuracy can be improved, making it possible to provide more personalized information.

[0092] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is excited, relevant information can be provided immediately to keep the user interested. If the user is relaxed, detailed information can be provided to deepen the user's understanding. Furthermore, if the user is tired, brief information can be provided to reduce the user's burden. As a result, user satisfaction can be improved by providing information at a timing that suits the user's emotions.

[0093] When collecting user feedback, the collection unit can adjust the collection method taking into account the user's current activity status. For example, if the user is visiting a tourist attraction, feedback about the tourist attraction can be collected preferentially. Also, if the user is eating at a restaurant, feedback about the restaurant can be collected preferentially. Furthermore, if the user is participating in an activity, feedback about the activity can be collected preferentially. In this way, more relevant information can be obtained by collecting feedback according to the user's current activity status.

[0094] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is excited, feedback related to that emotion can be analyzed with priority. Also, if the user is relaxed, detailed analysis can be performed to deepen understanding of the user. Furthermore, if the user is tired, a brief analysis can be performed to reduce the burden on the user. In this way, by determining the priority of analysis based on the user's emotions, important information can be analyzed with priority.

[0095] When collecting user feedback, the providing unit can analyze the user's social media activities to collect relevant feedback. For example, feedback regarding places where the user has checked in on social media can be collected. The providing unit can also analyze the content of the user's social media posts to collect relevant feedback. Furthermore, the providing unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, relevant feedback can be collected by analyzing the user's social media activities.

[0096] The providing unit can estimate the user's emotions and adjust the content of the information provided based on the estimated user's emotions. For example, if the user is excited, visually stimulating information can be provided. If the user is relaxed, detailed information can be provided to deepen the user's understanding. Furthermore, if the user is tired, concise information can be provided to reduce the user's burden. In this way, by providing information with content that corresponds to the user's emotions, user satisfaction can be improved.

[0097] When analyzing user feedback, the analysis unit can determine the priority of analysis based on the time of submission of the feedback. For example, by prioritizing analysis of the most recent feedback, it is possible to provide information in real time. Furthermore, by prioritizing analysis of feedback submitted immediately after an important event, it is possible to quickly provide information related to the event. Furthermore, by prioritizing analysis of feedback submitted by a user during a specific time period, it is possible to provide information according to the time period. In this way, by determining the priority of analysis based on the time of submission, it is possible to prioritize analysis of the most recent information.

[0098] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is excited, information can be provided that makes extensive use of videos and images. If the user is relaxed, detailed text-based information can be provided. Furthermore, if the user is tired, information can be provided in audio format, thereby reducing the burden on the user. In this way, user satisfaction can be improved by providing information in a format that suits the user's emotions.

[0099] When collecting user feedback, the collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, if the user is at a tourist spot, feedback about the tourist spot can be prioritized. Also, if the user is at a restaurant, feedback about the restaurant can be prioritized. Furthermore, if the user is participating in an activity, feedback about the activity can be prioritized. In this way, highly relevant feedback can be collected by taking into account the user's geographical location information.

[0100] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, if the user is excited, information that expresses heightened emotions can be provided preferentially. Also, if the user is relaxed, detailed information can be provided preferentially. Furthermore, if the user is tired, concise information can be provided preferentially. In this way, by determining the priority of information provision based on the user's emotions, important information can be provided preferentially.

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

[0102] Step 1: The collection unit collects user feedback. User feedback can include text, voice, and images. For example, a user can enter photos taken at tourist spots or their impressions of a restaurant they visited in text. The collection unit can also collect feedback in real time using AI. It can also collect user voice feedback using voice recognition technology and analyze photos taken by users using image analysis technology. Step 2: The analysis unit analyzes the feedback collected by the collection unit. The analysis unit uses AI to analyze the user's feedback and understand the user's interests and concerns. It can also analyze the user's text feedback using text analysis technology and analyze the user's emotions using emotion analysis technology. Furthermore, it can statistically analyze the user's feedback using statistical analysis technology. Step 3: The provision unit provides information based on the feedback analyzed by the analysis unit. The provision unit uses AI to provide information based on the user's interests. For example, if the user expresses interest in a particular tourist destination, the provision unit can provide detailed information about that tourist destination. If the user expresses interest in a particular food, the provision unit can also provide information about that food and recommended restaurants. Furthermore, if the user expresses interest in a particular activity, the provision unit can provide information about that activity and how to make a reservation.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following 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.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting user feedback; an analysis unit that analyzes the feedback collected by the collection unit; a providing unit that provides information based on the feedback analyzed by the analyzing unit; Equipped with A system characterized by:

2. The collecting unit Collect feedback in the form of text, audio, and images 2. The system of claim 1.

3. The analysis unit Analyze collected feedback to understand user interests and concerns 2. The system of claim 1.

4. The providing unit Providing you with travel destination information or restaurant and activity recommendations based on your analyzed feedback 2. The system of claim 1.

5. The providing unit Learn from your past feedback and suggest your next travel itinerary 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust the timing of feedback collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past feedback history and select the best collection method 2. The system of claim 1.

8. The collecting unit When collecting feedback, filter it based on the user's current travel situation and interests.

2. The system of claim 1.

9. The collecting unit When collecting feedback, choose the best method to collect it depending on the user's input method 2. The system of claim 1.

10. The collecting unit Estimate user emotions and prioritize feedback to be collected based on the estimated user emotions.

2. The system of claim 1.

Images