system

Abstract

The system according to the embodiment aims to efficiently find the most suitable hotel based on the user's requests. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a setting unit, a scoring unit, and a providing unit. The reception unit inputs a user's request. The analysis unit analyzes the request input by the reception unit. The setting unit sets filtering conditions based on the information analyzed by the analysis unit. The scoring unit scores hotel information and reviews based on the conditions set by the setting unit. The providing unit provides the information scored by the scoring unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to find the best hotel based on the user's requirements, and the search process takes a long time.

[0005] The system according to the embodiment aims to efficiently find the most suitable hotel based on the user's requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a setting unit, a scoring unit, and a providing unit. The reception unit inputs a user's request. The analysis unit analyzes the request input by the reception unit. The setting unit sets filtering conditions based on the information analyzed by the analysis unit. The scoring unit scores hotel information and reviews based on the conditions set by the setting unit. The providing unit provides the information scored by the scoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently find the most suitable hotel based on the user's request. [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 search system according to an embodiment of the present invention dynamically provides personalized search criteria by having users input their preferences on a home screen. The travel search system allows users to input their preferences on a home screen, and AI analyzes the requests and dynamically provides optimal search criteria. Furthermore, the travel search system analyzes hotel information and reviews to generate a score based on how well a hotel matches the user's preferences. For example, a user may input preferences such as "a room with an ocean view is preferred," "pets allowed," and "breakfast included." This information is input to an AI, which analyzes the input. Next, the travel search system provides optimal search criteria to the user based on the information analyzed by the AI. For example, in response to a request for "a room with an ocean view is preferred," the system provides the search criteria "a room with an ocean view." Furthermore, the travel search system analyzes hotel information and reviews to generate a score based on how well a hotel matches the user's preferences. For example, if a hotel receives many reviews stating that the rooms have excellent ocean views, the hotel receives a high score. Based on these scores, the user can select a hotel. This allows the travel search system to dynamically provide narrowing conditions based on the user's requests, and analyze and score hotel information and reviews, thereby providing the user with the hotel that is most suitable for the user. This allows the travel search system to dynamically provide narrowing conditions based on the user's requests, and analyze and score hotel information and reviews, thereby providing the user with the hotel that is most suitable for the user. For example, a user can easily find a hotel that meets their needs. For example, in response to a request for "pets allowed," hotels that are pet-friendly are displayed with high scores, allowing the user to enjoy traveling with their pet. In addition, in response to a request for "breakfast included," hotels with highly rated breakfasts are displayed, allowing the user to enjoy a satisfying breakfast.

[0029] A travel search system according to an embodiment includes a reception unit, an analysis unit, a setting unit, a scoring unit, and a providing unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, the dates of stay, budget, and desired facilities. For example, the reception unit allows the user to input requests such as "prefer a room with an ocean view," "pets allowed," and "breakfast included" on the top screen. The analysis unit uses AI to analyze the requests input by the reception unit. The analysis is performed using, for example, but not limited to, techniques such as natural language processing and data mining. For example, the analysis unit analyzes the user's requests using natural language processing technology to understand the content of the requests. The analysis unit can also extract information related to the user's requests from past data using data mining technology. The setting unit sets filtering conditions based on the information analyzed by the analysis unit. The filtering conditions include, for example, but are not limited to, price range, location, and rating. For example, the setting unit sets filtering conditions such as "room with ocean view," "pets allowed," and "breakfast included" based on the user's requests. The setting unit can also set priorities for the filtering conditions according to the user's requests. The scoring unit scores hotel information and reviews based on the conditions set by the setting unit. Scoring can be performed using, for example, an evaluation score or weighting, but is not limited to these examples. For example, if a hotel receives many reviews stating "great rooms with ocean views" based on the hotel's reviews, the scoring unit can increase the score of the hotel. The scoring unit can also score the degree to which the hotel matches the user's requests based on the hotel information. The providing unit provides the information scored by the scoring unit. The provision can be performed using, for example, a webpage, email, app notification, or other methods, but is not limited to these examples. For example, the providing unit displays scored hotel information on a webpage, allowing the user to easily select a hotel. The providing unit can also provide the scored information to the user via email or app notification. As a result, the travel search system according to the embodiment can dynamically provide filtering conditions based on the user's requests, and by analyzing and scoring hotel information and reviews, can provide the user with the most suitable hotel.

[0030] The reception unit can analyze the user's past request history and select the optimal input method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used in a specific time period based on the user's past request history. In this way, the optimal input method can be provided by analyzing the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] When a request is input, the reception unit can filter the requests based on the user's current travel plans and areas of interest. The reception unit can, for example, preferentially display related requests based on the destination of a trip currently planned by the user. The reception unit can also filter related requests based on the user's areas of interest (e.g., beach resorts, mountainous areas, etc.). The reception unit can also suggest appropriate requests based on the duration of the user's travel plans. In this way, by filtering requests based on the user's current travel plans and areas of interest, more relevant requests can be provided. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI.

[0032] When a request is input, the reception unit can select the optimal input means depending on the user's input method. For example, when a user inputs a request by voice, the reception unit automatically converts the request into text using voice recognition technology. Furthermore, when a user inputs a request by text, the reception unit can provide an input completion function to enable the request to be input quickly. Furthermore, when a user inputs a request using an image, the reception unit can analyze the request using image recognition technology and propose appropriate filtering conditions. This allows the request to be input smoothly by providing the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When inputting a request, the reception unit can prioritize inputting highly relevant requests by taking into account the user's geographical location information. The reception unit can, for example, prioritize displaying related requests based on the user's current location. Furthermore, when the user inputs geographical location information of a travel destination, the reception unit can also prioritize displaying requests related to that location. Furthermore, the reception unit can also prioritize displaying requests related to locations close to the user's current location. In this way, by taking the user's geographical location information into account, highly relevant requests can be prioritized and input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] When a request is input, the reception unit can analyze the user's social media activity and input related requests. For example, the reception unit can preferentially display requests related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related requests. The reception unit can also suggest related requests by referring to the activities of the user's friends on social media. In this way, related requests can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis on an important request. The analysis unit can also perform a simplified analysis on a less important request. The analysis unit can also determine the priority of the analysis according to the importance of the request. In this way, by adjusting the level of detail of the analysis based on the importance of the request, a detailed analysis can be performed on an important request. 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.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit applies an analysis algorithm dedicated to accommodations to requests related to accommodations. The analysis unit can also apply an analysis algorithm dedicated to tourist spots to requests related to tourist spots. The analysis unit can also apply an analysis algorithm dedicated to restaurants to requests related to restaurants. By applying different analysis algorithms depending on the category of request, 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.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also reflect feedback from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the request. For example, the analysis unit prioritizes analysis of the most recently submitted request. The analysis unit can also perform a simplified analysis on requests that were submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the request, the most recent request can be analyzed preferentially. 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.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also perform a simplified analysis for less relevant requests. The analysis unit can also adjust the order of analysis based on the relevance of the requests. In this way, by adjusting the order of analysis based on the relevance of the requests, highly relevant requests can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] 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 uses detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without using AI.

[0042] When setting the filtering conditions, the setting unit can adjust the level of detail of the setting based on the importance of the request. For example, the setting unit provides detailed filtering conditions for important requests. The setting unit can also provide simplified filtering conditions for requests with low importance. The setting unit can also determine the priority of the filtering conditions according to the importance of the request. In this way, by adjusting the level of detail of the setting based on the importance of the request, detailed filtering conditions can be provided for important requests. Some or all of the above-mentioned processing in the setting unit may be performed using AI, for example, or may be performed without using AI.

[0043] When setting the narrowing-down conditions, the setting unit can apply different setting algorithms depending on the category of the request. For example, the setting unit applies a setting algorithm dedicated to accommodations to a request regarding accommodations. Furthermore, the setting unit can also apply a setting algorithm dedicated to tourist spots to a request regarding tourist spots. Furthermore, the setting unit can also apply a setting algorithm dedicated to restaurants to a request regarding restaurants. In this way, by applying different setting algorithms depending on the category of the request, more accurate narrowing-down conditions can be provided. Some or all of the above-mentioned processing in the setting unit may be performed using AI, for example, or may be performed without using AI.

[0044] When setting the filtering conditions, the setting unit can improve the accuracy of the settings by referring to the user's past setting results. The setting unit, for example, optimizes the setting algorithm based on the user's past setting results. The setting unit can also reflect feedback to improve the accuracy of the settings from the user's past setting results. The setting unit can also adjust the level of detail of the settings by referring to the user's past setting results. In this way, the accuracy of the settings can be improved by referring to the user's past setting results. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI.

[0045] When setting the filtering conditions, the setting unit can determine the priority of the settings based on the time of submission of the requests. For example, the setting unit can prioritize the most recently submitted requests. The setting unit can also perform simplified settings for requests that were submitted earlier. The setting unit can also determine the priority of the settings based on the time of submission. In this way, by determining the priority of the settings based on the time of submission of the requests, the most recent requests can be prioritized. Some or all of the above-mentioned processing in the setting unit can be performed, for example, using AI or without using AI.

[0046] The setting unit can adjust the order of settings based on the relevance of requests when setting the filtering conditions. For example, the setting unit prioritizes setting highly relevant requests. The setting unit can also perform simplified settings for less relevant requests. The setting unit can also adjust the order of settings based on the relevance of requests. In this way, by adjusting the order of settings based on the relevance of requests, highly relevant requests can be prioritized. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI.

[0047] When setting the filtering conditions, the setting unit can adjust the use of technical terminology in the settings according to the user's level of expertise. For example, if the user has technical expertise, the setting unit uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the setting unit can provide the setting results in simple language. Furthermore, the setting unit can adjust the use of technical terminology in the settings according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the settings according to the user's level of expertise, it is possible to provide filtering conditions that are easier to understand. Some or all of the above-described processing in the setting unit may be performed, for example, using AI or without using AI.

[0048] When scoring, the scoring unit can adjust the level of detail of the scoring based on the importance of the hotel information and reviews. For example, the scoring unit performs detailed scoring for important information and reviews. The scoring unit can also perform simplified scoring for less important information and reviews. The scoring unit can also determine the priority of the scoring based on the importance of the information and reviews. In this way, by adjusting the level of detail of the scoring based on the importance of the hotel information and reviews, detailed scoring can be performed for important information. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The scoring unit can apply different scoring algorithms depending on the hotel category when scoring. For example, the scoring unit applies a scoring algorithm dedicated to luxury hotels to luxury hotels. The scoring unit can also apply a scoring algorithm dedicated to business hotels to business hotels. The scoring unit can also apply a scoring algorithm dedicated to resort hotels to resort hotels. This allows for more accurate scoring by applying different scoring algorithms depending on the hotel category. Some or all of the above-mentioned processing in the scoring unit may be performed using AI, for example, or may be performed without using AI.

[0050] The scoring unit can improve the accuracy of the scoring by referring to the user's past scoring results when scoring. The scoring unit, for example, optimizes the scoring algorithm based on the user's past scoring results. The scoring unit can also reflect feedback from the user's past scoring results to improve the accuracy of the scoring. The scoring unit can also adjust the level of detail of the scoring by referring to the user's past scoring results. In this way, the accuracy of the scoring can be improved by referring to the user's past scoring results. Some or all of the above-mentioned processing in the scoring unit may be performed, for example, using AI or without using AI.

[0051] When scoring, the scoring unit can determine the priority of scoring based on the time of submission of the hotel information and reviews. For example, the scoring unit prioritizes scoring of recently submitted information and reviews. The scoring unit can also perform simplified scoring for older submitted information and reviews. The scoring unit can also determine the priority of scoring based on the time of submission. In this way, by determining the priority of scoring based on the time of submission of the hotel information and reviews, the most recent information can be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI.

[0052] When scoring, the scoring unit can adjust the order of scoring based on the relevance of the hotel information and reviews. For example, the scoring unit prioritizes scoring of highly relevant information and reviews. The scoring unit can also perform a simplified scoring for less relevant information and reviews. The scoring unit can also adjust the order of scoring based on the relevance of the information and reviews. In this way, by adjusting the order of scoring based on the relevance of the hotel information and reviews, highly relevant information can be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The scoring unit can adjust the use of technical terms for scoring according to the user's level of expertise when scoring. For example, if the user has technical expertise, the scoring unit uses detailed technical terms. Furthermore, if the user does not have technical expertise, the scoring unit can provide a scoring result in simple terms. Furthermore, the scoring unit can adjust the use of technical terms for scoring according to the user's level of expertise. In this way, by adjusting the use of technical terms for scoring according to the user's level of expertise, it is possible to provide a scoring result that is easier to understand. Some or all of the above-mentioned processing in the scoring unit may be performed, for example, using AI or without using AI.

[0054] When providing information, the providing unit can select the optimal information providing method by referring to the user's past information providing history. The providing unit, for example, suggests the optimal information providing method based on the user's past information providing history. The providing unit can also reflect feedback to improve the accuracy of information provision from the user's past information providing history. The providing unit can also adjust the level of detail of information provision by referring to the user's past information providing history. In this way, the optimal information providing method can be provided by referring to the user's past information providing history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0055] The providing unit can customize the provided content based on the user's current travel plan when providing information. The providing unit, for example, prioritizes providing relevant information based on the user's current travel plan. The providing unit can also provide appropriate information based on the period of the user's travel plan. The providing unit can also provide relevant information based on the destination of the user's travel plan. In this way, by customizing the provided content based on the user's current travel plan, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When providing information, the providing unit can provide optimal information by taking into account the user's current location information. The providing unit, for example, prioritizes providing relevant information based on the user's current location. The providing unit can also provide information related to locations close to the user's current location. The providing unit can also select an optimal information provision method based on the user's current location. This makes it possible to provide highly relevant information by taking into account the user's current location information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0057] When providing information, the providing unit can provide optimal information by taking into account the user's geographical location information. For example, the providing unit provides information on related tourist spots and stores based on the user's current location. Furthermore, when the user inputs geographical location information of the user's travel destination, the providing unit can also provide information related to that location. Furthermore, the providing unit can preferentially provide information related to locations close to the user's current location. In this way, by taking into account the user's geographical location information, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0058] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit can provide, for example, 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 information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI.

[0059] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The providing unit can, for example, suggest an optimal information providing method based on the user's past feedback. The providing unit can also reflect feedback for improving the accuracy of information provision from the user's past feedback. The providing unit can also adjust the level of detail of information provision by referring to the user's past feedback. In this way, the optimal information providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can adjust the request input method taking into account the user's current health condition. For example, if the user is tired, simple options can be provided to allow the user to input their request quickly. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is ill, voice input or auto-completion functions can be prioritized to reduce the burden of input. This allows the user to smoothly input their request by providing the optimal input method according to their health condition.

[0062] The setting unit can analyze the user's past travel history and suggest optimal filtering conditions. For example, it can automatically set relevant filtering conditions based on data on places and accommodations visited by the user in the past. It can also extract the characteristics of facilities that the user has given high ratings to and suggest filtering conditions based on these. It can also analyze the user's past travel patterns and predict filtering conditions suitable for the next trip. This makes it possible to provide more accurate filtering conditions by utilizing the user's past travel history.

[0063] The providing unit can adjust the method of providing information taking into account the user's current device usage status. For example, if the user is using a smartphone, information can be provided using a mobile-friendly interface. If the user is using a personal computer, detailed information can be provided on a large screen. Furthermore, if the user is using a tablet, information can be provided using an interface optimized for touch operation. This allows the user to receive information smoothly by providing the optimal information providing method according to the user's device usage status.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, a detailed analysis is performed for an important request. The analysis unit can also perform a simplified analysis for a less important request. The analysis unit can also determine the priority of the analysis according to the importance of the request. In this way, by adjusting the level of detail of the analysis based on the importance of the request, a detailed analysis can be performed for an important request.

[0065] When scoring, the scoring unit can determine the priority of scoring based on the time of submission of the hotel information and reviews. For example, the scoring unit can prioritize scoring of recently submitted information and reviews. The scoring unit can also perform simplified scoring for information and reviews that were submitted older. The scoring unit can also determine the priority of scoring based on the time of submission. In this way, by determining the priority of scoring based on the time of submission of the hotel information and reviews, the most recent information can be prioritized in scoring.

[0066] When providing information, the providing unit can analyze the user's social media activity and provide related 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 information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs the user's requests. The user's requests include the date of stay, budget, desired facilities, etc. For example, the user can input requests such as "I would like a room with an ocean view," "Pets allowed," and "Breakfast included" on the top screen. Step 2: The analysis unit uses AI to analyze the request entered by the reception unit. The analysis is performed using technologies such as natural language processing and data mining. For example, the analysis unit analyzes the user's request using natural language processing technology to understand the content of the request. It can also use data mining technology to extract information related to the user's request from past data. Step 3: The setting unit sets the filtering conditions based on the information analyzed by the analysis unit. The filtering conditions include price range, location, and rating. For example, the setting unit sets filtering conditions such as "room with ocean view," "pets allowed," and "breakfast included" based on the user's requests. The setting unit can also set the priority of the filtering conditions according to the user's requests. Step 4: The scoring unit scores the hotel information and reviews based on the conditions set by the setting unit. Scoring is performed using methods such as evaluation scores and weighting. For example, if there are many reviews of a hotel that say, "The rooms with ocean views are great," the hotel will be given a high score. It is also possible to score the degree to which the hotel matches the user's requirements based on the hotel information. Step 5: The providing unit provides the information scored by the scoring unit. The provision is performed by a method such as a web page, email, or app notification. For example, the scored hotel information can be displayed on a web page to allow the user to easily select a hotel. The scored information can also be provided to the user by email or app notification.

[0069] (Example 2) A travel search system according to an embodiment of the present invention dynamically provides personalized search criteria by having users input their preferences on a home screen. The travel search system allows users to input their preferences on a home screen, and AI analyzes the requests and dynamically provides optimal search criteria. Furthermore, the travel search system analyzes hotel information and reviews to generate a score based on how well a hotel matches the user's preferences. For example, a user may input preferences such as "a room with an ocean view is preferred," "pets allowed," and "breakfast included." This information is input to an AI, which analyzes the input. Next, the travel search system provides optimal search criteria to the user based on the information analyzed by the AI. For example, in response to a request for "a room with an ocean view is preferred," the system provides the search criteria "a room with an ocean view." Furthermore, the travel search system analyzes hotel information and reviews to generate a score based on how well a hotel matches the user's preferences. For example, if a hotel receives many reviews stating that the rooms have excellent ocean views, the hotel receives a high score. Based on these scores, the user can select a hotel. This allows the travel search system to dynamically provide narrowing conditions based on the user's requests, and analyze and score hotel information and reviews, thereby providing the user with the hotel that is most suitable for the user. This allows the travel search system to dynamically provide narrowing conditions based on the user's requests, and analyze and score hotel information and reviews, thereby providing the user with the hotel that is most suitable for the user. For example, a user can easily find a hotel that meets their needs. For example, in response to a request for "pets allowed," hotels that are pet-friendly are displayed with high scores, allowing the user to enjoy traveling with their pet. In addition, in response to a request for "breakfast included," hotels with highly rated breakfasts are displayed, allowing the user to enjoy a satisfying breakfast.

[0070] A travel search system according to an embodiment includes a reception unit, an analysis unit, a setting unit, a scoring unit, and a providing unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, the dates of stay, budget, and desired facilities. For example, the reception unit allows the user to input requests such as "prefer a room with an ocean view," "pets allowed," and "breakfast included" on the top screen. The analysis unit uses AI to analyze the requests input by the reception unit. The analysis is performed using, for example, but not limited to, techniques such as natural language processing and data mining. For example, the analysis unit analyzes the user's requests using natural language processing technology to understand the content of the requests. The analysis unit can also extract information related to the user's requests from past data using data mining technology. The setting unit sets filtering conditions based on the information analyzed by the analysis unit. The filtering conditions include, for example, but are not limited to, price range, location, and rating. For example, the setting unit sets filtering conditions such as "room with ocean view," "pets allowed," and "breakfast included" based on the user's requests. The setting unit can also set priorities for the filtering conditions according to the user's requests. The scoring unit scores hotel information and reviews based on the conditions set by the setting unit. Scoring can be performed using, for example, an evaluation score or weighting, but is not limited to these examples. For example, if a hotel receives many reviews stating "great rooms with ocean views" based on the hotel's reviews, the scoring unit can increase the score of the hotel. The scoring unit can also score the degree to which the hotel matches the user's requests based on the hotel information. The providing unit provides the information scored by the scoring unit. The provision can be performed using, for example, a webpage, email, app notification, or other methods, but is not limited to these examples. For example, the providing unit displays scored hotel information on a webpage, allowing the user to easily select a hotel. The providing unit can also provide the scored information to the user via email or app notification. As a result, the travel search system according to the embodiment can dynamically provide filtering conditions based on the user's requests, and by analyzing and scoring hotel information and reviews, can provide the user with the most suitable hotel.

[0071] The reception unit can estimate the user's emotions and adjust the timing of request input based on the estimated user emotions. For example, when the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input requests. This allows for more appropriate request input by adjusting the timing of request input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 reception unit may be performed using, for example, AI, or without AI.

[0072] The reception unit can analyze the user's past request history and select the optimal input method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used in a specific time period based on the user's past request history. In this way, the optimal input method can be provided by analyzing the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0073] When a request is input, the reception unit can filter the requests based on the user's current travel plans and areas of interest. The reception unit can, for example, preferentially display related requests based on the destination of a trip currently planned by the user. The reception unit can also filter related requests based on the user's areas of interest (e.g., beach resorts, mountainous areas, etc.). The reception unit can also suggest appropriate requests based on the duration of the user's travel plans. In this way, by filtering requests based on the user's current travel plans and areas of interest, more relevant requests can be provided. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI.

[0074] When a request is input, the reception unit can select the optimal input means depending on the user's input method. For example, when a user inputs a request by voice, the reception unit automatically converts the request into text using voice recognition technology. Furthermore, when a user inputs a request by text, the reception unit can provide an input completion function to enable the request to be input quickly. Furthermore, when a user inputs a request using an image, the reception unit can analyze the request using image recognition technology and propose appropriate filtering conditions. This allows the request to be input smoothly by providing the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The reception unit can estimate the user's emotions and determine the priority of requests to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prompts the user to input important requests first. Furthermore, when the user is relaxed, the reception unit can prompt the user to input detailed requests. Furthermore, when the user is in a hurry, the reception unit can display requests that can be input quickly first. In this way, by determining the priority of requests according to the user's emotions, important requests can be input first. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI.

[0076] When inputting a request, the reception unit can prioritize inputting highly relevant requests by taking into account the user's geographical location information. The reception unit can, for example, prioritize displaying related requests based on the user's current location. Furthermore, when the user inputs geographical location information of a travel destination, the reception unit can also prioritize displaying requests related to that location. Furthermore, the reception unit can also prioritize displaying requests related to locations close to the user's current location. In this way, by taking the user's geographical location information into account, highly relevant requests can be prioritized and input. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0077] When a request is input, the reception unit can analyze the user's social media activity and input related requests. For example, the reception unit can preferentially display requests related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related requests. The reception unit can also suggest related requests by referring to the activities of the user's friends on social media. In this way, related requests can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0078] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 may be performed using, for example, an AI, or may be performed without using an AI.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis on an important request. The analysis unit can also perform a simplified analysis on a less important request. The analysis unit can also determine the priority of the analysis according to the importance of the request. In this way, by adjusting the level of detail of the analysis based on the importance of the request, a detailed analysis can be performed on an important request. 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.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit applies an analysis algorithm dedicated to accommodations to requests related to accommodations. The analysis unit can also apply an analysis algorithm dedicated to tourist spots to requests related to tourist spots. The analysis unit can also apply an analysis algorithm dedicated to restaurants to requests related to restaurants. By applying different analysis algorithms depending on the category of request, 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.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also reflect feedback from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0083] 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 provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, using AI, or without AI.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the request. For example, the analysis unit prioritizes analysis of the most recently submitted request. The analysis unit can also perform a simplified analysis on requests that were submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the request, the most recent request can be analyzed preferentially. 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.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also perform a simplified analysis for less relevant requests. The analysis unit can also adjust the order of analysis based on the relevance of the requests. In this way, by adjusting the order of analysis based on the relevance of the requests, highly relevant requests can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0086] 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 uses detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without using AI.

[0087] The setting unit can estimate the user's emotions and adjust the method for setting the filtering conditions based on the estimated user emotions. For example, if the user is nervous, the setting unit can provide simple, highly visible filtering conditions. Furthermore, if the user is relaxed, the setting unit can provide detailed filtering conditions. Furthermore, if the user is in a hurry, the setting unit can provide filtering conditions that focus on the main points. This allows for adjusting the method for setting the filtering conditions according to the user's emotions, thereby providing more appropriate filtering conditions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI.

[0088] When setting the filtering conditions, the setting unit can adjust the level of detail of the setting based on the importance of the request. For example, the setting unit provides detailed filtering conditions for important requests. The setting unit can also provide simplified filtering conditions for requests with low importance. The setting unit can also determine the priority of the filtering conditions according to the importance of the request. In this way, by adjusting the level of detail of the setting based on the importance of the request, detailed filtering conditions can be provided for important requests. Some or all of the above-mentioned processing in the setting unit may be performed using AI, for example, or may be performed without using AI.

[0089] When setting the narrowing-down conditions, the setting unit can apply different setting algorithms depending on the category of the request. For example, the setting unit applies a setting algorithm dedicated to accommodations to a request regarding accommodations. Furthermore, the setting unit can also apply a setting algorithm dedicated to tourist spots to a request regarding tourist spots. Furthermore, the setting unit can also apply a setting algorithm dedicated to restaurants to a request regarding restaurants. In this way, by applying different setting algorithms depending on the category of the request, more accurate narrowing-down conditions can be provided. Some or all of the above-mentioned processing in the setting unit may be performed using AI, for example, or may be performed without using AI.

[0090] When setting the filtering conditions, the setting unit can improve the accuracy of the settings by referring to the user's past setting results. The setting unit, for example, optimizes the setting algorithm based on the user's past setting results. The setting unit can also reflect feedback to improve the accuracy of the settings from the user's past setting results. The setting unit can also adjust the level of detail of the settings by referring to the user's past setting results. In this way, the accuracy of the settings can be improved by referring to the user's past setting results. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using AI or may be performed without using AI.

[0091] The setting unit can estimate the user's emotions and determine the priority of the filtering conditions based on the estimated user emotions. For example, when the user is feeling stressed, the setting unit can prioritize important filtering conditions. Furthermore, when the user is relaxed, the setting unit can also set detailed filtering conditions. Furthermore, when the user is in a hurry, the setting unit can prioritize displaying filtering conditions that can be set quickly. In this way, by determining the priority of the filtering conditions according to the user's emotions, important filtering conditions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the setting unit may be performed, for example, using AI or without AI.

[0092] When setting the filtering conditions, the setting unit can determine the priority of the settings based on the time of submission of the requests. For example, the setting unit can prioritize the most recently submitted requests. The setting unit can also perform simplified settings for requests that were submitted earlier. The setting unit can also determine the priority of the settings based on the time of submission. In this way, by determining the priority of the settings based on the time of submission of the requests, the most recent requests can be prioritized. Some or all of the above-mentioned processing in the setting unit can be performed, for example, using AI or without using AI.

[0093] The setting unit can adjust the order of settings based on the relevance of requests when setting the filtering conditions. For example, the setting unit prioritizes setting highly relevant requests. The setting unit can also perform simplified settings for less relevant requests. The setting unit can also adjust the order of settings based on the relevance of requests. In this way, by adjusting the order of settings based on the relevance of requests, highly relevant requests can be prioritized. Some or all of the above-described processing in the setting unit may be performed using, for example, AI, or may be performed without using AI.

[0094] When setting the filtering conditions, the setting unit can adjust the use of technical terminology in the settings according to the user's level of expertise. For example, if the user has technical expertise, the setting unit uses detailed technical terminology. Furthermore, if the user does not have technical expertise, the setting unit can provide the setting results in simple language. Furthermore, the setting unit can adjust the use of technical terminology in the settings according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the settings according to the user's level of expertise, it is possible to provide filtering conditions that are easier to understand. Some or all of the above-described processing in the setting unit may be performed, for example, using AI or without using AI.

[0095] The scoring unit can estimate the user's emotions and adjust the scoring method based on the estimated user's emotions. For example, if the user is relaxed, the scoring unit can perform detailed scoring. Furthermore, if the user is in a hurry, the scoring unit can also perform simplified scoring. Furthermore, if the user is excited, the scoring unit can also perform scoring with a visually stimulating effect. This allows for more appropriate scoring by adjusting the scoring method according 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 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 scoring unit can be performed, for example, using AI, or without AI.

[0096] When scoring, the scoring unit can adjust the level of detail of the scoring based on the importance of the hotel information and reviews. For example, the scoring unit performs detailed scoring for important information and reviews. The scoring unit can also perform simplified scoring for less important information and reviews. The scoring unit can also determine the priority of the scoring based on the importance of the information and reviews. In this way, by adjusting the level of detail of the scoring based on the importance of the hotel information and reviews, detailed scoring can be performed for important information. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI.

[0097] The scoring unit can apply different scoring algorithms depending on the hotel category when scoring. For example, the scoring unit applies a scoring algorithm dedicated to luxury hotels to luxury hotels. The scoring unit can also apply a scoring algorithm dedicated to business hotels to business hotels. The scoring unit can also apply a scoring algorithm dedicated to resort hotels to resort hotels. This allows for more accurate scoring by applying different scoring algorithms depending on the hotel category. Some or all of the above-mentioned processing in the scoring unit may be performed using AI, for example, or may be performed without using AI.

[0098] The scoring unit can improve the accuracy of the scoring by referring to the user's past scoring results when scoring. The scoring unit, for example, optimizes the scoring algorithm based on the user's past scoring results. The scoring unit can also reflect feedback from the user's past scoring results to improve the accuracy of the scoring. The scoring unit can also adjust the level of detail of the scoring by referring to the user's past scoring results. In this way, the accuracy of the scoring can be improved by referring to the user's past scoring results. Some or all of the above-mentioned processing in the scoring unit may be performed, for example, using AI or without using AI.

[0099] The scoring unit can estimate the user's emotions and determine the priority of scoring based on the estimated user's emotions. For example, when the user is feeling stressed, the scoring unit can prioritize scoring important information and reviews. Furthermore, when the user is relaxed, the scoring unit can also prioritize scoring detailed information and reviews. Furthermore, when the user is in a hurry, the scoring unit can prioritize displaying information and reviews that can be scored quickly. Thus, by determining the priority of scoring according to the user's emotions, important information can be prioritized for scoring. 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 scoring unit can be performed, for example, using AI, or without AI.

[0100] When scoring, the scoring unit can determine the priority of scoring based on the time of submission of the hotel information and reviews. For example, the scoring unit prioritizes scoring of recently submitted information and reviews. The scoring unit can also perform simplified scoring for older submitted information and reviews. The scoring unit can also determine the priority of scoring based on the time of submission. In this way, by determining the priority of scoring based on the time of submission of the hotel information and reviews, the most recent information can be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI.

[0101] When scoring, the scoring unit can adjust the order of scoring based on the relevance of the hotel information and reviews. For example, the scoring unit prioritizes scoring of highly relevant information and reviews. The scoring unit can also perform a simplified scoring for less relevant information and reviews. The scoring unit can also adjust the order of scoring based on the relevance of the information and reviews. In this way, by adjusting the order of scoring based on the relevance of the hotel information and reviews, highly relevant information can be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The scoring unit can adjust the use of technical terms for scoring according to the user's level of expertise when scoring. For example, if the user has technical expertise, the scoring unit uses detailed technical terms. Furthermore, if the user does not have technical expertise, the scoring unit can provide a scoring result in simple terms. Furthermore, the scoring unit can adjust the use of technical terms for scoring according to the user's level of expertise. In this way, by adjusting the use of technical terms for scoring according to the user's level of expertise, it is possible to provide a scoring result that is easier to understand. Some or all of the above-mentioned processing in the scoring unit may be performed, for example, using AI or without using AI.

[0103] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide an information provision method that focuses on the main points. This allows for more appropriate information provision by adjusting the information provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0104] When providing information, the providing unit can select the optimal information providing method by referring to the user's past information providing history. The providing unit, for example, suggests the optimal information providing method based on the user's past information providing history. The providing unit can also reflect feedback to improve the accuracy of information provision from the user's past information providing history. The providing unit can also adjust the level of detail of information provision by referring to the user's past information providing history. In this way, the optimal information providing method can be provided by referring to the user's past information providing history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0105] The providing unit can customize the provided content based on the user's current travel plan when providing information. The providing unit, for example, prioritizes providing relevant information based on the user's current travel plan. The providing unit can also provide appropriate information based on the period of the user's travel plan. The providing unit can also provide relevant information based on the destination of the user's travel plan. In this way, by customizing the provided content based on the user's current travel plan, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0106] When providing information, the providing unit can provide optimal information by taking into account the user's current location information. The providing unit, for example, prioritizes providing relevant information based on the user's current location. The providing unit can also provide information related to locations close to the user's current location. The providing unit can also select an optimal information provision method based on the user's current location. This makes it possible to provide highly relevant information by taking into account the user's current location information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0107] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can provide important information preferentially. Furthermore, when the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the providing unit can preferentially display information that can be provided quickly. In this way, by determining the priority of information provision according to the user's emotions, important information can be preferentially provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0108] When providing information, the providing unit can provide optimal information by taking into account the user's geographical location information. For example, the providing unit provides information on related tourist spots and stores based on the user's current location. Furthermore, when the user inputs geographical location information of the user's travel destination, the providing unit can also provide information related to that location. Furthermore, the providing unit can preferentially provide information related to locations close to the user's current location. In this way, by taking into account the user's geographical location information, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When providing information, the providing unit can analyze the user's social media activity and provide related information. The providing unit can provide, for example, 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 information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI.

[0110] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The providing unit can, for example, suggest an optimal information providing method based on the user's past feedback. The providing unit can also reflect feedback for improving the accuracy of information provision from the user's past feedback. The providing unit can also adjust the level of detail of information provision by referring to the user's past feedback. In this way, the optimal information providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, setting unit, scoring unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and causes the user to input their requests on a top screen. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the requests using AI. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets filtering conditions based on the analysis results. The scoring unit is realized by the specific processing unit 290 of the data processing device 12 and scores hotel information and reviews. The providing unit is realized by the control unit 46A of the smart device 14 and provides the scored information to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, setting unit, scoring unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and causes the user's requests to be input on the top screen. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the requests using AI. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets filtering conditions based on the analysis results. The scoring unit is realized by the specific processing unit 290 of the data processing device 12 and scores hotel information and reviews. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides the scored information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, setting unit, scoring unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and causes the user's requests to be input on the top screen. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the requests using AI. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets filtering conditions based on the analysis results. The scoring unit is realized by the specific processing unit 290 of the data processing device 12 and scores hotel information and reviews. The providing unit is realized by the control unit 46A of the headset type terminal 314 and provides the scored information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, setting unit, scoring unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and causes the user to input their requests on the top screen. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the requests using AI. The setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets filtering conditions based on the analysis results. The scoring unit is realized by the specific processing unit 290 of the data processing device 12 and scores hotel information and reviews. The providing unit is realized by the control unit 46A of the robot 414 and provides the scored information to the user.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The reception unit can adjust the request input method taking into account the user's current health condition. For example, if the user is tired, simple options can be provided to allow the user to input their request quickly. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is ill, voice input or auto-completion functions can be prioritized to reduce the burden of input. This allows the user to smoothly input their request by providing the optimal input method according to their health condition.

[0113] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can be performed quickly and the results can be provided quickly. If the user is relaxed, the analysis can be performed in detail and deep insights can be provided. Furthermore, if the user is in a hurry, the analysis can be provided in a concise manner that focuses on the main points. In this way, by adjusting the timing of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0114] The setting unit can analyze the user's past travel history and suggest optimal filtering conditions. For example, it can automatically set relevant filtering conditions based on data on places and accommodations visited by the user in the past. It can also extract the characteristics of facilities that the user has given high ratings to and suggest filtering conditions based on these. It can also analyze the user's past travel patterns and predict filtering conditions suitable for the next trip. This makes it possible to provide more accurate filtering conditions by utilizing the user's past travel history.

[0115] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed scoring criteria can be applied to perform a detailed evaluation. If the user is in a hurry, simplified scoring criteria can be applied to perform a quick evaluation. Furthermore, if the user is excited, a visually appealing scoring result can be provided. This allows for more appropriate evaluation by adjusting the scoring criteria according to the user's emotions.

[0116] The providing unit can adjust the method of providing information taking into account the user's current device usage status. For example, if the user is using a smartphone, information can be provided using a mobile-friendly interface. If the user is using a personal computer, detailed information can be provided on a large screen. Furthermore, if the user is using a tablet, information can be provided using an interface optimized for touch operation. This allows the user to receive information smoothly by providing the optimal information providing method according to the user's device usage status.

[0117] The reception unit can estimate the user's emotions and determine the priority of requests to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input important requests with priority. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed requests with priority. Furthermore, if the user is in a hurry, the reception unit can also display requests that can be input quickly with priority. In this way, by determining the priority of requests according to the user's emotions, important requests can be input with priority.

[0118] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, a detailed analysis is performed for an important request. The analysis unit can also perform a simplified analysis for a less important request. The analysis unit can also determine the priority of the analysis according to the importance of the request. In this way, by adjusting the level of detail of the analysis based on the importance of the request, a detailed analysis can be performed for an important request.

[0119] 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 feeling stressed, important information is provided preferentially. The providing unit can also provide detailed information if the user is relaxed. The providing unit can also display information that can be provided quickly preferentially if the user is in a hurry. In this way, by determining the priority of information provision according to the user's emotions, important information can be provided preferentially.

[0120] When scoring, the scoring unit can determine the priority of scoring based on the time of submission of the hotel information and reviews. For example, the scoring unit can prioritize scoring of recently submitted information and reviews. The scoring unit can also perform simplified scoring for information and reviews that were submitted older. The scoring unit can also determine the priority of scoring based on the time of submission. In this way, by determining the priority of scoring based on the time of submission of the hotel information and reviews, the most recent information can be prioritized in scoring.

[0121] When providing information, the providing unit can analyze the user's social media activity and provide related 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 information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit inputs the user's requests. The user's requests include the date of stay, budget, desired facilities, etc. For example, the user can input requests such as "I would like a room with an ocean view," "Pets allowed," and "Breakfast included" on the top screen. Step 2: The analysis unit uses AI to analyze the request entered by the reception unit. The analysis is performed using technologies such as natural language processing and data mining. For example, the analysis unit analyzes the user's request using natural language processing technology to understand the content of the request. It can also use data mining technology to extract information related to the user's request from past data. Step 3: The setting unit sets the filtering conditions based on the information analyzed by the analysis unit. The filtering conditions include price range, location, and rating. For example, the setting unit sets filtering conditions such as "room with ocean view," "pets allowed," and "breakfast included" based on the user's requests. The setting unit can also set the priority of the filtering conditions according to the user's requests. Step 4: The scoring unit scores the hotel information and reviews based on the conditions set by the setting unit. Scoring is performed using methods such as evaluation scores and weighting. For example, if there are many reviews of a hotel that say, "The rooms with ocean views are great," the hotel will be given a high score. It is also possible to score the degree to which the hotel matches the user's requirements based on the hotel information. Step 5: The providing unit provides the information scored by the scoring unit. The provision is performed by a method such as a web page, email, or app notification. For example, the scored hotel information can be displayed on a web page to allow the user to easily select a hotel. The scored information can also be provided to the user by email or app notification.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's request; an analysis unit that analyzes the request input by the reception unit; a setting unit that sets narrowing conditions based on the information analyzed by the analysis unit; a scoring unit that scores hotel information and reviews based on the conditions set by the setting unit; a providing unit that provides the information scored by the scoring unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of request input based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit Filter requests based on the user's current travel plans and interests as they are entered 2. The system of claim 1.

5. The reception unit When inputting a request, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the requests to be input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering requests, consider the user's geographic location to prioritize relevant requests.

2. The system of claim 1.

8. The reception unit When you submit a request, analyze your social media activity and submit relevant requests 2. The system of claim 1.

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