system

Abstract

The system according to the embodiment aims to enable users to efficiently acquire word-of-mouth information. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit inputs desired conditions. The analysis unit analyzes word-of-mouth reviews based on the information received by the receiving unit. The generation unit generates information based on the information analyzed by the analysis unit. The provision unit provides the information generated by the generation 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 requires users to read each and every word-of-mouth review to obtain information, which makes it difficult to obtain information efficiently.

[0005] The system according to the embodiment aims to enable users to efficiently acquire word-of-mouth information. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs desired conditions. The analyzing unit analyzes the word-of-mouth based on the information received by the receiving unit. The generating unit generates information based on the information analyzed by the analyzing unit. The providing unit provides the information generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to efficiently obtain word-of-mouth information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A search system according to an embodiment of the present invention uses a generation AI to respond to a user's desired conditions based on reviews stored in a company database. The search system allows users to input their desired conditions, and the generation AI analyzes the reviews to generate and provide information to the user. This mechanism eliminates various drawbacks currently associated with users when browsing reviews. For example, if a user inputs specific conditions such as "a quiet cafe with Wi-Fi," the generation AI analyzes the reviews in the company database and generates and provides relevant information. This allows users to obtain information efficiently, eliminating the need to read each review individually. The search system analyzes reviews based on the user's desired conditions and generates and provides information, improving user convenience. For example, this eliminates problems such as the overwhelming volume of reviews and the difficulty of comparing them due to the inconsistent content of the reviews. Furthermore, the generation AI understands the content of the reviews and provides information that matches the user's desired conditions, allowing users to obtain more accurate information. Furthermore, this search system has the potential to replace current search engines in the future.

[0029] A search system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives a user's input of desired conditions. The user may input the desired conditions using, for example, text input, voice input, or image input. The receiving unit receives a user's input of specific conditions, such as "a quiet cafe with Wi-Fi." The analysis unit analyzes the reviews based on the information received by the receiving unit. The analysis unit may analyze the content of the reviews using, for example, text mining technology, and extract information that matches the user's desired conditions. The analysis unit may also analyze the emotional nuances of the reviews using sentiment analysis technology. The generation unit generates information based on the information analyzed by the analysis unit. The generation unit may summarize the content of the reviews using, for example, a generation AI, and generate information that matches the user's desired conditions. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which understands the content of the reviews and generates appropriate information. The provision unit provides the information generated by the generation unit to the user. The providing unit can provide information by, for example, a web page, email, notification, etc. As a result, the search system according to the embodiment analyzes reviews based on the user's desired conditions and generates and provides information, thereby improving user convenience.

[0030] The search system is equipped with an understanding unit that understands the content of reviews. The understanding unit understands the content of reviews. The understanding unit analyzes the content of reviews using, for example, natural language processing technology and extracts important information. For example, the understanding unit uses keyword extraction technology to pick out particularly important information from reviews. The understanding unit can also understand the emotional nuances of reviews using sentiment analysis technology. For example, the understanding unit identifies positive and negative emotions in reviews and understands the content of the reviews based on that information. This allows for understanding the content of reviews and providing more accurate information.

[0031] The search system includes an extraction unit that extracts information that matches the user's desired conditions. The extraction unit extracts information that matches the user's desired conditions. For example, the extraction unit uses text mining technology to analyze the content of reviews and extract information that matches the user's desired conditions. For example, if a user enters a condition such as "a quiet cafe with Wi-Fi," the extraction unit extracts reviews that match the condition. The extraction unit can also analyze the emotional nuances of reviews using sentiment analysis technology to extract information that matches the user's desired conditions. For example, the extraction unit preferentially extracts reviews that express positive emotions. This improves user convenience by extracting information that matches the user's desired conditions.

[0032] The reception unit can analyze the user's past search history and present appropriate input candidates. The reception unit analyzes the user's past search history. For example, the reception unit performs a frequency analysis of search keywords and automatically displays related input candidates based on the user's past search conditions. The reception unit can also analyze click history and preferentially suggest search keywords that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, if the user previously searched for "cafe" during a specific time period, the reception unit will preferentially suggest "cafe" during that time period. This reduces the user's input effort by presenting optimal input candidates based on the user's past search history.

[0033] The reception unit can provide input assistance based on the user's current situation and areas of interest when inputting desired conditions. The reception unit provides input assistance taking the user's current situation and areas of interest into consideration. For example, the reception unit suggests related conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are preferentially displayed. The reception unit can also preferentially display related conditions based on the user's areas of interest. For example, if the user has frequently searched for "cafe" in the past, the reception unit will preferentially display conditions related to "cafe." Furthermore, the reception unit can also suggest optimal conditions based on the user's current situation (time of day, weather, etc.). For example, if the user is searching at night, stores that are open at night are preferentially suggested. In this way, input assistance can be provided based on the user's current situation and areas of interest, thereby suggesting more appropriate conditions.

[0034] When a user inputs desired conditions, the reception unit can select an appropriate input means according to the user's input method. The reception unit selects the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the desired conditions using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the desired conditions using image recognition technology. For example, if a user uploads a photo of a cafe, the reception unit analyzes the photo and suggests related conditions. This allows for improved input convenience by selecting the optimal input means according to the user's input method.

[0035] When inputting desired conditions, the reception unit can prioritize input of highly relevant conditions taking into account the user's geographical location information. The reception unit provides input support taking into account the user's geographical location information. For example, the reception unit prioritizes displaying relevant conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are prioritized. The reception unit can also suggest optimal conditions based on the user's geographical location information. For example, if the user is in a specific area, conditions related to that area are prioritized. In this way, more relevant conditions can be suggested by taking the user's geographical location information into account.

[0036] When the user inputs desired conditions, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit analyzes the user's social media activity. For example, the reception unit can suggest related conditions based on the locations 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 conditions. Furthermore, the reception unit can suggest related conditions by taking into account the activity of the user's friends on social media. For example, if the user's friends have checked in at a specific cafe, the reception unit can suggest conditions related to that cafe. In this way, by analyzing the user's social media activity, more relevant conditions can be suggested.

[0037] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired conditions. The reception unit reflects the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input method based on the user's past feedback. For example, if the user previously provided feedback that "the input form is complicated," the reception unit provides a simple input form. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, if the user provides feedback that "voice input is convenient," the reception unit will preferentially suggest voice input. In this way, a more appropriate input method can be provided by reflecting the user's past feedback.

[0038] When analyzing reviews, the analysis unit can adjust the level of detail of the analysis based on the importance of the review. The analysis unit adjusts the level of detail of the analysis based on the importance of the review. For example, the analysis unit performs a detailed analysis on reviews with high importance. It can also perform a concise analysis on reviews with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the review. For example, it prioritizes the analysis of reviews with high evaluation scores. In this way, adjusting the level of detail of the analysis based on the importance of the review enables efficient analysis.

[0039] When analyzing reviews, the analysis unit can apply different analysis algorithms depending on the category of the review. The analysis unit applies different analysis algorithms depending on the category of the review. For example, the analysis unit applies an analysis algorithm related to the quality of food and service to restaurant reviews. It can also apply an analysis algorithm related to the cleanliness of the room and the attitude of the staff to hotel reviews. It can also apply an analysis algorithm related to the scenery and access to reviews of tourist destinations. In this way, by applying an analysis algorithm depending on the category of the review, it is possible to provide more accurate analysis results.

[0040] When analyzing reviews, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit refers to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the analysis results performed by the user in the past. The analysis unit can also select the optimal analysis algorithm from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, if the user requested a detailed analysis in the past, the analysis unit will perform a detailed analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0041] When analyzing reviews, the analysis unit can determine the priority of analysis based on when the reviews were submitted. The analysis unit determines the priority of analysis based on when the reviews were submitted. For example, the analysis unit prioritizes analyzing the most recent reviews. It is also possible to analyze older reviews later. Furthermore, the analysis unit can adjust the priority of analysis based on when the reviews were submitted. For example, it prioritizes analyzing reviews that were submitted recently. In this way, efficient analysis is possible by determining the priority of analysis based on when the reviews were submitted.

[0042] When analyzing reviews, the analysis unit can adjust the order of analysis based on the relevance of the reviews. The analysis unit adjusts the order of analysis based on the relevance of the reviews. For example, the analysis unit prioritizes analysis of highly relevant reviews. It can also postpone analysis of less relevant reviews. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the reviews. For example, it prioritizes analysis of reviews that include common keywords. In this way, adjusting the order of analysis based on the relevance of the reviews enables efficient analysis.

[0043] When analyzing reviews, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. On the other hand, if the user does not have technical knowledge, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user is not familiar with technical terms, the analysis unit provides simplified technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate analysis results can be provided.

[0044] When generating information, the generation unit can adjust the level of detail of the generated information based on the importance of the review. The generation unit adjusts the level of detail of the generated information based on the importance of the review. For example, the generation unit generates detailed information based on reviews with high importance. It can also generate concise information based on reviews with low importance. Furthermore, the generation unit can adjust the level of detail of the information to be generated according to the importance of the review. For example, it generates detailed information based on reviews with high evaluation scores. In this way, by adjusting the level of detail of the generated information based on the importance of the review, efficient information generation is possible.

[0045] When generating information, the generation unit can apply different generation algorithms depending on the category of the review. The generation unit applies different generation algorithms depending on the category of the review. For example, the generation unit can generate information about the quality of food and service based on restaurant reviews. It can also generate information about the cleanliness of rooms and the responsiveness of staff based on hotel reviews. It can also generate information about scenery and access based on reviews of tourist attractions. In this way, by applying a generation algorithm depending on the category of the review, it is possible to provide more accurate information.

[0046] When generating information, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit refers to the user's past generation results. For example, the generation unit improves the accuracy of generation based on information generated by the user in the past. The generation unit can also select the optimal generation algorithm from the user's past generation results. Furthermore, the generation unit can adjust the level of detail of the information to be generated by referring to the user's past generation results. For example, if the user requested detailed information in the past, the generation unit generates detailed information. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0047] When generating information, the generation unit can determine the generation priority based on the time when the reviews were submitted. The generation unit determines the generation priority based on the time when the reviews were submitted. For example, the generation unit generates information preferentially based on the most recent reviews. It can also generate information later based on older reviews. Furthermore, the generation unit can adjust the generation priority based on the time when the reviews were submitted. For example, it generates information based on reviews that have a recent submission date. In this way, efficient information generation is possible by determining the generation priority based on the time when the reviews were submitted.

[0048] When generating information, the generation unit can adjust the order of generation based on the relevance of the reviews. The generation unit adjusts the order of generation based on the relevance of the reviews. For example, the generation unit generates information preferentially based on highly relevant reviews. It can also generate information later based on less relevant reviews. Furthermore, the generation unit can adjust the order of generation according to the relevance of the reviews. For example, information is generated based on reviews that include common keywords. In this way, adjusting the order of generation based on the relevance of the reviews enables efficient information generation.

[0049] When generating information, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate information that uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate concise and easy-to-understand information. Furthermore, the generation unit can adjust the expression method of the information to be generated according to the user's level of expertise. For example, if the user is not familiar with technical terminology, the generation unit provides simplified technical terminology. In this way, more appropriate information can be provided by adjusting the use of technical terminology according to the user's level of expertise.

[0050] When providing information, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit refers to the user's past operation history. For example, the providing unit analyzes click history and browsing history and suggests an optimal display method based on display methods used by the user in the past. The providing unit can also select an optimal display method from the user's past operation history. Furthermore, the providing unit can customize the display method by referring to the user's past operation history. For example, if the user has preferred list view in the past, the providing unit will preferentially suggest list view. In this way, a more appropriate display method can be provided by referring to the user's past operation history.

[0051] The providing unit can customize the display content according to the user's current task when providing information. The providing unit customizes the display content according to the user's current task. For example, the providing unit preferentially displays information related to the task the user is currently performing. The providing unit can also customize the display content according to the user's current task. Furthermore, when the user is performing a specific task, the providing unit can also preferentially display information related to the task. For example, when the user is looking for a cafe, the providing unit preferentially displays information related to the cafe. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0052] The providing unit can improve the display method by reflecting user feedback when providing information. The providing unit reflects user feedback. For example, the providing unit improves the display method based on feedback provided by the user. The providing unit can also select the optimal display method from the user feedback. Furthermore, the providing unit can customize the display method by reflecting user feedback. For example, if the user gives feedback that "the display is difficult to see," the providing unit provides a display method with high visibility. In this way, a more appropriate display method can be provided by reflecting user feedback.

[0053] When providing information, the providing unit can select the optimal display method by taking into consideration the user's device information. The providing unit selects the display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible display method. In this way, a more appropriate display method can be provided by taking into consideration the user's device information.

[0054] When providing information, the providing unit can make the display content multilingual according to the user's language setting. The providing unit makes the display content multilingual according to the user's language setting. For example, the providing unit automatically sets the display content based on the language setting of the user's device. In addition, if the user uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, more appropriate information can be provided.

[0055] When providing information, the providing unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The providing unit provides information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying information relevant to the user's current location. The providing unit can also suggest optimal information based on the user's geographical location information. Furthermore, when the user is in a specific area, the providing unit can prioritize displaying information related to that area. For example, when the user is in a specific cafe, the providing unit prioritizes displaying information related to that cafe. In this way, more relevant information can be provided by taking into account the user's geographical location information.

[0056] When understanding a review, the understanding unit can adjust the level of detail of the understanding based on the importance of the review. The understanding unit adjusts the level of detail of the understanding based on the importance of the review. For example, the understanding unit performs a detailed understanding of a review with a high importance. It can also perform a concise understanding of a review with a low importance. Furthermore, the understanding unit can determine the priority of the understanding according to the importance of the review. For example, it prioritizes understanding of reviews with a high evaluation score. In this way, by adjusting the level of detail of the understanding based on the importance of the review, efficient understanding is possible.

[0057] When understanding reviews, the understanding unit can apply different understanding algorithms depending on the category of the review. The understanding unit applies different understanding algorithms depending on the category of the review. For example, the understanding unit applies understanding algorithms related to the quality of food and service to restaurant reviews. It can also apply understanding algorithms related to the cleanliness of rooms and the responsiveness of staff to hotel reviews. It can also apply understanding algorithms related to scenery and access to reviews of tourist destinations. In this way, by applying understanding algorithms according to the category of the review, it is possible to provide more accurate understanding results.

[0058] When understanding reviews, the understanding unit can improve the accuracy of the understanding by referring to the user's past understanding results. The understanding unit refers to the user's past understanding results. For example, the understanding unit improves the accuracy of the understanding based on the user's past understanding results. The understanding unit can also select the optimal understanding algorithm from the user's past understanding results. Furthermore, the understanding unit can adjust the level of detail of the understanding by referring to the user's past understanding results. For example, if the user has requested a detailed understanding in the past, the understanding unit will perform a detailed understanding. In this way, the accuracy of the understanding is improved by referring to the user's past understanding results.

[0059] When understanding reviews, the understanding unit can determine the priority of understanding based on the time of submission of the reviews. The understanding unit determines the priority of understanding based on the time of submission of the reviews. For example, the understanding unit prioritizes understanding the most recent reviews. It is also possible to postpone understanding older reviews. Furthermore, the understanding unit can adjust the priority of understanding based on the time of submission of the reviews. For example, it prioritizes understanding reviews that were submitted recently. In this way, efficient understanding is possible by determining the priority of understanding based on the time of submission of the reviews.

[0060] When understanding the reviews, the understanding unit can adjust the order of understanding based on the relevance of the reviews. The understanding unit adjusts the order of understanding based on the relevance of the reviews. For example, the understanding unit prioritizes understanding of highly relevant reviews. It can also postpone understanding of less relevant reviews. Furthermore, the understanding unit can adjust the order of understanding based on the relevance of the reviews. For example, it prioritizes understanding of reviews that include common keywords. This allows for efficient understanding by adjusting the order of understanding based on the relevance of the reviews.

[0061] When understanding reviews, the understanding unit can adjust the use of technical terms in the understanding according to the user's level of expertise. The understanding unit adjusts the use of technical terms in the understanding according to the user's level of expertise. For example, if the user has technical knowledge, the understanding unit provides an understanding result that makes heavy use of technical terms. Also, if the user does not have technical knowledge, the understanding unit can provide a concise and easy-to-understand understanding result. Furthermore, the understanding unit can adjust the way the understanding result is expressed according to the user's level of expertise. For example, if the user is not familiar with technical terms, the understanding unit provides simplified technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate understanding results can be provided.

[0062] When extracting information, the extraction unit can improve the accuracy of extraction by taking into account the interrelationships between reviews. The extraction unit improves the accuracy of extraction by taking into account the interrelationships between reviews. For example, the extraction unit analyzes the relevance between reviews and preferentially extracts related information. The extraction unit can also extract important information based on the interrelationships between reviews. Furthermore, the extraction unit can improve the accuracy of information extracted by taking into account the interrelationships between reviews. For example, information is extracted based on reviews that include common keywords. In this way, the accuracy of extraction is improved by taking into account the interrelationships between reviews.

[0063] When extracting information, the extraction unit can perform the extraction while taking into consideration the attribute information of the review submitter. The extraction unit performs the extraction while taking into consideration the attribute information of the review submitter. For example, the extraction unit extracts related information based on the age and gender of the review submitter. The extraction unit can also extract related information based on the occupation and hobbies of the review submitter. Furthermore, the extraction unit can improve the accuracy of the extracted information by taking into consideration the attribute information of the review submitter. For example, information is extracted based on reviews related to a specific age group. In this way, more appropriate information can be provided by taking into consideration the attribute information of the review submitter.

[0064] When extracting information, the extraction unit can weight the extraction based on the frequency of reviews submitted. The extraction unit weights the extraction based on the frequency of reviews submitted. For example, the extraction unit prioritizes extracting reviews that are submitted more frequently. It is also possible to extract reviews that are submitted less frequently later. Furthermore, the extraction unit can weight the information to be extracted according to the frequency of reviews submitted. For example, information is extracted based on frequently submitted reviews. In this way, by weighting the extraction based on the frequency of reviews submitted, more appropriate information can be provided.

[0065] When extracting information, the extraction unit can perform the extraction taking into account the geographical distribution of reviews. The extraction unit performs the extraction taking into account the geographical distribution of reviews. For example, the extraction unit extracts related information based on the geographical distribution of reviews. The extraction unit can also preferentially extract geographically close reviews. Furthermore, the extraction unit can improve the accuracy of the extracted information by taking into account the geographical distribution of reviews. For example, the extraction unit extracts information based on reviews related to a specific region. In this way, more appropriate information can be provided by taking into account the geographical distribution of reviews.

[0066] When extracting information, the extraction unit can improve the accuracy of extraction by referring to literature related to reviews. The extraction unit improves the accuracy of extraction by referring to literature related to reviews. For example, the extraction unit extracts important information based on literature related to reviews. The extraction unit can also improve the accuracy of the extracted information by referring to related literature. Furthermore, the extraction unit can weight the information to be extracted by taking into account literature related to reviews. For example, the reliability of reviews is evaluated based on related literature and important information is extracted. In this way, by referring to literature related to reviews, the accuracy of extraction is improved.

[0067] The extraction unit can extract information while taking into consideration the market value of the word-of-mouth. The extraction unit extracts information while taking into consideration the market value of the word-of-mouth. For example, the extraction unit can prioritize extracting word-of-mouth with high market value. It can also extract word-of-mouth with low market value later. Furthermore, the extraction unit can weight the information to be extracted according to the market value of the word-of-mouth. For example, the market value of the word-of-mouth is evaluated based on sales data or market share, and important information is extracted. In this way, by taking into consideration the market value of the word-of-mouth, more appropriate information can be provided.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The reception unit can analyze the user's past search history and automatically suggest search conditions that the user frequently searches for. For example, if the user frequently searches for "quiet cafes" in the past, the reception unit will preferentially suggest "quiet cafes." Also, if the user tends to search for specific conditions during a specific time of day, the reception unit can suggest conditions related to that time of day. Furthermore, the reception unit can learn the user's preferences from the user's past search history and make more personalized suggestions. This allows for more efficient searches by utilizing the user's past search history.

[0070] The understanding unit can evaluate the reliability of reviews when understanding the content of reviews. For example, the understanding unit analyzes the past posting history of the review poster and prioritizes understanding reviews from highly reliable posters. It can also check whether the content of a review matches other reviews and determine that reviews with a high degree of match are highly reliable. Furthermore, the understanding unit can take into account the posting date and time of the review and prioritize understanding the most recent reviews. This allows for more accurate information to be provided by evaluating the reliability of reviews.

[0071] The extraction unit can extract information taking into account the user's current geographic location information. For example, based on the user's current location, reviews of nearby cafes and restaurants can be preferentially extracted. Also, if the user is in a specific area, reviews related to that area can be preferentially extracted. Furthermore, the extraction unit can also take into account the user's movement history and extract reviews related to places visited in the past. This makes it possible to provide more relevant information by utilizing the user's current geographic location information.

[0072] The reception unit can analyze the user's past search history and present appropriate input candidates. For example, the reception unit can analyze the frequency of search keywords and automatically display related input candidates based on the user's past search conditions. The reception unit can also analyze click history and preferentially suggest search keywords that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, if the user previously searched for "cafe" during a specific time period, the reception unit will preferentially suggest "cafe" during that time period. This reduces the user's input effort by presenting optimal input candidates based on the user's past search history.

[0073] The reception unit can provide input assistance based on the user's current situation and areas of interest when inputting desired conditions. For example, the reception unit suggests related conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are preferentially displayed. The reception unit can also preferentially display related conditions based on the user's areas of interest. For example, if the user has frequently searched for "cafe" in the past, the reception unit will preferentially display conditions related to "cafe." Furthermore, the reception unit can also suggest optimal conditions based on the user's current situation (time of day, weather, etc.). For example, if the user is searching at night, stores that are open at night are preferentially suggested. In this way, input assistance based on the user's current situation and areas of interest can suggest more appropriate conditions.

[0074] When the user inputs desired conditions, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the desired conditions using voice recognition technology. If the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the desired conditions using image recognition technology. For example, if the user uploads a photo of a cafe, the reception unit analyzes the photo and suggests related conditions. This allows the user to select the optimal input means according to the user's input method, thereby improving input convenience.

[0075] When inputting desired conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying relevant conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes can be prioritized. The reception unit can also suggest optimal conditions based on the user's geographical location information. For example, if the user is in a specific area, conditions related to that area can be prioritized. In this way, more relevant conditions can be suggested by taking into account the user's geographical location information.

[0076] When the user inputs desired conditions, the reception unit can analyze the user's social media activity and suggest related conditions. For example, the reception unit can suggest related conditions based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. Furthermore, the reception unit can suggest related conditions by taking into account the activity of the user's friends on social media. For example, if the user's friends have checked in at a specific cafe, the reception unit can suggest conditions related to that cafe. In this way, by analyzing the user's social media activity, more relevant conditions can be suggested.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives the user's desired conditions. The user can input the desired conditions using methods such as text input, voice input, and image input. For example, the reception unit receives the user's input of specific conditions such as "a quiet cafe with Wi-Fi." Step 2: The analysis unit analyzes the reviews based on the information received by the reception unit. The analysis unit uses text mining technology to analyze the content of the reviews and extract information that matches the user's desired conditions. It can also analyze the emotional nuances of the reviews using sentiment analysis technology. Step 3: The generation unit generates information based on the information analyzed by the analysis unit. The generation unit uses a generation AI to summarize the content of the reviews and generate information that meets the user's desired conditions. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which understands the content of the reviews and generates appropriate information. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit can provide the information by a method such as a web page, email, or notification.

[0079] (Example 2) A search system according to an embodiment of the present invention uses a generation AI to respond to a user's desired conditions based on reviews stored in a company database. The search system allows users to input their desired conditions, and the generation AI analyzes the reviews to generate and provide information to the user. This mechanism eliminates various drawbacks currently associated with users when browsing reviews. For example, if a user inputs specific conditions such as "a quiet cafe with Wi-Fi," the generation AI analyzes the reviews in the company database and generates and provides relevant information. This allows users to obtain information efficiently, eliminating the need to read each review individually. The search system analyzes reviews based on the user's desired conditions and generates and provides information, improving user convenience. For example, this eliminates problems such as the overwhelming volume of reviews and the difficulty of comparing them due to the inconsistent content of the reviews. Furthermore, the generation AI understands the content of the reviews and provides information that matches the user's desired conditions, allowing users to obtain more accurate information. Furthermore, this search system has the potential to replace current search engines in the future.

[0080] A search system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives a user's input of desired conditions. The user may input the desired conditions using, for example, text input, voice input, or image input. The receiving unit receives a user's input of specific conditions, such as "a quiet cafe with Wi-Fi." The analysis unit analyzes the reviews based on the information received by the receiving unit. The analysis unit may analyze the content of the reviews using, for example, text mining technology, and extract information that matches the user's desired conditions. The analysis unit may also analyze the emotional nuances of the reviews using sentiment analysis technology. The generation unit generates information based on the information analyzed by the analysis unit. The generation unit may summarize the content of the reviews using, for example, a generation AI, and generate information that matches the user's desired conditions. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which understands the content of the reviews and generates appropriate information. The provision unit provides the information generated by the generation unit to the user. The providing unit can provide information by, for example, a web page, email, notification, etc. As a result, the search system according to the embodiment analyzes reviews based on the user's desired conditions and generates and provides information, thereby improving user convenience.

[0081] The search system is equipped with an understanding unit that understands the content of reviews. The understanding unit understands the content of reviews. The understanding unit analyzes the content of reviews using, for example, natural language processing technology and extracts important information. For example, the understanding unit uses keyword extraction technology to pick out particularly important information from reviews. The understanding unit can also understand the emotional nuances of reviews using sentiment analysis technology. For example, the understanding unit identifies positive and negative emotions in reviews and understands the content of the reviews based on that information. This allows for understanding the content of reviews and providing more accurate information.

[0082] The search system includes an extraction unit that extracts information that matches the user's desired conditions. The extraction unit extracts information that matches the user's desired conditions. For example, the extraction unit uses text mining technology to analyze the content of reviews and extract information that matches the user's desired conditions. For example, if a user enters a condition such as "a quiet cafe with Wi-Fi," the extraction unit extracts reviews that match the condition. The extraction unit can also analyze the emotional nuances of reviews using sentiment analysis technology to extract information that matches the user's desired conditions. For example, the extraction unit preferentially extracts reviews that express positive emotions. This improves user convenience by extracting information that matches the user's desired conditions.

[0083] The reception unit can estimate the user's emotions and change the input method for desired conditions based on the estimated user emotions. The reception unit estimates the user's emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate the user's emotions. The reception unit can also estimate the user's emotions from the user's input using text analysis technology. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input the desired conditions. This makes it possible to reduce the user's stress by adjusting the input method according to the user's emotions.

[0084] The reception unit can analyze the user's past search history and present appropriate input candidates. The reception unit analyzes the user's past search history. For example, the reception unit performs a frequency analysis of search keywords and automatically displays related input candidates based on the user's past search conditions. The reception unit can also analyze click history and preferentially suggest search keywords that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, if the user previously searched for "cafe" during a specific time period, the reception unit will preferentially suggest "cafe" during that time period. This reduces the user's input effort by presenting optimal input candidates based on the user's past search history.

[0085] The reception unit can provide input assistance based on the user's current situation and areas of interest when inputting desired conditions. The reception unit provides input assistance taking the user's current situation and areas of interest into consideration. For example, the reception unit suggests related conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are preferentially displayed. The reception unit can also preferentially display related conditions based on the user's areas of interest. For example, if the user has frequently searched for "cafe" in the past, the reception unit will preferentially display conditions related to "cafe." Furthermore, the reception unit can also suggest optimal conditions based on the user's current situation (time of day, weather, etc.). For example, if the user is searching at night, stores that are open at night are preferentially suggested. In this way, input assistance can be provided based on the user's current situation and areas of interest, thereby suggesting more appropriate conditions.

[0086] When a user inputs desired conditions, the reception unit can select an appropriate input means according to the user's input method. The reception unit selects the optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the desired conditions using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the desired conditions using image recognition technology. For example, if a user uploads a photo of a cafe, the reception unit analyzes the photo and suggests related conditions. This allows for improved input convenience by selecting the optimal input means according to the user's input method.

[0087] The reception unit can estimate the user's emotions and determine the priority of desired conditions based on the estimated user emotions. The reception unit estimates the user's emotions. For example, the reception unit analyzes the user's facial expressions using facial expression recognition technology to estimate the emotions. The reception unit can also estimate the emotions from the user's input content using text analysis technology. For example, if the user is feeling stressed, the reception unit can prioritize displaying simple and important conditions. Also, if the user is relaxed, the reception unit can prioritize displaying detailed conditions. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying conditions that can be entered quickly. In this way, by determining the priority of desired conditions based on the user's emotions, more appropriate information can be provided.

[0088] When inputting desired conditions, the reception unit can prioritize input of highly relevant conditions taking into account the user's geographical location information. The reception unit provides input support taking into account the user's geographical location information. For example, the reception unit prioritizes displaying relevant conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are prioritized. The reception unit can also suggest optimal conditions based on the user's geographical location information. For example, if the user is in a specific area, conditions related to that area are prioritized. In this way, more relevant conditions can be suggested by taking the user's geographical location information into account.

[0089] When the user inputs desired conditions, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit analyzes the user's social media activity. For example, the reception unit can suggest related conditions based on the locations 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 conditions. Furthermore, the reception unit can suggest related conditions by taking into account the activity of the user's friends on social media. For example, if the user's friends have checked in at a specific cafe, the reception unit can suggest conditions related to that cafe. In this way, by analyzing the user's social media activity, more relevant conditions can be suggested.

[0090] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired conditions. The reception unit reflects the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input method based on the user's past feedback. For example, if the user previously provided feedback that "the input form is complicated," the reception unit provides a simple input form. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, if the user provides feedback that "voice input is convenient," the reception unit will preferentially suggest voice input. In this way, a more appropriate input method can be provided by reflecting the user's past feedback.

[0091] The analysis unit can estimate the user's emotions and change the review analysis method based on the estimated user's emotions. The analysis unit estimates the user's emotions. For example, the analysis unit uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The analysis unit can also estimate the emotions from the user's input using text analysis technology. For example, if the user is relaxed, the analysis unit performs a detailed analysis. If the user is in a hurry, the analysis unit can also perform a concise analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This makes it possible to provide more appropriate analysis results by adjusting the analysis method based on the user's emotions.

[0092] When analyzing reviews, the analysis unit can adjust the level of detail of the analysis based on the importance of the review. The analysis unit adjusts the level of detail of the analysis based on the importance of the review. For example, the analysis unit performs a detailed analysis on reviews with high importance. It can also perform a concise analysis on reviews with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the review. For example, it prioritizes the analysis of reviews with high evaluation scores. In this way, adjusting the level of detail of the analysis based on the importance of the review enables efficient analysis.

[0093] When analyzing reviews, the analysis unit can apply different analysis algorithms depending on the category of the review. The analysis unit applies different analysis algorithms depending on the category of the review. For example, the analysis unit applies an analysis algorithm related to the quality of food and service to restaurant reviews. It can also apply an analysis algorithm related to the cleanliness of the room and the attitude of the staff to hotel reviews. It can also apply an analysis algorithm related to the scenery and access to reviews of tourist destinations. In this way, by applying an analysis algorithm depending on the category of the review, it is possible to provide more accurate analysis results.

[0094] When analyzing reviews, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit refers to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the analysis results performed by the user in the past. The analysis unit can also select the optimal analysis algorithm from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, if the user requested a detailed analysis in the past, the analysis unit will perform a detailed analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0095] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user's emotions. The analysis unit estimates the user's emotions. For example, the analysis unit uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The analysis unit can also estimate the emotions from the user's input content using text analysis technology. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method based on the user's emotions, more appropriate analysis results can be provided.

[0096] When analyzing reviews, the analysis unit can determine the priority of analysis based on when the reviews were submitted. The analysis unit determines the priority of analysis based on when the reviews were submitted. For example, the analysis unit prioritizes analyzing the most recent reviews. It is also possible to analyze older reviews later. Furthermore, the analysis unit can adjust the priority of analysis based on when the reviews were submitted. For example, it prioritizes analyzing reviews that were submitted recently. In this way, efficient analysis is possible by determining the priority of analysis based on when the reviews were submitted.

[0097] When analyzing reviews, the analysis unit can adjust the order of analysis based on the relevance of the reviews. The analysis unit adjusts the order of analysis based on the relevance of the reviews. For example, the analysis unit prioritizes analysis of highly relevant reviews. It can also postpone analysis of less relevant reviews. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the reviews. For example, it prioritizes analysis of reviews that include common keywords. In this way, adjusting the order of analysis based on the relevance of the reviews enables efficient analysis.

[0098] When analyzing reviews, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. On the other hand, if the user does not have technical knowledge, the analysis unit can also provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user is not familiar with technical terms, the analysis unit provides simplified technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate analysis results can be provided.

[0099] The generation unit can estimate the user's emotion and change the expression method of the generated information based on the estimated user's emotion. The generation unit estimates the user's emotion. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also estimate the emotion from the user's input using text analysis technology. For example, if the user is relaxed, the generation unit can provide an expression method that includes detailed information. If the user is in a hurry, the generation unit can provide an expression method that is concise and to the point. Furthermore, if the user is excited, the generation unit can provide an expression method that is visually stimulating. In this way, by adjusting the expression method of information based on the user's emotion, more appropriate information can be provided.

[0100] When generating information, the generation unit can adjust the level of detail of the generated information based on the importance of the review. The generation unit adjusts the level of detail of the generated information based on the importance of the review. For example, the generation unit generates detailed information based on reviews with high importance. It can also generate concise information based on reviews with low importance. Furthermore, the generation unit can adjust the level of detail of the information to be generated according to the importance of the review. For example, it generates detailed information based on reviews with high evaluation scores. In this way, by adjusting the level of detail of the generated information based on the importance of the review, efficient information generation is possible.

[0101] When generating information, the generation unit can apply different generation algorithms depending on the category of the review. The generation unit applies different generation algorithms depending on the category of the review. For example, the generation unit can generate information about the quality of food and service based on restaurant reviews. It can also generate information about the cleanliness of rooms and the responsiveness of staff based on hotel reviews. It can also generate information about scenery and access based on reviews of tourist attractions. In this way, by applying a generation algorithm depending on the category of the review, it is possible to provide more accurate information.

[0102] When generating information, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit refers to the user's past generation results. For example, the generation unit improves the accuracy of generation based on information generated by the user in the past. The generation unit can also select the optimal generation algorithm from the user's past generation results. Furthermore, the generation unit can adjust the level of detail of the information to be generated by referring to the user's past generation results. For example, if the user requested detailed information in the past, the generation unit generates detailed information. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0103] The generation unit can estimate the user's emotion and change the length of the information to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion. For example, the generation unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can also estimate the emotion from the user's input using text analysis technology. For example, if the user is in a hurry, the generation unit can generate short, to-the-point information. If the user is relaxed, the generation unit can generate longer information including detailed explanations. Furthermore, if the user is excited, the generation unit can generate information with visually stimulating effects. In this way, by adjusting the length of the information based on the user's emotion, more appropriate information can be provided.

[0104] When generating information, the generation unit can determine the generation priority based on the time when the reviews were submitted. The generation unit determines the generation priority based on the time when the reviews were submitted. For example, the generation unit generates information preferentially based on the most recent reviews. It can also generate information later based on older reviews. Furthermore, the generation unit can adjust the generation priority based on the time when the reviews were submitted. For example, it generates information based on reviews that have a recent submission date. In this way, efficient information generation is possible by determining the generation priority based on the time when the reviews were submitted.

[0105] When generating information, the generation unit can adjust the order of generation based on the relevance of the reviews. The generation unit adjusts the order of generation based on the relevance of the reviews. For example, the generation unit generates information preferentially based on highly relevant reviews. It can also generate information later based on less relevant reviews. Furthermore, the generation unit can adjust the order of generation according to the relevance of the reviews. For example, information is generated based on reviews that include common keywords. In this way, adjusting the order of generation based on the relevance of the reviews enables efficient information generation.

[0106] When generating information, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate information that uses a lot of technical terminology. Also, if the user does not have technical expertise, the generation unit can generate concise and easy-to-understand information. Furthermore, the generation unit can adjust the expression method of the information to be generated according to the user's level of expertise. For example, if the user is not familiar with technical terminology, the generation unit provides simplified technical terminology. In this way, more appropriate information can be provided by adjusting the use of technical terminology according to the user's level of expertise.

[0107] The providing unit can estimate the user's emotions and change the display method of the information to be provided based on the estimated user's emotions. The providing unit estimates the user's emotions. For example, the providing unit analyzes the user's facial expressions using facial expression recognition technology to estimate the emotions. The providing unit can also estimate the emotions from the user's input using text analysis technology. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, more appropriate information can be provided by adjusting the display method based on the user's emotions.

[0108] When providing information, the providing unit can select an appropriate display method by referring to the user's past operation history. The providing unit refers to the user's past operation history. For example, the providing unit analyzes click history and browsing history and suggests an optimal display method based on display methods used by the user in the past. The providing unit can also select an optimal display method from the user's past operation history. Furthermore, the providing unit can customize the display method by referring to the user's past operation history. For example, if the user has preferred list view in the past, the providing unit will preferentially suggest list view. In this way, a more appropriate display method can be provided by referring to the user's past operation history.

[0109] The providing unit can customize the display content according to the user's current task when providing information. The providing unit customizes the display content according to the user's current task. For example, the providing unit preferentially displays information related to the task the user is currently performing. The providing unit can also customize the display content according to the user's current task. Furthermore, when the user is performing a specific task, the providing unit can also preferentially display information related to the task. For example, when the user is looking for a cafe, the providing unit preferentially displays information related to the cafe. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0110] The providing unit can improve the display method by reflecting user feedback when providing information. The providing unit reflects user feedback. For example, the providing unit improves the display method based on feedback provided by the user. The providing unit can also select the optimal display method from the user feedback. Furthermore, the providing unit can customize the display method by reflecting user feedback. For example, if the user gives feedback that "the display is difficult to see," the providing unit provides a display method with high visibility. In this way, a more appropriate display method can be provided by reflecting user feedback.

[0111] The providing unit can estimate the user's emotions and determine the priority of information based on the estimated user's emotions. The providing unit estimates the user's emotions. For example, the providing unit analyzes the user's facial expressions using facial expression recognition technology to estimate the emotions. The providing unit can also estimate the emotions from the user's input using text analysis technology. For example, if the user is feeling stressed, the providing unit can prioritize displaying important information. Also, if the user is relaxed, the providing unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize displaying information that can be provided quickly. In this way, by determining the priority of information based on the user's emotions, more appropriate information can be provided.

[0112] When providing information, the providing unit can select the optimal display method by taking into consideration the user's device information. The providing unit selects the display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible display method. In this way, a more appropriate display method can be provided by taking into consideration the user's device information.

[0113] When providing information, the providing unit can make the display content multilingual according to the user's language setting. The providing unit makes the display content multilingual according to the user's language setting. For example, the providing unit automatically sets the display content based on the language setting of the user's device. In addition, if the user uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, more appropriate information can be provided.

[0114] When providing information, the providing unit can prioritize displaying highly relevant information by taking into account the user's geographical location information. The providing unit provides information by taking into account the user's geographical location information. For example, the providing unit prioritizes displaying information relevant to the user's current location. The providing unit can also suggest optimal information based on the user's geographical location information. Furthermore, when the user is in a specific area, the providing unit can prioritize displaying information related to that area. For example, when the user is in a specific cafe, the providing unit prioritizes displaying information related to that cafe. In this way, more relevant information can be provided by taking into account the user's geographical location information.

[0115] The understanding unit can estimate the user's emotions and change the review understanding method based on the estimated user's emotions. The understanding unit estimates the user's emotions. For example, the understanding unit uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The understanding unit can also estimate the emotions from the user's input content using text analysis technology. For example, if the user is relaxed, the understanding unit can provide detailed understanding. If the user is in a hurry, the understanding unit can also provide concise understanding. Furthermore, if the user is excited, the understanding unit can provide visually stimulating understanding results. In this way, by adjusting the understanding method based on the user's emotions, more appropriate understanding results can be provided.

[0116] When understanding a review, the understanding unit can adjust the level of detail of the understanding based on the importance of the review. The understanding unit adjusts the level of detail of the understanding based on the importance of the review. For example, the understanding unit performs a detailed understanding of a review with a high importance. It can also perform a concise understanding of a review with a low importance. Furthermore, the understanding unit can determine the priority of the understanding according to the importance of the review. For example, it prioritizes understanding of reviews with a high evaluation score. In this way, by adjusting the level of detail of the understanding based on the importance of the review, efficient understanding is possible.

[0117] When understanding reviews, the understanding unit can apply different understanding algorithms depending on the category of the review. The understanding unit applies different understanding algorithms depending on the category of the review. For example, the understanding unit applies understanding algorithms related to the quality of food and service to restaurant reviews. It can also apply understanding algorithms related to the cleanliness of rooms and the responsiveness of staff to hotel reviews. It can also apply understanding algorithms related to scenery and access to reviews of tourist destinations. In this way, by applying understanding algorithms according to the category of the review, it is possible to provide more accurate understanding results.

[0118] When understanding reviews, the understanding unit can improve the accuracy of the understanding by referring to the user's past understanding results. The understanding unit refers to the user's past understanding results. For example, the understanding unit improves the accuracy of the understanding based on the user's past understanding results. The understanding unit can also select the optimal understanding algorithm from the user's past understanding results. Furthermore, the understanding unit can adjust the level of detail of the understanding by referring to the user's past understanding results. For example, if the user has requested a detailed understanding in the past, the understanding unit will perform a detailed understanding. In this way, the accuracy of the understanding is improved by referring to the user's past understanding results.

[0119] The understanding unit can estimate the user's emotions and determine the priority of reviews based on the estimated user emotions. The understanding unit estimates the user's emotions. For example, the understanding unit analyzes the user's facial expressions using facial expression recognition technology to estimate emotions. The understanding unit can also estimate emotions from the user's input using text analysis technology. For example, if the user is feeling stressed, the understanding unit prioritizes understanding important reviews. Also, if the user is relaxed, the understanding unit can prioritize understanding detailed reviews. Furthermore, if the user is in a hurry, the understanding unit prioritizes understanding reviews that can be understood quickly. In this way, by determining the priority of reviews based on the user's emotions, more appropriate understanding results can be provided.

[0120] When understanding reviews, the understanding unit can determine the priority of understanding based on the time of submission of the reviews. The understanding unit determines the priority of understanding based on the time of submission of the reviews. For example, the understanding unit prioritizes understanding the most recent reviews. It is also possible to postpone understanding older reviews. Furthermore, the understanding unit can adjust the priority of understanding based on the time of submission of the reviews. For example, it prioritizes understanding reviews that were submitted recently. In this way, efficient understanding is possible by determining the priority of understanding based on the time of submission of the reviews.

[0121] When understanding the reviews, the understanding unit can adjust the order of understanding based on the relevance of the reviews. The understanding unit adjusts the order of understanding based on the relevance of the reviews. For example, the understanding unit prioritizes understanding of highly relevant reviews. It can also postpone understanding of less relevant reviews. Furthermore, the understanding unit can adjust the order of understanding based on the relevance of the reviews. For example, it prioritizes understanding of reviews that include common keywords. This allows for efficient understanding by adjusting the order of understanding based on the relevance of the reviews.

[0122] When understanding reviews, the understanding unit can adjust the use of technical terms in the understanding according to the user's level of expertise. The understanding unit adjusts the use of technical terms in the understanding according to the user's level of expertise. For example, if the user has technical knowledge, the understanding unit provides an understanding result that makes heavy use of technical terms. Also, if the user does not have technical knowledge, the understanding unit can provide a concise and easy-to-understand understanding result. Furthermore, the understanding unit can adjust the way the understanding result is expressed according to the user's level of expertise. For example, if the user is not familiar with technical terms, the understanding unit provides simplified technical terms. In this way, by adjusting the use of technical terms according to the user's level of expertise, more appropriate understanding results can be provided.

[0123] The extraction unit can estimate the user's emotions and prioritize information based on the estimated user's emotions. The extraction unit estimates the user's emotions. For example, the extraction unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The extraction unit can also estimate emotions from the user's input using text analysis technology. For example, if the user is feeling stressed, the extraction unit can prioritize extracting important information. Also, if the user is relaxed, the extraction unit can prioritize extracting detailed information. Furthermore, if the user is in a hurry, the extraction unit can prioritize extracting information that can be extracted quickly. In this way, by prioritizing information based on the user's emotions, more appropriate information can be provided.

[0124] When extracting information, the extraction unit can improve the accuracy of extraction by taking into account the interrelationships between reviews. The extraction unit improves the accuracy of extraction by taking into account the interrelationships between reviews. For example, the extraction unit analyzes the relevance between reviews and preferentially extracts related information. The extraction unit can also extract important information based on the interrelationships between reviews. Furthermore, the extraction unit can improve the accuracy of information extracted by taking into account the interrelationships between reviews. For example, information is extracted based on reviews that include common keywords. In this way, the accuracy of extraction is improved by taking into account the interrelationships between reviews.

[0125] When extracting information, the extraction unit can perform the extraction while taking into consideration the attribute information of the review submitter. The extraction unit performs the extraction while taking into consideration the attribute information of the review submitter. For example, the extraction unit extracts related information based on the age and gender of the review submitter. The extraction unit can also extract related information based on the occupation and hobbies of the review submitter. Furthermore, the extraction unit can improve the accuracy of the extracted information by taking into consideration the attribute information of the review submitter. For example, information is extracted based on reviews related to a specific age group. In this way, more appropriate information can be provided by taking into consideration the attribute information of the review submitter.

[0126] When extracting information, the extraction unit can weight the extraction based on the frequency of reviews submitted. The extraction unit weights the extraction based on the frequency of reviews submitted. For example, the extraction unit prioritizes extracting reviews that are submitted more frequently. It is also possible to extract reviews that are submitted less frequently later. Furthermore, the extraction unit can weight the information to be extracted according to the frequency of reviews submitted. For example, information is extracted based on frequently submitted reviews. In this way, by weighting the extraction based on the frequency of reviews submitted, more appropriate information can be provided.

[0127] The extraction unit can estimate the user's emotion and change the display method of the extracted information based on the estimated user's emotion. The extraction unit estimates the user's emotion. For example, the extraction unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The extraction unit can also estimate the emotion from the user's input using text analysis technology. For example, if the user is nervous, the extraction unit can provide a simple, highly visible display method. If the user is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. In this way, by adjusting the display method based on the user's emotion, more appropriate information can be provided.

[0128] When extracting information, the extraction unit can perform the extraction taking into account the geographical distribution of reviews. The extraction unit performs the extraction taking into account the geographical distribution of reviews. For example, the extraction unit extracts related information based on the geographical distribution of reviews. The extraction unit can also preferentially extract geographically close reviews. Furthermore, the extraction unit can improve the accuracy of the extracted information by taking into account the geographical distribution of reviews. For example, the extraction unit extracts information based on reviews related to a specific region. In this way, more appropriate information can be provided by taking into account the geographical distribution of reviews.

[0129] When extracting information, the extraction unit can improve the accuracy of extraction by referring to literature related to reviews. The extraction unit improves the accuracy of extraction by referring to literature related to reviews. For example, the extraction unit extracts important information based on literature related to reviews. The extraction unit can also improve the accuracy of the extracted information by referring to related literature. Furthermore, the extraction unit can weight the information to be extracted by taking into account literature related to reviews. For example, the reliability of reviews is evaluated based on related literature and important information is extracted. In this way, by referring to literature related to reviews, the accuracy of extraction is improved.

[0130] The extraction unit can extract information while taking into consideration the market value of the word-of-mouth. The extraction unit extracts information while taking into consideration the market value of the word-of-mouth. For example, the extraction unit can prioritize extracting word-of-mouth with high market value. It can also extract word-of-mouth with low market value later. Furthermore, the extraction unit can weight the information to be extracted according to the market value of the word-of-mouth. For example, the market value of the word-of-mouth is evaluated based on sales data or market share, and important information is extracted. In this way, by taking into consideration the market value of the word-of-mouth, more appropriate information can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, understanding unit, and extraction 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 accepts input of a user's desired conditions using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit analyzes the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the generation unit generates information using a generation AI. The provision unit provides the information to the user through the display 40A or speaker 40B of the smart device 14. The understanding unit understands the content of the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the extraction unit extracts information that matches the user's desired conditions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, understanding unit, and extraction 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 accepts input of a user's desired conditions using the microphone 238 of the smart glasses 214. The analysis unit analyzes the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the generation unit generates information using a generation AI. The provision unit provides the information to the user through the speaker 240 of the smart glasses 214. The understanding unit understands the content of the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the extraction unit extracts information that matches the user's desired conditions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, understanding unit, and extraction 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 accepts input of the user's desired conditions using the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the generation unit generates information using a generation AI. The provision unit provides the information to the user through the speaker 240 of the headset-type terminal 314. The understanding unit understands the content of the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the extraction unit extracts information that matches the user's desired conditions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, understanding unit, and extraction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts input of the user's desired conditions using the microphone 238 of the robot 414. The analysis unit analyzes the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the generation unit generates information using a generation AI. The provision unit provides the information to the user through the speaker 240 of the robot 414. The understanding unit understands the content of the word-of-mouth using the specific processing unit 290 of the data processing device 12, and the extraction unit extracts information that matches the user's desired conditions.

[0131] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0132] The reception unit can analyze the user's past search history and automatically suggest search conditions that the user frequently searches for. For example, if the user frequently searches for "quiet cafes" in the past, the reception unit will preferentially suggest "quiet cafes." Also, if the user tends to search for specific conditions during a specific time of day, the reception unit can suggest conditions related to that time of day. Furthermore, the reception unit can learn the user's preferences from the user's past search history and make more personalized suggestions. This allows for more efficient searches by utilizing the user's past search history.

[0133] The understanding unit can evaluate the reliability of reviews when understanding the content of reviews. For example, the understanding unit analyzes the past posting history of the review poster and prioritizes understanding reviews from highly reliable posters. It can also check whether the content of a review matches other reviews and determine that reviews with a high degree of match are highly reliable. Furthermore, the understanding unit can take into account the posting date and time of the review and prioritize understanding the most recent reviews. This allows for more accurate information to be provided by evaluating the reliability of reviews.

[0134] The extraction unit can extract information taking into account the user's current geographic location information. For example, based on the user's current location, reviews of nearby cafes and restaurants can be preferentially extracted. Also, if the user is in a specific area, reviews related to that area can be preferentially extracted. Furthermore, the extraction unit can also take into account the user's movement history and extract reviews related to places visited in the past. This makes it possible to provide more relevant information by utilizing the user's current geographic location information.

[0135] The reception unit can estimate the user's emotions and change the input method for desired conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input desired conditions. In this way, the input method can be adjusted according to the user's emotions, thereby reducing the user's stress.

[0136] The reception unit can analyze the user's past search history and present appropriate input candidates. For example, the reception unit can analyze the frequency of search keywords and automatically display related input candidates based on the user's past search conditions. The reception unit can also analyze click history and preferentially suggest search keywords that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, if the user previously searched for "cafe" during a specific time period, the reception unit will preferentially suggest "cafe" during that time period. This reduces the user's input effort by presenting optimal input candidates based on the user's past search history.

[0137] The reception unit can provide input assistance based on the user's current situation and areas of interest when inputting desired conditions. For example, the reception unit suggests related conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes are preferentially displayed. The reception unit can also preferentially display related conditions based on the user's areas of interest. For example, if the user has frequently searched for "cafe" in the past, the reception unit will preferentially display conditions related to "cafe." Furthermore, the reception unit can also suggest optimal conditions based on the user's current situation (time of day, weather, etc.). For example, if the user is searching at night, stores that are open at night are preferentially suggested. In this way, input assistance based on the user's current situation and areas of interest can suggest more appropriate conditions.

[0138] When the user inputs desired conditions, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the desired conditions using voice recognition technology. If the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the desired conditions using image recognition technology. For example, if the user uploads a photo of a cafe, the reception unit analyzes the photo and suggests related conditions. This allows the user to select the optimal input means according to the user's input method, thereby improving input convenience.

[0139] The reception unit can estimate the user's emotions and prioritize the desired conditions based on the estimated user emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The reception unit can also estimate the emotions from the user's input using text analysis technology. For example, if the user is feeling stressed, the reception unit can prioritize displaying simple and important conditions. If the user is relaxed, the reception unit can prioritize displaying detailed conditions. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying conditions that can be entered quickly. In this way, by prioritizing the desired conditions based on the user's emotions, more appropriate information can be provided.

[0140] When inputting desired conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying relevant conditions based on the user's current location. For example, if the user's current location is a cafe, conditions related to cafes can be prioritized. The reception unit can also suggest optimal conditions based on the user's geographical location information. For example, if the user is in a specific area, conditions related to that area can be prioritized. In this way, more relevant conditions can be suggested by taking into account the user's geographical location information.

[0141] When the user inputs desired conditions, the reception unit can analyze the user's social media activity and suggest related conditions. For example, the reception unit can suggest related conditions based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. Furthermore, the reception unit can suggest related conditions by taking into account the activity of the user's friends on social media. For example, if the user's friends have checked in at a specific cafe, the reception unit can suggest conditions related to that cafe. In this way, by analyzing the user's social media activity, more relevant conditions can be suggested.

[0142] The processing flow of the second embodiment will be briefly explained below.

[0143] Step 1: The reception unit receives the user's desired conditions. The user can input the desired conditions using methods such as text input, voice input, and image input. For example, the reception unit receives the user's input of specific conditions such as "a quiet cafe with Wi-Fi." Step 2: The analysis unit analyzes the reviews based on the information received by the reception unit. The analysis unit uses text mining technology to analyze the content of the reviews and extract information that matches the user's desired conditions. It can also analyze the emotional nuances of the reviews using sentiment analysis technology. Step 3: The generation unit generates information based on the information analyzed by the analysis unit. The generation unit uses a generation AI to summarize the content of the reviews and generate information that meets the user's desired conditions. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, which understands the content of the reviews and generates appropriate information. Step 4: The providing unit provides the information generated by the generating unit to the user. The providing unit can provide the information by a method such as a web page, email, or notification.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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."

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Explanation of symbols]

[0216] 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 section for inputting desired conditions; an analysis unit that analyzes word-of-mouth based on the information received by the reception unit; a generation unit that generates information based on the information analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit A system characterized by:

2. Equipped with an understanding department that understands the content of reviews 2. The system of claim 1.

3. Equipped with an extraction unit that extracts information that matches the user's desired conditions 2. The system of claim 1.

4. The reception unit Estimate the user's emotions and change the input method for desired conditions based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyzes the user's past search history and provides appropriate suggestions 2. The system of claim 1.

6. The reception unit When entering desired conditions, input assistance is provided based on the user's current situation and areas of interest.

2. The system of claim 1.

7. The reception unit When entering desired conditions, select the appropriate input method according to the user's input method.

2. The system of claim 1.

8. The reception unit Estimate the user's feelings and determine the priority of desired conditions based on the estimated user's feelings.

2. The system of claim 1.

Images