system

Abstract

To provide a system for quickly and accurately determining the reliability of a mail-order site and providing it to a user.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. A reception part inputs the URL of a mail order site. The analysis part analyzes the URL inputted by the reception part and determines the degree of risk on the basis of the contents and structure of the site and past evaluation. The providing unit provides the user with the determination result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to quickly and accurately determine the reliability of an online shopping site, and there is a risk that users may be deceived by a fraudulent site.

[0005] The system according to the embodiment aims to quickly and accurately determine the reliability of an online shopping site and provide the result to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the URL of an online shopping site. The analysis unit analyzes the URL input by the reception unit and determines the risk level based on the site's content, structure, and past reviews. The provision unit provides the user with the determination result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine the reliability of an online shopping site and provide the result to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention loads the URL of an online shopping site into an AI and determines whether the site is a reputable site or a dark site. In this system, a user inputs the URL, and the AI ​​analyzes the URL and determines the risk level based on the site's content, structure, past reviews, and other factors. This determination is based on unique criteria and does not constitute business interference. For example, a user inputs the URL of a specific online shopping site into the system, such as "https: / / example.com." This information is then input into the AI. The AI ​​then analyzes the input URL. The AI ​​determines the risk level based on the URL's content, structure, past reviews, and other factors. For example, the AI ​​analyzes the presence or absence of an SSL certificate, past user reviews, the site's design and structure, and domain registration information. This allows the system to determine whether the site is a reputable site or a dark site. Furthermore, the AI ​​determines the risk level based on its own criteria. These criteria are used to evaluate the reliability and safety of the site and do not constitute business interference. For example, the AI ​​may determine whether the site has a valid SSL certificate, past user reviews, and whether the site's design and structure indicate trustworthiness. This allows the system to safely use online shopping sites. For example, before using a particular online shopping site, users can check whether the site is a reputable site or a dark site. This allows users to avoid fraudulent and unauthorised sites and enjoy online shopping safely.

[0029] An online shopping site evaluation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a URL of an online shopping site entered by a user. For example, the user can manually enter the URL of a specific online shopping site. The reception unit can also receive the URL by copy and paste, voice input, or other methods. The analysis unit analyzes the URL entered by the reception unit and determines the risk level based on the site's content, structure, and past evaluations. For example, the analysis unit checks whether an SSL certificate is present. The analysis unit can evaluate the site's reliability by checking the issuer and expiration date of the SSL certificate. The analysis unit also analyzes past user reviews. For example, the analysis unit can determine the site's rating based on the review content and rating score. The analysis unit also evaluates the site's design and structure. For example, the analysis unit can evaluate the site's usability, visual aesthetics, ease of navigation, and so on. The analysis unit also checks domain registration information. For example, the analysis unit can evaluate the site's reliability based on the domain's registrant information, registration date, registration agency, and so on. The provision unit provides the user with the results of the analysis unit's evaluation. For example, the providing unit can provide the analysis results to the user in text format or graphical display. The providing unit can also use email, push notification, or the like as a notification method. This allows the online shopping site evaluation system according to the embodiment to analyze the URL of an online shopping site entered by the user, determine its risk level, and provide the result.

[0030] The analysis unit can check whether an SSL certificate is present. For example, the analysis unit checks whether an SSL certificate is present. For example, the analysis unit checks the issuer and expiration date of the SSL certificate to evaluate the reliability of the site. The analysis unit can also verify the certificate chain to confirm the validity of the SSL certificate. For example, the analysis unit checks whether the certificate chain is correctly configured and determines whether it was issued by a trusted certificate authority. In this way, the reliability of a site can be evaluated by checking whether an SSL certificate is present.

[0031] The analysis unit can analyze past user reviews. The analysis unit, for example, analyzes past user reviews. For example, the analysis unit determines the site's rating based on the review content and rating score. The analysis unit can also check the reliability of the review poster to evaluate the reliability of the review. For example, the analysis unit can check whether the review poster actually purchased the product and prioritize reviews with high reliability. The analysis unit can also analyze the content of the review using natural language processing technology and classify positive and negative reviews. For example, the analysis unit can analyze keywords and phrases used in the review and comprehensively determine the site's rating. In this way, the site's rating can be determined by analyzing past user reviews.

[0032] The analysis unit can evaluate the design or structure of a site. For example, the analysis unit evaluates the design of a site. For example, the analysis unit evaluates the site based on its usability and visual beauty. The analysis unit can also evaluate the ease of navigation of a site. For example, the analysis unit analyzes the link arrangement and page hierarchical structure of a site to determine whether users can easily find information. The analysis unit can also evaluate the structure of a site. For example, the analysis unit evaluates the way information is organized on the site and the layout of the pages. In this way, by evaluating the design and structure of a site, the reliability of the site can be determined.

[0033] The analysis unit can check the domain registration information. The analysis unit can, for example, check the domain registration information. For example, the analysis unit can check the domain registrant information and registration date to evaluate the reliability of the site. The analysis unit can also check the domain registration agency. For example, the analysis unit can check whether the domain is registered by a reliable registration agency to determine the reliability of the site. The analysis unit can also check the domain update history. For example, the analysis unit can check whether the domain is frequently updated to evaluate the reliability of the site. In this way, the reliability of the site can be evaluated by checking the domain registration information.

[0034] The providing unit can provide the determination results obtained by the analysis unit to the user. The providing unit, for example, provides the analysis results to the user in text format. For example, the providing unit displays the analysis results in concise text so that the user can easily understand them. The providing unit can also provide the analysis results in a graphical display. For example, the providing unit displays the analysis results as graphs or charts so that the results are provided in a format that is visually easy to understand. The providing unit can also use email or push notification as a notification method. For example, the providing unit sends the analysis results to the user by email to provide the information quickly. In this way, by providing the analysis results to the user, the user can confirm the reliability of the site.

[0035] The reception unit can analyze the user's past URL input history and select the optimal input method. The reception unit, for example, analyzes the user's past URL input history. For example, the reception unit automatically displays URLs that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that have been used in the past. For example, the reception unit can predict and suggest URLs that will be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to a generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can filter URLs based on the user's current areas of interest when the URL is input. The reception unit, for example, identifies the user's current areas of interest. For example, the reception unit can preferentially display related URLs based on keywords recently searched by the user. The reception unit can also filter URLs based on categories in which the user is interested (e.g., fashion, electronics, etc.). For example, the reception unit can analyze the user's past browsing history and suggest highly relevant URLs. This makes it possible to provide highly relevant URLs by filtering URLs based on the user's areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform filtering.

[0037] When a user inputs a URL, the reception unit can select the optimal input means depending on the user's input method. The reception unit, for example, identifies the user's input method. For example, when the user inputs a URL by voice, the reception unit accurately inputs the URL using voice recognition technology. Furthermore, when the user inputs a URL using text, the reception unit can provide an autocomplete function to simplify input. For example, when the user inputs a URL using an image, the reception unit can extract the URL using image recognition technology. This improves input convenience by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0038] When a URL is input, the reception unit can prioritize inputting highly relevant URLs by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit can identify the user's location information using GPS data or an IP address. The reception unit can also acquire the user's geographical location information by using a location information service. For example, if the user is in a specific area, the reception unit can prioritize displaying URLs of online shopping sites related to that area. Furthermore, if the user is traveling, the reception unit can prioritize displaying URLs of online shopping sites related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize displaying URLs of local online shopping sites. This makes it possible to provide highly relevant URLs by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant URLs.

[0039] The reception unit can analyze the user's social media activity when a URL is input and input related URLs. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit automatically displays URLs shared by the user on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related URLs. For example, the reception unit can suggest related URLs by referring to the activity of the user's friends on social media. In this way, highly relevant URLs can be provided by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related URLs.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when entering a URL. The reception unit, for example, acquires the user's past feedback. For example, the reception unit customizes the input method based on the user's ratings and comments. The reception unit can also customize the input method based on the user's past usage history. For example, the reception unit preferentially suggests input methods that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit adjusts the input interface to avoid input methods that the user has been dissatisfied with in the past. This allows the user to be provided with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the site. The analysis unit, for example, evaluates the importance of the site. For example, the analysis unit evaluates the importance based on the number of visitors and transaction volume of the site. The analysis unit can also evaluate the importance based on industry influence. For example, the analysis unit performs a detailed analysis for a site with high importance. The analysis unit can also perform a simplified analysis for a site with low importance. Furthermore, the analysis unit performs an analysis with a moderate level of detail for a site with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the site. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the site. The analysis unit, for example, identifies the category of the site. For example, in the case of a fashion site, the analysis unit may perform an analysis that emphasizes product reviews and ratings. In addition, in the case of an electronics site, the analysis unit may perform an analysis that emphasizes product specifications and technical information. Furthermore, in the case of a food site, the analysis unit may perform an analysis that emphasizes product safety and quality. This enables highly accurate analysis by applying an analysis algorithm depending on the site category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input site category data into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, acquires the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past analysis data and feedback results. The analysis unit can also adjust the analysis algorithm based on past analysis results. For example, the analysis unit performs analysis by referring to the characteristics of sites that the user has previously rated highly. The analysis unit can also perform analysis in a way that avoids the characteristics of sites that the user has previously rated poorly. In this way, by referring to past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine analysis priorities based on the registration date of the site. The analysis unit, for example, checks the registration date of the site. For example, the analysis unit checks the domain registration date and the publication date of the site to evaluate the reliability of the site. The analysis unit can also check the update history of the site. For example, the analysis unit checks whether the site is frequently updated and evaluates its reliability. Furthermore, the analysis unit can prioritize analysis of sites registered within a specific period. This enables efficient analysis by determining the analysis priorities based on the registration date of the site. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site registration date data into the generation AI and have the generation AI determine the analysis priorities.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sites. The analysis unit, for example, evaluates the relevance of the sites. For example, the analysis unit prioritizes analyzing sites in categories in which the user is interested. The analysis unit can also prioritize analyzing sites that the user has visited in the past. Furthermore, the analysis unit prioritizes analyzing sites that are highly relevant based on the user's search history. This enables efficient analysis by adjusting the order of analysis based on the relevance of the sites. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history. The analysis unit can also evaluate the level of expertise based on survey results. For example, if the user has technical expertise, the analysis unit provides analysis results using detailed technical terminology. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit provides analysis results using appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The providing unit can adjust the level of detail of the analysis results based on the importance of the analysis results when providing the analysis results. The providing unit, for example, evaluates the importance of the analysis results. For example, the providing unit evaluates the importance based on the impact of the analysis results or the user's interest. The providing unit can also evaluate the importance based on the urgency of the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. Furthermore, the providing unit provides information with an appropriate level of detail for analysis results with medium importance. This enables efficient information provision by adjusting the level of detail of the analysis results based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0048] The providing unit can improve the accuracy of the information provided by referring to the user's past providing results when providing the information. The providing unit, for example, acquires the user's past providing results. For example, the providing unit improves the accuracy of the information provided based on past providing data and feedback results. The providing unit can also adjust the providing algorithm based on the past providing results. For example, the providing unit provides information by referring to the characteristics of information that the user has previously rated highly. The providing unit can also provide information in a way that avoids the characteristics of information that the user has previously rated poorly. In this way, the accuracy of the information provided is improved by referring to the past providing results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past providing result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0049] The providing unit can customize the provided content based on the user's current areas of interest at the time of providing the content. The providing unit, for example, identifies the user's current areas of interest. For example, the providing unit provides relevant information based on keywords recently searched by the user. The providing unit can also provide information based on categories in which the user is interested (e.g., fashion, electronics, etc.). Furthermore, the providing unit analyzes the user's past browsing history and provides highly relevant information. This allows the provided content to be customized based on the user's areas of interest, thereby providing highly relevant information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's area of ​​interest data into a generating AI and cause the generating AI to customize the provided content.

[0050] The providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit, for example, acquires the user's geographical location information. For example, the providing unit identifies the user's location information using GPS data or an IP address. The providing unit can also acquire the user's geographical location information using a location information service. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, if the user is at home, the providing unit prioritizes providing local information. In this way, highly relevant information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0051] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit automatically displays information shared by the user on social media as candidates. The providing unit can also analyze the content of the user's social media posts and suggest related information. Furthermore, the providing unit can suggest related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data to a generation AI and cause the generation AI to select related information.

[0052] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, acquires the user's past feedback. For example, the providing unit customizes the delivery method based on the user's ratings and comments. The providing unit can also customize the delivery method based on the user's past usage history. For example, the providing unit preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also customize the delivery interface based on the user's past feedback. For example, the providing unit adjusts the interface to avoid delivery methods that the user has been dissatisfied with in the past. This allows the delivery method that is optimal for the user to be provided by reflecting the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past feedback data into a generation AI and cause the generation AI to customize the delivery method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze environmental sounds when the user is entering input and suggest the optimal input method. For example, if the user is in a noisy environment, the reception unit can recommend text input over voice input. The reception unit can also suggest voice input as a priority when the user is in a quiet environment. Furthermore, if the user is on the move, the reception unit can provide an interface that allows the user to enter a URL with a simple tap operation. This improves input convenience by providing the optimal input method according to the user's environment.

[0055] The analysis unit can evaluate how frequently a site's content is updated to determine its trustworthiness. For example, the analysis unit can check how frequently a site's blog or news section is updated. The analysis unit can also evaluate how frequently product inventory information and prices are updated. Furthermore, the analysis unit can check the site's maintenance history to determine whether it is updated regularly. This allows the reliability of a site to be determined by evaluating how frequently its content is updated.

[0056] The analysis unit can evaluate the security measures of a site and determine its trustworthiness. For example, the analysis unit can check whether the site has implemented two-step authentication. The analysis unit can also evaluate whether the site regularly applies security patches. Furthermore, the analysis unit can check whether the site encrypts and stores user data. In this way, the reliability of a site can be determined by evaluating its security measures.

[0057] The analysis unit can evaluate the performance of a site and determine its usability. For example, the analysis unit measures the loading speed of a site. The analysis unit can also evaluate the response time of a site. Furthermore, the analysis unit can check the mobile compatibility of a site and determine its usability. In this way, usability can be determined by evaluating the performance of a site.

[0058] The analysis unit can analyze the site's social media ratings to determine its trustworthiness. For example, the analysis unit can check the number of followers of the site's official social media accounts. The analysis unit can also analyze user comments and ratings on social media. Furthermore, the analysis unit can evaluate the number of shares and likes on social media to determine its trustworthiness. In this way, the analysis unit can determine the site's trustworthiness by analyzing the site's social media ratings.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives the URL of a shopping site entered by the user. For example, the user can manually enter the URL of a specific shopping site. The reception unit can also receive the URL by copy and paste, voice input, or other methods. Step 2: The analysis unit analyzes the URL entered by the reception unit and determines the risk level based on the site's content, structure, and past ratings. For example, the analysis unit checks whether or not the site has an SSL certificate and evaluates the site's reliability based on its issuer and expiration date. It also analyzes past user reviews and determines the site's rating based on the review content and rating score. It also evaluates the site's design and structure, and evaluates it based on factors such as usability, visual beauty, and ease of navigation. It also checks the domain registration information and evaluates the site's reliability based on registrant information, registration date, and registration agency. Step 3: The providing unit provides the determination result obtained by the analysis unit to the user. For example, the analysis result can be provided to the user in text format or in a graphical display. Notification methods such as email and push notification can also be used.

[0061] (Example 2) A system according to an embodiment of the present invention loads the URL of an online shopping site into an AI and determines whether the site is a reputable site or a dark site. In this system, a user inputs the URL, and the AI ​​analyzes the URL and determines the risk level based on the site's content, structure, past reviews, and other factors. This determination is based on unique criteria and does not constitute business interference. For example, a user inputs the URL of a specific online shopping site into the system, such as "https: / / example.com." This information is then input into the AI. The AI ​​then analyzes the input URL. The AI ​​determines the risk level based on the URL's content, structure, past reviews, and other factors. For example, the AI ​​analyzes the presence or absence of an SSL certificate, past user reviews, the site's design and structure, and domain registration information. This allows the system to determine whether the site is a reputable site or a dark site. Furthermore, the AI ​​determines the risk level based on its own criteria. These criteria are used to evaluate the reliability and safety of the site and do not constitute business interference. For example, the AI ​​may determine whether the site has a valid SSL certificate, past user reviews, and whether the site's design and structure indicate trustworthiness. This allows the system to safely use online shopping sites. For example, before using a particular online shopping site, users can check whether the site is a reputable site or a dark site. This allows users to avoid fraudulent and unauthorised sites and enjoy online shopping safely.

[0062] An online shopping site evaluation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a URL of an online shopping site entered by a user. For example, the user can manually enter the URL of a specific online shopping site. The reception unit can also receive the URL by copy and paste, voice input, or other methods. The analysis unit analyzes the URL entered by the reception unit and determines the risk level based on the site's content, structure, and past evaluations. For example, the analysis unit checks whether an SSL certificate is present. The analysis unit can evaluate the site's reliability by checking the issuer and expiration date of the SSL certificate. The analysis unit also analyzes past user reviews. For example, the analysis unit can determine the site's rating based on the review content and rating score. The analysis unit also evaluates the site's design and structure. For example, the analysis unit can evaluate the site's usability, visual aesthetics, ease of navigation, and so on. The analysis unit also checks domain registration information. For example, the analysis unit can evaluate the site's reliability based on the domain's registrant information, registration date, registration agency, and so on. The provision unit provides the user with the results of the analysis unit's evaluation. For example, the providing unit can provide the analysis results to the user in text format or graphical display. The providing unit can also use email, push notification, or the like as a notification method. This allows the online shopping site evaluation system according to the embodiment to analyze the URL of an online shopping site entered by the user, determine its risk level, and provide the result.

[0063] The analysis unit can check whether an SSL certificate is present. For example, the analysis unit checks whether an SSL certificate is present. For example, the analysis unit checks the issuer and expiration date of the SSL certificate to evaluate the reliability of the site. The analysis unit can also verify the certificate chain to confirm the validity of the SSL certificate. For example, the analysis unit checks whether the certificate chain is correctly configured and determines whether it was issued by a trusted certificate authority. In this way, the reliability of a site can be evaluated by checking whether an SSL certificate is present.

[0064] The analysis unit can analyze past user reviews. The analysis unit, for example, analyzes past user reviews. For example, the analysis unit determines the site's rating based on the review content and rating score. The analysis unit can also check the reliability of the review poster to evaluate the reliability of the review. For example, the analysis unit can check whether the review poster actually purchased the product and prioritize reviews with high reliability. The analysis unit can also analyze the content of the review using natural language processing technology and classify positive and negative reviews. For example, the analysis unit can analyze keywords and phrases used in the review and comprehensively determine the site's rating. In this way, the site's rating can be determined by analyzing past user reviews.

[0065] The analysis unit can evaluate the design or structure of a site. For example, the analysis unit evaluates the design of a site. For example, the analysis unit evaluates the site based on its usability and visual beauty. The analysis unit can also evaluate the ease of navigation of a site. For example, the analysis unit analyzes the link arrangement and page hierarchical structure of a site to determine whether users can easily find information. The analysis unit can also evaluate the structure of a site. For example, the analysis unit evaluates the way information is organized on the site and the layout of the pages. In this way, by evaluating the design and structure of a site, the reliability of the site can be determined.

[0066] The analysis unit can check the domain registration information. The analysis unit can, for example, check the domain registration information. For example, the analysis unit can check the domain registrant information and registration date to evaluate the reliability of the site. The analysis unit can also check the domain registration agency. For example, the analysis unit can check whether the domain is registered by a reliable registration agency to determine the reliability of the site. The analysis unit can also check the domain update history. For example, the analysis unit can check whether the domain is frequently updated to evaluate the reliability of the site. In this way, the reliability of the site can be evaluated by checking the domain registration information.

[0067] The providing unit can provide the determination results obtained by the analysis unit to the user. The providing unit, for example, provides the analysis results to the user in text format. For example, the providing unit displays the analysis results in concise text so that the user can easily understand them. The providing unit can also provide the analysis results in a graphical display. For example, the providing unit displays the analysis results as graphs or charts so that the results are provided in a format that is visually easy to understand. The providing unit can also use email or push notification as a notification method. For example, the providing unit sends the analysis results to the user by email to provide the information quickly. In this way, by providing the analysis results to the user, the user can confirm the reliability of the site.

[0068] The reception unit can estimate the user's emotions and adjust the timing of URL input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The reception unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize 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 enable quick URL input. This improves user convenience by adjusting the timing of URL input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past URL input history and select the optimal input method. The reception unit, for example, analyzes the user's past URL input history. For example, the reception unit automatically displays URLs that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that have been used in the past. For example, the reception unit can predict and suggest URLs that will be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to a generation AI and have the generation AI select the optimal input method.

[0070] The reception unit can filter URLs based on the user's current areas of interest when the URL is input. The reception unit, for example, identifies the user's current areas of interest. For example, the reception unit can preferentially display related URLs based on keywords recently searched by the user. The reception unit can also filter URLs based on categories in which the user is interested (e.g., fashion, electronics, etc.). For example, the reception unit can analyze the user's past browsing history and suggest highly relevant URLs. This makes it possible to provide highly relevant URLs by filtering URLs based on the user's areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform filtering.

[0071] When a user inputs a URL, the reception unit can select the optimal input means depending on the user's input method. The reception unit, for example, identifies the user's input method. For example, when the user inputs a URL by voice, the reception unit accurately inputs the URL using voice recognition technology. Furthermore, when the user inputs a URL using text, the reception unit can provide an autocomplete function to simplify input. For example, when the user inputs a URL using an image, the reception unit can extract the URL using image recognition technology. This improves input convenience by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0072] The reception unit can estimate the user's emotions and determine the priority of URLs to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The reception unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. For example, if the user is feeling anxious, the reception unit can prioritize displaying highly reliable URLs. If the user is relaxed, the reception unit can prioritize displaying interesting URLs. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying URLs that can be accessed quickly. This improves user convenience by determining the priority of URLs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When a URL is input, the reception unit can prioritize inputting highly relevant URLs by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit can identify the user's location information using GPS data or an IP address. The reception unit can also acquire the user's geographical location information by using a location information service. For example, if the user is in a specific area, the reception unit can prioritize displaying URLs of online shopping sites related to that area. Furthermore, if the user is traveling, the reception unit can prioritize displaying URLs of online shopping sites related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize displaying URLs of local online shopping sites. This makes it possible to provide highly relevant URLs by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant URLs.

[0074] The reception unit can analyze the user's social media activity when a URL is input and input related URLs. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit automatically displays URLs shared by the user on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related URLs. For example, the reception unit can suggest related URLs by referring to the activity of the user's friends on social media. In this way, highly relevant URLs can be provided by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related URLs.

[0075] The reception unit can customize the input method by reflecting the user's past feedback when entering a URL. The reception unit, for example, acquires the user's past feedback. For example, the reception unit customizes the input method based on the user's ratings and comments. The reception unit can also customize the input method based on the user's past usage history. For example, the reception unit preferentially suggests input methods that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit adjusts the input interface to avoid input methods that the user has been dissatisfied with in the past. This allows the user to be provided with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit provides concise analysis results that focus on the main points. This allows the analysis results to be easily understood by adjusting the way the analysis is presented depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the site. The analysis unit, for example, evaluates the importance of the site. For example, the analysis unit evaluates the importance based on the number of visitors and transaction volume of the site. The analysis unit can also evaluate the importance based on industry influence. For example, the analysis unit performs a detailed analysis for a site with high importance. The analysis unit can also perform a simplified analysis for a site with low importance. Furthermore, the analysis unit performs an analysis with a moderate level of detail for a site with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the site. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the site. The analysis unit, for example, identifies the category of the site. For example, in the case of a fashion site, the analysis unit may perform an analysis that emphasizes product reviews and ratings. In addition, in the case of an electronics site, the analysis unit may perform an analysis that emphasizes product specifications and technical information. Furthermore, in the case of a food site, the analysis unit may perform an analysis that emphasizes product safety and quality. This enables highly accurate analysis by applying an analysis algorithm depending on the site category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input site category data into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, acquires the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past analysis data and feedback results. The analysis unit can also adjust the analysis algorithm based on past analysis results. For example, the analysis unit performs analysis by referring to the characteristics of sites that the user has previously rated highly. The analysis unit can also perform analysis in a way that avoids the characteristics of sites that the user has previously rated poorly. In this way, by referring to past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis length to be adjusted according to the user's emotions, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can determine analysis priorities based on the registration date of the site. The analysis unit, for example, checks the registration date of the site. For example, the analysis unit checks the domain registration date and the publication date of the site to evaluate the reliability of the site. The analysis unit can also check the update history of the site. For example, the analysis unit checks whether the site is frequently updated and evaluates its reliability. Furthermore, the analysis unit can prioritize analysis of sites registered within a specific period. This enables efficient analysis by determining the analysis priorities based on the registration date of the site. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site registration date data into the generation AI and have the generation AI determine the analysis priorities.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sites. The analysis unit, for example, evaluates the relevance of the sites. For example, the analysis unit prioritizes analyzing sites in categories in which the user is interested. The analysis unit can also prioritize analyzing sites that the user has visited in the past. Furthermore, the analysis unit prioritizes analyzing sites that are highly relevant based on the user's search history. This enables efficient analysis by adjusting the order of analysis based on the relevance of the sites. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input site relevance data into the generation AI and cause the generation AI to adjust the order of analysis.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history. The analysis unit can also evaluate the level of expertise based on survey results. For example, if the user has technical expertise, the analysis unit provides analysis results using detailed technical terminology. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit provides analysis results using appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0084] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can analyze the user's facial expressions using facial expression recognition technology to estimate the emotions. The providing unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information that focuses on the main points. This allows the information presentation method to be adjusted according to the user's emotions, thereby providing information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The providing unit can adjust the level of detail of the analysis results based on the importance of the analysis results when providing the analysis results. The providing unit, for example, evaluates the importance of the analysis results. For example, the providing unit evaluates the importance based on the impact of the analysis results or the user's interest. The providing unit can also evaluate the importance based on the urgency of the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. Furthermore, the providing unit provides information with an appropriate level of detail for analysis results with medium importance. This enables efficient information provision by adjusting the level of detail of the analysis results based on the importance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0086] The providing unit can improve the accuracy of the information provided by referring to the user's past providing results when providing the information. The providing unit, for example, acquires the user's past providing results. For example, the providing unit improves the accuracy of the information provided based on past providing data and feedback results. The providing unit can also adjust the providing algorithm based on the past providing results. For example, the providing unit provides information by referring to the characteristics of information that the user has previously rated highly. The providing unit can also provide information in a way that avoids the characteristics of information that the user has previously rated poorly. In this way, the accuracy of the information provided is improved by referring to the past providing results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past providing result data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0087] The providing unit can customize the provided content based on the user's current areas of interest at the time of providing the content. The providing unit, for example, identifies the user's current areas of interest. For example, the providing unit provides relevant information based on keywords recently searched by the user. The providing unit can also provide information based on categories in which the user is interested (e.g., fashion, electronics, etc.). Furthermore, the providing unit analyzes the user's past browsing history and provides highly relevant information. This allows the provided content to be customized based on the user's areas of interest, thereby providing highly relevant information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's area of ​​interest data into a generating AI and cause the generating AI to customize the provided content.

[0088] The providing unit can estimate the user's emotions and determine the priority of information to provide based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The providing unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing highly reliable information. If the user is relaxed, the providing unit can prioritize providing interesting information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that can be accessed quickly. In this way, by determining the priority of information according to the user's emotions, it is possible to provide optimal information to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit, for example, acquires the user's geographical location information. For example, the providing unit identifies the user's location information using GPS data or an IP address. The providing unit can also acquire the user's geographical location information using a location information service. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, if the user is at home, the providing unit prioritizes providing local information. In this way, highly relevant information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0090] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit automatically displays information shared by the user on social media as candidates. The providing unit can also analyze the content of the user's social media posts and suggest related information. Furthermore, the providing unit can suggest related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing social media activity. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data to a generation AI and cause the generation AI to select related information.

[0091] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, acquires the user's past feedback. For example, the providing unit customizes the delivery method based on the user's ratings and comments. The providing unit can also customize the delivery method based on the user's past usage history. For example, the providing unit preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also customize the delivery interface based on the user's past feedback. For example, the providing unit adjusts the interface to avoid delivery methods that the user has been dissatisfied with in the past. This allows the delivery method that is optimal for the user to be provided by reflecting the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past feedback data into a generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a URL of an online shopping site entered by a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and determine the risk level based on the content, structure, and past evaluation of the URL. For example, the provision unit can provide the analysis result to the user via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a URL of an online shopping site entered by a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and determines the risk level based on the content, structure, and past evaluation of the URL. For example, the provision unit can provide the analysis result to the user via the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive a URL of an online shopping site entered by a user via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and determines the risk level based on the content, structure, and past evaluations of the URL. For example, the provision unit can provide the analysis result to the user via the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive a URL of an online shopping site entered by a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and determines the risk level based on the content, structure, and past evaluations of the URL. For example, the provision unit can provide the analysis result to the user via the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can analyze environmental sounds when the user is entering input and suggest the optimal input method. For example, if the user is in a noisy environment, the reception unit can recommend text input over voice input. The reception unit can also suggest voice input as a priority when the user is in a quiet environment. Furthermore, if the user is on the move, the reception unit can provide an interface that allows the user to enter a URL with a simple tap operation. This improves input convenience by providing the optimal input method according to the user's environment.

[0094] The analysis unit can evaluate how frequently a site's content is updated to determine its trustworthiness. For example, the analysis unit can check how frequently a site's blog or news section is updated. The analysis unit can also evaluate how frequently product inventory information and prices are updated. Furthermore, the analysis unit can check the site's maintenance history to determine whether it is updated regularly. This allows the reliability of a site to be determined by evaluating how frequently its content is updated.

[0095] The analysis unit can evaluate the security measures of a site and determine its trustworthiness. For example, the analysis unit can check whether the site has implemented two-step authentication. The analysis unit can also evaluate whether the site regularly applies security patches. Furthermore, the analysis unit can check whether the site encrypts and stores user data. In this way, the reliability of a site can be determined by evaluating its security measures.

[0096] The analysis unit can evaluate the performance of a site and determine its usability. For example, the analysis unit measures the loading speed of a site. The analysis unit can also evaluate the response time of a site. Furthermore, the analysis unit can check the mobile compatibility of a site and determine its usability. In this way, usability can be determined by evaluating the performance of a site.

[0097] The analysis unit can analyze the site's social media ratings to determine its trustworthiness. For example, the analysis unit can check the number of followers of the site's official social media accounts. The analysis unit can also analyze user comments and ratings on social media. Furthermore, the analysis unit can evaluate the number of shares and likes on social media to determine its trustworthiness. In this way, the analysis unit can determine the site's trustworthiness by analyzing the site's social media ratings.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can display the analysis results in colors and designs that give a sense of security. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. Furthermore, if the user is relaxed, the analysis unit can also provide detailed analysis results. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0099] The providing unit can estimate the user's emotions and adjust the amount of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can reduce the amount of information and provide concise information. If the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide short information that focuses on the main points. In this way, by adjusting the amount of information according to the user's emotions, it is possible to provide optimal information for the user.

[0100] The providing unit can estimate the user's emotion and adjust the format of the information to be provided based on the estimated user's emotion. For example, if the user prefers visual information, the providing unit can provide the information in a graphical display. If the user prefers text information, the providing unit can also provide the information in detailed text. Furthermore, if the user prefers audio information, the providing unit can provide the information in audio. In this way, by adjusting the format of the information according to the user's emotion, it is possible to provide information that is easy for the user to understand.

[0101] The providing unit can estimate the user's emotions and adjust the timing of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information slowly. If the user is in a hurry, the providing unit can also provide information that quickly covers the main points. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. In this way, by adjusting the timing of information according to the user's emotions, it is possible to provide optimal information for the user.

[0102] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide highly reliable information with priority. Also, if the user is relaxed, the providing unit can provide interesting information with priority. Furthermore, if the user is in a hurry, the providing unit can provide information that can be accessed quickly with priority. In this way, by determining the priority of information according to the user's emotions, it is possible to provide the most suitable information for the user.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives the URL of a shopping site entered by the user. For example, the user can manually enter the URL of a specific shopping site. The reception unit can also receive the URL by copy and paste, voice input, or other methods. Step 2: The analysis unit analyzes the URL entered by the reception unit and determines the risk level based on the site's content, structure, and past ratings. For example, the analysis unit checks whether or not the site has an SSL certificate and evaluates the site's reliability based on its issuer and expiration date. It also analyzes past user reviews and determines the site's rating based on the review content and rating score. It also evaluates the site's design and structure, and evaluates it based on factors such as usability, visual beauty, and ease of navigation. It also checks the domain registration information and evaluates the site's reliability based on registrant information, registration date, and registration agency. Step 3: The providing unit provides the determination result obtained by the analysis unit to the user. For example, the analysis result can be provided to the user in text format or in a graphical display. Notification methods such as email and push notification can also be used.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 desk where you can enter the URL of the online shopping site, an analysis unit that analyzes the URL input by the reception unit and determines the risk level based on the content, structure, and past evaluation of the site; a providing unit that provides a user with the determination result obtained by the analysis unit. A system characterized by:

2. The analysis unit Check for the presence of an SSL certificate 2. The system of claim 1.

3. The analysis unit Analyze past user reviews 2. The system of claim 1.

4. The analysis unit Evaluate the design or structure of the site 2. The system of claim 1.

5. The analysis unit Check your domain registration information 2. The system of claim 1.

6. The providing unit The determination result obtained by the analysis unit is provided to the user.

2. The system of claim 1.

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

2. The system of claim 1.

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

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