system

Abstract

The system according to this embodiment aims to enable users to efficiently select the most suitable products and services. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes the optimal option based on the results of the analysis performed by the analysis unit. The provision unit provides the option proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes time to compare multiple sites when purchasing daily necessities or food products, searching for a parking lot at the destination, etc., and it is difficult to find the optimal option.

[0005] The system according to the embodiment aims to enable a user to efficiently select an optimal product or service.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes the optimal option based on the results of the analysis performed by the analysis unit. The provision unit provides the option proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to efficiently select the most suitable products and services. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online platform according to an embodiment of the present invention is a system for eliminating the need to search and compare multiple websites when purchasing daily necessities or groceries, or when searching for parking while traveling by car. This online platform allows users to compare products from various perspectives, such as price, quality, function, and ratings. An AI algorithm analyzes a large amount of data and proposes the optimal choice that suits the user's needs and preferences. Users can use this service to help them choose the best product or service that suits their individual needs. For example, the user inputs information they want to compare, such as information for purchasing daily necessities or groceries, or information for searching for parking while traveling by car. This information is input into the AI. Next, the AI ​​analyzes the input information and compares various products and services. The AI ​​analyzes data such as price, quality, function, and ratings of products and proposes the optimal choice that suits the user's needs and preferences. For example, if a user is looking for groceries of a specific brand, the AI ​​will propose the highest-rated and reasonably priced product within that brand. Furthermore, the AI ​​learns the user's past behavior and preferences and provides customized recommendations. For example, based on data of products and services the user has purchased in the past, the AI ​​will propose new products and services that suit the user's preferences. This allows the user to find the best choice for themselves. Furthermore, the AI ​​updates market information in real time, providing users with information such as product price fluctuations and new product releases. This allows users to always compare products and services based on the latest information. Finally, the comparison results can be viewed in a spreadsheet or similar format. Users can see the comparison results at a glance and choose the best option. For example, they can compare the fees and availability of multiple parking lots and choose the most suitable one. This system allows users to save time and effort and efficiently compare products and services. It also enables them to find the best option based on reliable information and objective analysis. For example, when users are purchasing daily necessities or groceries, or looking for parking when traveling by car, they can use this service to efficiently find the best option.This allows online platforms to efficiently receive and analyze user information, and then propose and provide the most suitable options.

[0029] The online platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives information from users. User information includes, but is not limited to, information regarding the purchase of daily necessities and groceries, or information regarding finding parking when traveling by car. The reception unit receives information entered by users in digital format, for example. The reception unit can also receive information in various formats, such as voice input and image input. For example, the reception unit receives information entered by users using smartphones or personal computers. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes data to suggest the best options that match the user's needs and preferences, for example, using an AI algorithm. The analysis unit analyzes data such as product price, quality, function, and evaluation. For example, the analysis unit collects and analyzes data on product price fluctuations and quality evaluations. The analysis unit can also learn the user's past behavior and preferences and analyze data to provide customized recommendations. For example, the analysis unit suggests new products and services that match the user's preferences based on data of products and services the user has purchased in the past. The proposal unit proposes the optimal option based on the results analyzed by the analysis unit. For example, the proposal unit proposes products and services that match the user's needs and preferences. The proposal unit proposes the optimal option to the user based on data such as product price, quality, function, and ratings. For example, if the user is looking for a specific brand of food products, the proposal unit will propose the highest-rated and reasonably priced product within that brand. The supply unit provides the options proposed by the proposal unit. For example, the supply unit provides the user with detailed information on the proposed products and services. The supply unit can also provide comparison results in a spreadsheet or similar format. For example, the supply unit compares the fees and availability of multiple parking lots and provides information to help the user choose the most suitable parking lot. As a result, the online platform according to the embodiment can efficiently receive, analyze, propose, and provide user information and optimal options.

[0030] The reception department receives information from users. This information includes, but is not limited to, information about purchasing daily necessities and groceries, or information about finding parking when traveling by car. The reception department accepts information entered by users in digital format. It can also accept information in various other formats, such as voice input and image input. For example, the reception department accepts information entered by users using smartphones or personal computers. Specifically, it can accept text information entered by users through smartphone applications, information entered via voice recognition technology, and even image information captured using a camera. This allows users to provide information in the most convenient way for them. Furthermore, the reception department has the function of centrally managing this information and storing it in a database. For example, information entered by users is stored in the database in real time, making it accessible to subsequent analysis and proposal departments. The reception department also implements security measures such as information encryption and access control to protect user privacy. This allows users to provide information with confidence. Furthermore, the reception area prioritizes user-friendliness, employing an intuitive design. For example, voice input allows users to input information simply by speaking, and image input allows for easy uploading of images captured with a camera. This enables the reception area to efficiently and reliably receive information from users.

[0031] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses AI algorithms to analyze data in order to suggest the best options that match the user's needs and preferences. Specifically, it analyzes data such as product price, quality, features, and ratings. For instance, the analysis unit collects and analyzes data on product price fluctuations and quality ratings. The analysis unit can also learn from the user's past behavior and preferences and analyze data to provide customized recommendations. For example, based on data of products and services the user has previously purchased, the analysis unit can suggest new products and services that match the user's preferences. AI algorithms, using machine learning and deep learning technologies, can analyze vast amounts of data quickly and accurately. For example, natural language processing technology is used to analyze text information entered by the user to understand the user's intent and needs. Image recognition technology is also used to extract product features from images uploaded by the user and identify appropriate products. Furthermore, the analysis unit can analyze data in real time and provide rapid feedback to the user. For example, when a user searches for a product, it can instantly display a list of related products. The analysis unit can also perform trend analysis based on past data to predict future demand. This allows the analysis unit to efficiently analyze data to propose the optimal option tailored to user needs, thereby improving the overall accuracy and reliability of the system.

[0032] The suggestion department proposes the optimal options based on the results analyzed by the analysis department. For example, the suggestion department proposes products and services that match the user's needs and preferences. Based on data such as product price, quality, function, and ratings, the suggestion department proposes the best options for the user. For example, if a user is looking for a specific brand of food products, the suggestion department will propose the highest-rated and reasonably priced product within that brand. Specifically, based on data provided by the analysis department, the suggestion department considers the user's past purchase and search history to list products and services that match the user's preferences. Furthermore, the suggestion department can also make suggestions tailored to the user's current situation and environment. For example, it may suggest services and stores available near the user's current location. In addition, the suggestion department displays the suggested content in a visually easy-to-understand format through the user interface. For example, it may display product images, ratings, and prices in a list so that users can easily compare them. Furthermore, the suggestion department can collect feedback from users and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user purchases a suggested product, the suggestion department will adjust the next suggestion based on that result. The suggestion department can also flexibly update its suggestions in response to changes in the user's preferences and needs. This allows the proposal department to consistently provide users with the best possible options, thereby improving user satisfaction.

[0033] The service provider provides the options proposed by the suggestion provider. For example, the service provider provides users with detailed information on the products and services suggested to them. The service provider can also provide comparison results in spreadsheets or other formats. For example, the service provider compares the fees and availability of multiple parking lots and provides information to help users choose the most suitable parking lot. Specifically, the service provider displays a list of detailed specifications, prices, ratings, and available options for the suggested products and services, making it easy for users to compare them. The service provider also supports the procedures for users to actually use the suggested products and services. For example, it makes it easy to complete online purchase and reservation procedures. Furthermore, the service provider provides information compatible with multiple platforms and devices to improve user convenience. For example, it allows users to access information anytime, anywhere through smartphone apps and websites. The service provider can also collect user feedback and continuously improve the accuracy and usefulness of the information it provides. For example, it collects feedback on the results of actions taken by users based on the information provided and reflects this in future information provision. In this way, the service provider can always provide users with the latest and most accurate information and support their decision-making. Furthermore, the service provider has implemented security measures regarding the handling of information to protect user privacy. This allows users to use the information with peace of mind.

[0034] The recommendation unit can learn the user's past behavior and preferences and provide customized recommendations. For example, the recommendation unit can suggest new products and services that match the user's preferences based on data of products and services the user has purchased in the past. For example, the recommendation unit can analyze ratings and reviews of products the user has purchased in the past and suggest products that match the user's preferences. The recommendation unit can also learn patterns of user preferences based on the user's past behavior data and provide customized recommendations. For example, the recommendation unit can suggest new products that match the user's preferences based on the categories and brands of products the user has purchased in the past. This allows for more appropriate suggestions based on the user's past behavior and preferences. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's past behavior data into a generating AI and have the generating AI perform the generation of customized recommendations.

[0035] The analysis unit can analyze data such as product price, quality, function, and evaluation. For example, the analysis unit collects and analyzes data on product price fluctuations and quality evaluations. For example, the analysis unit analyzes price fluctuation patterns based on product price data. The analysis unit can also analyze quality evaluation criteria based on product quality evaluation data. For example, the analysis unit analyzes the details of a product's functions based on product function data. The analysis unit can also analyze the reliability of evaluations based on product evaluation data. For example, the analysis unit analyzes the reliability of evaluations based on product reviews and ratings. By analyzing data such as product price, quality, function, and evaluation, the analysis unit can provide users with the best possible choices. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product price data into a generating AI and have the generating AI perform an analysis of price fluctuation patterns.

[0036] The service provider can provide comparison results in a spreadsheet. For example, the service provider can provide the price, quality, features, and ratings of multiple products in a spreadsheet format. For example, the service provider can compile data such as the price, quality, features, and ratings of products into a spreadsheet and provide it to the user. The service provider can also customize the format and method of providing the spreadsheet. For example, the service provider can adjust the layout and display format of the spreadsheet according to the user's needs. By providing comparison results in a spreadsheet, the user can see the comparison results at a glance. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input product comparison data into a generating AI and have the generating AI generate the spreadsheet.

[0037] The analysis unit can update market information in real time and provide information on product price fluctuations and new product releases. For example, the analysis unit can collect the latest market information from the internet and update it in real time. For example, the analysis unit can collect product price fluctuation data and update it in real time. The analysis unit can also collect new product release information and provide it to users. For example, the analysis unit can collect news articles and press releases from the internet and provide new product release information. This allows users to compare products and services based on the latest information at all times by updating market information in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input market information into a generating AI and have the generating AI perform real-time updates.

[0038] The reception desk can analyze the user's past information input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze patterns of information entered by the user in the past and automatically generate the optimal input form. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past information input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past information input history into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying nearby stores and services based on the user's current location. For example, the reception unit can display only relevant products based on the user's areas of interest. The reception unit can also filter and display appropriate information according to the user's current situation (e.g., traveling, resting, etc.). By filtering information based on the user's current situation and areas of interest, it is possible to provide more relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0040] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about stores and services close to the user's current location. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving information about their travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant information.

[0041] The reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, the reception unit can receive information about related products based on information shared by the user on social media. For example, the reception unit can analyze a user's interests on social media and receive information about related services. The reception unit can also receive information about related events based on the user's social media activity history. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for a more detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a price comparison algorithm to product price information. For example, the analysis unit can apply a quality evaluation algorithm to product quality information. Furthermore, the analysis unit can also apply a function comparison algorithm to product function information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit will prioritize the analysis of the most recent information. For example, the analysis unit will lower the priority of analysis for information that has been submitted a long time ago. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for prioritizing the analysis of the most recent information by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit can also adjust the analysis schedule based on the relevance of the information. This allows for prioritizing the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the options. For example, it can provide detailed proposals for high-importance options and simplified proposals for low-importance options. The proposal unit can also determine the priority of proposals based on the importance of the options. This allows for more detailed proposals for more important options by adjusting the level of detail based on the importance of the options. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input option importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the category of the options when making suggestions. For example, the suggestion unit can apply a price comparison algorithm to product price information. For example, the suggestion unit can apply a quality evaluation algorithm to product quality information. Furthermore, the suggestion unit can also apply a function comparison algorithm to product function information. By applying different suggestion algorithms depending on the category of the options, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category data of the options into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0048] The proposal department can determine the priority of proposals based on when the options were submitted. For example, the proposal department will prioritize the most recent options. For example, it will lower the priority of older options. The proposal department can also adjust the proposal schedule based on the submission dates. This allows the proposal department to prioritize the most recent options by determining the priority of proposals based on the submission dates. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission date data of the options into a generating AI and have the generating AI perform the determination of proposal priorities.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the options when making a proposal. For example, the proposal unit will prioritize proposing options that are highly relevant. For example, the proposal unit will postpone proposing options that are less relevant. The proposal unit can also adjust the proposal schedule based on the relevance of the options. This allows for prioritizing the proposal of more relevant options by adjusting the order of proposals based on the relevance of the options. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevance data of the options into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0050] The service provider can select the optimal display method by referring to the user's past behavior history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. For example, the service provider may predict and provide the optimal display method based on the user's past behavior history. The service provider can also provide customized display methods based on the user's past behavior history. This allows the service provider to select the optimal display method by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past behavior history data into a generating AI and have the generating AI select the optimal display method.

[0051] The service provider can customize the information display method based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide a concise and highly visible display method. For example, if the user is taking a break, the service provider can provide a display method that includes detailed information. The service provider can also provide the most appropriate display method according to the user's current situation. This allows for the provision of more appropriate information by customizing the information display method based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the customization of the information display method.

[0052] The information delivery unit can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information delivery unit may prioritize providing information about stores and services close to the user's current location. For example, if the user is in a specific region, the information delivery unit may prioritize providing information related to that region. Furthermore, if the user is traveling, the information delivery unit may prioritize providing information about their travel destination. In this way, the optimal information delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit may input the user's geographical location information data into a generating AI and have the generating AI select the optimal information delivery method.

[0053] The information provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the information provider can provide information on related products based on information shared by the user on social media. For example, the information provider can analyze the user's interests on social media and provide information on related services. The information provider can also provide information on related events based on the user's social media activity history. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity data into a generating AI and have the generating AI propose means of providing information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a user's past behavior history and select the most suitable reception method. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns in information entered by the user in the past and automatically generate the most suitable input form. Furthermore, it can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, the system can select the most suitable reception method by analyzing the user's past behavior history.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. It can also determine the priority of the analysis according to the importance of the information. This allows for a more detailed analysis of more important information by adjusting the level of detail based on the importance of the information.

[0057] The proposal function can apply different proposal algorithms depending on the category of the options. For example, a price comparison algorithm can be applied to product price information, a quality evaluation algorithm to product quality information, and a function comparison algorithm to product function information. By applying different proposal algorithms depending on the category of the options, more appropriate proposals can be provided.

[0058] The information delivery system can select the most appropriate method of information delivery by considering the user's geographical location. For example, it can prioritize providing information about stores and services close to the user's current location. If the user is in a specific region, it can prioritize providing information related to that region. Furthermore, if the user is traveling, it can prioritize providing information about their travel destination. This allows the system to select the most appropriate method of information delivery by considering the user's geographical location.

[0059] The information provision department can analyze users' social media activity and propose means of providing information. For example, it can provide information on related products based on information users have shared on social media. It can also analyze users' interests on social media and provide information on related services. Furthermore, it can provide information on related events based on users' social media activity history. In this way, by analyzing users' social media activity, it is possible to provide relevant information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives information from users. This information includes details about purchasing daily necessities and groceries, as well as information for finding parking when traveling by car. The reception desk can accept information entered by users in digital format, as well as in various other formats such as voice input and image input. For example, it can accept information entered by users using smartphones or personal computers. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI algorithms to analyze data in order to suggest the best options that match the user's needs and preferences. It analyzes data such as product price, quality, features, and ratings, and analyzes data to provide customized recommendations by learning the user's past behavior and preferences. For example, it collects and analyzes data on product price fluctuations and quality ratings. Step 3: The Proposal Department proposes the optimal option based on the results analyzed by the Analysis Department. The Proposal Department suggests products and services that match the user's needs and preferences, and proposes the optimal option based on data such as price, quality, function, and ratings. For example, if a user is looking for a specific brand of food products, the Proposal Department will suggest the highest-rated and reasonably priced product within that brand. Step 4: The supply department provides the options proposed by the proposal department. The supply department provides users with detailed information about the proposed products and services, and can also provide comparison results in a spreadsheet or similar format. For example, it can compare the fees and availability of multiple parking lots and provide information to help users choose the most suitable parking lot.

[0062] (Example of form 2) An online platform according to an embodiment of the present invention is a system for eliminating the need to search and compare multiple websites when purchasing daily necessities or groceries, or when searching for parking while traveling by car. This online platform allows users to compare products from various perspectives, such as price, quality, function, and ratings. An AI algorithm analyzes a large amount of data and proposes the optimal choice that suits the user's needs and preferences. Users can use this service to help them choose the best product or service that suits their individual needs. For example, the user inputs information they want to compare, such as information for purchasing daily necessities or groceries, or information for searching for parking while traveling by car. This information is input into the AI. Next, the AI ​​analyzes the input information and compares various products and services. The AI ​​analyzes data such as price, quality, function, and ratings of products and proposes the optimal choice that suits the user's needs and preferences. For example, if a user is looking for groceries of a specific brand, the AI ​​will propose the highest-rated and reasonably priced product within that brand. Furthermore, the AI ​​learns the user's past behavior and preferences and provides customized recommendations. For example, based on data of products and services the user has purchased in the past, the AI ​​will propose new products and services that suit the user's preferences. This allows the user to find the best choice for themselves. Furthermore, the AI ​​updates market information in real time, providing users with information such as product price fluctuations and new product releases. This allows users to always compare products and services based on the latest information. Finally, the comparison results can be viewed in a spreadsheet or similar format. Users can see the comparison results at a glance and choose the best option. For example, they can compare the fees and availability of multiple parking lots and choose the most suitable one. This system allows users to save time and effort and efficiently compare products and services. It also enables them to find the best option based on reliable information and objective analysis. For example, when users are purchasing daily necessities or groceries, or looking for parking when traveling by car, they can use this service to efficiently find the best option.This allows online platforms to efficiently receive and analyze user information, and then propose and provide the most suitable options.

[0063] The online platform according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit receives information from users. User information includes, but is not limited to, information regarding the purchase of daily necessities and groceries, or information regarding finding parking when traveling by car. The reception unit receives information entered by users in digital format, for example. The reception unit can also receive information in various formats, such as voice input and image input. For example, the reception unit receives information entered by users using smartphones or personal computers. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes data to suggest the best options that match the user's needs and preferences, for example, using an AI algorithm. The analysis unit analyzes data such as product price, quality, function, and evaluation. For example, the analysis unit collects and analyzes data on product price fluctuations and quality evaluations. The analysis unit can also learn the user's past behavior and preferences and analyze data to provide customized recommendations. For example, the analysis unit suggests new products and services that match the user's preferences based on data of products and services the user has purchased in the past. The proposal unit proposes the optimal option based on the results analyzed by the analysis unit. For example, the proposal unit proposes products and services that match the user's needs and preferences. The proposal unit proposes the optimal option to the user based on data such as product price, quality, function, and ratings. For example, if the user is looking for a specific brand of food products, the proposal unit will propose the highest-rated and reasonably priced product within that brand. The supply unit provides the options proposed by the proposal unit. For example, the supply unit provides the user with detailed information on the proposed products and services. The supply unit can also provide comparison results in a spreadsheet or similar format. For example, the supply unit compares the fees and availability of multiple parking lots and provides information to help the user choose the most suitable parking lot. As a result, the online platform according to the embodiment can efficiently receive, analyze, propose, and provide user information and optimal options.

[0064] The reception department receives information from users. This information includes, but is not limited to, information about purchasing daily necessities and groceries, or information about finding parking when traveling by car. The reception department accepts information entered by users in digital format. It can also accept information in various other formats, such as voice input and image input. For example, the reception department accepts information entered by users using smartphones or personal computers. Specifically, it can accept text information entered by users through smartphone applications, information entered via voice recognition technology, and even image information captured using a camera. This allows users to provide information in the most convenient way for them. Furthermore, the reception department has the function of centrally managing this information and storing it in a database. For example, information entered by users is stored in the database in real time, making it accessible to subsequent analysis and proposal departments. The reception department also implements security measures such as information encryption and access control to protect user privacy. This allows users to provide information with confidence. Furthermore, the reception area prioritizes user-friendliness, employing an intuitive design. For example, voice input allows users to input information simply by speaking, and image input allows for easy uploading of images captured with a camera. This enables the reception area to efficiently and reliably receive information from users.

[0065] The analysis unit analyzes information received by the reception unit. For example, the analysis unit uses AI algorithms to analyze data in order to suggest the best options that match the user's needs and preferences. Specifically, it analyzes data such as product price, quality, features, and ratings. For instance, the analysis unit collects and analyzes data on product price fluctuations and quality ratings. The analysis unit can also learn from the user's past behavior and preferences and analyze data to provide customized recommendations. For example, based on data of products and services the user has previously purchased, the analysis unit can suggest new products and services that match the user's preferences. AI algorithms, using machine learning and deep learning technologies, can analyze vast amounts of data quickly and accurately. For example, natural language processing technology is used to analyze text information entered by the user to understand the user's intent and needs. Image recognition technology is also used to extract product features from images uploaded by the user and identify appropriate products. Furthermore, the analysis unit can analyze data in real time and provide rapid feedback to the user. For example, when a user searches for a product, it can instantly display a list of related products. The analysis unit can also perform trend analysis based on past data to predict future demand. This allows the analysis unit to efficiently analyze data to propose the optimal option tailored to user needs, thereby improving the overall accuracy and reliability of the system.

[0066] The suggestion department proposes the optimal options based on the results analyzed by the analysis department. For example, the suggestion department proposes products and services that match the user's needs and preferences. Based on data such as product price, quality, function, and ratings, the suggestion department proposes the best options for the user. For example, if a user is looking for a specific brand of food products, the suggestion department will propose the highest-rated and reasonably priced product within that brand. Specifically, based on data provided by the analysis department, the suggestion department considers the user's past purchase and search history to list products and services that match the user's preferences. Furthermore, the suggestion department can also make suggestions tailored to the user's current situation and environment. For example, it may suggest services and stores available near the user's current location. In addition, the suggestion department displays the suggested content in a visually easy-to-understand format through the user interface. For example, it may display product images, ratings, and prices in a list so that users can easily compare them. Furthermore, the suggestion department can collect feedback from users and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user purchases a suggested product, the suggestion department will adjust the next suggestion based on that result. The suggestion department can also flexibly update its suggestions in response to changes in the user's preferences and needs. This allows the proposal department to consistently provide users with the best possible options, thereby improving user satisfaction.

[0067] The service provider provides the options proposed by the suggestion provider. For example, the service provider provides users with detailed information on the products and services suggested to them. The service provider can also provide comparison results in spreadsheets or other formats. For example, the service provider compares the fees and availability of multiple parking lots and provides information to help users choose the most suitable parking lot. Specifically, the service provider displays a list of detailed specifications, prices, ratings, and available options for the suggested products and services, making it easy for users to compare them. The service provider also supports the procedures for users to actually use the suggested products and services. For example, it makes it easy to complete online purchase and reservation procedures. Furthermore, the service provider provides information compatible with multiple platforms and devices to improve user convenience. For example, it allows users to access information anytime, anywhere through smartphone apps and websites. The service provider can also collect user feedback and continuously improve the accuracy and usefulness of the information it provides. For example, it collects feedback on the results of actions taken by users based on the information provided and reflects this in future information provision. In this way, the service provider can always provide users with the latest and most accurate information and support their decision-making. Furthermore, the service provider has implemented security measures regarding the handling of information to protect user privacy. This allows users to use the information with peace of mind.

[0068] The recommendation unit can learn the user's past behavior and preferences and provide customized recommendations. For example, the recommendation unit can suggest new products and services that match the user's preferences based on data of products and services the user has purchased in the past. For example, the recommendation unit can analyze ratings and reviews of products the user has purchased in the past and suggest products that match the user's preferences. The recommendation unit can also learn patterns of user preferences based on the user's past behavior data and provide customized recommendations. For example, the recommendation unit can suggest new products that match the user's preferences based on the categories and brands of products the user has purchased in the past. This allows for more appropriate suggestions based on the user's past behavior and preferences. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input the user's past behavior data into a generating AI and have the generating AI perform the generation of customized recommendations.

[0069] The analysis unit can analyze data such as product price, quality, function, and evaluation. For example, the analysis unit collects and analyzes data on product price fluctuations and quality evaluations. For example, the analysis unit analyzes price fluctuation patterns based on product price data. The analysis unit can also analyze quality evaluation criteria based on product quality evaluation data. For example, the analysis unit analyzes the details of a product's functions based on product function data. The analysis unit can also analyze the reliability of evaluations based on product evaluation data. For example, the analysis unit analyzes the reliability of evaluations based on product reviews and ratings. By analyzing data such as product price, quality, function, and evaluation, the analysis unit can provide users with the best possible choices. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product price data into a generating AI and have the generating AI perform an analysis of price fluctuation patterns.

[0070] The service provider can provide comparison results in a spreadsheet. For example, the service provider can provide the price, quality, features, and ratings of multiple products in a spreadsheet format. For example, the service provider can compile data such as the price, quality, features, and ratings of products into a spreadsheet and provide it to the user. The service provider can also customize the format and method of providing the spreadsheet. For example, the service provider can adjust the layout and display format of the spreadsheet according to the user's needs. By providing comparison results in a spreadsheet, the user can see the comparison results at a glance. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input product comparison data into a generating AI and have the generating AI generate the spreadsheet.

[0071] The analysis unit can update market information in real time and provide information on product price fluctuations and new product releases. For example, the analysis unit can collect the latest market information from the internet and update it in real time. For example, the analysis unit can collect product price fluctuation data and update it in real time. The analysis unit can also collect new product release information and provide it to users. For example, the analysis unit can collect news articles and press releases from the internet and provide new product release information. This allows users to compare products and services based on the latest information at all times by updating market information in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input market information into a generating AI and have the generating AI perform real-time updates.

[0072] The reception unit can estimate the user's emotions and adjust the timing of information reception based on the estimated emotions. For example, if the user is stressed, the reception unit's AI can estimate their emotions and receive information during a time when they are relaxed. For example, if the user is in a hurry, the reception unit's AI can estimate their emotions and provide an interface for quick information reception. The reception unit can also estimate the user's emotions when they are relaxed and provide an interface that prompts for detailed information input. This allows for information to be received at a more appropriate time by adjusting the timing of information reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information reception.

[0073] The reception desk can analyze the user's past information input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze patterns of information entered by the user in the past and automatically generate the optimal input form. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past information input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past information input history into a generating AI and have the generating AI select the optimal reception method.

[0074] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying nearby stores and services based on the user's current location. For example, the reception unit can display only relevant products based on the user's areas of interest. The reception unit can also filter and display appropriate information according to the user's current situation (e.g., traveling, resting, etc.). By filtering information based on the user's current situation and areas of interest, it is possible to provide more relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0075] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving high-priority information. If the user is relaxed, the reception desk will prioritize receiving detailed information. The reception desk can also prioritize receiving information that needs to be processed quickly if the user is in a hurry. In this way, by prioritizing information according to the user's emotions, more important information can be received first. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0076] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about stores and services close to the user's current location. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving information about their travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant information.

[0077] The reception unit can analyze a user's social media activity when receiving information and receive relevant information. For example, the reception unit can receive information about related products based on information shared by the user on social media. For example, the reception unit can analyze a user's interests on social media and receive information about related services. The reception unit can also receive information about related events based on the user's social media activity history. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result if the user is in a hurry. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for a more detailed analysis of important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a price comparison algorithm to product price information. For example, the analysis unit can apply a quality evaluation algorithm to product quality information. Furthermore, the analysis unit can also apply a function comparison algorithm to product function information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit will prioritize the analysis of the most recent information. For example, the analysis unit will lower the priority of analysis for information that has been submitted a long time ago. The analysis unit can also adjust the analysis schedule based on the submission date. This allows for prioritizing the analysis of the most recent information by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit can also adjust the analysis schedule based on the relevance of the information. This allows for prioritizing the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. This allows for more appropriate suggestions to be provided by adjusting the presentation of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0085] The proposal unit can adjust the level of detail of its proposals based on the importance of the options. For example, it can provide detailed proposals for high-importance options and simplified proposals for low-importance options. The proposal unit can also determine the priority of proposals based on the importance of the options. This allows for more detailed proposals for more important options by adjusting the level of detail based on the importance of the options. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input option importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the options when making suggestions. For example, the suggestion unit can apply a price comparison algorithm to product price information. For example, the suggestion unit can apply a quality evaluation algorithm to product quality information. Furthermore, the suggestion unit can also apply a function comparison algorithm to product function information. By applying different suggestion algorithms depending on the category of the options, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category data of the options into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. The suggestion unit can also provide visually stimulating suggestions if the user is excited. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0088] The proposal department can determine the priority of proposals based on when the options were submitted. For example, the proposal department will prioritize the most recent options. For example, it will lower the priority of older options. The proposal department can also adjust the proposal schedule based on the submission dates. This allows the proposal department to prioritize the most recent options by determining the priority of proposals based on the submission dates. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission date data of the options into a generating AI and have the generating AI perform the determination of proposal priorities.

[0089] The proposal unit can adjust the order of proposals based on the relevance of the options when making a proposal. For example, the proposal unit will prioritize proposing options that are highly relevant. For example, the proposal unit will postpone proposing options that are less relevant. The proposal unit can also adjust the proposal schedule based on the relevance of the options. This allows for prioritizing the proposal of more relevant options by adjusting the order of proposals based on the relevance of the options. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevance data of the options into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0090] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. The service provider can also provide a concise display method if the user is in a hurry. By adjusting how the information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust how the information is displayed.

[0091] The service provider can select the optimal display method by referring to the user's past behavior history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. For example, the service provider may predict and provide the optimal display method based on the user's past behavior history. The service provider can also provide customized display methods based on the user's past behavior history. This allows the service provider to select the optimal display method by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past behavior history data into a generating AI and have the generating AI select the optimal display method.

[0092] The service provider can customize the information display method based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide a concise and highly visible display method. For example, if the user is taking a break, the service provider can provide a display method that includes detailed information. The service provider can also provide the most appropriate display method according to the user's current situation. This allows for the provision of more appropriate information by customizing the information display method based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the customization of the information display method.

[0093] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the provider will prioritize providing information of high importance. If the user is relaxed, the provider will prioritize providing detailed information. The provider can also prioritize providing information that needs to be processed quickly if the user is in a hurry. In this way, by prioritizing information according to the user's emotions, more important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0094] The information delivery unit can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information delivery unit may prioritize providing information about stores and services close to the user's current location. For example, if the user is in a specific region, the information delivery unit may prioritize providing information related to that region. Furthermore, if the user is traveling, the information delivery unit may prioritize providing information about their travel destination. In this way, the optimal information delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit may input the user's geographical location information data into a generating AI and have the generating AI select the optimal information delivery method.

[0095] The information provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the information provider can provide information on related products based on information shared by the user on social media. For example, the information provider can analyze the user's interests on social media and provide information on related services. The information provider can also provide information on related events based on the user's social media activity history. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity data into a generating AI and have the generating AI propose means of providing information.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception system can estimate the user's emotions and customize how information is received based on those estimates. For example, if the user is stressed, it can provide a concise and quick input method. If the user is relaxed, it can provide an interface that encourages detailed information input. Furthermore, if the user is in a hurry, it can prioritize quick methods such as voice input. This allows the system to provide the most appropriate information reception method according to the user's emotions.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is stressed, it will prioritize analyzing information of high importance. If the user is relaxed, it will perform a more detailed analysis. Furthermore, if the user is in a hurry, it can provide analysis results quickly. This allows for more appropriate analysis results by adjusting the analysis priority according to the user's emotions.

[0099] The suggestion function can estimate the user's emotions and customize the content of suggestions based on those emotions. For example, if the user is stressed, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. This allows for more appropriate suggestions to be provided by customizing the content of suggestions according to the user's emotions.

[0100] The information provider can estimate the user's emotions and adjust how information is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can also provide a display that gets straight to the point. By adjusting how information is displayed according to the user's emotions, it is possible to provide more appropriate information.

[0101] The information delivery unit can estimate the user's emotions and prioritize the information to be delivered based on those emotions. For example, if the user is stressed, it will prioritize providing high-priority information. If the user is relaxed, it will prioritize providing detailed information. If the user is in a hurry, it can also prioritize providing information that needs to be processed quickly. In this way, by prioritizing information according to the user's emotions, it is possible to deliver more important information first.

[0102] The reception desk can analyze a user's past behavior history and select the most suitable reception method. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns in information entered by the user in the past and automatically generate the most suitable input form. Furthermore, it can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, the system can select the most suitable reception method by analyzing the user's past behavior history.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information and a simplified analysis on less important information. It can also determine the priority of the analysis according to the importance of the information. This allows for a more detailed analysis of more important information by adjusting the level of detail based on the importance of the information.

[0104] The proposal function can apply different proposal algorithms depending on the category of the options. For example, a price comparison algorithm can be applied to product price information, a quality evaluation algorithm to product quality information, and a function comparison algorithm to product function information. By applying different proposal algorithms depending on the category of the options, more appropriate proposals can be provided.

[0105] The information delivery system can select the most appropriate method of information delivery by considering the user's geographical location. For example, it can prioritize providing information about stores and services close to the user's current location. If the user is in a specific region, it can prioritize providing information related to that region. Furthermore, if the user is traveling, it can prioritize providing information about their travel destination. This allows the system to select the most appropriate method of information delivery by considering the user's geographical location.

[0106] The information provision department can analyze users' social media activity and propose means of providing information. For example, it can provide information on related products based on information users have shared on social media. It can also analyze users' interests on social media and provide information on related services. Furthermore, it can provide information on related events based on users' social media activity history. In this way, by analyzing users' social media activity, it is possible to provide relevant information.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives information from users. This information includes details about purchasing daily necessities and groceries, as well as information for finding parking when traveling by car. The reception desk can accept information entered by users in digital format, as well as in various other formats such as voice input and image input. For example, it can accept information entered by users using smartphones or personal computers. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses AI algorithms to analyze data in order to suggest the best options that match the user's needs and preferences. It analyzes data such as product price, quality, features, and ratings, and analyzes data to provide customized recommendations by learning the user's past behavior and preferences. For example, it collects and analyzes data on product price fluctuations and quality ratings. Step 3: The Proposal Department proposes the optimal option based on the results analyzed by the Analysis Department. The Proposal Department suggests products and services that match the user's needs and preferences, and proposes the optimal option based on data such as price, quality, function, and ratings. For example, if a user is looking for a specific brand of food products, the Proposal Department will suggest the highest-rated and reasonably priced product within that brand. Step 4: The supply department provides the options proposed by the proposal department. The supply department provides users with detailed information about the proposed products and services, and can also provide comparison results in a spreadsheet or similar format. For example, it can compare the fees and availability of multiple parking lots and provide information to help users choose the most suitable parking lot.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information entered by the user using a smartphone or personal computer. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data using an AI algorithm to propose the optimal choice that suits the user's needs and preferences. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal choice based on the analysis results. The provision unit is implemented by the output device 40 of the smart device 14 and provides detailed information about the proposed product or service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user receives information using voice input. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where an AI algorithm is used to analyze data in order to propose the optimal choice that suits the user's needs and preferences. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the optimal choice is proposed based on the analyzed results. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, where detailed information about the proposed product or service is provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user receives information using voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an AI algorithm is used to analyze data in order to propose the optimal choice that suits the user's needs and preferences. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the optimal choice is proposed based on the analyzed results. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, where detailed information about the proposed products and services is provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user receives information using voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an AI algorithm is used to analyze data in order to propose the optimal choice that suits the user's needs and preferences. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the optimal choice is proposed based on the analyzed results. The provision unit is implemented by, for example, the speaker 240 of the robot 414, where detailed information about the proposed product or service is provided. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that receives information from users, An analysis unit that analyzes the information received by the reception unit, A proposal unit that proposes the optimal option based on the results of the analysis performed by the aforementioned analysis unit, The system comprises a providing unit that provides the options proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It learns the user's past behavior and preferences to provide customized recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze data such as product price, quality, features, and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide comparison results in a spreadsheet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We update market information in real time, providing updates on product price changes and new product releases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past information input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the options. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the options. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when each option was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the order of the options based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the way information is displayed is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and propose methods for providing information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, An analysis unit that analyzes the information received by the reception unit, A proposal unit that proposes the optimal option based on the results of the analysis performed by the aforementioned analysis unit, The system comprises a providing unit that provides the options proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, It learns the user's past behavior and preferences to provide customized recommendations. The system according to feature 1.

3. The aforementioned analysis unit, Analyze data such as product price, quality, features, and ratings. The system according to feature 1.

4. The aforementioned supply unit is, Provide comparison results in a spreadsheet. The system according to feature 1.

5. The aforementioned analysis unit, We update market information in real time, providing updates on product price changes and new product releases. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past information input history to select the optimal reception method. The system according to feature 1.

8. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system according to feature 1.

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