system

Abstract

The system according to this embodiment aims to enable users to efficiently search for and compare solutions simply by registering their problems. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, a ranking unit, and a creation unit. The reception unit receives a user's request for a problem. The analysis unit analyzes the content of the problem registered by the reception unit. The search unit searches for solutions based on the content analyzed by the analysis unit. The ranking unit ranks the solutions found by the search unit. The creation unit creates a comparison table of the solutions ranked by the ranking unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 the process of comparing and considering multiple solutions requires time and effort and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to enable a user to efficiently search for and compare solutions only by registering a problem.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a ranking unit, and a creation unit. The reception unit receives a user's request for a problem. The analysis unit analyzes the content of the problem registered by the reception unit. The search unit searches for solutions based on the content analyzed by the analysis unit. The ranking unit ranks the solutions found by the search unit. The creation unit creates a comparison table of the solutions ranked by the ranking unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently search for and compare solutions simply by registering their problems. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 agent system according to an embodiment of the present invention is a system that provides new solutions to address challenges in business operations. This agent system automatically searches for target solutions and creates a comparison table that includes company information, detailed solution information, and even an estimated cost, simply by having the user register a problem. Specifically, it consists of the following steps: First, the user registers a problem on the instruction screen. Next, the generating AI analyzes the content of the problem using natural language processing to determine the essence of the problem. The generating AI prioritizes searching platform information, and then searches internet information. The generating AI analyzes each piece of information on the solutions and ranks the solutions that are most relevant to solving the problem. Finally, the generating AI creates a comparison table of solutions and provides it to the user. Because this agent system automatically searches for multiple solutions and creates a comparison table simply by having the user register a problem, the time required for comparison and evaluation can be significantly reduced. Furthermore, by providing a platform, a mechanism is adopted to have solution providers register information, allowing for the use of more detailed information. As a result, the agent system automatically searches for solutions, ranks them, and creates a comparison table simply by having the user register a problem.

[0029] The agent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a ranking unit, and a creation unit. The reception unit receives a user's task. Tasks registered by the user include, but are not limited to, technical tasks and business tasks. The reception unit provides, for example, an interface for the user to input the task into an instruction screen. The analysis unit analyzes the content of the task registered by the reception unit. The analysis unit analyzes the content of the task using, for example, natural language processing technology and determines the essence of the task. The analysis unit can use methods such as text analysis and data analysis. The search unit searches for solutions based on the content analyzed by the analysis unit. The search unit includes, for example, a priority search unit that prioritizes searching for platform information. The search unit may also include an internet search unit that searches for internet information. The ranking unit ranks the solutions found by the search unit. The ranking unit ranks the solutions based on, for example, evaluation criteria and scoring methods. The ranking unit ranks the solutions considering their relevance and reliability. The creation unit creates a comparison table in the form of a table of solutions ranked by the ranking unit. The creation unit includes, for example, a registration unit for solution providers to register information. The creation unit can adjust the items and display methods of the comparison table. As a result, the agent system according to the embodiment automatically searches for solutions, ranks them, and creates a comparison table simply by the user registering a problem. Some or all of the above-described processes in the reception unit, analysis unit, search unit, ranking unit, and creation unit may be performed using AI, for example, or not using AI. For example, the reception unit provides an interface for the user to input a problem on an instruction screen, the analysis unit analyzes the content of the problem using natural language processing technology, the search unit prioritizes searching for platform information, the ranking unit ranks solutions based on evaluation criteria and scoring methods, and the creation unit can adjust the items and display methods of the comparison table.

[0030] The reception desk allows users to register issues. These issues may include, but are not limited to, technical or business issues. The reception desk provides an interface for users to input issues into a guidance screen. Specifically, it provides an intuitive user interface, displaying text boxes and options for entering issue details. Users can enter the issue title, detailed description, relevant keywords, desired resolution deadline, etc. The reception desk also includes a function to allow users to view a history of previously registered issues, which is helpful when registering similar issues again. Furthermore, the reception desk provides a function for users to upload related files and documents when registering issues. This allows users to provide the system with materials related to the issue, providing supplementary information for the analysis department to understand the issue more accurately. After a user registers an issue, the reception desk displays a preview screen for the user to review and modify the registration details. This reduces the risk of users registering incorrect information and allows them to provide accurate issue information to the system.

[0031] The analysis unit analyzes the content of the issues registered by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the issues and determine their essence. Specifically, the analysis unit tokenizes the text data of the issues and uses dictionaries and models to analyze the meaning of each token. Furthermore, to understand the context, the analysis unit analyzes the sentence structure and contextual information to extract the subject and important elements of the issues. The analysis unit can use methods such as text analysis and data analysis. For example, text analysis involves morphological analysis and dependency structure analysis to analyze the content of the issues in detail. Data analysis involves referring to a database of similar past issues and solutions to analyze issues' patterns and trends. Based on these analysis results, the analysis unit determines the category and priority of the issues and provides basic information for searching for appropriate solutions. Furthermore, based on the content of the issues, the analysis unit leverages expertise in relevant technologies and business fields to identify the elements necessary to solve the issues. This allows the analysis unit to accurately understand the essence of the issues registered by users and build a foundation for providing appropriate solutions.

[0032] The search unit searches for solutions based on the analysis performed by the analysis unit. The search unit includes, for example, a priority search unit that prioritizes platform information. Specifically, the search unit searches databases and the internet for relevant solutions based on the problem categories and keywords provided by the analysis unit. The priority search unit prioritizes searching reliable platforms and specialized databases to provide high-quality solutions. The search unit can also include an internet search unit that searches internet information. The internet search unit searches for solutions from a wide range of sources and provides solutions based on the latest technologies and trends. The search unit also has a function to filter search results and prioritize the display of highly relevant solutions. This allows the search unit to quickly provide the optimal solution for the user's problem. Furthermore, the search unit can continuously improve its search algorithm based on user feedback to enhance search accuracy. This allows the search unit to always provide the latest and most optimal solutions, supporting users in solving their problems.

[0033] The ranking unit ranks the solutions found by the search unit. The ranking unit ranks solutions based on evaluation criteria and scoring methods, for example. Specifically, the ranking unit sets evaluation criteria such as relevance, reliability, track record, and user reviews, and assigns a score to each solution. Relevance evaluates the degree of match between the problem and the solution; reliability evaluates the reliability and past track record of the solution provider; track record evaluates how many problems the solution has solved in the past; and user reviews assign scores based on evaluations and feedback from other users. The ranking unit comprehensively considers these evaluation criteria to calculate an overall score for each solution. Furthermore, the ranking unit can also perform customized rankings according to the user's priorities and specific needs. This allows the ranking unit to prioritize displaying the most appropriate solutions for the user, supporting efficient problem solving.

[0034] The creation unit creates a comparison table of solutions ranked by the ranking unit. The creation unit includes, for example, a registration unit where solution providers can register their information. Specifically, the creation unit collects detailed information on ranked solutions and provides it to users in the form of a comparison table. The comparison table includes features, benefits, costs, and provider information for each solution, allowing users to easily compare them. The creation unit can adjust the items and display methods of the comparison table, allowing for customization to meet user needs. For example, if a user prioritizes specific evaluation criteria, a comparison table based on those criteria can be created. Furthermore, the creation unit also provides functions for users to download and share the comparison table with other users. This allows users to efficiently compare multiple solutions and select the optimal solution. Based on user feedback, the creation unit can continuously improve the content and display methods of the comparison table, enhancing the user experience. This allows the creation unit to provide users with a powerful tool for finding the optimal solution and supporting problem-solving.

[0035] The search unit includes a priority search unit that prioritizes searching for platform information. For example, the search unit prioritizes retrieving information from specific websites or databases. The search unit can provide reliable information by prioritizing the retrieval of reliable sources. For example, the search unit prioritizes platform information in a specific industry to provide reliable information. Furthermore, the search unit can maintain information consistency by prioritizing platform information. For example, the search unit prioritizes retrieving information from a specific platform to maintain information consistency. This allows the search unit to provide reliable information by prioritizing platform information retrieval. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can retrieve information using an AI model that prioritizes the retrieval of platform information.

[0036] The search unit includes an internet search unit that searches for information on the internet. The search unit retrieves information from, for example, web pages and online databases. The search unit can provide a wide range of information by collecting information from a wide range of sources. For example, the search unit searches for the latest information on the internet and provides it to the user. In addition, the search unit can quickly obtain the latest information by searching for information on the internet. For example, the search unit searches for news articles and blog posts on the internet and provides the latest information. In this way, a wide range of information can be collected by searching for information on the internet. Some or all of the above-described processes in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can obtain information using an AI model that searches for information on the internet.

[0037] The reception unit allows users to register tasks on the instruction screen. The reception unit provides, for example, an interface for users to input tasks. The reception unit provides an intuitive user interface so that users can easily register tasks. For example, the reception unit provides text boxes and dropdown menus for users to input tasks. The reception unit also has a function to guide users to the necessary information when they register tasks. For example, the reception unit displays the necessary items when the user is inputting a task and prompts them to input. This makes it easier for users to input tasks by registering them on the instruction screen. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can provide an interface for users to input tasks and can use AI to analyze the input content.

[0038] The creation unit includes a registration unit for solution providers to register information. The creation unit provides, for example, an interface for solution providers to register information. The creation unit provides an intuitive user interface so that solution providers can register detailed information. For example, the creation unit provides text boxes and dropdown menus for solution providers to input information. The creation unit also includes a function to guide solution providers to the necessary information when they register information. For example, the creation unit displays the necessary items and prompts solution providers to input information. This allows for the utilization of detailed information by having solution providers register information. Some or all of the above processing in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit provides an interface for solution providers to input information and can analyze the input content using AI.

[0039] The reception desk analyzes the user's past task registration history and selects the optimal registration method. For example, the reception desk may prioritize suggesting registration methods that the user has frequently used in the past (e.g., voice, text). The reception desk can also analyze the user's past registration history to identify tendencies for registration at specific times and encourage registration during those times. The reception desk can also analyze the content of tasks the user has previously registered and automatically suggest similar tasks. In this way, the reception desk can suggest the optimal registration method by analyzing the user's past task registration history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use an AI model to analyze the user's past task registration history to select the optimal registration method.

[0040] The reception system filters tasks based on the user's current projects and areas of interest when they are registered. For example, the reception system prioritizes tasks related to the user's current projects. The reception system can also automatically suggest relevant tasks based on the user's areas of interest. The reception system can also register tasks at appropriate times depending on the user's project progress. This allows for the registration of highly relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can perform filtering using an AI model that analyzes the user's current projects and areas of interest.

[0041] The reception desk prioritizes registering highly relevant issues by considering the user's geographical location when an issue is registered. For example, if the user is in a specific region, the reception desk prioritizes registering issues related to that region. The reception desk can also prioritize displaying information on nearby solution providers based on the user's location. If the user is on the move, the reception desk can also suggest the most suitable issues based on their current location. This allows for the priority registration of highly relevant issues by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use an AI model to acquire the user's geographical location information to prioritize registering highly relevant issues.

[0042] The reception desk analyzes the user's social media activity when a problem is registered and registers relevant problems. For example, the reception desk automatically registers problems that the user has mentioned on social media. The reception desk can also analyze topics of interest from the user's social media activity and suggest relevant problems. The reception desk can also register relevant problems by referring to the activities of the user's followers and friends on social media. In this way, relevant problems can be automatically registered by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can register relevant problems using an AI model that analyzes the user's social media activity.

[0043] The analysis unit adjusts the level of detail of the analysis based on the importance of the issues during the analysis. For example, the analysis unit performs a detailed analysis for high-importance issues. The analysis unit can also perform a simplified analysis for low-importance issues. The analysis unit can also determine the priority of the analysis according to the importance of the issues. By adjusting the level of detail of the analysis based on the importance of the issues, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of the issues.

[0044] The analysis unit applies different analysis algorithms depending on the category of the problem during the analysis. For example, the analysis unit applies a technical analysis algorithm to technical problems. The analysis unit can also apply a management analysis algorithm to management problems. The analysis unit can also apply a market analysis algorithm to market research problems. By applying different analysis algorithms depending on the category of the problem, 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 perform analysis using an AI model that applies different analysis algorithms depending on the category of the problem.

[0045] The analysis unit determines the priority of analyses based on the submission dates of assignments. For example, the analysis unit prioritizes analyses of assignments with approaching deadlines. It may also postpone analyses of assignments with later submission dates. The analysis unit can also adjust the analysis schedule according to the submission dates. This allows for the provision of more appropriate analysis results by determining the priority of analyses based on the submission dates of assignments. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analyses using an AI model that evaluates the submission dates of assignments.

[0046] The analysis unit adjusts the order of analysis based on the relevance of the issues during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant issues. The analysis unit may also postpone the analysis of less relevant issues. The analysis unit can also adjust the order of analysis according to the relevance of the issues. By adjusting the order of analysis based on the relevance of the issues, 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 adjust the order of analysis using an AI model that evaluates the relevance of the issues.

[0047] The search unit improves search accuracy by considering the interrelationships between solutions during the search process. For example, the search unit analyzes the relationships between solutions and provides optimal search results. The search unit can also prioritize displaying relevant solutions by considering their interrelationships. The search unit can also improve the accuracy of search results based on the interrelationships between solutions. This improves search accuracy by considering the interrelationships between solutions. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can improve search accuracy by using an AI model that evaluates the interrelationships between solutions.

[0048] The search unit performs searches while considering the attribute information of the solution provider. For example, the search unit provides optimal search results based on the industry and size of the solution provider. The search unit can also provide highly reliable search results by considering the solution provider's past performance. The search unit can also provide optimal search results by considering the solution provider's regional information. In this way, optimal search results can be provided by considering the attribute information of the solution provider. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can perform searches using an AI model that evaluates the attribute information of the solution provider.

[0049] The search unit performs searches while considering the geographical distribution of solutions. For example, the search unit provides optimal search results based on the regional information of solution providers. The search unit can also prioritize displaying solution providers that are geographically close. The search unit can also provide optimal search results by considering the characteristics of solutions in each region. In this way, optimal search results can be provided by considering the geographical distribution of solutions. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can perform searches using an AI model that evaluates the geographical distribution of solutions.

[0050] The search unit improves search accuracy by referring to relevant literature for the solution during the search. For example, the search unit provides the best search results based on literature related to the solution. The search unit can also provide reliable search results by referring to relevant literature. The search unit can also analyze relevant literature for the solution to improve the accuracy of the search results. This improves search accuracy by referring to relevant literature for the solution. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can improve search accuracy by using an AI model that evaluates relevant literature for the solution.

[0051] The ranking unit improves the accuracy of ranking by considering the interrelationships between solutions during the ranking process. For example, the ranking unit analyzes the relationships between solutions and provides the optimal ranking result. The ranking unit can also prioritize ranking related solutions by considering their interrelationships. The ranking unit can also improve the accuracy of the ranking result based on the interrelationships of solutions. This improves the accuracy of ranking by considering the interrelationships of solutions. Some or all of the above processes in the ranking unit may be performed using AI, for example, or not. For example, the ranking unit can improve the accuracy of ranking by using an AI model that evaluates the interrelationships of solutions.

[0052] The ranking unit performs ranking by considering the attribute information of the solution provider. For example, the ranking unit provides the optimal ranking result based on the industry and size of the solution provider. The ranking unit can also provide a highly reliable ranking result by considering the solution provider's past performance. The ranking unit can also provide the optimal ranking result by considering the solution provider's regional information. In this way, the optimal ranking result can be provided by considering the attribute information of the solution provider. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can perform ranking using an AI model that evaluates the attribute information of the solution provider.

[0053] The ranking unit considers the geographical distribution of solutions when ranking them. For example, the ranking unit provides the optimal ranking result based on the regional information of the solution providers. The ranking unit can also prioritize the ranking of solution providers that are geographically close. The ranking unit can also provide the optimal ranking result by considering the characteristics of solutions in each region. This allows for the provision of the optimal ranking result by considering the geographical distribution of solutions. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can perform ranking using an AI model that evaluates the geographical distribution of solutions.

[0054] The ranking unit improves the accuracy of ranking by referring to relevant literature for the solutions during the ranking process. The ranking unit provides the optimal ranking result, for example, based on literature related to the solutions. The ranking unit can also provide a more reliable ranking result by referring to relevant literature. The ranking unit can also improve the accuracy of the ranking result by analyzing the relevant literature for the solutions. This improves the accuracy of ranking by referring to relevant literature for the solutions. Some or all of the above processes in the ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can improve the accuracy of ranking by using an AI model that evaluates relevant literature for solutions.

[0055] The creation unit adjusts the level of detail in the comparison table based on the importance of the solutions when creating it. For example, the creation unit creates a detailed comparison table for high-importance solutions. It can also create a concise comparison table for low-importance solutions. The creation unit can also determine the priority of the comparison table according to the importance of the solutions. This allows for the provision of a more appropriate comparison table by adjusting the level of detail based on the importance of the solutions. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can adjust the level of detail in the comparison table using an AI model that evaluates the importance of the solutions.

[0056] The creation unit applies different creation algorithms depending on the solution category when creating the comparison table. For example, the creation unit applies a technology comparison algorithm to technical solutions. The creation unit can also apply a management comparison algorithm to management solutions. The creation unit can also apply a market comparison algorithm to market research solutions. By applying different creation algorithms depending on the solution category, a more appropriate comparison table can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can create a comparison table using an AI model that applies different creation algorithms depending on the solution category.

[0057] The creation team determines the priority of the comparison table based on the submission timing of the solutions when creating the comparison table. For example, the creation team will prioritize creating comparison tables for solutions with approaching deadlines. The creation team may also postpone creating comparison tables for solutions with later submission deadlines. The creation team can also adjust the schedule of the comparison table according to the submission timing. This allows for the provision of more appropriate comparison tables by prioritizing the comparison table based on the submission timing of the solutions. Some or all of the above processes in the creation team may be performed using AI, for example, or not. For example, the creation team may use an AI model to evaluate the submission timing of solutions to determine the priority of the comparison table.

[0058] The creation unit adjusts the order of the comparison table based on the relevance of the solutions when creating the comparison table. For example, the creation unit prioritizes creating comparison tables for highly relevant solutions. The creation unit may also postpone creating comparison tables for less relevant solutions. The creation unit can also adjust the order of the comparison table according to the relevance of the solutions. This allows for the provision of a more appropriate comparison table by adjusting the order of the comparison table based on the relevance of the solutions. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can adjust the order of the comparison table using an AI model that evaluates the relevance of the solutions.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze a user's past task registration history when they register a task and suggest the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (such as voice or text). Furthermore, it can analyze the user's past registration history to identify their tendency to register at specific times and encourage them to register during those times. In this way, by analyzing a user's past task registration history, the system can suggest the most suitable registration method.

[0061] The ranking section can improve the accuracy of ranking by considering the interrelationships between solutions. For example, it can analyze the relationships between solutions and provide the optimal ranking result. It can also prioritize ranking related solutions by considering their interrelationships. This improves the accuracy of ranking by considering the interrelationships between solutions.

[0062] The reception system can filter tasks based on the user's current projects and areas of interest when they are registered. For example, it can prioritize tasks related to the user's current projects. It can also automatically suggest relevant tasks based on the user's areas of interest. This allows for the registration of highly relevant tasks by filtering based on the user's current projects and areas of interest.

[0063] The search function can perform searches while considering the attribute information of solution providers. For example, it can provide optimal search results based on the industry and size of the solution provider. It can also provide highly reliable search results by considering the solution provider's past performance. In this way, by considering the attribute information of solution providers, it can provide optimal search results.

[0064] The creation unit can apply different creation algorithms depending on the solution category when creating comparison tables. For example, a technical comparison algorithm can be applied to technical solutions, and a management comparison algorithm can be applied to management solutions. By applying different creation algorithms depending on the solution category, a more appropriate comparison table can be provided.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the user's issue. The issues registered by the user include technical issues and business issues. The reception desk provides an interface for the user to enter the issue on the instruction screen. Step 2: The analysis unit analyzes the content of the assignment registered by the reception unit. The analysis unit uses natural language processing technology to analyze the content of the assignment and determine its essence. Methods such as text analysis and data analysis can be used. Step 3: The search unit searches for solutions based on the analysis performed by the analysis unit. The search unit may include a priority search unit that prioritizes platform information and an internet search unit that searches internet information. Step 4: The ranking unit ranks the solutions found by the search unit. The ranking unit ranks the solutions based on evaluation criteria and scoring methods, taking into account the relevance and reliability of the solutions. Step 5: The creation section creates a comparison table of the ranked solutions from the ranking section. The creation section includes a registration section where solution providers can register their information, and it can adjust the items and display methods of the comparison table.

[0067] (Example of form 2) An agent system according to an embodiment of the present invention is a system that provides new solutions to address challenges in business operations. This agent system automatically searches for target solutions and creates a comparison table that includes company information, detailed solution information, and even an estimated cost, simply by having the user register a problem. Specifically, it consists of the following steps: First, the user registers a problem on the instruction screen. Next, the generating AI analyzes the content of the problem using natural language processing to determine the essence of the problem. The generating AI prioritizes searching platform information, and then searches internet information. The generating AI analyzes each piece of information on the solutions and ranks the solutions that are most relevant to solving the problem. Finally, the generating AI creates a comparison table of solutions and provides it to the user. Because this agent system automatically searches for multiple solutions and creates a comparison table simply by having the user register a problem, the time required for comparison and evaluation can be significantly reduced. Furthermore, by providing a platform, a mechanism is adopted to have solution providers register information, allowing for the use of more detailed information. As a result, the agent system automatically searches for solutions, ranks them, and creates a comparison table simply by having the user register a problem.

[0068] The agent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a ranking unit, and a creation unit. The reception unit receives a user's task. Tasks registered by the user include, but are not limited to, technical tasks and business tasks. The reception unit provides, for example, an interface for the user to input the task into an instruction screen. The analysis unit analyzes the content of the task registered by the reception unit. The analysis unit analyzes the content of the task using, for example, natural language processing technology and determines the essence of the task. The analysis unit can use methods such as text analysis and data analysis. The search unit searches for solutions based on the content analyzed by the analysis unit. The search unit includes, for example, a priority search unit that prioritizes searching for platform information. The search unit may also include an internet search unit that searches for internet information. The ranking unit ranks the solutions found by the search unit. The ranking unit ranks the solutions based on, for example, evaluation criteria and scoring methods. The ranking unit ranks the solutions considering their relevance and reliability. The creation unit creates a comparison table in the form of a table of solutions ranked by the ranking unit. The creation unit includes, for example, a registration unit for solution providers to register information. The creation unit can adjust the items and display methods of the comparison table. As a result, the agent system according to the embodiment automatically searches for solutions, ranks them, and creates a comparison table simply by the user registering a problem. Some or all of the above-described processes in the reception unit, analysis unit, search unit, ranking unit, and creation unit may be performed using AI, for example, or not using AI. For example, the reception unit provides an interface for the user to input a problem on an instruction screen, the analysis unit analyzes the content of the problem using natural language processing technology, the search unit prioritizes searching for platform information, the ranking unit ranks solutions based on evaluation criteria and scoring methods, and the creation unit can adjust the items and display methods of the comparison table.

[0069] The reception desk allows users to register issues. These issues may include, but are not limited to, technical or business issues. The reception desk provides an interface for users to input issues into a guidance screen. Specifically, it provides an intuitive user interface, displaying text boxes and options for entering issue details. Users can enter the issue title, detailed description, relevant keywords, desired resolution deadline, etc. The reception desk also includes a function to allow users to view a history of previously registered issues, which is helpful when registering similar issues again. Furthermore, the reception desk provides a function for users to upload related files and documents when registering issues. This allows users to provide the system with materials related to the issue, providing supplementary information for the analysis department to understand the issue more accurately. After a user registers an issue, the reception desk displays a preview screen for the user to review and modify the registration details. This reduces the risk of users registering incorrect information and allows them to provide accurate issue information to the system.

[0070] The analysis unit analyzes the content of the issues registered by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the content of the issues and determine their essence. Specifically, the analysis unit tokenizes the text data of the issues and uses dictionaries and models to analyze the meaning of each token. Furthermore, to understand the context, the analysis unit analyzes the sentence structure and contextual information to extract the subject and important elements of the issues. The analysis unit can use methods such as text analysis and data analysis. For example, text analysis involves morphological analysis and dependency structure analysis to analyze the content of the issues in detail. Data analysis involves referring to a database of similar past issues and solutions to analyze issues' patterns and trends. Based on these analysis results, the analysis unit determines the category and priority of the issues and provides basic information for searching for appropriate solutions. Furthermore, based on the content of the issues, the analysis unit leverages expertise in relevant technologies and business fields to identify the elements necessary to solve the issues. This allows the analysis unit to accurately understand the essence of the issues registered by users and build a foundation for providing appropriate solutions.

[0071] The search unit searches for solutions based on the analysis performed by the analysis unit. The search unit includes, for example, a priority search unit that prioritizes platform information. Specifically, the search unit searches databases and the internet for relevant solutions based on the problem categories and keywords provided by the analysis unit. The priority search unit prioritizes searching reliable platforms and specialized databases to provide high-quality solutions. The search unit can also include an internet search unit that searches internet information. The internet search unit searches for solutions from a wide range of sources and provides solutions based on the latest technologies and trends. The search unit also has a function to filter search results and prioritize the display of highly relevant solutions. This allows the search unit to quickly provide the optimal solution for the user's problem. Furthermore, the search unit can continuously improve its search algorithm based on user feedback to enhance search accuracy. This allows the search unit to always provide the latest and most optimal solutions, supporting users in solving their problems.

[0072] The ranking unit ranks the solutions found by the search unit. The ranking unit ranks solutions based on evaluation criteria and scoring methods, for example. Specifically, the ranking unit sets evaluation criteria such as relevance, reliability, track record, and user reviews, and assigns a score to each solution. Relevance evaluates the degree of match between the problem and the solution; reliability evaluates the reliability and past track record of the solution provider; track record evaluates how many problems the solution has solved in the past; and user reviews assign scores based on evaluations and feedback from other users. The ranking unit comprehensively considers these evaluation criteria to calculate an overall score for each solution. Furthermore, the ranking unit can also perform customized rankings according to the user's priorities and specific needs. This allows the ranking unit to prioritize displaying the most appropriate solutions for the user, supporting efficient problem solving.

[0073] The creation unit creates a comparison table of solutions ranked by the ranking unit. The creation unit includes, for example, a registration unit where solution providers can register their information. Specifically, the creation unit collects detailed information on ranked solutions and provides it to users in the form of a comparison table. The comparison table includes features, benefits, costs, and provider information for each solution, allowing users to easily compare them. The creation unit can adjust the items and display methods of the comparison table, allowing for customization to meet user needs. For example, if a user prioritizes specific evaluation criteria, a comparison table based on those criteria can be created. Furthermore, the creation unit also provides functions for users to download and share the comparison table with other users. This allows users to efficiently compare multiple solutions and select the optimal solution. Based on user feedback, the creation unit can continuously improve the content and display methods of the comparison table, enhancing the user experience. This allows the creation unit to provide users with a powerful tool for finding the optimal solution and supporting problem-solving.

[0074] The search unit includes a priority search unit that prioritizes searching for platform information. For example, the search unit prioritizes retrieving information from specific websites or databases. The search unit can provide reliable information by prioritizing the retrieval of reliable sources. For example, the search unit prioritizes platform information in a specific industry to provide reliable information. Furthermore, the search unit can maintain information consistency by prioritizing platform information. For example, the search unit prioritizes retrieving information from a specific platform to maintain information consistency. This allows the search unit to provide reliable information by prioritizing platform information retrieval. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can retrieve information using an AI model that prioritizes the retrieval of platform information.

[0075] The search unit includes an internet search unit that searches for information on the internet. The search unit retrieves information from, for example, web pages and online databases. The search unit can provide a wide range of information by collecting information from a wide range of sources. For example, the search unit searches for the latest information on the internet and provides it to the user. In addition, the search unit can quickly obtain the latest information by searching for information on the internet. For example, the search unit searches for news articles and blog posts on the internet and provides the latest information. In this way, a wide range of information can be collected by searching for information on the internet. Some or all of the above-described processes in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can obtain information using an AI model that searches for information on the internet.

[0076] The reception unit allows users to register tasks on the instruction screen. The reception unit provides, for example, an interface for users to input tasks. The reception unit provides an intuitive user interface so that users can easily register tasks. For example, the reception unit provides text boxes and dropdown menus for users to input tasks. The reception unit also has a function to guide users to the necessary information when they register tasks. For example, the reception unit displays the necessary items when the user is inputting a task and prompts them to input. This makes it easier for users to input tasks by registering them on the instruction screen. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can provide an interface for users to input tasks and can use AI to analyze the input content.

[0077] The creation unit includes a registration unit for solution providers to register information. The creation unit provides, for example, an interface for solution providers to register information. The creation unit provides an intuitive user interface so that solution providers can register detailed information. For example, the creation unit provides text boxes and dropdown menus for solution providers to input information. The creation unit also includes a function to guide solution providers to the necessary information when they register information. For example, the creation unit displays the necessary items and prompts solution providers to input information. This allows for the utilization of detailed information by having solution providers register information. Some or all of the above processing in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit provides an interface for solution providers to input information and can analyze the input content using AI.

[0078] The reception desk estimates the user's emotions and adjusts the timing of task registration based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to register a task at a time when they can relax. If the user is concentrating, the reception desk may prompt them to register a task at that time. If the user is tired, the reception desk may prompt them to register a task after a break. By adjusting the timing of task registration according to the user's emotions, tasks can be registered at a more appropriate time. 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, for example, or not using AI. For example, the reception desk may use an AI model to estimate the user's emotions and adjust the timing of task registration based on those emotions.

[0079] The reception desk analyzes the user's past task registration history and selects the optimal registration method. For example, the reception desk may prioritize suggesting registration methods that the user has frequently used in the past (e.g., voice, text). The reception desk can also analyze the user's past registration history to identify tendencies for registration at specific times and encourage registration during those times. The reception desk can also analyze the content of tasks the user has previously registered and automatically suggest similar tasks. In this way, the reception desk can suggest the optimal registration method by analyzing the user's past task registration history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use an AI model to analyze the user's past task registration history to select the optimal registration method.

[0080] The reception system filters tasks based on the user's current projects and areas of interest when they are registered. For example, the reception system prioritizes tasks related to the user's current projects. The reception system can also automatically suggest relevant tasks based on the user's areas of interest. The reception system can also register tasks at appropriate times depending on the user's project progress. This allows for the registration of highly relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can perform filtering using an AI model that analyzes the user's current projects and areas of interest.

[0081] The reception desk estimates the user's emotions and determines the priority of tasks to register based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone tasks of lower importance. If the user is relaxed, the reception desk may also prioritize tasks of higher importance. If the user is in a hurry, the reception desk may also prioritize tasks that require immediate attention. This allows for the prioritization of more appropriate tasks by determining the priority of tasks 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 processing in the reception desk may be performed using AI or not. For example, the reception desk may use an AI model to estimate the user's emotions and determine the priority of tasks based on those emotions.

[0082] The reception desk prioritizes registering highly relevant issues by considering the user's geographical location when an issue is registered. For example, if the user is in a specific region, the reception desk prioritizes registering issues related to that region. The reception desk can also prioritize displaying information on nearby solution providers based on the user's location. If the user is on the move, the reception desk can also suggest the most suitable issues based on their current location. This allows for the priority registration of highly relevant issues by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use an AI model to acquire the user's geographical location information to prioritize registering highly relevant issues.

[0083] The reception desk analyzes the user's social media activity when a problem is registered and registers relevant problems. For example, the reception desk automatically registers problems that the user has mentioned on social media. The reception desk can also analyze topics of interest from the user's social media activity and suggest relevant problems. The reception desk can also register relevant problems by referring to the activities of the user's followers and friends on social media. In this way, relevant problems can be automatically registered by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can register relevant problems using an AI model that analyzes the user's social media activity.

[0084] The analysis unit estimates the user's emotions and adjusts 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. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. 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 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to estimate the user's emotions and adjust the presentation of the analysis based on those emotions.

[0085] The analysis unit adjusts the level of detail of the analysis based on the importance of the issues during the analysis. For example, the analysis unit performs a detailed analysis for high-importance issues. The analysis unit can also perform a simplified analysis for low-importance issues. The analysis unit can also determine the priority of the analysis according to the importance of the issues. By adjusting the level of detail of the analysis based on the importance of the issues, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of the issues.

[0086] The analysis unit applies different analysis algorithms depending on the category of the problem during the analysis. For example, the analysis unit applies a technical analysis algorithm to technical problems. The analysis unit can also apply a management analysis algorithm to management problems. The analysis unit can also apply a market analysis algorithm to market research problems. By applying different analysis algorithms depending on the category of the problem, 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 perform analysis using an AI model that applies different analysis algorithms depending on the category of the problem.

[0087] The analysis unit estimates the user's emotions and adjusts 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. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model to estimate the user's emotions and adjust the length of the analysis based on those emotions.

[0088] The analysis unit determines the priority of analyses based on the submission dates of assignments. For example, the analysis unit prioritizes analyses of assignments with approaching deadlines. It may also postpone analyses of assignments with later submission dates. The analysis unit can also adjust the analysis schedule according to the submission dates. This allows for the provision of more appropriate analysis results by determining the priority of analyses based on the submission dates of assignments. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analyses using an AI model that evaluates the submission dates of assignments.

[0089] The analysis unit adjusts the order of analysis based on the relevance of the issues during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant issues. The analysis unit may also postpone the analysis of less relevant issues. The analysis unit can also adjust the order of analysis according to the relevance of the issues. By adjusting the order of analysis based on the relevance of the issues, 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 adjust the order of analysis using an AI model that evaluates the relevance of the issues.

[0090] The search unit estimates the user's emotions and adjusts the search criteria based on the estimated emotions. For example, if the user is stressed, the search unit provides simple and highly visible search results. If the user is relaxed, the search unit can also provide detailed search results. If the user is in a hurry, the search unit can also provide concise and to-the-point search results. This allows for more appropriate search results by adjusting the search criteria 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can use an AI model to estimate the user's emotions and adjust the search criteria based on those emotions.

[0091] The search unit improves search accuracy by considering the interrelationships between solutions during the search process. For example, the search unit analyzes the relationships between solutions and provides optimal search results. The search unit can also prioritize displaying relevant solutions by considering their interrelationships. The search unit can also improve the accuracy of search results based on the interrelationships between solutions. This improves search accuracy by considering the interrelationships between solutions. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can improve search accuracy by using an AI model that evaluates the interrelationships between solutions.

[0092] The search unit performs searches while considering the attribute information of the solution provider. For example, the search unit provides optimal search results based on the industry and size of the solution provider. The search unit can also provide highly reliable search results by considering the solution provider's past performance. The search unit can also provide optimal search results by considering the solution provider's regional information. In this way, optimal search results can be provided by considering the attribute information of the solution provider. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can perform searches using an AI model that evaluates the attribute information of the solution provider.

[0093] The search unit estimates the user's emotions and adjusts the order in which search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit may prioritize displaying high-priority search results. If the user is relaxed, the search unit may also display detailed search results. If the user is in a hurry, the search unit may also prioritize displaying concise search results. This allows for more appropriate search results to be provided by adjusting the display order of search results 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 search unit may be performed using AI, for example, or not using AI. For example, the search unit may use an AI model to estimate the user's emotions and adjust the order in which search results are displayed based on those emotions.

[0094] The search unit performs searches while considering the geographical distribution of solutions. For example, the search unit provides optimal search results based on the regional information of solution providers. The search unit can also prioritize displaying solution providers that are geographically close. The search unit can also provide optimal search results by considering the characteristics of solutions in each region. In this way, optimal search results can be provided by considering the geographical distribution of solutions. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can perform searches using an AI model that evaluates the geographical distribution of solutions.

[0095] The search unit improves search accuracy by referring to relevant literature for the solution during the search. For example, the search unit provides the best search results based on literature related to the solution. The search unit can also provide reliable search results by referring to relevant literature. The search unit can also analyze relevant literature for the solution to improve the accuracy of the search results. This improves search accuracy by referring to relevant literature for the solution. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can improve search accuracy by using an AI model that evaluates relevant literature for the solution.

[0096] The ranking unit estimates the user's emotions and adjusts the ranking criteria based on the estimated emotions. For example, if the user is stressed, the ranking unit provides a simple and easy-to-understand ranking result. If the user is relaxed, the ranking unit can also provide a detailed ranking result. If the user is in a hurry, the ranking unit can also provide a concise ranking result that gets straight to the point. This allows for more appropriate ranking results by adjusting the ranking criteria 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 ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can use an AI model to estimate the user's emotions and adjust the ranking criteria based on those emotions.

[0097] The ranking unit improves the accuracy of ranking by considering the interrelationships between solutions during the ranking process. For example, the ranking unit analyzes the relationships between solutions and provides the optimal ranking result. The ranking unit can also prioritize ranking related solutions by considering their interrelationships. The ranking unit can also improve the accuracy of the ranking result based on the interrelationships of solutions. This improves the accuracy of ranking by considering the interrelationships of solutions. Some or all of the above processes in the ranking unit may be performed using AI, for example, or not. For example, the ranking unit can improve the accuracy of ranking by using an AI model that evaluates the interrelationships of solutions.

[0098] The ranking unit performs ranking by considering the attribute information of the solution provider. For example, the ranking unit provides the optimal ranking result based on the industry and size of the solution provider. The ranking unit can also provide a highly reliable ranking result by considering the solution provider's past performance. The ranking unit can also provide the optimal ranking result by considering the solution provider's regional information. In this way, the optimal ranking result can be provided by considering the attribute information of the solution provider. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can perform ranking using an AI model that evaluates the attribute information of the solution provider.

[0099] The ranking unit estimates the user's emotions and adjusts the order in which ranking results are displayed based on the estimated emotions. For example, if the user is stressed, the ranking unit will prioritize displaying high-importance ranking results. If the user is relaxed, the ranking unit may also display detailed ranking results. If the user is in a hurry, the ranking unit may also prioritize displaying concise ranking results. This allows for more appropriate ranking results to be provided by adjusting the display order of ranking results 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 ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can use an AI model to estimate the user's emotions and adjust the order in which ranking results are displayed based on those emotions.

[0100] The ranking unit considers the geographical distribution of solutions when ranking them. For example, the ranking unit provides the optimal ranking result based on the regional information of the solution providers. The ranking unit can also prioritize the ranking of solution providers that are geographically close. The ranking unit can also provide the optimal ranking result by considering the characteristics of solutions in each region. This allows for the provision of the optimal ranking result by considering the geographical distribution of solutions. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can perform ranking using an AI model that evaluates the geographical distribution of solutions.

[0101] The ranking unit improves the accuracy of ranking by referring to relevant literature for the solutions during the ranking process. The ranking unit provides the optimal ranking result, for example, based on literature related to the solutions. The ranking unit can also provide a more reliable ranking result by referring to relevant literature. The ranking unit can also improve the accuracy of the ranking result by analyzing the relevant literature for the solutions. This improves the accuracy of ranking by referring to relevant literature for the solutions. Some or all of the above processes in the ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can improve the accuracy of ranking by using an AI model that evaluates relevant literature for solutions.

[0102] The creation unit estimates the user's emotions and adjusts the method of creating the comparison table based on the estimated user emotions. For example, if the user is stressed, the creation unit provides a simple and highly visual comparison table. If the user is relaxed, the creation unit can also provide a detailed comparison table. If the user is in a hurry, the creation unit can also provide a concise comparison table that gets straight to the point. In this way, by adjusting the method of creating the comparison table according to the user's emotions, a more appropriate comparison table can be provided. 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 creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can use an AI model that estimates the user's emotions to adjust the method of creating the comparison table based on the emotions.

[0103] The creation unit adjusts the level of detail in the comparison table based on the importance of the solutions when creating it. For example, the creation unit creates a detailed comparison table for high-importance solutions. It can also create a concise comparison table for low-importance solutions. The creation unit can also determine the priority of the comparison table according to the importance of the solutions. This allows for the provision of a more appropriate comparison table by adjusting the level of detail based on the importance of the solutions. Some or all of the above processes in the creation unit may be performed using AI, for example, or not. For example, the creation unit can adjust the level of detail in the comparison table using an AI model that evaluates the importance of the solutions.

[0104] The creation unit applies different creation algorithms depending on the solution category when creating the comparison table. For example, the creation unit applies a technology comparison algorithm to technical solutions. The creation unit can also apply a management comparison algorithm to management solutions. The creation unit can also apply a market comparison algorithm to market research solutions. By applying different creation algorithms depending on the solution category, a more appropriate comparison table can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can create a comparison table using an AI model that applies different creation algorithms depending on the solution category.

[0105] The creation unit estimates the user's emotions and adjusts the display method of the comparison table based on the estimated user emotions. For example, if the user is stressed, the creation unit provides a simple and highly visible display method. If the user is relaxed, the creation unit can also provide a detailed display method. If the user is in a hurry, the creation unit can also provide a concise display method. This allows for the provision of a more appropriate comparison table by adjusting the display method 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 creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can use an AI model to estimate the user's emotions and adjust the display method of the comparison table based on those emotions.

[0106] The creation team determines the priority of the comparison table based on the submission timing of the solutions when creating the comparison table. For example, the creation team will prioritize creating comparison tables for solutions with approaching deadlines. The creation team may also postpone creating comparison tables for solutions with later submission deadlines. The creation team can also adjust the schedule of the comparison table according to the submission timing. This allows for the provision of more appropriate comparison tables by prioritizing the comparison table based on the submission timing of the solutions. Some or all of the above processes in the creation team may be performed using AI, for example, or not. For example, the creation team may use an AI model to evaluate the submission timing of solutions to determine the priority of the comparison table.

[0107] The creation unit adjusts the order of the comparison table based on the relevance of the solutions when creating the comparison table. For example, the creation unit prioritizes creating comparison tables for highly relevant solutions. The creation unit may also postpone creating comparison tables for less relevant solutions. The creation unit can also adjust the order of the comparison table according to the relevance of the solutions. This allows for the provision of a more appropriate comparison table by adjusting the order of the comparison table based on the relevance of the solutions. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can adjust the order of the comparison table using an AI model that evaluates the relevance of the solutions.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception desk can analyze a user's past task registration history when they register a task and suggest the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (such as voice or text). Furthermore, it can analyze the user's past registration history to identify their tendency to register at specific times and encourage them to register during those times. In this way, by analyzing a user's past task registration history, the system can suggest the most suitable registration method.

[0110] The search engine can estimate the user's emotions and adjust search criteria based on that estimation. For example, if the user is stressed, it can provide simple, highly visual search results. If the user is relaxed, it can provide detailed search results. If the user is in a hurry, it can provide concise search results that get straight to the point. By adjusting search criteria according to the user's emotions, it can provide more relevant search results.

[0111] The ranking section can improve the accuracy of ranking by considering the interrelationships between solutions. For example, it can analyze the relationships between solutions and provide the optimal ranking result. It can also prioritize ranking related solutions by considering their interrelationships. This improves the accuracy of ranking by considering the interrelationships between solutions.

[0112] The creation process can estimate the user's emotions and adjust how the comparison table is created based on those estimates. For example, if the user is stressed, a simple and highly visual comparison table can be provided. If the user is relaxed, a detailed comparison table can be provided. If the user is in a hurry, a concise comparison table focusing on the key points can be provided. By adjusting how the comparison table is created according to the user's emotions, a more appropriate comparison table can be provided.

[0113] The reception system can filter tasks based on the user's current projects and areas of interest when they are registered. For example, it can prioritize tasks related to the user's current projects. It can also automatically suggest relevant tasks based on the user's areas of interest. This allows for the registration of highly relevant tasks by filtering based on the user's current projects and areas of interest.

[0114] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0115] The search function can perform searches while considering the attribute information of solution providers. For example, it can provide optimal search results based on the industry and size of the solution provider. It can also provide highly reliable search results by considering the solution provider's past performance. In this way, by considering the attribute information of solution providers, it can provide optimal search results.

[0116] The ranking unit can estimate the user's emotions and adjust the ranking criteria based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand ranking result. If the user is relaxed, it can provide a more detailed ranking result. If the user is in a hurry, it can provide a concise ranking result that gets straight to the point. By adjusting the ranking criteria according to the user's emotions, it can provide more appropriate ranking results.

[0117] The creation unit can apply different creation algorithms depending on the solution category when creating comparison tables. For example, a technical comparison algorithm can be applied to technical solutions, and a management comparison algorithm can be applied to management solutions. By applying different creation algorithms depending on the solution category, a more appropriate comparison table can be provided.

[0118] The reception desk can estimate the user's emotions and determine the priority of tasks to register based on those emotions. For example, if the user is stressed, less important tasks will be postponed. If the user is relaxed, more important tasks can be prioritized. This allows for the prioritization of tasks according to the user's emotions, ensuring that more appropriate tasks are registered first.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the user's issue. The issues registered by the user include technical issues and business issues. The reception desk provides an interface for the user to enter the issue on the instruction screen. Step 2: The analysis unit analyzes the content of the assignment registered by the reception unit. The analysis unit uses natural language processing technology to analyze the content of the assignment and determine its essence. Methods such as text analysis and data analysis can be used. Step 3: The search unit searches for solutions based on the analysis performed by the analysis unit. The search unit may include a priority search unit that prioritizes platform information and an internet search unit that searches internet information. Step 4: The ranking unit ranks the solutions found by the search unit. The ranking unit ranks the solutions based on evaluation criteria and scoring methods, taking into account the relevance and reliability of the solutions. Step 5: The creation section creates a comparison table of the ranked solutions from the ranking section. The creation section includes a registration section where solution providers can register their information, and it can adjust the items and display methods of the comparison table.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, ranking unit, and creation 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 control unit 46A of the smart device 14 and provides an interface for the user to input a task on the instruction screen. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the task using natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and prioritizes searching for platform information. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12 and ranks solutions based on evaluation criteria and scoring methods. The creation unit is implemented by the control unit 46A of the smart device 14 and adjusts the items and display method of the comparison table. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, ranking unit, and creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input a task on the instruction screen. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the task using natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and prioritizes searching for platform information. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12 and ranks solutions based on evaluation criteria and scoring methods. The creation unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the items and display method of the comparison table. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, ranking unit, and creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input a task on the instruction screen. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the task using natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and prioritizes searching for platform information. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12 and ranks solutions based on evaluation criteria and scoring methods. The creation unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the items and display method of the comparison table. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, ranking unit, and creation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input a task on an instruction screen. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the task using natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and prioritizes searching for platform information. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12 and ranks solutions based on evaluation criteria and scoring methods. The creation unit is implemented by the control unit 46A of the robot 414 and adjusts the items and display method of the comparison table. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area where users register their tasks, An analysis unit analyzes the content of the tasks registered by the aforementioned reception unit, A search unit searches for a solution based on the content analyzed by the aforementioned analysis unit, A ranking unit that ranks the solutions found by the search unit, The system includes a creation unit that creates a comparison table of the solutions ranked by the ranking unit. A system characterized by the following features. (Note 2) The aforementioned search unit, It includes a priority search unit that prioritizes searching for platform information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, It is equipped with an internet search unit for searching internet information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The user registers the task on the instruction screen. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, It includes a registration section where solution providers can register their information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of issue registration based on those estimated 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 task registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When registering a task, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of tasks to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When registering a task, the system prioritizes the registration of tasks that are highly relevant to the user, taking into account 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 registering a task, the system analyzes the user's social media activity and registers relevant tasks. 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 the analysis, adjust the level of detail based on the importance of the issues. 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 the problem. 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 will be determined based on the submission deadline for the assignment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, consider the interrelationships between solutions to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, the search will take into account the attribute information of the solution provider. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, When searching, consider the geographical distribution of solutions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When searching, refer to related literature for solutions to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ranking unit, It estimates user sentiment and adjusts ranking criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ranking unit, When ranking solutions, consider their interrelationships to improve the accuracy of the ranking. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ranking unit, When ranking solutions, attribute information of the solution providers is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ranking unit, It estimates user sentiment and adjusts the order in which ranking results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ranking unit, When ranking solutions, the geographical distribution of those solutions should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ranking unit, When ranking solutions, refer to relevant literature to improve the accuracy of the ranking. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, We estimate the user's emotions and adjust how the comparison table is created based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned creation unit, When creating a comparison table, adjust the level of detail in the table based on the importance of the solutions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned creation unit, When creating comparison tables, different creation algorithms are applied depending on the solution category. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned creation unit, The system estimates the user's emotions and adjusts how the comparison table is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned creation unit, When creating a comparison table, prioritize the table based on when the solutions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned creation unit, When creating a comparison table, adjust the order of the table based on the relevance of the solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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. The reception area where users register their tasks, An analysis unit analyzes the content of the tasks registered by the aforementioned reception unit, A search unit searches for a solution based on the content analyzed by the aforementioned analysis unit, A ranking unit that ranks the solutions found by the search unit, The system includes a creation unit that creates a comparison table of the solutions ranked by the ranking unit. A system characterized by the following features.

2. The aforementioned search unit, It includes a priority search unit that prioritizes searching for platform information. The system according to feature 1.

3. The aforementioned search unit, It is equipped with an internet search unit for searching internet information. The system according to feature 1.

4. The aforementioned reception unit is The user registers the task on the instruction screen. The system according to feature 1.

5. The aforementioned creation unit, It includes a registration section where solution providers can register their information. The system according to feature 1.

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

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

8. The aforementioned reception unit is When registering a task, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of tasks to register based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When registering a task, the system prioritizes the registration of tasks that are highly relevant to the user, taking into account the user's geographical location. The system according to feature 1.

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