system

Abstract

The system according to this embodiment aims to enable engineers to quickly catch up on the latest technological trends and apply them to their work. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a reception unit, and a proposal unit. The collection unit collects the latest engineering news and trends. The generation unit selects the information collected by the collection unit and generates learning content. The reception unit allows engineers to register their assigned tasks. The proposal unit proposes methods for applying the latest technologies based on the information registered by the reception unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

[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 character of the chatbot, 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 conventional technology, there is a problem that it is difficult for engineers to quickly catch up with the latest technological trends.

[0005] The system according to the embodiment aims to enable engineers to quickly catch up with the latest technological trends and apply them to their work.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a reception unit, and a proposal unit. The collection unit collects the latest engineering news and trends. The generation unit selects the information collected by the collection unit and generates learning content. The reception unit allows engineers to register their assigned tasks. The proposal unit proposes methods for applying the latest technologies based on the information registered by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows engineers to quickly catch up on the latest technological trends and apply them to their work. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The technology trend curation system according to an embodiment of the present invention is a system that helps engineers keep up with the latest technology trends. This technology trend curation system collects the latest engineering news and trends, generates and provides learning content. In addition, by registering the engineer's assigned tasks, it also suggests ways to apply the latest technologies. This enables engineers to quickly utilize the latest information in their work. For example, the technology trend curation system collects data from multiple reliable sources, and the AI ​​analyzes it. By collecting information from technology blogs, research papers, news sites, etc., it can grasp the latest technology trends. Next, the AI ​​selects the collected information and generates learning content useful for engineers. For example, it generates tutorials for new programming languages ​​and explanatory articles on the latest technology trends. This allows engineers to efficiently learn the latest technologies. Furthermore, by registering the engineer's assigned tasks, the AI ​​suggests ways to apply the latest technologies. For example, if an engineer is in charge of "database management," the AI ​​suggests how to use the latest database technologies and tools. This allows engineers to quickly apply the latest technologies to their work. Through this mechanism, engineers can efficiently learn the latest technology trends and apply them to their work. For example, it will become possible to quickly learn new programming languages ​​and adopt the latest database technologies. This is expected to improve engineers' productivity and enhance the overall technical capabilities of the company. In this way, the technology trend curation system will enable engineers to efficiently learn the latest technology trends and apply them to their work.

[0029] The technology trend curation system according to this embodiment comprises a collection unit, a generation unit, a reception unit, and a proposal unit. The collection unit collects the latest engineering news and trends. The collection unit collects data from multiple reliable sources, for example. The collection unit can collect information from technology blogs, research papers, news sites, etc. The collection unit analyzes the collected information using AI to grasp the latest technology trends. The generation unit selects the information collected by the collection unit and generates learning content. The generation unit generates, for example, tutorials for new programming languages ​​or explanatory articles on the latest technology trends. The generation unit uses AI to generate learning content useful for engineers. The reception unit allows engineers to register their assigned tasks. For example, if an engineer is in charge of "database management," the reception unit can register that information. The proposal unit proposes ways to apply the latest technologies based on the information registered by the reception unit. The proposal unit proposes, for example, how to use the latest database technologies and tools. The proposal unit uses AI to make useful suggestions for engineers. As a result, the technology trend curation system according to this embodiment enables engineers to efficiently learn about the latest technology trends and apply them to their work.

[0030] The data collection unit gathers the latest engineering news and trends. For example, it collects data from multiple reliable sources. Specifically, it can gather information from technical blogs, research papers, news sites, open-source project repositories, technical conference presentations, patent databases, and more. The data collection unit uses AI to analyze the collected information and grasp the latest technology trends. The AI ​​uses natural language processing techniques to analyze the collected text data and extract important keywords and topics. For example, machine learning algorithms can be used to identify trends in specific technology fields and classify relevant information. Furthermore, the data collection unit can calculate reliability scores for information sources to evaluate their reliability and filter out unreliable information. In addition, the data collection unit regularly updates the collected information to ensure it is always up-to-date. This allows the data collection unit to efficiently gather the latest technology information that engineers need and improve the overall information accuracy of the system.

[0031] The generation unit selects information collected by the collection unit and generates learning content. For example, the generation unit can generate tutorials for new programming languages ​​or explanatory articles on the latest technology trends. Specifically, it uses AI to analyze the collected information and generate learning content useful for engineers. The AI ​​uses natural language generation technology to generate easy-to-understand text based on the collected information. For example, the generation unit can summarize the content of collected technical blogs and research papers so that engineers can understand them quickly. Furthermore, the generation unit can also generate interactive learning content based on the collected information. For example, in programming language tutorials, it can generate interactive content including code examples and exercises, allowing engineers to learn by actually working through the material. In addition, the generation unit can generate personalized learning content according to the engineer's skill level and interests. This allows the generation unit to support engineers in learning efficiently and quickly acquire the latest technology trends.

[0032] The reception desk allows engineers to register their assigned tasks. For example, if an engineer is responsible for "database management," the reception desk can register that information. Specifically, it provides an interface for engineers to log in to the system and input their assigned tasks and areas of technical interest. The reception desk stores the entered information in a database, making it accessible to other departments. Furthermore, the reception desk can also register detailed information such as the engineer's skill level and years of experience. This allows the system to make suggestions tailored to the individual needs of each engineer. For example, if an engineer is proficient in a particular programming language, the system can prioritize providing the latest technology trends and learning content related to that language. The reception desk can also periodically update the information registered by engineers, accommodating changes in engineers' skills or assigned tasks. This allows the reception desk to always be aware of the engineers' current status, improving the overall flexibility and adaptability of the system.

[0033] The Proposal Department proposes ways to apply the latest technologies based on information registered by the Reception Department. For example, the Proposal Department proposes how to use the latest database technologies and tools. Specifically, it uses AI to make useful suggestions for engineers. The AI ​​selects the most suitable technologies and tools based on the engineer's registered responsibilities, skill level, and areas of interest. For example, if an engineer is responsible for database management, the AI ​​can propose the latest database technologies and tools for performance improvement. The Proposal Department can also provide solutions to specific challenges faced by engineers. For example, if an engineer is facing a performance issue with a particular database, the Proposal Department can propose the best methods and tools to solve that problem. Furthermore, the Proposal Department can propose relevant learning content and training programs to help engineers quickly acquire new technologies. In this way, the Proposal Department helps engineers efficiently learn the latest technology trends and apply them to their work. The Proposal Department can also collect feedback from engineers and continuously improve the accuracy and usefulness of its suggestions. In this way, the Proposal Department can provide engineers with the best technology suggestions and maximize the overall effectiveness of the system.

[0034] The data collection unit collects data from multiple reliable sources. For example, it collects information from academic papers, industry experts, and official news sites. The data collection unit uses AI to analyze the collected information and select the most reliable information. For example, the data collection unit evaluates reliability based on the number of citations and ratings of academic papers. The data collection unit can also evaluate reliability based on the opinions and evaluations of industry experts. The data collection unit can also prioritize the collection of information from official news sites. In this way, the data collection unit can provide useful information to engineers by collecting highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from academic papers into a generating AI and have the generating AI perform the reliability evaluation.

[0035] The generation unit sorts the collected information and generates useful learning content for engineers. For example, the generation unit generates tutorials for new programming languages ​​or explanatory articles on the latest technology trends. The generation unit uses AI to analyze the collected information and select information useful for engineers. For example, the generation unit sorts based on factors such as the reliability, relevance, and importance of the information. The generation unit generates learning content based on the selected information. For example, the generation unit generates learning content in the form of text, video, or interactive materials. The generation unit can generate learning content using AI. This allows the generation unit to enable engineers to efficiently learn the latest technologies. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the collected information into a generation AI and have the generation AI generate the learning content.

[0036] The proposal department proposes ways to apply the latest technologies based on the tasks registered by engineers. For example, if an engineer is in charge of "database management," the proposal department will propose how to use the latest database technologies and tools. The proposal department uses AI to make useful suggestions for engineers. The proposal department makes suggestions based on, for example, algorithms, past user behavior data, and industry best practices. The proposal department proposes the optimal way to apply technologies based on the tasks registered by engineers. The proposal department proposes, for example, implementation procedures, usage examples, and best practices. This allows the proposal department to enable engineers to quickly apply the latest technologies to their work. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data on the tasks registered by engineers into a generating AI and have the generating AI generate suggestions on how to apply the latest technologies.

[0037] The proposal department, when engineers are responsible for "database management," proposes the use of the latest database technologies and tools. For example, the proposal department might suggest the latest database software or new data management methods. The proposal department uses AI to make useful suggestions for engineers. For example, the proposal department makes suggestions based on algorithms, past user behavior data, and industry best practices. When engineers are responsible for "database management," the proposal department proposes the optimal use of database technologies and tools. For example, the proposal department might suggest implementation procedures, usage examples, and best practices. This allows the proposal department to enable engineers to quickly acquire and apply the latest database technologies and tools to their work. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data managed by engineers into a generating AI and have the generating AI generate suggestions on how to use the latest database technologies and tools.

[0038] The data collection unit evaluates the reliability of information sources in real time during collection and prioritizes the collection of highly reliable information. For example, the data collection unit evaluates reliability in real time based on the past reliability evaluation of the information source. The data collection unit can also evaluate reliability by considering the expertise of the information source's sender. The data collection unit can also evaluate reliability based on the number of citations and ratings of the information source. The data collection unit uses AI to evaluate the reliability of information sources in real time and prioritizes the collection of highly reliable information. As a result, the data collection unit can provide useful information to engineers by prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the reliability evaluation data of the information source into a generating AI and have the generating AI perform the reliability evaluation.

[0039] The data collection unit prioritizes collecting the latest information, taking into account its freshness. For example, the data collection unit prioritizes the latest information based on the information's publication date and time. The data collection unit can also prioritize the latest information based on the frequency of information updates. The data collection unit can also prioritize the latest information based on its timeliness. The data collection unit uses AI to evaluate the freshness of the information and prioritizes collecting the latest information. As a result, the data collection unit can provide engineers with the latest technology trends by prioritizing the collection of the latest information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input information publication date and time data into a generating AI and have the generating AI perform an evaluation of the information' freshness.

[0040] The data collection unit collects region-specific technology trends, taking into account the user's geographical location information during collection. For example, the data collection unit collects technology event information in the user's area. The data collection unit can also collect technology trends of companies in the user's area. The data collection unit can also collect the latest research from research institutions in the user's area. The data collection unit uses AI to analyze the user's geographical location information and collect region-specific technology trends. As a result, the data collection unit can provide the user with highly relevant information by collecting region-specific technology trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of region-specific technology trends.

[0041] The data collection unit analyzes the user's social media activity and collects relevant technology trends during the collection process. For example, the data collection unit collects posts from technology influencers that the user follows. The data collection unit can also collect topics from technology communities that the user participates in. The data collection unit can also collect relevant information from technology articles that the user has shared. The data collection unit uses AI to analyze the user's social media activity and collect relevant technology trends. This allows the data collection unit to collect relevant technology trends based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant technology trends.

[0042] The generation unit adjusts the level of detail in the learning content based on the importance of the information during generation. For example, the generation unit generates content with detailed explanations for highly important information. The generation unit can also generate concise content for less important information. The generation unit can also generate content that includes diagrams or videos depending on the importance. The generation unit uses AI to evaluate the importance of the information and adjust the level of detail in the learning content. This allows the generation unit to provide optimal learning content according to the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the learning content.

[0043] The generation unit applies different generation algorithms depending on the category of information during generation. For example, the generation unit applies an algorithm that includes code examples to programming language tutorials. The generation unit may also apply an algorithm that includes diagrams to explanations of technology trends. The generation unit may also apply a text summarization algorithm to summaries of research papers. The generation unit uses AI to analyze the category of information and applies the most suitable generation algorithm. This allows the generation unit to apply the most suitable generation algorithm depending on the category of information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input information category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0044] The generation unit determines the priority of learning content based on the information submission timing during generation. For example, the generation unit generates content prioritizing the most recent information. The generation unit can also postpone the generation of older information. The generation unit can also adjust the content generation order according to the submission timing. The generation unit uses AI to evaluate the information submission timing and determine the priority of learning content. This allows the generation unit to provide optimal learning content according to the information submission timing. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of learning content priorities.

[0045] The generation unit adjusts the order of learning content based on the relevance of the information during generation. For example, the generation unit prioritizes generating content based on highly relevant information. The generation unit can also postpone less relevant information. The generation unit can also adjust the order of content according to relevance. The generation unit uses AI to evaluate the relevance of information and adjust the order of learning content. This allows the generation unit to provide the optimal order of learning content according to the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of learning content.

[0046] The reception desk, upon receiving a request, refers to the user's past work history to suggest the most suitable registration method. For example, the reception desk can automatically display as candidates tasks that the user has frequently registered in the past. The reception desk can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks to be used during specific time periods based on the user's past work history. The reception desk uses AI to analyze the user's past work history and suggest the most suitable registration method. This allows the reception desk to provide the most suitable registration method based on the user's past work history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past work history data into a generating AI and have the generating AI suggest the most suitable registration method.

[0047] The reception desk customizes the registration content based on the user's current work status upon receiving the information. For example, the reception desk prioritizes registering tasks related to projects the user is currently working on. The reception desk can also suggest appropriate tasks considering the user's current workload. The reception desk can also automatically adjust the registration content based on the user's current work status. The reception desk uses AI to analyze the user's current work status and customize the registration content. This allows the reception desk to provide optimal registration content according to the user's current work status. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current work status data into a generating AI and have the generating AI perform the customization of the registration content.

[0048] The reception desk registers region-specific job content, taking into account the user's geographical location information, upon receiving a request. For example, the reception desk registers information on technology events in the user's region. The reception desk can also register the technology trends of companies in the user's region. The reception desk can also register the latest research from research institutions in the user's region. The reception desk uses AI to analyze the user's geographical location information and register region-specific job content. This allows the reception desk to provide users with highly relevant services by registering region-specific job content. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the registration of region-specific job content.

[0049] The reception desk analyzes the user's social media activity upon registration and registers relevant work content. For example, the reception desk registers posts from tech influencers the user follows. The reception desk can also register topics from tech communities the user participates in. The reception desk can also register relevant information from tech articles the user has shared. The reception desk uses AI to analyze the user's social media activity and register relevant work content. This allows the reception desk to register relevant work content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the registration of relevant work content.

[0050] The proposal department adjusts the level of detail in proposals based on the importance of the technology. For example, the proposal department will provide detailed explanations for highly important technologies. For less important technologies, the proposal department may provide concise proposals. The proposal department may also include diagrams or videos depending on the importance. The proposal department uses AI to evaluate the importance of technologies and adjust the level of detail in proposals. This allows the proposal department to provide the optimal level of detail in proposals according to the importance of the technologies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0051] The proposal unit applies different proposal algorithms depending on the technology category during the proposal process. For example, the proposal unit applies an algorithm that includes code examples to proposals of programming languages. The proposal unit may also apply an algorithm that includes diagrams to proposals of technology trends. The proposal unit may also apply a text summarization algorithm to proposals of research papers. The proposal unit uses AI to analyze technology categories and applies the most suitable proposal algorithm. This allows the proposal unit to provide the most suitable proposal algorithm for each technology category. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input technology category data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0052] The proposal department determines the priority of proposals based on the timing of technology submission. For example, the proposal department prioritizes the latest technologies. The proposal department may also postpone older technologies. The proposal department may also adjust the order of proposals according to their submission timing. The proposal department uses AI to evaluate the timing of technology submission and determine the priority of proposals. This allows the proposal department to provide the optimal priority of proposals according to the timing of technology submission. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology submission timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0053] The proposal department adjusts the order of proposals based on the relevance of the technologies. For example, the proposal department prioritizes proposing technologies with high relevance. The proposal department may also postpone less relevant technologies. The proposal department can also adjust the order of proposals according to their relevance. The proposal department uses AI to evaluate the relevance of technologies and adjust the order of proposals. This allows the proposal department to provide the optimal order of proposals according to the relevance of the technologies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

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

[0055] The technology trend curation system can also analyze a user's past learning history and provide a personalized learning plan. For example, the collection unit records the technologies and topics the user has learned in the past, and the generation unit generates new learning content based on that history. The suggestion unit can combine what the user has learned in the past with the latest technology trends to suggest a more effective learning method. As a result, users can receive an optimal learning plan based on their learning history, enabling them to acquire new technologies efficiently.

[0056] The technology trend curation system can also take into account the user's geographical location to provide region-specific technology trends and event information. For example, the collection unit can gather information on technology conferences and workshops in the user's area. The generation unit can generate learning content based on regional technology trends. The suggestion unit can also suggest regional technology events and networking opportunities that the user can participate in. This allows users to efficiently obtain the latest technology information relevant to their region and connect with the local technology community.

[0057] The technology trend curation system can also analyze users' social media activity and provide relevant technology trends and learning content. For example, the collection unit collects posts from technology influencers that users follow and topics from technology communities they participate in. The generation unit can generate learning content based on technology topics that are trending on social media. The suggestion unit can also suggest relevant technology trends and learning resources based on technology articles and comments that users have shared. This allows users to efficiently obtain the latest technology information based on their social media activity and use it to their advantage in learning.

[0058] The technology trend curation system can also refer to the user's past work history to suggest the most suitable technology trends and learning content. For example, the collection unit records projects the user has worked on in the past and the technologies they have used, and the generation unit generates content including the latest relevant technology trends based on that history. The suggestion unit can suggest the latest technology trends and tools similar to those the user has successfully implemented in the past. This allows users to receive the most suitable technology trends and learning content based on their work history, thereby improving their work efficiency.

[0059] The technology trend curation system can also analyze the user's current work situation and provide optimal technology trends and learning content. For example, the collection unit collects technology information related to the user's current project. The generation unit can generate learning content based on the current work situation. The suggestion unit can also suggest the most suitable technology trends and tools for the user's current project. As a result, users can receive optimal technology trends and learning content tailored to their work situation, thereby improving work efficiency.

[0060] The technology trend curation system can also evaluate a user's past learning achievements and provide optimal learning content. For example, the collection unit records what the user has learned in the past and their achievements, and the generation unit generates new learning content based on that evaluation. The suggestion unit can suggest relevant, up-to-date technology trends and tools based on the learning methods and topics in which the user has achieved high success in the past. This allows users to receive optimal learning content based on their own learning achievements, enabling them to acquire new technologies efficiently.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit gathers the latest engineering news and trends. The data collection unit collects data from multiple reliable sources, including technical blogs, research papers, and news sites. The data collection unit also uses AI to analyze the collected information and grasp the latest technology trends. Step 2: The generation unit selects the information collected by the collection unit and generates learning content. The generation unit generates tutorials for new programming languages, explanatory articles on the latest technology trends, and other learning content useful for engineers using AI. Step 3: The reception desk allows engineers to register their assigned tasks. For example, if an engineer is responsible for "database management," they can register that information. Step 4: The proposal department proposes ways to apply the latest technologies based on the information registered by the reception department. The proposal department proposes ways to use the latest database technologies and tools, and uses AI to make useful suggestions for engineers.

[0063] (Example of form 2) The technology trend curation system according to an embodiment of the present invention is a system that helps engineers keep up with the latest technology trends. This technology trend curation system collects the latest engineering news and trends, generates and provides learning content. In addition, by registering the engineer's assigned tasks, it also suggests ways to apply the latest technologies. This enables engineers to quickly utilize the latest information in their work. For example, the technology trend curation system collects data from multiple reliable sources, and the AI ​​analyzes it. By collecting information from technology blogs, research papers, news sites, etc., it can grasp the latest technology trends. Next, the AI ​​selects the collected information and generates learning content useful for engineers. For example, it generates tutorials for new programming languages ​​and explanatory articles on the latest technology trends. This allows engineers to efficiently learn the latest technologies. Furthermore, by registering the engineer's assigned tasks, the AI ​​suggests ways to apply the latest technologies. For example, if an engineer is in charge of "database management," the AI ​​suggests how to use the latest database technologies and tools. This allows engineers to quickly apply the latest technologies to their work. Through this mechanism, engineers can efficiently learn the latest technology trends and apply them to their work. For example, it will become possible to quickly learn new programming languages ​​and adopt the latest database technologies. This is expected to improve engineers' productivity and enhance the overall technical capabilities of the company. In this way, the technology trend curation system will enable engineers to efficiently learn the latest technology trends and apply them to their work.

[0064] The technology trend curation system according to this embodiment comprises a collection unit, a generation unit, a reception unit, and a proposal unit. The collection unit collects the latest engineering news and trends. The collection unit collects data from multiple reliable sources, for example. The collection unit can collect information from technology blogs, research papers, news sites, etc. The collection unit analyzes the collected information using AI to grasp the latest technology trends. The generation unit selects the information collected by the collection unit and generates learning content. The generation unit generates, for example, tutorials for new programming languages ​​or explanatory articles on the latest technology trends. The generation unit uses AI to generate learning content useful for engineers. The reception unit allows engineers to register their assigned tasks. For example, if an engineer is in charge of "database management," the reception unit can register that information. The proposal unit proposes ways to apply the latest technologies based on the information registered by the reception unit. The proposal unit proposes, for example, how to use the latest database technologies and tools. The proposal unit uses AI to make useful suggestions for engineers. As a result, the technology trend curation system according to this embodiment enables engineers to efficiently learn about the latest technology trends and apply them to their work.

[0065] The data collection unit gathers the latest engineering news and trends. For example, it collects data from multiple reliable sources. Specifically, it can gather information from technical blogs, research papers, news sites, open-source project repositories, technical conference presentations, patent databases, and more. The data collection unit uses AI to analyze the collected information and grasp the latest technology trends. The AI ​​uses natural language processing techniques to analyze the collected text data and extract important keywords and topics. For example, machine learning algorithms can be used to identify trends in specific technology fields and classify relevant information. Furthermore, the data collection unit can calculate reliability scores for information sources to evaluate their reliability and filter out unreliable information. In addition, the data collection unit regularly updates the collected information to ensure it is always up-to-date. This allows the data collection unit to efficiently gather the latest technology information that engineers need and improve the overall information accuracy of the system.

[0066] The generation unit selects information collected by the collection unit and generates learning content. For example, the generation unit can generate tutorials for new programming languages ​​or explanatory articles on the latest technology trends. Specifically, it uses AI to analyze the collected information and generate learning content useful for engineers. The AI ​​uses natural language generation technology to generate easy-to-understand text based on the collected information. For example, the generation unit can summarize the content of collected technical blogs and research papers so that engineers can understand them quickly. Furthermore, the generation unit can also generate interactive learning content based on the collected information. For example, in programming language tutorials, it can generate interactive content including code examples and exercises, allowing engineers to learn by actually working through the material. In addition, the generation unit can generate personalized learning content according to the engineer's skill level and interests. This allows the generation unit to support engineers in learning efficiently and quickly acquire the latest technology trends.

[0067] The reception desk allows engineers to register their assigned tasks. For example, if an engineer is responsible for "database management," the reception desk can register that information. Specifically, it provides an interface for engineers to log in to the system and input their assigned tasks and areas of technical interest. The reception desk stores the entered information in a database, making it accessible to other departments. Furthermore, the reception desk can also register detailed information such as the engineer's skill level and years of experience. This allows the system to make suggestions tailored to the individual needs of each engineer. For example, if an engineer is proficient in a particular programming language, the system can prioritize providing the latest technology trends and learning content related to that language. The reception desk can also periodically update the information registered by engineers, accommodating changes in engineers' skills or assigned tasks. This allows the reception desk to always be aware of the engineers' current status, improving the overall flexibility and adaptability of the system.

[0068] The Proposal Department proposes ways to apply the latest technologies based on information registered by the Reception Department. For example, the Proposal Department proposes how to use the latest database technologies and tools. Specifically, it uses AI to make useful suggestions for engineers. The AI ​​selects the most suitable technologies and tools based on the engineer's registered responsibilities, skill level, and areas of interest. For example, if an engineer is responsible for database management, the AI ​​can propose the latest database technologies and tools for performance improvement. The Proposal Department can also provide solutions to specific challenges faced by engineers. For example, if an engineer is facing a performance issue with a particular database, the Proposal Department can propose the best methods and tools to solve that problem. Furthermore, the Proposal Department can propose relevant learning content and training programs to help engineers quickly acquire new technologies. In this way, the Proposal Department helps engineers efficiently learn the latest technology trends and apply them to their work. The Proposal Department can also collect feedback from engineers and continuously improve the accuracy and usefulness of its suggestions. In this way, the Proposal Department can provide engineers with the best technology suggestions and maximize the overall effectiveness of the system.

[0069] The data collection unit collects data from multiple reliable sources. For example, it collects information from academic papers, industry experts, and official news sites. The data collection unit uses AI to analyze the collected information and select the most reliable information. For example, the data collection unit evaluates reliability based on the number of citations and ratings of academic papers. The data collection unit can also evaluate reliability based on the opinions and evaluations of industry experts. The data collection unit can also prioritize the collection of information from official news sites. In this way, the data collection unit can provide useful information to engineers by collecting highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from academic papers into a generating AI and have the generating AI perform the reliability evaluation.

[0070] The generation unit sorts the collected information and generates useful learning content for engineers. For example, the generation unit generates tutorials for new programming languages ​​or explanatory articles on the latest technology trends. The generation unit uses AI to analyze the collected information and select information useful for engineers. For example, the generation unit sorts based on factors such as the reliability, relevance, and importance of the information. The generation unit generates learning content based on the selected information. For example, the generation unit generates learning content in the form of text, video, or interactive materials. The generation unit can generate learning content using AI. This allows the generation unit to enable engineers to efficiently learn the latest technologies. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the collected information into a generation AI and have the generation AI generate the learning content.

[0071] The proposal department proposes ways to apply the latest technologies based on the tasks registered by engineers. For example, if an engineer is in charge of "database management," the proposal department will propose how to use the latest database technologies and tools. The proposal department uses AI to make useful suggestions for engineers. The proposal department makes suggestions based on, for example, algorithms, past user behavior data, and industry best practices. The proposal department proposes the optimal way to apply technologies based on the tasks registered by engineers. The proposal department proposes, for example, implementation procedures, usage examples, and best practices. This allows the proposal department to enable engineers to quickly apply the latest technologies to their work. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data on the tasks registered by engineers into a generating AI and have the generating AI generate suggestions on how to apply the latest technologies.

[0072] The proposal department, when engineers are responsible for "database management," proposes the use of the latest database technologies and tools. For example, the proposal department might suggest the latest database software or new data management methods. The proposal department uses AI to make useful suggestions for engineers. For example, the proposal department makes suggestions based on algorithms, past user behavior data, and industry best practices. When engineers are responsible for "database management," the proposal department proposes the optimal use of database technologies and tools. For example, the proposal department might suggest implementation procedures, usage examples, and best practices. This allows the proposal department to enable engineers to quickly acquire and apply the latest database technologies and tools to their work. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input data managed by engineers into a generating AI and have the generating AI generate suggestions on how to use the latest database technologies and tools.

[0073] The data collection unit estimates the user's emotions and determines the priority of information sources to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting from reliable information sources. If the user is relaxed, the data collection unit may also collect from a wide range of information sources. If the user is in a hurry, the data collection unit may also prioritize collecting from information sources that provide timely updates. The data collection unit uses AI to estimate the user's emotions and determine the priority of information sources to collect. This allows the data collection unit to collect information from the most suitable information sources 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 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information sources to collect.

[0074] The data collection unit evaluates the reliability of information sources in real time during collection and prioritizes the collection of highly reliable information. For example, the data collection unit evaluates reliability in real time based on the past reliability evaluation of the information source. The data collection unit can also evaluate reliability by considering the expertise of the information source's sender. The data collection unit can also evaluate reliability based on the number of citations and ratings of the information source. The data collection unit uses AI to evaluate the reliability of information sources in real time and prioritizes the collection of highly reliable information. As a result, the data collection unit can provide useful information to engineers by prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the reliability evaluation data of the information source into a generating AI and have the generating AI perform the reliability evaluation.

[0075] The data collection unit prioritizes collecting the latest information, taking into account its freshness. For example, the data collection unit prioritizes the latest information based on the information's publication date and time. The data collection unit can also prioritize the latest information based on the frequency of information updates. The data collection unit can also prioritize the latest information based on its timeliness. The data collection unit uses AI to evaluate the freshness of the information and prioritizes collecting the latest information. As a result, the data collection unit can provide engineers with the latest technology trends by prioritizing the collection of the latest information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input information publication date and time data into a generating AI and have the generating AI perform an evaluation of the information' freshness.

[0076] The data collection unit estimates the user's emotions and adjusts the categories of information to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit collects concise and to-the-point information. If the user is relaxed, the data collection unit may also collect detailed information. If the user is excited, the data collection unit may also collect visually stimulating information. The data collection unit uses AI to estimate the user's emotions and adjusts the categories of information to be collected. This allows the data collection unit to collect the most appropriate information categories 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the categories of information to be collected.

[0077] The data collection unit collects region-specific technology trends, taking into account the user's geographical location information during collection. For example, the data collection unit collects technology event information in the user's area. The data collection unit can also collect technology trends of companies in the user's area. The data collection unit can also collect the latest research from research institutions in the user's area. The data collection unit uses AI to analyze the user's geographical location information and collect region-specific technology trends. As a result, the data collection unit can provide the user with highly relevant information by collecting region-specific technology trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of region-specific technology trends.

[0078] The data collection unit analyzes the user's social media activity and collects relevant technology trends during the collection process. For example, the data collection unit collects posts from technology influencers that the user follows. The data collection unit can also collect topics from technology communities that the user participates in. The data collection unit can also collect relevant information from technology articles that the user has shared. The data collection unit uses AI to analyze the user's social media activity and collect relevant technology trends. This allows the data collection unit to collect relevant technology trends based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant technology trends.

[0079] The generation unit estimates the user's emotions and adjusts the presentation of the learning content based on the estimated emotions. For example, if the user is relaxed, the generation unit generates content with detailed explanations. If the user is in a hurry, the generation unit can also generate concise, to-the-point content. If the user is excited, the generation unit can also generate visually stimulating content. The generation unit uses AI to estimate the user's emotions and adjusts the presentation of the learning content. This allows the generation unit to provide optimal learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the learning content.

[0080] The generation unit adjusts the level of detail in the learning content based on the importance of the information during generation. For example, the generation unit generates content with detailed explanations for highly important information. The generation unit can also generate concise content for less important information. The generation unit can also generate content that includes diagrams or videos depending on the importance. The generation unit uses AI to evaluate the importance of the information and adjust the level of detail in the learning content. This allows the generation unit to provide optimal learning content according to the importance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the learning content.

[0081] The generation unit applies different generation algorithms depending on the category of information during generation. For example, the generation unit applies an algorithm that includes code examples to programming language tutorials. The generation unit may also apply an algorithm that includes diagrams to explanations of technology trends. The generation unit may also apply a text summarization algorithm to summaries of research papers. The generation unit uses AI to analyze the category of information and applies the most suitable generation algorithm. This allows the generation unit to apply the most suitable generation algorithm depending on the category of information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input information category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0082] The generation unit estimates the user's emotions and adjusts the length of the learning content based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. If the user is excited, the generation unit can also generate content with visually stimulating effects. The generation unit uses AI to estimate the user's emotions and adjusts the length of the learning content. This allows the generation unit to provide the optimal length of learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the learning content.

[0083] The generation unit determines the priority of learning content based on the information submission timing during generation. For example, the generation unit generates content prioritizing the most recent information. The generation unit can also postpone the generation of older information. The generation unit can also adjust the content generation order according to the submission timing. The generation unit uses AI to evaluate the information submission timing and determine the priority of learning content. This allows the generation unit to provide optimal learning content according to the information submission timing. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of learning content priorities.

[0084] The generation unit adjusts the order of learning content based on the relevance of the information during generation. For example, the generation unit prioritizes generating content based on highly relevant information. The generation unit can also postpone less relevant information. The generation unit can also adjust the order of content according to relevance. The generation unit uses AI to evaluate the relevance of information and adjust the order of learning content. This allows the generation unit to provide the optimal order of learning content according to the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of learning content.

[0085] The reception desk estimates the user's emotions and adjusts the task registration method based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk may also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk may prioritize voice input to allow for quick task registration. The reception desk uses AI to estimate the user's emotions and adjust the task registration method. This allows the reception desk to provide the optimal task registration 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the task registration method.

[0086] The reception desk, upon receiving a request, refers to the user's past work history to suggest the most suitable registration method. For example, the reception desk can automatically display as candidates tasks that the user has frequently registered in the past. The reception desk can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks to be used during specific time periods based on the user's past work history. The reception desk uses AI to analyze the user's past work history and suggest the most suitable registration method. This allows the reception desk to provide the most suitable registration method based on the user's past work history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past work history data into a generating AI and have the generating AI suggest the most suitable registration method.

[0087] The reception desk customizes the registration content based on the user's current work status upon receiving the information. For example, the reception desk prioritizes registering tasks related to projects the user is currently working on. The reception desk can also suggest appropriate tasks considering the user's current workload. The reception desk can also automatically adjust the registration content based on the user's current work status. The reception desk uses AI to analyze the user's current work status and customize the registration content. This allows the reception desk to provide optimal registration content according to the user's current work status. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current work status data into a generating AI and have the generating AI perform the customization of the registration content.

[0088] 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 will prioritize registering high-priority tasks. If the user is relaxed, the reception desk may also prioritize registering detailed tasks. If the user is in a hurry, the reception desk may also prioritize registering tasks that can be completed quickly. The reception desk uses AI to estimate the user's emotions and determine the priority of tasks to register. This allows the reception desk to provide the optimal priority of tasks to register according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of tasks to register.

[0089] The reception desk registers region-specific job content, taking into account the user's geographical location information, upon receiving a request. For example, the reception desk registers information on technology events in the user's region. The reception desk can also register the technology trends of companies in the user's region. The reception desk can also register the latest research from research institutions in the user's region. The reception desk uses AI to analyze the user's geographical location information and register region-specific job content. This allows the reception desk to provide users with highly relevant services by registering region-specific job content. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the registration of region-specific job content.

[0090] The reception desk analyzes the user's social media activity upon registration and registers relevant work content. For example, the reception desk registers posts from tech influencers the user follows. The reception desk can also register topics from tech communities the user participates in. The reception desk can also register relevant information from tech articles the user has shared. The reception desk uses AI to analyze the user's social media activity and register relevant work content. This allows the reception desk to register relevant work content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the registration of relevant work content.

[0091] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit may provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit may provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit may provide visually stimulating suggestions. The suggestion unit uses AI to estimate the user's emotions and adjusts the way it presents suggestions. This allows the suggestion unit to provide the most appropriate way to present suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0092] The proposal department adjusts the level of detail in proposals based on the importance of the technology. For example, the proposal department will provide detailed explanations for highly important technologies. For less important technologies, the proposal department may provide concise proposals. The proposal department may also include diagrams or videos depending on the importance. The proposal department uses AI to evaluate the importance of technologies and adjust the level of detail in proposals. This allows the proposal department to provide the optimal level of detail in proposals according to the importance of the technologies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0093] The proposal unit applies different proposal algorithms depending on the technology category during the proposal process. For example, the proposal unit applies an algorithm that includes code examples to proposals of programming languages. The proposal unit may also apply an algorithm that includes diagrams to proposals of technology trends. The proposal unit may also apply a text summarization algorithm to proposals of research papers. The proposal unit uses AI to analyze technology categories and applies the most suitable proposal algorithm. This allows the proposal unit to provide the most suitable proposal algorithm for each technology category. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input technology category data into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0094] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit may provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit may also provide suggestions with visually stimulating effects. The suggestion unit uses AI to estimate the user's emotions and adjust the length of the suggestions. This allows the suggestion unit to provide the optimal suggestion length 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0095] The proposal department determines the priority of proposals based on the timing of technology submission. For example, the proposal department prioritizes the latest technologies. The proposal department may also postpone older technologies. The proposal department may also adjust the order of proposals according to their submission timing. The proposal department uses AI to evaluate the timing of technology submission and determine the priority of proposals. This allows the proposal department to provide the optimal priority of proposals according to the timing of technology submission. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology submission timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0096] The proposal department adjusts the order of proposals based on the relevance of the technologies. For example, the proposal department prioritizes proposing technologies with high relevance. The proposal department may also postpone less relevant technologies. The proposal department can also adjust the order of proposals according to their relevance. The proposal department uses AI to evaluate the relevance of technologies and adjust the order of proposals. This allows the proposal department to provide the optimal order of proposals according to the relevance of the technologies. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input technology relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The technology trend curation system can also analyze a user's past learning history and provide a personalized learning plan. For example, the collection unit records the technologies and topics the user has learned in the past, and the generation unit generates new learning content based on that history. The suggestion unit can combine what the user has learned in the past with the latest technology trends to suggest a more effective learning method. As a result, users can receive an optimal learning plan based on their learning history, enabling them to acquire new technologies efficiently.

[0099] The technology trend curation system can also estimate the user's emotions and adjust the difficulty level of the learning content based on those emotions. For example, if the user is feeling stressed, the collection unit will prioritize collecting easy, introductory-level content. If the user is relaxed, the generation unit can generate content that includes more advanced technology topics. If the user is excited, the suggestion unit can suggest challenging tasks or projects. This allows users to receive learning content that is optimal for their emotional state, maximizing the effectiveness of their learning.

[0100] The technology trend curation system can also take into account the user's geographical location to provide region-specific technology trends and event information. For example, the collection unit can gather information on technology conferences and workshops in the user's area. The generation unit can generate learning content based on regional technology trends. The suggestion unit can also suggest regional technology events and networking opportunities that the user can participate in. This allows users to efficiently obtain the latest technology information relevant to their region and connect with the local technology community.

[0101] The technology trend curation system can also analyze users' social media activity and provide relevant technology trends and learning content. For example, the collection unit collects posts from technology influencers that users follow and topics from technology communities they participate in. The generation unit can generate learning content based on technology topics that are trending on social media. The suggestion unit can also suggest relevant technology trends and learning resources based on technology articles and comments that users have shared. This allows users to efficiently obtain the latest technology information based on their social media activity and use it to their advantage in learning.

[0102] The technology trend curation system can also estimate the user's emotions and adjust the format of the learning content based on those emotions. For example, if the user is feeling stressed, the collection unit will prioritize collecting concise, text-based information. If the user is relaxed, the generation unit can generate content including videos and interactive learning materials. If the user is excited, the suggestion unit can suggest content including visually stimulating infographics and animations. This allows users to receive learning content in the most appropriate format for their emotional state, maximizing the effectiveness of their learning.

[0103] The technology trend curation system can also refer to the user's past work history to suggest the most suitable technology trends and learning content. For example, the collection unit records projects the user has worked on in the past and the technologies they have used, and the generation unit generates content including the latest relevant technology trends based on that history. The suggestion unit can suggest the latest technology trends and tools similar to those the user has successfully implemented in the past. This allows users to receive the most suitable technology trends and learning content based on their work history, thereby improving their work efficiency.

[0104] The technology trend curation system can also estimate the user's emotions and adjust the timing of learning content delivery based on those emotions. For example, the collection unit can temporarily refrain from providing learning content if the user is feeling stressed. The generation unit can proactively provide learning content if the user is relaxed. The suggestion unit can prioritize suggesting content that can be learned in a short amount of time if the user is in a hurry. This allows users to receive learning content at the optimal time according to their emotional state, maximizing the effectiveness of their learning.

[0105] The technology trend curation system can also analyze the user's current work situation and provide optimal technology trends and learning content. For example, the collection unit collects technology information related to the user's current project. The generation unit can generate learning content based on the current work situation. The suggestion unit can also suggest the most suitable technology trends and tools for the user's current project. As a result, users can receive optimal technology trends and learning content tailored to their work situation, thereby improving work efficiency.

[0106] The technology trend curation system can also estimate the user's emotions and adjust the feedback method of learning content based on those emotions. For example, if the user is stressed, the collection unit will prioritize collecting positive feedback. If the user is relaxed, the generation unit can generate content with detailed feedback. If the user is excited, the suggestion unit can suggest content with visually stimulating feedback. This allows users to receive optimal feedback tailored to their emotional state, maximizing the effectiveness of their learning.

[0107] The technology trend curation system can also evaluate a user's past learning achievements and provide optimal learning content. For example, the collection unit records what the user has learned in the past and their achievements, and the generation unit generates new learning content based on that evaluation. The suggestion unit can suggest relevant, up-to-date technology trends and tools based on the learning methods and topics in which the user has achieved high success in the past. This allows users to receive optimal learning content based on their own learning achievements, enabling them to acquire new technologies efficiently.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit gathers the latest engineering news and trends. The data collection unit collects data from multiple reliable sources, including technical blogs, research papers, and news sites. The data collection unit also uses AI to analyze the collected information and grasp the latest technology trends. Step 2: The generation unit selects the information collected by the collection unit and generates learning content. The generation unit generates tutorials for new programming languages, explanatory articles on the latest technology trends, and other learning content useful for engineers using AI. Step 3: The reception desk allows engineers to register their assigned tasks. For example, if an engineer is responsible for "database management," they can register that information. Step 4: The proposal department proposes ways to apply the latest technologies based on the information registered by the reception department. The proposal department proposes ways to use the latest database technologies and tools, and uses AI to make useful suggestions for engineers.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, generation unit, reception unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing unit 12. The reception unit registers the engineer's assigned tasks using the control unit 46A of the smart device 14. The proposal unit proposes methods for applying the latest technology using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, generation unit, reception unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing unit 12. The reception unit registers the engineer's assigned tasks using the control unit 46A of the smart glasses 214. The proposal unit proposes methods for applying the latest technology using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, generation unit, reception unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing unit 12. The reception unit registers the engineer's assigned tasks using the control unit 46A of the headset terminal 314. The proposal unit proposes methods for applying the latest technology using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, generation unit, reception unit, and proposal unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The generation unit generates learning content based on the information collected by the specific processing unit 290 of the data processing unit 12. The reception unit registers the engineer's assigned tasks using the control unit 46A of the robot 414. The proposal unit proposes methods for applying the latest technology using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The collection department gathers the latest engineering news and trends, A generation unit that selects the information collected by the collection unit and generates learning content, The reception area where engineers register their assigned tasks, Based on the information registered by the reception department, the proposal department proposes methods for applying the latest technology. Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from multiple reliable sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The collected information is filtered and useful learning content is generated for engineers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the assigned tasks registered by the engineers, we propose ways to apply the latest technologies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, If an engineer is in charge of "database management," they should propose the use of the latest database technologies and tools. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and determines the priority of information sources to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the reliability of information sources is evaluated in real time, and reliable information is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, we prioritize the collection of the latest information, taking into consideration its timeliness. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the categories of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system takes into account the user's geographical location to gather region-specific technology trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, we analyze users' social media activity and gather relevant technology trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts how the learning content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, adjust the level of detail in the learning content based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the learning content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, learning content is prioritized based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of learning content is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of registering assigned tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is During registration, we will refer to the user's past work history to suggest the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is At the time of registration, the registration details are customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of assigned tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is During registration, the system takes into account the user's geographical location and registers region-specific job details. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is Upon registration, the system analyzes the user's social media activity and registers relevant work details. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of technology. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of technical submission. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the technologies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 collection department gathers the latest engineering news and trends, A generation unit that selects the information collected by the collection unit and generates learning content, The reception area where engineers register their assigned tasks, Based on the information registered by the reception department, the proposal department proposes methods for applying the latest technology. Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect data from multiple reliable sources. The system according to feature 1.

3. The generating unit is The collected information is filtered and useful learning content is generated for engineers. The system according to feature 1.

4. The aforementioned proposal section is, Based on the assigned tasks registered by the engineers, we propose ways to apply the latest technologies. The system according to feature 1.

5. The aforementioned proposal section is, If an engineer is in charge of database management, they should suggest the use of the latest database technologies and tools. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and determines the priority of information sources to collect based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the reliability of information sources is evaluated in real time, and reliable information is prioritized for collection. The system according to feature 1.

8. The aforementioned collection unit is When collecting information, we prioritize the collection of the latest information, taking into consideration its timeliness. The system according to feature 1.

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