system

Abstract

The system according to this embodiment aims to grasp the degree of penetration and evolution of generated AI and to count down to a singularity. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a learning unit, and a countdown generation unit. The data collection unit collects patent information and trend data. The analysis unit analyzes the data collected by the data collection unit. The learning unit learns the degree of penetration and evolution of the generated AI based on the results analyzed by the analysis unit. The countdown generation unit generates a countdown of singularities based on the results learned by the learning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the degree of penetration and evolution of generative AI has not been sufficiently grasped and the countdown to the singularity has not been sufficiently carried out.

[0005] The system according to the embodiment aims to grasp the degree of penetration and evolution of generative AI and perform a countdown to the singularity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a learning unit, and a countdown generation unit. The data collection unit collects patent information and trend data. The analysis unit analyzes the data collected by the data collection unit. The learning unit learns the degree of penetration and evolution of the generated AI based on the results analyzed by the analysis unit. The countdown generation unit generates a countdown to singularities based on the results learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the degree of penetration and evolution of the generated AI and count down to the singularity. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Generative AI Evolution Countdown Service according to an embodiment of the present invention is a service that displays the time remaining until a Generative AI takes over a job, with "midnight" being the point at which such a time occurs, in the form of "minutes (seconds) until midnight." The Generative AI Evolution Countdown Service learns the degree of penetration and evolution of Generative AI in each field from patent information and trends, and generates a countdown to the singularity. Furthermore, it is equipped with a Generative AI chatbot function, making it a helpful clock that also provides information on services offered by specific groups and tips on Generative AI in all fields. For example, the Generative AI Evolution Countdown Service collects patent information and trend data, and the Generative AI analyzes this data. Next, based on the analysis results, it learns the degree of penetration and evolution of Generative AI in each field. As a result, it displays the time remaining until a Generative AI takes over a job, with "midnight" being the point at which such a time occurs, in the form of "minutes (seconds) until midnight." Furthermore, it is equipped with a Generative AI chatbot function, and when the user enters a question, the Generative AI will provide information on services offered by specific groups and tips on Generative AI in all fields. For example, if a user asks, "I want to know how generative AI is evolving," the generative AI will answer based on the latest patent information and trend data. This service allows users to understand the degree of evolution of generative AI in real time and prepare for future changes in their work. In addition, users can easily obtain the latest information and tips about generative AI through the generative AI's chatbot function. In this way, the generative AI evolution countdown service can provide users with the degree of evolution of generative AI in real time and help them prepare for future changes in their work.

[0029] The generative AI evolution countdown service according to this embodiment comprises a collection unit, an analysis unit, a learning unit, and a countdown generation unit. The collection unit collects patent information and trend data. For example, the collection unit can collect patent information in a specific technical field or trend data for a specific market. The collection unit can also collect data from publicly available databases and news sites on the internet. Furthermore, the collection unit can also collect data from social media and industry reports. For example, the collection unit automatically collects patent information in a specific technical field and stores it in a database. The collection unit can also periodically collect trend data for a specific market and provide it to the analysis unit. Furthermore, the collection unit can analyze social media posts and industry reports and extract relevant data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using data mining techniques. Furthermore, the analysis unit can analyze the data using statistical analysis and machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing techniques. For example, the analysis unit uses data mining techniques to extract important patterns and trends from the collected data. Furthermore, the analysis unit can analyze the correlation of data using statistical analysis and evaluate the degree of evolution of the generative AI. In addition, the analysis unit can use machine learning algorithms to build a predictive model of the generative AI's evolution from the collected data. The learning unit learns the degree of penetration and evolution of the generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of the generative AI using, for example, supervised learning. The learning unit can also learn the degree of evolution of the generative AI using unsupervised learning or reinforcement learning. Furthermore, the learning unit can learn the degree of evolution of the generative AI in real time. For example, the learning unit can use supervised learning to learn based on the collected data and the known degree of evolution of generative AI. The learning unit can also use unsupervised learning to automatically extract patterns of generative AI evolution from the collected data. Furthermore, the learning unit can use reinforcement learning to optimize the degree of evolution of the generative AI.The countdown generation unit generates a countdown to a singularity based on the results learned by the learning unit. The countdown generation unit can generate a countdown based on, for example, the definition of a singularity. It can also generate a countdown based on the countdown start conditions. Furthermore, the countdown generation unit can customize the display method of the countdown. For example, based on the definition of a singularity, the countdown generation unit generates a countdown with the timing when the generating AI takes over a job set to "midnight". The countdown generation unit can also start the countdown from the point when a specific condition is met, based on the countdown start conditions. Furthermore, the countdown generation unit can customize the display method of the countdown to the user's preference. As a result, the generating AI evolution countdown service according to the embodiment can grasp the degree of evolution of the generating AI in real time by collecting, analyzing, learning, and generating countdowns from patent information and trend data.

[0030] The data collection unit collects patent information and trend data. For example, it can collect patent information in specific technological fields or trend data for specific markets. Specifically, for patent information, it utilizes the Japan Patent Office database and international patent databases to obtain the latest patent application and registration information. This allows for the understanding of new inventions and technological advancements in specific technological fields. For trend data collection, it leverages market research company reports and industry news sites to track trends in specific markets and technologies. Furthermore, the data collection unit can collect data from publicly available databases and news sites on the internet. For example, it can automatically collect articles from technology blogs and professional journals using scraping techniques and store them in a database. Data collection from social media is also important; it monitors specific keywords and hashtags and collects relevant posts. This allows for the understanding of real-time reactions from the technology community and market. It also collects data from industry reports, such as regularly obtaining quarterly market analysis reports and technology forecast reports and providing them to the analysis unit. This enables the data collection unit to gather a wide range of data from diverse sources and build a comprehensive database on the evolution of generative AI.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. Specifically, it uses data mining techniques to extract important patterns and trends from the collected data. For example, it can identify increasing trends in the number of patent applications in specific technological fields or identify technological areas that specific companies are focusing on, using a patent information database. Furthermore, statistical analysis and machine learning algorithms are used to analyze trend data. Statistical analysis can be used to analyze data correlations and evaluate the degree of evolution of generative AI. For example, it can analyze the correlation between the number of patent applications in a specific technological field and the market growth rate to evaluate the impact of technological evolution on the market. In addition, machine learning algorithms can be used to build predictive models of generative AI evolution from the collected data. For example, it can create models that predict the rate of evolution of generative AI and the time it takes for a particular technology to become widespread, based on historical data. Natural language processing techniques can also be used to analyze text data. For example, it can analyze text data from patent documents and news articles to extract important keywords and phrases related to technological evolution. This allows the analysis unit to analyze the collected data from multiple perspectives and gain deep insights into the evolution of generative AI.

[0032] The learning unit learns the degree of penetration and evolution of generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of generative AI using, for example, supervised learning. Specifically, it learns based on past data and known degrees of evolution of generative AI. For example, it can train a model to predict the degree of evolution of generative AI by using the number of patent applications or market growth rate in a specific technology field as input data. It can also automatically extract patterns of evolution of generative AI from collected data using unsupervised learning. For example, it can use a clustering algorithm to analyze patterns of patent applications in a specific technology field and identify trends in technological evolution. Furthermore, it can use reinforcement learning to optimize the degree of evolution of generative AI. For example, it can learn the optimal strategy to promote the evolution of generative AI and find a way to maximize the rate of evolution under specific conditions. The learning unit can combine these learning algorithms to learn the degree of evolution of generative AI in real time. For example, it can update the model each time new data is collected and predict the degree of evolution based on the latest information. This allows the learning unit to provide highly accurate predictions about the evolution of the generative AI, thereby improving the overall performance of the system.

[0033] The countdown generation unit generates a countdown to a singularity based on the results learned by the learning unit. For example, the countdown generation unit can generate a countdown based on the definition of a singularity. Specifically, it can define a singularity as the point in time when the generative AI surpasses human capabilities in a particular technological field and display a countdown to that point. It can also generate a countdown based on the start condition of the countdown. For example, the start condition of the countdown is the point in time when a particular technology is introduced to the market, and the period from that point to the singularity is counted down. Furthermore, the display method of the countdown can be customized. For example, the countdown can be displayed in a digital clock format or a graphical interface according to the user's preference. Based on the data provided by the learning unit, the countdown generation unit updates the period to the singularity in real time, providing the user with the latest information. For example, if the evolution of the generative AI is progressing faster than expected, it shortens the countdown period and issues a warning to the user. The countdown generation unit can also manage countdowns for multiple singularities simultaneously. This allows the user to grasp the progress of the generative AI's evolution in different technological fields and markets at a glance. This allows the countdown generation unit to provide users with important information regarding the evolution of the generation AI and to support them in taking appropriate measures.

[0034] The system includes a chatbot unit that provides generative AI chatbot functionality. The chatbot unit can, for example, respond to user questions using natural language processing technology. It can also manage user interactions using a dialogue management system. Furthermore, the chatbot unit can generate optimal responses to user questions using generative AI. For example, the chatbot unit analyzes user questions using natural language processing technology and generates appropriate responses. It can also manage the flow of user interactions using a dialogue management system, ensuring smooth conversations. Additionally, the chatbot unit can generate optimal responses to user questions using generative AI and provide them to the user. This allows users to easily obtain information and tips about generative AI through the chatbot functionality.

[0035] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can discover from past collected data that data collection efficiency is high during specific time periods and concentrate data collection during those times. The data collection unit can also analyze past collected data to confirm that specific data sources are highly reliable and prioritize the use of those data sources. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. For example, the data collection unit can analyze past collected data to identify time periods with high collection efficiency and concentrate data collection during those times. The data collection unit can also identify highly reliable data sources based on past collected data and prioritize the use of those data sources. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. This makes efficient data collection possible by analyzing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter data based on specific industries or fields during collection. For example, when collecting data in the medical field, the collection unit can filter and collect only medical-related patent information and trend data. Similarly, when collecting data in the IT field, the collection unit can filter and collect only IT-related patent information and trend data. Furthermore, when collecting data in the education field, the collection unit can filter and collect only education-related patent information and trend data. For example, the collection unit can automatically filter medical patent information and collect only relevant data. Similarly, the collection unit can filter IT trend data and collect only relevant data. Furthermore, the collection unit can filter education patent information and trend data and collect only relevant data. This allows for the efficient collection of data relevant to specific industries or fields. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data relevant to a specific industry or field into a generating AI and have the generating AI perform the data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information and trend data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. For example, the data collection unit obtains the user's geographical location information from GPS data or IP address and collects relevant data based on that information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Furthermore, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. This allows for the efficient collection of highly relevant data by considering geographical location 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 the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0038] The data collection unit can analyze social media activity and collect relevant data during the collection process. For example, if a user uses a specific hashtag, the data collection unit can collect patent information and trend data related to that hashtag. Furthermore, if a user follows a specific social media account, the data collection unit can also collect patent information and trend data related to that account. Additionally, if a user posts about a specific topic, the data collection unit can collect patent information and trend data related to that topic. For example, the data collection unit can analyze social media posts and extract relevant data. It can also collect relevant data based on hashtags used by the user. Furthermore, the data collection unit can analyze posts from accounts followed by the user and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. 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 social media post data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. It can also apply an IT-specific analysis algorithm to IT data. Furthermore, it can apply an education-specific analysis algorithm to educational data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent data based on the data collection timing. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific category. For example, the analysis unit evaluates the relevance of the data and prioritizes the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and automatically optimize the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and automatically optimize the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0044] The learning unit can filter learning data based on specific industries or fields during the learning process. For example, the learning unit can filter and learn from learning data in the medical field. It can also filter and learn from learning data in the IT field. Furthermore, it can filter and learn from learning data in the education field. For example, the learning unit can filter learning data in the medical field and learn only the relevant data. It can also filter learning data in the IT field and learn only the relevant data. Furthermore, it can filter learning data in the education field and learn only the relevant data. This allows for efficient learning of data relevant to specific industries or fields. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data relevant to a specific industry or field into a generating AI and have the generating AI perform the data filtering.

[0045] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. Furthermore, the learning unit can assign appropriate weights to data collected during a specific period. For example, the learning unit can assign a higher weight to the most recent data based on when the data was collected. It can also assign a lower weight to older data. Furthermore, the learning unit can assign appropriate weights to data collected during a specific period. This enables efficient learning by weighting the training data based on when the data was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection period into a generating AI and have the generating AI perform the weighting of the training data.

[0046] The learning unit can improve the accuracy of its learning by referring to relevant literature during the learning process. For example, the learning unit can learn by referring to the latest relevant research papers. It can also learn by referring to relevant patent information. Furthermore, the learning unit can learn by referring to relevant industry reports. For example, the learning unit can learn by referring to the latest relevant research papers. It can also learn by referring to relevant patent information. Furthermore, the learning unit can learn by referring to relevant industry reports. This improves the accuracy of learning by referring to relevant literature. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0047] The countdown generation unit can improve the accuracy of singularity prediction by referring to past data when generating the countdown. For example, the countdown generation unit can improve the accuracy of singularity prediction based on past data. The countdown generation unit can also analyze past data and optimize the singularity prediction algorithm. Furthermore, the countdown generation unit can improve the singularity prediction model by referring to past data. For example, the countdown generation unit can improve the accuracy of singularity prediction based on past data. Furthermore, the countdown generation unit can analyze past data and optimize the singularity prediction algorithm. Furthermore, the countdown generation unit can improve the singularity prediction model by referring to past data. As a result, the accuracy of singularity prediction is improved by referring to past data. Some or all of the above processing in the countdown generation unit may be performed using AI, for example, or without using AI. For example, the countdown generation unit can input past data into the generation AI and have the generation AI perform the improvement of singularity prediction accuracy.

[0048] The countdown generation unit can customize the countdown based on a specific industry or field when generating it. For example, when generating a singularity countdown in the medical field, the countdown generation unit customizes it based on medical-related data. Similarly, when generating a singularity countdown in the IT field, the countdown generation unit can customize it based on IT-related data. Furthermore, when generating a singularity countdown in the education field, the countdown generation unit can customize it based on education-related data. This allows for the provision of more relevant information by customizing the countdown based on a specific industry or field. Some or all of the above-described processes in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input data related to a specific industry or field into the generation AI and have the generation AI perform customization of the countdown.

[0049] The countdown generation unit can generate an optimal countdown by considering geographical location information during countdown generation. For example, if the user is in a specific region, the countdown generation unit can generate a singularity countdown related to that region. Furthermore, if the user is traveling, the countdown generation unit can generate a singularity countdown related to the travel destination. Additionally, if the user is in a specific city, the countdown generation unit can generate a singularity countdown related to that city. For example, the countdown generation unit obtains the user's geographical location information from GPS data or IP address and generates a relevant countdown based on that information. Furthermore, if the user is traveling, the countdown generation unit can generate a singularity countdown related to the travel destination. Furthermore, if the user is in a specific city, the countdown generation unit can generate a singularity countdown related to that city. This allows for the generation of highly relevant countdowns by considering geographical location information. Some or all of the above-described processes in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input the user's geographical location information into the generation AI, allowing the AI ​​to generate the optimal countdown.

[0050] The countdown generation unit can improve the accuracy of the countdown by referring to relevant literature during countdown generation. For example, the countdown generation unit can generate a countdown by referring to the latest relevant research papers. The countdown generation unit can also generate a countdown by referring to relevant patent information. Furthermore, the countdown generation unit can generate a countdown by referring to relevant industry reports. For example, the countdown generation unit can generate a countdown by referring to the latest relevant research papers. The countdown generation unit can also generate a countdown by referring to relevant patent information. Furthermore, the countdown generation unit can generate a countdown by referring to relevant industry reports. This improves the accuracy of the countdown by referring to relevant literature. Some or all of the above processing in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input relevant literature into a generation AI and have the generation AI perform the countdown accuracy improvement.

[0051] The chatbot unit can provide the most appropriate response by referring to the user's past question history when responding. For example, the chatbot unit can provide relevant information based on the content of questions the user has asked in the past. The chatbot unit can also select the most appropriate response from the user's past question history. Furthermore, the chatbot unit can analyze the user's past question history and suggest the most appropriate response method. For example, the chatbot unit can refer to the user's past question history from a database and provide relevant information. The chatbot unit can also select the most appropriate response based on the user's past question history. Furthermore, the chatbot unit can analyze the user's past question history and suggest the most appropriate response method. This allows for the provision of more appropriate responses by referring to the user's past question history. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the user's past question history into a generating AI and have the generating AI perform the task of providing the most appropriate response.

[0052] The chatbot can customize its responses based on specific industries or fields. For example, it can provide medical-related information in response to questions in the medical field, IT-related information in response to questions in the IT field, and education-related information in response to questions in the education field. By customizing responses based on specific industries or fields, it can provide more relevant information. Some or all of the above processing in the chatbot may be performed using AI, for example, or not. For example, the chatbot can input data related to specific industries or fields into a generating AI and have the generating AI customize the response content.

[0053] The chatbot unit can provide the optimal response by considering the user's device information when responding. For example, if the user is using a smartphone, the chatbot unit will provide a response that is appropriate for the screen size. Furthermore, if the user is using a tablet, the chatbot unit can provide a response optimized for a larger screen. Additionally, if the user is using a smartwatch, the chatbot unit can provide a concise and easily readable response. This allows for the provision of more appropriate responses by considering the user's device information. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal response.

[0054] The chatbot unit can improve the accuracy of its responses by referring to relevant literature when responding. For example, the chatbot unit can respond by referring to the latest relevant research papers. It can also respond by referring to relevant patent information. Furthermore, the chatbot unit can respond by referring to relevant industry reports. For example, the chatbot unit can respond by referring to the latest relevant research papers. It can also respond by referring to relevant patent information. Furthermore, the chatbot unit can respond by referring to relevant industry reports. This improves the accuracy of responses by referring to relevant literature. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of responses.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze past collected data and select the optimal collection method. For example, it can discover from past collected data that data collection efficiency is high during specific time periods and concentrate data collection during those times. It can also analyze past collected data to confirm that specific data sources are highly reliable and prioritize their use. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. This means that efficient data collection becomes possible by analyzing past data.

[0057] The data collection unit can filter data based on specific industries or fields during the collection process. For example, when collecting data in the medical field, the unit can filter and collect only medical-related patent information and trend data. Similarly, when collecting data in the IT field, the unit can filter and collect only IT-related patent information and trend data. Furthermore, when collecting data in the education field, the unit can filter and collect only education-related patent information and trend data. For instance, the unit can automatically filter medical patent information and collect only relevant data. It can also filter IT trend data and collect only relevant data. Furthermore, it can filter education patent information and trend data and collect only relevant data. This allows for the efficient collection of data relevant to specific industries or fields.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information and trend data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. For instance, the data collection unit obtains the user's geographical location information from GPS data or IP address and collects relevant data based on that information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0060] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. It can also apply an IT-specific analysis algorithm to IT data. Furthermore, it can apply an education-specific analysis algorithm to educational data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category.

[0061] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, efficient analysis becomes possible.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects patent information and trend data. For example, the data collection unit can collect patent information in a specific technological field or trend data for a specific market. It can also collect data from publicly available databases and news sites on the internet. Furthermore, it can collect data from social media and industry reports. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis or machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing techniques. Step 3: The learning unit learns the degree of penetration and evolution of the generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of the generative AI using, for example, supervised learning. It can also learn the degree of evolution of the generative AI using unsupervised learning or reinforcement learning. Furthermore, the learning unit can learn the degree of evolution of the generative AI in real time. Step 4: The countdown generation unit generates a countdown for the singularity based on the results learned by the learning unit. The countdown generation unit can generate a countdown based on, for example, the definition of a singularity. It can also generate a countdown based on the start condition of the countdown. Furthermore, the countdown generation unit can customize how the countdown is displayed.

[0064] (Example of form 2) The Generative AI Evolution Countdown Service according to an embodiment of the present invention is a service that displays the time remaining until a Generative AI takes over a job, with "midnight" being the point at which such a time occurs, in the form of "minutes (seconds) until midnight." The Generative AI Evolution Countdown Service learns the degree of penetration and evolution of Generative AI in each field from patent information and trends, and generates a countdown to the singularity. Furthermore, it is equipped with a Generative AI chatbot function, making it a helpful clock that also provides information on services offered by specific groups and tips on Generative AI in all fields. For example, the Generative AI Evolution Countdown Service collects patent information and trend data, and the Generative AI analyzes this data. Next, based on the analysis results, it learns the degree of penetration and evolution of Generative AI in each field. As a result, it displays the time remaining until a Generative AI takes over a job, with "midnight" being the point at which such a time occurs, in the form of "minutes (seconds) until midnight." Furthermore, it is equipped with a Generative AI chatbot function, and when the user enters a question, the Generative AI will provide information on services offered by specific groups and tips on Generative AI in all fields. For example, if a user asks, "I want to know how generative AI is evolving," the generative AI will answer based on the latest patent information and trend data. This service allows users to understand the degree of evolution of generative AI in real time and prepare for future changes in their work. In addition, users can easily obtain the latest information and tips about generative AI through the generative AI's chatbot function. In this way, the generative AI evolution countdown service can provide users with the degree of evolution of generative AI in real time and help them prepare for future changes in their work.

[0065] The generative AI evolution countdown service according to this embodiment comprises a collection unit, an analysis unit, a learning unit, and a countdown generation unit. The collection unit collects patent information and trend data. For example, the collection unit can collect patent information in a specific technical field or trend data for a specific market. The collection unit can also collect data from publicly available databases and news sites on the internet. Furthermore, the collection unit can also collect data from social media and industry reports. For example, the collection unit automatically collects patent information in a specific technical field and stores it in a database. The collection unit can also periodically collect trend data for a specific market and provide it to the analysis unit. Furthermore, the collection unit can analyze social media posts and industry reports and extract relevant data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using data mining techniques. Furthermore, the analysis unit can analyze the data using statistical analysis and machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing techniques. For example, the analysis unit uses data mining techniques to extract important patterns and trends from the collected data. Furthermore, the analysis unit can analyze the correlation of data using statistical analysis and evaluate the degree of evolution of the generative AI. In addition, the analysis unit can use machine learning algorithms to build a predictive model of the generative AI's evolution from the collected data. The learning unit learns the degree of penetration and evolution of the generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of the generative AI using, for example, supervised learning. The learning unit can also learn the degree of evolution of the generative AI using unsupervised learning or reinforcement learning. Furthermore, the learning unit can learn the degree of evolution of the generative AI in real time. For example, the learning unit can use supervised learning to learn based on the collected data and the known degree of evolution of generative AI. The learning unit can also use unsupervised learning to automatically extract patterns of generative AI evolution from the collected data. Furthermore, the learning unit can use reinforcement learning to optimize the degree of evolution of the generative AI.The countdown generation unit generates a countdown to a singularity based on the results learned by the learning unit. The countdown generation unit can generate a countdown based on, for example, the definition of a singularity. It can also generate a countdown based on the countdown start conditions. Furthermore, the countdown generation unit can customize the display method of the countdown. For example, based on the definition of a singularity, the countdown generation unit generates a countdown with the timing when the generating AI takes over a job set to "midnight". The countdown generation unit can also start the countdown from the point when a specific condition is met, based on the countdown start conditions. Furthermore, the countdown generation unit can customize the display method of the countdown to the user's preference. As a result, the generating AI evolution countdown service according to the embodiment can grasp the degree of evolution of the generating AI in real time by collecting, analyzing, learning, and generating countdowns from patent information and trend data.

[0066] The data collection unit collects patent information and trend data. For example, it can collect patent information in specific technological fields or trend data for specific markets. Specifically, for patent information, it utilizes the Japan Patent Office database and international patent databases to obtain the latest patent application and registration information. This allows for the understanding of new inventions and technological advancements in specific technological fields. For trend data collection, it leverages market research company reports and industry news sites to track trends in specific markets and technologies. Furthermore, the data collection unit can collect data from publicly available databases and news sites on the internet. For example, it can automatically collect articles from technology blogs and professional journals using scraping techniques and store them in a database. Data collection from social media is also important; it monitors specific keywords and hashtags and collects relevant posts. This allows for the understanding of real-time reactions from the technology community and market. It also collects data from industry reports, such as regularly obtaining quarterly market analysis reports and technology forecast reports and providing them to the analysis unit. This enables the data collection unit to gather a wide range of data from diverse sources and build a comprehensive database on the evolution of generative AI.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. Specifically, it uses data mining techniques to extract important patterns and trends from the collected data. For example, it can identify increasing trends in the number of patent applications in specific technological fields or identify technological areas that specific companies are focusing on, using a patent information database. Furthermore, statistical analysis and machine learning algorithms are used to analyze trend data. Statistical analysis can be used to analyze data correlations and evaluate the degree of evolution of generative AI. For example, it can analyze the correlation between the number of patent applications in a specific technological field and the market growth rate to evaluate the impact of technological evolution on the market. In addition, machine learning algorithms can be used to build predictive models of generative AI evolution from the collected data. For example, it can create models that predict the rate of evolution of generative AI and the time it takes for a particular technology to become widespread, based on historical data. Natural language processing techniques can also be used to analyze text data. For example, it can analyze text data from patent documents and news articles to extract important keywords and phrases related to technological evolution. This allows the analysis unit to analyze the collected data from multiple perspectives and gain deep insights into the evolution of generative AI.

[0068] The learning unit learns the degree of penetration and evolution of generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of generative AI using, for example, supervised learning. Specifically, it learns based on past data and known degrees of evolution of generative AI. For example, it can train a model to predict the degree of evolution of generative AI by using the number of patent applications or market growth rate in a specific technology field as input data. It can also automatically extract patterns of evolution of generative AI from collected data using unsupervised learning. For example, it can use a clustering algorithm to analyze patterns of patent applications in a specific technology field and identify trends in technological evolution. Furthermore, it can use reinforcement learning to optimize the degree of evolution of generative AI. For example, it can learn the optimal strategy to promote the evolution of generative AI and find a way to maximize the rate of evolution under specific conditions. The learning unit can combine these learning algorithms to learn the degree of evolution of generative AI in real time. For example, it can update the model each time new data is collected and predict the degree of evolution based on the latest information. This allows the learning unit to provide highly accurate predictions about the evolution of the generative AI, thereby improving the overall performance of the system.

[0069] The countdown generation unit generates a countdown to a singularity based on the results learned by the learning unit. For example, the countdown generation unit can generate a countdown based on the definition of a singularity. Specifically, it can define a singularity as the point in time when the generative AI surpasses human capabilities in a particular technological field and display a countdown to that point. It can also generate a countdown based on the start condition of the countdown. For example, the start condition of the countdown is the point in time when a particular technology is introduced to the market, and the period from that point to the singularity is counted down. Furthermore, the display method of the countdown can be customized. For example, the countdown can be displayed in a digital clock format or a graphical interface according to the user's preference. Based on the data provided by the learning unit, the countdown generation unit updates the period to the singularity in real time, providing the user with the latest information. For example, if the evolution of the generative AI is progressing faster than expected, it shortens the countdown period and issues a warning to the user. The countdown generation unit can also manage countdowns for multiple singularities simultaneously. This allows the user to grasp the progress of the generative AI's evolution in different technological fields and markets at a glance. This allows the countdown generation unit to provide users with important information regarding the evolution of the generation AI and to support them in taking appropriate measures.

[0070] The system includes a chatbot unit that provides generative AI chatbot functionality. The chatbot unit can, for example, respond to user questions using natural language processing technology. It can also manage user interactions using a dialogue management system. Furthermore, the chatbot unit can generate optimal responses to user questions using generative AI. For example, the chatbot unit analyzes user questions using natural language processing technology and generates appropriate responses. It can also manage the flow of user interactions using a dialogue management system, ensuring smooth conversations. Additionally, the chatbot unit can generate optimal responses to user questions using generative AI and provide them to the user. This allows users to easily obtain information and tips about generative AI through the chatbot functionality.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of collecting patent information and trend data based on the estimated emotions. For example, if the user is excited, the data collection unit can increase the collection frequency to provide the latest information quickly. Conversely, if the user is relaxed, the data collection unit can decrease the collection frequency to provide stable information. Furthermore, if the user is stressed, the data collection unit can adjust the collection timing to provide information during the time when the user is most relaxed. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of information at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0072] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can discover from past collected data that data collection efficiency is high during specific time periods and concentrate data collection during those times. The data collection unit can also analyze past collected data to confirm that specific data sources are highly reliable and prioritize the use of those data sources. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. For example, the data collection unit can analyze past collected data to identify time periods with high collection efficiency and concentrate data collection during those times. The data collection unit can also identify highly reliable data sources based on past collected data and prioritize the use of those data sources. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. This makes efficient data collection possible by analyzing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0073] The data collection unit can filter data based on specific industries or fields during collection. For example, when collecting data in the medical field, the collection unit can filter and collect only medical-related patent information and trend data. Similarly, when collecting data in the IT field, the collection unit can filter and collect only IT-related patent information and trend data. Furthermore, when collecting data in the education field, the collection unit can filter and collect only education-related patent information and trend data. For example, the collection unit can automatically filter medical patent information and collect only relevant data. Similarly, the collection unit can filter IT trend data and collect only relevant data. Furthermore, the collection unit can filter education patent information and trend data and collect only relevant data. This allows for the efficient collection of data relevant to specific industries or fields. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data relevant to a specific industry or field into a generating AI and have the generating AI perform the data filtering.

[0074] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the unit will prioritize collecting the latest patent information and trend data. If the user is relaxed, the unit can also prioritize collecting stable information. Furthermore, if the user is stressed, the unit can prioritize collecting data in areas of the user's greatest interest. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate information by prioritizing data 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information and trend data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. For example, the data collection unit obtains the user's geographical location information from GPS data or IP address and collects relevant data based on that information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Furthermore, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. This allows for the efficient collection of highly relevant data by considering geographical location 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 the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0076] The data collection unit can analyze social media activity and collect relevant data during the collection process. For example, if a user uses a specific hashtag, the data collection unit can collect patent information and trend data related to that hashtag. Furthermore, if a user follows a specific social media account, the data collection unit can also collect patent information and trend data related to that account. Additionally, if a user posts about a specific topic, the data collection unit can collect patent information and trend data related to that topic. For example, the data collection unit can analyze social media posts and extract relevant data. It can also collect relevant data based on hashtags used by the user. Furthermore, the data collection unit can analyze posts from accounts followed by the user and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. 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 social media post data into a generating AI and have the generating AI collect the relevant data.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and highly visual analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. It can also apply an IT-specific analysis algorithm to IT data. Furthermore, it can apply an education-specific analysis algorithm to educational data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0081] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent data based on the data collection timing. It can also postpone the analysis of older data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific category. For example, the analysis unit evaluates the relevance of the data and prioritizes the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, the analysis unit can prioritize the analysis of data related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0083] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. If the user is in a hurry, the learning unit can select simplified training data. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0084] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and automatically optimize the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and automatically optimize the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0085] The learning unit can filter learning data based on specific industries or fields during the learning process. For example, the learning unit can filter and learn from learning data in the medical field. It can also filter and learn from learning data in the IT field. Furthermore, it can filter and learn from learning data in the education field. For example, the learning unit can filter learning data in the medical field and learn only the relevant data. It can also filter learning data in the IT field and learn only the relevant data. Furthermore, it can filter learning data in the education field and learn only the relevant data. This allows for efficient learning of data relevant to specific industries or fields. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data relevant to a specific industry or field into a generating AI and have the generating AI perform the data filtering.

[0086] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed, and decrease it when the user is in a hurry. Furthermore, if the user is excited, the learning unit can adjust the learning frequency to achieve optimal learning. For example, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0087] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. Furthermore, the learning unit can assign appropriate weights to data collected during a specific period. For example, the learning unit can assign a higher weight to the most recent data based on when the data was collected. It can also assign a lower weight to older data. Furthermore, the learning unit can assign appropriate weights to data collected during a specific period. This enables efficient learning by weighting the training data based on when the data was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection period into a generating AI and have the generating AI perform the weighting of the training data.

[0088] The learning unit can improve the accuracy of its learning by referring to relevant literature during the learning process. For example, the learning unit can learn by referring to the latest relevant research papers. It can also learn by referring to relevant patent information. Furthermore, the learning unit can learn by referring to relevant industry reports. For example, the learning unit can learn by referring to the latest relevant research papers. It can also learn by referring to relevant patent information. Furthermore, the learning unit can learn by referring to relevant industry reports. This improves the accuracy of learning by referring to relevant literature. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0089] The countdown generation unit can estimate the user's emotions and adjust the display method of the countdown based on the estimated emotions. For example, if the user is nervous, the countdown generation unit can provide a simple and highly visible display method. If the user is relaxed, the countdown generation unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the countdown generation unit can provide a display method that adds visually stimulating effects. For example, the countdown generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The countdown generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the countdown generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the countdown display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0090] The countdown generation unit can improve the accuracy of singularity prediction by referring to past data when generating the countdown. For example, the countdown generation unit can improve the accuracy of singularity prediction based on past data. The countdown generation unit can also analyze past data and optimize the singularity prediction algorithm. Furthermore, the countdown generation unit can improve the singularity prediction model by referring to past data. For example, the countdown generation unit can improve the accuracy of singularity prediction based on past data. Furthermore, the countdown generation unit can analyze past data and optimize the singularity prediction algorithm. Furthermore, the countdown generation unit can improve the singularity prediction model by referring to past data. As a result, the accuracy of singularity prediction is improved by referring to past data. Some or all of the above processing in the countdown generation unit may be performed using AI, for example, or without using AI. For example, the countdown generation unit can input past data into the generation AI and have the generation AI perform the improvement of singularity prediction accuracy.

[0091] The countdown generation unit can customize the countdown based on a specific industry or field when generating it. For example, when generating a singularity countdown in the medical field, the countdown generation unit customizes it based on medical-related data. Similarly, when generating a singularity countdown in the IT field, the countdown generation unit can customize it based on IT-related data. Furthermore, when generating a singularity countdown in the education field, the countdown generation unit can customize it based on education-related data. This allows for the provision of more relevant information by customizing the countdown based on a specific industry or field. Some or all of the above-described processes in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input data related to a specific industry or field into the generation AI and have the generation AI perform customization of the countdown.

[0092] The countdown generation unit can estimate the user's emotions and determine the priority of the countdown based on the estimated emotions. For example, if the user is excited, the countdown generation unit will prioritize displaying the most recent singularity countdown. It can also prioritize displaying a stable singularity countdown if the user is relaxed. Furthermore, if the user is stressed, the countdown generation unit can prioritize displaying singularity countdowns in areas of the user's greatest interest. For example, the countdown generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the countdown generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate information to be provided by prioritizing the countdown according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the countdown generation unit may be performed using AI, or not using AI. For example, the countdown generation unit can input user image data captured by a camera into the generating AI and have the generating AI perform the estimation of the user's emotions.

[0093] The countdown generation unit can generate an optimal countdown by considering geographical location information during countdown generation. For example, if the user is in a specific region, the countdown generation unit can generate a singularity countdown related to that region. Furthermore, if the user is traveling, the countdown generation unit can generate a singularity countdown related to the travel destination. Additionally, if the user is in a specific city, the countdown generation unit can generate a singularity countdown related to that city. For example, the countdown generation unit obtains the user's geographical location information from GPS data or IP address and generates a relevant countdown based on that information. Furthermore, if the user is traveling, the countdown generation unit can generate a singularity countdown related to the travel destination. Furthermore, if the user is in a specific city, the countdown generation unit can generate a singularity countdown related to that city. This allows for the generation of highly relevant countdowns by considering geographical location information. Some or all of the above-described processes in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input the user's geographical location information into the generation AI, allowing the AI ​​to generate the optimal countdown.

[0094] The countdown generation unit can improve the accuracy of the countdown by referring to relevant literature during countdown generation. For example, the countdown generation unit can generate a countdown by referring to the latest relevant research papers. The countdown generation unit can also generate a countdown by referring to relevant patent information. Furthermore, the countdown generation unit can generate a countdown by referring to relevant industry reports. For example, the countdown generation unit can generate a countdown by referring to the latest relevant research papers. The countdown generation unit can also generate a countdown by referring to relevant patent information. Furthermore, the countdown generation unit can generate a countdown by referring to relevant industry reports. This improves the accuracy of the countdown by referring to relevant literature. Some or all of the above processing in the countdown generation unit may be performed using AI, for example, or without AI. For example, the countdown generation unit can input relevant literature into a generation AI and have the generation AI perform the countdown accuracy improvement.

[0095] The chatbot can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the chatbot will respond in a calm voice. If the user is relaxed, the chatbot can respond in a cheerful voice. Furthermore, if the user is in a hurry, the chatbot can provide a quick and concise response. For example, the chatbot can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the chatbot can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the chatbot to provide more appropriate responses by adjusting its response 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0096] The chatbot unit can provide the most appropriate response by referring to the user's past question history when responding. For example, the chatbot unit can provide relevant information based on the content of questions the user has asked in the past. The chatbot unit can also select the most appropriate response from the user's past question history. Furthermore, the chatbot unit can analyze the user's past question history and suggest the most appropriate response method. For example, the chatbot unit can refer to the user's past question history from a database and provide relevant information. The chatbot unit can also select the most appropriate response based on the user's past question history. Furthermore, the chatbot unit can analyze the user's past question history and suggest the most appropriate response method. This allows for the provision of more appropriate responses by referring to the user's past question history. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the user's past question history into a generating AI and have the generating AI perform the task of providing the most appropriate response.

[0097] The chatbot can customize its responses based on specific industries or fields. For example, it can provide medical-related information in response to questions in the medical field, IT-related information in response to questions in the IT field, and education-related information in response to questions in the education field. By customizing responses based on specific industries or fields, it can provide more relevant information. Some or all of the above processing in the chatbot may be performed using AI, for example, or not. For example, the chatbot can input data related to specific industries or fields into a generating AI and have the generating AI customize the response content.

[0098] The chatbot can estimate the user's emotions and determine the chatbot's response priority based on the estimated emotions. For example, if the user is excited, the chatbot can prioritize providing the latest information. If the user is relaxed, the chatbot can also prioritize providing stable information. Furthermore, if the user is stressed, the chatbot can prioritize providing information in areas of greatest interest to the user. For example, the chatbot can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Additionally, the chatbot can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the chatbot to provide more appropriate information by determining its response priority 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0099] The chatbot unit can provide the optimal response by considering the user's device information when responding. For example, if the user is using a smartphone, the chatbot unit will provide a response that is appropriate for the screen size. Furthermore, if the user is using a tablet, the chatbot unit can provide a response optimized for a larger screen. Additionally, if the user is using a smartwatch, the chatbot unit can provide a concise and easily readable response. This allows for the provision of more appropriate responses by considering the user's device information. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal response.

[0100] The chatbot unit can improve the accuracy of its responses by referring to relevant literature when responding. For example, the chatbot unit can respond by referring to the latest relevant research papers. It can also respond by referring to relevant patent information. Furthermore, the chatbot unit can respond by referring to relevant industry reports. For example, the chatbot unit can respond by referring to the latest relevant research papers. It can also respond by referring to relevant patent information. Furthermore, the chatbot unit can respond by referring to relevant industry reports. This improves the accuracy of responses by referring to relevant literature. Some or all of the above processing in the chatbot unit may be performed using AI, for example, or without AI. For example, the chatbot unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of responses.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of collecting patent information and trend data based on the estimated emotions. For example, if the user is excited, the data collection unit can increase the collection frequency to provide the latest information quickly. Conversely, if the user is relaxed, the data collection unit can decrease the collection frequency to provide stable information. Furthermore, if the user is stressed, the data collection unit can adjust the collection timing to provide information during the time when the user is most relaxed. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of information at a more appropriate time by adjusting the collection timing according to the user's emotions.

[0103] The data collection unit can analyze past collected data and select the optimal collection method. For example, it can discover from past collected data that data collection efficiency is high during specific time periods and concentrate data collection during those times. It can also analyze past collected data to confirm that specific data sources are highly reliable and prioritize their use. Furthermore, the data collection unit can automatically optimize the collection method based on past collected data to achieve efficient data collection. This means that efficient data collection becomes possible by analyzing past data.

[0104] The data collection unit can filter data based on specific industries or fields during the collection process. For example, when collecting data in the medical field, the unit can filter and collect only medical-related patent information and trend data. Similarly, when collecting data in the IT field, the unit can filter and collect only IT-related patent information and trend data. Furthermore, when collecting data in the education field, the unit can filter and collect only education-related patent information and trend data. For instance, the unit can automatically filter medical patent information and collect only relevant data. It can also filter IT trend data and collect only relevant data. Furthermore, it can filter education patent information and trend data and collect only relevant data. This allows for the efficient collection of data relevant to specific industries or fields.

[0105] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the unit will prioritize collecting the latest patent information and trend data. If the user is relaxed, the unit can also prioritize collecting stable information. Furthermore, if the user is stressed, the unit can prioritize collecting data in areas of the user's greatest interest. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize data according to the user's emotions, providing more relevant information.

[0106] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of patent information and trend data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. For instance, the data collection unit obtains the user's geographical location information from GPS data or IP address and collects relevant data based on that information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of patent information and trend data related to the travel destination. Additionally, if the user is in a specific city, the data collection unit can prioritize the collection of patent information and trend data related to that city. This allows for the efficient collection of highly relevant data by considering geographical location information.

[0107] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and highly visual analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide more appropriate results by adjusting the presentation of the analysis according to the user's emotions.

[0108] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on highly important data. It can also perform a simplified analysis on less important data. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0109] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. It can also apply an IT-specific analysis algorithm to IT data. Furthermore, it can apply an education-specific analysis algorithm to educational data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category.

[0110] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide more appropriate results by adjusting the length of the analysis according to the user's emotions.

[0111] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. By determining the priority of analysis based on the data collection period, efficient analysis becomes possible.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects patent information and trend data. For example, the data collection unit can collect patent information in a specific technological field or trend data for a specific market. It can also collect data from publicly available databases and news sites on the internet. Furthermore, it can collect data from social media and industry reports. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining techniques. It can also analyze the data using statistical analysis or machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing techniques. Step 3: The learning unit learns the degree of penetration and evolution of the generative AI based on the results analyzed by the analysis unit. The learning unit can learn the degree of evolution of the generative AI using, for example, supervised learning. It can also learn the degree of evolution of the generative AI using unsupervised learning or reinforcement learning. Furthermore, the learning unit can learn the degree of evolution of the generative AI in real time. Step 4: The countdown generation unit generates a countdown for the singularity based on the results learned by the learning unit. The countdown generation unit can generate a countdown based on, for example, the definition of a singularity. It can also generate a countdown based on the start condition of the countdown. Furthermore, the countdown generation unit can customize how the countdown is displayed.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, learning unit, countdown generation unit, and chatbot unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the specific processing unit 290 of the data processing device 12. The countdown generation unit is implemented by the specific processing unit 290 of the data processing device 12. The chatbot unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, learning unit, countdown generation unit, and chatbot unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The countdown generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The chatbot unit is implemented by the control unit 46A of the smart glasses 214 or 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 examples described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, learning unit, countdown generation unit, and chatbot unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The countdown generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The chatbot unit is implemented by the control unit 46A of the headset terminal 314 or 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 examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, learning unit, countdown generation unit, and chatbot unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The countdown generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The chatbot unit is implemented by the control unit 46A of the robot 414 or 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 examples described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A collection unit that collects patent information and trend data, An analysis unit analyzes the data collected by the aforementioned collection unit, A learning unit learns the degree of penetration and evolution of the generated AI based on the results analyzed by the aforementioned analysis unit, The system includes a countdown generation unit that generates a countdown of singularities based on the results learned by the learning unit. A system characterized by the following features. (Note 2) It includes a chatbot section that provides AI-generated chatbot functionality. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate user sentiment and adjust the timing of patent information and trend data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting data, filter it based on specific industries or sectors. 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 prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, filter the training data based on specific industries or fields. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During learning, refer to relevant literature to improve the accuracy of learning. The system described in Appendix 1, characterized by the features described herein. (Note 21) The countdown generation unit, It estimates the user's emotions and adjusts the countdown display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The countdown generation unit, When generating the countdown, historical data is referenced to improve the accuracy of singularity prediction. The system described in Appendix 1, characterized by the features described herein. (Note 23) The countdown generation unit, When generating a countdown, customize the countdown based on specific industries or sectors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The countdown generation unit, It estimates the user's emotions and determines the countdown priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The countdown generation unit, When generating a countdown, the system considers geographical location information to generate the optimal countdown. The system described in Appendix 1, characterized by the features described herein. (Note 26) The countdown generation unit, When generating the countdown, we refer to relevant literature to improve the accuracy of the countdown. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned chatbot unit is It estimates the user's emotions and adjusts the chatbot's response based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned chatbot unit is When the chatbot responds, it refers to the user's past question history to provide the most appropriate response. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned chatbot unit is Customize chatbot responses based on specific industries or sectors. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned chatbot unit is It estimates the user's emotions and determines the chatbot's response priority based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned chatbot unit is When the chatbot responds, it takes the user's device information into consideration to provide the most appropriate response. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned chatbot unit is When the chatbot responds, it references relevant literature to improve the accuracy of the response. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects patent information and trend data, An analysis unit analyzes the data collected by the aforementioned collection unit, A learning unit learns the degree of penetration and evolution of the generated AI based on the results analyzed by the aforementioned analysis unit, The system includes a countdown generation unit that generates a countdown of singularities based on the results learned by the learning unit. A system characterized by the following features.

2. It includes a chatbot section that provides AI-generated chatbot functionality. The system according to feature 1.

3. The aforementioned collection unit is We estimate user sentiment and adjust the timing of patent information and trend data collection based on the estimated user sentiment. The system according to feature 1.

4. The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting data, filter it based on specific industries or sectors. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

8. The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system according to feature 1.

Images