WO2019103199A1 - Système intelligent personnalisé et procédé de fonctionnement associé - Google Patents
Système intelligent personnalisé et procédé de fonctionnement associé Download PDFInfo
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- WO2019103199A1 WO2019103199A1 PCT/KR2017/013509 KR2017013509W WO2019103199A1 WO 2019103199 A1 WO2019103199 A1 WO 2019103199A1 KR 2017013509 W KR2017013509 W KR 2017013509W WO 2019103199 A1 WO2019103199 A1 WO 2019103199A1
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
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- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
Definitions
- the present invention relates to a customized intelligent system, and more particularly, to a customized intelligent system capable of providing an immediate and active intelligent service to a user by using a customized intelligent model for each user apparatus and an operation method thereof.
- AI artificial intelligence
- Machine learning is a technique in which a computer learns data on its own and performs predictions based on the learning results to derive useful information.
- Machine learning generally increases the accuracy of prediction as the quantity and quality of learning data is richer. Therefore, machine learning has a close relationship with Big Data, which is a technology for processing large data, and a typical example is machine learning based personalized service using Big Data.
- An intelligent service is a service that provides customized content according to the user's personal information. For example, a service that constructs a customized initial screen for each user by analyzing usage patterns of portal site users, or analyzes a search pattern and suggests a recommendation word to other users. Such an intelligent service is provided to a user by an intelligent system. Referring to FIG. 1, a general intelligent system according to the prior art will be described.
- FIG. 1 is a schematic view of a general intelligent system according to the prior art.
- the general purpose intelligent system 100 may include a user device 110 and an intelligent server 130.
- the user device 110 may include a smart phone or a tablet PC as an agent for providing an intelligent service to a user.
- the intelligence server 130 collects a large amount of user data stored in at least one user equipment 110, for example, service usage histories, and performs machine learning based on the big data to generate a general intelligence model.
- an intelligent model refers to a software platform that includes a prediction function or pattern information generated through learning about big data.
- the intelligent server 130 distributes the predicted data, for example, information predictive of a potential user's intention, to the user device 110 using the general intelligence model.
- the distributed prediction data is provided to the user through the user device 110.
- generation of predicted data is performed by intelligent server 130, so that real-time service provision may be delayed due to increase in user connection traffic or network failure, and service may be disconnected.
- the general-purpose intelligent system 100 since the general-purpose intelligent system 100 generates and provides the predictive data only when there is a request from the user (i.e., user data transmission), there is a problem that it is difficult to provide the instantaneous service.
- the embodiments of the present invention have been made to solve the above-described problems, and in particular, the present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide a user data management system capable of providing an immediate and active intelligent service based on small data using a customized intelligent model created and distributed by an intelligent server Intelligent systems that allow them to do so.
- an intelligent system for providing an intelligent service based on Small Data comprising: an intelligent server for generating and distributing intelligent models from Big Data; And at least one user device for generating predictive data from the user data using the distributed intelligence model and providing intelligent services to the user through a decision based on the predictive data,
- a big data collection unit for collecting big data from a big data source including a device;
- a general intelligence model generation unit for generating a predictive function or pattern information by performing learning by the preprocessing and learning algorithm on the collected big data and analyzing the predictive function or the pattern information to generate a general intelligence model;
- a customized intelligent model generation unit for extracting a customized intelligent model from the general intelligent model using profile data of each of the user devices.
- an operation method of an intelligent system including an intelligent server for generating and distributing an intelligent model and at least one user device for providing an intelligent service based on Small Data, Collecting big data from a Big Data source including the user device; Generating a general function intelligence model by analyzing the prediction function or pattern information by performing learning by the preprocessing and learning algorithm on the collected big data to generate a prediction function or pattern information; Generating a customized intelligence model from the general intelligence model using profile data of each of the user devices; Distributing the customized intelligence model to each of the user devices; And each of the user devices generates predictive data from the user data using the distributed customized intelligent model and provides intelligent services to the user through a decision based on the predictive data. .
- the intelligent system according to the present embodiment is effective in allowing the user device to provide an instant and active intelligent service based on small data using a customized intelligent model generated and distributed by the intelligent server.
- FIG. 1 is a schematic view of a general intelligent system according to the prior art.
- FIG. 2 schematically shows a customized intelligent system according to the present embodiment.
- FIG. 3 is a diagram showing a configuration of a user apparatus according to the present embodiment.
- FIG. 4 is a diagram showing a configuration of an intelligent server according to the present embodiment.
- FIG. 5 is a diagram illustrating a process of generating and distributing a customized intelligent model by the intelligent server shown in FIG.
- FIG. 6 is a diagram illustrating a process in which the user equipment shown in FIG. 3 provides an intelligent service to a user.
- module refers to a unit that processes at least one function or operation, which may be implemented in hardware, software, or a combination of hardware and software.
- FIG. 2 schematically shows a customized intelligent system according to the present embodiment.
- the customized intelligent system 200 may include a user device 210 and an intelligent server 210.
- the user device 210 provides the user with his / her own intelligence service using the customized intelligence model generated and distributed by the intelligence server 210.
- the user device 210 may be an electronic device having various kinds of sensors for measuring the biometric information (e.g., heart rate, blood pressure) of the user IOT device, the user 110, a smart phone, A mobile digital device such as a smart watch, a tablet PC and the like, a fixed digital device such as a smart TV, a smart car, a drone, and the like.
- biometric information e.g., heart rate, blood pressure
- User device 210 is a big data source and data stored in user device 210 is collected by intelligence server 230 and used for intelligent model generation.
- the data stored in the user device 210 may include user data and profile data of the user device.
- the user profile data includes the service purpose and timing of the user device 210, the S / W performance analysis result (for example, the type and version of the operating system framework or library) and the H / W performance analysis result RAM, capacity of the storage device), and the like.
- the intelligent model is generated by the intelligence server 230 and distributed to the user device 210 and may be updated by the user device 210 or the intelligence server 230 as the user data is added.
- the user data provided to the intelligent server 230 and the profile data of the user device are encrypted and decrypted using various encryption algorithms (e.g., Advanced Encryption Standard (AES), Cipher-based Message Authentication Code (CMAC) Sex can be strengthened.
- AES Advanced Encryption Standard
- CMAC Cipher-based Message Authentication Code
- the user device 210 may also send a learning algorithm stored in a memory (not shown) to the intelligence server 230.
- the memory may include non-volatile memory (e.g., NAND flash) and / or volatile memory (e.g., DRAM, etc.).
- the transmitted learning algorithm can be used by the intelligence server 230 to learn the big data and generate the intelligence model.
- the intelligence server 130 collects a large amount of user data stored in at least one user equipment 110, for example, service usage histories, and performs machine learning based on the big data to generate a general intelligence model.
- the intelligence server 230 can generate a customized intelligence model for each user device 210 by preprocessing, learning, and analyzing the user profile data received from the user device 210 using the general purpose intelligence model.
- performance degradation such as slowing down the predicted speed, may occur when the big data-based general purpose intelligent model is directly applied to the user device 210 degradation may occur.
- the intelligent service provided by the user device 210 is based on Small Data, when the general-purpose intelligent model is directly applied, resources (e.g., CPU, memory capacity, etc.) It can be wasted more than necessary.
- the intelligent server 230 acquires, from the general intelligence model, personalized intelligent models based on the device characteristics, performance, and service information of each of the user devices 210 Create and distribute intelligence models.
- the user device 210 generates prediction data for the intelligent service, that is, the small data, using the distributed customized intelligent model, and provides it to the user.
- the customized intelligent system 200 can provide an active active intelligent service to the user by using the customized intelligent model itself, unlike the general intelligent system 100 according to the related art .
- FIG. 3 is a diagram showing a configuration of a user apparatus according to the present embodiment.
- the user device 210 includes an intelligent service unit 310, a communication unit 330, and a memory 350 as components necessary for providing an intelligent service to a user using a customized intelligent model .
- the intelligent service unit 310 The intelligent service unit 310,
- the intelligent service unit 310 can generate the predictive data from the user data using the customized intelligent model distributed from the intelligent server 230 and provide the intelligent service to the user. Specifically, the intelligent service unit 310 receives the customized intelligence model distributed from the intelligence server 230 through the communication unit 330.
- the intelligent service unit 310 may update the customized intelligent model distributed from the intelligent server 230 by preprocessing, learning and analyzing the user data received from the user.
- preprocessing learning and analysis operations
- the intelligent service unit 310 performs prediction using the customized intelligence model for new user data received from the user, and generates predetermined prediction data. Then, the intelligent service unit 310 provides the intelligent service to the user through the decision process based on the generated predictive data.
- the intelligent service unit 310 may be implemented by at least one processor.
- the intelligent service unit 310 may include a plurality of GPUs (Graphics Processing Units) that perform parallel processing to improve the real- . ≪ / RTI >
- GPUs Graphics Processing Units
- the communication unit 330 The communication unit 330,
- the communication unit 330 includes a wireless communication module (LTE, Wi-Fi, etc.), a short-range communication module (such as Bluetooth, ZigBee, etc.) as components necessary for exchanging data with the intelligent server 230 via a wired / (ZigBee), Near Field Communication (NFC), etc.).
- LTE Long Term Evolution
- Wi-Fi Wireless Fidelity
- NFC Near Field Communication
- the communication unit 330 functions to support communication connection with the intelligent server 230 so as to provide the intelligent server 230 with the big data.
- the communication unit 330 performs a function of receiving the customized intelligence model distributed from the intelligence server 230.
- the memory 350 may include non-volatile memory (e.g., NAND flash) and / or volatile memory (e.g., DRAM, etc.) as components necessary to store learning algorithms required for intelligent model generation.
- the learning algorithm stored in the memory is transmitted to the intelligence server 230 through the communication unit 330, and can be used for big data learning for intelligent model generation.
- FIG. 4 is a diagram showing a configuration of an intelligent server according to the present embodiment.
- the intelligent server 230 includes components such as a big data collection unit 410, a general intelligent model generation unit 430, a customized intelligent model A generating unit 450, a communication unit 470, and a storage unit 490.
- the big data collection unit 410 The big data collection unit 410,
- the big data collecting unit 410 collects big data actively or passively from a big data source connected through the communication unit 470.
- the big data includes structured data such as a database, a spreadsheet, semi-structured data such as XML and HTML, and unstructured data such as text documents, (unstructured data).
- the big data source may be an internal source (e.g., an internal database management system (DBMS) or the like) existing in the intelligence server 230 and an external source (e.g., a user device, the Internet, etc.) . ≪ / RTI >
- DBMS database management system
- the big data collection unit 410 may store the collected big data in the storage unit 490 or a separate external storage (not shown).
- the general intelligence model generation unit 430 The general intelligence model generation unit 430
- the general intelligence model generation unit 430 performs a preprocessing operation for cleaning data in order to easily learn and analyze the collected big data in real time or at the request of the user device 210.
- the big data refinement means an operation of detecting and correcting data where a missing value or an error exists in order to correct the inconsistency of the collected data.
- the big data preprocessing operation may include a filtering operation, a data transformation operation, and a data integration operation.
- the filtering operation means an operation of removing data that does not fit the purpose of collecting big data in order to shorten learning time and efficiently utilize the storage space.
- the filtering operation means an operation (i.e., data mining) for eliminating errors or duplication of irregular data.
- the data conversion operation refers to operations such as switching the unstructured data into a structured form or normalizing the data as a premise of the big data learning.
- the data integration operation refers to the operation of merging data from multiple sources.
- the big data collecting unit 410 may store the preprocessed big data in the storage unit 490 or a separate external storage (not shown).
- the general intelligence model generation unit 430 performs a big data processing operation for learning and analyzing the big data in order to generate an intelligent model from the big data.
- the general intelligence model generation unit 430 learns big data using various learning algorithms to generate a prediction function from big data or infer pattern information.
- the learning algorithm may be an algorithm stored in a memory (not shown) of intelligence server 230 or an algorithm received from user device 210.
- the learning algorithms can be classified into supervised learning algorithms (eg, multilayer neural networks (Perceptron), support vector machines (SVM), kernel machines, decision trees, etc.), unsupervised learning algorithms : K-Means, hierarchical clustering, self-organizing maps (SOM), etc.) and reinforcement learning algorithms.
- supervised learning algorithms eg, multilayer neural networks (Perceptron), support vector machines (SVM), kernel machines, decision trees, etc.
- unsupervised learning algorithms K-Means, hierarchical clustering, self-organizing maps (SOM), etc.
- reinforcement learning algorithms eg., this is merely an example, and the present embodiment is not limited thereto.
- the general intelligent model generation unit 430 may learn (i.e., perform ensemble learning) of the big data using two or more learning algorithms in order to improve the prediction performance of the intelligent model.
- the general intelligent model generation unit 430 analyzes the error of the prediction function or the pattern information generated as a result of the big data learning. For example, the general intelligence model generation unit 430 may perform cross-validation to solve an overfitting problem of learning results.
- the general intelligence model generation unit 430 generates a general intelligence model based on the error function or the pattern information.
- the general purpose intelligent model may be distributed to the user device 210 as a prediction model for generating predictive data based on new user data.
- the customized intelligent model generation unit 450 generates,
- the customized intelligence model generation unit 450 prepares, learns, and analyzes the user profile data received from the user device 210 using the general-purpose intelligence model to generate a customized intelligence model for each user device.
- the user profile data includes the service purpose and timing of the user device 210, the S / W performance analysis result (for example, the type and version of the operating system framework or library) and the H / W performance analysis result RAM, capacity of the storage device), and the like.
- the user device 210 Since the user device 210 has a relatively low computing performance compared to the intelligent server 230, when the big data-based general purpose intelligent model is directly applied to the user device 210, degradation ). Since the intelligent service provided by the user device 210 is based on Small Data, when the general-purpose intelligent model is directly applied, resources (e.g., CPU, memory capacity, etc.) Waste more than necessary.
- resources e.g., CPU, memory capacity, etc.
- the customized intelligent model generation unit 450 generates the customized intelligent model from the general intelligence model based on the device characteristics, performance, and service information (i.e., user profile data) of each of the user devices 210 ) Create customized intelligence models. Then, the user device 210 generates prediction data for the intelligent service, that is, the small data, using the customized intelligence model distributed from the intelligence server 230, and provides it to the user.
- the communication unit 470 The communication unit 470
- the communication unit 470 includes a wireless communication module (LTE, Wi-Fi, etc.), a short-range communication module (such as Bluetooth, ZigBee, etc.) as components necessary for exchanging data with the user device 210 via a wired / (ZigBee), Near Field Communication (NFC), etc.).
- LTE Long Term Evolution
- Wi-Fi Wireless Fidelity
- NFC Near Field Communication
- the communication unit 470 functions to support a communication connection with a big data source so that the big data collection unit 410 can collect big data.
- the communication unit 470 then distributes the generated customized intelligence model to the user device 210.
- the storage unit 490 stores the collected big data or the preprocessed big data.
- the storage unit 490 may be a relational database (e.g., MS-SQL), a non-relational database (e.g., NoSQL, etc.), or a distributed file system For example, Hadoop DFS, etc.).
- FIG. 5 is a diagram illustrating a process of generating and distributing a customized intelligent model by the intelligent server shown in FIG.
- step S510 the big data collection unit 410 collects big data actively or passively from a big data source connected through the communication unit 470.
- the big data collection unit 410 may store the collected big data in the storage unit 490 or a separate external storage (not shown).
- step S520 the general intelligent model generation unit 430 performs a pre-processing operation for cleaning data in order to easily learn and analyze the collected big data in real time.
- the big data preprocessing operation may include a filtering operation, a data transformation operation, and a data integration operation.
- step S530 the general intelligence model generation unit 430 learns big data in real time using various learning algorithms to generate a prediction function from big data or infer pattern information.
- the learning algorithm may be an algorithm stored in the storage unit 490 of the intelligence server 230 or an algorithm received from the user device 210.
- step S540 the general-purpose intelligent model generator 430 analyzes errors of the predictive function or pattern information generated as a result of the big data learning in real time in order to improve the prediction performance of the intelligent model.
- step S550 the general-purpose intelligent model generation unit 430 generates the general intelligent model on the basis of the error function analyzed or the pattern information in real time.
- step S560 the customized intelligent model generation unit 450 generates a customized intelligent model based on the device characteristics, performance, and service information (i.e., user profile data) of each user device 210, Generate intelligent models in real time.
- step S570 the communication unit 470 distributes the customized intelligent model to the user device 210 through the wired / wireless communication connection in real time. Then, the user device 210 generates prediction data for the intelligent service, that is, the small data, using the distributed customized intelligent model, and provides it to the user.
- FIG. 6 is a diagram illustrating a process in which the user equipment shown in FIG. 3 provides an intelligent service to a user.
- step S610 the intelligent service unit 310 can receive the customized intelligent model distributed from the intelligent server 230 through the communication unit 350.
- the intelligent service unit 310 may generate predetermined prediction data by performing prediction on the user data newly received from the user based on the customized intelligence model.
- the intelligent service unit 310 may provide an intelligent service to the user through a decision process based on the generated predictive data.
- FIGS. 5 and 6 illustrate that a plurality of processes are performed sequentially, this is merely illustrative of the technical idea of this embodiment. In other words, those skilled in the art will understand that the present invention can be implemented by changing the order described in FIGS. 5 and 6 or by changing some of the plurality of processes in parallel It should be understood that the present invention is not limited to the above-described embodiments.
- a computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. That is, a computer-readable recording medium includes a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.), an optical reading medium (e.g., CD ROM, And the like).
- the computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.
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Abstract
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US11797550B2 (en) | 2019-01-30 | 2023-10-24 | Uptake Technologies, Inc. | Data science platform |
KR102241311B1 (ko) * | 2019-06-20 | 2021-04-16 | 경상국립대학교산학협력단 | 인공지능의 자율적 도덕 판단 및 수행을 위한 시스템 |
KR102348174B1 (ko) * | 2019-08-09 | 2022-01-06 | 재단법인 아산사회복지재단 | 빅데이터 기반 휘발성 영역 내 학습 방법, 프로그램 및 시스템 |
KR102230991B1 (ko) * | 2019-09-24 | 2021-03-23 | 허해연 | 인공지능 및 사물인터넷 기반 고객행동분석 및 공급사슬관리를 위한 모션 식별 서비스 제공 방법 |
KR102345410B1 (ko) * | 2019-11-19 | 2021-12-30 | 주식회사 피씨엔 | 빅데이터 지능형 수집 방법 및 장치 |
KR102283523B1 (ko) * | 2019-12-16 | 2021-07-28 | 박병훈 | 인공지능 서비스를 제공하기 위한 방법 |
KR102366153B1 (ko) * | 2020-08-20 | 2022-02-23 | 한국전력공사 | 실시간 어플리케이션을 위한 기계학습의 앙상블 추론 방법 및 앙상블 추론을 가속화한 기계 학습 방법 |
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KR101787613B1 (ko) * | 2017-01-20 | 2017-11-15 | 주식회사 더디엔에이시스템 | 딥 러닝 기반의 자가 적응 학습 기술을 이용한 인공지능 플랫폼 시스템 |
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2017
- 2017-11-23 KR KR1020170157067A patent/KR20190060021A/ko not_active Application Discontinuation
- 2017-11-24 WO PCT/KR2017/013509 patent/WO2019103199A1/fr active Application Filing
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KR20120033597A (ko) * | 2010-09-30 | 2012-04-09 | 성균관대학교산학협력단 | 사용자 상황 예측 장치 및 방법 |
KR20130035660A (ko) * | 2011-09-30 | 2013-04-09 | 주식회사 케이티 | 추천 시스템 및 추천 방법 |
KR20170009991A (ko) * | 2014-08-26 | 2017-01-25 | 구글 인코포레이티드 | 글로벌 모델로부터의 로컬화된 학습 |
JP2016048417A (ja) * | 2014-08-27 | 2016-04-07 | 石井 美恵子 | サービス提供システムおよびプログラム |
KR101787613B1 (ko) * | 2017-01-20 | 2017-11-15 | 주식회사 더디엔에이시스템 | 딥 러닝 기반의 자가 적응 학습 기술을 이용한 인공지능 플랫폼 시스템 |
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