Patent Publication Number: US-2023153626-A1

Title: Method and apparatus for supporting automated re-learning in machine to machine system

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims priority to a U.S. provisional application 63/280,319, filed Nov. 17, 2021, the entire contents of which is incorporated herein for all purposes by this reference. 
    
    
     BACKGROUND OF THE DISCLOSURE 
     Field of the Disclosure 
     The present disclosure relates to a machine-to-machine (M2M) system and, more particularly, to a method and apparatus for supporting automated re-learning in the M2M system. 
     Description of the Related Art 
     Recently, introduction of Machine-to-Machine (M2M) system has become active. An M2M communication may refer to a communication performed between machines without human intervention. M2M may refer to Machine Type Communication (MTC), Internet of Things (IoT) or Device-to-Device (D2D). In the following description, the term “M2M” may be uniformly used for convenience of explanation, but the present disclosure may not be limited thereto. A terminal used for M2M communication may be an M2M terminal or an M2M device. An M2M terminal may generally be a device having low mobility while transmitting a small amount of data. Herein, the M2M terminal may be used in connection with an M2M server that centrally stores and manages inter-machine communication information. In addition, an M2M terminal may be applied to various systems such as object tracking, automobile linkage, and power metering. 
     Meanwhile, with respect to an M2M terminal, the oneM2M standardization organization provides requirements for M2M communication, things to things communication and IoT technology, and technologies for architecture, Application Program Interface (API) specifications, security solutions and interoperability. The specifications of the oneM2M standardization organization provide a framework to support a variety of applications and services such as smart cities, smart grids, connected cars, home automation, security and health. 
     SUMMARY 
     The present disclosure provides a method and apparatus for effectively performing learning for an artificial intelligence (AI) model in a machine-to-machine (M2M) system. 
     The present disclosure provides a method and apparatus for supporting automated re-learning in an M2M system. 
     The present disclosure provides a method and apparatus for properly triggering re-learning for an AI model in an M2M system. 
     According to an embodiment of the present disclosure, a method for operating a device in an M2M system may include: generating a resource for training an artificial intelligence (AI) model; controlling to perform initial learning of the AI model; collecting learning data for re-learning of the AI model; and controlling to perform re-learning of the AI model by using the learning data. 
     According to an embodiment of the present disclosure, a transceiver and a processor coupled with the transceiver may be included. The processor may be configured to generate a resource for training an artificial intelligence (AI) model, to perform initial learning of the AI model, to collect learning data for re-learning the AI model, and to perform re-learning of the AI model by using the learning data. 
     According to the present disclosure, learning for an artificial intelligence (AI) model may be effectively performed in a machine-to-machine (M2M) system. 
     Effects obtained in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned above may be clearly understood by those skilled in the art from the following description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects, features and advantages of the present disclosure will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings. 
         FIG.  1    illustrates a layered structure of a machine-to-machine (M2M) system according to the present disclosure. 
         FIG.  2    illustrates a reference point in an M2M system according to the present disclosure. 
         FIG.  3    illustrates each node in an M2M system according to the present disclosure. 
         FIG.  4    illustrates a common service function in an M2M system according to the present disclosure. 
         FIG.  5    illustrates a method in which an originator and a receiver exchange a message in an M2M system according to the present disclosure. 
         FIG.  6    illustrates a concept of re-learning supported in an M2M system according to the present disclosure. 
         FIG.  7    illustrates an example of a resource including information on re-learning in an M2M system according to the present disclosure. 
         FIG.  8    illustrates an example of a procedure of controlling learning for an AI model in an M2M system according to the present disclosure. 
         FIG.  9    illustrates an example of a procedure of performing re-learning in an M2M system according to the present disclosure. 
         FIG.  10    illustrates a configuration of an M2M device in an M2M system according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. In adding the reference numerals to the components of each drawing, it should be noted that the identical or equivalent component is designated by the identical numeral even when they are displayed on other drawings. Further, in describing the embodiment of the present disclosure, a detailed description of the related known configuration or function will be omitted when it is determined that it interferes with the understanding of the embodiment of the present disclosure. 
     In the present disclosure, the terms first, second, etc. may be used only for the purpose of distinguishing one component from another, and do not limit the order or importance of components, etc. unless specifically stated otherwise. Thus, within the scope of this disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly a second component in one embodiment may be referred to as a first component. In addition, as understood by a person of skill in the art reading the present disclosure, the components may not be separated, but merely indicate different functions for a single component structure. For example, a first memory for storing data A and a second memory for storing data B may include either separate memory for storing the separate data or could, in fact, be implemented in a single memory unit that stores both data A and data B. 
     In the present disclosure, when a component may be referred to as being “linked”, “coupled”, or “connected” to another component, it may be understood that not only a direct connection relationship but also an indirect connection relationship through an intermediate component may also be included. Also, when a component may be referred to as “comprising” or “having” another component, it may mean further inclusion of another component not the exclusion thereof, unless explicitly described to the contrary. 
     In the present disclosure, components that may be distinguished from each other may be intended to clearly illustrate each feature. However, it does not necessarily mean that the components may be separate. In other words, a plurality of components may be integrated into one hardware or software unit, or a single component may be distributed into a plurality of hardware or software units. Thus, unless otherwise noted, such integrated or distributed embodiments are also included within the scope of the present disclosure. 
     In the present disclosure, components described in the various embodiments are not necessarily essential components, and some may be optional components. Accordingly, embodiments consisting of a subset of the components described in one embodiment may be also included within the scope of the present disclosure. Also, exemplary embodiments that include other components in addition to the components described in the various exemplary embodiments may also be included in the scope of the present disclosure. 
     In the following description of the embodiments of the present disclosure, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present disclosure rather unclear. Parts not related to the description of the present disclosure in the drawings may be omitted, and like parts may be denoted by similar reference numerals. 
     Although an exemplary embodiment may be described as using a plurality of units to perform the exemplary process, it may be understood that the exemplary processes may also be performed by one or plurality of modules. Additionally, it may be understood that the term controller/control unit refers to a hardware device that includes a memory and a processor and may be specifically programmed to execute the processes described herein. The memory may be configured to store the modules and the processor may be specifically configured to execute said modules to perform one or more processes which may be described further below. 
     In addition, the present specification describes a network based on Machine-to-Machine (M2M) communication, and a work in M2M communication network may be performed in a process of network control and data transmission in a system managing the communication network. In the present specification, an M2M terminal may be a terminal performing M2M communication. However, in consideration of backward compatibility, it may be a terminal operating in a wireless communication system. In other words, an M2M terminal may refer to a terminal operating based on M2M communication network but may not be limited thereto. An M2M terminal may operate based on another wireless communication network and may not be limited to the exemplary embodiment described above. 
     In addition, an M2M terminal may be fixed or have mobility. An M2M server refers to a server for M2M communication and may be a fixed station or a mobile station. In the present specification, an entity may refer to hardware like M2M device, M2M gateway and M2M server. In addition, for example, an entity may be used to refer to software configuration in a layered structure of M2M system and may not be limited to the embodiment described above. 
     In addition, for example, the present disclosure mainly describes an M2M system but may not be solely applied thereto. In addition, an M2M server may be a server that performs communication with an M2M terminal or another M2M server. In addition, an M2M gateway may be a connection point between an M2M terminal and an M2M server. For example, when an M2M terminal and an M2M server have different networks, the M2M terminal and the M2M server may be connected to each other through an M2M gateway. Herein, for example, both an M2M gateway and an M2M server may be M2M terminals and may not be limited to the embodiment described above. 
     It is understood that the term “vehicle” or “vehicular” or other similar term as used herein is inclusive of motor vehicles in general such as passenger automobiles including sports utility vehicles (SUV), buses, trucks, various commercial vehicles, watercraft including a variety of boats and ships, aircraft, and the like, and includes hybrid vehicles, electric vehicles, plug-in hybrid electric vehicles, hydrogen-powered vehicles and other alternative fuel vehicles (e.g. fuels derived from resources other than petroleum). As referred to herein, a hybrid vehicle is a vehicle that has two or more sources of power, for example both gasoline-powered and electric-powered vehicles. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. These terms are merely intended to distinguish one component from another component, and the terms do not limit the nature, sequence or order of the constituent components. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Throughout the specification, unless explicitly described to the contrary, the word “comprise” and variations such as “comprises” or “comprising” will be understood to imply the inclusion of stated elements but not the exclusion of any other elements. In addition, the terms “unit”, “-er”, “-or”, and “module” described in the specification mean units for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof. 
     Although exemplary embodiment is described as using a plurality of units to perform the exemplary process, it is understood that the exemplary processes may also be performed by one or plurality of modules. Additionally, it is understood that the term controller/control unit refers to a hardware device that includes a memory and a processor and is specifically programmed to execute the processes described herein. The memory is configured to store the modules and the processor is specifically configured to execute said modules to perform one or more processes which are described further below. 
     Further, the control logic of the present disclosure may be embodied as non-transitory computer readable media on a computer readable medium containing executable program instructions executed by a processor, controller or the like. Examples of computer readable media include, but are not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices. The computer readable medium can also be distributed in network coupled computer systems so that the computer readable media is stored and executed in a distributed fashion, e.g., by a telematics server or a Controller Area Network (CAN). 
     Unless specifically stated or obvious from context, as used herein, the term “about” is understood as within a range of normal tolerance in the art, for example within 2 standard deviations of the mean. “About” can be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear from the context, all numerical values provided herein are modified by the term “about”. 
     The present disclosure relates to a method and device for performing learning for an artificial intelligence (AI) model in a machine-to-machine (M2M) system. More particularly, the present disclosure describes a technology of supporting re-learning after initial learning for an AI model in an M2M system. 
     oneM2M may be a de facto standards organization that was founded to develop a communal IoT service platform sharing and integrating application service infrastructure (platform) environments beyond fragmented service platform development structures limited to separate industries like energy, transportation, national defense and public service. oneM2M aims to render requirements for things to things communication and IoT technology, architectures, Application Program Interface (API) specifications, security solutions and interoperability. For example, the specifications of oneM2M provide a framework to support a variety of applications and services such as smart cities, smart grids, connected cars, home automation, security and health. In this regard, oneM2M has developed a set of standards defining a single horizontal platform for data exchange and sharing among all the applications. Applications across different industrial sections may also be considered by oneM2M. Like an operating system, oneM2M provides a framework connecting different technologies, thereby creating distributed software layers facilitating unification. Distributed software layers may be implemented in a common services layer between M2M applications and communication Hardware/Software (HW/SW) rendering data transmission. For example, a common services layer may be a part of a layered structure illustrated in  FIG.  1   . The oneM2M standards are referred to herein and incorporated in their entirety into this application. Specifically, the technical specification of the oneM2M Functional Architecture is referred to herein and incorporated herein in its entirety. See Document No. TS-0001-V4.8.0, Functional Architecture and Document No. TS-0001-V3.15.1, Functional Architecture. 
       FIG.  1    illustrates a layered structure of an Machine-to-Machine (M2M) system according to the present disclosure. Referring to  FIG.  1   , a layered structure of an M2M system may include an application layer  110 , a common services layer  120  and a network services layer  130 . Herein, the application layer  110  may be configured as a layer operating based on a specific application. For example, an application may be a fleet tracking application, a remote blood sugar monitoring application, a power metering application or a controlling application. In other words, an application layer may be a layer for a specific application. Herein, an entity operating based on an application layer may be an application entity (AE). 
     The common services layer  120  may be configured as a layer for a common service function (CSF). For example, the common services layer  120  may be a layer for providing common services like data management, device management, M2M service subscription management and location service. For example, an entity operating based on the common services layer  120  may be a common service entity (CSE). 
     The common services layer  120  may be configured to provide a set of services that may be grouped into CSFs according to functions. A multiplicity of instantiated CSFs constitutes CSEs. CSEs may interface with applications (for example, application entities or AEs in the terminology of oneM2M), other CSEs and base networks (for example, network service entities or NSEs in the terminology of oneM2M). The network services layer  130  may be configured to provide the common services layer  120  with services such as device management, location service and device triggering. Herein, an entity operating based on the network layer  120  may be a network service entity (NSE). 
       FIG.  2    illustrates reference points in an M2M system according to the present disclosure. Referring to  FIG.  2   , an M2M system structure may be distinguished into a field domain and an infrastructure domain. Herein, in each domain, each of the entities may perform communication through a reference point (for example, Mca or Mcc). For example, a reference point may indicate a communication flow between each entity. In particular, referring to  FIG.  2   , the reference point Mca between AE  210  or  240  and CSE  220  or  250 , the reference point Mcc between different CSEs and Mcn reference point between CSE  220  or  250  and NSE  230  or  260  may be set. 
       FIG.  3    illustrates each node in an M2M system according to the present disclosure. Referring to  FIG.  3   , an infrastructure domain of a specific M2M service provider may be configured to provide a specific infrastructure node (IN)  310 . Herein, the CSE of the IN may be configured to perform communication based on the AE and the reference point Mca of another infrastructure node. In particular, one IN may be set for each M2M service provider. In other words, the IN may be a node that performs communication with the M2M terminal of another infrastructure based on an infrastructure structure. In addition, for example, conceptually, a node may be configured as a logical entity or a software configuration. 
     Next, an application dedicated node (ADN)  320  may be a node including at least one AE but not CSE. In particular, an ADN may be set in the field domain. In other words, an ADN may be a dedicated node for AE. For example, an ADN may be a node that may be set in an M2M terminal in hardware. In addition, the application service node (ASN)  330  may be a node including one CSE and at least one AE. ASN may be set in the field domain. In other words, it may be a node including AE and CSE. In particular, an ASN may be a node connected to an IN. For example, an ASN may be a node that may be set in an M2M terminal in hardware. 
     In addition, a middle node (MN)  340  may be a node including a CSE and including zero or more AEs. In particular, the MN may be set in the field domain. An MN may be connected to another MN or IN based on a reference point. In addition, for example, an MN may be set in an M2M gateway in hardware. As an example, a non-M2M terminal node  350  (Non-M2M device node, NoDN) may be a node that does not include M2M entities. It may be a node that performs management or collaboration together with an M2M system. 
       FIG.  4    illustrates a common service function in an M2M system according to the present disclosure. Referring to  FIG.  4   , common service functions may be provided. For example, a common service entity may be configured to provide at least one or more CSFs among application and service layer management  402 , communication management and delivery handling  404 , data management and repository  406 , device management  408 , discovery  410 , group management  412 , location  414 , network service exposure/service execution and triggering  416 , registration  418 , security  420 , service charging and accounting  422 , service session management and subscription/notification  424 . At this time, M2M terminals may operate based on a common service function. In addition, a common service function may be possible in other embodiments and may not be limited to the above-described exemplary embodiment. 
     The application and service layer management  402  CSF may be configured to provide management of AEs and CSEs. The application and service layer management  402  CSF may be configured to include not only the configuring, problem solving and upgrading of CSE functions but also the capability of upgrading AEs. The communication management and delivery handling  404  CSF may be configured to provide communications with other CSEs, AEs and NSEs. The communication management and delivery handling  404  CSF may be configured to determine at what time and through what connection communications may be delivered, and also determine to buffer communication requests to deliver the communications later, if necessary and permitted. 
     The data management and repository  406  CSF may be configured to provide data storage and transmission functions (for example, data collection for aggregation, data reformatting, and data storage for analysis and sematic processing). The device management  408  CSF may be configured to provide the management of device capabilities in M2M gateways and M2M devices. 
     The discovery  410  CSF may be configured to provide an information retrieval function for applications and services based on filter criteria. The group management  412  CSF may be configured to provide processing of group-related requests. The group management  412  CSF may be configured to enable an M2M system to support bulk operations for many devices and applications. The location  414  CSF may be configured to enable AEs to obtain geographical location information. 
     The network service exposure/service execution and triggering  416  CSF may be configured to manage communications with base networks for access to network service functions. The registration  418  CSF may be configured to provide AEs (or other remote CSEs) to a CSE. The registration  418  CSF may be configured to allow AEs (or remote CSE) to use services of CSE. The security  420  CSF may be configured to provide a service layer with security functions like access control including identification, authentication and permission. The service charging and accounting  422  CSF may be configured to provide charging functions for a service layer. The subscription/notification  424  CSF may be configured to allow subscription to an event and notifying the occurrence of the event. 
       FIG.  5    illustrates an exchange of a message between an originator and a receiver in an M2M system according to the present disclosure. Referring to  FIG.  5   , the originator  501  may be configured to transmit a request message to the receiver  520 . In particular, the originator  510  and the receiver  520  may be the above-described M2M terminals. However, the originator  510  and the receiver  520  may not be limited to M2M terminals but may be other terminals. They may not be limited to the above-described exemplary embodiment. In addition, for example, the originator  510  and the receiver  520  may be nodes, entities, servers or gateways, which may be described above. In other words, the originator  510  and the receiver  520  may be hardware or software configurations and may not be limited to the above-described embodiment. 
     Herein, for example, a request message transmitted by the originator  510  may include at least one parameter. Additionally, a parameter may be a mandatory parameter or an optional parameter. For example, a parameter related to a transmission terminal, a parameter related to a receiving terminal, an identification parameter and an operation parameter may be mandatory parameters. In addition, optional parameters may be related to other types of information. In particular, a transmission terminal-related parameter may be a parameter for the originator  510 . In addition, a receiving terminal-related parameter may be a parameter for the receiver  520 . An identification parameter may be a parameter required for identification of each other. 
     Further, an operation parameter may be a parameter for distinguishing operations. For example, an operation parameter may be set to any one among Create, Retrieve, Update, Delete or Notify. In other words, the parameter may aim to distinguish operations. In response to receiving a request message from the originator  510 , the receiver  520  may be configured to process the message. For example, the receiver  520  may be configured to perform an operation included in a request message. For the operation, the receiver  520  may be configured to determine whether a parameter may be valid and authorized. In particular, in response to determining that a parameter may be valid and authorized, the receiver  520  may be configured to check whether there may be a requested resource and perform processing accordingly. 
     For example, in case an event occurs, the originator  510  may be configured to transmit a request message including a parameter for notification to the receiver  520 . The receiver  520  may be configured to check a parameter for a notification included in a request message and may perform an operation accordingly. The receiver  520  may be configured to transmit a response message to the originator  510 . 
     A message exchange process using a request message and a response message, as illustrated in  FIG.  5   , may be performed between AE and CSE based on the reference point Mca or between CSEs based on the reference point Mcc. In other words, the originator  510  may be AE or CSE, and the receiver  520  may be AE or CSE. According to an operation in a request message, such a message exchange process as illustrated in  FIG.  5    may be initiated by either AE or CSE. 
     A request from a requestor to a receiver through the reference points Mca and Mcc may include at least one mandatory parameter and at least one optional parameter. In other words, each defined parameter may be either mandatory or optional according to a requested operation. For example, a response message may include at least one parameter among those listed in Table 1 below. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Response message parameter/success or not 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 Response Status Code - successful, unsuccessful, ack 
               
               
                   
                 Request Identifier - uniquely identifies a Request message 
               
               
                   
                 Content - to be transferred 
               
               
                   
                 To - the identifier of the Originator or the Transit CSE 
               
               
                   
                 that sent the corresponding non-blocking request 
               
               
                   
                 From - the identifier of the Receiver 
               
               
                   
                 Originating Timestamp - when the message was built 
               
               
                   
                 Result Expiration Timestamp - when the message expires 
               
               
                   
                 Event Category - what event category shall be used for 
               
               
                   
                 the response message 
               
               
                   
                 Content Status 
               
               
                   
                 Content Offset 
               
               
                   
                 Token Request Information 
               
               
                   
                 Assigned Token Identifiers 
               
               
                   
                 Authorization Signature Request Information 
               
               
                   
                 Release Version Indicator - the oneM2M release version that this 
               
               
                   
                 response message conforms to 
               
               
                   
                   
               
            
           
         
       
     
     A filter criteria condition, which may be used in a request message or a response message, may be defined as in Table 2 and Table 3 below. 
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Condition tag 
                 Multiplicity 
                 Description 
               
            
           
           
               
            
               
                 Matching Conditions 
               
               
                   
               
            
           
           
               
               
               
            
               
                 createdBefore 
                 0 . . . 1 
                 The creationTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically before the specified value. 
               
               
                 createdAfter 
                 0 . . . 1 
                 The creationTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically after the specified value. 
               
               
                 modifiedSince 
                 0 . . . 1 
                 The lastModifiedTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically after the specified value. 
               
               
                 unmodifiedSince 
                 0 . . . 1 
                 The lastModifiedTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically before the specified value. 
               
               
                 stateTagSmaller 
                 0 . . . 1 
                 The stateTag attribute of the matched resource is smaller than the 
               
               
                   
                   
                 specified value. 
               
               
                 stateTagBigger 
                 0 . . . 1 
                 The stateTag attribute of the matched resource is bigger than the 
               
               
                   
                   
                 specified value. 
               
               
                 expireBefore 
                 0 . . . 1 
                 The expirationTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically before the specified value. 
               
               
                 expireAfter 
                 0 . . . 1 
                 The expirationTime attribute of the matched resource is 
               
               
                   
                   
                 chronologically after the specified value. 
               
               
                 labels 
                 0 . . . 1 
                 The labels attribute of the matched resource matches the specified 
               
               
                   
                   
                 value. 
               
               
                 labelsQuery 
                 0 . . . 1 
                 The value is an expression for the filtering of labels attribute of 
               
               
                   
                   
                 resource when it is of key-value pair format. The expression is 
               
               
                   
                   
                 about the relationship between label-key and label-value which 
               
               
                   
                   
                 may include equal to or not equal to, within or not within a 
               
               
                   
                   
                 specified set etc. For example, label-key equals to label value, or 
               
               
                   
                   
                 label-key within {label-value1, label-value2}. 
               
               
                 childLabels 
                 0 . . . 1 
                 A child of the matched resource has labels attributes matching the 
               
               
                   
                   
                 specified value. The evaluation is the same as for the labels 
               
               
                   
                   
                 attribute above. 
               
               
                 parentLabels 
                 0 . . . 1 
                 The parent of the matched resource has labels attributes matching 
               
               
                   
                   
                 the specified value. The evaluation is the same as for the labels 
               
               
                   
                   
                 attribute above. 
               
               
                 resourceType 
                 0 . . . n 
                 The resourceType attribute of the matched resource is the same as 
               
               
                   
                   
                 the specified value. It also allows differentiating between normal 
               
               
                   
                   
                 and announced resources. 
               
               
                 childResourceType 
                 0 . . . n 
                 A child of the matched resource has the resourceType attribute the 
               
               
                   
                   
                 same as the specified value. 
               
               
                 parentResourceType 
                 0 . . . 1 
                 The parent of the matched resource has the resourceType attribute 
               
               
                   
                   
                 the same as the specified value. 
               
               
                 sizeAbove 
                 0 . . . 1 
                 The contentsize attribute of the &lt;contentInstance&gt; matched 
               
               
                   
                   
                 resource is equal to or greater than the specified value. 
               
               
                 sizeBelow 
                 0 . . . 1 
                 The contentsize attribute of the &lt;contentInstance&gt; matched 
               
               
                   
                   
                 resource is smaller than the specified value. 
               
               
                 contentType 
                 0 . . . n 
                 The contentinfo attribute of the &lt;contentInstance&gt; matched 
               
               
                   
                   
                 resource matches the specified value. 
               
               
                 attribute 
                 0 . . . n 
                 This is an attribute of resource types (clause 9.6). Therefore, a real 
               
               
                   
                   
                 tag name is variable and depends on its usage and the value of the 
               
               
                   
                   
                 attribute can have wild card *. E.g. creator of container resource 
               
               
                   
                   
                 type can be used as a filter criteria tag as “creator = Sam”, 
               
               
                   
                   
                 “creator = Sam*”, “creator = *Sam”. 
               
               
                 childAttribute 
                 0 . . . n 
                 A child of the matched resource meets the condition provided. 
               
               
                   
                   
                 The evaluation of this condition is similar to the attribute 
               
               
                   
                   
                 matching condition above. 
               
               
                 parentAttribute 
                 0 . . . n 
                 The parent of the matched resource meets the condition provided. 
               
               
                   
                   
                 The evaluation of this condition is similar to the attribute 
               
               
                   
                   
                 matching condition above. 
               
               
                 semanticsFilter 
                 0 . . . n 
                 Both semantic resource discovery and semantic query use 
               
               
                   
                   
                 semanticsFilter to specify a query statement that shall be specified 
               
               
                   
                   
                 in the SPARQL query language [5]. When a CSE receives a 
               
               
                   
                   
                 RETRIEVE request including a semanticsFilter, and the Semantic 
               
               
                   
                   
                 Query Indicator parameter is also present in the request, the 
               
               
                   
                   
                 request shall be processed as a semantic query; otherwise, the 
               
               
                   
                   
                 request shall be processed as a semantic resource discovery. 
               
               
                   
                   
                 In the case of semantic resource discovery targeting a specific 
               
               
                   
                   
                 resource, if the semantic description contained in 
               
               
                   
                   
                 the &lt;semanticDescriptor&gt; of a child resource matches the 
               
               
                   
                   
                 semanticFilter, the URI of this child resource will be included in 
               
               
                   
                   
                 the semantic resource discovery result. 
               
               
                   
                   
                 In the case of semantic query, given a received semantic query 
               
               
                   
                   
                 request and its query scope, the SPARQL query statement shall be 
               
               
                   
                   
                 executed over aggregated semantic information collected from the 
               
               
                   
                   
                 semantic resource(s) in the query scope and the produced output 
               
               
                   
                   
                 will be the result of this semantic query. 
               
               
                   
                   
                 Examples for matching semantic filters in SPARQL to semantic 
               
               
                   
                   
                 descriptions can be found in [i.28]. 
               
               
                 filterOperation 
                 0 . . . 1 
                 Indicates the logical operation (AND/OR) to be used for different 
               
               
                   
                   
                 condition tags. The default value is logical AND. 
               
               
                 contentFilterSyntax 
                 0 . . . 1 
                 Indicates the Identifier for syntax to be applied for content-based 
               
               
                   
                   
                 discovery. 
               
               
                 contentFilterQuery 
                 0 . . . 1 
                 The query string shall be specified when contentFilterSyntax 
               
               
                   
                   
                 parameter is present. 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Condition tag 
                 Multiplicity 
                 Description 
               
            
           
           
               
            
               
                 Filter Handling Conditions 
               
               
                   
               
            
           
           
               
               
               
            
               
                 filterUsage 
                 0 . . . 1 
                 Indicates how the filter criteria is used. If provided, possible values 
               
               
                   
                   
                 are ‘discovery’ and ‘IPEOnDemandDiscovery’. 
               
               
                   
                   
                 If this parameter is not provided, the Retrieve operation is a 
               
               
                   
                   
                 generic retrieve operation and the content of the child resources 
               
               
                   
                   
                 fitting the filter criteria is returned. 
               
               
                   
                   
                 If filterUsage is ‘discovery’, the Retrieve operation is for resource 
               
               
                   
                   
                 discovery (clause 10.2.6), i.e. only the addresses of the child 
               
               
                   
                   
                 resources are returned. 
               
               
                   
                   
                 If filterUsage is ‘IPEOnDemandDiscovery’, the other filter 
               
               
                   
                   
                 conditions are sent to the IPE as well as the discovery Originator 
               
               
                   
                   
                 ID. When the IPE successfully generates new resources matching 
               
               
                   
                   
                 with the conditions, then the resource address(es) shall be returned. 
               
               
                   
                   
                 This value shall only be valid for the Retrieve request targeting 
               
               
                   
                   
                 an &lt;AE&gt; resource that represents the IPE. 
               
               
                 limit 
                 0 . . . 1 
                 The maximum number of resources to be included in the filtering 
               
               
                   
                   
                 result. This may be modified by the Hosting CSE. When it is 
               
               
                   
                   
                 modified, then the new value shall be smaller than the suggested 
               
               
                   
                   
                 value by the Originator. 
               
               
                 level 
                 0 . . . 1 
                 The maximum level of resource tree that the Hosting CSE shall 
               
               
                   
                   
                 perform the operation starting from the target resource (i.e. To 
               
               
                   
                   
                 parameter). This shall only be applied for Retrieve operation. The 
               
               
                   
                   
                 level of the target resource itself is zero and the level of the direct 
               
               
                   
                   
                 children of the target is one. 
               
               
                 offset 
                 0 . . . 1 
                 The number of direct child and descendant resources that a Hosting 
               
               
                   
                   
                 CSE shall skip over and not include within a Retrieve response 
               
               
                   
                   
                 when processing a Retrieve request to a targeted resource. 
               
               
                 applyRelativePath 
                 0 . . . 1 
                 This attribute contains a resource tree relative path 
               
               
                   
                   
                 (e.g. . . . /tempContainer/LATEST). This condition applies after all 
               
               
                   
                   
                 the matching conditions have been used (i.e. a matching result has 
               
               
                   
                   
                 been obtained). The attribute determines the set of resource(s) in 
               
               
                   
                   
                 the final filtering result. The filtering result is computed by 
               
               
                   
                   
                 appending the relative path to the path(s) in the matching result. 
               
               
                   
                   
                 All resources whose Resource-IDs match that combined path(s) 
               
               
                   
                   
                 shall be returned in the filtering result. If the relative path does not 
               
               
                   
                   
                 represent a valid resource, the outcome is the same as if no match 
               
               
                   
                   
                 was found, i.e. there is no corresponding entry in the filtering 
               
               
                   
                   
                 result. 
               
               
                   
               
            
           
         
       
     
     A response to a request for accessing a resource through the reference points Mca and Mcc may include at least one mandatory parameter and at least one optional parameter. In other words, each defined parameter may be either mandatory or optional according to a requested operation or a mandatory response code. For example, a request message may include at least one parameter among those listed in Table 4 below. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Request message parameter 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 Mandatory 
                 Operation - operation to be executed/CREAT, Retrieve, Update, Delete, Notify 
               
               
                   
                 To - the address of the target resource on the target CSE 
               
               
                   
                 From - the identifier of the message Originator 
               
               
                   
                 Request Identifier - uniquely identifies a Request message 
               
               
                 Operation 
                 Content - to be transferred 
               
               
                 dependent 
                 Resource Type - of resource to be created 
               
               
                 Optional 
                 Originating Timestamp - when the message was built 
               
               
                   
                 Request Expiration Timestamp - when the request message expires 
               
               
                   
                 Result Expiration Timestamp - when the result message expires 
               
               
                   
                 Operational Execution Time - the time when the specified operation is to be 
               
               
                   
                 executed by the target CSE 
               
               
                   
                 Response Type - type of response that shall be sent to the Originator 
               
               
                   
                 Result Persistence - the duration for which the reference containing the responses is 
               
               
                   
                 to persist 
               
               
                   
                 Result Content - the expected components of the result 
               
               
                   
                 Event Category - indicates how and when the system should deliver the message 
               
               
                   
                 Delivery Aggregation - aggregation of requests to the same target CSE is to be used 
               
               
                   
                 Group Request Identifier - Identifier added to the group request that is to be fanned 
               
               
                   
                 out to each member of the group 
               
               
                   
                 Group Request Target Members-indicates subset of members of a group 
               
               
                   
                 Filter Criteria - conditions for filtered retrieve operation 
               
               
                   
                 Desired Identifier Result Type - format of resource identifiers returned 
               
               
                   
                 Token Request Indicator - indicating that the Originator may attempt Token Request 
               
               
                   
                 procedure (for Dynamic Authorization) if initiated by the Receiver 
               
               
                   
                 Tokens - for use in dynamic authorization 
               
               
                   
                 Token IDs - for use in dynamic authorization 
               
               
                   
                 Role IDs - for use in role based access control 
               
               
                   
                 Local Token IDs - for use in dynamic authorization 
               
               
                   
                 Authorization Signature Indicator - for use in Authorization Relationship Mapping 
               
               
                   
                 Authorization Signature - for use in Authorization Relationship Mapping 
               
               
                   
                 Authorization Relationship Indicator - for use in Authorization Relationship 
               
               
                   
                 Mapping 
               
               
                   
                 Semantic Query Indicator - for use in semantic queries 
               
               
                   
                 Release Version Indicator - the oneM2M release version that this request message 
               
               
                   
                 conforms to. 
               
               
                   
                 Vendor Information 
               
               
                   
               
            
           
         
       
     
     A normal resource includes a complete set of representations of data constituting the base of information to be managed. Unless qualified as either “virtual” or “announced”, the resource types in the present document may be normal resources. A virtual resource may be used to trigger processing and/or a retrieve result. However, a virtual resource may not have a permanent representation in a CSE. An announced resource may contain a set of attributes of an original resource. When an original resource changes, an announced resource may be automatically updated by the hosting CSE of the original resource. The announced resource contains a link to the original resource. Resource announcement enables resource discovery. An announced resource at a remote CSE may be used to create a child resource at a remote CSE, which may not be present as a child of an original resource or may not be an announced child thereof. 
     To support resource announcement, an additional column in a resource template may specify attributes to be announced for inclusion in an associated announced resource type. For each announced &lt;resourceType&gt;, the addition of suffix “Annc” to the original &lt;resourceType&gt; may be used to indicate its associated announced resource type. For example, resource &lt;containerAnnc&gt; may indicate the announced resource type for &lt;container&gt; resource, and &lt;groupAnnc&gt; may indicate the announced resource type for &lt;group&gt; resource. 
     An IoT system like oneM2M should support communication among numerous devices. In addition, since many devices generate massive amounts of data, fast processing of such a large amount of data may be required. To this end, an AI technology may be used. In case an AI technology is used, it may be necessary to use an AI model that is sufficiently trained. In some cases, follow-up re-learning may be needed for an initially trained AI model. 
     There may exist various reasons why re-learning may be performed. For example, in case an environment changes over time, re-learning may be desired to build a better model and to generate a high-quality accurate prediction. 
     A learning rate of automated machine learning (autoML) may be described as follows. In machine learning and statistics, a learning rate may be a tuning parameter in an optimization algorithm. For example, a learning rate may determine a step size at each iteration while moving toward a minimum of a loss function. A learning rate may be determined according to time and the number of learning datasets, which the present disclosure proposes to describe in detail through an IoT platform. 
       FIG.  6    illustrates a concept of re-learning supported in an M2M system according to the present disclosure.  FIG.  6    illustrates a concept of controlling, by an IoT platform  620 , initial training and re-learning for an AI/ML model  630 . 
     Referring to  FIG.  6   , after initial training for building the AI/ML model  630 , the IoT platform  620  collects data. Herein, the initial training may be performed using training data  614 . Real time data thus collected may be used for prediction. That is, the IoT platform  620  performs a predicting operation using the AI/ML model  630  by means of raw data. The IoT platform  620  collects some datasets for future training (e.g., re-learning). When criteria may be satisfied, the IoT platform  620  performs re-learning based on new training data  616 . According to various embodiments, the criteria may be defined based on time, an amount of data, and a demand. Re-learning may be performed based on a re-learning criterion as follows. 
     A criterion may be defined based on time. In this case, re-learning may be performed when a specific time is satisfied. For example, every hour or every specified time (e.g.,  00 : 00 ) may be set as a time for re-learning. 
     A criterion may be defined based on an amount of data. In this case, re-learning may be performed when an amount of new training data reaches a given value. For example, if the criterion is set to 1,000 labeled datasets, the IoT platform may perform re-learning when 1,000 labeled datasets are collected for training. 
     A criterion may be defined based on a size of data. In this case, re-learning may be performed when a size of new training data reaches a given value. For example, if the criterion is set to 1 gigabyte, the IoT platform may perform re-learning when a size of data reaches 1 gigabyte. 
     A criterion may be defined on demand. In this case, when an administrator application generates a request to perform re-learning, the IoT platform may perform re-learning. In case there is no data collected for re-learning, the IoT platform may neglect the request. 
     A criterion may be defined based on an accuracy rate. In this case, when the accuracy rate is below a specific level, the IoT platform may perform re-learning. An accuracy rate for prediction may be measured for this scheme. 
     According to the above-listed various criteria, re-learning may be performed. However, the above-described criteria are only examples, and other criteria may be applied for re-learning according to various embodiments. Furthermore, conditions for re-learning may be combined. For example, in case an amount of data and a size of data may be combined, when the amount of data exceeds 1,000 and the size of data exceeds 1 gigabyte, re-learning may be performed. 
       FIG.  7    illustrates an example of a resource including information on re-learning in an M2M system according to the present disclosure. In the example of  FIG.  7   , a resource containing information on re-learning may be expressed as ‘learningAlgorithm’  710 , but the name of a resource may be different according to various embodiments. Referring to  FIG.  7   , the resource ‘learningAlgorithm’  710  may include a plurality of attributes or resources  711  to  716 . Each attribute or resource may be described in Table 5 below. 
     
       
         
           
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 Attribute/Resource 
                 Description 
               
               
                   
               
             
            
               
                 learningAlgorithm 
                 A resource representing a specific 
               
               
                   
                 learning algorithm 
               
               
                 reLearningCriteria 
                 Combination of criteria for re-learning 
               
               
                 newLearningData 
                 A set of new learning data 
               
               
                 onDemandReLearning 
                 A resource to trigger re-learning. 
               
               
                   
                 If the resource is set to a positive 
               
               
                   
                 value, it is interpreted 
               
               
                   
                 to request re-learning, and re- 
               
               
                   
                 learning on demand is supported. 
               
               
                 resultParameters 
                 Result tuning parameters after re-learning 
               
               
                 initialData 
                 A set of initial learning data 
               
               
                 accuracyRate 
                 Represents an average accuracy rate 
               
               
                   
                 for the prediction 
               
               
                   
               
            
           
         
       
     
     Information on re-learning according to various embodiments may be managed through resources in  FIG.  7   . 
       FIG.  8    illustrates an example of a procedure of controlling learning for an AI model in an M2M system according to the present disclosure. The operation subject of  FIG.  8    may be a device (e.g., CSE) that controls the training of an AI model. In the description below, the operation subject of  FIG.  8    is referred to as ‘device’. 
     Referring to  FIG.  8   , at step S 801 , the device generates a resource for training of an AI model. For example, the device may generate a resource in response to establishing a connection with another entity that operates the AI model. According to an embodiment, the resource may include at least one of information indicating a learning algorithm, information defining a re-learning triggering criterion, information for storing learning data, information for storing a (re-)learning result, information for storing initial learning data, and information indicating an accuracy rate for prediction using the AI model. Herein, the information may be understood as a resource or an attribute. At this time, information may be generated without having a value. 
     At step S 803 , the device performs initial learning and performs prediction. The device may perform initial learning for the AI model by using initial learning data stored in a resource. At this time, an operation (e.g., prediction, loss function calculation, back propagation) for the initial learning may be performed by the device or another device (e.g., CSF). In case the operation for the initial learning may be performed by another device, the device may transmit information on the AI model and learning data to the another device and receive a learning result. In addition, a predicting operation may be performed by the device or another device (e.g., AE). 
     At step S 805 , the device collects learning data. After the initial learning is completed, while the AI model thus learned is being operated, the device may collect learning data for re-learning. For example, the device may collect at least a portion of data obtained for prediction as learning data for re-learning. According to an embodiment, the device may obtain newly labeled data by providing data, which is input for prediction, to a third entity generating a label and obtaining the label. According to another embodiment, the device may obtain newly labeled data by performing data augmentation based on data, which is input for prediction, and a prediction result. Apart from these, many other methods may be used to obtain newly labeled data. Learning data collected for re-learning may be stored in the resource that is generated at step S 801 . 
     At step S 807 , the device checks whether or not a re-learning condition is satisfied. The re-learning condition is stored in the resource generated at step S 801  and may be defined based on at least one of various factors. For example, the re-learning condition may be defined based on at least one of a time, an amount of collected data, a size of collected data, an accuracy rate of AI model, and a demand. In case the re-learning condition is not satisfied, the device returns to step S 803 . 
     In case the re-learning condition is satisfied, at step S 809 , the device performs re-learning and updates a resource. The device may perform initial learning for the AI model by using learning data for re-learning stored in the resource. At this time, an operation (e.g., prediction, loss function calculation, back propagation) for the initial learning may be performed by the device or another device (e.g., CSF). In case the operation for the initial learning is performed by another device, the device may transmit information on the AI model and learning data to the another device and receive a learning result. When the re-learning is completed, the device may store information on the re-learned AI model in the resource. For example, the device may store information on a re-learning history and information on a result of re-learning in the resource. In addition, the device may delete the learning data used for re-learning from the resource. 
       FIG.  9    illustrates an example of a procedure of performing re-learning in an M2M system according to the present disclosure.  FIG.  9    exemplifies signal exchanges among a learning CSF  910  that performs training, a server IN-CSE  920  that controls training for an AI model, and an AI application  930  using the AI model. Herein, using an AI model may be understood as operating the AI model itself or providing input data to another device operating the AI model and receiving output data. 
     Referring to  FIG.  9   , at step S 901 , the AI application  930  transmits a request for initial learning to the server IN-CSE  920 . In other words, the AI application  930  transmits a message for requesting initial learning for building an AI model to the server IN-CSE  920 . Herein, the message includes information necessary to perform training for the AI model. For example, the message may include at least one of information on a structure of the AI model, information on a weight, and information on a training method. 
     At step S 903 , the server IN-CSE  920  transmits information on the initial learning request to the learning CSF  910 . In other words, the server IN-CSE  920  informs the learning CSF  910  of occurrence of the request to perform initial learning and transmits information necessary to perform initial learning. For example, the information necessary to perform the initial learning may include at least one of information on a structure of the AI model, information on a weight, and information on a training method. In addition, according to an embodiment, the server IN-CSE  920  may provide a set of learning data for initial learning. 
     At step S 905 , the learning CSF  910  performs initial learning by using an initial dataset. Being a set of learning data, the initial dataset may be provided from the server IN-CSE  920  or be collected by the learning CSF  910 . The learning CSF  910  may build an AI model by performing initial learning based on information that is provided from the server IN-CSE  920 . Specifically, the learning CSF  910  may perform prediction by using learning data, determine a loss value based on a prediction result and a label, and update weight values by performing back-propagation using the loss value. 
     At step S 907 , the learning CSF  910  transmits a learning result to the server IN-C SE  920 . The learning result includes information on the learned AI model. That is, the learning CSF  910  requests to update a resource for AI model training by means of the learning result. For example, the learning result may include information on weights of the AI model. Accordingly, the server IN-CSE  920  obtains the AI model, for which initial learning is completed, and updates the resource for AI model training. 
     At step S 909 , the server IN-CSE  920  transmits a result of learning to the AI application  930 . That is, the server IN-CSE  920  returns the result of learning to the AI application  930 . Accordingly, the AI application  930  may obtain the AI model thus built and be in a state where it can use the AI model. 
     At step S 911 , the server IN-CSE  920  collects a dataset for re-learning. Although not illustrated in  FIG.  9   , the server IN-CSE  920  may transmit data, which is received from at least one of devices connected to the server IN-CSE  920 , to the AI application  930 , receive a prediction result using the AI model from the AI application  930 , and perform a necessary operation by using the prediction result. At this time, the server IN-CSE  920  may collect at least a portion of received data as learning data for re-learning. 
     At step S 913 , the AI application  930  transmits a request for re-learning to server IN-CSE  920 . In other words, the AI application  930  transmits a message for requesting re-learning for the AI model to the server IN-CSE  920 . Herein, the request of the AI application  930  may be meaningful when an on-demand criterion is applied. Herein, the message includes information necessary to perform training for the AI model. For example, the message may include at least one of information on a structure of the AI model, information on a weight, and information on a training method. 
     At step S 915 , the server IN-CSE  920  transmits information on the re-learning request to the learning CSF  910 . In other words, the server IN-CSE  920  informs the learning CSF  910  of occurrence of a request to perform re-learning and transmits information necessary to perform re-learning. For example, the information necessary to perform the initial learning may include at least one of information on a structure of the AI model, information on a weight, and information on a training method. In addition, according to an embodiment, the server IN-CSE  920  may provide a set of learning data for re-learning. For example, the learning data for re-learning may include at least a portion of the dataset collected at step S 911 . 
     At step S 917 , the learning CSF  910  performs re-learning by using a new dataset. The new dataset, which is a set of learning data, may be received from the server IN-CSE  920 . The learning CSF  910  may update or reinforce the AI model by performing re-learning based on information that is provided from the server IN-CSE  920 . Specifically, the learning CSF  910  may perform prediction by using learning data, determine a loss value based on a prediction result and a label, and update weight values by performing back-propagation using the loss value. 
     At step S 919 , the learning CSF  910  transmits a learning result to the server IN-C SE  920 . The learning result includes information on the learned AI model. That is, the learning CSF  910  requests to update a resource for AI model training by means of the learning result. For example, the learning result may include information on weights of the AI model. Accordingly, the server IN-CSE  920  obtains the AI model, for which initial learning is completed, and updates the resource for AI model training. 
     At step S 921 , the server IN-CSE  920  transmits a result of learning to the AI application  930 . That is, the server IN-CSE  920  returns the result of learning to the AI application  930 . Accordingly, the AI application  930  may obtain the AI model thus built and be in a state where it can use the AI model. Accordingly, at step S 923 , the AI application  930  performs an operation by using an AI/ML model that is trained with labeled data. 
       FIG.  10    illustrates a configuration of an M2M device in an M2M system according to the present disclosure. An M2M device  1010  or an M2M device  1020  illustrated in  FIG.  10    may be understood as hardware functioning as at least one among the above-described AE, CSE and NSE. 
     Referring to  FIG.  10   , the M2M device  1010  may include a processor  1012  controlling a device and a transceiver  1014  transmitting and receiving a signal. Herein, the processor  1012  may control the transceiver  1014 . In addition, the M2M device  1010  may communicate with another M2M device  1020 . The another M2M device  1020  may also include a processor  1022  and a transceiver  1024 , and the processor  1022  and the transceiver  1024  may perform the same function as the processor  1012  and the transceiver  1014 . 
     As an example, the originator, the receiver, AE and CSE, which may be described above, may be one of the M2M devices  1010  and  1020  of  FIG.  10   , respectively. In addition, the devices  1010  and  1020  of  FIG.  10    may be other devices. As an example, the devices  1010  and  1020  of  FIG.  10    may be communication devices, vehicles, or base stations. That is, the devices  1010  and  1020  of  FIG.  10    refer to devices capable of performing communication and may not be limited to the above-described embodiment. 
     The above-described exemplary embodiments of the present disclosure may be implemented by various means. For example, the exemplary embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. 
     The foregoing description of the exemplary embodiments of the present disclosure has been presented for those skilled in the art to implement and perform the disclosure. While the foregoing description has been presented with reference to the preferred embodiments of the present disclosure, it will be apparent to those skilled in the art that various modifications and variations may be made in the present disclosure without departing from the spirit or scope of the present disclosure as defined by the following claims. 
     Accordingly, the present disclosure is not intended to be limited to the exemplary embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, while the exemplary embodiments of the present specification have been particularly shown and described, it is to be understood that the present specification is not limited to the above-described exemplary embodiments, but, on the contrary, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the present specification as defined by the claims below, and such changes and modifications should not be individually understood from the technical thought and outlook of the present specification. 
     In this specification, both the disclosure and the method disclosure are explained, and the description of both disclosures may be supplemented as necessary. In addition, the present disclosure has been described with reference to exemplary embodiments thereof. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the essential characteristics of the present disclosure. Therefore, the disclosed exemplary embodiments should be considered in an illustrative sense rather than in a restrictive sense. The scope of the present disclosure is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present disclosure.