Patent Application
20220284003
The present disclosure relates generally to information processing. Various embodiments of the teachings herein include methods and/or apparatuses for labeling a data point.
Data visualization refers to visual representation of data, which is aimed at communicating information contained in the data clearly and efficiently in a graphical manner. A data set consisting of a plurality of data points provides a basis for data visualization. In an exemplary typical application scenario, a data visualization tool may obtain data, such as temperature data, pressure data, and humidity data, collected by Internet of Things (IoT) sensors at a certain frequency, and a large number of data points formed by such sensor data are visualized, plotted as a graph (for example, a data distribution graph), and presented on a visual user interface.
With the increase in the number of application scenarios, service instances represented by data points have become increasingly complex, and, for example, a data point may represent a combination of data collected by more than one IoT sensor at a time. Generally, only experts in the service field who are familiar with specific services can understand and distinguish between different situations reflected by different data points, for example, whether a corresponding service instance is normal. Therefore, it is necessary, by experts in the service field, or at least with the assistance of experts in the service field, to label each obtained data point to be used for data visualization, and add marks to illustrate corresponding situations, thereby facilitating related data analysis, including classification of data points.
Some embodiments of the teachings of the present disclosure include a method for labeling a data point comprising: executing a labeling operation on a target data set, wherein the target data set comprises a plurality of data points, each data point representing a service instance, and the labeling operation comprises: dividing the target data set into a plurality of first subsets; for each of the first subsets: receiving a user input used for designating a mark for at least one data point in the first subset, wherein the mark is used for illustrating the situation of the service instance represented by the data point; determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition; in response to determining that the preset condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation; and in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set.
As another example, some embodiments include an apparatus for labeling a data point comprising: a module for executing a labeling operation on a target data set, wherein the target data set comprises a plurality of data points, each data point representing a service instance, and the module for executing a labeling operation on a target data set comprises: a module for dividing the target data set into a plurality of first subsets; for each of the plurality of first subsets: a module for receiving an user input used for designating a mark for at least one data point in the first subset, wherein the mark is used for illustrating the situation of the service instance represented by the data point; a module for determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition; a module for, in response to determining that the preset condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation; and a module for, in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set.
As another example, some embodiments include a computing device comprising: a memory for storing an instruction; and at least one processor coupled to the memory, wherein the instruction, when executed by the at least one processor, causes the at least one processor to execute a labeling operation on a target data set, wherein the target data set comprises a plurality of data points, each data point representing a service instance, and the labeling operation comprises: dividing the target data set into a plurality of first subsets; for each of the first subsets: receiving a user input used for designating a mark for at least one data point in the first subset, wherein the mark is used for illustrating the situation of the service instance represented by the data point; determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition; in response to determining that the preset condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation; and in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set.
As another example, some embodiments include a computer-readable storage medium on which an instruction is stored which, when executed by at least one processor, causes the at least one processor to execute a labeling operation on a target data set, wherein the target data set comprises a plurality of data points, each data point representing a service instance, and the labeling operation comprises: dividing the target data set into a plurality of first subsets; for each of the first subsets: receiving a user input used for designating a mark for at least one data point in the first subset, wherein the mark is used for illustrating the situation of the service instance represented by the data point; determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition; in response to determining that the preset condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation; and in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set.
Implementations of the present disclosure are illustrated in an exemplary manner, rather than in a limiting manner, in the drawings, and the same reference numerals in the drawings denote the same or similar components, wherein:
Various aspects of the present disclosure provide a mechanism for labeling a data point on the basis of machine learning, wherein the provided adaptive labeling mechanism only requires users (experts in the service field, or operators at least assisted by experts in the service field) to designate marks for a small number of data points in a target data set, and, relying on artificial intelligence, manually designated marks are applied to all the data points in the target data set, which allows a remarkable reduction in manual labor required of users while maintaining high classification accuracy.
In some embodiments, a method further comprise: dividing an initial data set into a plurality of second subsets; for each of the plurality of second subsets, receiving a user input used for designating a mark for at least one data point in the second subset; and selecting one of the plurality of second subsets as the target data set. The operations on the initial data set in the above-described examples may also be implemented by some operations similar to those in the aforementioned adaptive labeling mechanism, so that set operation processes and corresponding modules may be partially reused during implementation, and manual labor required of users may be further reduced, which improves the user experience.
In some embodiments, in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set, a method may comprise: setting a mark previously designated for at least one data point in the target data set as the mark for each data point in the first subset. In the above-described example, setting the mark previously designated for at least one data point in the target data set as the mark for each data point in the current subset can simplify the operation while ensuring the labeling accuracy of the labeling operation.
In some embodiments, when determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition, a method may comprise: determining the semantic similarity between the mark and the mark previously designated for at least one data point in the target data set; and determining whether the semantic similarity exceeds a preset threshold. In the above-described example, since determination is performed on the basis of semantic similarity and more complex marks may be processed, more complex service situations may be handled, and the requirements for user input are also relaxed.
In the following description, for explanation purposes, meticulous details are given. It is understood, however, that implementations of the teachings of the present disclosure may be achieved without such meticulous details. In other instances, no well-known circuits, structures or techniques are shown in detail, so that an understanding of this description is not affected.
References throughout the description to “one implementation”, “implementation”, “exemplary implementation”, “some implementations”, “various implementations,” etc. mean that the described implementations of the invention may include particular features, structures or characteristics, but this does not mean that every implementation must contain these particular features, structures or characteristics. Further, some implementations may have some, all, or none of the features described for other implementations.
In the following description and claims, the terms “coupled” and “connected” as well as derivatives thereof may be used. It should be understood that these terms are not intended as synonyms for each other. Conversely, in a particular implementation, “connected” is used to mean that two or more components are in direct physical or electrical contact with each other, while “coupled” is used to mean that two or more components cooperate or interact with each other, but that they may or may not be in direct physical or electrical contact with each other.
A data visualization tool can plot a data distribution map of a large number of data points and present it to a user. With the increase in the number of application scenarios, service instances represented by data points have become increasingly complex, and, in order to carry out meaningful data analysis in terms of services, including correct classification of data points, it is necessary, by experts in the service field, or at least with the assistance of experts in the service field, to add marks for each obtained data point to illustrate the corresponding situation. However, this requires considerable manual labor.
The teachings of the present disclosure include a mechanism for labeling a data point on the basis of machine learning, wherein, by the mechanism, users (experts in the service field, or operators at least assisted by experts in the service field) are only required to designate marks for a small number of data points in a target data set, and, relying on artificial intelligence, manually designated marks are applied to all the data points in the target data set, which allows a remarkable reduction in manual labor required of users while maintaining high classification accuracy.
See
In some embodiments, a data visualization tool may run on the device 110 for visualizing data obtained from at least one data source 120. In some examples, a mechanism for labeling a data point on the basis of machine learning provided in the present disclosure may be implemented as part of a data visualization tool, for example, as a plug-in thereof. In some other examples, the mechanism may be implemented as a separate component on the device 110.
Examples of the device 110 may include, but are not limited to: mobile devices, personal digital assistants (PDAs), wearable devices, smartphones, cellular phones, handheld devices, messaging devices, computers, personal computers (PCs), desktop computers, laptop computers, notebook computers, handheld computers, tablet computers, workstations, mini computers, mainframe computers, supercomputers, network devices, web devices, processor-based systems, multiprocessor systems, consumer electronics, programmable consumer electronics, televisions, digital televisions, set-top boxes, or a combination thereof.
At least one data source 120 is used to provide data for manipulation by a data visualization tool on the device 110. In some embodiments, the data source 120 may include various types of sensors, such as image sensor, temperature sensor, pressure sensor, humidity sensor, and current sensor. In some examples, the sensor 120 may be configured to collect data at a fixed frequency, while in some other examples, the data sampling frequency of the sensor 120 is adjustable, for example, in response to an indicator signal from the outside (for example, the device 110). In some embodiments, the data source 120 may be a database, a memory device, or any other type of device for providing data. The present disclosure is not limited to a particular type of data source.
In some embodiments, data collected by at least one data source 120 may be directly provided to the device 110 for data visualization operations, or may first be stored in the device 110 (for example, in a memory contained therein) or in a database/server (not shown) communicatively coupled to the device 110 and/or the data source 120 through the network 130, before being accessed as needed.
The network 130 may comprise any type of wired or wireless communication network, or a combination of wired and wireless networks. In some examples, the network 130 may comprise a wide area network (WAN), a local area network (LAN), a wireless network, a public telephone network, an intranet, and the Internet of Things (IoT). Further, although a single network 130 is shown here, the network 130 may be configured to comprise a plurality of networks.
In some embodiments, the device 110 and the at least one data source 120 may also be communicatively coupled to each other directly, without using a network. In some examples, the device 110 may be deployed on an industrial site, and data collected by various industrial sensors on the industrial site may be considered as the data source 120. The present disclosure is not limited to the specific architecture shown in
In some embodiments, the data visualization tool mentioned above and the mechanism for labeling a data point on the basis of machine learning of the present disclosure may be deployed in a distributed computing environment, and may also be implemented using cloud computing technologies.
Common data analysts lacking professional knowledge in the CNC field, only from the data distribution shape of these data points shown in
Although a service instance has been explained above in combination with the current values of the two spindles of the CNC, those of ordinary skill in the art can understand that a service instance for a CNC may comprise more complex structures, and, for example, a service instance may be included in a combination of more sensor data that may be used to describe the CNC operation state at one moment. Further, as the number of data points contained in a data set increases, the labeling of these data points one by one by experts in the service field will become a very time-consuming and labor-intensive task.
Some appropriate mechanisms may be used to reduce the dimensionality of complex service instances, and, for example, in the previous example, the average value of all pixels in the relationship graph or image may be calculated to compress the relationship graph or image to a data point, so that such data points are displayed in a low-dimensional (for example, two-dimensional) data distribution map. In addition, as the complexity of the service instance represented by a data point increases, the amount and complexity of the content of the marks that may be added to the data point will also increase, because more descriptions of the situation of the service instance, such as interpretation of a specific operating status of the corresponding machine, analysis of the cause of a failure, and matters needing attention, are required.
In
It is clear from
For example, each feature element is data collected by one corresponding sensor. As exemplified above in conjunction with
The process of the method 500 then proceeds to step 520, where a labeling operation is executed on the target data set to set a mark for each data point in the target data set. As mentioned above, the mark is used to illustrate the situation of the service instance represented by the corresponding data point. In some embodiments, the labeling operation in step 520 may be executed iteratively until all the data points in the target data set are labeled.
Specifically, executing a labeling operation on a target data set may comprise dividing the target data set into a plurality of first subsets in step 521. In some examples, cluster analysis may be performed to divide the target data set into a plurality of first subsets. Cluster analysis is a machine learning technique and unsupervised learning, in which attempts are made to divide the data points in a data set into a plurality of different subsets, and each subset may be called a cluster. The K-means clustering algorithm, as one of the most commonly used and important clustering algorithms for cluster analysis, is simple and efficient, and can still operate effectively for a large number of data points to meet actual production needs. In a preferred implementation, the K-means clustering algorithm may be used in step 521 to divide the data points in the target data set into a plurality of first subsets.
Relevant parameters of the K-means clustering algorithm may be set depending on the actual situation. For example, relevant parameters may include the number of clusters/subsets, and the setting of the number of subsets may depend on the number of data points contained in the processed data set; additionally or alternatively, the setting of the number of subsets may also depend on the requirements of the services represented by the data points in the data set; additionally or alternatively, the setting of the number of subsets may also depend on the experience of experts in the service field and/or data analysts. It is also feasible to consider other factors.
In addition, in step 521, any other suitable clustering algorithms other than the K-means clustering algorithm, including, but not limited to, those clustering algorithms whose parameters are the number of clusters/subsets, may also be used. In some embodiments, the graphical user interface of the data visualization tool may present a data distribution map of data points (including the target data set) collected for a service, and for classification of a data set (target data set), for example, classification using the above-mentioned K-means clustering algorithm, can provide controls allowing a user to execute operations such as viewing/inputting/changing related parameters of the K-means clustering algorithm. In addition, the graphical user interface of the data visualization tool can also provide an animated display of the division process, so that the user can visually monitor the entire process.
After step 521, the operation process proceeds to step 522, that is, receiving a user input for each of the plurality of first subsets divided from the target data set, the user input being used for designating a mark for at least one data point in the current first subset. In some embodiments, selection of at least one data point in the first subset may be performed automatically, which means that at least one data point is automatically selected from the first subset by, for example, a data visualization tool, and the selected data points are highlighted to the user together or one by one for the user to designate marks for them respectively. In some other examples, the user may manually select at least one data point from the presented first subset and then designate marks for them respectively. The present disclosure is not limited to the specific examples described above.
In some embodiments, selection of at least one data point in the first subset is random. In addition, in some examples given below, for convenience of description, it is assumed that a user input received in step 522 is to designate a mark for a data point in the first subset, which, as is comprehensible in conjunction with the following description, minimizes the amount of manual labor required of the user.
The mark is used for illustrating the situation of the service instance represented by the corresponding data point. In some implementations of the present disclosure, such a mark may be a simple symbolic sign, a word, a phrase, a more complex sentence, even a paragraph, and so on. In some examples, the mark may come from a preset mark set, and a user may select an appropriate mark from the preset mark set when designating a mark for a data point. In some other examples, the mark may also come from a user's free input, for example, a user inputting a body of text. The present disclosure is not limited to the specific examples described above, and a combination of these and other examples is possible as long as it helps to clearly and accurately illustrate the situation of the corresponding service instance.
In some embodiments, in order to provide the user with convenience in labeling a selected data point, some relevant information on the data point may be presented to the user, for example, presenting real data on the service instance represented by the data point, such as values of each sensor, graphs, and images, to the user, as exemplified previously.
In some embodiments, on the graphical user interface of the data visualization tool, for the current first subset, a selected data point to be labeled may be stressed by means of highlighting and/or zooming-in. For example, when manually selecting data points, the user can select a data point by hovering over or clicking the data point with a pointing tool, for example, a mouse, or by another means, and the selected data point is highlighted and/or zoomed-in. Additionally, after a data point is selected, relevant information on the selected data point may be presented in the form of a pop-up menu or a bubble, so that the user can perform labeling with reference thereto. Correspondingly, at least one control, such as a drop-down menu and a text box, is further provided on the graphical user interface of the data visualization tool, allowing the user to conveniently select/input the content of the mark to be added. In some embodiments, a mark designated for a data point in the first subset may also be stored in association with the identifiers of the first subset and/or the identifier of the data point.
The operation process then proceeds to step 523, where it is determined whether the similarity between a mark designated by a user input in step 522 for at least one data point in the current first subset and a mark previously designated for at least one data point in the target data set satisfies a preset condition. The preset condition may be a condition or a combination of a series of conditions, and the series of conditions may further comprise constraints on the current first subset and/or the target data set, and in other aspects. It is understandable that from the perspective of the set relationship, the target data set is the parent set of the first subset here.
In some embodiments, the determining operation in step 523 may comprise determining whether a mark designated for at least one data point in the first subset is equal to a mark previously designated for at least one data point in the target data set. This may be particularly applicable in some scenarios where marks are relatively simple. For example, if the user designates a mark “N” selected from a preset mark set for a data point in the current first subset (assuming N here stands for normal), and a mark previously designated by the user for a data point in the target data set (that is, the parent set of the first subset) is also the mark “N” selected from the preset mark set, it may be directly determined that the two are equal and the similarity between them is deemed to satisfy the preset condition.
In some embodiments, the determining operation may comprise determining whether the similarity between a mark designated for at least one data point in the first subset and a mark previously designated for at least one data point in the target data set exceeds a preset threshold. Here, any available form of measuring similarity between two marks is feasible, and the present disclosure is not limited to a particular implementation. A threshold may be set by standards for the same type of services, or may be manually designated by the user, for example, depending on specific needs of services, experience of experts in the service field and/or data analysts, and so on. In a preferred implementation of the present disclosure, especially in a relatively complex scenario where the mark is a sentence, a paragraph, etc., it is possible to determine the semantic similarity between two marks and then determine whether the semantic similarity exceeds a preset threshold (for example, 80%), so as to perform step 523. Using a determination method based on semantic similarity, on the one hand, more complex mark content may be processed, so that more complex service situations may be handled, and, on the other hand, the requirements for a user input are relaxed, so that a user can provide an input freely, rather than having to make a selection from a preset mark set. In addition, in some examples, for the calculation of semantic similarity, a natural language processing technique, for example, word embedding or sentence embedding, may be used to convert two marks into vector representations respectively, and the semantic similarity between them may be calculated correspondingly. In some embodiments, after the user has inputted a mark designated for at least one data point in the first subset, a result of the above-described similarity determination may be displayed on a graphical user interface of the data visualization tool.
Next, the operation process proceeds to step 524, in which, in response to determining that the similarity does not satisfy the preset condition (in other words, the determination result in step 523 is “No”), the current first subset is used as the target data set, which means that the target data set used as the parent set in this case has been updated to the first subset that does not meet the preset condition in the determination performed in step 523, and then the operation process returns to step 521 to reperform this step and steps subsequent thereto.
In some embodiments, the above-described operations included in step 520 are a series of operations performed iteratively until it is found that in a certain iteration, the similarity determination executed on the current first subset satisfies a preset condition. In addition, the preset condition, for example, may further comprise: the number of current iterations having exceeded a specified number of times (for example, the specified number of times is 10) relative to the initially set target data set; in the current iteration, the number of data points in the first subset already being not greater than a specified number (for example, the specified number is 1), and so on. Those of ordinary skill in the art can understand that the preset condition may comprise any combination of the above-described and other constraints. In some embodiments, in response to determining that the similarity satisfies the preset condition (in other words, the determination result in step 523 is “Yes”), in step 525, a mark associated with a mark previously designated for at least one data point in the target data set is set for each data point in the current first subset.
In some embodiments, setting of marks in step 525 may comprise setting a mark previously designated for at least one data point in the target data set as the mark for each data point in the current first subset. For example, if the mark previously designated for a data point in the target data set is X1, and the mark designated for a data point in the current first subset (which is one of the subsets of the target data set) is X2, where X2 and X1 are the same, or although the two are different but the similarity still satisfies the preset condition, then X1 may be set as the mark of each data point in the first subset. In other words, if the determination result in step 523 is “Yes”, it may be considered that the mark previously designated for a data point in the target data set (parent set) is appropriate, and in this case, the mark may be directly used as the mark of each data point in the current first subset, without the need to use a different mark for the latter.
In some embodiments, a mark set in step 525 may also be determined on the basis of a mark previously designated for at least one data point in the target data set (parent set), a mark designated for at least one data point in the current first subset, and/or a mark designated for at least one data point in a brother subset of the current first subset (that is, at least one other first subset of the plurality of first subsets divided from the target data set), etc. The present disclosure is not limited to the above-described or other specific examples.
In some embodiments, after the operation in step 525 is performed for the current first subset, for all the brother subsets of the first subset (that is, other first subsets in the plurality of first subsets divided from the target data set in step 521), the operations in 522-525 also apply, so that all the data points in the target data set in each iteration are appropriately labeled, and ultimately all the data points in the initially set target data set are also appropriately labeled.
In some embodiments, in each iteration of the labeling operation on the target data set, only one data point is selected from each of the plurality of first subsets of the target data set, and a user designates a mark therefor, then an adaptive labeling mechanism according to the present disclosure makes it possible to minimize the manual labor required of the user. On the other hand, the user may also designate marks for more than one data point in the current first subset in step 522, in which case, in the subsequent steps 523-525, if a mark designated for at least one data point in the first subset is involved, and, similarly, a mark previously designated for at least one data point in the target data set is involved, this may refer to the result of performing a specific operation on the corresponding marks of more than one data point, the operation including, but not limited to, calculating an average of more than one mark.
In some embodiments, a graphical user interface of the data visualization tool may also provide an animated display of the classification of the data points in the data set according to the marks set for these data points, wherein different colors/grayscales/shapes/patterns and various possible forms, or any combination thereof, are used to indicate that the corresponding data points belong to different clusters.
In some embodiments, setting a portion of an initial data set as a target data set in step 510 may comprise: dividing the initial data set into a plurality of second subsets; for each of the plurality of second subsets, receiving a user input used for designating a mark for at least one data point in the second subset; and selecting one of the plurality of second subsets as the target data set.
The operation process of dividing the initial data set into a plurality of second subsets is similar to the operation process of dividing the target data set into a plurality of first subsets in step 521 as discussed above, for which reference may be made to the preceding description. In some examples, cluster analysis may be performed to divide the initial data set into a plurality of second subsets. In some embodiments, the K-means clustering algorithm may be employed in this step to divide the initial data set into a plurality of second subsets. It should be noted that, although in the previous description of a specific example of step 521 it is exemplified that the K-means clustering algorithm may be used, since the operation object in this step is different from that in step 521, specific parameter settings for the K-means clustering algorithm in the two processes may differ.
Further, for each of the plurality of second subsets, the operation process of receiving a user input used for designating a mark for at least one data point in the second subset is similar to the operation process of receiving a mark designated for at least one data point in the first subset in step 522 as discussed above, for which reference may also be made to the preceding description. In some embodiments, selection of at least one data point to be labeled in the second subset may be performed automatically, or may be performed manually by a user, and the present disclosure is not limited thereto. In some embodiments, selection of at least one data point in the second subset is random. In some embodiments, by sequentially setting each of the plurality of second subsets divided from the initial data set into a target data set, the labeling operation 520 as detailed above may be performed to set proper marks for all the data points in the initial data set.
In the above-described example, an operation on the initial data set may also be implemented by some operations similar to the aforementioned labeling operation on the target data set, so that a set operation process and corresponding module may be partially reused in implementation and the manual labor required of a user is further reduced, which improves user experience. Although a specific operation example of step 510 is given above, those of ordinary skill in the art can understand that it is also possible to set a target data set from an initial data set by another means, and the present disclosure is not limited to the above-described specific example.
Next, in conjunction with the specific example shown in
Taking the example on the left in
Next, referring to the example shown in the middle of
The example on the right of
As shown in
In addition, the apparatus 800 may further comprise a module 830, and the module 830 is used for determining whether the similarity between the mark and a mark previously designated for at least one data point in the target data set satisfies a preset condition. In addition, the apparatus 800 may further comprise a module 840, and the module 840 is used for, in response to determining that the preset condition is not satisfied, taking the first subset as a target data set to re-execute the labeling operation. In addition, the apparatus 800 may further comprise a module 850, and the module 850 is used for, in response to determining that the preset condition is satisfied, setting, for each data point in the first subset, a mark associated with the mark previously designated for at least one data point in the target data set.
It should be noted that although the apparatus 800 is shown as comprising the modules 810-850, the apparatus 800 may comprise more or fewer modules to implement various functions described herein. In some embodiments, the modules 810-850 may be included in one module for executing a labeling operation on a target data set, for example, corresponding to step 520 in
In some embodiments, the computing device 900 may further be equipped with one or more peripheral components, which may include, but are not limited to, a display, a speaker, a mouse, and a keyboard. Furthermore, in some implementations, the computing device 900 may further be equipped with a communication interface, which may support various types of wired/wireless communication protocols to communicate with external communication networks. Examples of communication networks may include, but are not limited to: local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), public telephone networks, Internet, intranets, Internet of Things, infrared networks, Bluetooth networks, and near-field communication (NFC) networks.
In some embodiments, the above-described and other components may communicate with each other via one or more buses/interconnections that may support any suitable bus/interconnection protocols, including Peripheral Component Interconnect (PCI), PCI Express, Universal Serial Bus (USB), Serial Attached SCSI (SAS), Serial ATA (SATA), Fiber Channel (FC), System Management Bus (SMBus), or other suitable protocols.
In some embodiments, there is a hardware unit, a software unit, or a combination thereof. Examples of hardware units may include devices, components, processors, microprocessors, circuits, circuit elements (for example, transistors, resistors, capacitors, inductors, etc.), integrated circuits, application specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), memory cells, logic gates, registers, semiconductor devices, chips, microchips, chipsets, etc. Examples of software elements may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application programming interfaces (APIs), instruction sets, computational code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determination of whether an implementation is to be achieved using a hardware unit and/or a software unit can vary depending on factors such as desired computation rate, power level, thermal tolerance, processing cycle budget, input data rate, output data rate, memory resources, data bus speed, and other design or performance constraints, as expected for a given implementation.
In some embodiments, there are manufactured articles. A manufactured article may comprise a storage medium for storing logic. Examples of storage media may include one or more types of computer-readable storage media capable of storing electronic data, such as volatile or nonvolatile memory, removable or non-removable memory, erasable or non-erasable memory, writable or rewritable memory, etc. Examples of logic may include various software elements, such as software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application programming interfaces (APIs), instruction sets, computational code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. In some implementations, for example, a manufactured article may store an executable computer program instruction that, when executed by a processing unit, causes the processing unit to execute a method and/or operation described herein. Executable computer program instructions may include any suitable type of code, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. An executable computer program instruction may be implemented according to a predefined computer language, method or syntax for instructing a computer to execute a designated function. The instruction may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language.
What has been described above includes examples of the disclosed architecture. While it is indeed impossible to describe every conceivable combination of components and/or methods, those of ordinary skill in the art will appreciate that many other combinations and permutations are possible. Therefore, this novel architecture is intended to cover all such substitutions, alterations and variations that fall within the spirit and scope of the appended claims.
This application is a U.S. National Stage Application of International Application No. PCT/CN2019/102080 filed Aug. 22, 2019, which designates the United States of America. The contents of which is hereby incorporated by reference in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2019/102080 | 8/22/2019 | WO |
