Patent ID: 12210981

DETAILED DESCRIPTION

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. 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.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. The following detailed description will generally follow the summary of the disclosure, as set forth above, further explaining and expanding the definitions of the various aspects and embodiments of the disclosure as necessary.

FIG.1illustrates information handling system100, which is a simplified example of a computer system capable of performing the computing operations described herein. Information handling system100includes one or more processors110coupled to processor interface bus112. Processor interface bus112connects processors110to Northbridge115, which is also known as the Memory Controller Hub (MCH). Northbridge115connects to system memory120and provides a means for processor(s)110to access the system memory. Graphics controller125also connects to Northbridge115. In one embodiment, Peripheral Component Interconnect (PCI) Express bus118connects Northbridge115to graphics controller125. Graphics controller125connects to display device130, such as a computer monitor.

Northbridge115and Southbridge135connect to each other using bus119. In some embodiments, the bus is a Direct Media Interface (DMI) bus that transfers data at high speeds in each direction between Northbridge115and Southbridge135. In some embodiments, a PCI bus connects the Northbridge and the Southbridge. Southbridge135, also known as the Input/Output (I/O) Controller Hub (ICH) is a chip that generally implements capabilities that operate at slower speeds than the capabilities provided by the Northbridge. Southbridge135typically provides various busses used to connect various components. These busses include, for example, PCI and PCI Express busses, an ISA bus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count (LPC) bus. The LPC bus often connects low-bandwidth devices, such as boot ROM196and “legacy” I/O devices (using a “super I/O” chip). The “legacy” I/O devices (198) can include, for example, serial and parallel ports, keyboard, mouse, and/or a floppy disk controller. Other components often included in Southbridge135include a Direct Memory Access (DMA) controller, a Programmable Interrupt Controller (PIC), and a storage device controller, which connects Southbridge135to nonvolatile storage device185, such as a hard disk drive, using bus184.

ExpressCard155is a slot that connects hot-pluggable devices to the information handling system. ExpressCard155supports both PCI Express and Universal Serial Bus (USB) connectivity as it connects to Southbridge135using both the USB and the PCI Express bus. Southbridge135includes USB Controller140that provides USB connectivity to devices that connect to the USB. These devices include webcam (camera)150, infrared (IR) receiver148, keyboard and trackpad144, and Bluetooth device146, which provides for wireless personal area networks (PANs). USB Controller140also provides USB connectivity to other miscellaneous USB connected devices142, such as a mouse, removable nonvolatile storage device145, modems, network cards, Integrated Services Digital Network (ISDN) connectors, fax, printers, USB hubs, and many other types of USB connected devices. While removable nonvolatile storage device145is shown as a USB-connected device, removable nonvolatile storage device145could be connected using a different interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device175connects to Southbridge135via the PCI or PCI Express bus172. LAN device175typically implements one of the Institute of Electrical and Electronic Engineers (IEEE) 802.11 standards of over-the-air modulation techniques that all use the same protocol to wireless communicate between information handling system100and another computer system or device. Optical storage device190connects to Southbridge135using Serial Analog Telephone Adapter (ATA) (SATA) bus188. Serial ATA adapters and devices communicate over a high-speed serial link. The Serial ATA bus also connects Southbridge135to other forms of storage devices, such as hard disk drives. Audio circuitry160, such as a sound card, connects to Southbridge135via bus158. Audio circuitry160also provides functionality associated with audio hardware such as audio line-in and optical digital audio in port162, optical digital output and headphone jack164, internal speakers166, and internal microphone168. Ethernet controller170connects to Southbridge135using a bus, such as the PCI or PCI Express bus. Ethernet controller170connects information handling system100to a computer network, such as a Local Area Network (LAN), the Internet, and other public and private computer networks.

WhileFIG.1shows one information handling system, an information handling system may take many forms. For example, an information handling system may take the form of a desktop, server, portable, laptop, notebook, or other form factor computer or data processing system. In addition, an information handling system may take other form factors such as a personal digital assistant (PDA), a gaming device, Automated Teller Machine (ATM), a portable telephone device, a communication device or other devices that include a processor and memory.

FIG.2provides an extension of the information handling system environment shown inFIG.1to illustrate that the methods described herein can be performed on a wide variety of information handling systems that operate in a networked environment. Types of information handling systems range from small handheld devices, such as handheld computer/mobile telephone210to large mainframe systems, such as mainframe computer270. Examples of handheld computer210include personal digital assistants (PDAs), personal entertainment devices, such as Moving Picture Experts Group Layer-3 Audio (MP3) players, portable televisions, and compact disc players. Other examples of information handling systems include pen, or tablet, computer220, laptop, or notebook, computer230, workstation240, personal computer system250, and server260. Other types of information handling systems that are not individually shown inFIG.2are represented by information handling system280. As shown, the various information handling systems can be networked together using computer network200. Types of computer network that can be used to interconnect the various information handling systems include Local Area Networks (LANs), Wireless Local Area Networks (WLANs), the Internet, the Public Switched Telephone Network (PSTN), other wireless networks, and any other network topology that can be used to interconnect the information handling systems. Many of the information handling systems include nonvolatile data stores, such as hard drives and/or nonvolatile memory. The embodiment of the information handling system shown inFIG.2includes separate nonvolatile data stores (more specifically, server260utilizes nonvolatile data store265, mainframe computer270utilizes nonvolatile data store275, and information handling system280utilizes nonvolatile data store285). The nonvolatile data store can be a component that is external to the various information handling systems or can be internal to one of the information handling systems. In addition, removable nonvolatile storage device145can be shared among two or more information handling systems using various techniques, such as connecting the removable nonvolatile storage device145to a USB port or other connector of the information handling systems.

As discussed above, existing solutions focus on optimizing machine learning mode prediction components instead of optimizing constructed features preparation. Some solutions build their feature cache for performance but they are designed for specific applications and cannot be used by general solutions. Some customers rely on an ETL process for data preparation and therefore cannot use machine learning models in scenarios with low latency demand. For example, a card fraud detection model may require the following:Average amount spent per transactions in the past 1/2/4 weeks;Maximum amount spent per transactions in the past 1/2/4 weeks;Average amount spent per day in the past 1/2/4 weeks;Maximum amount spent per day in the past 1/2/4 weeks;Total number of transactions with the same merchant; andNumber of retailer locations per day and the duration between these locations.

In the example above, some requirements require a multiple amount of aggregations to prepare the data for inferencing (e.g., a first aggregation using the past 1 week of data, a second aggregation using the past 2 weeks of data, etc.). As such, three aggregations are required to fulfill the average transaction amount requirement (1/2/4 weeks); three aggregations are required to fulfill the max transaction amount requirement (1/2/4 weeks); three aggregations are required to fulfill the average amount spent per day requirement (1/2/4 weeks); and three aggregations are required to fulfill the max amount spent per day query (1/2/4 weeks), for a total of 12 aggregations to fulfill the first four requirements. Then, one aggregation is required to fulfill the total number of transactions with the same merchant requirement, one aggregation is required to fulfill the number of retailer locations per day requirement, and one aggregation is required to fulfill the duration between retailer locations requirement, which adds up to 15 aggregations. As a result, in this example, the feature preparation time could be 15*8 ms per aggregation=120 ms feature preparation time compared against 5 ms for model inferencing time. In other words, the data preparation time could take tens/hundreds times longer than the modeling inferencing time.

FIGS.3through9depict an approach that can be executed on an information handling system that automatically identifies time consuming data transformation operators in a machine learning pipeline and generates scheduled jobs (batches) to prepare the data for machine learning model inferencing. The approach automatically generates optimized feature construction pipelines for deployment from the feature construction pipeline that is built during machine learning model training. The deployed package then selects the most cost-effective approach to process runtime data according to the runtime data features' construction complexity and latency requirements. As discussed herein, feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features.

FIG.3is an exemplary diagram depicting a model generation system that trains a machine learning model and creates a deployment package that includes optimized feature construction pipelines and machine learning model components based on the machine learning model training. System300trains a machine learning model via training stage305. During training stage305, data preparation320retrieves training data from training data/code store310and works in conjunction with model training330and pipeline builder335to iteratively train a machine learning model for predictive analysis. Data preparation320includes feature construction pipeline325that data preparation320adjusts during the machine learning model stage.

Pipeline builder335includes feature evaluator340that evaluates the “cost” (e.g., time) of feature preparation and the importance of feature latency to the machine learning model. To evaluate the cost, feature evaluator340samples the training data and probes the samples to estimate preparation cost of each of the features. Feature evaluator340adjusts a time series window (N) to understand the latency impact of the various features. In one embodiment, as feature evaluator340increases N, feature evaluator340identifies the refresh rate requirements of the various data transformation operators (DTOs) discussed below (seeFIGS.5,6, and corresponding text for further details).

Once model training330completes, pipeline builder335analyzes the data transformation operators in the feature construction pipeline325and converts hard-coded time series data transformation operators to operators fitting the need of deployment with auto-matching of time series columns. For example, an operator may use a query “SELECT CARD_ID, AVG(AMOUNT) WHERE TRANS_DATE BETWEEN ‘2020-06-01’ AND ‘2020-06-07’ FROM CARD TRANS GROUP BY CARD_ID” to compute the average amount spent per transaction in the last week as a feature in feature construction pipeline325. ‘2020-06-01’ and ‘2020-06-07’ are hard coded by data scientists because they work on a training dataset extracted from historical data and, as such, pipeline builder335needs to convert the operator to use a query “SELECT CARD_ID, AVG(AMOUNT) WHERE TRANS_DATE BETWEEN CURRENT DATE— 1 WEEK AND CURRENT DATE FROM CARD TRANS GROUP BY CARD_ID” to extract from current data. In this example, TRNAS_DATE is the time series column. Pipeline builder335detects any hard-coded date on time series columns in operators, correlates these dates to those in records to be scored, and then converts the operators to auto-matching time series operators accordingly.

Pipeline builder335creates optimized feature construction pipelines335from feature construction pipeline325. Optimized feature construction pipelines355include batch pipeline360streaming pipeline365and online pipeline370. As discussed herein, batch pipeline360prepares features that can be prepared through infrequent batch processes (e.g., daily); streaming pipeline365prepares features that must be prepared with near-real time data (e.g., prior transaction's location); and online pipeline370prepares features that can be quickly computed (e.g., customer input, seeFIG.7and corresponding text for further details).

System300also inserts key components of the trained machine learning model into deployment package350. This includes features store375, inferencing pipeline380, and scoring engine385. Features store375stores the features that are constructed during the training stage. Inferencing pipeline380is the trained pipeline for predictive analysis. And, scoring engine385is the trained scoring engine to score the results from inferencing pipeline380.

FIG.4is an exemplary flowchart showing steps taken to generate a deployment package to send to a runtime system.FIG.4processing commences at400whereupon, at step410, the process constructs transformation graphs415from data transformation code (SQL/Pandas/Data Refinery) in training data/code store310. Referring toFIG.6, the process constructs transformation graph415from the training data code.

At step420, the process estimates execution times of individual feature preparation by sampling training data310and probing the execution result times (seeFIG.6and corresponding text for further details).

At predefined process430, the process evaluates latency requirements of the data transformation operators and assigns data transformation operators to a specific optimized pipeline (seeFIG.5and corresponding text for processing details). For example, the process may categorize the different batch cycles based on pre-defined threshold times.

At step440, the process converts hard-coded time series data transformation operators to auto-matching time series data transformation operators. For example, as discussed above, when an operator uses a query “SELECT CARD_ID, AVG(AMOUNT) WHERE TRANS_DATE BETWEEN ‘2020-06-01’ AND ‘2020-06-07’ FROM CARD TRANS GROUP BY CARD_ID,” where TRNAS_DATE is the time series column, pipeline builder335converts the query to “SELECT CARD_ID, AVG(AMOUNT) WHERE TRANS_DATE BETWEEN CURRENT DATE— 1 WEEK AND CURRENT DATE FROM CARD TRANS GROUP BY CARD_ID”.

At step450, the process creates optimized feature construction pipelines355that include batch pipeline360, streaming pipeline365, and online pipeline370based on the latency impact evaluation. At step460, the process adds optimized feature construction pipelines355and machine learning model components (features store375, inferencing pipeline380, and scoring engine385) into deployment package350. At step470, the process deploys deployment package350to runtime system390andFIG.4processing thereafter ends at495.

FIG.5is an exemplary flowchart showing steps taken to evaluate latency impacts of feature sets.FIG.5processing commences at500whereupon, at step510, the process excludes transactions that happened in the last N minutes. For example, a feature “the average amount spent per transaction in the last week” is constructed with the transaction data between ‘2020-06-01 00:00:00’ and ‘2020-06-07 23:59:59.999’. After the process excludes transactions that happened in the last 5 minutes, the feature will use the transaction data between “2020-06-01 00:00:00’ and ‘2020-06-07 23:54:59.999’.

At step520, the process trains the machine learning model with features in the remaining transactions and, at step530, the process checks the new model performance and identifies the data transformation operators that fail to meet their corresponding performance thresholds, such as by an F1 score or AUC (Area Under ROC Curve). An F-score is a measure of a test's accuracy and the F1 score is the harmonic mean of the test's precision and recall.

The process determines as to whether the data refresh rate requirements for each of the data transformation operators can be identified (decision540). For example, if the process increases N and data transformation operator X causes a drop in performance, then the process knows that data transformation operator X requires up to date data and should be assigned to the streaming pipeline or online pipeline. Continuing with the examine, if data transformation operator Y still performs at N but falls off at N+5 minutes, the process knows that data transformation operator Y should have a refresh rate less than N+5 minutes (e.g., N+4 minutes). The process continues to increase N until the process identifies drop off points for each of the data transformation operators.

If the data refresh rate requirements for each of the data transformation operators cannot be identified, then decision540branches to the ‘no’ branch which loops back to increase the amount of N minutes (step550) to exclude more transactions that happened in the last N minutes. This looping continues until the data refresh rate requirements for each of the data transformation operators can be identified, at which point decision540branches to the ‘yes’ branch exiting the loop.

At step560, the process assigns the data transformation operators with their identified data refresh rates to specific pipelines based on the performance results. For example, if a data transformation operator does not require recent data to maintain sufficient performance, the data transformation operator is assigned to a batch pipeline. Conversely, if a data transformation operator requires up-to-date data, the data transformation operator is assigned to the streaming pipeline or the online pipeline.FIG.5processing thereafter returns to the calling routine (seeFIG.4) at595.

FIG.6is an exemplary diagram of a transformation graph based on feature preparation executions. System300generates transformation graph415from training data executions that prepare the training data during training stage305(e.g., aggregations). Transformation graph415is a transformation “tree” with multiple nodes that each correspond to a particular feature preparation execution. Transformation graph415starts with node600, which corresponds to the original training data in training data/code store310. Node610branches into multiple nodes, such as node610, node670, and etc.

Node610branches to nodes620630and640, that each correspond to particular queries. For example node620corresponds to a query that retrieves customer ID and their corresponding transactions per week. Node630corresponds to a query that retrieves customer ID, merchant, and transactions per week. Node640corresponds to a query that retrieves customer ID, channel, and transactions per week.

Nodes620,630, and640further branch into other nodes. For example, node620branches to three more nodes650,660, and665. Node650corresponds to the average amount of transactions and the maximum amount of transactions in one week. Node660corresponds to an average amount of transactions at a maximum of transactions in two weeks.

System300determines that node650takes 15 ms to execute based on sampling and probing; node660takes 40 ms to execute; node665takes 18 ms to execute; and node670takes 7 ms to execute. In turn, system300joins each of the nodes via join680to create a data frame as the final feature-set695. Final feature-set695is then stored in features set375.

FIG.7is an exemplary diagram depicting a runtime system390invoking deployment package350to perform predictive analysis with runtime application700using runtime data395.

Batch pipeline360and streaming pipeline365process runtime data395at predetermined intervals (N) such as once per week, once per day, once per hour, etc. The output of batch pipeline360and streaming pipeline365are stored in feature store375(seeFIG.8and corresponding text for further details). Runtime application700uses the various optimized feature construction pipelines355, along with inferencing pipeline380and scoring engine385, to perform predictive analysis. As discussed herein, the overall amount of time to perform predictive analysis greatly decreases due to the fact that batch pipeline360and streaming pipeline365pre-prepare the constructed features requiring sufficient time such that the constructed features are ready for inferencing pipeline380when needed.

FIG.8is an exemplary diagram depicting a runtime system390pre-processing runtime data using a batch pipeline360and streaming pipeline365. Runtime data395includes customer info800and transaction info810. Batch pipeline360and streaming pipeline365process the runtime data at predefined intervals. For example batch pipeline360may process the runtime data every day, every week, every hour, etc. based on runtime system390's requirements.

Feature store375stores the results of batch pipeline360and streaming pipeline365. Features850shows an example the constructed features that are ready for inferencing pipeline380to receive and process without delay.

FIG.9is an exemplary flowchart showing steps taken by a runtime system to utilize deployment package350in a manner customized to the customer's data.FIG.9processing commences at900whereupon, at step910, the process receives deployment package910.

At step920, the process configures optimized feature construction pipelines350and creates/initializes feature store375on runtime system390. During configuration, the process determines where each pipeline should access data at inferencing time where i) online pipeline370prepares features that need to be quickly computed (e.g., customer input); ii) batch pipeline360prepares features that can be prepared through infrequent batch (e.g., daily); and iii) streaming pipeline365prepares features that require near real-time preparation (e.g., a prior transaction's location).

At step930, the process identifies batch operations that can be merged and merges the batch operations accordingly. For example, an operator to compute the average amount per transactions in the last one week may be merged with the operator to compute the average amount per transactions in the last two weeks.

At step940, the process evaluates runtime data and operations in runtime data395and, at step950, the process invokes batch pipeline360and streaming pipeline365to generate constructed features data from runtime data395. At step960, the process invokes online pipeline370and processes customer inquiries using the constructed features in features store. The process updates the batch data every N minutes and updates the streaming data as needed.FIG.9processing thereafter ends at995.

While particular embodiments of the present disclosure have been shown and described, it will be obvious to those skilled in the art that, based upon the teachings herein, that changes and modifications may be made without departing from this disclosure and its broader aspects. Therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of this disclosure. Furthermore, it is to be understood that the disclosure is solely defined by the appended claims. It will be understood by those with skill in the art that if a specific number of an introduced claim element is intended, such intent will be explicitly recited in the claim, and in the absence of such recitation no such limitation is present. For non-limiting example, as an aid to understanding, the following appended claims contain usage of the introductory phrases “at least one” and “one or more” to introduce claim elements. However, the use of such phrases should not be construed to imply that the introduction of a claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to disclosures containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an”; the same holds true for the use in the claims of definite articles.