Patent Application
20230169515
Companies, such as manufacturers, retailers, OEMs, etc., often provide warranties to consumers of products that they manufacture and/or sell. A warranty is a type of written promise or guarantee that a company or similar party makes regarding the condition of its products. The warranty may provide for repair, replacement, and/or service of a product within a specified period to the consumer should the product fail to meet promised quality or performance standards. A warranty that is provided with a product is typically intended to instill confidence in the consumer in the quality of the product as well as set expectations with respect to what can be expected if the product fails to perform as promised. Warranties may often affect a consumer's purchasing decision.
This Summary is provided to introduce a selection of concepts in simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features or combinations of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In accordance with one illustrative embodiment provided to illustrate the broader concepts, systems, and techniques described herein, a computer implemented method to determine pricing for an extended warranty for a product includes, by a warranty system, receiving a customer-specific usage-related data for a product at a customer location and generating a feature vector for the product, wherein the feature vector represents one or more features from the customer-specific usage-related data. The method also includes, by the warranty system using a trained incident prediction module, predicting a number of future incidents for the product at the customer location based on the first feature vector. The method further includes, by the warranty system, determining a price for an extended warranty for the product based on the predicted number of future incidents for the product at the customer location.
According to another illustrative embodiment provided to illustrate the broader concepts described herein, a system includes one or more non-transitory machine-readable mediums configured to store instructions and one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums. Execution of the instructions causes the one or more processors to receive a customer-specific usage-related data for a product at a customer location and generate a feature vector for the product, wherein the feature vector represents one or more features from the customer-specific usage-related data. Execution of the instructions also causes the one or more processors to predict, using a trained incident prediction module, a number of future incidents for the product at the customer location based on the first feature vector. Execution of the instructions further causes the one or more processors to determine a price for an extended warranty for the product based on the predicted number of future incidents for the product at the customer location.
In some embodiments, the trained incident prediction module is trained using a training dataset generated from a corpus of historical product utilization and environment data.
According to another illustrative embodiment provided to illustrate the broader concepts described herein, a non-transitory, computer-readable storage medium has encoded thereon instructions that, when executed by one or more processors, causes a process to be carried out. The process includes receiving a customer-specific usage-related data for a product at a customer location and generating a feature vector for the product, wherein the feature vector represents one or more features from the customer-specific usage-related data. The process also includes predicting, using a trained incident prediction module, a number of future incidents for the product at the customer location based on the first feature vector, wherein the trained incident prediction module is trained using a training dataset generated from a corpus of historical product utilization and environment data. The process further includes determining a price for an extended warranty for the product based on the predicted number of future incidents for the product at the customer location.
In some embodiments, the trained incident prediction module includes a dense neural network (DNN). In one aspect, the DNN of the trained incident prediction module functions as a regression-based model.
In some embodiments, the one or more features includes a feature regarding utilization of the product by a customer.
In some embodiments, the one or more features includes a feature regarding a location at which the product is used.
In some embodiments, receiving the customer-specific usage-related data includes receiving at least some of the customer-specific usage-related data from the product.
In some embodiments, receiving the customer-specific usage-related data includes receiving at least some of the customer-specific usage-related data from a device at the customer location.
The foregoing and other objects, features and advantages will be apparent from the following more particular description of the embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments.
Warranties usually have exceptions that limit the conditions in which company, such as a manufacturer, will be obligated to rectify a problem. For example, many warranties for products such as common household items only cover a product for up to one year from the date of purchase and usually only if the product in question contains problems resulting from defective parts or workmanship. As a result of these limited manufacturer warranties, many vendors offer extended warranties. These extended warranties are essentially insurance policies for products that consumers pay for upfront. Extended warranty coverage may last for a specified duration (e.g., a number of years) above and beyond the manufacturer's warranty and may be more lenient in terms of the limited terms and conditions. Such an extended warranty for a product may be offered to customers during the purchase of the product or subsequent to the purchase of the product. However, the pricing for such extended warranties are often based on static and heuristic-based rules. For example, many extended warranties use a one size fits all approach which uses a uniform price for an extended warranty for a product based only on the age of the product.
It is appreciated herein that such trivial pricing approaches fail to consider other factors which can significantly impact the operation of the product and contribute to efficiency, defects and eventual lifespan of the product and its parts. Examples of such factors may include product usage behavior of the customer (e.g., gently, roughly, frequently, less frequently, constantly, heavy utilization, light utilization, indoor, outdoor, covered, uncovered, etc.), and customer location environmental conditions (e.g., ambient temperature, humidity, air pressure, among others), to provide a couple examples. Typically, product utilization or usage varies from one customer to another. For example, while one customer can use a product gently (e.g., carefully), another customer can use the same product roughly or in a manner that is not so gentle. In the case of an information technology (IT) product, such as a server device or a storage device, product utilization can also include numbers of Install, Move, Add, Change (IMAC), deployments, system alerts, error logs, etc. Similarly, the environmental conditions can also vary from one customer location to another. In short, the manner and the conditions in which a customer uses a product can affect the number of issues and/or defects experienced by the product as well as the lifespan of the product. The number of issues and/or defects experienced by the product can influence the costs that can be incurred by a vendor (e.g., a company) in supporting and maintaining the product under an extended warranty.
A potential application of machine learning (ML) is for predicting the number of future incidents/defects (e.g., sometimes referred to herein more simply as “future incidents”) that may be experienced by a product. The predicted number of future incidents for a product is a good indicator of an estimate of the product maintenance costs that can be expected to be incurred by a company (e.g., a company manufacturing and/or selling the product). This, in turn, may allow the company to determine an accurate and fair price for an extended warranty for the product. A fair price for an extended warranty may be important to a customer's decision to purchase as well as ultimate satisfaction with the product. Higher product satisfaction can result in improved product and brand value for the company.
To this end, certain embodiments of the concepts, techniques, and structures disclosed herein are directed to predicting a number of future incidents that can be expected for a product based on customer-specific usage-related data for the product. Customer-specific usage-related data for a product may include customer-specific product utilization data (e.g., product utilization metrics) and customer-specific location environmental data (e.g., environmental condition metrics). In some embodiments, a learning model (e.g., a regression-based deep learning model) may be trained using machine learning techniques (including neural networks) to predict a number of future incidents that can be expected for a product that is being used by a customer. For example, to train the model, historical product utilization and environment data can be collected. As alluded to above, historical product utilization and environmental data is a good indicator for predicting the number of future incidents for products (e.g., predicting the number of future incidents that will be encountered by the product) owned and/or used by a customer. The product utilization and environment data may be collected from variety of sources, as will be further described below at least in conjunction with
Once the historical product utilization and environment metrics data is collected, the variables or parameters (also called features) that are correlated to or influence (or contribute to) the number of incidents encountered by a product can be determined (e.g., identified) from the corpus of historical product utilization and environment data. These relevant features can then be used to generate a dataset (e.g., a training dataset) that can be used to train the model. A feature (also known as an independent variable in machine learning) is an attribute that is useful or meaningful to the problem that is being modeled (i.e., predicting the number of incidents for a product). For example, in the case of electronic devices, the relevant features may include information regarding the customer who is utilizing the product, customer location, the product itself (e.g., product configuration), age (i.e., age of the product), product utilization by the customer (e.g., manner in which the product is being utilized, the number of IMAC, number of deployments, etc.), ambient temperature, ambient humidity, ambient air pressure, number of product system alerts, and the number of product error logs or reports. As another example, in the case of home appliances (e.g., a refrigerator), the relevant features may include information regarding the customer who is utilizing the refrigerator, customer location, the product itself (e.g., refrigerator model), product utilization by the customer (e.g., manner in which the refrigerator is being utilized such as the number of times the doors are opened/closed, the refrigerator/freezer temperature setting(s), number and frequency of change to the temperature setting(s), the frequency in which the motor (e.g., compressor) runs, etc.), ambient temperature, and ambient humidity. The above are only two examples and other types of products are envisioned. In any case, being able to accurately estimate a number of future incidents for a product used by a customer allows a company to determine an accurate and fair price for an extended warranty for the product to offer to the customer.
Although certain embodiments and/or examples are described herein in the context of electronic devices, it will be appreciated in light of this disclosure that such embodiments and/or examples are not restricted as such, but are applicable to any type of product that is manufactured and sold, in the general sense. Numerous variations and configurations will be apparent in light of this disclosure.
Referring now to the figures,
The various components of architecture 100, including the components of warranty system 102, may be communicably coupled to one another via one or more networks (not shown). The network may correspond to one or more wired or wireless computer networks including, but not limited to, local area networks (LANs), wide area networks (WANs), personal area networks (PANs), metropolitan area networks (MANs), storage area networks (SANs), virtual private networks (VPNs), wireless local-area networks (WLAN), primary public networks, primary private networks, Wi-Fi (i.e., 802.11) networks, other types of networks, or some combination of the above.
Telemetry collection system 106 is operable to collect or otherwise obtain telemetry data form the company's products. For example, the company's products may include smart clients (e.g., Internet of Things (IoT) devices) that periodically or continuously capture product utilization and environment metrics and send or otherwise provide the captured telemetry data to telemetry collection system 106. Non-limiting examples of types of product utilization and environment telemetry data that can be provided include error logs (e.g., number and/or records of the errors encountered by the product during use), system alerts (e.g., number and/or records of the special circumstances encountered during product operation), On/Off statistics (e.g., number of times the product is turned On/Off), configuration changes (e.g., number of changes to the configuration of the product), network changes (e.g., number of changes to the network and/or networking capabilities of the product), IMAC (e.g., number of installs, moves, adds, and/or changes experienced by the product), resource utilization (e.g., resources utilized by the product), ambient temperature, ambient humidity, ambient air pressure, and vibration (e.g., product and/or parts/components vibration statistics), among others. The company's products at the customer locations may provide the telemetry data while utilized by the customer (e.g., during the product's utilization by the customer). It will be appreciated that the types of metrics data provided above are merely illustrative and that the types of metrics data may vary depending on the product and/or type of product. Also, some products may provide other types of metrics data in addition to some or all of those illustrated above.
Product data repository 104 stores or otherwise records the historical product utilization and environment data. The historical product utilization and environment data may include information regarding the products sold or otherwise provided by the company (e.g., customers who are using the products, parts included in the products, configuration of the products, etc.), the manner in which these products are used and/or have been used by the customers (e.g., customer-specific product utilization data), the environmental conditions in which these products are used and/or have been used by the customers (e.g., customer-specific location environmental data), and the number of incidents (e.g., issues and/or defects) experienced by these products, for example, during use by the customers. For example, as can be seen in
In some embodiments, the historical product utilization and environment data may be stored in a tabular format. In the table, the structured columns represent the features (also called variables) and each row represents an observation or instance (e.g., a product at a customer location). Thus, each column in the table shows a different feature of the instance. In some embodiments, product data repository 104 can perform preliminary operations with the collected historical product utilization and environment data (e.g., customer-specific product utilization and environment information regarding the past products sold by the company) to generate the training dataset. For example, the preliminary operations may include null data handling (e.g., the handling of missing values in the table). According to one embodiment, null or missing values in a column (a feature) may be replaced by a mode or median value of the values in that column. According to alternative embodiments, observations in the table with null or missing values in a column may be removed from the table.
The preliminary operations may also include feature selection and/or data engineering to determine (e.g., identify) the relevant features from the historical product utilization and environment data. The relevant features are the features that are more correlated with the thing being predicted by the trained model (e.g., number of incidents encountered by the product). A variety of feature engineering techniques, such as exploratory data analysis (EDA) and/or bivariate data analysis with multivariate-variate plots and/or correlation heatmaps and diagrams, among others, may be used to determine the relevant features. Such feature engineering may be performed to reduce the dimension and complexity of the trained model, hence improving its accuracy and performance.
The preliminary operations may also include data preprocessing to place the data (information) in the table into a format that is suitable for training a model. For example, since machine learning deals with numerical values, textual categorical values (i.e., free text) in the columns (e.g., customer, product, utilization, customer location, etc.) can be converted (i.e., encoded) into numerical values. According to one embodiment, the textual categorical values may be encoded using label encoding. According to alternative embodiments, the textual categorical values may be encoded using one-hot encoding.
As shown in
In data structure 200, each row may represent a training sample (i.e., an instance of a training sample) in the training dataset, and each column may show a different relevant feature of the training sample. Each training sample may correspond to a past product that was sold or otherwise provided to a customer by the company. As can be seen in
Referring again to
In brief, the DNN includes an input layer for all input variables such as customer, product, ae (e.g., age of the product), utilization, temperature, customer location, system alerts, error logs, etc., multiple hidden layers for feature extraction, and an output layer. Each layer may be comprised of a number of nodes or units embodying an artificial neuron (or more simply a “neuron”). As a DNN, each neuron in a layer receives an input from all the neurons in the preceding layer. In other words, every neuron in each layer is connected to every neuron in the preceding layer and the succeeding layer. As a regression model, the output layer is comprised of a single neuron, which outputs a numerical value representing the number of incidents.
In more detail, and as shown in
Although
Each neuron in hidden layers 304 and the neuron in output layer 306 may be associated with an activation function. For example, according to one embodiment, the activation function for the neurons in hidden layers 304 may be a rectified linear unit (ReLU) activation function. As DNN 300 is to function as a regression model, the neuron in output layer 306 will not contain an activation function.
Since this is a dense neural network, as can be seen in
During a first pass (epoch) in the training phase, the weight and bias values may be set randomly by the neural network. For example, according to one embodiment, the weight and bias values may all be set to 1 (or 0). Each neuron may then perform a linear calculation by combining the multiplication of each input variables (x1, x2, . . . ) with their weight factors and then adding the bias of the neuron. The equation for this calculation may be as follows:
ws1=x1·w1+x2·w2+ ⋅ ⋅ ⋅ +b1,
where ws1 is the weighted sum of the neuron1, x1, x2, etc. are the input values to the model, w1, w2, etc. are the weight values applied to the connections to the neuron1, and b1 is the bias value of neuron1. This weighted sum is input to an activation function (e.g., ReLU) to compute the value of the activation function. Similarly, the weighted sum and activation function values of all the other neurons in a layer are calculated. These values are then fed to the neurons of the succeeding (next) layer. The same process is repeated in the succeeding layer neurons until the values are fed to the neuron of output layer 306. Here, the weighted sum may also be calculated and compared to the actual target value. Based on the difference, a loss value is calculated. The loss value indicates the extent to which the model is trained (i.e., how well the model is trained). This pass through the neural network is a forward propagation, which calculates the error and drives a backpropagation through the network to minimize the loss or error at each neuron of the network. Considering the error/loss is generated by all the neurons in the network, backpropagation goes through each layer from back to forward and attempts to minimize the loss using, for example, a gradient descent-based optimization mechanism or some other optimization method. Since the neural network is used as a regressor, mean squared error may be used as the loss function and adaptive movement estimation (Adam) used as the optimization algorithm.
The result of this backpropagation is used to adjust (update) the weight and bias values at each connection and neuron level to reduce the error/loss. An epoch (one pass of the entire training dataset) is completed once all the observations of the training data are passed through the neural network. Another forward propagation (e.g., epoch 2) may then be initiated with the adjusted weight and bias values and the same process of forward and backpropagation may be repeated in the subsequent epochs. Note that a higher loss value means the model is not sufficiently trained. In this case, hyperparameter tuning may be performed. Hyperparameter tuning may include, for example, changing the loss function, changing optimizer algorithm, and/or changing the neural network architecture by adding more hidden layers. Additionally or alternatively, the number of epochs can be also increased to further train the model. In any case, once the loss is reduced to a very small number (ideally close to zero (0)), the neural network is sufficiently trained for prediction.
For example, DNN 300 can be built by first creating a shell model and then adding desired number of individual layers to the shell model. For each layer, the number of neurons to include in the layer can be specified along with the type of activation function to use and any kernel parameter settings. Once DNN 300 is built, a loss function (e.g., mean squared error), an optimizer algorithm (e.g., Adam), and validation metrics (e.g., mean squared error (mse); mean absolute error (mae)) can be specified for training, validating, and testing DNN 300.
DNN 300 can then be trained by passing the portion of the training dataset (e.g., 70% of the training dataset) designated for training and specifying a number of epochs. An epoch (one pass of the entire training dataset) is completed once all the observations of the training data are passed through DNN 300. DNN 300 can be validated once DNN 300 completes the specified number of epochs. For example, DNN 300 can process the training dataset and the loss/error value can be calculated and used to assess the performance of DNN 300. The loss value indicates how well DNN 300 is trained. Note that a higher loss value means DNN 300 is not sufficiently trained. In this case, hyperparameter tuning may be performed. Hyperparameter tuning may include, for example, changing the loss function, changing optimizer algorithm, and/or changing the neural network architecture by adding more hidden layers. Additionally or alternatively, the number of epochs can be also increased to further train DNN 300. In any case, once the loss is reduced to a very small number (ideally close to 0), DNN 300 is sufficiently trained for prediction. Prediction of the model (e.g., DNN 300) can be achieved by passing the independent variables of test data (i.e., for comparing train vs. test) or the real values that need to be predicted to predict the estimated number of future incidents (i.e., target variable).
Once sufficiently trained, as illustrated in
Referring again to
With reference to process 500 of
At 504, the customer-specific usage-related data for the product is retrieved. The customer-specific usage-related data for that product (i.e., the product at the customer location), which can include customer location specific general environment/weather data, can be retrieved from product data repository 104.
At 506, a number of future incidents for the product at the customer location is predicted based on the customer-specific usage-related data for that product. The prediction of the number of future incidents for the product can be made using incident prediction module 108. For example, a feature vector representing some or all the customer-specific usage-related data for that product can be generated and input to incident prediction module 108 which outputs a predicted number of future incidents for that product.
At 508, a price for an extended warranty for the product at the customer location id determined based on the predicted number of future incidents for the product at the customer location. The price for the extended warranty can be determined using warranty pricing module 110. For example, the price for the extended warranty can be determined by applying one or more pricing rules which consider the predicted number of future incidents for the product as one factor, determinant, or limitation in determining a price. As one example, the determined price for the extended warranty for the product at the customer location may then be provided to, for example, the company's warranty team for consideration in offering to the customer.
Non-volatile memory 606 may include: one or more hard disk drives (HDDs) or other magnetic or optical storage media; one or more solid state drives (SSDs), such as a flash drive or other solid-state storage media; one or more hybrid magnetic and solid-state drives; and/or one or more virtual storage volumes, such as a cloud storage, or a combination of such physical storage volumes and virtual storage volumes or arrays thereof.
User interface 608 may include a graphical user interface (GUI) 614 (e.g., a touchscreen, a display, etc.) and one or more input/output (I/O) devices 616 (e.g., a mouse, a keyboard, a microphone, one or more speakers, one or more cameras, one or more biometric scanners, one or more environmental sensors, and one or more accelerometers, etc.).
Non-volatile memory 606 stores an operating system 618, one or more applications 620, and data 622 such that, for example, computer instructions of operating system 618 and/or applications 620 are executed by processor(s) 602 out of volatile memory 604. In one example, computer instructions of operating system 618 and/or applications 620 are executed by processor(s) 602 out of volatile memory 604 to perform all or part of the processes described herein (e.g., processes illustrated and described in reference to
The illustrated computing device 600 is shown merely as an illustrative client device or server and may be implemented by any computing or processing environment with any type of machine or set of machines that may have suitable hardware and/or software capable of operating as described herein.
Processor(s) 602 may be implemented by one or more programmable processors to execute one or more executable instructions, such as a computer program, to perform the functions of the system. As used herein, the term “processor” describes circuitry that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the circuitry or soft coded by way of instructions held in a memory device and executed by the circuitry. A processor may perform the function, operation, or sequence of operations using digital values and/or using analog signals.
In some embodiments, the processor can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors (DSPs), graphics processing units (GPUs), microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory.
Processor 602 may be analog, digital or mixed signal. In some embodiments, processor 602 may be one or more physical processors, or one or more virtual (e.g., remotely located or cloud computing environment) processors. A processor including multiple processor cores and/or multiple processors may provide functionality for parallel, simultaneous execution of instructions or for parallel, simultaneous execution of one instruction on more than one piece of data.
Communications interfaces 610 may include one or more interfaces to enable computing device 600 to access a computer network such as a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or the Internet through a variety of wired and/or wireless connections, including cellular connections.
In described embodiments, computing device 600 may execute an application on behalf of a user of a client device. For example, computing device 600 may execute one or more virtual machines managed by a hypervisor. Each virtual machine may provide an execution session within which applications execute on behalf of a user or a client device, such as a hosted desktop session. Computing device 600 may also execute a terminal services session to provide a hosted desktop environment. Computing device 600 may provide access to a remote computing environment including one or more applications, one or more desktop applications, and one or more desktop sessions in which one or more applications may execute.
In the foregoing detailed description, various features of embodiments are grouped together for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited. Rather, inventive aspects may lie in less than all features of each disclosed embodiment.
As will be further appreciated in light of this disclosure, with respect to the processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in differing order. Additionally or alternatively, two or more operations may be performed at the same time or otherwise in an overlapping contemporaneous fashion. Furthermore, the outlined actions and operations are only provided as examples, and some of the actions and operations may be optional, combined into fewer actions and operations, or expanded into additional actions and operations without detracting from the essence of the disclosed embodiments.
Elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above. Other embodiments not specifically described herein are also within the scope of the following claims.
Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the claimed subject matter. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”
As used in this application, the words “exemplary” and “illustrative” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “exemplary” and “illustrative” is intended to present concepts in a concrete fashion.
In the description of the various embodiments, reference is made to the accompanying drawings identified above and which form a part hereof, and in which is shown by way of illustration various embodiments in which aspects of the concepts described herein may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made without departing from the scope of the concepts described herein. It should thus be understood that various aspects of the concepts described herein may be implemented in embodiments other than those specifically described herein. It should also be appreciated that the concepts described herein are capable of being practiced or being carried out in ways which are different than those specifically described herein.
Terms used in the present disclosure and in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including, but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes, but is not limited to,” etc.).
Additionally, if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
In addition, even if a specific number of an introduced claim recitation is explicitly recited, such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two widgets,” without other modifiers, means at least two widgets, or two or more widgets). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” is used, in general such a construction is intended to include A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together, etc.
All examples and conditional language recited in the present disclosure are intended for pedagogical examples to aid the reader in understanding the present disclosure, and are to be construed as being without limitation to such specifically recited examples and conditions. Although illustrative embodiments of the present disclosure have been described in detail, various changes, substitutions, and alterations could be made hereto without departing from the scope of the present disclosure. Accordingly, it is intended that the scope of the present disclosure be limited not by this detailed description, but rather by the claims appended hereto.
