FORECASTING DEMAND FOR GROUPS OF ITEMS, SUCH AS MOBILE PHONES

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First Claim
1. A computerized method of improving accuracy and reducing processing time in forecasting demand for a set of items, comprising:
 obtaining, by a computing device, parameters for a set of items;
wherein the obtained parameters include volume information, price information, and perceived value information for each item of the set of items;
determining, by the computing device, substitution factors for the obtained parameters associated with the items within the set of items,wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices;
generating, by the computing device, a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and
determining, by the computing device, a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.
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Abstract
In some embodiments, the systems and methods obtain parameters for a set of mobile devices (or, other items), such as volume information, price information, and perceived value information for each mobile device within the set of mobile devices. The set of mobile devices may be a tier of substitute mobile devices that each affect consumer demand of other mobile devices within the set of mobile devices. The systems and methods may then determine substitution factors for the obtained parameters associated with the set of mobile devices, generate a substitution forecast for the set of mobile devices that is based on the substitution factors determined for the obtained parameters associated with the mobile devices, and determine a consensus forecast for the set of mobile devices that adjusts a baseline forecast for the demand of the set of mobile devices using the substitution forecast.
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20 Claims
 1. A computerized method of improving accuracy and reducing processing time in forecasting demand for a set of items, comprising:
obtaining, by a computing device, parameters for a set of items; wherein the obtained parameters include volume information, price information, and perceived value information for each item of the set of items; determining, by the computing device, substitution factors for the obtained parameters associated with the items within the set of items, wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices; generating, by the computing device, a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and determining, by the computing device, a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
 10. At least one nontransitory computerreadable storage medium, carrying instructions, that when executed by at least one data processor cause the data processor to perform a method that improves accuracy and processing time in forecasting demand for a set of items, the method comprising:
obtaining parameters for a set of items; wherein the obtained parameters include volume information, price information, and perceived value information for each item of the set of items; determining substitution factors for the obtained parameters associated with the items within the set of items, wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices; generating a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and determining a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.  View Dependent Claims (11, 12, 13, 14, 15, 16)
 17. A system for improving accuracy and reducing processing time in forecasting demand of a set of items, the system comprising:
at least one hardware computer, wherein the computer is configured to execute software modules, including; a substitution set module that obtains parameters for a set of items, wherein the obtained parameters include volume information, price information, and perceived value information for each item in the set of items; one or more matrix modules that determines substitution factors for the obtained parameters associated with the items within the set of items, wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices; a substitution forecast module that generates a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and a consensus forecast module that determines a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.  View Dependent Claims (18, 19, 20)
1 Specification
This application is a continuation of U.S. patent application Ser. No. 15/344,408 filed Nov. 4, 2016, entitled FORECASTING DEMAND FOR GROUPS OF SUBSTITUTE ITEMS, SUCH AS MOBILE PHONES; which claims priority to U.S. Provisional Patent Application No. 62/252,398, filed on Nov. 6, 2015, entitled DEMAND AND SUBSTITUTION MODEL; and U.S. Provisional Patent Application No. 62/316,450 filed on Mar. 31, 2016, entitled FORECASTING INVENTORY FOR GROUPS OF SUBSTITUTE ITEMS, SUCH AS MOBILE PHONES; all of which are incorporated by reference in their entireties for all purposes.
Companies rely on practical and effective demand planning processes to create reliable forecasts associated with consumer demand for their products, such as mobile phones. Ideally, proper demand planning improves the accuracy of inventory and revenue forecasts, aligns inventory levels with peaks and troughs in demand, and helps to maximize the profitability of products and channels. Companies, therefore, strive to improve upon their current demand planning processes.
Embodiments of the disclosed technology will be described and explained through the use of the accompanying drawings.
The drawings have not necessarily been drawn to scale. Similarly, some components and/or operations may be separated into different blocks or combined into a single block for the purposes of discussion of some of the embodiments of the present technology. Moreover, while the technology is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the technology to the particular embodiments described. On the contrary, the technology is intended to cover all modifications, equivalents, and alternatives falling within the scope of the technology as defined by the appended claims.
Systems and methods for demand planning, including systems and methods that utilize demand and substitution models, processes, and/or algorithms, are described. For example, the systems and methods, via automated tools that utilize the models, may consider volume information, pricing information, and/or perceived value information as substitution parameters when modelling and/or automating cannibalization and stock out substitution, among other things, during demand planning for mobile devices, accessories, and other items.
In some embodiments, the systems and methods obtain parameters for a set of mobile devices, such as volume information, price information, and perceived value information for each mobile device within the set of mobile devices. The set of mobile devices may be a tier of substitute mobile devices that each affect consumer demand of other mobile devices within the set of mobile devices.
The systems and methods may then determine substitution factors for the obtained parameters associated with the set of mobile devices, generate a substitution forecast for the set of mobile devices that is based on the substitution factors determined for the obtained parameters associated with the mobile devices, and determine a consensus forecast for the set of mobile devices that adjusts a baseline forecast for the demand of the set of mobile devices using the substitution forecast.
Thus, in some embodiments, the systems and methods described herein may increase accuracy and functionality of a demand planning process, which may further standardize forecasting of item inventories and consumer demand with increased scale and flexibility, among other benefits.
The systems and methods, therefore, may determine the quantifiable parameters that have an impact on demand and customers'"'"' substitution preferences, build or utilize an automated tool that leverages advanced theoretical demand planning concepts while maintaining simple definitions and parameters that are practicable for implementation, in order to maximize and/or improve forecast accuracy and reduce time required to achieve an optimal or beneficial forecast, among other benefits.
In other words, the systems and methods described herein enhance conventional inventory and demand forecasting methods by quantifying consumer habits, preferences, prejudices and buying patterns (e.g., biases associated with prices and perceived values of similar devices) into specific algorithms, and then applying the algorithms to typical forecasting methods. These applications enable an entity to dynamically generate targeted demand forecasts for groups of substitute items, effectively changing and updating the processes of forecasting the consumer demand of physical items, such as mobile devices.
Various embodiments of the system will now be described. The following description provides specific details for a thorough understanding and an enabling description of these embodiments. One skilled in the art will understand, however, that the system may be practiced without many of these details. Additionally, some wellknown structures or functions may not be shown or described in detail, so as to avoid unnecessarily obscuring the relevant description of the various embodiments. The terminology used in the description presented below is intended to be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific embodiments of the invention.
Suitable Computing Environments
The product inventory server 110 may include databases that contain information for each of the devices, accessories, and other items, such as type information, make information, size information, color information, item specifications, fit information, form information, function information, and so on. Further, the databases may include information associated with current or projected inventory of items (e.g., volume information), information associated with current or projected retail pricing of items (e.g., price information), information associated with current or projected value of the items (e.g., perceived value or ranking information), and so on.
A user, such as an employee of a carrier associated with the product inventory server 110, may access the various databases via graphical user interfaces (GUIs) presented by the server 110 and displayed by a user device 120, in order to add information, modify or adjust information, and so on. The user device 130 may access the server 110 over a network 120, such as a telecommunications network, wireless network, storage area network, and so on. Further, a promotion server 140 may provide additional information associated with the inventory of items, such as information identifying current or future promotions for the items.
In some embodiments, a demand forecasting system 150 may access, via the network 120, the information stored at the product inventory server 110, in order to perform various demand planning processes, such as demand forecasting for the various substitutable items or products. Further details regarding the processes and components of the demand forecasting system 150 are described herein.
Aspects of the invention can also be practiced in distributed computing environments, where tasks or modules are performed by remote processing devices, which are linked through a communications network, such as a Local Area Network (“LAN”), Wide Area Network (“WAN”), or the Internet. In a distributed computing environment, program modules or subroutines may be located in both local and remote memory storage devices. Aspects of the invention described herein may be stored or distributed on tangible, nontransitory computerreadable media, including magnetic and optically readable and removable computer discs, stored in firmware in chips (e.g., EEPROM chips). Alternatively, aspects of the invention may be distributed electronically over the Internet or over other networks (including wireless networks). Those skilled in the relevant art will recognize that portions of the invention may reside on a server computer, while corresponding portions reside on a client computer. Data structures and transmission of data particular to aspects of the invention are also encompassed within the scope of the invention.
Examples of Determining Substitution Forecasts for Groups of Physical Items
As described herein, in some embodiments, the demand forecasting system 150 determines substitute forecasts for groups, sets, or tiers of substitutable items, and generates final or consensus demand forecasts for the items based on the determined substitute forecasts. In some cases, a set of substitutable items refers to a group of physical items whose sales affect one another. For example, a price change or stock out of any item in a substitution set will result in a change in a quantity sold of the remaining items in the set.
The substitution set module 210 is configured and/or programmed to obtain parameters for a set of items, such as a set of mobile devices, and provide the obtained information to various matrix modules. For example, the substitution set module 210 may obtain volume information, price information, perceived value information, and/or other information (fit, form, function information, such as color or appearance information) for each mobile device of the set of mobile device, such as a set of mobile devices that includes multiple substitutable mobile devices within the set of devices.
The diagram illustrates example information that may be obtained for each of the devices, including volume information (e.g., “3000” for device 1 and “14000” for the device 4), price information (e.g., “$89” for device 2, and “$229” for device 6), perceived value information (e.g., “6” for device 3, and “8” for device 6), and baseline forecast information (e.g., at this point, is the same as the volume information).
The perceived value information may include or refer to a rating system from 110 (with 10 being the best) that indicates the perceived value of an item based on form, fit, functionality, and brand with respect to other items in a substitutable set of items. Various factors may influence the perceived value ranking. For example, for mobile devices, the perceived value of a device may be based on the brand, recent marketing/advertising campaign, operating system, design, screen size, RAM/ROM, camera megapixels, screen resolution, processor, unique features/newest technology, and so on.
One or more matrix modules receive the parameter information from the substitution set module 210 and determine substitution factors for the obtained parameters associated with the mobile devices within the set of the mobile devices. For example, volume information, price information, and perceived or real value information (e.g., “form,” “fit,” and “function”) may capture or represent a customer substitution effect, and may be part of a combined substitution factor based on different weights assigned to the different types of information.
In some embodiments, the volume matrix module 220 is configured and/or programmed to generate a volume based substitution factor matrix that is based on the volume information for the set of mobile devices. In other words, the volume matrix module 220 may utilize the baseline forecast information at the channel/item level (e.g., for each of the mobile devices) to calculate or otherwise determine a volume based substitution factor for each item in a substitution set.
As shown in diagram 400 of
volume based substitution factor=(volume amount of the substitute mobile device)/(total volume amount of set of mobile devices−volume amount of device 4).
For example, diagram 400 depicts device 1 to have a volume based substitution factor of 0.065, device 7 to have a volume based substitution factor of 0.196, and device 4 to have a volume based substitution factor of 0 (a device will not cannibalize its own volume).
In some embodiments, the price matrix module 230 is configured and/or programmed to generate a price based substitution factor matrix that is based on the price information for the set of mobile devices. In other words, the price matrix module 230 may utilize timephased customer facing price information at the channel/item level (e.g., for each of the mobile devices) to calculate or otherwise determine a price based substitution factor for each item in a substitution set.
As shown in diagram 410 of
price based substitution factor=(1/abs(price of device 4−price of substitute mobile device)/(1/(price of device 4)*0.05), where the multiplier 0.05 (and other similar multipliers described herein is a flexible parameter set to group close prices to a common factor or value within the calculation. For example, diagram 410 depicts device 2 to have a price based substitution factor of 0.562, device 5 to have a price based substitution factor of 0.112, and device 7 to have a price based substitution factor of 0.03 (the greater the difference in prices, the smaller the cannibalization factor between devices, and vice versa).
In some embodiments, the value matrix module 240 is configured and/or programmed to generate a perceived value based substitution factor matrix that is based on the perceived value information for the set of mobile devices. In other words, the value matrix module 240 may utilize the timephased perceived value (e.g., represented as comparative rankings, such as 110) at the channel/item level (e.g., for each of the mobile devices) to calculate or otherwise determine the perceived value substitution factor for each item in a substitution set.
As shown in diagram 420 of
perceived value based substitution factor=(1/abs(perceived value of device 4−perceived value of substitute mobile device)/(1/(perceived value of device 4)*0.05).
For example, diagram 420 depicts device 3 to have a perceived value based substitution factor of 0.199, and depicts device 5 to have a perceived value based substitution factor of 0.397 (e.g., where the smaller the difference in perceived value, the larger the cannibalization factor between devices).
In some embodiments, the combined matrix module 250 is configured and/or programmed to generate a combined substitution factor matrix that is based on a weighted combination of values within the volume based substitution factor matrix, the price based substitution factor matrix, and the perceived value based substitution factor matrix. In other words, the combined matrix module 250 may apply different weights to the different factors to calculate or otherwise determine a combined or overall weighted substitution factor for each of the devices in the substitution set, which is used to determine substitution forecasts for devices.
As shown in diagram 500 of
Combined weighted substitution factor=(weight_{volume})(volume based substitution factor)+(weight_{price})(price based substitution factor)+(weight_{pv})(perceived value based substitution factor).
For example, given weight_{volume}=0.5, weight_{price}=0.3, and weight_{pv}=0.2, diagram 500 depicts device 1 to have a combined weighed substitution factor of 0.074, and depicts device 6 to have a combined weighed substitution factor of 0.183. Of course, other weights may be used to vary the different effects of the factors on the combined factor.
Thus, the various matrix modules determine substitution factors for different parameters associated with the substitution set of devices (or other items), including a combined (weighted) factor to be utilized when determining a substitution forecast for each device of the substitute set of devices.
Referring back to
The substitution forecast module 260, in some embodiments, receives information identifying the amount of cannibalized devices from the cannibalization module 265, which is configured and/or programmed to determine an amount of cannibalized units of the substitute set of devices (e.g., devices 17). The substitution forecast module 260, therefore, determines the substitution forecast for the set of mobile devices as a function of the amount of cannibalized units multiplied by a substitution forecast value obtained from the combined substitution factor matrix for each mobile device of the set of mobile devices.
The cannibalization module 265 may identify a percentage or level of increased demand of an item (e.g., because of price decrease or promotion of the item), and/or percentage or level of decreased demand of an item (e.g., because of price increase of the item), which would be cannibalized from the demand of other items in the substitution set (with the remaining percentage of items leading to an increase in the tier volume of overall items).
The cannibalization module 265, in some embodiments, generates a substitution coefficient that determines a cannibalization factor across all devices or accessories in a substitution set. The substitution coefficient may be based on various factors, including the perceived value substitution factor, and may be generated in a variety of ways.
For example, when an item first launches (e.g., an affordable tier device), a tier trend was 50,000 units. Post launch, the tier trend was 54,000 units and the device trend was 10,000 units, and the substitution coefficient as may be determined as 0.6=[1−(4000/10000)]. Thus, the substitution coefficient may be equal to [1−(Tier incremental volume)/item trend volume)].
As another example, the substitution coefficient may be based on comparisons between similar or substitutable items. For example, if an item in a tier has many similar items in terms of “price” and “perceived value,” then it follows that the item will have a substitution factor closer to 1, but if an item in a tier has few similar items in terms of “price” and “perceived value,” then it follows that the item will have a substitution factor closer to 0.
For example, if the cannibalization module 265 sets 0.8 (80%) as the cannibalization factor for an item, then 80% of the increased (or, decreased) demand will come at the cost of a decrease (or, increase) in the demand of other items in the set, and effectively only a 20% increase (or, decrease) in the tier volume, because of cannibalization of items within the substitution set. In addition, multiple devices may substitute the demand in a week or other time period. The substitution coefficient, therefore, represents, in the event of a stock out, the percentage of unsatisfied demand that will be transferred to the other items in the substitution set (where the remaining percentage is lost demand).
For examples, as depicted in diagram 600, device 1 is determined to have a substitution forecast of −520 devices, where the substitution forecast is equal to the number of cannibalized devices (7000) multiplied by the combined substitution factor (0.074) for device 1, or (7000)(0.074)=518, which is rounded to 520 devices (the substitution factor taken only to three significant digits). The diagram 600 depicts the substitution forecasts for the other devices, which, when totaled equal 7000 devices, the number of total cannibalized devices. It also follows that the combined substitution factors for all the devices equals 1.0.
In some embodiments, a consensus forecast module (not shown) is configured and/or programmed to generate a consensus forecast for the inventory of the set of mobile devices that is based on a combination of a baseline forecast for the set of mobile devices, the promotion demand of the set of mobile devices, and/or the substitution forecast for the set of mobile devices.
As described herein, the demand forecasting system 150 performs various processes or operations in order to determine an accurate and reliable forecast of the demand for items (e.g., devices) within a substitution set of items.
In operation 710, the demand forecasting system 150 calculates a baseline forecast for a group of items (e.g., devices) For example, the demand forecasting system 150 may calculate a statistical forecast based on historical corrections or performances of similar items.
In operation 720, the demand forecasting system 150 determines substitution forecasts for the group of items. For example, the substitution forecast module 260 determines a substitution forecast for a set of mobile devices that is based on applying combined substitution factors to a determined amount of cannibalized devices for the substitution set of items, as described herein.
In operation 730, the demand forecasting system 150 generates a consensus or overall forecast for the group of items that is based on the baseline forecast and the substitution forecast. For example, the diagram 650 of
The demand forecasting system 150 may perform other operations when generating the consensus or overall demand forecast. For example, the system 150 may utilize consumer or other user override information, substitutionin information, wherein quantities are increased/decreased, and so on.
Although the processes and methods described herein are directed to unconstrained demand scenarios, where there is a limitless, or theoretically limitless, supply of items available to consumers, the demand forecasting system 150 may utilize similar processes and methods when determining demand forecasts for items within constrained demand scenarios, where there is a limited supply of items available to consumers.
For example,
As shown in diagram 1000, for each device, a forecast reconciled value is calculated as:
Forecast Reconciled=If Forecast(Check)>Projected Inventory(Cycle1), then “Projected Inventory(Cycle1)”, else Forecast(Check).
Also, for each device, an unsatisfied demand value is calculated as:
Unsatisfied demand=If Forecast(Check)>Projected Inventory(Cycle1), then Forecast(Check)−Projected Inventory(Cycle1), else Zero(0).
Then, a Sub Out Cannibalized value of Unsatisfied Demand(Cycle1)*Substitution Coefficient is determined.
Thus, “Sub In Cannibalized” is determined as the “Unsatisfied demand” *“substitution coefficient,” and is used for determining the increase in demand for other devices in Cycle2 using coefficient matrices. As shown in diagram 1150, the Sub Out Cannibalized units for device 4 and device 6 are applied to the respective combined matrix to determine the spread of cannibalized units across other devices in the substitution set. Then, the Sub Quantity units for each device are added to determine the Substitution In units, which becomes the input for calculating an overall constrained substitution forecast.
Substitution Forecast(Constrained)=“Forecast Reconciled(Cycle1)”+“Sub In(1) Reconciled”+“Sub In(2) Reconciled”.
Therefore, some embodiments, the methods and systems described herein facilitate the determination of substitution forecasts for device inventories under unconstrained demand scenarios and/or constrained demand scenarios.
Thus, in some embodiments, the demand forecasting system 150 determines forecasts for the demand of substitute sets of devices and other items based on substitution forecasts that are determined for each devices within the substitute set of devices. For example, the demand forecasting system 150 may forecast consumer demand for mobile devices within a group of substitute mobile devices by obtaining parameters for a set of mobile devices, where the obtained parameters includes volume information, price information, and perceived value information for each mobile device within the set of mobile devices.
The system 150 then determines substitution factors for the obtained parameters associated with the set of mobile devices, generates a substitution forecast for the set of mobile devices that is based on the substitution factors determined for the obtained parameters associated with the mobile devices, and determines a consensus forecast for the set of mobile devices that is an adjustment of a baseline forecast for demand of the set of mobile devices based on the substitution forecast for the set of mobile devices.
As described herein, the system 150 may be employed in a variety of inventory processes and associated systems For example, the system 150 may provide various information to an advanced planning and optimization (APO) system that provided planning and optimization functionalities for business processes, such as the SCM APO, provided by SAP®.
As another example, the system 150 may provide various information to assortment optimization processes, which are utilized to determine what items in a substation set an entity (e.g., an entity having many retail locations) may stock across their different retail locations.
For example, a carrier may use the system 150 to determine assortments of six different device cases to stock across their retail stores. Using substitution forecasts determined by the system 150, the carrier may modify assortment percentages for the different cases based on the forecasts in order to determine a percentage of satisfied demand for their customers given the modified assortments, and provide the assortments to the different stores based on the assortment determinations. Therefore, the system 150 may provide information that optimizes an assortment of substitutable items across different retail or other locations.
Of course, the system 150 may be implemented for use with other demand or inventory planning processes or systems.
Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. The word “or” in reference to a list of two or more items covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.
The above Detailed Description of examples of the invention is not intended to be exhaustive or to limit the invention to the precise form disclosed above. While specific examples for the invention are described above for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. For example, while processes or blocks are presented in a given order, alternative implementations may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or subcombinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed or implemented in parallel, or may be performed at different times. Further any specific numbers noted herein are only examples: alternative implementations may employ differing values or ranges.
The teachings of the invention provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various examples described above can be combined to provide further implementations of the invention. Some alternative implementations of the invention may include not only additional elements to those implementations noted above, but also may include fewer elements.
These and other changes can be made to the invention in light of the above Detailed Description. While the above description describes certain examples of the invention, and describes the best mode contemplated, no matter how detailed the above appears in text, the invention can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the invention disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the invention should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the invention with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the invention to the specific examples disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the invention encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the invention under the claims