Claims:WE CLAIM:

A processor-implemented method for pre-emptive product selection from an inventory, comprising:
receiving a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval (202);
analyzing the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection (204);
estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques(206); and
iteratively optimizing an action objective function (u_p) for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products (208).

The method of claim 1, wherein the pre-emptive product selection further comprises facilitating a product order received after a threshold cut-off time for pre-emptive picking from the inventory, wherein the pre-emptive selection is dependent of the physical constraints in the inventory.

The method of claim2, wherein the physical constraints comprises capacity constraints, space capacity constraints and loss function, and is independent of labor constraints.

The method of claim 1, wherein the demand matching index (R) is matched when the objective function is maximized and is based on one or more constraints, wherein the one or more constraints comprises estimation of order arrival, capacity constraints, price, revenue and margin of short-list products.

The method of claim 1, wherein the multi-criterion analysis comprises assigning a score for each of the plurality of datasets and arriving at a single score for each of a product order from the plurality of product orders using a learning database.

A system comprising:
an optimization unit(102) for pre-emptive product selection from an inventory;
a memory (104) for storing instructions;
one or more communication interfaces(106);
one or more hardware processors(108) communicatively coupled to the memory using the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions for :
receiving a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval;
analyzing the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection;
estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques; and
iteratively optimizing an action objective function (U_p) for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products.

A non-transitory computer-readable medium having embodied thereon a computer readable program, wherein the computer readable program, when executed by one or more hardware processors, cause:
receiving a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval;
analyzing the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection;
estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques; and
iteratively optimizing an action objective function (U_p) for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products.

Dated this 30th day of November 2018.
Tata Consultancy Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg. No. IN/PA-1086
, Description:
FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

SYSTEM AND METHOD FOR PRE-EMPTIVE PRODUCT SELECTION FROM AN INVENTORY

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

PREAMBLE OF THE INVENTION:
The following specification particularly describes the invention and the manner in which it is to be performed.


TECHNICAL FIELD
The disclosure herein generally relates to field of product selection from an inventory and, more particularly, to a system and a method for pre-emptive product selection from an inventory.
BACKGROUND
The rapid rise of internet, has led to popularization and enormous use of electronic commerce (e-commerce) for shopping, customer services as well as delivery of various services at a customer’s doorstep. The benefits of e-commerce include its around-the-clock availability, speed of access, a wider selection of goods and services, accessibility, and international reach. Due to the various benefits, e-commerce is gaining popularity among customers and hence numerous suppliers have entered the e-commerce shopping domain, where they can provide customers with an almost immeasurable amount of possibilities. With numerous suppliers online, customers can analyze, compare, choose and decide best seller for a product. Hence for online suppliers to thrive the existing stiff competition, customer service is an important factor wherein a promise of expedited shipping is a popular distinguishing factor/feature between various online competitors.
The fulfilment of shipping online orders occurs in two main phases wherein during the first phase, the ordered products are prepared/packed for shipping inside a distribution center (DC) or inventories. Further during the second phase, the prepared/packed products are shipped to their destinations. Further for expedited shipping orders for orders received after a deadline, either both the phases should be efficiently processed or pre-emptively executed. The first phase can be pre-empted by accurate forecasting of upcoming orders followed by pre-emptive movement of an optimal set of products within the DC before the orders are actually generated. Traditional forecasting techniques such as moving averages, exponential smoothing, and time-series approaches do not account for non-linearity in demand patterns, leading to relatively low accuracies in order prediction. Furthermore, simple forecasting of upcoming orders is not effective for extending expedited shipping deadline as it does not provide any decision support under constraints imposed by ongoing processes within the DC and its capacity-limiting factors. Recent studies have shown that machine learning based approaches can outperform traditional approaches in demand forecasting, in several realistic scenarios.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for pre-emptive product selection from an inventory is provided. The proposed pre-emptive product selection is performed by a combination of predictive analysis and learning techniques in multiple-stages. The proposed multi-stage pre-emptive product selection include short-listing product types for pre-emptive selection, further an order arrival rate is estimated for each short-listed product, and finally the quantity of product of each type to be pre-emptively picked is iteratively optimized and determined.
In another aspect, a method for pre-emptive product selection from an inventory is provided. The method includes receiving a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval. Further the method includes analyzing the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection. Furthermore the method includes estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques. Finally the method iteratively optimizes an action objective function for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products.
In another aspect, a system for pre-emptive product selection from an inventory is provided. The system comprises a memory storing instructions, one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by instructions to: includes a pre-processing module for plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval. Further the systems analyzes the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection. Furthermore the method includes estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques. Finally the system iteratively optimizes an action objective function for the estimated order arrival rate of the short-listed products, the operational constraints of the distribution center, and the expected revenue potential of pre-emptive decisions, to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products.
Another embodiment provides a non-transitory computer-readable medium having embodied thereon a computer readable program, wherein the computer readable program, when executed by one or more hardware processors, causes pre-emptive product selection from an inventory. The program includes receiving a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order for a predetermined time interval. Further the program includes analyzing the received datasets using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection. Furthermore the program includes estimating order arrival rate of the short-listed products for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques. Finally the program iteratively optimizes an action objective function for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index, of the estimated order arrival rate of the short-listed products.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates an exemplary block diagram of a system for pre-emptive product selection from an inventory in accordance with some embodiments of the present disclosure.
FIG. 2 is an exemplary flow diagram illustrating a method for pre-emptive product selection from an inventory using the system of FIG. 1 in accordance with some embodiments of the present disclosure
FIG. 3A and 3B is a functional block diagram of existing online order fulfillment workflow and proposed pre-emptive product selection for online order fulfillment workflow in accordance with some embodiments of the present disclosure.
FIG. 4 illustrates graphs of e-commerce transactions of product orders for a plurality of online retailers for a pre-determined time interval, according to some embodiments of the present disclosure.
FIG. 5 illustrates graphs of short-listing of product types based on multi-criterion techniques, according to some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
A distribution center (DC) or inventory is a combined warehouse and operations center which stocks products for shipping to retail stores or individual customers. An online order fulfilment occurs in two main stages as shown in FIG 3A, wherein during the first stage, the ordered products are selected and prepared/packed for shipping inside a distribution center (DC) or inventories while during the second stage, the prepared/packed products are shipped to their destinations. The first and second stages have multiple sub-stages with in them, wherein during first stage of packing, upon receiving an online order, a work-order is generated, product is selected, packed and sent to a pick-up area, where it is ready to be dispatched/shipped. Further during second phase, the prepared/packed products are shipped and delivered by logistics to their destinations.
An order fulfilment workflow can be expedited either by expediting the first stage or the second stage, or by pre-emptively executing one or both stages. The proposed pre-empting product selection pre-empts the first stage of product selection as shown in FIG.3B by estimating product types and quantities to be pre-emptively selected by predictively analyzing and iterative optimizing based on learning techniques. The pre-emptively selected product is picked and kept ready at a ‘pre-selection area’ close to the departure point in the DC, from where it is ready to be dispatched/shipped to the destination. Hence the proposed pre-emptive selection techniques, also facilitates a product order received after a threshold cut-off time, wherein the threshold cut-off time may be any pre-defined time which acts as a booking deadline for products to be delivered within any timeframe, such as next day or within some other promised time window. The proposed pre-emptive selection techniques facilitates a product order received after a threshold cut-off time as the product is pre-emptively selected, it is ready to be dispatched immediately without the need for selection from storage. Hence orders placed after a normal booking deadline can also be shipped but only before a delivery truck departs. This time period after normal booking deadline but before departure of truck is referred to as the extended booking deadline, which explained in the further section.
In an embodiment, considering online order fulfillment workflow as shown in FIG.3A , time required for processing an order within a DC , T_(norm ) is can be represented as follow;,
T_(norm )= T_pick + T_(conv )+ T_(pack1 )
Where T_pick is time for picking a product, T_(pack ) is time for packing a picked product and T_(conv ) for processing from DC to pre-selection area
In an embodiment, considering online order fulfillment workflow as shown in FIG.3B, products retrieved from a pre-selection area require a packing time T_pack and a time period of T_retr minutes to be processed to a pre-selected area from a DC. Hence time required for normally processing selected products, T_prep, from DC to a pre-pick area is as follows;
T_prep= T_retr+ T_pack2
Hence if a product is pre-emptively picked, the time is reduced as the product is ready to be shipped as shown in FIG.3B. Further the reduction in time, ? can be represented as follows;
? = T_norm- T_prep
The reduction time ? may be used to extend booking time as the products have already been pre-selected and made ready for dispatch. Hence ? is equal to extension of the booking deadline for expedited shipping orders. In an embodiment, considering online order fulfillment workflow as shown in FIG.3A if booking deadline of day ‘m’ is D_m, by performing pre-emptive selection ( as shown in FIG.3B ), deadline for ordering items available in the pre-selection area is ?( D?_m- ? )as pre-emptively selected products arrive in the pre-selected area before D_m . However, during this extended period, an expedited shipping order is only accepted if the requested product is already present in the pre-selection area and further any pre-selected products that is unsold at time ?( D?_m+ ? ) are returned to their normal storage location in the DC.
Referring now to the drawings, and more particularly to FIGS. 1 through FIG.5, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 is a block diagram of a system 100 for collaborative product configuration optimization model, in accordance with an example embodiment. The system 100 includes an optimizer unit 102 for collaborative product configuration optimization model. The optimizer unit 102 includes or is otherwise in communication with at least one memory such as a memory 104, at least one processor such as a communication interface 106, and a processor 108. The memory 104, communication interface 106, and the processor 108 may be coupled by a system bus such as a system bus 110 or a similar mechanism. Although FIG. 1 shows example components of optimizer unit 102, in other implementations, system 100 may contain fewer components, additional components, different components, or differently arranged components than depicted in FIG. 1.
The at least one processor such as the processor 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that facilitates in designing polymeric carrier for controlled release of molecules. Further, the processor 108 may comprise a multi-core architecture. Among other capabilities, the processor 108 is configured to fetch and execute computer-readable instructions or modules stored in the memory 104. The processor 108 may include circuitry implementing, among others, audio and logic functions associated with the communication. For example, the processor 106 may include, but are not limited to, one or more digital signal processors (DSPs), one or more microprocessor, one or more special-purpose computer chips, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more computer(s), various analog to digital converters, digital to analog converters, and/or other support circuits. The processor 108 thus may also include the functionality to encode messages and/or data or information. The processor 106 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processor 106. Further, the processor 106 may include functionality to execute one or more software programs, which may be stored in the memory 104 or otherwise accessible to the processor 108.
The memory 104, may store any number of pieces of information, and data, used by the system 100 to implement the functions of the system 100. The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. Examples of volatile memory may include, but are not limited to volatile random access memory (RAM). The non-volatile memory may additionally or alternatively comprise an electrically erasable programmable read only memory (EEPROM), flash memory, hard drive, or the like. The memory 104 may be configured to store information, data, applications, instructions or the like for enabling the system 100 to carry out various functions in accordance with various example embodiments. Additionally or alternatively, the memory 104 may be configured to store instructions which when executed by the processor 108 causes the system 100 to behave in a manner as described in various embodiments.
The communication interface(s) 106 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the communication interface (s) 108 may include one or more ports. One or more functionalities of the system 100 and components thereof, is further explained in detail with respect to flow diagram described in FIG. 2.
FIG. 2, with reference to FIGS. 1, is an exemplary flow diagram illustrating a method for pre-emptive product selection in an inventory using the system 100 of FIG. 1 according to an embodiment of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processors 108 and is configured to store instructions for execution of steps of the method by the one or more processors 108. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIGS. 1, and the flow diagram as depicted in FIG.2 explained below.
At step 202, a plurality of datasets corresponding to a plurality of e-commerce transactions of a product order are received for a predetermined time interval. In an embodiment a publicly available dataset of e-commerce transactions of product orders for a plurality of online retailers for a pre-determined time interval that could be for a day or few months as shown in FIG 4, where purchase quantity is plotted against y-axis while a pre-determined time interval such as hour of day or number of week is plotted against x-axis.
In the next step at step 204, the received datasets are analyzing using multi-criterion analysis techniques to short-list products eligible for pre-emptive selection. The proposed multi-criterion analysis comprises assigning a score for each of the plurality of datasets and arriving at a single score for each of a product order from the plurality of product orders using a learning database. The multi-criterion analysis utilizes several additional parameters such as revenue generated, size of customer base total quantity purchased, the number of unique orders for the product type, the revenue generated, and the number of unique customers ordering that product, and combines values of each parameter into a single score for each product type to arrive at a short-list of products eligible for pre-emptive selection. In an embodiment, the number of products eligible for short-listing may be pre-determined number. The pre-determined number of products eligible for short-listing is dependent on of product types (p) to choose from and number of products sold ( S) and may be determined as follows;
p?S=> u_p=0 and p ? S=>u_p=0
Where (u_p) is an action objective function, which would explained in further sections.
FIG.5 illustrates graphs of short-listing of product types based on multi-criterion techniques, where rank of product type is plotted against x-axis while a ranking criteria such as quantity purchased or revenue is plotted against y-axis.
In the next step at 206, order arrival rate of the short-listed products is estimated for the predetermined time interval using Long Term Short Memory (LSTM) based on one or more pre-determined parameters associated with each of the product orders wherein order arrival rate is predicting a type and quantity of product order for the predetermined using LSTM techniques. In an embodiment , order arrival rate is an average of customers ordering at least one product of a product type on previous day, and on same day of week one week, which can be represented as (y_(p,m) ) ^of product type p ? S ordered on a given day m by number of customers (y_(c,m) ) ^. , which can be represented as shown below;
(y_(p,m) ) ^=(?¦?(_o,i^)[q_(o,i)]D=t_o=D+??)/(?¦(_o,i^)[q_(o,i)] )
Where D is an extended booking deadline.
Hence order arrival rate, (y_(p,m) ) ^ , which is a type and quantity of product order based on LSTM techniques is estimated for a predetermined time interval.
In the next step at 208, an action objective function (u_p) is iteratively optimizing for the estimated order arrival rate of the short-listed products to obtain a final type and quantity of product order for the pre-emptive product selection, wherein iteratively optimizing the action objective function is performed for a pre-determined time to arrive at a demand matching index (R), of the estimated order arrival rate of the short-listed products.
In an embodiment, if ?s ?_p is a number of items of a product type p that are actually sold during the extended period, and q_p is a number that were requested by customers, then q_p can be represented as;
q_p= ?¦?[ q_(o,i) |p_(o,i) ?=p,D_m= t_o= D_m+?]
If a cost of product p is d_p, demand matching index (R) comprises revenue (R_sold) and unsold product revenue loss that includes refused products R_(refused )and returned products R_returned, can be represented shown below;
R= R_sold+ R_refused+R_returned (1)
Where
R_sold= ?_(p?S)¦(d_p.s_p ) (2)
The refused product orders ( R_(refused )) are defined to be those orders arriving within the extended window ? for a product type in S, but for which a matching item is not available in the pre-pick area, while and returned products orders ( R_returned) is defined as selected products that remain unsold at the end of each day , which would be returned to the DC. Hence the pre-emptive product selection also defines and includes the refused products and returned products in demand matching index (R),which can be represented as follows;
R= R_sold+ R_refused+R_returned= ?_(p?S)¦(d_p.s_p ) - ?_(p?S)¦(d_p.f_p ) - ?_(p?S)¦?(d.r_p)?
Where
f_p=max?(0,q_p-u_p)
r_p=max?(0,?u_p-q?_p)
The pre-emptive selection process maximizes the demand matching index value (R) for each day by using optimizing an action objective function (u_p) for the estimated order arrival, where relationship between ?s ?_p and u_p is? s ?_p=min?(u_p,q_p). The action objective function (u_p) is iteratively optimized to arrive at a demand matching index (R) based on a combination of predictive analysis and learning techniques such as reinforcement learning in an artificial neural network (ANN) .In an embodiment, since R must be maximum ( R^(* )) , the u_p is iteratively optimized based on learning techniques, wherein the ANN is trained on a loss function (t ) , where loss function is sum of squares of refused and returned products is defined for refused and returned products as explained below;
Hence the loss function used for training ANN is;
t = R^(* )-R= 0
Where
t= R_refused^2+ R_returned^2= ?[?_(p?S)¦?(d_p.f_p )]??^2- ?[?_(p?S)¦?(d.r_p)?]?^2
Thus based predictive analysis and learning techniques iteratively optimized till the R is maximized based on action objective function (u_p) and R, to finally estimate the products to be pre-emptively selected. Further the pre-emptively selected product is kept at a ‘pre-selection area’ from where it is ready to be dispatched/shipped to the destination.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
Hence the proposed method and system for pre-emptive product selection is provided. The proposed pre-emptive product selection is performed by a combination of predictive analysis and learning techniques in multiple-stages. The proposed multi-stage pre-emptive product selection include short-listing product types for pre-emptive selection, further an order arrival rate is estimated for each short-listed product, and finally the quantity of product of each type to be pre-emptively picked is iteratively optimized and determined. The pre-emptively selected product is kept ready at a ‘pre-selection area’ from where it can be immediately dispatched/shipped to a destination upon receiving an online line. Hence the proposed pre-emptive selection techniques, also facilitates a product order received after a normal booking deadline, as the product is already pre-emptively selected and ready to be dispatched.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.