Claims:1. A processor implemented method (200) comprising:
recursively identifying one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state (?) to one or more cause nodes in an observation iteration, the one or more cause nodes being not directly observable (202); and
assigning either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations (204).

2. The processor implemented method of claim 1, wherein the one or more errant sub-graph configurations is at least one of:
a sub-graph wherein two cause nodes have an observation node in the form of a common descendant and another observation node provides an indirect influence on one of the two cause nodes; and
a cyclic sub-graph wherein there are at least three active negative product synergy edges and at least one of the nodes associated thereof is common to two of the negative product synergy edges.

3. The processor implemented method of claim 2, wherein the step of assigning either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) comprises:
allowing the indirect influence on one of the two cause nodes to dominate a prior state associated thereof and behave as a direct influence, if the sub-graph involving the indirect influence leads one of the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences (204a); and
blocking propagation of intercausal influence between two cause nodes, if the cyclic sub-graph involving at least three active negative product synergy edges leads the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences during mutual propagation of opposing influences therebetween (204b).

4. A system (100) comprising:
one or more data storage devices (102) operatively coupled to one or more hardware processors (104) and configured to store instructions configured for execution by the one or more hardware processors to:
recursively identify one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state (?) to one or more cause nodes in an observation iteration, the one or more cause nodes being not directly observable; and
assign either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations.

5. The system of claim 4, wherein the one or more errant sub-graph configuration is at least one of:
a sub-graph wherein two cause nodes have an observation node in the form of a common descendant and another observation node provides an indirect influence on one of the two cause nodes; and
a cyclic sub-graph wherein there are at least three active negative product synergy edges and at least one of the nodes associated thereof is common to two of the negative product synergy edges.

6. The system of claim 5, wherein the one or more hardware processors are further configured to assign either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) by:
allowing the indirect influence on one of the two cause nodes to dominate a prior state associated thereof and behave as a direct influence, if the sub-graph involving the indirect influence leads one of the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences; and
blocking propagation of intercausal influence between two cause nodes, if the cyclic sub-graph involving at least three active negative product synergy edges leads the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences during mutual propagation of opposing influences therebetween.
, Description:FORM 2

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

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR ELIMINATING NODE AMBIGUITIES WITHIN SUB-GRAPHS OF QUALITATIVE BELIEF NETWORKS

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

The following specification particularly describes the embodiments and the manner in which it is to be performed.

TECHNICAL FIELD
[001] The embodiments herein generally relate to belief networks having decision graphs, and more particularly to systems and methods for eliminating node ambiguities within sub-graphs of qualitative belief networks.

BACKGROUND
[002] In many real-life analyses, where inferencing is relied upon to arrive at a conclusion, it may not be possible to use crisp and deductive logic simply because statements within the premises (hypotheses) may not be provably correct, for instance, ‘It rains in Mumbai every Thursday’. Hence inductive or abductive reasoning is employed to reach a conclusion, without a guarantee, but with a fair degree of confidence (also called belief) in the conclusion. For inductive reasoning with probability measures over the hypotheses’ set, Bayes’ framework is employed to predict the confidence measure in the conclusion, given the confidence in the premises. However, probability measures being a statistical measure, it is best calculated on an outcome/observation set of size infinity. In real world, the number of observations of certain outcomes is fairly limited, and may not even be a hundred of them, for instance, ‘City of Kolkata had more than 100 earthquakes since 0 AD’. Hence Bayesian logic is traded off with qualitative logic in many scenarios. Though a quantitative probabilistic framework such as Bayesian logic can provide more accurate and less ambiguous results, qualitative reasoning is often preferred, to leverage the advantage of faster computations, and the ease with which qualitative knowledge bases can be constructed with expert beliefs on qualitative influences across nodes without requiring quantitative prior probability values. As a disadvantage, though, such inferencing is prone to assignment of ambiguous confidence values to various unobserved statements (interim or final/outcome). Once a statement acquires ambiguous confidence, it can never come out to have a well-defined confidence value.

SUMMARY
[003] 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.
[004] In an aspect, there is provided a processor implemented method comprising: recursively identifying one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state (?) to one or more cause nodes in an observation iteration, the one or more cause nodes being not directly observable; and assigning either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?)in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations.
[005] In another aspect, there is provided a system comprising: one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to: recursively identify one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state (?) to one or more cause nodes in an observation iteration, the one or more cause nodes being not directly observable; and assign either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations.
[006] In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: recursively identify one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state (?) to one or more cause nodes in an observation iteration, the one or more cause nodes being not directly observable; and assign either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations in each of the observation iteration.
[007] In an embodiment of the present disclosure, the one or more errant sub-graph configuration is at least one of: a sub-graph wherein two cause nodes have an observation node in the form of a common descendant and another observation node provides an indirect influence on one of the two cause nodes; and a cyclic sub-graph wherein there are at least three active negative product synergy edges and at least one of the nodes associated thereof is common to two of the negative product synergy edges.
[008] In an embodiment of the present disclosure, the one or more hardware processors are further configured to assign either a positive belief (+1) state or a negative belief (-1) state to the one or more cause nodes being led to the ambiguous state (?) by: allowing the indirect influence on one of the two cause nodes to dominate a prior state associated thereof and behave as a direct influence, if the sub-graph involving the indirect influence leads one of the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences; and blocking propagation of intercausal influence between two cause nodes, if the cyclic sub-graph involving at least three active negative product synergy edges leads the two cause nodes to the ambiguous state (?) and thereby to ambiguous inferences during mutual propagation of opposing influences therebetween.
[009] 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 embodiments of the present disclosure, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[010] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[011] FIG.1 illustrates an exemplary Qualitative Belief Network (QBN) that captures interaction of various variables related to low level of car engine oil, as known in the art;
[012] FIG.2 illustrates an exemplary block diagram of a system for eliminating node ambiguities within sub-graphs of qualitative belief networks, in accordance with an embodiment of the present disclosure;
[013] FIG.3 is an exemplary flow diagram illustrating a computer implemented method for eliminating node ambiguities within sub-graphs of qualitative belief networks, in accordance with an embodiment of the present disclosure;
[014] FIG.4A1, FIG.4A2, FIG.4B1, FIG.4B2, FIG.4C1 and FIG.4C2 illustrate exemplary errant sub-graph of a QBN, wherein an indirect influence on a cause node leads to an ambiguous state (?); and
[015] FIG.5A, FIG.5B and FIG.5C illustrate an exemplary errant sub-graph of a QBN, wherein mutual propagation of opposite influences leads associated cause nodes to an ambiguous state (?).
[016] It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

DETAILED DESCRIPTION
[017] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[018] The words "comprising," "having," "containing," and "including," and other forms thereof, 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.
[019] 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. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described.
[020] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[021] Before setting forth the detailed explanation, it is noted that all of the discussion below, regardless of the particular implementation being described, is exemplary in nature, rather than limiting.
[022] Reasoning using Qualitative Belief Network (QBN) often leads a node (statement) to an ambiguous state, when inferencing fails. The present disclosure thus provides systems and methods for eliminating node ambiguities within sub-graphs of QBNs. FIG.1 illustrates an exemplary Qualitative Belief Network (QBN) that captures interaction of various variables related to low level of car engine oil. All variables in the exemplary QBN are propositional. Owner’s negligence in replenishing oil leads to low oil level and in the long run to worn piston rings and excessive oil consumption. Low oil level can be also caused by an oil leak (e.g. through a cracked gasket). Oil leak and possible oil spills during adding or replacing oil can give the engine block a greasy look. Some of the variables in the QBN are directly observable; the others may be inferred. In a QBN, a node can assume discrete belief values, namely ‘+’, ‘-’, ‘0’ and ‘?’. Belief values ‘+’ and ‘-’ represent positive and negative beliefs respectively, in a corresponding proposition. For example, a ‘+’ for “oil leak” implies that some visual observation positively corroborates this proposition, directly or indirectly. The value ‘0’ indicates that no information is (yet) available about the node. The value ‘?’ indicates that contradictory information has been obtained for that node and therefore, its value cannot be ascertained. Edges between nodes in a QBN may be associated with positive or negative influences and may be labeled with S+ and S- respectively. Generally, a positive (negative) influence between two nodes A and B signifies that a positive belief in A induces a positive (negative) belief in B and vice-versa. Similarly, a negative influence between the nodes signifies that a positive belief in A induces a negative belief in B and vice-versa. A positive or negative product synergy exists between two nodes A and B, if they have a common descendant C, and observation of C or any of its descendants, followed by observation of one of A and B leads to increase or decrease in belief of the other node. In such cases, the nodes A and B are connected with an edge labeled X+ or X-, depending on increase or decrease of belief. For example, “Excessive oil consumption” and “oil leak” do not cooperate but compete each other, to explain the effect of positive, prior to direct or indirect observation at “low oil level” node. Hence the edge between them is annotated with X-.
[023] Reasoning in QBNs entails establishing the most probable cause, or set of causes, for an effect, given a set of evidences produced by various observations of effects related to propositions that the individual evidence nodes encapsulate within. The fastest and most popular way for that is by doing sign-propagation. Sign propagation involves first assigning a sign to an effect node, given the hard evidence produced/input towards the corresponding proposition. Then the sign is recursively propagated to all active neighbors of each of those nodes, which had their signs updated during the previous recursion, thereby getting their assignments of soft evidences. The propagation over links representing synergies happens in a special way. The propagation along the synergy edges does not take place till the common descendant node has been observed. Belief flow down an X- link is often referred to as “explain away”. As a simple example, an observed “low oil level” can have been caused by either “Excessive oil consumption” or “oil leak”, and we may have equal belief in the two competing causes. Subsequent observation of “oil leak” to have occurred explains away i.e. reduces the belief of, “excessive oil consumption”. The method of the present disclosure fully mimics belief propagation algorithm in QBN.
[024] Referring now to the drawings, and more particularly to FIGS. 2 through 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 method.
[025] FIG.2 illustrates an exemplary block diagram of a system 100 for eliminating node ambiguities within sub-graphs of qualitative belief networks, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
[026] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
[027] The memory 102 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. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory 102.
[028] FIG.3 is an exemplary flow diagram illustrating a computer implemented method 200 for eliminating node ambiguities within sub-graphs of qualitative belief networks, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or memory 102 operatively coupled to the one or more processors 104 and is configured to store instructions configured for execution of steps of the method 200 by the one or more processors 104.
[029] In accordance with the present disclosure, the one or more processors 104 are configured to recursively identify, at step 202, one or more errant sub-graph configurations within a Qualitative Belief Network (QBN), wherein belief propagation at runtime leads to assignment of an ambiguous state ‘?’ to one or more cause nodes in an observation iteration; the one or more cause nodes being not directly observable. Further, the one or more processors 104 are configured to assign at step 204, either a positive belief ‘+’ state or a negative belief ‘-1’ state to the one or more cause nodes being led to the ambiguous state ‘?’ in each of the observation iteration, the assigning being based on the identified one or more errant sub-graph configurations.
[030] In an embodiment, the errant sub-graph configuration may be a sub-graph wherein two cause nodes have an observation node in the form of a common descendant and another observation node provides an indirect influence on one of the two cause nodes. There are several sub-graph configurations that may take this form as illustrated in FIG.4A1, FIG.A2, FIG.B1, FIG.B2, FIGC1 and FIG.4C2. Assuming that the states of all nodes in the graph are initialized to ‘0’, it may be noted that nodes A and B have a common descendant C, i.e. both the nodes A and B are potential causes for C. If a positive evidence is presented at C, then both A and B compete for being its cause. Thus, a change of state from ‘0’ to ‘+’ at node C, as a consequence of observation of C (or any of its descendants, if they exist) results in belief propagation to both A and B and their states also change from ‘0’ to ‘+’. Intuitively, more evidences need to be gathered in favor of either the node A or node B to explain away the other. For instance, if a positive evidence is received for node B, then it correctly explains away node A by virtue of belief propagation down the X- link connecting B and A. On the other hand, if a negative evidence is received for node D as illustrated in FIG.4A1, intuitively, a negative evidence for node B explains for the competing cause node A of the common effect C. The observed state at node C being ‘+’, the belief state of B becomes ‘?’ and further belief propagation stops. There is no way to recover from this ambiguous situation. Such situation arises because one of the competing causes gets an indirect negative influence from some other node. In the exemplary subgraph of FIG.4A2, the observed state at node C is ‘-’ and if a positive evidence is received for node D, again the belief state of node B becomes ‘?’
[031] In FIG.4A1 and FIG.4A2, there is a single edge between nodes B and D. FIG.4B1 and FIG.4B2 represent exemplary sub-graph configurations wherein there may be an arbitrary graph between nodes B and D. As long as the sign arriving at node B, post propagation of the observed state at node D is opposite of observed state at node C, the belief state of node B becomes ‘?’.
[032] FIG.4C1 and FIG.C2 juxtapose the configurations of FIG.4A1-FIG.4B1 and FIG.4A2-FIG4B2 respectively. As long as the observed state that is propagated to node B post observation at node D is opposite of the belief of an earlier observation at node C, the belief state of node B becomes ‘?’.
[033] In case of identification of an errant sub-graph wherein two cause nodes (A and B) have an observation node in the form of a common descendant (C) and another observation node (D) provides an indirect influence on one of the two cause nodes (A), as illustrated in the exemplary sub-graph configurations of FIG.4A1, FIG.A2, FIG.B1, FIG.B2, FIG.C1 and FIG.4C2; the method 200 provides a resolution for the ambiguous belief state of node B. In accordance with the present disclosure, the one or more processors 104 are configured to allow, at step 204a, the indirect influence on one of the two cause nodes to dominate a prior state associated thereof and behave as a direct influence. Accordingly, in the exemplary errant sub-graph configuration of FIG. 4A1, FIG.4B1, FIG.4B2 and FIG.4C1, node B will take a ‘-’ sign rather than ‘?’ and hence allow one of the competing reasons to be explained away. Likewise, in the exemplary errant sub-graph configuration of FIG.4A2 and FIG.4C2, node B will take a ‘+’ sign rather than ‘?’.
[034] As is known, product synergy links are latent links. A product synergy link comes into being, or becomes active, only when its common effect (or its descendants) is directly observed. Hence the topology of the graph keeps on evolving as more and more product synergies become active. At certain point, some of these active product synergies may enter into a cyclic configuration, which is when a possibility of another errant sub-graph configuration exists. In another embodiment, the errant sub-graph configuration may be a cyclic sub-graph wherein there are at least three active negative product synergy edges and at least one of the nodes associated thereof is common to two of the negative product synergy edges as illustrated in FIG.5A, FIG.5B and FIG.5C. In FIG.5A, a common descendant of nodes A and B, A and F, and M and N (e.g., nodes D, C and E) may have either been directly observed, or inferred by virtue of their descendants being directly observed, towards having a ‘+’ sign, in the first few cycles. If a ‘+’ or a ‘-’ evidence is later presented to node A, then both of its neighbors, nodes B and F, get the opposite (‘-’ or ‘+’) but same sign. The (same) signs then propagate from node B to M, and similarly, from node F to N. This happens in all such subgraph configurations, where the signs of all minimum non-blocked trails between nodes B to M, and similarly, from node F to N are consonant with one-another, as in FIG.5A, a predicate. Nodes M and N try to propagate the same sign to each other, using the X- link, opposite of what they receive, which is the common sign of nodes B and F. This propagation to each-other may happen in same iteration, or in different iterations. Hence each of the nodes, M and N, have same sign already, and get an opposite sign from the other node, since they participate in negative product synergy themselves. Hence, in the end, both end up with an ambiguous ‘?’ sign, which is undesired. There is no way to recover from this ambiguous situation. In FIG.5B, observation at node A induces appropriate signs at nodes D, E and G. The prior observations at nodes B, C, F and K do not block the propagation of signs from the nodes D, E and G to M and N, since paths/trails from these nodes to nodes M and N are present which do not pass via nodes B, C, F and K (which block). As long as such blocking is absent, the size of all trails from node A to each of the competing nodes M and N can be all different, and yet have the same effect, assuming sign consistency among them. For instance, in FIG.5B, prior observations are made at nodes B, C, F and K, before observation at node A is made. The active trails from node A to e.g. node N are: F->Y->N, K->X->N, A->D->G->L->N. All these trails have different lengths, though there cumulative sign are all same: `+’. It may be noted that in FIG.5B there are an odd number of negative product synergies participating in the cyclic sub-graph and irrespective of the length of paths from A to M and A to N, as long as the propagated belief reaching nodes M and N bear the same sign, the belief state of nodes M and N become ‘?’. In FIG.5C, it may be noted that there are an even number of negative product synergies participating in the cyclic sub-graph. Nodes B, C, F, J and X are pre-observed, leading to competing causes in the corresponding product synergy formations. In the next iteration, the sign (e.g., `+’) of node A propagates via the only active trail, A->E->H->I->M as a `+’ sign to node M (nodes B and J are blocked since they are prior observations). Similarly, the same sign of node A propagates via only active trail, A->D->G->K->L->N as a `+’ sign to node N (nodes C, F and X are blocked since they are prior observations). Both signs being consistent with each other, and also consistent with sign of node X that propagated to nodes M and N in previous iteration, an ambiguity in signs results at nodes M and N in the current iteration.
[035] In case of identification of an errant sub-graph wherein there are at least three active negative product synergy edges as illustrated in the exemplary sub-graph configurations of FIG.5A, FIG.5B and FIG.5C; the method 200 provides a resolution for the ambiguous belief state of nodes M and N. In accordance with the present disclosure, the one or more processors 104 are configured to block, at step 204b, propagation of intercausal influence between the two cause nodes. Accordingly, in the illustrated exemplary errant sub-graph configurations in FIG.5A through FIG.5C, propagation of intercausal influence between nodes M and N that arises due to propagation of observed state at node A in the current iteration is blocked, thereby preventing the QBN from entering a non-recoverable ambiguous state.
[036] Thus in accordance with the present disclosure, systems and methods described herein above identify illustrated and described errant subgraph configurations and provide specific methods to remove ambiguities during inferencing by emulating a human mind’s approach to resolution of conflicting propositions by blocking irrelevant sign propagations.
[037] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments of the present disclosure. The scope of the subject matter embodiments defined here may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope 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.
[038] The scope of the subject matter embodiments defined here may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope 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.
[039] It is, however 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 of the present disclosure may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[040] 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 comprising the system of the present disclosure and 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 various modules described herein may be implemented as software and/or hardware modules and may be stored in any type of non-transitory computer readable medium or other storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives.
[041] Further, although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[042] The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.