The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/18/2025 has been entered.
Notice to Applicant
In response to the communication received on 12/18/2025, the following is a Non-Final Office Action for Application No. 18354274.
Status of Claims
Claims 1-13 and 21-28 are pending.
Claims 14-20 are cancelled.
Claims 28 is new.
Response to Amendments
Applicant’s amendments have been fully considered. Applicant’s amendments to the claims overcome the Claim Interpretation with respect to electronic user device of prior amended claims 1-13 and hence the Claim Interpretation with respect to claims 1-13 has been withdrawn.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the updated grounds of rejection, as necessitated by amendment. Arguments that are not moot are as follow:
Applicant argues that Shukhija in view of Bahramshahry fails to teach as recited in independent claim 1 (and similar claims):
wherein the iteratively optimizing comprises:creating a summary for each of the allocation of ground resources and manpower,wherein the summary illustrates how the ground resources and manpower are allocated to a particular resource type and sum up total manpower in different statuses including in use, idle, allocated, and unavailable:comparing the summary for each of the allocation of ground resources and manpower to a next iteration of the allocation of ground resources and manpower and determining, by the comparing, if the next iteration is an improvement. The Examiner respectfully disagrees, and in particular (Shukhija ¶0035 The smart monitoring, prediction and analytics system 112 alerts [e.g., summary provided to stakeholders] the plurality of stakeholders 114 in real time about the weakest link due to which the flights might be getting delayed [particular resource type], any cascading impact on further ground operations[sum up total manpower], network operations of the aircrafts or cascading impacts on further planning and operations at one/multiple airports. Accordingly, the plurality of stakeholders 114 may take necessary action for rectifying the errors in ground operations. [e.g., comparing ground operations and subsequent iterations to rectify errors and determining improvement]. The improvement in the on time performance can be seen again and again through the real time visibility provided by the smart monitoring, prediction and analytics system 112 with the aid of the plurality of loT sensors 108. Shukhija ¶0036 The smart monitoring, prediction and analytics system 112 helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities Shukhija ¶0045 The activities are performed during one or multiple journeys at the airport (aircraft and ground operations). The system analyses the data associated with the equipment currently in operation and equipment available for allotment [e.g., different statuses including in use/allocated/unavailable and idle/available], data related to incoming flights and other outgoing flights scheduled for take-off at a later time, current status of all the ground equipment and the like. The system makes a set of predictions based on the analysis of the data related to availability status of the equipment for future flights, predicted time at which the operation by each of the equipment will be complete, predicted time in which each operation will be complete and the like. The system provides a set of recommendations for the allotment of the plurality of ground equipment for different flights based on the predictive analysis. The system may utilize machine learning algorithms [e.g., iterative optimization] for enabling efficient utilization and allotment of ground equipment in real time for on time turnaround of the aircrafts). Although idleness, allocations and iterative allocations per se are not explicitly taught by Shukhija, Bahramshahry teaches in the analogous art of systems for implementing a scheduler and workload manager with dynamic workload termination based on cost-benefit analysis: (Bahramshahry ¶0089 Conventional solutions utilize a status allocation model in which a prediction is made for any scheduled work or expected work in terms of how much computing resources should be reserved. Unfortunately, such a model locks up resources for defined periods of time and risks having sub-sets of computing resources sitting idle or under-utilized if the expected workload does not arrive while other computing resources are over-utilized or the overall system is indicating an over-allocated state and therefore refusing to accept new work leading to overall performance degradation. Bahramshahry ¶0106 According to described embodiments, such a scheduler 125 may implement simple and deterministic policies which are easily understandable, extendable, testable, and debuggable. Such a scheduler may therefore generate sub-optimal results, yet through the iterative processing permit improvement as the scheduler cycles over and over evaluating and analyzing the work to be performed and making its allocations. Moreover, though the analysis 132 phase it is possible then to make adjustments to the output and selected 129 work as determined by the scheduler. Bahramshahry ¶0107 Within the scheduling service 145 from FIG. 1B, the scheduler 125 as depicted here works from locally cached data and to allocate the available resources using the following exemplary allocation scheme. For instance, the scheduler 125 first produces 126 all tasks possible for a given workload type along with an associated priority for each task, with the production operating in isolation from other workload types. Next, the scheduler calculates a next allocation round 128 including the round's priority and allowed resources for each workload type.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for implementing a scheduler and workload manager with dynamic workload termination based on cost-benefit analysis of Bahramshahry with the system for auto task allocation & recommending ground equipment, manpower to improve turnaround time of aircrafts in airport of Shukhija for the reasons stated in the Office action.
For the reasons detailed above, Examiner is not persuaded that the claims are patentably distinguishable over the Shukhija in view of Bahramshahry disclosure. Rather, Examiner maintains that the Shukhija in view of Bahramshahry combination renders obvious the claimed invention. Accordingly, the previous prior art rejection is maintained.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The electronic user device, processor and/or aircraft is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, electronic user device, processor and/or aircraft to inter alia perform the function of showing the report is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of showing the report which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: electronic user device, processor and/or aircraft. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, electronic user device, processor and/or aircraft to inter alia perform the function of showing the report is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
Applicant directs the Examiner's attention to Example 20 of the USPTO's Section 101 Examples for Subject Matter Eligibility. The claim of Example 20 is directed towards a robotic arm assembly where a control system used sensor information to adjust the velocity of an end effector. Applicant is providing that the claims are analogous to claims of Example 20, claiming the additional element of an aircraft (e.g., mechanical device) having operations varied (to improve aircraft turnaround time) based on analysis of data. However, the aircraft is not similar to an end effector. The robotic arm assembly is a particular tool whereby the velocity of end effector is adjusted based on sensor data which is similar to Diamond v. Diehr whereby the press is opened at a calculated time. The claims of the present application, however, direct ground resources and manpower which may include Certain Methods of Organizing Human Activity. As per the last amended limitation, the operating the aircraft per se is Certain Methods of Organizing Human Activity and further it is based on Certain Methods of Organizing Human Activity. Therefore, there exists a Broadest Reasonable Interpretation of the claims that includes at least Certain Methods of Organizing Human Activity. Thus, the rejection is maintained.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: electronic user device in claims 21-27.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 21-28 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Claims 1-13 and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of process or machine or manufacture. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
receiving, by one or more processors, input information regarding the airport, wherein the input information at least includes turnaround process information for aircraft at the airport; determining, by the one or more processors, an allocation of ground resources and manpower at the airport with a ground resource and manpower model based on the input information; iteratively optimizing, by the one or more processors, the allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport, wherein the iteratively optimizing comprises:creating a summary for each of the allocation of ground resources and manpower,wherein the summary illustrates how the ground resources and manpower are allocated to a particular resource type and sum up total manpower in different statuses including in use, idle, allocated, and unavailable:comparing the summary for each of the allocation of ground resources and manpower to a next iteration of the allocation of ground resources and manpower and determining, by the comparing, if the next iteration is an improvement; operating the aircraft based on the optimized allocation of ground resources and manpower.
[or]
an electronic user device configured to be operated to input information regarding the airport, wherein the input information at least includes turnaround process information for aircraft at the airport; and one or more processors configured to:receive_the information;determine an allocation of ground resources and manpower at the airport with a ground resource and manpower model based on the input information;iteratively optimize the allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport by, at least in part,:creating a summary for each of the allocation of ground resources and manpower, wherein the summary illustrates how the ground resources and manpower are allocated to a particular resource type and sum up total manpower in different statuses including in use, idle, allocated, and unavailable;comparing the summary for each of the allocation of ground resources and manpower to a next iteration of the allocation of ground resources and manpower;and determining, by the comparing, if the next iteration is an improvement;generate a report based on the optimized allocation of ground resources and manpower;and communicate the report to the electronic user device wherein the electronic user device shows the report.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The electronic user device, processor and/or aircraft is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic electronic user device, processor and/or aircraft limitation is no more than mere instructions to apply the exception using a generic computer component. Further, generating a report based on the optimized allocation of ground resources and manpower by a electronic user device, processor and/or aircraft is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: electronic user device, processor and aircraft. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, generating a report based on the optimized allocation of ground resources and manpower by a electronic user device, processor and/or aircraft is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0030 wherein “When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13 and 21-28 are rejected under 35 U.S.C. 103 as being unpatentable over Shukhija (WO 2019186594 A1) hereinafter referred to as Shukhija in view of Bahramshahry et al. (US 20200026564 A1) hereinafter referred to as Bahramshahry.
Shukhija teaches:
Claim 1. A method of optimizing ground operations at an airport, the method comprising:
receiving, by one or more processors, input information regarding the airport, wherein the input information at least includes turnaround process information for aircraft at the airport (¶0041 FIG. 2 illustrates a block diagram of a computing system 200 to recommend ground equipment for improving turnaround operations of aircraft in an airport including atleast one processor. The computing system 200 further includes a memory 204 coupled to the at least one processor. The memory comprises an analytics module 206 to obtain at least one operational parameter from atleast one ground equipment during turnaround operations of an aircraft from atleast one sensor. The computing system 200 is capable to analyze the atleast one operational parameter related to the atleast one ground equipment to determine the availability of the atleast one equipment using artificial intelligence and machine learning. The computing system 200 also generate atleast one recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment.);
determining, by the one or more processors, an allocation of ground resources and manpower at the airport with a ground resource and manpower model based on the input information (¶0010 Yet another object of the present disclosure is to recommend ground equipment for automatic task allocation to improve the aircraft turnaround time in airport. Yet another object of the present disclosure is to recommend manpower (ground support, ramp staff) for automatic task allocation to improve the aircraft turnaround time in airport. ¶0024 FIG. 1 illustrates a block diagram 100 of an interactive computing environment for enabling real-time monitoring of multiple ground, airport operations, baggage and passenger journey for airlines and airports, in accordance with various embodiments of the present disclosure. The real-time monitoring of multiple ground and airport operations are performed between a time when an incoming flight lands and arrives at an assigned gate near airport runway and when the flight departs for another trip. The multiple ground operations are the operations which are essential operations to be performed in order for the flight or aircraft to get ready for another trip on time. CL 4. The computing system of claim 1, further comprising: atleast one machine learning & artificial intelligence model to understand the situational awareness of airport environment and the generated recommendation for allotment of the atleast one manpower or equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time);
iteratively optimizing, by the one or more processors, the allocation of ground resources and manpower determined by the ground resource and manpower model with a machine learning model until an optimized allocation of ground recourse and manpower is generated that reduces idling of at least one of ground resources or manpower at the airport (¶0043 FIG. 3 illustrates a flow diagram of a method 300 to recommend ground equipment for improving turnaround operations of aircraft in an airport. The method 300 includes steps for obtaining 302 at least one operational parameter during turnaround operations of an aircraft from atleast one sensor. The method 300 further includes step for analyzing 304, the atleast one operational parameter related to the atleast one ground equipment to determine the availability 304 of the atleast one equipment using artificial intelligence and machine learning to improve the efficiency of the turnaround operations of the aircraft. The method 300 also include step to generate 308 atleast one recommendation based on the analysis of the at least one obtained operational parameter for allotment of the atleast one ground equipment… ¶0036 The smart monitoring, prediction and analytics system 112 provides a recommendation associated with a best way to optimize the use of the ground equipment in order to avoid flight delay. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 utilizes machine learning and artificial intelligence techniques to predict delay in flights. The delay in flights can be predicted based on the past set of data and real time data associated with the ground operations. In an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports. ¶0014 Yet another object of the present disclosure is to detect situational awareness of airport environment - Weather conditions like temperature, cloud conditions, sunlight, phase of the day, total flights at the airport at that time, upcoming flights, total flights being served by the ground handler under turnaround, available drivers for the ground equipment vs the total drivers, ground equipment performance history, age of the ground equipment vehicle, nearby available flight bays, upcoming flights & outgoing flights.),
wherein the iteratively optimizing comprises:creating a summary for each of the allocation of ground resources and manpower,wherein the summary illustrates how the ground resources and manpower are allocated to a particular resource type and sum up total manpower in different statuses including in use, idle, allocated, and unavailable:comparing the summary for each of the allocation of ground resources and manpower to a next iteration of the allocation of ground resources and manpower and determining, by the comparing, if the next iteration is an improvement (¶0035 The smart monitoring, prediction and analytics system 112 alerts the plurality of stakeholders 114 in real time about the weakest link due to which the flights might be getting delayed, any cascading impact on further ground operations, network operations of the aircrafts or cascading impacts on further planning and operations at one/multiple airports. Accordingly, the plurality of stakeholders 114 may take necessary action for rectifying the errors in ground operations. The improvement in the on time performance can be seen again and again through the real time visibility provided by the smart monitoring, prediction and analytics system 112 with the aid of the plurality of loT sensors 108. ¶0036 The smart monitoring, prediction and analytics system 112 helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities ¶0045 The activities are performed during one or multiple journeys at the airport (aircraft and ground operations). The system analyses the data associated with the equipment currently in operation and equipment available for allotment, data related to incoming flights and other outgoing flights scheduled for take-off at a later time, current status of all the ground equipment and the like. The system makes a set of predictions based on the analysis of the data related to availability status of the equipment for future flights, predicted time at which the operation by each of the equipment will be complete, predicted time in which each operation will be complete and the like. The system provides a set of recommendations for the allotment of the plurality of ground equipment for different flights based on the predictive analysis. The system may utilize machine learning algorithms for enabling efficient utilization and allotment of ground equipment in real time for on time turnaround of the aircrafts);
operating the aircraft based on the optimized allocation of ground resources and manpower (¶0019 The smart monitoring, prediction and analytics system helps the airlines improve the on time performance. A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations ¶0020 FIG. 1 illustrates a block diagram of an interactive computing environment for enabling real-time monitoring of multiple ground operations for airlines, in accordance with various embodiments of the present disclosure; and FIG. 2 illustrates another block diagram of interactive computing system to recommend ground equipment for improving turnaround operations of aircraft in an airport ¶0042 The computing system 200 further includes atleast one user interface 208 to present the at least one operational parameter and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time.).
Although not explicitly taught by Shukhija, Bahramshahry teaches in the analogous art of systems for implementing a scheduler and workload manager with dynamic workload termination based on cost-benefit analysis:
wherein the summary illustrates how the ground resources and manpower are allocated to a particular resource type and sum up total manpower in different statuses including in use, idle, allocated, and unavailable (¶0089 Conventional solutions utilize a status allocation model in which a prediction is made for any scheduled work or expected work in terms of how much computing resources should be reserved. Unfortunately, such a model locks up resources for defined periods of time and risks having sub-sets of computing resources sitting idle or under-utilized if the expected workload does not arrive while other computing resources are over-utilized or the overall system is indicating an over-allocated state and therefore refusing to accept new work leading to overall performance degradation. ¶0090 Through a dynamic allocation process as implemented by the scheduling service it is possible to realize both more efficient computing architecture utilization while simultaneously delivering greater compliance with expected QoS and SLTs. ¶0091 The scheduling service must make many decisions in quick succession and therefore, the various services are provided to support the scheduler's core function of creating, selecting, and planning the execution of tasks.);
comparing the summary for each of the allocation of ground resources and manpower to a next iteration of the allocation of ground resources and manpower and determining, by the comparing, if the next iteration is an improvement (¶0106 According to described embodiments, such a scheduler 125 may implement simple and deterministic policies which are easily understandable, extendable, testable, and debuggable. Such a scheduler may therefore generate sub-optimal results, yet through the iterative processing permit improvement as the scheduler cycles over and over evaluating and analyzing the work to be performed and making its allocations. Moreover, though the analysis 132 phase it is possible then to make adjustments to the output and selected 129 work as determined by the scheduler. ¶0107 Within the scheduling service 145 from FIG. 1B, the scheduler 125 as depicted here works from locally cached data and to allocate the available resources using the following exemplary allocation scheme. For instance, the scheduler 125 first produces 126 all tasks possible for a given workload type along with an associated priority for each task, with the production operating in isolation from other workload types. Next, the scheduler calculates a next allocation round 128 including the round's priority and allowed resources for each workload type. Selection 129 to capacity then proceeds by first selecting tasks for each workload type from the produced set of tasks according to both the round's priority and also the workload type's resource allocation. Planning 127 then effectuates a stated plan for the given round according to all tasks that were selected for that round and according also to the available resources for that round. ¶0422 calculating the efficiency of distribution value for each distribution quantity in a range and then checking the efficiency improvement surpasses a delta threshold helps to systematically find an appropriate distribution quantity for any given workload, even when the workload is not evenly distributable).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for implementing a scheduler and workload manager with dynamic workload termination based on cost-benefit analysis of Bahramshahry with the system for auto task allocation & recommending ground equipment, manpower to improve turnaround time of aircrafts in airport of Shukhija for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Shukhija ¶0002 teaches that there is a continuous need to increase the on time performance of the ground and airports operations so that the turnaround time is decreased for each flight;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Shukhija Abstract teaches a computing system for task automation & recommending ground equipment to improve turnaround operations of aircraft, and Bahramshahry Abstract teaches apparatuses for implementing a stateless, deterministic scheduler and work discovery system with interruption recovery; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Shukhija at least the above cited paragraphs, and Bahramshahry at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for implementing a scheduler and workload manager with dynamic workload termination based on cost-benefit analysis of Bahramshahry with the system for auto task allocation & recommending ground equipment, manpower to improve turnaround time of aircrafts in airport of Shukhija. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Shukhija teaches:
Claim 2. The method of claim 1, wherein the machine learning model is generated by a machine learning algorithm trained with a training dataset that includes historical operations information at the airport (¶0036 The smart monitoring, prediction and analytics system 112 provides a recommendation associated with a best way to optimize the use of the ground equipment in order to avoid flight delay. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 utilizes machine learning and artificial intelligence techniques to predict delay in flights. The delay in flights can be predicted based on the past set of data and real time data associated with the ground operations. In an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports. ¶0014 Yet another object of the present disclosure is to detect situational awareness of airport environment - Weather conditions like temperature, cloud conditions, sunlight, phase of the day, total flights at the airport at that time, upcoming flights, total flights being served by the ground handler under turnaround, available drivers for the ground equipment vs the total drivers, ground equipment performance history, age of the ground equipment vehicle, nearby available flight bays, upcoming flights & outgoing flights).
Shukhija teaches:
Claim 3. The method of claim 2, wherein the historical operations information at the airport includes information regarding vehicle resource inventory, manpower inventory, and turnaround activities (¶0036 The smart monitoring, prediction and analytics system 112 provides a recommendation associated with a best way to optimize the use of the ground equipment in order to avoid flight delay. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 utilizes machine learning and artificial intelligence techniques to predict delay in flights. The delay in flights can be predicted based on the past set of data and real time data associated with the ground operations. In an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports. ¶0014 Yet another object of the present disclosure is to detect situational awareness of airport environment - Weather conditions like temperature, cloud conditions, sunlight, phase of the day, total flights at the airport at that time, upcoming flights, total flights being served by the ground handler under turnaround, available drivers for the ground equipment vs the total drivers, ground equipment performance history, age of the ground equipment vehicle, nearby available flight bays, upcoming flights & outgoing flights ¶0043 FIG. 3 illustrates a flow diagram of a method 300 to recommend ground equipment for improving turnaround operations of aircraft in an airport. The method 300 includes steps for obtaining 302 at least one operational parameter during turnaround operations of an aircraft from atleast one sensor. The method 300 further includes step for analyzing 304, the atleast one operational parameter related to the atleast one ground equipment to determine the availability 304 of the atleast one equipment using artificial intelligence and machine learning to improve the efficiency of the turnaround operations of the aircraft…).
Shukhija teaches:
Claim 4. The method of claim 2, wherein the training dataset includes the input information regarding the airport (¶0036 The smart monitoring, prediction and analytics system 112 provides a recommendation associated with a best way to optimize the use of the ground equipment in order to avoid flight delay. In an embodiment of the present disclosure, the smart monitoring, prediction and analytics system 112 utilizes machine learning and artificial intelligence techniques to predict delay in flights. The delay in flights can be predicted based on the past set of data and real time data associated with the ground operations. In an embodiment of the present disclosure, the live data can be utilized to simulate different scenarios and peak load factor at the airports.).
Shukhija teaches:
Claim 5. The method of claim 1, wherein the report includes a turnaround procedures summary having a schedule of activities required to turnaround the aircraft at the airport (¶0028 The aircraft 104 may be about to take off or just checked in to the airport facility 102. In an example, let's say the aircraft 104 has just completed a trip and landed on the runway of the airport facility 102 at 8 am and scheduled to take off at 8.45 am from the airport facility 102. The airline, ground handling, airport operations and airport authorities need to perform multiple ground operations on or near the aircraft 104 in a time interval of say 8 am to 8.45 am in order for the aircraft 104 to depart on time. The multiple ground operations include transporting passengers through coaches, re-fueling of the aircraft, cleaning of the aircraft, de boarding and boarding of passengers, unloading and loading of baggages, security and frisking of passengers, below and above the wing ground operations and other necessary ground operations known in the art.).
Shukhija teaches:
Claim 6. The method of claim 1, wherein the report includes a flight delay summary identifying a deficiency in resources that will result in a delay of at least one aircraft at the airport (¶0019 The real time visibility provided by the real time data is utilized for determining the critical path, artificial intelligence based model to predict delays and simulation of future load factors of sectors/airport terminal and ground operations. The critical path corresponds to one or more airport/ground/baggage and passenger journey related operations undertaken at the same time. The smart monitoring, prediction and analytics system determines the weakest link in the multiple operations because of which the flight may get delayed…A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations. The smart monitoring, prediction and analytics system takes into account data associated with the plurality of parameters in order to determine the critical path for each different aircraft. The smart monitoring, prediction and analytics system helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities.).
Shukhija teaches:
Claim 7. The method of claim 1, further comprising generating an optimized flight schedule for a plurality of the aircraft based on the allocation of ground resources and manpower, wherein the report includes a flight schedule summary providing an optimized flight schedule for the airport based on the ground resources and manpower available at the airport (¶0019 The real time visibility provided by the real time data is utilized for determining the critical path, artificial intelligence based model to predict delays and simulation of future load factors of sectors/airport terminal and ground operations. The critical path corresponds to one or more airport/ground/baggage and passenger journey related operations undertaken at the same time. The smart monitoring, prediction and analytics system determines the weakest link in the multiple operations because of which the flight may get delayed… ¶0032 The smart monitoring, prediction and analytics system 112 can help in recalibration of KPIs for below and above the wing operations of the aircraft. Further, it can help with network planning of the aircraft and change management of further turn around planned for an aircraft. A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations.).
Shukhija teaches:
Claim 8. The method of claim 1, wherein the turnaround process information includes at least one of a vehicle resource inventory at the airport or a manpower inventory at the airport (¶0041 FIG. 2 illustrates a block diagram of a computing system 200 to recommend ground equipment for improving turnaround operations of aircraft in an airport including atleast one processor. The computing system 200 further includes a memory 204 coupled to the at least one processor. The memory comprises an analytics module 206 to obtain at least one operational parameter from atleast one ground equipment during turnaround operations of an aircraft from atleast one sensor. The computing system 200 is capable to analyze the atleast one operational parameter related to the atleast one ground equipment to determine the availability of the atleast one quipment using artificial intelligence and machine learning.).
Shukhija teaches:
Claim 9. The method of claim 8, wherein the turnaround process information includes turnaround activities for at least one aircraft at the airport (¶0031 the smart monitoring, prediction and analytics system 112 enables reduction in turnaround time for the aircraft 104 and further avoidance of delays in the entire network operations of the associated aircraft. The turnaround time corresponds to a time taken for the aircraft 104 to depart for the next flight after completing a previous flight. ¶0032 The smart monitoring, prediction and analytics system 112 allows real time visibility of the various ground operations performed in and around the aircraft 104. The real time visibility is facilitated with the help of the data received from the plu rality of loT sensors 108. The real time visibility of the ground operations helps the airlines to discover blind spots due to which the turnaround time for the aircraft is getting increased.).
Shukhija teaches:
Claim 10. The method of claim 1, wherein the input information includes flight scheduling information for airplanes at the airport (¶0019 The smart monitoring, prediction and analytics system helps the airlines improve the on time performance. A precision time schedule can be a set of KPIs for various operations and sub operations for optimal performance of each flight turn around operations. The smart monitoring, prediction and analytics system takes into account data associated with the plurality of parameters in order to determine the critical path for each different aircraft. ¶0025 The airport facility 102 corresponds to an airport for providing facility of travelling by air to passengers. In addition, multiple flights arrive and depart from the airport facility 102 at fixed intervals of time as per the schedule. The airport facility 102 includes multiple airport authorities who are responsible for carrying out various ground operations. The airport authorities include multiple personnel inside control towers, terminal buildings, security personnel, air sight operations, baggage management and the like.).
Shukhija teaches:
Claim 11. The method of claim 1, wherein the input information includes weather conditions at the airport (¶0014 Yet another object of the present disclosure is to detect situational awareness of airport environment - Weather conditions like temperature, cloud conditions, sunlight, phase of the day, total flights at the airport at that time, upcoming flights, total flights being served by the ground handler under turnaround CL. 5. The computing system of claim 1, further comprising: atleast one system to understand the daily roster of allocation equipment & manpower, at least one system to understand the current flights & operational load at airportport, at least one system to understand the weather information).
Shukhija teaches:
Claim 12. The method of claim 1, wherein the input information includes at least one of an airport layout, airport resources, airport NOTAMS, or gate availability at the airport (¶0019 In yet another aspect, the present disclosure provides a computer system for enabling real-time monitoring of multiple ground, airport, passenger and baggages operations for airlines. The real-time monitoring of multiple ground operations are performed between a time when an incoming flight lands and arrives at an assigned gate near airport runway and when the flight departs for another trip. The airport or airline authorities employ the plurality of ground equipment as soon as the aircraft reaches the specified gate. Further, computer system includes a plurality of loT sensors 108 installed at different places. The different places where the sensors can be installed are ground equipment, identity cards associated with personnel deployed for turnaround of aircraft, inside airport and the like, baggage tags reader and passenger biometric/cameras/AI based edge devices throughout the journey of passengers.).
Shukhija teaches:
Claim 13. The method of claim 1, wherein the optimized allocation of ground resources and manpower determined by the ground resource and manpower model is customizable with user preferences (¶0033 In an example, a passenger coach may be taking more time than usual in reaching near the aircraft and then leaving from the aircraft. Similarly, there may be multiple ground operations which are taking more time. The smart monitoring, prediction and analytics system 112 helps the airlines improve the on time performance. In general, a precision time schedule for each aircraft is pre-defined according to a plurality of parameters. The plurality of parameters includes origin, destination, type of aircraft, load factor and the like. The smart monitoring, prediction and analytics system 112 takes into account data associated with the plurality of parameters in order to determine the critical path for each different aircraft and precision time schedule of the aircraft turn around operations.).
As per claims 21-27, the system tracks the method of claims 1-7, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7 are applied to claims 21-27, respectively. Shukhija discloses that the embodiment may be found as a system (Fig. 1 and ¶0047).
Shukhija teaches:
Claim 28. The method of claim 1, further comprising: generating, by the one or more processors remote from an electronic user device that communicated the input to the one or more processors, a report based on the optimized allocation of ground resources and manpower; and showing, by the electronic user device, the report on an electronic user device (¶0035 The smart monitoring, prediction and analytics system 112 alerts the plurality of stakeholders 114 in real time about the weakest link due to which the flights might be getting delayed, any cascading impact on further ground operations, network operations of the aircrafts or cascading impacts on further planning and operations at one/multiple airports. Accordingly, the plurality of stakeholders 114 may take necessary action for rectifying the errors in ground operations. The improvement in the on time performance can be seen again and again through the real time visibility provided by the smart monitoring, prediction and analytics system 112 with the aid of the plurality of loT sensors 108. ¶0036 The smart monitoring, prediction and analytics system 112 helps in optimization of use of ground equipment based on the real time data associated with the ground handling activities ¶0037 The smart monitoring, prediction and analytics system 112 enables increase in efficiency in ground operations and enables cost savings. In an embodiment of the present disclosure, the smart monitoring, prediction and ana lytics system 112 generates reports and performs analytics in real time. ¶0042 The computing system 200 further includes atleast one user interface 208 to present the at least one operational parameter and the generated recommendation for allotment of the atleast one ground equipment to increase the efficiency of the turnaround operations for improving the turnaround time for the aircraft in real time)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2013006367 A2
BERTSIMAS D J et al.
Computerized method for optimizing airport operations to uniformly optimize air traffic flow management issues, involves generating flights for time period based on runway configuration, flight-to-runway assignment and flight information
US 20130013182 A1
Bertsimas; Dimitris J. et al.
AIRPORT OPERATIONS OPTIMIZATION
CA 2357975 C
GREENSTEIN IRA LOUIS
REAL TIME TERTIARY OPERATION FOR RESOLVING IRREGULARITIES IN AIRCRAFT OPERATIONS
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/KURTIS GILLS/Primary Examiner, Art Unit 3624