CTNF 19/077,056 CTNF 96216 Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This office action is in response to the amendment filed on 03/12/2025. Claims 1-20 are currently pending in the filing of 03/12/2025, with no claims added, cancelled, and/or amended since filing. Copies of Foreign Priority Documents Copies of the foreign priority document 202411096638 from India were not provided by the applicant, and have not been retrieved by the Office, as indicated in the PTO-326 section regarding Priority under 35 U.S.C. 119. Examiner notes that an access code for the foreign priority document from India was not provided, as indicated on the bottom of the first page of the Filing Receipt of 4/11/2025. Additionally, after conducting a search, the examiner notes that an Indian application / publication numbered 202411096638 could not be found. Objection to Specification The numbering in the applicant’s specification is objected to for misnumbering the paragraphs in the specification. First, there are two paragraphs numbered as [0001] and [0002]. Additionally, the initial numbering of paragraphs includes the first two instances of [0001] for the Background and Summary of Invention, followed by [0002-16] for the Summary of the Invention, followed by [0002-56] for the Detailed Description. Appropriate correction required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 12-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The examiner interprets the features of “a data processing engine”, as recited in claims 12, 13, 14, 16, & 17, “a flight parameter generation engine”, as recited in claims 12 & 13, and “a feedback engine”, as recited in claim 17 as software, which is supported by the applicant’s specification at [0018] that the “engines” 104 of fig. 1, may be programmable instructions. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims is directed to “a data processing engine”, as recited in claims 12, 13, 14, 16, & 17, “a flight parameter generation engine” as recited in claims 12 & 13, and “a feedback engine”, as recited in claim 17, which are interpreted as programmable instructions, thus, cover only software embodiments, not one of the four statutory categories (process, machine, manufacture, or composition of matter). Therefore, independent claim 12 and claims 13-17, which depend from independent claim 12, are rejected as “software per se.” Claim Rejections - 35 USC § 103 07-20-aia AIA 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, 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. 07-21-aia AIA Claim s 1-4, 12, 14-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220085981 to Selvarajan et al. (hereinafter Selvarajan), in view of US 20140361923 to Bunch et al. (hereinafter Bunch) . Regarding claim 1, Selvarajan teaches, A method comprising: obtaining an input data associated with at least one of flight assistance parameters of an aircraft; (Abstract, teaching obtaining data from Electronic Flight Bag (EFB) by Flight Management System (FMS).) processing the input data at each of a predetermined number of flight plan applications, ([0004] teaches cybersecurity of config files from / to EFB applications.) wherein at least one flight plan application, from among the predetermined number of flight plan applications, is heterogeneous with respect to other flight plan applications; ([0027-28] teaches EFB applications and same / different information available from the EFBs. ) (Applicant’s specification at [0005] teaches “flight plan application” as an EFB.) obtaining an output data from each of the flight plan applications; ([0008] teaches EFB applications and config files data access, where config files affect complex flight system.) determining a confidence level of the output data depending on a degree of agreement between the output data, wherein the confidence level is associated with a data acceptability level; (Abstract & [0004] teach use of encryption for EFB data, and cybersecurity of config files. [0008] teaches FMS using digital signature comparison / matching to validate data.) for the output data determined to have the confidence level in a prespecified threshold , calculating flight modification parameters using the output data; and (See discussion of Abstract, [0004], & [0008] above.) transmitting the flight modification parameters to a flight management system of the aircraft. (Abstract & [0053] teaches EFB config file / “flight modification parameters” and FMS.) Selvarajan fails to explicitly teach the use of thresholds, However, Bunch teaches, for the output data determined to have the confidence level in a prespecified threshold, calculating flight modification parameters using the output data; and (Bunch, [0059-60] teaches alternative flight plans / routes being used with weather thresholds, and the use of EFBs.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch , which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan with the added ability to utilize thresholds, as taught by Bunch, for the purpose of maintaining security (Selvarajan, [0004-8]) while increasing flight planning efficiency and safety by using EFBs with weather thresholds to improve flight planning. Regarding claim 2, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising: verifying a nearby waypoint to the output data to authenticate context sensitivity of the output data when the output data is determined to have the confidence level below the prespecified threshold, wherein the nearby waypoint is an indicator associated with a flight plan summary. (Bunch, [0059] teaches alternative flight plans / routes being used with weather thresholds.) Regarding claim 3, Selvarajan and Bunch teach, The method as claimed in claim 2, wherein the flight plan summary comprises one or more of fuel, distance, travel route, and travel time. (Bunch, [0059] teaches flight plans and alternative routes. [0056] teaches weather being used with time and distance.) Regarding claim 4, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising rejecting the output data determined to have the confidence level below the prespecified threshold. (Bunch, fig. 1 and [0005-7] teach identifying green weather that is safe and red / yellow code weather that is convective weather, such as thunderstorms to be avoided. [0054-55] teaches different types of aircraft having different weather severity thresholds.) Regarding claim 12, Selvarajan and Bunch teach, A system comprising: a data processing engine to: (Examiner interprets this feature as software, as described in the applicant’s specification at [0018]) obtain an input data associated with at least one of flight assistance parameters of an aircraft; process the input data at each of a predetermined number of flight plan applications, wherein at least one flight plan application, from among the predetermined number of flight plan applications, is heterogeneous with respect to other flight plan applications; obtain an output data from each of the flight plan applications; and determine a confidence level of the output data depending on using a degree of agreement between the output data, wherein the confidence level is associated with a data acceptability level; and a flight parameter generation engine to: (Examiner interprets this feature as software, as described in the applicant’s specification at [0018]) for the output data determined to have the confidence level in a prespecified threshold, calculate flight modification parameters the output data; and transmit the flight modification parameters to a flight management system of the aircraft. Claim 12 is rejected using the same basis of arguments used to reject claim 1 above. Regarding claim 14, Selvarajan and Bunch teach, The system as claimed in claim 13, wherein for the authentication of the output data for the output data determined to have the confidence level below the prespecified threshold, the data processing engine is to: verify a nearby waypoint to the output data to authenticate context sensitivity of the output data, wherein the nearby waypoint is an indicator associated with a flight plan summary. Claim 14 is rejected using the same basis of arguments used to reject claim 2 above. Regarding claim 15, Selvarajan and Bunch teach, The system as claimed in claim 14, wherein the flight plan summary comprises one or more of fuel, distance, travel route, and travel time. Claim 15 is rejected using the same basis of arguments used to reject claim 3 above. Regarding claim 16, Selvarajan and Bunch teach, The system as claimed in claim 12, wherein the data processing engine is to reject the output data determined to have the confidence level below the prespecified threshold. Claim 16 is rejected using the same basis of arguments used to reject claim 4 above. Regarding claim 18, Selvarajan and Bunch teach, A non-transitory computer readable medium having instructions stored thereon, the instructions, when executed by a processor, cause the processor to perform operations comprising: obtaining an input data associated with at least one of flight assistance parameters of an aircraft; processing the input data at each of a predetermined number of flight plan applications, wherein at least one flight plan application, from among the predetermined number of flight plan applications, is heterogeneous with respect to other flight plan applications; obtaining an output data from each of the flight plan applications; determining a confidence level of the output data depending on a degree of agreement between the output data, wherein the confidence level is associated with a data acceptability level; for the output data determined to have the confidence level in a prespecified threshold, calculating flight modification parameters using the output data; and transmitting the flight modification parameters to a flight management system of the aircraft. Claim 18 is rejected using the same basis of arguments used to reject claim 1 above. Regarding claim 19, Selvarajan and Bunch teach, The non-transitory computer readable medium as claimed in claim 18, further comprising verifying a nearby waypoint to the output data to authenticate context sensitivity of the output data, wherein the nearby waypoint is an indicator associated with a flight plan summary. Claim 19 is rejected using the same basis of arguments used to reject claim 2 above. Regarding claim 20, Selvarajan and Bunch teach, The non-transitory computer readable medium as claimed in claim 18, further comprising rejecting the output data determined to have the confidence level below the prespecified value. Claim 20 is rejected using the same basis of arguments used to reject claim 4 above . 07-21-aia AIA Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of US 20210024224 to Mohan et al. (hereinafter Mohan) . Regarding claim 5, Selvarajan and Bunch teach, The method as claimed in claim 1, Selvarajan and Bunch fail to explicitly teach processing data at each of the flight plan applications / EFBs, However, Mohan teaches, wherein the processing of the input data comprises independently processing the input data at each of the flight plan applications. (Mohan, [0039] teaches each of the EFB application components identifying anomalies.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Mohan , which also teaches EFBs ([0014]), and additionally teaches EFBs identifying anomalies ([0039]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to use the EFBs to identify anomalies in data, as taught by Mohan, for the purpose of increasing security by using the EFBs to identify anomalies . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of US 9535158 to Breiholz et al. (hereinafter Breiholz), in view of US 20140025808 to Ito et al. (hereafter Ito) . Regarding claim 6, Selvarajan and Bunch teach, The method as claimed in claim 1, Selvarajan and Bunch fail to explicitly teach determining similar outputs, However, Breiholz teaches, wherein the determining of the confidence level of the output data comprises: determining number of similar output data; (Col. 20, lines 10-50 (65-67) teach multiple weather sources.) determining total number of output data received from the flight plan applications; and (fig. 5 steps 560-570 & Col. 20, lines 10-50 (65-67) teach a confidence level based on multiple weather sources.) (Selvarajan and Bunch both teach EFBs. Col. 16, lines 10-41 (54) teach using the number of forecast values, radar beams to make the determination.) computing the confidence level in percentage by dividing the number of similar output data with the total number of output data and further multiplying by hundred . (fig. 5 steps 560-570 & Col. 20, lines 10-50 (65-67) teach a confidence level, Col. 12 line 58 to Col. 13, line 17 (45), teaches use of multiplying.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Breiholz , which also teaches flight avionics equipment (figs. 1-2), and additionally teaches confidence levels applied to multiple sources of weather information (Col. 20, lines 10-50). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to determine confidence levels when multiple sources of weather data are being used by aircraft, as taught by Breiholz, for the purpose of increasing flight safety while maintaining security. Selvarajan, Bunch, and Breiholz fail to explicitly teach dividing by number of output data and multiplying by 100, However, Ito teaches, computing the confidence level in percentage by dividing the number of similar output data with the total number of output data and further multiplying by hundred. ([0115] teaches calculating confidence degree based on number of conditions and multiplying by 100.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Breiholz, which also teaches flight avionics equipment (figs. 1-2), and additionally teaches confidence levels applied to multiple sources of weather information (Col. 20, lines 10-50), with Ito , which also teaches data monitoring to detect conditions (Abstract), and additionally teaches calculating confidence degree by dividing by number of outputs / conditions and multiplying by 100 ([0115]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan, Bunch, and Breiholz with the added ability to use multiplication and division to calculate a confidence level, as taught by Ito, for the purpose of increasing flight safety while maintaining security . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of Breiholz, in view of Ito, in view of US 20240411753 to Sivaraj et al. (hereinafter Sivaraj) . Regarding claim 7, Selvarajan, Bunch, Breiholz, and Ito teach, The method as claimed in claim 6, further comprising calculating flight modification parameters using the output data, when the output data is determined to have the confidence level of 100%, (Ito, [0115] teaches calculating confidence level.) wherein the confidence level of 100% indicates that the output data from each of the flight plan applications are the same . (Selvarajan teaches verifying data confidence using encryption and signatures, Bunch teaches using thresholds to determine confidence, as discussed in the rejection of claim 1.) Selvarajan, Bunch, Breiholz, and Ito fail to explicitly teach determining that data is the same, However, Sivaraj teaches, … wherein the confidence level of 100% indicates that the output data from each of the flight plan applications are the same. ([0127] teaches using matching scores to determine if data is the same.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Breiholz, which also teaches flight avionics equipment (figs. 1-2), and additionally teaches confidence levels applied to multiple sources of weather information (Col. 20, lines 10-50), with Ito, which also teaches data monitoring to detect conditions (Abstract), and additionally teaches calculating confidence degree by dividing by number of outputs / conditions and multiplying by 100 ([0115]), with Sivaraj , which also teaches networking ([0044]) and data distributions ([0027]), and additionally teaches matching the same data ([0127]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan, Bunch, Breiholz, and Ito with the added ability to determine matching data, as taught by Sivaraj, for the purpose of increasing flight safety while increasing computational efficiency by detecting the same data from different sources based on a matching score . 07-21-aia AIA Claim 8-9 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of Breiholz . Regarding claim 8, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising initiating a pilot review prompt to authenticate context sensitivity of the output data when the output data determined to have similar values at each of the flight plan applications. (Bunch, figs. 1-2e teach pilot displayed weather radar maps which may be from an EFB.) (Selvarajan, as discussed in the rejection of claim 1, teaches multiple EFBs as inputs.) Selvarajan and Bunch fail to explicitly teach similar data that is output, However, Breiholz teaches, initiating a pilot review prompt to authenticate context sensitivity of the output data when the output data determined to have similar values at each of the flight plan applications. (Breiholz, figs. 11a-11f and Col. Col. 21, line 38 to Col. 22, line 15 (73-74) teach ground and aircraft radar data being used together to identify a region of unsafe weather / thunderstorms, where the map is color coded. Figs 11a-11f show similar data from different sources.) (Selvarajan and Bunch both teach EFB.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Breiholz , which also teaches flight avionics equipment (figs. 1-2), and additionally teaches confidence levels applied to multiple sources of weather information (Col. 20, lines 10-50) and displaying information (figs. 11a-11f). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to determine confidence levels when multiple sources of weather data are being used by aircraft, and showing similar data from different sources, as taught by Breiholz, for the purpose of increasing flight safety while maintaining security. Regarding claim 9, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising Selvarajan and Bunch fail to explicitly teach rejecting data that is different, However, Breiholz teaches, rejecting the output data on determining that the output data at each of the flight plan applications is different. (Breiholz, Col. 11, line 58 to Col. 12, line 19 (42) teaches rejecting / discarding / removing data that is different due to quality differences and/or distance of measurement.) (Selvarajan and Bunch both teach EFB.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Breiholz , which also teaches flight avionics equipment (figs. 1-2), and additionally teaches confidence levels applied to multiple sources of weather information (Col. 20, lines 10-50) and displaying information and rejecting differences (figs. 11a-11f and Col. 11, line 58 to Col. 12, line 19). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to determine confidence levels when multiple sources of weather data are being used by aircraft, and reject differences, as taught by Breiholz, for the purpose of increasing flight safety while maintaining security . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of US 20230195712 to Carpentier et al. (hereinafter Carpentier) . Regarding claim 10, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising: determining the output data having the confidence level in the prespecified threshold as graded data; (See rejection of claim 1, including discussion of Selvarajan, [0004], & [0008].) (Applicant’s specification at [0043] teaches graded data with deviations is invalid data.) transmitting the graded data to each of the flight plan applications; (Selvarajan, Abstract & [0053] teaches EFB config file / “flight modification parameters” and FMS exchanging data. Selvarajan and Bunch both teach EFB.) ascertaining if the graded data is an actual output corresponding to the input data received for the processing at each of the flight plan applications; and (Selvarajan, Abstract & [0004] teach use of encryption for EFB data, and cybersecurity of config files. [0008] teaches FMS using digital signature comparison / matching to validate data.) authenticating the graded data as valid data. (Selvarajan, Abstract & [0004] teach use of encryption for EFB data, and cybersecurity of config files. [0008] teaches FMS using digital signature comparison / matching to validate data.) Selvarajan and Bunch fail to explicitly teach graded data, However, Carpentier teaches, determining the output data having the confidence level in the prespecified threshold as graded data; ( Carpentier , [0008] & [0086] teach use of thresholds with deviations, [0055] teaches applying a grade between 0-1. Abstract teaching applying the deviation analysis to flight parameters) (Applicant’s specification at [0043] teaches graded data with deviations is invalid data.) transmitting the graded data to each of the flight plan applications; ( Carpentier , Abstract teaches comparing two databases using deviations.) ascertaining if the graded data is an actual output corresponding to the input data received for the processing at each of the flight plan applications; and ( Carpentier , [0054-58] teach grading and deviations..) authenticating the graded data as valid data. ( Carpentier , [0059] teaches validating data. [0015-16] & [0065] teach validating data.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Carpentier , which also teaches flight parameters (Abstract), and additionally teaches grading using deviations ([0055]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to use deviations to determine graded data, as taught by Carpentier, for the purpose of increasing flight safety while maintaining security by filtering data using deviations . 07-21-aia AIA Claim s 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of US 20250306242 to Xavier et al. (hereinafter Xavier) . Regarding claim 11, Selvarajan and Bunch teach, The method as claimed in claim 1, further comprising: obtaining a feedback for each of the flight plan applications based on computation of the output data at each of the flight plan applications; (Selvarajan, Abstract & [0004] teach use of encryption for EFB data, and cybersecurity of config files. [0008] teaches FMS using digital signature comparison / matching to validate data.) updating weightage of each of the flight plan applications based on the feedback ; and (Selvarajan, Abstract & [0004] teach use of encryption for EFB data, and cybersecurity of config files. [0008] teaches FMS using digital signature comparison / matching to validate data.) determining the confidence level of the output data depending on the degree of agreement between the output data and the updated weightage of each of the flight plan applications. (Selvarajan, Abstract & [0004] & [0008] teach verifying data between EFB and FMS.) (Bunch, [0059-60] teaches alternative flight plans / routes being used with weather thresholds, and the use of EFBs.) Selvarajan and Bunch fail to explicitly teach the use of weights and machine learning / feedback, However, Xavier teaches, obtaining a feedback for each of the flight plan applications based on computation of the output data at each of the flight plan applications; ([0055] teaches a learning model using weights used in flight planning. Learning models update the initially obtained weights.) updating weightage of each of the flight plan applications based on the feedback; and ([0055] teaches a learning model using weights used in flight planning. Learning models update the initially obtained weights. [0063] teaches updating.) determining the confidence level of the output data depending on the degree of agreement between the output data and the updated weightage of each of the flight plan applications. ([0055] teaches a learning model using weights used in flight planning.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Xavier , which also teaches FMS and flight planning ([0011]), and additionally teaches a learning model using weights used in flight planning ([0055]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan and Bunch with the added ability to use machine learning feedback to update weights, as taught by Xavier, for the purpose of increasing flight safety while maintaining security. Regarding claim 17, Selvarajan, Bunch, and Xavier teach, The system as claimed in claim 12, further comprising a feedback engine to: (Examiner interprets this feature as software, as described in the applicant’s specification at [0018]) obtain a feedback for each of the flight plan applications based on computation of the output data at each of the flight plan applications; and update weightage of each of the flight plan applications based on the feedback, wherein the data processing engine is to determine the confidence level of the output data depending on the degree of agreement between the output data and the updated weightage of each of the flight plan applications. Claim 17 is rejected using the same basis of arguments used to reject claim 11 above . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Selvarajan, in view of Bunch, in view of Ito . Regarding claim 13, Selvarajan and Bunch teach, The system as claimed in claim 12, wherein for the output data determined to have the confidence level below the prespecified value indicating that a degree of disagreement is present, the data processing engine is to: (See rejection of claim 4.) authenticate the output data; (Selvarajan, [0008] teaches EFB applications and config files data access, where config files affect complex flight system.) on determining that the output data is authentic, the flight parameter generation engine is to (Selvarajan, [0008] teaches EFB applications and config files data access, where config files affect complex flight system.) transmit the flight modification parameters to the flight management system of the aircraft. (See rejection of claim 1. Selvarajan, Abstract & [0053] teaches EFB config file / “flight modification parameters” and FMS. [0004] & [0008] teach communications with FMS.) Selvarajan and Bunch fail to explicitly teach calculating modification parameters, However, Ito teaches, calculate flight modification parameters using the output data; and (See rejection of claim 7. Ito, [0115] teaches calculating confidence level.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Selvarajan, which teaches EFBs and FMS that are connected for flight planning (Abstract), and while maintaining security ([0004-8]), with Bunch, which also teaches Electronic Flight Bags (EFB) ([0060]) and flight planning ([0057]), and additionally teaches thresholds and flight planning / routing (Abstract & [0056-59]), with Ito , which also teaches data monitoring to detect conditions (Abstract), and additionally teaches calculating confidence degree ([0115]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Selvarajan, Bunch, and Breiholz with the added ability to use multiplication and division to calculate a confidence level, as taught by Ito, for the purpose of increasing flight safety while maintaining security. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN WILLIAM AVERY whose telephone number is (571) 272-3942. The examiner can normally be reached on 9AM-5PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Farid Homayounmehr can be reached on (571) 272-3739. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.W.A./ /JASON K GEE/Primary Examiner, Art Unit 2495 Application/Control Number: 19/077,056 Page 2 Art Unit: 2495 Application/Control Number: 19/077,056 Page 3 Art Unit: 2495 Application/Control Number: 19/077,056 Page 4 Art Unit: 2495 Application/Control Number: 19/077,056 Page 5 Art Unit: 2495 Application/Control Number: 19/077,056 Page 6 Art Unit: 2495 Application/Control Number: 19/077,056 Page 7 Art Unit: 2495 Application/Control Number: 19/077,056 Page 8 Art Unit: 2495 Application/Control Number: 19/077,056 Page 9 Art Unit: 2495 Application/Control Number: 19/077,056 Page 10 Art Unit: 2495 Application/Control Number: 19/077,056 Page 11 Art Unit: 2495 Application/Control Number: 19/077,056 Page 12 Art Unit: 2495 Application/Control Number: 19/077,056 Page 13 Art Unit: 2495 Application/Control Number: 19/077,056 Page 14 Art Unit: 2495 Application/Control Number: 19/077,056 Page 15 Art Unit: 2495 Application/Control Number: 19/077,056 Page 16 Art Unit: 2495 Application/Control Number: 19/077,056 Page 17 Art Unit: 2495 Application/Control Number: 19/077,056 Page 18 Art Unit: 2495 Application/Control Number: 19/077,056 Page 19 Art Unit: 2495
Prosecution Insights