DETAILED ACTION
This Office Action is taken in response to Applicant’s Amendment and Remarks filed on 10/02/2025 regarding Application No. 17/929,194 originally filed on 09/01/2022. Claims 1-2, 6-12 and 14-20 are pending for consideration:
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 .
Response to Arguments
The applicant argues “Independent claims 1, 9, and 15, as currently amended, are not directed to merely a principle that is ‘a fundamental truth,’ ‘an original cause,’ or ‘a motive’… For example, independent claim 1 recites determining whether a vehicle is being actively tracked using backup tracking data received from sensors… As a result, the claims recite more than merely the abstract idea and a mental process… the claims recite a solution necessarily rooted in computer technology to overcome a problem specifically arising in the realm of computer networks… (DDR Holdings)” [Remarks, p.9-12]. The examiner respectfully disagrees.
As amended, the claims remain directed to the abstract mental process of data record management and reconciliation: detecting a missing data field, deciding whether a current record/vehicle matches a previously received record/vehicle, using a prior value to fill a missing field, conditionally generating a “mapped data record,” and otherwise assigning a new identifier. These are observation/evaluation steps that, under the broadest reasonable interpretation, can be performed mentally (field-by-field comparison, match/no-match decision, copy/use prior value), and the recited “computing device / processor / memory / mapped data record” merely automate that logic on generic computer components. Merely receiving sensor data and performing the above comparisons does not take the claim out of the mental-process grouping, and the airport context/primary-backup systems are, at most, field-of-use and data gathering. Accordingly, the § 101 rejection of claims 1-2, 6-12, and 14-20 is maintained.
The applicant argues “Cummings, Du, Riess, and Park, individually or in combination, do not teach or suggest… ‘generate a mapped data record using the primary tracking data and the generated data for the missing data field…’ and ‘refrain from generating the mapped data record and assign a new identifier…’ as recited in independent claims 1 and 15, as amended” [Remarks, p.12-14]. The examiner respectfully disagrees.
Riess expressly discloses comparing current vehicle attributes to attributes stored in a tracked-vehicle database and, if a match is found, updating the existing tracked vehicle record with the newly acquired information; and if no match is found, “flagging” the vehicle as new and creating/storing a new record (i.e., a new identifier). (as per “compares the attributes… If a match is found… database… is updated… If no match is found… designates or ‘flags’ the vehicle as a new element… stored in… new record” in ¶65, as per “new entry is logged… for that particular vehicle… If a match is not found… template is then added to the database” in ¶67, as per Claim 27). This corresponds to updating the mapped data record when the vehicle is the same, and refraining from updating that mapped record and instead assigning a new identifier when the vehicle is not the same. It would have been obvious to apply Du’s missing-data recovery to Cummings’ airport tracking records and to apply Riess’ known match/no-match record update vs new-record creation logic to maintain consistent vehicle records when fields are missing or incomplete. Applicant’s traversal is therefore unpersuasive, and the obviousness rejection of claims 1-2, 6, 15-16, 18-20 is maintained.
The applicant argues “Riess does not teach or suggest… [the mapped data record / new identifier limitations]” [Remarks, p.13-14]. The examiner respectfully disagrees.
Riess discloses exactly the claimed conditional record handling: compare newly acquired attributes to stored attributes, update the tracked vehicle database when a match is found, and designate as a new vehicle and store attributes in a new record when no match is found. (as per ¶65, ¶67, Claim 27). This is the same record/identifier management logic recited by Applicant (mapped record if same; new identifier if not same), merely applied to Cummings’ airport tracking records.
The applicant argues “Park does not cure the deficiencies of Cummings, Du, and Riess” [Remarks, p.13-14]. The examiner respectfully disagrees.
Park teaches operating an auxiliary surveillance processing function when the main surveillance data processing is unavailable, including receiving surveillance data and generating track output based on the available surveillance input. (as per P3¶2). This corresponds to the claimed fallback to backup tracking data when primary tracking data is not available, and generating a mapped data record/track output from the backup data, as applied in the rejection.
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 1-2, 6-12 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
1. A computing device for vehicle tracking, comprising:
a memory; and
a processor configured to execute instructions stored in the memory to:
receive tracking data for a vehicle at an airport, wherein the tracking data includes primary tracking data including a data record including a number of data fields from sensors of a primary tracking data system and backup tracking data from sensors of a backup tracking data system;
in response to receiving the primary tracking data, determine whether the vehicle is being actively tracked using the primary tracking data;
determine whether the data record included in the primary tracking data has a missing data field;
generate, in response to determining the data record has a missing data field, data for the missing data field by:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; and
generate a mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; and
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate the mapped data record using the backup tracking data to track the vehicle at the airport in response to determining that the vehicle being tracked using the backup tracking data is the same vehicle associated with the previously received data record
101 Analysis - Step 1: Statutory category – Yes
The claims recites a device including at least one step. The claims falls within one of the four statutory categories. MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claims recite the limitations “receive tracking data…”, “determine…”, “utilize”, “refrain…” and “generate…”. The determining limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of “computing device”, “memory”, “processor”, and “mapped data record”. That is, other than reciting the additional limitations, nothing in the claims precludes the step from practically being performed in the mind. For example, but for the “computing device”, and “mapped data record” language, the claim encompasses looking at data collected and forming a simple judgement. The mere nominal recitation of sensors, memory, and processor do not take the claim limitations out of the mental process grouping. Thus, the claims recite a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claims beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
The claims recite the additional elements of “computing device”, “memory”, “processor”, and “mapped data record” that performs receiving, determining, utilizing, and generating steps. The steps by the additional elements are recited at a high level of generality and merely automates the steps, therefore acting as a generic computer to perform the abstract idea. The additional elements are claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a computer. (i.e. “computing device”)
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to the abstract idea.
Step 2B evaluation: Inventive Concept: - No
The claim(s) are evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claims.
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f).
Therefore, Claim 1 is ineligible.
9. A non-transitory computer readable medium having computer-readable instructions stored thereon that are executable by a processor to:
receive tracking data for a vehicle at an airport, wherein the tracking data includes primary tracking data including a data record having an identifier and including a number of data fields from sensors of a primary tracking data system and backup tracking data from sensors of a backup tracking data system;
determine whether the tracking data has a missing data field from the number of data fields;
in response to determining the tracking data has a missing data field, generate data for the missing data field via K-Means clustering machine learning by;
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle is a same vehicle associated with the previously received data record; and
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record using the backup tracking data to track the vehicle at the airport;
generate the mapped data record to track the vehicle at the airport using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record.
101 Analysis - Step 1: Statutory category – Yes
The claims recites a device including at least one step. The claims falls within one of the four statutory categories. MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claims recite the limitations “receive tracking data…”, “determine…”, “utilize”, “refrain…”, and “generate…”. The determining limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of “via K-Means clustering machine learning”, “non-transitory computer readable medium”, “processor”, and “mapped data record”. That is, other than reciting the additional limitations, nothing in the claims precludes the step from practically being performed in the mind. For example, but for the “processor”, and “via K-Means clustering machine learning” language, the claim encompasses looking at data collected and forming a simple judgement. The mere nominal recitation of sensors, clustering machine learning, and processor do not take the claim limitations out of the mental process grouping. Thus, the claims recite a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claims beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
The claims recite the additional elements of “via K-Means clustering machine learning”, “non-transitory computer readable medium”, “processor”, and “mapped data record” that performs receiving, determining, utilizing, and generating steps. The steps by the additional elements are recited at a high level of generality and merely automates the steps, therefore acting as a generic computer to perform the abstract idea. The additional elements are claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a computer. (i.e. “non-transitory computer readable medium having computer-readable instructions”)
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to the abstract idea.
Step 2B evaluation: Inventive Concept: - No
The claim(s) are evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claims.
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f).
Therefore, Claim 9 is ineligible.
15. A system for vehicle tracking, comprising:
a primary tracking system located at an airfield of an airport to provide primary tracking data from sensors of the primary tracking system, wherein the primary tracking data includes a data record including a number of data fields;
a backup tracking system located at the airfield of the airport to provide backup tracking data from sensors of the backup tracking system;
a computing device configured to:
determine, in response to receiving primary tracking data, whether the data record included in the primary tracking data has a missing data field;
generate, in response to determining the data record has a missing data field, data the missing data field by:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether a vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
generate a mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; and
in response to the primary tracking data not including primary tracking data for a vehicle at the airport from the primary tracking system;
receive backup tracking data for the vehicle from the backup tracking system, wherein the backup tracking data includes a data record having an identifier and including a number of data fields;
determine whether the vehicle is being actively tracked using the backup tracking data;
match a data field of the data record of the backup tracking data to a data field included in a cluster of a number of data clusters associated with a number of vehicles at the airport; and
generate a mapped data record to track the vehicle at the airport using the backup tracking data and the matched data field.
101 Analysis - Step 1: Statutory category – Yes
The claims recites a system including at least one step. The claims falls within one of the four statutory categories. MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claims recite the limitations “receive”, “determine…”, “match…”, “utilize”, “refrain…”, and “generate…”. The determining limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of “primary tracking system / backup tracking system”, “computing device”, and “mapped data record”. That is, other than reciting the additional limitations, nothing in the claims precludes the step from practically being performed in the mind. For example, but for the “computing device”, and “mapped data record” language, the claim encompasses looking at data collected and forming a simple judgement. The mere nominal recitation of a sensors, primary/backup tracking system, etc. does not take the claim limitations out of the mental process grouping. Thus, the claims recite a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claims beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
The claims recite the additional elements of “primary tracking system / backup tracking system”, “computing device”, and “mapped data record” that performs receiving, matching, determining, utilizing, and generating steps. The steps by the additional elements are recited at a high level of generality and merely automates the steps, therefore acting as a generic computer to perform the abstract idea. The additional elements are claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a computer. (i.e. “computing device”)
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to the abstract idea.
Step 2B evaluation: Inventive Concept: - No
The claim(s) are evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claims.
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f).
Therefore, Claim 15 is ineligible.
Dependent claim(s) 2, 6-8, 10-12, 14, and 16-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or generic additional elements that do not integrate the judicial exception into a practical application. Claims 7, 10-12, and 14 are mere instructions to implement an abstract idea or other exception on a computer. Claims 2, 6, 8, and 16-20 recite limitations that are insignificant extra-solution activity as they are nominally or tangentially related to the invention and well-known. Therefore, dependent claim(s) 2, 6-8, 10-12 and 14, and 16-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 9, and 15.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 6, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cummings (US Pub. No. 20220068145) in view of Du (NPL Title: Missing Data Problem in the Monitoring System: A Review) in view of Riess (US Pub. No. 20060200307) in further view of Park (KR Pub. No. 20140092433).
As per Claim 1, Cummings discloses of a method for improved airport and related vehicle operations and tracking (as per Abstract), comprising:
a memory; (as per ¶35)
a processor configured to execute instructions stored in the memory (as per ¶23) to:
receive tracking data for a vehicle at an airport, wherein the tracking data includes primary tracking data including a data record including a number of data fields (as per “Under ADS-B In, an aircraft receives ADS-B and other data from nearby aircraft. On-the-ground infrastructure can also operate ADS-B In to receive messages and other data from nearby aircraft.” in ¶3, as per “The information contained in an ADS-B Message varies according to the message type. There are six different ADS-B message types and eight message sub-types which adhere to the Surveillance and Broadcast Services (SBS) format.” in ¶4) from sensors of a primary tracking data system and backup tracking data from sensors of a backup tracking data system; (as per “each SDR 44 is connected to an antenna 30 that is mounted outside in a location that will allow the ADS-B signals from a given vehicle to be received” in ¶43, as per “collect ADS-B data broadcast by aircraft and optionally other vehicles, optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data” in ¶8)
in response to receiving the primary tracking data, determine whether the vehicle is being actively tracked using the primary tracking data; (as per “Determining if the vehicle is entering a geofence volume, leaving the geofence, within the airport runway geometry, airborne or on the ground, if this vehicle is currently being tracked, updating the vehicle and/or aircraft state array, adding a new vehicle of interest, determining if an event has occurred, processing the event, removing “stale” vehicles from the vehicle state array” in ¶75)
determine whether the data record included in the primary tracking data has a missing data field; (as per “in Tables 2 and 3. underlining or dashes indicate fields that are sent and blanks indicate fields for which null data is transmitted.” in ¶5, as per “The method then checks to see if the raw data is valid as at 304, if the data fields are valid as at 306 and if the sub-message type is 1, 2, 3, 4 or 6 as at 308” in ¶62)
Cummings fails to expressly disclose:
generate, in response to determining the data record has a missing data field, data for the missing data field by:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; and
generate a mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; and
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate the mapped data record using the backup tracking data to track the vehicle at the airport in response to determining that the vehicle being tracked using the backup tracking data is the same vehicle associated with the previously received data record.
Du discloses of missing data recovery (as per Abstract), comprising:
generate, in response to determining the data record has a missing data field, data for the missing data field (as per “using the well-learned model, we can recover the missing data by the predicted value and get a complete monitoring dataset.” in P13990, C1 - Data Recovery Based on Data Mining Algorithms)
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In this way, Du operates to solve the missing data problem. (Abstract) Like Cummings, Du is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings with the missing data recovery system as taught by Du to enable another standard means of recovering missing data. (P13990, C1 - Data Recovery Based on Data Mining Algorithms) Such modification also allows the system to gather real-time data from multiple channels and address the issue of missing data. (P13984, C1 – Introduction)
Cummings and Du fail to expressly disclose:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
generate a mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; and
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate the mapped data record using the backup tracking data to track the vehicle at the airport in response to determining that the vehicle being tracked using the backup tracking data is the same vehicle associated with the previously received data record.
Riess discloses of a vehicle identification and tracking system (as per Abstract), comprising
determining whether the missing data field is a same data field included in a previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610.” in ¶65)
determining whether the vehicle being tracked is a same vehicle associated with the previously received data record; (as per “Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle, as well as the location and time each image was acquired.” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65)
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610.” in ¶67)
generate a mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record; (as per “The database 130 also includes a tracked or monitored vehicle database which is configured to store tracked vehicle records. Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110,” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle” in ¶67)
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; and (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610” in ¶67, as per “If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “flag the monitored vehicle as a new vehicle if the measured monitored vehicle attributes do not correspond to the measured tracked vehicle attributes stored in the tracked vehicle records” in Claim 27)
in response to determining that the vehicle being tracked using the backup tracking data is the same vehicle associated with the previously received data record. (as per “the database 130 also includes a tracked or monitored vehicle database which is configured to store tracked vehicle records” in ¶64, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610” in ¶67)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings and Du, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings and the missing data recovery system as taught by Du with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
Cummings, Du, and Riess fail to expressly disclose:
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate the mapped data record using the backup tracking data to track the vehicle at the airport
Park discloses of an air traffic control integrated system, comprising:
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data; (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
generate the mapped data record using the backup tracking data to track the vehicle at the airport (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
In this way, Park operates to improve the leading design and integration technologies of next-generation air traffic management systems. Like Cummings, Du, and Riess, Park is concerned with large-scale monitoring systems (Abstract).
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the missing data recovery system of Du, and the tracking system of Riess with the air traffic control integrated system of Park to operate a surveillance data bypass processing system when the operation of the main surveillance data processing system is impossible (P3¶2).
As per Claim 2, the combination of Cummings, Du, Riess, and Park teaches or suggests all limitations of Claim 1. Cummings further discloses:
in response to receiving the primary tracking data, the processor is configured to execute the instructions to determine whether the vehicle is being actively tracked using the primary tracking data. (as per “receive an ADS-B transmission from the ADS-B radio receiver when the vehicle is entering, within or leaving the defined geofence volume; detect whether the vehicle is performing a pre-established aircraft operation; and upon detecting that the vehicle is performing a pre-established aircraft operation, generate a detected event notification” in Claim 1, as per “if the aircraft is on the tracking list, tracking info status data is obtained as at 318, the method copies valid data into the aircraft state array as at 316 and then proceeds to encircled notation “2” in FIG. 13” in ¶62)
As per Claim 6, the combination of Cummings, Du, Riess, and Park teaches or suggests all limitations of Claim 1. Cummings further discloses:
determine whether the data record included in the backup tracking data has a missing data field, wherein the data record includes a first identifier; (as per “Receiving the ADS-B message (e.g., SBS formatted) data from the decoding programs, parsing the data into individual variables” in ¶74, as per “in Tables 2 and 3. underlining or dashes indicate fields that are sent and blanks indicate fields for which null data is transmitted” in ¶5)
Cummings fails to expressly disclose:
in response to determining the data record included in the backup tracking data has a missing data field, generate data for the missing data field via machine learning.
See Claim 1 for teachings of Du. Du further discloses:
in response to determining the data record included in the backup tracking data has a missing data field, generate data for the missing data field via machine learning. (as per “using the well-learned model, we can recover the missing data by the predicted value and get a complete monitoring dataset.” in P7C1, as per “Since the monitoring system usually consists of a large number of sensor nodes, it can gather real-time data from different geographical locations, which helps for monitoring the operating status of the system and making intelligent decisions” in P1C1)
In this way, Du operates to solve the missing data problem. (Abstract) Like Cummings, Riess, and Park, Du is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the tracking system of Riess, and air traffic control integrated system of Park with the missing data recovery system as taught by Du to enable another standard means of recovering missing data. (P13990, C1 - Data Recovery Based on Data Mining Algorithms) Such modification also allows the system to gather real-time data from multiple channels and address the issue of missing data. (P13984, C1 – Introduction)
As per Claim 15, Cummings discloses of a method for improved airport and related vehicle operations and tracking (as per Abstract), comprising:
a primary tracking system located at an airfield of an airport to provide primary tracking data from sensors of the primary tracking system, wherein the primary tracking data includes a data record including a number of data fields; (as per “Under ADS-B In, an aircraft receives ADS-B and other data from nearby aircraft. On-the-ground infrastructure can also operate ADS-B In to receive messages and other data from nearby aircraft.” in ¶3, as per “The information contained in an ADS-B Message varies according to the message type. There are six different ADS-B message types and eight message sub-types which adhere to the Surveillance and Broadcast Services (SBS) format.” in ¶4)
a backup tracking system located at the airfield of the airport to provide backup tracking data from sensors of the backup tracking system; (as per “optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data.” in ¶8, as per “The system can fuse data received from sensors with data from the FAA, airports and other stakeholders to support analysis and reporting on parameters that include make/model of aircraft, aircraft gross weight, aircraft number of seats, local vs transient operations, aircraft registration information and VFR vs IFR aircraft (the use of Air Traffic Control services), for example.” in ¶70)
a computing device configured to:
determine, in response to receiving primary tracking data, whether the data record included in the primary tracking data has a missing data field; (as per “in Tables 2 and 3. underlining or dashes indicate fields that are sent and blanks indicate fields for which null data is transmitted.” in ¶5, as per “The method then checks to see if the raw data is valid as at 304, if the data fields are valid as at 306 and if the sub-message type is 1, 2, 3, 4 or 6 as at 308” in ¶62)
match a data field of the data record of the backup tracking data to a data field included in a cluster of a number of data clusters associated with a number of vehicles at the airport; (as per “All of the ADS-B, FAA and airport home based aircraft data can be merged into a single data record and stored in database” in ¶59, as per “the aircraft or vehicle “state” array is a data structure that stores multiple ADS-B vehicle values… In various embodiments, each vehicle being tracked is allocated one state array structure. The identifying (index) is the vehicle's six-character (Hexadecimal) ICAO ID Code.” in ¶77, as per “Use the aircraft ICAO code to lookup FAA owner registration data, FAA aircraft reference data and lookup if the aircraft is based at the airport using a list of home-based aircraft provided by an airport database system.” in ¶85)
Cummings fails to expressly disclose:
generate, in response to determining the data record has a missing data field, data for the missing data field by:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether a vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
generate the mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record;
in response to primary tracking data not including primary tracking data for a vehicle at the airport from the primary tracking system, receive backup tracking data for the vehicle from the backup tracking system, wherein the backup tracking data includes a data record having an identifier and including a number of data fields;
determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record to track the vehicle at the airport using the backup tracking data and the matched data field.
Du discloses of missing data recovery (as per Abstract), comprising:
generate, in response to determining the data record has a missing data field, data for the missing data field (as per “using the well-learned model, we can recover the missing data by the predicted value and get a complete monitoring dataset.” in P13990, C1 - Data Recovery Based on Data Mining Algorithms)
In this way, Du operates to solve the missing data problem. (Abstract) Like Cummings, Du is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings with the missing data recovery system as taught by Du to enable another standard means of recovering missing data. (P13990, C1 - Data Recovery Based on Data Mining Algorithms) Such modification also allows the system to gather real-time data from multiple channels and address the issue of missing data. (P13984, C1 – Introduction)
Cummings and Du fail to expressly disclose:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether a vehicle being tracked is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
generate the mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record;
in response to primary tracking data not including primary tracking data for a vehicle at the airport from the primary tracking system, receive backup tracking data for the vehicle from the backup tracking system, wherein the backup tracking data includes a data record having an identifier and including a number of data fields;
determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record to track the vehicle at the airport using the backup tracking data and the matched data field.
Riess discloses of a vehicle identification and tracking system (as per Abstract), comprising
determining whether the missing data field is a same data field included in a previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610.” in ¶65)
determining whether the vehicle being tracked is a same vehicle associated with the previously received data record; (as per “Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle, as well as the location and time each image was acquired.” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65)
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610.” in ¶67)
generate the mapped data record using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record; (as per “The database 130 also includes a tracked or monitored vehicle database which is configured to store tracked vehicle records. Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110,” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle” in ¶67)
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610” in ¶67, as per “If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “flag the monitored vehicle as a new vehicle if the measured monitored vehicle attributes do not correspond to the measured tracked vehicle attributes stored in the tracked vehicle records” in Claim 27)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings and Du, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings and the missing data recovery system as taught by Du with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
Cummings, Du, and Riess fail to expressly disclose:
in response to primary tracking data not including primary tracking data for a vehicle at the airport from the primary tracking system, receive backup tracking data for the vehicle from the backup tracking system, wherein the backup tracking data includes a data record having an identifier and including a number of data fields;
determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record to track the vehicle at the airport using the backup tracking data and the matched data field.
Park discloses of an air traffic control integrated system, comprising:
in response to primary tracking data not including primary tracking data for a vehicle at the airport from the primary tracking system, (as per “The ADS-B / A system 107 can receive the data information of the aircraft equipped with the ADS-B / MODE-S transmitter. Then, the ADS-B / A system 107 can process the received data information and provide a surveillance service for the area without the radar network” in P2¶1) receive backup tracking data for the vehicle from the backup tracking system, (as per “The local control, low-altitude terminal airspace and airport ground area can include the ADS-B data monitoring function, which has the positioning function and the identification function of the ground mobile device to perform the function to assist the existing radar” in P2¶2) wherein the backup tracking data includes a data record having an identifier and including a number of data fields; (as per “The ADS-B system 107 processes the data such as the aircraft identification code (ID), the location information (altitude, latitude and longitude) of the aircraft, the aircraft speed-ground speed and the air speed, Function” in P3¶3, as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
determine whether the vehicle is being actively tracked using the backup tracking data; (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
generate a mapped data record to track the vehicle at the airport using the backup tracking data and the matched data field. (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
In this way, Park operates to improve the leading design and integration technologies of next-generation air traffic management systems. Like Cummings, Du, and Riess, Park is concerned with large-scale monitoring systems (Abstract).
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the missing data recovery system of Du, and the tracking system of Riess with the air traffic control integrated system of Park to operate a surveillance data bypass processing system when the operation of the main surveillance data processing system is impossible (P3¶2).
As per Claim 16, the combination of Cummings, Du, Riess and Park teaches or suggests all limitations of Claim 15. Cummings further discloses wherein the computing device is configured to receive primary tracking data for the number of vehicles at the airport. (as per “embodiments of the present disclosure collect ADS-B data broadcast by aircraft and optionally other vehicles… optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data.” in ¶8)
As per Claim 18, the combination of Cummings, Du, Riess and Park teaches or suggests all limitations of Claim 15. Cummings further discloses wherein the data fields included in the data record of tracking data include at least one of:
a tracking identifier; (as per “Use the aircraft ICAO code to lookup FAA owner registration data, FAA aircraft reference data and lookup if the aircraft is based at the airport using a list of home-based aircraft provided by an airport database system.” in ¶85)
a callsign associated with the vehicle, wherein the vehicle is an aircraft; and
a movement type associated with the aircraft. (as per “A fixed (stationary) geofence may be used for airport operations counting, noise sensitive areas or any airspace that needs to be monitored for air traffic. A dynamic or moving geofence can be used to monitor any aircraft within a defined volume relative to a moving reference point (e.g., drone, aircraft).” in ¶90)
As per Claim 19, the combination of Cummings, Du, Riess and Park teaches or suggests all limitations of Claim 15. Cummings further discloses wherein the primary tracking system and the backup tracking system comprise at least one of:
radar associated with the airport; (as per “optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data.” in ¶8)
navigational aids at the airport; (as per “optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data.” in ¶8)
cameras at the airport.
As per Claim 20, the combination of Cummings, Du, Riess and Park teaches or suggests all limitations of Claim 15. Cummings further discloses:
the generated mapped data record is utilized to track at least one of a location of the vehicle at the airport, (as per “the presently disclosed system can generate reports containing ground time for aircraft located in specified locations, such as a state, over the course of a period of time” in ¶19) a movement type of the vehicle through the airport, and a clearance status of the vehicle; and
the computing device includes a user interface configured to display at least one of the location of the vehicle at the airport, the movement type of the vehicle through the airport, and the clearance status of the vehicle. (as per “details of various operations and the aircraft involved can be presented in user interface 56 in organized form including, for example, date/time of the operation 56 a, manufacturer of the aircraft 56 b, model of the aircraft 56 c, ADS-B-N number 56 d, operation involved 56 e, runway used 56 f, ADS-B-ICAO ID 56 g, and the aircraft registrant's name and address 56 h. As shown in FIG. 8, details of operations can be recorded and presented in user interface 57, including operations by aircraft type 57 a, operations by aircraft engine 57 b and operations by aircraft manufacturer 57 c.” in ¶58)
Claim(s) 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Cummings (US Pub. No. 20220068145) in view of Du (NPL Title: Missing Data Problem in the Monitoring System: A Review) in view of Riess (US Pub. No. 20060200307) in view of Park (KR Pub. No. 20140092433) in further view of Copper (US Pub. No. 20190340533).
As per Claim 7, the combination of Cummings, Du, Riess, and Park teaches or suggests all limitations of Claim 6. Cummings, Du, Riess, and Park fail to expressly disclose:
determine whether a data field including a first identifier included in the backup tracking data corresponds to an existing data field via machine learning;
in response to the data field corresponding to the existing data field, utilize the existing data field to generate the mapped data record;
in response to the data field not corresponding to the existing data field, assign a second identifier to the data record including the data field.
Copper discloses of a method for preparing data (as per Abstract), comprising:
determine whether a data field including a first identifier included in the backup tracking data corresponds to an existing data field via machine learning; (as per “For each additional entry, (a) the “Fld Name” (field name) element 804 a contains the name of the field to which the entry corresponds, as associated with the field in data records in dataset 210; (b) the “Fld Index” (field index) element 804 b indicates the zero-based position of the field's value in an input data record 520 for the primary model” in ¶69, as per “the list of data structure entries 804 (that is, 806 1, 806, etc.) in the transitory memory 114 serves to map fields from a data record with the structure 320 to input fields with the structure of a data record 420” in ¶74)
in response to the data field corresponding to the existing data field, utilize the existing data field to generate the mapped data record; (as per “and the data record is missing only the value for field 2, the computing system will access the entry in data structure 800 corresponding to field 2, use the secondary model for field 2 to process all other values of input fields in the data record 320 that were not eliminated in phase 3 processing to generate a replacement value for field 2, and then combine the replacement value of field 2 with the values of other non-eliminated input fields from data record 320 to prepare a complete data record 420 for use in training the primary model, or a complete data record 520 for processing by the primary model.” in ¶70)
in response to the data field not corresponding to the existing data field, assign a second identifier to the data record including the data field. (as per “and the status/elimination code “5” indicates that the field was eliminated from the reduced clean dataset” in ¶55)
In this way, Copper operates to generate replacement values for invalid data values and exploit more fully the available historical data to create more robust models. (¶19) Like Cummings, Du, Riess, and Park, Copper is concerned with large-scale data handling. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the missing data recovery system of Du, the tracking system of Riess, and the air traffic control integrated system of Park with the data preparation method as taught by Copper to enable another standard means of sorting and classifying missing/invalid data. (¶55)
As per Claim 8, the combination of Cummings, Du, Riess, Park, and Copper teaches or suggests all limitations of Claim 7. Cummings, Du, Riess, and Park fail to expressly disclose wherein the processor is configured to generate the mapped data record using at least one of the backup tracking data, the generated data for the missing data field, the data record having the first identifier, and the data record having the second identifier.
See Claim 7 for teachings of Copper. Copper further discloses wherein the processor is configured to generate the mapped data record using at least one of the backup tracking data, (as per “with phase 3 processing a field status data structure 440, an abstraction of which is shown in FIG. 4C, is constructed for each data field in the phase 1 dataset 330” in ¶55) the generated data for the missing data field, (as per “improved systems and methods are needed to generate replacement values for invalid data values that account for the context in which the invalid values occurred” in ¶19) the data record having the first identifier, and the data record having the second identifier. (as per “field number “1” (v1) is associated with the field named “Sequence” in the dataset, and the status/elimination code “5” indicates that the field was eliminated from the reduced clean dataset because it was sequence information not useful to the machine learning algorithm” in ¶55)
In this way, Copper operates to generate replacement values for invalid data values and exploit more fully the available historical data to create more robust models. (¶19) Like Cummings, Du, Riess, and Park, Copper is concerned with large-scale data handling. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the missing data recovery system of Du, the tracking system of Riess, and the air traffic control integrated system of Park with the data preparation method as taught by Copper to enable another standard means of sorting and classifying missing/invalid data. (¶55)
Claim(s) 9-10 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Cummings (US Pub. No. 20220068145) in view of Fekade (NPL Title: Probabilistic Recovery of Incomplete Sensed Data in IoT) in view of Riess (US Pub. No. 20060200307) in further view of Park (KR Pub. No. 20140092433).
As per Claim 9, Cummings discloses of a method for improved airport and related vehicle operations and tracking (as per Abstract), comprising:
receive tracking data for a vehicle at an airport, wherein the tracking data includes primary tracking data including a data record having an identifier and including a number of data fields; (as per “Under ADS-B In, an aircraft receives ADS-B and other data from nearby aircraft. On-the-ground infrastructure can also operate ADS-B In to receive messages and other data from nearby aircraft.” in ¶3, as per “The information contained in an ADS-B Message varies according to the message type. There are six different ADS-B message types and eight message sub-types which adhere to the Surveillance and Broadcast Services (SBS) format.” in ¶4) from sensors of a primary tracking data system and backup tracking data from sensors of a backup tracking data system; (as per “each SDR 44 is connected to an antenna 30 that is mounted outside in a location that will allow the ADS-B signals from a given vehicle to be received” in ¶43, as per “collect ADS-B data broadcast by aircraft and optionally other vehicles, optionally collects other data from radar and other sensors, processes and even corrects the data as necessary, and fuses it with additional external data to provide rich, accurate, real-time vehicle and airport operations data” in ¶8)
determine whether the tracking data has a missing data field from the number of data fields; (as per “in Tables 2 and 3. underlining or dashes indicate fields that are sent and blanks indicate fields for which null data is transmitted.” in ¶5, as per “The method then checks to see if the raw data is valid as at 304, if the data fields are valid as at 306 and if the sub-message type is 1, 2, 3, 4 or 6 as at 308” in ¶62)
Cummings fails to expressly disclose wherein:
in response to determining the tracking data has a missing data field, generate data for the missing data field via K-Means clustering machine learning by;
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle is a same vehicle associated with the previously received data record; and
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; and
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record using the backup tracking data to track the vehicle at the airport;
generate the mapped data record to track the vehicle at the airport using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record.
Fekade discloses of incomplete data recovery (as per Abstract), comprising:
in response to determining the tracking data has a missing data field, generate data for the missing data field via K-Means clustering machine learning; (as per “Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm.” in Abstract, as per “Then, original matrix R is complemented with missing values obtained from recovered matrix R’.” in P2287, C1 - Extended PMF Approach to Improve the Precision of Data Recovery)
In this way, Fekade operates to solve the incomplete data problem. (Abstract) Like Cummings, Fekade is concerned with large-scale monitoring systems. (Fig. 1)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings with the incomplete data recovery system as taught by Fekade to enable another standard means of recovering incomplete data. (Fig. 6) Such modification also allows the system to gather real-time data from multiple channels and address the issue of incomplete data. (Fig. 1)
Cummings and Fekade fail to expressly disclose:
determining whether the missing data field is a same data field included in a previously received data record;
determining whether the vehicle is a same vehicle associated with the previously received data record;
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record;
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record using the backup tracking data to track the vehicle at the airport;
generate the mapped data record to track the vehicle at the airport using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record;
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record.
Riess discloses of a vehicle identification and tracking system (as per Abstract), comprising
determining whether the missing data field is a same data field included in a previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610.” in ¶65)
determining whether the vehicle is a same vehicle associated with the previously received data record; (as per “Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle, as well as the location and time each image was acquired.” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65)
utilizing the same data field to generate data for the missing data field in response to the vehicle being tracked being the same vehicle associated with the previously received data record; (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610.” in ¶67)
generate the mapped data record to track the vehicle at the airport using the primary tracking data and the generated data for the missing data field in response to determining that the vehicle being tracked is the same vehicle associated with the previously received data record; (as per “The database 130 also includes a tracked or monitored vehicle database which is configured to store tracked vehicle records. Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110,” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle” in ¶67)
refrain from generating the mapped data record and assign a new identifier to the data record responsive to a determination that the vehicle being tracked is not the same vehicle associated with the previously received data record. (as per “In step 604, computer 110, FIG. 2, compares the attributes of the acquired image to attributes stored in the tracked vehicle database shown in 610. If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured. If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “Once the best match is determined by means of the above comparison, the new entry is logged as an updated time and location for that particular vehicle. If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610” in ¶67, as per “If no match is found, computer 110, FIG. 2, designates or “flags” the vehicle as a new element, and the attributes of the newly monitored vehicle are stored in the appropriate fields of the new record in the database 610” in ¶65, as per “flag the monitored vehicle as a new vehicle if the measured monitored vehicle attributes do not correspond to the measured tracked vehicle attributes stored in the tracked vehicle records” in Claim 27)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings and Fekade, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings and the incomplete data recovery system as taught by Fekade with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
Cummings, Fekade, and Riess fail to expressly disclose:
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data;
generate a mapped data record using the backup tracking data to track the vehicle at the airport;
Park discloses of an air traffic control integrated system, comprising:
in response to the primary tracking data not including primary tracking data for the vehicle, determine whether the vehicle is being actively tracked using the backup tracking data; (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
generate a mapped data record using the backup tracking data to track the vehicle at the airport; (as per “The surveillance data bypass processing system 206 may be an auxiliary system of the surveillance data processing system 204. In other words, the surveillance data bypass processing system 206 may operate when the operation of the surveillance data processing system 204 is impossible. The surveillance data bypass processing system 206 can determine the estimated position of the aircraft after receiving the surveillance data by receiving the surveillance data depending on whether the surveillance data processing system 204 is operated or not. Accordingly, the surveillance data bypass processing system 206 may include a surveillance data input function, a plot or track generation function using the input surveillance data, and a function of outputting the generated surveillance data.” in P3¶2)
In this way, Park operates to improve the leading design and integration technologies of next-generation air traffic management systems. Like Cummings, Fekade, and Riess, Park is concerned with large-scale monitoring systems (Abstract).
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the incomplete data recovery of Fekade, and the tracking system of Riess with the air traffic control integrated system of Park to operate a surveillance data bypass processing system when the operation of the main surveillance data processing system is impossible (P3¶2).
As per Claim 10, the combination of Cummings, Fekade, Riess, and Park teaches or suggests all limitations of Claim 9. Cummings fails to expressly disclose wherein generating the data for the missing data field includes determining whether the missing data field is the same data field included in the previously received data record via the K-Means clustering machine learning.
See Claim 9 for teachings of Fekade. Fekade further discloses generating the data for the missing data field via the K-Means clustering machine learning. (as per “Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm.” in Abstract, as per “Then, original matrix R is complemented with missing values obtained from recovered matrix R’.” in P2287, C1 - Extended PMF Approach to Improve the Precision of Data Recovery)
In this way, Fekade operates to solve the incomplete data problem. (Abstract) Like Cummings, Riess, and Park, Fekade is concerned with large-scale monitoring systems. (Fig. 1)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the tracking system of Riess, and the air traffic control integrated system of Park with the incomplete data recovery system as taught by Fekade to enable another standard means of recovering incomplete data. (Fig. 6) Such modification also allows the system to gather real-time data from multiple channels and address the issue of incomplete data. (Fig. 1)
Cummings, Fekade, and Park fail to expressly disclose determining whether the missing data field is the same data field included in the previously received data record.
See Claim 9 for teachings of Riess. Riess further discloses determining whether the missing data field is the same data field included in the previously received data record. (as per “Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle, as well as the location and time each image was acquired.” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings, Fekade, and Park, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the incomplete data recovery system of Fekade, and the air traffic control integrated system of Park with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
As per Claim 14, the combination of Cummings, Fekade, Riess, and Park teaches or suggests all limitations of Claim 10. Cummings, Fekade, and Park fail to expressly disclose wherein in response to determining the missing data field is not the same data field included in the previously received data record, the processor is to execute the instructions to generate a cluster associated with the data record.
See Claim 10 for teachings of Riess. Riess further discloses wherein in response to determining the missing data field is not the same data field included in the previously received data record, the processor is to execute the instructions to generate a cluster associated with the data record. (as per “If a match is not found, the vehicle is logged as originating within the monitored area at the location/time and the template is then added to the database 610.” in ¶67, “wherein the processor is programmed to flag the monitored vehicle as a new vehicle if the measured monitored vehicle attributes do not correspond to the measured tracked vehicle attributes stored in the tracked vehicle records... wherein a new tracked vehicle record is created in the tracked vehicle database, the new tracked vehicle record including a plurality of data fields corresponding to the measured monitored vehicle attributes.” in Claim 27-28)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings, Fekade, and Park, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the incomplete data recovery system of Fekade, and the air traffic control integrated system of Park with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
Claim(s) 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Cummings (US Pub. No. 20220068145) in view of Fekade (NPL Title: Probabilistic Recovery of Incomplete Sensed Data in IoT) in view of Riess (US Pub. No. 20060200307) in view of Park (KR Pub. No. 20140092433) in further view of Du (NPL Title: Missing Data Problem in the Monitoring System: A Review).
As per Claim 11, the combination of Cummings, Fekade, Riess, and Park teaches or suggests all limitations of Claim 10. Cummings, Fekade, Riess, and Park fail to expressly disclose wherein in response to determining the missing data field is the same data field included in the previously received data record, the processor is to execute the instructions to determine whether the vehicle associated with the tracking data including the data record is the same vehicle associated with the previously received data record using k-nearest neighbors (k-NN) machine learning.
Du discloses of missing data recovery (as per Abstract), wherein in response to determining the missing data field is the same data field included in the previously received data record, the processor is to execute the instructions to determine whether the vehicle associated with the tracking data including the data record is the same vehicle associated with the previously received data record using k-nearest neighbors (k-NN) machine learning. (as per “The specific steps of KNN algorithm are as follows: first, based on the distance calculation method, traverse the entire dataset and calculate the distance between each complete sample and the sample containing missing values. In most cases, the Euclidean distance is selected. The closer the samples are, the higher the similarity between the samples. Then, according to the parameter K, the K samples closest to the sample containing missing values are selected. Finally, the missing values are recovered by weighting and averaging these K samples. KNN based method can not only recover missing data in discrete datasets but also has good adaptability to continuous datasets.” in P3988, C2 - K-Nearest Neighbors (KNN) Based Method)
In this way, Du operates to solve the missing data problem. (Abstract) Like Cummings, Fekade, Riess, and Park, Du is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the incomplete data recovery system of Fekade, the tracking system of Riess, and the air traffic control integrated system of Park with the missing data recovery system as taught by Du to enable another standard means of recovering missing data. (P13990, C1 - Data Recovery Based on Data Mining Algorithms) Such modification also allows the system to gather real-time data from multiple channels and address the issue of missing data. (P13984, C1 – Introduction)
As per Claim 12, the combination of Cummings, Fekade, Riess, Park, and Du teaches or suggests all limitations of Claim 11. Cummings fails to expressly disclose wherein in response to determining the vehicle associated with the tracking data including the data record is the same vehicle, the processor is to execute the instructions to generate the mapped data record using the tracking data and the generated data.
See Claim 11 for teachings of Fekade. Fekade further discloses generating the mapped data record using the tracking data and the generated data. (as per “when there are missing values inside one cluster, we can estimate them from neighboring nodes within the same cluster group.” in P2288, C2 – Experimental Results, as per “After getting the latent vectors, it is possible to reconstruct the missing data and complete the original matrix.” in P2289, C1 – Simulation Parameters and Results, Fig. 5)
In this way, Fekade operates to solve the incomplete data problem. (Abstract) Like Cummings, Riess, Park, and Du, Fekade is concerned with large-scale monitoring systems. (Fig. 1)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the tracking system of Riess, the air traffic control integrated system of Park, and the missing data recovery system as taught by Du with the incomplete data recovery system as taught by Fekade to enable another standard means of recovering incomplete data. (Fig. 6) Such modification also allows the system to gather real-time data from multiple channels and address the issue of incomplete data. (Fig. 1)
Cummings and Fekade fail to expressly disclose determining the vehicle associated with the tracking data including the data record is the same vehicle.
See Claim 11 for teachings of Riess. Riess further discloses determining the vehicle associated with the tracking data including the data record is the same vehicle. (as per “Each tracked vehicle record includes data corresponding to the anonymous vehicle feature data that is extracted from a captured image of a previously monitored vehicle, as well as the location and time each image was acquired.” in ¶64, as per “If a match is found per step 606, the tracked vehicle database 610 is updated by storing the current location of the vehicle in the monitored area, as well as the time the image was captured.” in ¶65)
In this way, Riess operates to track the location and movement of selected vehicles. (¶2) Like Cummings, Fekade, Park, and Du, Riess is concerned with large-scale monitoring systems. (Abstract)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the incomplete data recovery system of Fekade, the air traffic control integrated system of Park, and the missing data recovery system as taught by Du with the tracking system of Riess to enable another standard means of utilizing previous data. (Fig. 7) Such modification also allows the system to compare new data fields of a vehicle with previous data fields of a vehicle. (¶32)
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cummings (US Pub. No. 20220068145) in view of Du (NPL Title: Missing Data Problem in the Monitoring System: A Review) in view of Riess (US Pub. No. 20060200307) in view of Park (KR Pub. No. 20140092433) in further view of Fekade (NPL Title: Probabilistic Recovery of Incomplete Sensed Data in IoT).
As per Claim 17, the combination of Cummings, Du, Riess, and Park teaches or suggests all limitations of Claim 16. Cummings, Du, Riess, and Park fail to expressly disclose wherein the computing device is configured to cluster the primary tracking data by K-Means clustering machine learning to generate the number of data clusters.
Fekade discloses of incomplete data recovery (as per Abstract), wherein the computing device is configured to cluster the primary tracking data by K-Means clustering machine learning to generate the number of data clusters. (as per “Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm.” in Abstract, as per “By clustering sensors that have a minimal distance, it is possible to recover missing sensor data from other sensors in the cluster. In order to use an imputation scheme that utilizes time and space information, several algorithms for estimating missing sensor data have been proposed so far” in P2283, C2 – Related Work)
In this way, Fekade operates to solve the incomplete data problem. (Abstract) Like Cummings, Du, Riess, and Park, Fekade is concerned with large-scale monitoring systems. (Fig. 1)
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the vehicle operations system of Cummings, the missing data recovery system of Du, the tracking system of Riess, and the air traffic control integrated system of Park with the incomplete data recovery system as taught by Fekade to enable another standard means of recovering incomplete data. (Fig. 6) Such modification also allows the system to gather real-time data from multiple channels and address the issue of incomplete data. (Fig. 1)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.R.R./Examiner, Art Unit 3658
/TRUC M DO/Primary Examiner, Art Unit 3658