DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claim 32 is canceled.
3. Claims 1-31 and 33 are pending and presented for examination.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-31 and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 1 recites:
A computer implemented method of identifying anomalous flight data, the method comprising:
receiving a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point;
mapping the flight data units as respective data points to a multi-dimensional space, wherein the dimensions of the multi-dimensional space comprise a dimension for each of the plurality of flight parameters; and
identifying one or more anomalous flight data units in the received plurality of flight data units by applying a local outlier factor algorithm to the mapped flight data units.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitation in the claim is only: receiving a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point. This limitation is recited at a high level of generality (i.e., gathering or collecting data) such that it amounts no more than mere instructions to apply the exception using a generic collecting or computer components.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional element is recited at a high level of generality (i.e., gathering or collecting data). Further, the additional element is conventional in the art, as evidenced by the art of record (see, Desell et al. US 2015/0324501 (hereinafter, Desell), ([0012], [0030], Fig. 1), and Melnyk et al. “Vector Autoregressive Model-Based Anomaly Detection
in Aviation Systems”, (hereinafter, Melnyk), (page 163, Fig. 2). Therefore, claim 1 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Dependent claims 2-26 and 29, add further details of the identified abstract idea. The claims are not patent eligible.
Dependent claim 27, recites addition element of “wherein the method is performed at a ground location”. However, this limitation is recited at a high level of generality (i.e., as place where the method of claim 1 is performed) such that it amounts no more than mere instructions to apply the exception at different location. Further, the additional element is conventional in the art, as evidenced by the art of record (see, Li et al. “Analysis of Flight Data Using Clustering Techniques
for Detecting Abnormal Operations”, (hereinafter, Li), (page 596), and Melnyk (page 161). Therefore, claim 27 is directed to an abstract idea without significantly more. The claim is not patent eligible.
Dependent claim 28, recites addition element of “providing an output representing the identified one or more anomalous flight data units”. However, this limitation is recited at a high level of generality (i.e., outputting information using a computer component) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, the additional element is conventional in the art, as evidenced by the art of record (see, Desell (Fig. 4), and
Li (Figs. 7-8). Therefore, claim 28 is directed to an abstract idea without significantly more. The claim is not patent eligible.
Dependent claim 30, recites the limitations “determining at least one flight parameter as responsible for the identification of one or more flight data units as anomalous according to the method of claim 29; and performing a physical operation on the aircraft based on the determined at least one flight parameter.” The claim recites addition element of “performing a physical operation on the aircraft based on the determined at least one flight parameter.” However, this limitation is recited at a high level of generality (i.e., any activity that includes physical operation on the aircraft) such that it amounts no more than mere a generic activity that includes physical operation on the aircraft. Further, the additional element is conventional in the art, as evidenced by the art of record (see, Desell ([0002]-[0003], [0012]), and
Li (page 593). Therefore, claim 30 is directed to an abstract idea without significantly more. The claim is not patent eligible.
Independent claim 31, recites the limitations “receiving unit configured to receive a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point; a mapping unit configured to map the flight data units as respective data points to a multi-dimensional space, wherein the dimensions of the multi-dimensional space comprise a dimension for each of the plurality of flight parameters; and an identification unit configured to identify one or more anomalous flight data units in the received plurality of flight data units by applying a local outlier factor algorithm to the mapping flight data units.”
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: receiving unit configured to receive a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point; a mapping unit, and…an identification unit. The claim limitation “receiving unit configured to receive a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point”, is recited at a high level of generality (i.e., gathering or collecting data) such that it amounts no more than mere instructions to apply the exception using a generic collecting or computer components.
Further, the claim limitations “a mapping unit, and…an identification unit” are recited at a high level of generality (i.e., as a computer structures performing a generic computer function of mapping and identifying information) such that they amount no more than mere instructions to apply the exception using a generic computer components.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 31 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional limitations recited at a high level of generality (i.e., gathering or collecting data and computer component). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Desell ([0012], [0030], Figs. 1, 4), and Melnyk (page 163, Fig. 2). Therefore, claim 31 is directed to an abstract idea without significantly more.
Dependent claim 33, the claim is rejected with the same rationale as in claim 1.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
12. Claims 4, 14, 15, 24, and 30 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
7. Claim 4 recites the limitations “wherein the local outlier factor algorithm comprises comparing each of one or more of the flight data units with flight data units from other flights recorded at a corresponding time point or time window in those flights, the time point or time window being defined relative to a reference time point in the respective flight path.” However, the claim language “the respective flight path,” is unclear. The claim does not include any earlier recitation or limitation of a “the respective flight path” and as a result, it is not clear what element the description “the respective flight path” refers to. Appropriate correction is required.
8. Claim 14 recites the limitation “wherein the predetermined factor is in the range of 1 to 2, optionally substantially equal to 1.5.” However, the claim language " optionally substantially " is a relative term which renders the claim indefinite. This term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
9. Claim 15 recites the limitations “wherein the calculated threshold is calculated based on a statistical distribution over the calculated outlier scores of a subset of the data points, the subset of data points corresponding to a predetermined category, such as a predetermined type of aircraft, a predetermined phase of flight, or involvement of a predetermined airport.” However, the claim language " such as" is not limiting the scope of the claim and which renders the claim indefinite, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
10. Claim 24 recites the limitations “wherein the average outlier score is calculated using the outlier score of each of the flight data units recorded at a time point falling within the said phase, wherein for each phase of the flight, that phase is identified as anomalous when the average outlier score of the said phase deviates from a normal value by more than a predetermined value.” However, the claim language “the said phase”, is unclear. The claim does not include any earlier recitation or limitation of a “the said phase” and as a result, it is not clear what element the description “the said phase” refers to. Appropriate correction is required.
11. Claim 30 recites the limitations “determining at least one flight parameter as responsible for the identification of one or more flight data units as anomalous according to the method of claim 29; and performing a physical operation on the aircraft based on the determined at least one flight parameter.” However, the claim language “the aircraft”, is unclear. The claim does not include any earlier recitation or limitation of a “the aircraft” and as a result, it is not clear what element the description “the aircraft” refers to. Appropriate correction is required.
Drawings
12. The drawings are objected to because Figures 3-7, 9, 12, 13, and 15-17 of the filed drawings is of a very poor quality, and therefore is unreadable. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
13. Claim 31 in this application is given its broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) Claim 31 limitation use the terms “receiving unit, mapping unit, and identification unit” that are generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the terms “receiving unit, mapping unit, and identification unit” or the generic placeholder are modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the terms “receiving unit, mapping unit, and identification unit” or the generic placeholder are not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the terms “receiving unit, mapping unit, and identification unit” in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the terms “receiving unit, mapping unit, and identification unit” in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the terms “receiving unit, mapping unit, and identification unit” are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the terms “receiving unit, mapping unit, and identification unit” are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 103
13. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
14. Claims 1, 2-10, 17, 18, 20-30, 31, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Desell et al. US 2015/0324501 (hereinafter, Desell), in view of Melnyk et al. “Vector Autoregressive Model-Based Anomaly Detection
in Aviation Systems”, April 2016 (hereinafter, Melnyk).
15. Regarding claim 1, Desell discloses a computer implemented method of identifying anomalous flight data, the method comprising:
receiving a plurality of flight data units in a time series from each of a plurality of different flights, wherein each flight data unit comprises a value for each of a plurality of flight parameters at the same time point ([0016], [0030], [0070]);
mapping the flight data units to a multi-dimensional space, wherein the dimensions of the multi-dimensional space comprise a dimension for each of the plurality of flight parameters ([0030], [0039], [0048]-[0051]); and
identifying one or more anomalous flight data units in the received plurality of flight data units by applying algorithm to the mapped flight data units ([0029], [0035], [0048).
Desell discloses identifying one or more anomalous flight data ([0029], [0035], [0090]).
Desell does not disclose:
identifying one or more anomalous flight data by applying a local outlier factor algorithm.
However, Melnyk discloses:
identifying one or more anomalous flight data by applying a local outlier factor algorithm (Abstract, page 165, section C. Anomaly Detection: local outlier factor (LOF)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use identifying one or more anomalous flight data by applying a local outlier factor algorithm as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
16. Regarding claims 31 and 33, the claims are rejected with the same rationale as in claim 1.
17. Regarding claim 2, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell further discloses wherein the dimensions of the multi-dimensional space further comprise a time dimension to represent the time series of each plurality of flight data units ([0026], [0030], [0070]).
18. Regarding claim 3, Desell in view of Melnyk disclose the method of claim 2, as disclosed above.
Desell further discloses wherein the time dimension is defined relative to a common reference time point in the flight paths of the plurality of flights ([0016], [0030]-[0031]).
19. Regarding claim 4, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell does not disclose:
wherein the local outlier factor algorithm comprises comparing each of one or more of the flight data units with flight data units from other flights recorded at a corresponding time point or time window in those flights, the time point or time window being defined relative to a reference time point in the respective flight path.
However, Melnyk discloses:
wherein the local outlier factor algorithm comprises comparing each of one or more of the flight data units with flight data units from other flights recorded at a corresponding time point or time window in those flights, the time point or time window being defined relative to a reference time point in the respective flight path (pages 161, 165-167, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the local outlier factor algorithm comprises comparing each of one or more of the flight data units with flight data units from other flights recorded at a corresponding time point or time window in those flights, the time point or time window being defined relative to a reference time point in the respective flight path as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
20. Regarding claim 5, Desell in view of Melnyk disclose the method of claim 3, as disclosed above.
Desell further discloses wherein the reference time point comprises a reference point defined relative to a characteristic feature of one of the following phases of the flight: take-off, initial climb, cruise, approach, descent and landing ([0016]). Also, see Melnyk (page 166-167, Section V. Experiments).
21. Regarding claim 6, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell further discloses preprocessing the received plurality of flight data units prior to the mapping the flight data units to the multi-dimensional space, the preprocessing comprising synchronizing the flight data units such that flight data units having the same time point from different flights will correspond to the same portion of each flight ([0030]-[0032], [0070]).
22. Regarding claim 7, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell does not disclose:
wherein the local outlier factor algorithm is used to calculate an outlier score for each of the plurality of flight data units.
However, Melnyk discloses:
wherein the local outlier factor algorithm is used to calculate an outlier score for each of the plurality of flight data units (page 165, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the local outlier factor algorithm is used to calculate an outlier score for each of the plurality of flight data units as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
23. Regarding claim 8, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell does not disclose:
wherein a flight data unit is identified as anomalous when the outlier score of the flight data unit derived by the local outlier factor algorithm deviates from a normal value by more than a predetermined value.
However, Melnyk discloses:
wherein a flight data unit is identified as anomalous when the outlier score of the flight data unit derived by the local outlier factor algorithm deviates from a normal value by more than a predetermined value (pages 165-166, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein a flight data unit is identified as anomalous when the outlier score of the flight data unit derived by the local outlier factor algorithm deviates from a normal value by more than a predetermined value as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
24. Regarding claim 9, Desell in view of Melnyk disclose the method of claim 8, as disclosed above.
Desell does not disclose:
the local outlier factor algorithm is configured to determine a spatial variation of a local density of the data points in the multi-dimensional space; and the outlier score is calculated for each flight data unit based on a position of the data point corresponding to the flight data unit relative to the determined spatial variation of local density.
However, Melnyk discloses:
the local outlier factor algorithm is configured to determine a spatial variation of a local density of the data points in the multi-dimensional space; and the outlier score is calculated for each flight data unit based on a position of the data point corresponding to the flight data unit relative to the determined spatial variation of local density (pages 165-166, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use the local outlier factor algorithm is configured to determine a spatial variation of a local density of the data points in the multi-dimensional space; and the outlier score is calculated for each flight data unit based on a position of the data point corresponding to the flight data unit relative to the determined spatial variation of local density as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
25. Regarding claim 10, Desell in view of Melnyk disclose the method of claim 9, as disclosed above.
Desell further discloses statistical distribution and determining outliner ([0021], [0025], [0046]-[0047]).
Desell does not disclose:
wherein the predetermined value is calculated based on a statistical distribution of the calculated outlier scores.
However, Melnyk discloses:
wherein the predetermined value is calculated based on a statistical distribution of the calculated outlier scores (pages 165-166, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the predetermined value is calculated based on a statistical distribution of the calculated outlier scores as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
26. Regarding claim 17, Desell in view of Melnyk disclose the method of claim 9, as disclosed above.
Desell does not disclose:
wherein the determination of the spatial variation of the local density of the data points is performed based on distances between data points and nearest neighbours of the data points.
However, Melnyk discloses:
wherein the determination of the spatial variation of the local density of the data points is performed based on distances between data points and nearest neighbours of the data points (pages 165-166, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the determination of the spatial variation of the local density of the data points is performed based on distances between data points and nearest neighbours of the data points as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
27. Regarding claim 18, Desell in view of Melnyk disclose the method of claim 17, as disclosed above.
Desell does not disclose:
wherein the local density of each data point is defined using a distance between the data point and a k-th nearest neighbour of the data point, where k is an integer.
However, Melnyk discloses:
wherein the local density of each data point is defined using a distance between the data point and a k-th nearest neighbour of the data point, where k is an integer (pages 165-166, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the local density of each data point is defined using a distance between the data point and a k-th nearest neighbour of the data point, where k is an integer as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
28. Regarding claim 20, Desell in view of Melnyk disclose the method of claim 18, as disclosed above.
Desell does not disclose:
wherein the local density of each data point is defined as a local reachability density according to the following formula: LRDk(A)=…. where LRDk(A) is the local reachability density of a data point A for a given value of k; reach distance k
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(A, B) is the reachability distance of data point A from data point B, defined as reach distance k
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(A, B) =max{k-distance(B), d(A, B)}, k-distance(B) being the distance from the data point B to the k-th nearest neighbour of B, and d(A,B) being the distance between data points A and B; EBENk(A) reach distance k(A, B) is the sum of the reach distance k
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(A, B) over all data points B that are equidistant or closer to the data point A than the k-th nearest neighbour of A; and INk(A) I is the number of data points that are equidistant or closer to the data point A than the k-th nearest neighbour of A.
However, Melnyk discloses:
wherein the local density of each data point is defined as a local reachability density according to the following formula: LRDk(A)=…. where LRDk(A) is the local reachability density of a data point A for a given value of k; reach distance k
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(A, B) is the reachability distance of data point A from data point B, defined as reach distance k
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(A, B) =max{k-distance(B), d(A, B)}, k-distance(B) being the distance from the data point B to the k-th nearest neighbour of B, and d(A,B) being the distance between data points A and B; EBENk(A) reach distance k(A, B) is the sum of the reach distance k
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(A, B) over all data points B that are equidistant or closer to the data point A than the k-th nearest neighbour of A; and INk(A) I is the number of data points that are equidistant or closer to the data point A than the k-th nearest neighbour of A (page 165, section C. Anomaly Detection: see the equations).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the local density of each data point is defined as a local reachability density according to the following formula: LRDk(A)=…. where LRDk(A) is the local reachability density of a data point A for a given value of k; reach distance k
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(A, B) is the reachability distance of data point A from data point B, defined as reach distance k
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(A, B) =max{k-distance(B), d(A, B)}, k-distance(B) being the distance from the data point B to the k-th nearest neighbour of B, and d(A,B) being the distance between data points A and B; EBENk(A) reach distance k(A, B) is the sum of the reach distance k
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(A, B) over all data points B that are equidistant or closer to the data point A than the k-th nearest neighbour of A; and INk(A) I is the number of data points that are equidistant or closer to the data point A than the k-th nearest neighbour of A as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
29. Regarding claim 21, Desell in view of Melnyk disclose the method of claim 20, as disclosed above.
Desell does not disclose:
wherein the outlier score for each data point is calculated by mathematically comparing the local reachability density of the data point with the local reachability density of a group of neighbouring data points.
However, Melnyk discloses:
wherein the outlier score for each data point is calculated by mathematically comparing the local reachability density of the data point with the local reachability density of a group of neighbouring data points (page 165, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the outlier score for each data point is calculated by mathematically comparing the local reachability density of the data point with the local reachability density of a group of neighbouring data points as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
30. Regarding claim 22, Desell in view of Melnyk disclose the method of claim 20, as disclosed above.
Desell does not disclose:
wherein the outlier score for a data point A for a given value of k, LOFk(A), is given by the following expression: LOFk(A)= ….
However, Melnyk discloses:
wherein the outlier score for a data point A for a given value of k, LOFk(A), is given by the following expression: LOFk(A)= …. (page 165, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the outlier score for a data point A for a given value of k, LOFk(A), is given by the following expression: LOFk(A)= …. as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
31. Regarding claim 23, Desell in view of Melnyk disclose the method of claim 18, as disclosed above.
Desell does not disclose:
the local outlier factor algorithm is applied a plurality of times using a plurality of different values of k; the method comprises selecting a value of k that achieves higher than average or maximal outlier scores; and the method comprises using the selected value of k to perform the identifying of the one or more anomalous flight data units.
However, Melnyk discloses:
the local outlier factor algorithm is applied a plurality of times using a plurality of different values of k; the method comprises selecting a value of k that achieves higher than average or maximal outlier scores; and the method comprises using the selected value of k to perform the identifying of the one or more anomalous flight data units (page 165-167, section C. Anomaly Detection and Experiments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use the local outlier factor algorithm is applied a plurality of times using a plurality of different values of k; the method comprises selecting a value of k that achieves higher than average or maximal outlier scores; and the method comprises using the selected value of k to perform the identifying of the one or more anomalous flight data units as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
32. Regarding claim 24, Desell in view of Melnyk disclose the method of claim 7, as disclosed above.
Desell further discloses calculating an average ([0039]-[0040], [0046]).
Desell does not disclose:
calculating an average outlier score of at least one of the following phases of at least one of the plurality of flights: take-off, initial climb, cruise, approach, descent or landing, wherein the average outlier score is calculated using the outlier score of each of the flight data units recorded at a time point falling within the said phase, wherein for each phase of the flight, that phase is identified as anomalous when the average outlier score of the said phase deviates from a normal value by more than a predetermined value.
However, Melnyk discloses:
calculating an average outlier score of at least one of the following phases of at least one of the plurality of flights: take-off, initial climb, cruise, approach, descent or landing, wherein the average outlier score is calculated using the outlier score of each of the flight data units recorded at a time point falling within the said phase, wherein for each phase of the flight, that phase is identified as anomalous when the average outlier score of the said phase deviates from a normal value by more than a predetermined value (page 165-167, section C. Anomaly Detection and V. Experiments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use calculating an average outlier score of at least one of the following phases of at least one of the plurality of flights: take-off, initial climb, cruise, approach, descent or landing, wherein the average outlier score is calculated using the outlier score of each of the flight data units recorded at a time point falling within the said phase, wherein for each phase of the flight, that phase is identified as anomalous when the average outlier score of the said phase deviates from a normal value by more than a predetermined value as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
33. Regarding claim 25, Desell in view of Melnyk disclose the method of claim 7, as disclosed above.
Desell further discloses calculating an average ([0039]-[0040], [0046]).
Desell does not disclose:
calculating an average outlier score of the group of flight data units corresponding to at least one of the plurality of different flights, wherein the at least one of the plurality of different flights is identified as anomalous when the average outlier score of said flight deviates from a normal value by more than a predetermined value.
However, Melnyk discloses:
calculating an average outlier score of the group of flight data units corresponding to at least one of the plurality of different flights, wherein the at least one of the plurality of different flights is identified as anomalous when the average outlier score of said flight deviates from a normal value by more than a predetermined value (page 165-167, section C. Anomaly Detection and V. Experiments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use calculating an average outlier score of the group of flight data units corresponding to at least one of the plurality of different flights, wherein the at least one of the plurality of different flights is identified as anomalous when the average outlier score of said flight deviates from a normal value by more than a predetermined value as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
34. Regarding claim 26, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell does not disclose:
wherein for at least one of the different flights the outlier score is determined for each of a plurality of different flight data units in a time series of flight data units received for that flight.
However, Melnyk discloses:
wherein for at least one of the different flights the outlier score is determined for each of a plurality of different flight data units in a time series of flight data units received for that flight (page 165, section C. Anomaly Detection).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein for at least one of the different flights the outlier score is determined for each of a plurality of different flight data units in a time series of flight data units received for that flight as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
35. Regarding claim 27, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell further discloses wherein the method is performed at a ground location ([0030], Fig. 2).
36. Regarding claim 28, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell further discloses providing an output representing the identified one or more anomalous flight data units ([0020], [0048]-[0052], [0067]).
37. Regarding claim 29, Desell in view of Melnyk disclose the method of claim 1, as disclosed above.
Desell further discloses performing further analysis to determine at least one of the flight parameters as responsible for the identification of one or more flight data units as anomalous ([0029], [0067]).
38. Regarding claim 30, Desell in view of Melnyk disclose the method of claim 29, as disclosed above.
Desell further discloses determining at least one flight parameter as responsible for the identification of one or more flight data units as anomalous according to the method of claim 29; and performing a physical operation on the aircraft based on the determined at least one flight parameter ([0016], [0029], [0035], [0048]). See also Melnyk (pages 165, 171-172, section VI. Conclusions).
39. Claims 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Desell, in view of Melnyk, in further view of Li et al. “Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations”, September 2015 (hereinafter, Li).
40. Regarding claim 11, Desell in view of Melnyk disclose the method of claim 10, as disclosed above.
Desell further discloses determining outliner ([0021], [0025], [0046]-[0047]).
Desell does not disclose:
wherein the predetermined value is calculated such that outlier scores higher than a calculated threshold are identified as anomalous, the calculated threshold being equal to the sum of a value of a predetermined percentile of the distribution; and a predetermined percentile range multiplied by a predetermined factor.
However, Melnyk discloses:
wherein the predetermined value is calculated such that outlier scores higher than a calculated threshold are identified as anomalous, the calculated threshold being equal to a [predetermined value] (page 165, section C. Anomaly Detection: the estimated distance matrix can now be used to detect outliers, which correspond to the anomalous flights in our case. For this purpose, we use the local outlier factor approach of. Intuitively, the LOF is based on comparing the local density of a point with the density of its neighbors using the pairwise distances between the points…the LOF score is computed by comparing the local reachability density of a flight with densities of its neighbors LOFk(Fi)... If a point is an inlier, the LOF is close to one; whereas for outliers, the LOF score is greater than one. Therefore, to find anomalies, we can sort all the LOF scores in decreasing order and select the top τ flights as anomalous). Desell discloses determining outliner as disclosed above. Desell in view of Melnyk does not disclose the calculated threshold being equal to the sum of a value of a predetermined [value] of the distribution; and a predetermined [value] range multiplied by a predetermined factor. However, calculated threshold being equal to the sum of a value of a predetermined [value] of the distribution; and a predetermined [value] range multiplied by a predetermined factor would have been obvious to one ordinary skill in the art based on the teaching of Desell in view of Melnyk as disclosed above.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell to use wherein the predetermined value is calculated such that outlier scores higher than a calculated threshold are identified as anomalous, the calculated threshold being equal to a [predetermined value] as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
Desell in view of Melnyk does not disclose:
wherein the predetermined value is a predetermined percentile of the distribution; and a predetermined percentile range.
However, Li discloses:
wherein the predetermined value is a predetermined percentile of the distribution; and a predetermined percentile range (Abstract, page 591, section B. Result, and Fig. 7).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Melnyk to use wherein the predetermined value is a predetermined percentile of the distribution; and a predetermined percentile range as taught by Li. The motivation for doing so would have been in order to show parameter values of abnormal flights and bands depict the value range of the common patterns (Li, page 591).
41. Regarding claim 12, Desell in view of Melnyk in view of Li disclose the method of claim 11, as disclosed above.
Desell in view of Melnyk does not disclose:
wherein the predetermined percentile is a first quartile or a third quartile.
However, Li discloses:
wherein the predetermined percentile is a first quartile or a third quartile (Abstract, page 591, section B. Result, and Fig. 7).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Melnyk to use wherein the predetermined percentile is a first quartile or a third quartile as taught by Li. The motivation for doing so would have been in order to show parameter values of abnormal flights and bands depict the value range of the common patterns (Li, page 591).
42. Regarding claim 13, Desell in view of Melnyk in view of Li disclose the method of claim 11, as disclosed above.
Desell in view of Melnyk does not disclose:
wherein the predetermined percentile range is the interquartile range.
However, Li discloses:
wherein the predetermined percentile range is the interquartile range (Abstract, page 591, section B. Result, and Fig. 7).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Melnyk to use wherein the predetermined percentile range is the interquartile range as taught by Li. The motivation for doing so would have been in order to show parameter values of abnormal flights and bands depict the value range of the common patterns (Li, page 591).
43. Regarding claim 14, Desell in view of Melnyk in view of Li disclose the method of claim 11, as disclosed above.
Desell discloses wherein the predetermined factor is in the range ([0026]). Further, Melnyk disclose wherein the predetermined factor is in the range of 1 to 2, optionally substantially equal to 1.5 (pages 165-166, section C. Anomaly Detection). See also Li (page 591, section B. Result, and Fig. 7).
44. Regarding claim 15, Desell in view of Melnyk in view of Li disclose the method of claim 11, as disclosed above.
Desell further discloses statistical distribution and determining outliner and a predetermined phase of flight ([0012], [0025], [0046]-[0047]).
Desell in view of Li does not disclose:
wherein the calculated threshold is calculated based on a statistical distribution over the calculated outlier scores of a subset of the data points, the subset of data points corresponding to a predetermined category, such as a predetermined type of aircraft, a predetermined phase of flight, or involvement of a predetermined airport.
However, Melnyk discloses:
wherein the calculated threshold is calculated based on a statistical distribution over the calculated outlier scores of a subset of the data points, the subset of data points corresponding to a predetermined category, such as a predetermined type of aircraft, a predetermined phase of flight, or involvement of a predetermined airport (page 165, section C. Anomaly Detection and 166, section V. Experiments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Li to use wherein the calculated threshold is calculated based on a statistical distribution over the calculated outlier scores of a subset of the data points, the subset of data points corresponding to a predetermined category, such as a predetermined type of aircraft, a predetermined phase of flight, or involvement of a predetermined airport as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
45. Regarding claim 16, Desell in view of Melnyk in view of Li disclose the method of claim 11, as disclosed above.
Desell further discloses receiving further flight data units, calculating new outlier corresponding to those flight data units, and updating the calculated threshold to take account of the new outlier ([0066], [0068]).
Desell in view of Li does not disclose:
receiving further flight data units, calculating outlier scores.
However, Melnyk discloses:
receiving further flight data units, calculating outlier scores (pages 165-166).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Li to use receiving further flight data units, calculating outlier scores as taught by Melnyk. The motivation for doing so would have been in order to determine anomalous of flight data efficiently (Melnyk, page 161).
16. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Desell, in view of Melnyk, in further view of Statler et al. US 7206674 (hereinafter, Statler).
47. Regarding claim 19, Desell in view of Melnyk disclose the method of claim 18, as disclosed above.
Desell in view of Melnyk does not disclose:
wherein the distances are determined using the Manhattan distance.
However, Statler discloses:
wherein the distances are determined using the Manhattan distance (column 7, lines 18-43).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desell in view of Melnyk to use wherein the distances are determined using the Manhattan distance as taught by Statler. The motivation for doing so would have been in order to compute the distance between data point efficiently (Statler, column 7, lines 15-20).
Conclusion
48. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
49. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857
Prosecution Insights