DETAILED ACTION
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 Remarks
The Office Action has been made issued in response to amendment filed January 03, 2026. Claims 1-15 and 20-27 are pending of which 1-15 are withdrawn. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Claim Objections
Applicant has amended claims 20-27 to uncapitalized the word ‘Claim’ to ‘claim’; therefore, the objections have been withdrawn.
Claim Rejections – 35 USC section § 103
Applicant's arguments with respect to claims 20-27 have been considered but are moot in view of the new ground(s) of rejection.
Terminal Disclaimer
The terminal disclaimer filed on disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No.: 12,080,048 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Election/Restrictions
Applicant’s election without traverse of Group 1 consisting of claims 1-15 in the reply filed on January 02, 2026 is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a visual media file designation tool in claim 20.
The filed specification on pages 8 and 18 discloses the designation is implemented using artificial intelligence tool or human in combination with software executed by a processor.
a graphic property identifier in claim 20
The filed specification on pages 8 and 18 discloses the graphics property identifier is implemented using graphics recognition algorithm executed by a processor.
element similarity profile generator in claim 20
The filed specification on pages 8-9 and 18 discloses the similarity profile generator is implemented using look up tables in combination with software executed by a processor.
The providing a visual media file… in claim 25, determining a first graphic property… in claim 25, determining a second graphic property… in claim 25
determining a third graphic property… in claim 25, saving the first graphic property… in claim 25, classifying the unclassified visual media file… in claim 25 steps are implemented by software/data structures executed by a processor disclosed on pages 8-10 and 18.
a visual media classifier… in claim 20 and 24 discloses a probability calculator (see [p][0067]) executed by processor connected to processor (see [p][0045])
The filed specification on pages 8-9 and 18 discloses the similarity profile generator is implemented using look up tables in combination with software executed by a processor.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 20, 24 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over FROLOV et al (Pub No.: 20170267371) in view of Weijun et al (NPL titled: Deep Learning for Aircraft Wake Vortex Identification).
As to independent claim 20, FROLOV discloses a vortex similarity engine (apparatus for close formation flight – see [p][0014]) comprising: a database of visual media files (a video or imaging camera may provide imaging data that may be used for imaging analysis – see [p][0158]); a graphics recognition algorithm configured to generate graphic elements based on characteristics of the visual media files from the database (a method 1500 of vortex sensing (shown in FIG. 15) is provided in which the following may be implemented by the follower aircraft, either manually, automatically or both: measuring and collecting data at 1510 characterizing airflow near the aircraft, analyzing the collected data at 1520, creating a computer model of a vortex field at 1530, and evaluation of errors or differences between the model and the real vortex field at 1540 – see [p][0098] and visual and other complimentary data for narrowing the search area, using neural network and deep learning algorithms for vortex patterns recognition, and the like – see [p][0101]); a visual media file designation tool configured to designate the visual media files as a travelling object vortex image if the visual media files depicts a travelling object vortex (robust vortex model may be defined as a model produced by a set of real-time measurements, in which at least some of its characteristic parameters (i.e., vortex core diameter, position, strength, etc) have converged to stable values. A flight control system may specify the accuracy or precision required for vortex identification, which then would determine the time when the vortex search may be considered completed – see [p][0101]); the visual media file designation tool configured to designate the visual media file as not travelling object vortex visual media file if the visual media file does not depict a travelling object vortex (robust vortex model may be defined as a model produced by a set of real-time measurements, in which at least some of its characteristic parameters (i.e., vortex core diameter, position, strength, etc.) have converged to stable values. A flight control system may specify the accuracy or precision required for vortex identification, which then would determine the time when the vortex search may be considered completed - see [p][0101]); a graphic property identifier configured to identify a graphic property that some of the travelling object vortex visual media files share (robust vortex model may be defined as a model produced by a set of real-time measurements, in which at least some of its characteristic parameters (i.e., vortex core diameter, position, strength, etc.) have converged to stable values. A flight control system may specify the accuracy or precision required for vortex identification, which then would determine the time when the vortex search may be considered completed – see [p][0101]); and an element similarity profile generator configured to generate an element similarity profile based on one or more graphic elements identified by the graphics property identifier (using neural network processing and deep learning algorithms for vortex pattern recognition – see [p][0104]); FROLOV does not expressly disclose a visual media classifier a probability
configured to determine that a travelling object passed through a field of view of a media collector that collected the visual media files at a first time based on the element similarity profile.
Weijun explicitly disclose an aircraft wake vortex identification including a visual media classifier (SoftMax loss function – see section 3.3, [p][004]) a probability configured to determine that a travelling object passed through a field of view of a media collector (Pr(object ) reflects the confidence of the target in the current bounding box, and truth pred IoU reflects the accuracy of the current bounding box's predicted target position. If there is no object in the bounding box, then Pr (object )=0, if there is an object, then Pr (object )=1 – see section 4.1, [p][006]) that collected the visual media files at a first time based on the element similarity profile ([a]t the time of inspection, the classification confidence of each category bounding box is equal to the product of the confidence of each target bounding box and the category information for each grid prediction see section 4.1, [p][009]).
Weijun and FROLOV are combinable because they are from the same field of endeavor of wake detection. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the aircraft wake vortex identification of Weijun into the apparatus for close formation flight of FROLOV in order to extract the candidate target information, and then the detection network is used to predict and identify the location and category of the candidate target (see section 3.1). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
As to independent claim 24, FROLOV discloses method for determining whether an unclassified visual media file depicts a vortex (apparatus for close formation flight – see [p][0014]); said method comprising the steps of: providing a visual media file database comprising a first visual media file and second visual media file (see para [101], collecting first data and second data; and see para [158], the first and second data collected by a camera); determining a first graphic property of the first visual media file with a first graphics recognition algorithm (see para [98] and [101], determining first measurement data from the first data and second measurement data from the second data, wherein the first and second measurement data are of vortex characteristic parameters); determining a second graphic property of the second visual media file with the first graphics recognition algorithm (see para [98] and [101], determining first measurement data from the first data and second measurement data from the second data, wherein the first and second measurement data are of vortex characteristic parameters); determining a third graphic property of the unclassified visual media file with the first graphics recognition algorithm; saving the first graphic property and second graphic property with a graphic property manager (see para [101] and [104], collecting third data and determining third measurement data); generating an element similarity profile with an element profile generator (see para [98] and [101], the first and second measurement data that converges to a stable value (i.e., is sufficiently similar) is stored as a vortex model); and classifying the unclassified visual media file as depicting or not depicting a vortex by using the element similarity profile and the third graphic property of the unclassified visual media file (see para [101] and [104], a trained network recognizing the third data as a vortex by using the vortex model and the third measurement data) and determining, by a visual media classifier, a probability that a travelling object passed through a field of view of a media collector that collected the unclassified visual media file at a first time based on the element similarity profile
FROLOV does not expressly disclose a visual media classifier a probability
configured to determine that a travelling object passed through a field of view of a media collector that collected the visual media files at a first time based on the element similarity profile.
Weijun explicitly disclose an aircraft wake vortex identification including a visual media classifier (SoftMax loss function – see section 3.3, [p][004]) a probability configured to determine that a travelling object passed through a field of view of a media collector (Pr(object ) reflects the confidence of the target in the current bounding box, and truth pred IoU reflects the accuracy of the current bounding box's predicted target position. If there is no object in the bounding box, then Pr (object )=0, if there is an object, then Pr (object )=1 – see section 4.1, [p][006]) that collected the visual media files at a first time based on the element similarity profile ([a]t the time of inspection, the classification confidence of each category bounding box is equal to the product of the confidence of each target bounding box and the category information for each grid prediction see section 4.1, [p][009]).
Weijun and FROLOV are combinable because they are from the same field of endeavor of wake detection. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the aircraft wake vortex identification of Weijun into the apparatus for close formation flight of FROLOV in order to extract the candidate target information, and then the detection network is used to predict and identify the location and category of the candidate target (see section 3.1). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
As to claim 27, FROLOV teaches the method comprising the step of storing the first graphic property in a data structure in the visual media file database (see [p][0014]).
Claims 21- 23 are rejected under 35 U.S.C. 103 as being unpatentable over FROLOV et al (Pub No.: 20170267371) in view of Weijun et al (NPL titled: Deep Learning for Aircraft Wake Vortex Identification) as applied to claims 20 further in view of Yingjie et al. (NPL titled: Research on wake turbulence detection and recognition technology of aircraft).
As to claim 21, FROLOV in view of Weijun does not expressly disclose engine wherein the graphic property identifier is additionally configured to identify a graphic element that some of the visual media files do not share.
Yingjie discloses a wake turbulence wherein the graphic property identifier is additionally configured to identify a graphic element that some of the visual media files do not share (see section III.Cand IV, receiving training images including positive (i.e., including vortices) and negative samples (i.e., not including vortices) for training a classifier).
Yingjie, Weijun and FROLOV are combinable because they are from the same field of endeavor of wake detection. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the wake detection system of Yingjie into the apparatus for close formation flight of FROLOV as modified by Weijun in order to provide an intelligent algorithm that can effectively identify an aircraft wake turbulence during approach and landing aircraft (see abstract). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
As to claim 22, the combination of FROLOV and Weijun as a whole does not teach the engine wherein the graphic property identifier is additionally configured to identify a graphic element that some of the not travelling object vortex images share.
Yingjie discloses a wake turbulence wherein the graphic property identifier is additionally configured to identify a graphic element that some of the not travelling object vortex images share (see section III.Cand IV, receiving training images including positive (i.e., including vortices) and negative samples (i.e., not including vortices) for training a classifier). Therefore, combining Yingjie, Weijun and FROLOV would meet the claim limitations for the same reasons as previously discussed in claim 21.
As to claim 23, the combination of FROLOV and Weijun as a whole does not teach the engine wherein the graphic property identifier is additionally configured to identify a graphic element that some of the not travelling vortex images do not share.
Yingjie discloses a wake turbulence wherein the graphic property identifier is additionally configured to identify a graphic element that some of the not travelling vortex images do not share (see section III.Cand IV, receiving training images including positive (i.e., including vortices) and negative samples (i.e., not including vortices) for training a classifier). Therefore, combining Yingjie and FROLOV would meet the claim limitations for the same reasons as previously discussed in claim 21.
Claims 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over FROLOV et al (Pub No.: 20170267371) in view of Yingjie et al. (NPL titled: Research on wake turbulence detection and recognition technology of aircraft) further in view of Beynel et al (Pub No.: 20190279124)
As to claim 25, the combination of FROLOV and Weijun as a whole does not teach the method of wherein the unclassified visual media file comprises configured to store a label designating the unclassified visual media file as depicting or not depicting a vortex.
Yingjie discloses a wake turbulence wherein the unclassified visual media file comprises configured to store a label designating the unclassified visual media file as depicting or not depicting a vortex (see Fig 5 – indicating a probability of 0).
Yingjie, Weijun and FROLOV are combinable because they are from the same field of endeavor of wake detection. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the wake detection system of Yingjie into the apparatus for close formation flight of FROLOV as modified by Weijun in order to provide an intelligent algorithm that can effectively identify an aircraft wake turbulence during approach and landing aircraft (see abstract). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
However, the combination of Yingjie, Weijun and FROLOV as a whole does not expressly disclose the file containing metadata.
Beynel discloses an in-transit disruption detection method including wherein the file containing metadata (see [p][0076]).
Yingjie, Beynel, Weijun and FROLOV are combinable because they are from the same field of endeavor of wake detection. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the in-transit disruption detection of Beynel into the apparatus for close formation flight of FROLOV as modified by Yingjie for an in-transit detection and mitigation of transportation service disruptions in the travel industry using fragmented source data. A notification including a record identifier is received (see abstract). Such a modification is the result of combining prior art elements according to known methods, they would have performed as expected, and the results would have been predictable.
As to claim 26, FROLOV teach the method comprising the steps of: identifying a visual media file as depicting a vortex or not depicting vortex (see [p][0101-0102] - identifying the location of a vortex core); however, the combination of FROLOV and Weijun as a whole does not a label documenting the identification in a data structure in the visual media file database for the given visual media file.
Yingjie discloses a wake turbulence wherein a label documenting the identification in a data structure in the visual media file database for the given visual media file (see Fig 5 – indicating a probability of 0).
Therefore, combining Yingjie, Weijun and FROLOV would meet the claim limitations for the same reasons as previously discussed in claim 25.
Beynel discloses an in-transit disruption detection method including a storing a label (see [p][0076]).
Therefore, combining Yingjie, Weijun and FROLOV as modified by Beynel would meet the claim limitations for the same reasons as previously discussed in claim 25.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
January 27, 2026