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 . A request for continued examination under 37 CFR 1.114, including
the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application
is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR
1.114.
The Applicant’s Remarks filed 07 April 2026 have been received and considered.
The IDS filed 13 May 2026 has been received and considered
Claims 1 – 24 are pending.
Claims 1, 8 – 9, and 18 – 21 have been amended.
Claim 2 has been canceled.
Claims 1 and 3 – 24, all of the remaining claims pending in this application, have been rejected.
Response to Applicant’s Remarks
In view of the Applicant’s remarks filed 07 April 2026, regarding amendments to independent claims 1, 8, and 18 – 21, the previously applied prior art rejections are withdrawn. Applicant's remarks are rendered moot in view of the new grounds of rejection set forth below.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claims 1, 3 – 4, 8 – 12, 14, and 18 – 24 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2019/0304100 A1 to HEVER et al. (hereinafter HEVER) in view of US Patent No. 10706321 B1 to Chen et al. (hereinafter Chen) in further view of US Publication No. 2007/0273471 A1 to Shilling et al. (hereinafter Shilling).
Claim 1
Regarding claim 1, an independent method claim, HEVER teaches a method for determining a fingerprint of a vehicle, comprising, by a processor and memory circuitry (PMC): a) receiving a vehicle pre-defined identifier ("The set of reference images can be retrieved using an instance descriptor uniquely identifying the vehicle in the input image.", Paragraph [0005]);
b) capturing by at least one sensor at least one vehicle appearance, said at least one sensor is selected from a group consisting of at least an RF sensor, an imaging device and an audio sensor ("The imaging device 120 can be any kind of image acquisition device(s) or general-purpose device(s) equipped with image acquisition functionalities that can be used to capture vehicle images at a certain resolution and frequency, such as, e.g., a digital camera with image and/or video recording functionalities. In some embodiments, the imaging device can refer to one image acquisition device that is located at a given relative position with respect to the vehicle. The input image can refer to one or more images captured by the given image acquisition device from a given perspective. In some embodiments, the imaging device can refer to a plurality of imaging acquisition units which can be located at different relative positions with respect to the vehicle so as to capture images from different perspectives. In such cases, the input image should be understood as referring to one or more images acquired by each or at least some of the plurality of imaging acquisition units, as will be described in further detail below with reference to FIG. 2.", Paragraph [0039]);
c) receiving, from the at least one sensor, at least one vehicle appearance each including at least one image data indicative of at least partial vehicle scan; said appearance is associated with a unique appearance time tag ("As aforementioned, in some embodiments, the input image can refer to one or more images captured by one image acquisition device from a given perspective/view/angle, such as, e.g., front, side (e.g., either left or right side), rear, top, and underside of a vehicle. The input image can therefore cover at least part of the vehicle exterior, depending on the specific perspective that the images are taken from, and the relative position between the image acquisition device and the vehicle. By way of example, an imaging device that is embedded underground of a passage that a vehicle passes by, can capture multiple undercarriage images at a given time interval (e.g., 100-250 frames per second). The multiple undercarriage images with overlapping field of view can be combined together to form a single stitched image of the vehicle undercarriage. Such a stitched image, which typically has a relatively high resolution, can be used as the input image. In some cases, such a stitched image can be a 3D image.", Paragraph [0056]; "FIG. 4 illustrates an example of an input image and corresponding segments in accordance with certain embodiments of the presently disclosed subject matter. As shown, the exemplary input image 404 captures the undercarriage of a vehicle.", Paragraph [0061]; "The instance descriptor can be a unique identifier of a vehicle instance in an image. By way of example, the instance descriptor can be obtained/generated by using license plate recognition. By way of another example, a manual entry of a identifier can be used as an instance descriptor. In some cases, a fingerprint representative of specific features of the vehicle instance in the image can be created and used as the instance descriptor. By way of example, the specific features can refer to one or more structural characteristics of elements/components/patterns within the image, such as, e.g., shape, size, location of elements, and geometrical relations and relative positions between elements, etc. Additionally or alternatively, the location and time of the acquisition of the input image can also be used as part of the identifier information. Accordingly, a specific instance descriptor is obtained (301) for the input image using any of the above described methods.", Paragraph [0071]);
d) segmenting said image data into segment data that includes segments each being informative of a respective at least one-sub-component of said vehicle, wherein the segmenting produces a component map that identifies mechanically distinct sub-components of the vehicle (Fig. 4; "FIG. 4 illustrates an example of an input image and corresponding segments in accordance with certain embodiments of the presently disclosed subject matter. As shown, the exemplary input image 404 captures the undercarriage of a vehicle. The input image 404 is segmented into multiple input segments as illustrated in 402. The segmentation is performed such that the segments in 402 correspond to the following exemplary mechanical components: exhaust, fuel tank, engine, wheel, suspension, and chassis, etc. Taking the segment 406 for example, in the current example, there is one segment 406 corresponding to the entire exhaust component. However, in other cases, the exhaust can be further divided into sub-components/parts, such as, e.g., one or more exhaust pipes, and the segment(s) can correspond to the sub-components, or to the entire component.", Paragraph [0061]); and
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HEVER does not teach e) determining a plurality of marker instances from said image scan or segment data, wherein each marker instance is associated with a marker class and at least one marker feature, and wherein for each marker instance, at least one marker feature instance is determined that is location dependent and component dependent, said location dependent marker feature instance being indicative of the position of the marker instance on a specific sub-component of the vehicle as identified in the component map;
controlling access of said vehicle to a facility, based on verification of the fingerprint of said vehicle.
However, Chen teaches e) determining a plurality of marker instances from said image scan or segment data, wherein each marker instance is associated with a marker class and at least one marker feature, and wherein for each marker instance, at least one marker feature instance is determined that is location dependent and component dependent, said location dependent marker feature instance being indicative of the position of the marker instance on a specific sub-component of the vehicle as identified in the component map (Figure 15; "While this description particularly indicates that the block 312 uses CNNs 134 to detect damage to the target vehicle, it should be noted that the block 312 could use other types of statistical processing techniques to detect or classify damage or changes to particular target object components", Column 27, lines 34 - 38; "Moreover, the CNNs 134 or other deep learning tools may provide other possible outputs including, for example, a probability of a patch having damage (e.g., a number between 0 and 1), an indication of one or more types of damage detected (e.g., creases, dents, missing parts, cracks, scuffs, scrapes, scratches, etc.), an indication of damage severity (e.g., different damage levels based on, for example, the amount of labor hours required to repair or replace the component), an indication of a probability of hidden damage (e.g., damage not visible in the target images), an indication of the age of damage (e.g., whether the damage is prior damage or not), an indication of a repair cost for each involved body panel, an indication of a final repair cost, a confidence level with respect to prediction accuracy, etc. Still further, the CNNs or other deep learning technique may use, as inputs, a full set of target vehicle photos (not local patches), telematics data, video data, geographic variation data, etc.", Column 27, lines 50 - 67), wherein the marker class in this case would be the vehicle damage sustained, and the feature correlates to the type of damage (i.e. dents, scratches, scrapes, etc…as previously stated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of HEVER to incorporate techniques for detecting or classifying damage/changes to particular target object components, as disclosed by Chen. The suggestion/motivation for doing so would have been to determine specific damage to specific components of a vehicle to update an existing car profile or to determine a predicted cost of repair.
HEVER, in view of Chen, further teaches f) storing in the storage data indicative of the vehicle's fingerprint, for subsequent authentication of the same vehicle in a later independent scan, including said vehicle pre- defined identifier and at least its corresponding (i) vehicle appearance and associated appearance time tag, (ii) the so determined marker instances together with their associated location dependent and component dependent marker feature instances (Paragraphs [0071 - 0072]).
Neither HEVER, or Chen, or the combination teach controlling access of said vehicle to a facility, based on verification of the fingerprint of said vehicle.
However, Shilling teaches controlling access of said vehicle to a facility, based on verification of the fingerprint of said vehicle ("One embodiment of the present invention is a system for authorizing a waste management vehicle to proceed beyond an access point of a waste receivable environment. The system includes an identification reader configured to obtain vehicle identification information, hauler identification information, and personnel identification information from one or more identification mechanisms…The computer system is also configured to determine whether the vehicle and the personnel are authorized to proceed beyond the access point using the identification information and biometric information, and transmit a signal to a control mechanism to allow the vehicle and the personnel to proceed beyond the access point.", Paragraph [0008]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of HEVER, in view of Chen, to incorporate control of vehicle access to a facility based on a vehicle profile verification, as disclosed by Shilling. The suggestion/motivation for doing so would have been to only allow authorized vehicles to certain facilities or parts of a facility.
Claim 3
Regarding claim 3, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
HEVER, in view of Chen and in further view of Shilling, further teaches including with respect to said vehicle, repeating said (c) to (f) for at least one more vehicle appearance having a corresponding unique time tag, and for at least one other vehicle (Rejected as applied to claim 1), wherein the repeating of (c) to (f) are applied to multiple vehicles and stored for reference images as specifically disclosed by HEVER.
Claim 4
Regarding claim 4, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
HEVER, in view of Chen and in further view of Shilling, further teaches wherein said marker class is selected from the group that includes: scratches, dents, handwriting, color, rust mark, cross- type screws, and printed text (Rejected as applied to claim 1, specifically Chen Column 27, lines 50 - 67 ), wherein scratches, dents, etc. are detected using deep learning tools.
Claim 8
Regarding claim 8, an independent method claim, HEVER, in view of Chen and Shilling, teach a method for verifying a fingerprint of a vehicle, comprising by a processor and associated memory storage: a) receiving a vehicle pre-defined identifier (Rejected as applied to claim 1);
b) capturing by at least one sensor at least one vehicle appearance, said at least one sensor is selected from a group consisting of at least an RF sensor, an imaging device and an audio sensor (Rejected as applied to claim 1);
c) receiving from the at least one sensor at least one new vehicle appearance, each including at least one image data indicative of at least a partial vehicle scan; said appearance is associated with a unique appearance time tag (Rejected as applied to claim 1);
d) segmenting said image data into segment data that includes segments each being informative of a respective at least one-sub-component of said vehicle, wherein the segmenting produces a component map that identifies mechanically distinct sub-components of the vehicle (Rejected as applied to claim 1);
e) determining a plurality of new marker instances from said image scan or segment data, wherein each new marker instance is associated with a marker class and at least one marker feature, and wherein for each new marker instance, at least one marker feature instance is determined that is location dependent and component dependent, said location dependent marker feature instance being indicative of the position of the new marker instance on a specific sub-component of the vehicle as identified in the component map (Rejected as applied to claim 1);
f) extracting from said storage previously stored at least one vehicle appearance that is associated with said vehicle pre-defined identifier and at least one of its corresponding marker instances, said corresponding marker instances including their associated location dependent and component dependent marker feature instances (Rejected as applied to claim 1); and
g) comparing at least one new marker instance of the new appearance with a corresponding marker instance of at least one previously stored appearance of the same vehicle pre-defined identifier (Rejected as applied to claim 1; also, Paragraphs [0041 - 0047], HEVER), and
validating said vehicle fingerprint if a matching criterion is met (Rejected as applied to claim 1);
h) controlling access of said vehicle to a facility, based on said validation (Rejected as applied to claim 1).
Claim 9
Regarding claim 9, dependent on claim 8, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 8.
HEVER, in view of Chen and in further view of Shilling, further teaches ("In some embodiments, each difference map candidate in the set of difference map candidates can indicate segment-wise probability of presence of DOI between the given input segment and the corresponding reference segment. In such cases, the difference map as provided in block 212 is selected from the set of difference map candidates according to ranking of the probability of each difference map candidate in the set. By way of example, the segment-wise probability can be represented by a numerical value within a range of [0, 1] for each given segment, with 1 indicating most likely there is presence of DOI in the given input segment and 0 indicating otherwise. It is appreciated that other kinds of representation of probability and/or ranges can be used in lieu of the above", Paragraph [0081]), wherein the probabilities would be similarity scores and it is implicit that determining if there is in fact a presence of a DOI (difference of image using a binary value of "1" or "0") would require the use of thresholding,
determining if said matching criterion is met based on at least the validated marker instances and the corresponding stored marker instances (Rejected as applied directly above), wherein the "0" value means there was no DOI, therefore, implying no change or "similar".
Claim 10
Regarding claim 10, dependent on claim 9, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 9.
HEVER, in view of Chen and in further view of Shilling, further teaches further comprising for at least one validated marker instance of the newly acquired appearance, determining, utilizing a narrowing criterion, at least one candidate reference marker instance out of a larger number of stored reference marker instances of at least one vehicle appearance, and determining whether said matching criterion is met based on at least the validated marker instances and the corresponding stored candidate reference marker instances (Rejected as applied to claim 1 and 9), wherein it is implicit that the obtained input image is analyzed and the fingerprint is used to pull up reference images of said vehicle so that the DOI can be obtained. Therefore, the matching criterion is met based on the known identifiers/markers.
Claim 11
Regarding claim 11, dependent on claim 9, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 9.
HEVER, in view of Chen and in further view of Shilling, further teaches wherein said matching criterion is met if the number of validated marker instances out of the corresponding stored marker instances exceeds a given threshold (Rejected as applied to claim 10).
Claim 12
Regarding claim 12, dependent on claim 8, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 8.
HEVER, in view of Chen and in further view of Shilling, further teaches wherein at least some of said features are component dependent, and wherein said comparison is segment dependent thereby reducing the false alarms and computational complexity of said comparison (Paragraphs [0079 -0080]).
Claim 22
Regarding claim 22, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
HEVER further teaches wherein said vehicle pre-defined identifier is a member of a group consisting of a vehicle license plate (LP) and a vehicle identification number (VIN) ("The set of reference images are selected using an instance descriptor. The instance descriptor can be a unique identifier of a vehicle instance in an image. By way of example, the instance descriptor can be obtained/generated by using license plate recognition.", Paragraph [0071]).
Claim 14, dependent on claim 8, is rejected for the same reasons as applied to the above claims.
Claims 18 and 19, both independent system claims, are rejected as applied to the above claims.
Claims 20 and 21, both independent non-transitory computer readable medium claims, are rejected for the same reasons as applied to the above claims.
Claim 23, dependent on claim 18, is rejected for the same reasons as applied to claim 22.
Claim 24, dependent on claim 20, is rejected for the same reasons as applied to claim 22.
Claims 5, 7, 13, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2019/0304100 A1 to HEVER et al. (hereinafter HEVER) in view of US Patent No. 10706321 B1 to Chen et al. (hereinafter Chen) in further view of US Publication No. 2007/0273471 A1 to Shilling et al. (hereinafter Shilling) in further view of US Publication No. 2015/0103341 A1 to Buehler et al. (hereinafter Buehler).
Claim 5
Regarding claim 5, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
Neither HEVER, or Chen, or Shilling, or the combination teach wherein at least one of said at least one sensor is an IR sensor and wherein at least one of said vehicle scans falls in the IR wavelength.
However, Buehler teaches wherein at least one of said at least one sensor is an IR sensor and wherein at least one of said vehicle scans falls in the IR wavelength (Figure 3; "The imaging device 14 may include one or more cameras capable of capturing and recording image data at specific wavelengths across the electromagnetic spectrum. Imaging devices for use in the present invention may capture imagery in wavelengths defined by the infrared, visible and ultraviolet bands.", Paragraph [0019]; “FIG. 3 is a flowchart showing a method of generating a spatial and spectral object model using the system described above in FIGS. 1 and 2. Initially at step 200, an imaging device 14 may acquire and track an object of interest 30 by capturing imagery that is both spatially and spectrally resolved. Herein referred to as "hyperspectral images" to indicate the presence of both spatial and spectral content or images, the actual imagery may be collected with imaging devices as described above and may include elements responsive to multiple ultraviolet, infrared and/or visible wavelengths.”, Paragraph [0024]; also Paragraph(s) [0033] and [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of HEVER, in view of Chen and Shilling, to incorporate the use if an IR sensor to capture IR scans of vehicles, as disclosed by Buehler. The suggestion/motivation for doing so would have been to acquire images that would allow the detection of hidden parts, objects, contraband, etc.
Claim 7
Regarding claim 7, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
Although it's implied that electromagnetic imaging is used by HEVER (refer to figure 5 #504), it is not explicitly taught.
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Therefore, neither HEVER, or Chen, or Shilling, or the combination “explicitly” teach wherein at least one of said at least one sensor is an electromagnetic sensor and further comprising obtaining at least one electromagnetic scan of the vehicle and determining at least one electromagnetic marker class from said scan informative of a mark concealed underneath a non-metal surface of the vehicle.
However, Buehler teaches wherein at least one of said at least one sensor is an electromagnetic sensor and further comprising obtaining at least one electromagnetic scan of the vehicle and determining at least one electromagnetic marker class from said scan informative of a mark concealed underneath a non-metal surface of the vehicle (Rejected as applied to claim 5), wherein it is implicit that the use of visible, infrared, ultraviolet, and hyperspectral imagers are used to detect hidden objects are objects invisible to the naked eye.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of HEVER, in view of Chen and Shilling, to incorporate obtaining electromagnetic scans of vehicles, as disclosed by Buehler. The suggestion/motivation for doing so would have been to acquire images that would allow the detection of hidden parts, objects, contraband, etc.
Claim 13
Regarding claim 13, dependent on claim 8, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 8.
Neither HEVER, or Chen, or Shilling, or the combination teach wherein in case that a matching criterion is met with respect to the validated vehicle, the new marker instances of the validated vehicle that did not meet the similarity score are stored together with their associated feature instances for improving future vehicle verification.
However, Buehler teaches wherein in case that a matching criterion is met with respect to the validated vehicle, the new marker instances of the validated vehicle that did not meet the similarity score are stored together with their associated feature instances for improving future vehicle verification ("Subsequent to the creation of the identifier, the identifier may provide a reference to the object for adding new key characteristics or retrieving known characteristics of the related object.", Paragraph [0043]), where the new characteristics can be stored and retrieved later for future reference.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of HEVER, in view of Chen and Shilling, to incorporate storing new features/characteristics for future reference, as disclosed by Buehler. The suggestion/motivation for doing so would have been to keep an updated database for all scanned vehicles and for improved system accuracy.
Claims 15 and 17, both dependent on claim 8, are rejected for the same reasons as applied to the above claims.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2019/0304100 A1 to HEVER et al. (hereinafter HEVER) in view of US Patent No. 10706321 B1 to Chen et al. (hereinafter Chen) in further view of US Publication No. 2007/0273471 A1 to Shilling et al. (hereinafter Shilling) in further view of Non Patent Literature "Vehicle detection and classification using audio-visual cues" to Piyush et al. (hereinafter Piyush).
Claim 6
Regarding claim 6, dependent on claim 1, HEVER, in view of Chen and Shilling, teach the invention as claimed in claim 1.
Neither HEVER, or Chen, or Shilling, or the combination teach wherein at least one of said at least one sensor is an audio sensor and further comprising obtaining at least one audio scan of the vehicle and determining at least one audio marker class from said scan informative of sound of at least one module associated with said vehicle.
However, Piyush teaches wherein at least one of said at least one sensor is an audio sensor and further comprising obtaining at least one audio scan of the vehicle and determining at least one audio marker class from said scan informative of sound of at least one module associated with said vehicle (Figure 3; Table I; "For vehicle detection, first the audio signal of the video file was separated using ‘Format factory’, a freeware program. The audio file originally at 44100 Hz with mono channel in way file format was then re-sampled into 11025 Hz. Then short term energy (STE) of the audio signal is computed using a Hamming window of 20 ms size and 5 ms shift. STE is then smoothed using Bessel's low pass filter in order to remove high frequency fluctuations [6]. The smoothed STE represented in logarithmic scale is shown in Fig. 3(b). Comparison of audio signal and it's STE in Fig. 3(a) & (b) clearly indicate that sharp peaks in the STE contour corresponds to the presence of vehicle in front of the camera.", Section III - The Proposed System: Part B 'Vehicle Dectection and Video Frame Extraction').
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of HEVER, in view of Chen and Shilling, to incorporate the use of an audio sensor and determining a vehicle audio class associated with the captured signal, as disclosed by Piyush. The suggestion/motivation for doing so would have been to further narrow the possible vehicle type candidates being observed by the sensors, leading to a much faster and more accurate determination of the unique vehicle identifier from a database.
Claim 16, dependent upon claim 8, is rejected for the same reasons as applied to claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONDE LEE MILLER/Examiner, Art Unit 2663
/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698
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