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 .
Reissue Applications
This application seeks to reissue US Patent No. 11,755,984 (“the ‘984 patent”). In response to the 04/13/2026 final Office action (FOA), the applicant has filed a request for continued examination (RCE) with amendments. Claims 1-39 are pending.
For reissue applications filed before September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the law and rules in effect on September 15, 2012. Where specifically designated, these are “pre-AIA ” provisions.
For reissue applications filed on or after September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the current provisions.
Applicant is reminded of the continuing obligation under 37 CFR 1.178(b), to timely apprise the Office of any prior or concurrent proceeding in which Patent No. 11,755,984 is or was involved. These proceedings would include any trial before the Patent Trial and Appeal Board, interferences, reissues, reexaminations, supplemental examinations, and litigation. Applicant is further reminded of the continuing obligation under 37 CFR 1.56, to timely apprise the Office of any information which is material to patentability of the claims under consideration in this reissue application. These obligations rest with each individual associated with the filing and prosecution of this application for reissue. See also MPEP §§ 1404, 1442.01 and 1442.04.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/14/2026 has been entered.
Applicant’s Response to the Final Office Action
Except as noted below, the applicant’s response that was submitted with the RCE overcomes all issues that were described in the final Office action (FOA). New grounds of prior art rejection are made in light of the recent claim amendments.
Claim Interpretation – Contingent Language
The FOA described that claims 1 and 13 contain contingent language that is not required to be included in the broadest reasonable interpretation (BRI) of the claim. See FOA at 7-8. This issue has not been overcome.
Claim 13 recites “determining that the first probability score is no greater than a threshold.” This is a clear requirement that the first probability score does not exceed the threshold. But claim 13 later recites steps that are performed “in response to determining that the second probability score is no greater than a threshold.” In light of this difference in language, performing a step “in response to determining that the second probability score does not exceed a threshold” is understood to describe what occurs if that condition is met, rather than requiring the condition to occur. The BRI of claim 13 does not require these contingent steps. See MPEP 2111.04(II).
This interpretation applies to claim 1 as well. Claim 1 recites that “in response to determining that the probability score does not exceed a threshold,” additional iterations are performed. This language also does not explicitly require that the probability score does not exceed the threshold. Because the additional iterations are only performed when a non-required condition is encountered, and the BRI of claim 1 does not require that the additional iterations are performed. This means that, except for the final two lines of the claim, every claim 1 limitation on page 3 and 4 of the recent response is not included in the claim’s BRI.
In the interest of compact prosecution, all claim language is addressed below. To overcome this interpretation, the examiner recommends amending claim 1 to recite “[in response to ]determining that the probability score does not exceed a threshold, and in response, …” A similar amendment to claim 13 would also overcome this interpretation.
Claim Rejections - 35 USC § 112
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.
Claims 14 and 27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 describes determining “a second probability score” by determining “a third probability score” based on the second facial image, and by aggregating the first and third probability scores. This is indefinite for a number of reasons. “Second” and “third probability scores” are introduced in claim 13, from which claim 14 depends. It is not clear if “a” second probability score and “a” third probability score that are recited in claim 14 refer to these. Further, claim 13 describes generating the second probability score based on the first probability score and feature vectors relating to the second image; and the third probability score is based on first and second probability scores. The manner in which claim 14 describes generating the second probability score (by aggregating first and third probability scores) is inconsistent with amended claim 13.
Claim 27 depends from claim 13, and recites “capturing one or more additional facial images after the second facial image, and updating the second probability score based on the one or more additional facial images.” But claim 13 recites that a third facial image is captured after the second facial image, and updating the second probability score based on this third image. It is not clear if the scope of claim 27 includes only what is already recited in claim 13, in which case it would not further limit the claim it depends from. Alternative, it is not clear if claim 27 requires further facial images beyond the third facial image.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 27 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 27 does not further limit claim 13, as described in the § 112(b) rejection above. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
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 1-6, 12-15, 21, 24-33, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over JP 6340538 (“JP ‘538”), in view of Myslinksi, US 9,643,722, in view of JP 3580129 (“JP ‘129”), in view of Chan, US 20190163962, in view of Applicant’s Admitted Prior Art (AAPA). Note: for the Japanese documents below, reference is made to the English translations that have been provided previously.
Claim 1: JP ‘538 discloses a computer implemented method of increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising:
identifying a positioning property of a target person based on analysis of an image captured by at least one imaging sensor mounted on a drone operated to approach the target person (After determining that the image contains a person, it is determined if the person’s face is observable. If not observable, the drone flies to a position on the “front side” of the person’s face. Pg. 5 ¶7. This shows that the drone determines the direction that the person is facing, i.e. a positioning property of the person.), the at least one imaging sensor locally controlled at the drone (The camera 350 is operated locally by the drone. Fig. 2 and pg. 2 ¶5.)
instructing the drone to adjust its position to an optimal facial image capturing position selected based on the positioning property (The drone flies to a position on the “front side” of the person’s face in order to capture another image and detect the face of the person. Pg. 5 ¶7. The location to which the drone is moved meets the BRI of the claimed “optimal … position” when that term is understood in light of the ‘984 patent at 4:44-62),
receiving a facial image of the target person captured by the at least one imaging sensor while the drone is located at the optimal facial image capturing position, receiving a face classification associated with an image recognition procedure wherein the image recognition procedure is executed by at least one remote system connected to the drone via at least one network, and outputting the face classification for use by at least one face recognition based system (The image is analyzed to identify the person, and an identification (i.e. a face classification) is output for recognition. Pg. 5 ¶9. The image recognition is performed on server 50, which is connected to the drone via a network. See Fig. 1 and 2),
recognizing the target person in at least one additional iteration (A person is recognized in the person recognition step S160. As shown in Fig. 3, if the person is not recognized in this step, the process returns to the moving step S120, and another iteration of the steps S120-S160 is performed.), and
initiating another additional iteration in case the probability score does not exceed a certain threshold (If a match rate is not reached (e.g. a probability of identification does not exceed a threshold), another iteration of the steps of Fig. 3 is performed. See Fig. 3 and its description.).
wherein the operation of the drone is controlled locally at the drone (Various drone operations are controlled on-board the drone. Fig. 2 and pg. 3 ¶¶1-3.).
JP ‘538 fails to disclose that the positioning property comprises a condition obstructing visibility of a face of the target person in the image captured by the at least one imaging sensor, wherein the condition is caused by an interference in a line of sight between the drone and the target person, instructing the drone to adjust its position to an optimal facial image capturing position selected based on the condition.
Myslinksi discloses a drone with a camera that attempts to capture images of a person, and determines a positioning property of the person that comprises a condition obstructing visibility of the person in the image captured by the at least one imaging sensor, wherein the condition is caused by an interference in a line of sight between the drone and the target person, instructing the drone to adjust its position to an optimal image capturing position selected based on the condition (The drone identifies that an obstruction is present, and repositions itself to an optimal position in order to capture images of the target that remove or diminish the amount of obstruction. Myslinksi 56:48-57:33).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify JP ‘538 with Myslinksi, the rationale being to ensure that the drone is able to obtain an unobstructed view of the target person’s face.
JP ‘538-Myslinksi fails to disclose receiving a face classification associated with a probability score from at least one machine learning model trained to recognize the target person which is applied to the facial image, wherein the at least one machine learning model is executed by at least one remote system connected to the drone via at least one network.
JP ‘538-Myslinkski also fails to disclose that each additional iteration comprises:
identifying at least one additional positioning property of the target person based on analysis of at least one additional image captured by the at least one imaging sensor;
instructing the drone to adjust its position to an additional optimal facial image capturing position selected based on the at least one additional positioning property;
receiving at least one additional facial image of the target person captured by the at least one imaging sensor while the drone is located at the additional optimal facial image capturing position,
receiving an additional face classification associated with an additional probability score from the at least one machine learning model, and
initiating another iteration in case the aggregated probability score does not exceed a certain threshold.
JP ‘129 discloses receiving a face classification associated with a probability score from at least one machine learning model trained to recognize a person which is applied to at least one facial image (A machine learning (neural network – see ¶37) routine processes an image of the face of a person and provides a classification (likeness) that is associated with a probability, or likeness degree. ¶¶38-39. This implicitly describes that the ML routine is trained to recognize the person),
identifying at least one additional positioning property of the target person based on analysis of at least one additional image captured by at least one imaging sensor (Based on captured images and the direction that the person is facing, a condition is estimated for acquiring another image that will effectively identify the person. ¶¶ 7 and 33-35. This estimated condition includes changing the camera position. ¶¶ 29 and 33-35. Therefore an additional positioning property of the person is identified based on imagery),
instructing the camera to adjust its position to an additional optimal facial image capturing position selected based on the at least one additional positioning property (The camera is moved to a position based on the estimated condition, or the additional positioning property. ¶¶ 29 and 34.),
receiving at least one additional facial image of the target person captured by the at least one imaging sensor while the camera is located at the additional optimal facial image capturing position, receiving an additional face classification associated with an additional probability score from the at least one machine learning model (¶¶ 29-35 and 38-39.),
and initiating another iteration in case the probability score does not exceed a certain threshold; and outputting the face classification (If the probability does not exceed the threshold, a new iteration of imaging and identification is performed. ¶42. The new iteration involves moving the imaging unit to capture an image of the person’s face from a new location. ¶¶ 17 and 29. When the identification probability exceeds the threshold, the iterations cease and the person is identified, i.e. the face classification is output. ¶39-41.).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify JP ‘538 with teachings found in JP ‘129 by enabling the face of the person to be re-captured from a different location, the motivation being to increase the likelihood of positively identifying the person. See JP ‘129 ¶¶ 5 and 29. When modifying JP ‘538 to include this feature, the POSITA would have found that the logical and common-sense manner to achieve this advantage would have been to adjust the drone’s location, similar to camera location adjustment in JP ‘129, in order to re-capture the face of the person from a different angle in order to generate a more confident identification. This combination would also have resulted in the JP ‘129 procedure being carried out in the network-based server of JP ‘538, which that reference discloses as performing the image analysis and person identification procedures.
The JP ‘538 – Myslinksi - JP ‘129 combination fails to disclose responsive to the additional iteration being an initial iteration of the at least one additional iterations, calculating an aggregated probability score based on the additional probability score received in a current iteration of the at least one additional iteration and based on the probability score, responsive to the additional iteration being an iteration subsequent to the initial iteration of the at least one additional iteration, calculating the aggregated probability score based on the additional probability score received in a current iteration of the at least one additional iteration and based on one or more aggregated probability scores calculated before said current iteration such that, in each iteration of the at least one additional iteration subsequent to the initial iteration, said aggregated probability score is determined based on the probability score and respective additional probability scores of the at least one additional iteration
However, this is suggested in Chan. Chan would have suggested calculating an updated aggregated probability score at each iteration that is based on an additional probability score and an aggregated score calculated before the current calculation such that, in each iteration of the at least one additional iteration subsequent to the initial iteration, said aggregated probability score is determined based on the probability score and respective additional probability scores of the at least one additional iteration (Multiple images of an unknown person are analyzed in an attempt to identify the person. ¶¶27-28. The confidence of a match of a first image is combined with the confidence of a match in a second image to generate an aggregated probability score. ¶¶28-34.).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of JP ‘539, Myslinski, and JP ‘129 with these teachings in Chan. The POSITA would have been motivated by Chan to calculate the probability score by updating a combined probability in each iteration, rather than simply determining if a probability score of the current iteration exceeds a threshold, as JP ‘129 discloses (see above). The POSITA would understand that calculating probability in this way would reflect the statistical fact that when multiple images are captured of a person, multiple lower-confidence identifications provide a higher confidence than is reflected by the individual scores. See Chan ¶34. Therefore calculating probability in this manner would enable higher-confidence results and would reduce the number of iterations that the JP ‘539-Myslinkski-JP ‘129 system would have to perform before the person is identified. Therefore when modifying the drone in this manner, the POSITA would have found it obvious to aggregate scores across iterations in the manner claimed.
The above combination fails to disclose that the probability score determined based on a distance between a first feature vector extracted from the facial image and a second feature vector extracted from training facial images of the target person, and the additional probability score determined based on a distance between a third feature vector extracted from the additional facial image and a fourth feature vector extracted from training facial images of the target person
However, AAPA admits that it was known in the art to extract a feature vector from an image and determine a probability score based on a distance between this feature vector and a feature vector extracted from training images. For example, the ‘984 patent describes:
As known in the art, the ML model(s) are configured and trained to extract feature vectors from the images and classify the extracted feature vectors to respective classes (labels), for example, an identifier (identity) of the target person according to matching of the feature vectors with feature vectors extracted from training images of the target person 204 processed and learned by the ML model(s) during their training phase.
The ML model(s) may typically compute a probability score (confidence score) indicating a probability (confidence level) of correct classification of the face of the target person 204 and hence the probability and confidence of correctly recognizing the target person 204.
The probability score expressing confidence of the correct recognition may be interpreted as the distance between the feature vector(s) extracted from the facial image(s) of the target person 204 captured by the imaging sensor(s) 216 from the optimal image capturing position and feature vectors which are extracted from training facial images of the target person 204 which were used to train the ML model(s). For example, as known in the art, cosine similarity may be measured between feature vectors A∙B extracted from facial image(s) of the target person 204 captured by the imaging sensor(s) 216 and ∥A∥ ∥B∥ extracted from the training facial image(s) of the target person.
‘984 patent at 14:15-45 (emphasis added). It would have been obvious to a skilled artisan to modify JP538-Myslinski-JP129-Chan combination with this by determining the probability of identification based on the distance of the claimed feature vectors, the rationale being to provide an improved confidence score.
Claim 2: the JP ‘538 – Myslinksi – JP ‘129 – Chan combination discloses that the condition is a blocking object, and wherein instructing the drone to adjust its position to the optimal facial image capturing position selected based on the blocking object comprises: determining a repositioning direction based on a type of the blocking object, and instructing the drone to adjust its position in the repositioning direction (Myslinski 56:48-57:33).
Claim 3: the JP ‘538 – Myslinksi – JP ‘129 – Chan combination discloses that the optimal facial image capturing position is defined by at least one position parameter of the drone, the at least one position parameter is a location of the drone with respect to the target person (JP ‘538 pg. 5 ¶7).
Claim 4 the JP ‘538 – Myslinksi – JP ‘129 – Chan combination does not disclose adjusting at least one operational parameter of the at least one imaging sensor based on the at least one positioning property, the at least one operational parameter is a member of a group consisting of: a resolution, a zoom, a color, a field of view, an aperture, a shutter speed, a sensitivity (ISO), a white balance and an auto exposure. However, official notice is taken that this was well known in the art before the effective filing date of the claimed invention. For example, it was well known to adjust an imaging property such as a zoom level based on a distance, or positioning property, from the object being photographed. Therefore it would have been obvious to the skilled artisan to modify the JP ‘538 – Myslinksi – JP ‘129 – Chan combination to include this, the rationale being to generate an improved image of the person’s face.
Claim 5: the JP ‘538 – Myslinksi – JP ‘129 – Chan combination discloses that the at least one machine learning model is based on a neural network (JP ‘129 ¶37).
Claim 6: the JP ‘538 – Myslinksi – JP ‘129 – Chan combination discloses that at least part of the face classification is done by the at least one remote system connected to the drone via at least one network to receive facial image captured by the at least one imaging sensor mounted on the drone (The image recognition is performed on server 50, which is connected to the drone via a network, based on images captured by the drone-mounted camera. See JP ‘538 pg. 5 ¶9 and Fig. 1 and 2. When modified to perform the face classification using the JP ‘129, the face classification is done by the remote system).
Claim 12: See rejection of claim 1. The JP ‘538 – Myslinksi – JP ‘129 – Chan combination also discloses a system for increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising: at least one processor executing a code, the code comprising: code instructions for performing the method recited in claim 1. See e.g. JP ‘538 Figures 1-2 and their description.
Claim 13 is similar in scope to claim 1. See the rejection of claim 1 for a discussion of how the references meet these claim limitations, and why combining the references in the proposed manner would have been obvious. The JP ‘538 – Myslinksi – JP ‘129 – Chan combination discloses a computer-implemented method, comprising:
receiving, by a remote server, a first facial image of a person captured by a drone, the drone including at least one imaging sensor configured to capture images and one or more processors configured to control the at least one imaging sensor (JP ‘538 pg. 5, Fig. 1, and Fig. 2);
applying, by the remote server, at least one machine learning model to the first facial image to extract a first feature vector (JP ‘538 pg. 5; JP ‘129 ¶¶ 37-39; AAPA – ‘984 patent at 14:15-45);
determining, based on a distance between the first feature vector and a second feature vector extracted from training facial images of a target person, a first probability score indicating a likelihood that the person corresponds to an identity of the target person based on the first facial image (JP ‘538 pg. 5; JP ‘129 ¶¶ 37-39; AAPA – ‘984 patent at 14:15-45);
determining that the first probability score is no greater than a threshold (JP ‘538 Fig. 3 and its description; JP ‘129 ¶¶ 7 and 29-35);
identifying, by the remote server, a condition obstructing visibility of a face of the person in the first facial image, wherein the condition is caused by an interference in a line of sight between the drone and the target person (Myslinksi 56:48-57:33);
transmitting, by the remote server, a command to the drone to capture a second facial image of the person, wherein the command comprises an instruction to adjust a position of the drone based on the condition (id.);
receiving, by the remote server, the second facial image of the person captured by the drone (JP ‘538 Fig. 3; JP ‘129 ¶¶ 9-35);
determining, by the remote server, a second probability score indicating a likelihood that the person corresponds to the identity of the target person, the second probability score determined based on the first probability score and a distance between a third feature vector extracted from the second facial image and a fourth feature vector extracted from training facial images of the target person (JP ‘538 pg. 5; JP ‘129 ¶¶ 37-39; AAPA – ‘984 patent at 14:15-45. Chan provides suggestion to aggregate probability scores across iterations. See the rejection of claim 1 for a discussion of this modification); and
in response to determining that the second probability score is no greater than the threshold, determining, by the remote server, whether a third probability score is greater than the threshold, the third probability score determined based on the first probability score and the second probability score, the third probability score determined using a third facial image of the person captured by the drone (JP ‘538 pg. 5; JP ‘129 ¶¶ 37-39; AAPA – ‘984 patent at 14:15-45. Chan provides suggestion to aggregate probability scores across iterations. See the rejection of claim 1 for a discussion of this modification); and
in response to determining that the third probability score is greater than the threshold, outputting the identity of the target person for use by at least one face recognition- based system (JP ‘538 pg. 5.).
Claim 14: the above combination discloses that determining, by the remote server, a second probability score indicating a likelihood that the person corresponds to the identity of the target person based on both the first facial image and the second facial image comprises: applying, by the remote server, the at least one machine learning model to the second facial image to determine a third probability score indicating a likelihood that the person corresponds to the identity of the target person based on the second facial image; and aggregating, by the remote server, the first probability score and the third probability score to determine the second probability score (See the rejection of claim 13 above, and the §112(b) rejection of this claim).
Claim 15: the above combination discloses that the second facial image is captured from a different view angle relative to the first facial image (JP ‘129 ¶¶17 and 29).
Claim 21: JP ‘538 discloses that drone comprises one or more imaging sensors selected from a visible-light camera, an infrared camera, or a thermal camera (Fig. 2 and its description – camera 350 is a visible light or IR camera).
Claim 24 the above combination discloses that the command transmitted to the drone to capture the second facial image comprises at least one of: a location of the drone with respect to the person (JP ‘538 pg. 5 ¶7).
Claims 25-26: see rejection of claim 4.
Claim 27: Chan discloses capturing one or more additional facial images after the second facial image, and updating the second probability score based on the one or more additional facial images (see ¶¶ 27-34).
Claim 28: see rejection of claims 1 and 13. The above combination further discloses a system comprising a remote server comprising one or more processors; and a first non-transitory storage medium, storing instructions, which when executed by the one or more processors, cause the one or more processors to perform a first plurality of steps including steps that are recited in claims 1 and 13 (JP ‘538 Fig. 1: 50 is a server containing these structural elements); and a drone comprising: at least one imaging sensor; at least one processor; and a second non-transitory storage medium, storing instructions, which when executed by the at least one processor, cause the at least one processor to perform a second plurality of steps including steps recited in claims 1 and 13 (JP ‘538 Fig. 2: 30 is a drone containing these elements).
Claim 29 JP ‘538 discloses that the command received from the remote server comprises at least one of: a location of the drone with respect to the person (pg. 5 ¶¶ 7-9).
Claim 30-31: see rejection of claim 4.
Claim 32: JP ‘538 – JP ‘129 discloses receiving, from the remote server, one or more additional commands to capture one or more additional facial image of the person (If the probability of identification does not exceed the threshold, a new iteration of imaging and identification is performed. JP ‘129 ¶42. The new iteration involves moving the imaging unit to capture an image of the person’s face from a new location. JP ‘129 ¶¶ 17 and 29.).
Claim 33: JP ‘538 – JP ‘129 discloses that the second facial image is captured from a different view angle relative to the first facial image (The new iteration involves moving the imaging unit to capture an image of the person’s face from a new location. JP ‘129 ¶¶ 17 and 29.)
Claim 39: see rejection of claim 21.
Claims 7, 16, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘538 – Myslinski – JP ‘129 – Chan combination in view of Okubo, JP 2004362079.
Claim 7: the JP ‘538 – Myslinski – JP ‘129 – Chan combination fails to disclose initiating additional iterations to capture a plurality of facial images depicting the face of the target person from a plurality of view angles to form an at least partial three dimensional (3D) representation of at least part of a head of the target person, the plurality of facial images different from the at least one additional facial image of the target person.
However, Okubo discloses initiating a plurality of iterations to capture a plurality of facial images depicting the face of the target person from a plurality of view angles to form an at least partial three dimensional (3D) representation of at least part of a head of the target person, the plurality of facial images different from the at least one additional facial image of the target person (Images of the face of a person is captured from a plurality of angles to generate a 3D model of the person. ¶12).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the JP ‘538 – Myslinksi – JP ‘129 – Chan combination with teachings found in Okubo in order to ensure proper identification of the person.
Claim 16: the JP ‘538 – Myslinski – JP ‘129 – Chan system does not disclose constructing at least a partial three-dimensional representation of a head of the person based on the first facial image and the second facial image, wherein the second probability score is determined based in part on the partial three-dimensional representation of the head of the person.
Okubo discloses initiating a plurality of iterations to capture a plurality of facial images depicting the face of the target person from a plurality of view angles, and constructing at least a partial three-dimensional representation of a head of the person based on the first facial image and the second facial image (Images of the face of a person is captured from a plurality of angles to generate a 3D model of the person. ¶12).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the JP ‘538 – JP ‘129 combination with teachings found in Okubo in order to ensure proper identification of the person. When motivated to make this modification, the skilled artisan would have found it obvious to calculate the second probability score based in part on the partial three-dimensional representation of the head of the person, as this would produce a more reliable probability of identification. Therefore the combined teachings of JP ‘538, JP ‘129, and Okubo suggest the invention that is recited in claim 16.
Claim 34: see rejection of claim 16.
Claims 8-11, 17-20, and 35-38 are rejected under 35 U.S.C. 103 as being unpatentable over the JP ‘538 – Myslinksi – JP ‘129 – Chan combination in view of Kelly, US 20180253805.
Claim 8: the JP ‘538 – Myslinksi – JP ‘129 – Chan combination fails to disclose that the face recognition based system is an automated delivery system using the face classification for authenticating an identity of the target person for delivery of goods to the target person.
However, this is suggested in Kelly, which discloses a face recognition based system, where an automated delivery system uses a face classification for authenticating an identity of a target person for delivery of goods to the target person (A drone-based delivery system identifies a recipient of a delivered good using a facial scan. ¶297).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the JP ‘538 – Myslinksi – JP ‘129 – Chan combination with teachings in Kelly, the rationale being to diversity the capabilities of the system, thereby providing additional opportunities for revenue.
Claim 9: Kelly discloses initiating the at least one additional iteration for recognizing the target person in correlation with the goods at a time of delivery (¶¶ 291 and 297).
Claim 10: the JP ‘538 – Myslinksi – JP ‘129 – Chan - Kelly combination does not disclose that a location of the target person where the at least one image is captured for recognizing the target person is different form the location of the delivery of goods. But official notice is taken that this was well known. For example, delivery drones were known to drop off goods at a location distant from their recipient. It would have been obvious to the POSITA to modify the combination to operate in this way, the rationale being to ensure that the recipient is not injured during delivery.
Claim 11: Kelly discloses instructing the drone to initiate at least one additional authentication sequence which is a member of a group consisting of: manual signature of the target person, a biometric authentication of the target person and a voice authentication of the target person (¶297).
Claims 17 and 18: see rejection of claims 8-11.
Claim 19: Kelly discloses that the outputted identity of the target person is used by a security system (¶291 describes a system to securely deliver goods, which is a security system).
Claim 20: Kelly discloses instantiating an additional authentication operation, the additional authentication operation including at least one of: manual signature capture, biometric authentication, or voice authentication (¶297).
Claims 35-38: see rejection of claims 17-20.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of references described in the rejection of claim 13 in view of Martirosyan, US 20180046187.
Claim 22: Martirosyan discloses a drone that determines a condition obstructing visibility of a target person, wherein the condition is a blocking object, the method further comprising: determining the instruction to adjust the position of the drone based on the blocking object by: determining. based on a type of the blocking object, a repositioning direction for adjusting the position of the drone (The drone determines that the person it is attempting to image is below a tree, and in response repositions to a lower altitude below the tree leaves in order to maintain a line of sight. ¶ 22.).
It would have been obvious to the skilled artisan before the effective filing date of the claimed invention to modify the combination of references to include these teachings in Martirosyan, the rationale being to enable the drone to quickly and accurately reposition in response to a variety of obstacles, which would enable it to capture improved facial images of the target person.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of references described in the rejection of claim 13 in view of Smith, US 20200331403.
Claim 23: the combination of references in claim 13 does not disclose that the condition is environmental precipitation, the method further comprising: determining, responsive to the condition being the environmental precipitation, that the instruction comprises adjusting the position of the drone to be closer to the person.
Smith discloses a drone that detects environmental precipitation, and instructs the drone to adjust its to be closer to the object being tracked to capture images of the object (¶ 107).
It would have been obvious to the skilled artisan before the effective filing date of the claimed invention to modify the combination of references to include these teachings in Smith, the rationale being to enable the drone to capture accurate images of the person under adverse environmental conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J HANCE whose telephone number is (571)270-5319. The examiner can normally be reached M-F 11:00am-7:00pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Fuelling can be reached at (571) 270-1367. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ROBERT J HANCE/ Reexamination Specialist, Art Unit 3992
Conferees:
/CHARLES R CRAVER/ Reexamination Specialist, Art Unit 3992 /M.F/Supervisory Patent Examiner, Art Unit 3992
