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 Applicant’s Amendments / Arguments Regarding 35 U.S.C. § 103
The applicant’s remarks, on pages 11-15 of the response / amendment, the applicant argues the features which allegedly distinguish over the previously cited references cited in the 35 U.S.C. § 103 rejections.
Applicant’s arguments have been considered but are moot in view of the new ground(s) of rejection.
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, 3-4, 6-9, 11, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190318153 to Liu et al. (hereinafter Liu), in view of US 20190327092 to Kareti (hereinafter Kareti), in view of US 20220121884 to Zadeh et al. (hereinafter Zadeh).
Regarding claim 1, Liu teaches,
A method, comprising:
determining a plurality of indexed face-signature features, wherein each corresponding face-signature feature is associated with (fig. 4 steps 440, 441, 450, 451, 460, and 470 and [0052] teaching multiple face images. Lui, [0038] teaches features being face key points (indexes).)
obtaining a plurality of images of a person's face; (fig. 4 steps 440, 441, 450, 451, 460, and 470 and [0052] teaching multiple face images of video of photos are taken of the same person, creating a face sequence. Abstract, teaches that face sequence is set of face images of a same person in multiple images such as video.)
generating a face signature for each of the plurality of images, wherein the face signature includes a plurality of values with each corresponding value representative of the corresponding face-signature feature of the a plurality of indexed face-signature features; ([0052] teaches the use of weighting and averages on features of a face based on the quality of the face images. [0038] teaches face key points / “indexes”.)
generating a feature significance statistic for each corresponding face-signature feature of the plurality of indexed face-signature features from the corresponding values of the face signatures for the plurality of images; ([0053] teaches face sequence / multiple images, being analyzed for similarity between features on the face. [0054] teaches determines at least one face feature of the face in different pictures of the same person. [0038] teaches face key points / “indexes”.)
identifying a plurality of possible significant features from the plurality of indexed face signature features having the feature significance statistic ([0053] teaches selecting face features based on similarities.)
selecting a plurality of significant features from the plurality of possible significant features, wherein the plurality of significant features ([0053] teaches selecting face features based on similarities. [0056] teaches selecting based on pose, such as similar facial expressions.)
generating a binary . ([0052] teaches generating face key points of the features of the biometric. See also [0037-38] from a plurality of images. It is well understood in the art of biometrics that biometric features are represented in binary.)
Lui fails to teach generating a biometric key using the features of the biometric,
However, Kareti teaches,
generating a binary cryptographic key corresponding to the plurality of significant features based on the corresponding values of the . (Abstract teaches encryption of data using a cryptographic key generated using biometric information. [0018] teaches that biometric features may include facial features / iris.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lui, which teaches using multiple images of a biometric / face with statistical features of the biometric to select specific feature, which are key points in the biometric, (Abstract & [0052]) with Kareti, which also teaches the selection and extraction of biometric features ([0019]) from video [0061], and additionally teaches using biometrics, including facial or iris information, to generate an biometric cryptographic key (Abstract, fig. 2A, & [0028]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Lui with the added ability to generate biometric cryptographic keys, as taught by Kareti, for the purpose of increasing security and increasing user convenience by allowing the user to authenticate / encrypt data / decrypt data without having to remember a credential, which may be stolen.
Lui and Kareti fail to teach generating a biometric key using the features of the biometric,
However, Zadeh teaches,
determining a plurality of indexed face-signature features, wherein each corresponding face-signature feature is associated with a unique index value; ([1900-1901] teaches labeling of features, where the features are used in image recognition, including biometric recognition (Abstract) [1541] teaches detection of facial features including shapes, positions of facial features, and smile vs anger detection, which is similar to the applicants printed publication at [0040] teaching recognition and indexing based on facial feature positions, whether mouth is smiling / state of mouth, and feature characteristics such as color.)
identifying a plurality of possible significant features from the plurality of indexed face signature features having the feature significance statistic above a selected threshold; ([1924-1925] teach the use of a threshold that is used to select a subset of features. [1900-1902] teach reliability facture used for selecting features.)
selecting a plurality of significant features from the plurality of possible significant features, wherein the plurality of significant features is a subset of the plurality of possible significant features; and (end of [1902] teaches labeling of features, then subsets of features are selected at random, and potentially unreliable (and reliable) annotations are determined and tagged with a reliability factor. [19024-1925] also teach sub-selecting to reduce the number of features of the subset. Zadeh, [2892] teaches the subset selection increases speed by reducing dimensionality.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lui, which teaches using multiple images of a biometric / face with statistical features of the biometric to select specific feature, which are key points in the biometric, (Abstract & [0052]) with Kareti, which also teaches the selection and extraction of biometric features ([0019]) from video [0061], and additionally teaches using biometrics, including facial or iris information, to generate an biometric cryptographic key (Abstract, fig. 2A, & [0028]), with Zadeh, which also teaches image recognition including biometric recognition (Abstract), and additionally teaches labeling (indexing) features and selecting a subset of the features based on thresholds / quality ([1902] & [1924-25]). One of ordinary skill in the art would have been motivated to perform such an addition to provide Lui and Kareti with the added ability to select a subset of features from the set of features, to reduce the set of features used in recognition, as taught by Zadeh, for the purpose of increasing computational efficiency by removing features / calculations that have little to no effect on recognition while maintaining security (Zadeh, [2892]).
Regarding claim 3, Lui, Kareti and Zadeh teach,
The method of claim 1, wherein generating the binary cryptographic key further comprises:
generating a first binary cryptographic key corresponding to the plurality of significant features for at least one of the plurality of images; (see rejection of claim 1.)
Kareti teaches,
generating a second binary cryptographic key; and (Abstract teaches enrollment biometric key and regenerated biometric key using a new biometric scan / second key. fig. 2a and [0029] teaches biometric input 204 being combined with random number i 210. [0054] teaches performing shifts in the biometric data to embed the random number i. )
combining the first binary cryptographic key with the second binary cryptographic key. (fig. 2a, biometric processor combines biometric 204 with random number 201, as described in [0029-31].)
Regarding claim 4, Lui, Kareti, and Zadeh teach,
The method of claim 1, further comprising:
Kareti teaches,
encrypting content using the binary cryptographic key; and (Abstract teaches encrypting data using the cryptographic key generated using the biometric.)
modifying the encrypted content to include the index values of the plurality of significant features. ([0031] teaches part of the data includes a randomized map extracted from input data. [0031] also teaches the use of a secure channel.)
Regarding claim 6, Lui, Kareti, and Zadeh teach,
The method of claim 4, further comprising:
obtaining at least one second image of the person's face;
generating a second face signature for each of the at least one second image, wherein the second face signature includes a second plurality of values with each corresponding value representative of the corresponding face-signature feature of the plurality of indexed face-signature features;
selecting a second plurality of significant features from the plurality of indexed face-signature features that correspond to the unique index values of the plurality of significant features;
The above features of claim 6 are rejected using the same basis of arguments using Lui, as included above in the rejection of claim 1, above.
Kareti teaches,
generating a second binary cryptographic key corresponding to the second plurality of significant features based on the corresponding values of the second plurality of significant features for the at least one second image; and (Abstract teaches decrypting data using newly captured biometric / second biometric, that is different than the enrollment data. Fig. 2a and [0052] teach circuit 216 decrypting the data.)
decrypting the encrypted content using the second binary cryptographic key. (Abstract teaches decrypting data using newly captured biometric, that is different than the enrollment data. Fig. 2a and [0052] teach circuit 216 decrypting the data.)
Regarding claim 7, Lui, Kareti, and Zadeh teach,
The method of claim 1, further comprising:
Kareti teaches,
comparing the binary cryptographic key to a previously generated binary cryptographic key for the person; and ([0051] teaches comparison.)
positively authenticating the person in response to a match between the binary cryptographic key and the previously generated binary cryptographic key. (Title & Abstract, teach biometric authentication. [0051-52] teaches regenerating the same cryptographic key 212, which is biometric, and comparing, and using the second biometric key to decrypt the data.)
Regarding claim 8, Lui, Kareti, and Zadeh teach,
The method of claim 1, wherein identifying the plurality of possible significant features further comprises:
Lui teaches,
selecting the plurality of possible significant features from the plurality of indexed face-signature features as those face-signature features having the feature significant statistic above a selected threshold value. (Liu, [0053-54] highest similarity feature is selected as a face feature in the face sequence, where there are N similarities. See also, [0067-68]) (Kareti, [0019-20] teaches selecting facial features and mapping to eigenfaces. [0027] teaches the use of Hamming distances and thresholds.)
Regarding claim 9, Lui, Kareti, and Zadeh teach,
The method of claim 1, wherein identifying the plurality of possible significant features further comprises:
selecting the plurality of possible significant features from the plurality of indexed face-signature features as those face-signature features having the feature significant statistic above a selected percentile threshold of the plurality of indexed face-signature features. (Liu, [0053-54] highest similarity feature is selected as a face feature in the face sequence, where there are N similarities. See also, [0067-68]) (Kareti, [0019-20] teaches selecting facial features and mapping to eigenfaces. [0027] teaches the use of Hamming distances and thresholds.)
Regarding claim 11, Lui, Kareti, and Zadeh teach,
The method of claim 1, wherein selecting the plurality of significant features from the plurality of possible significant features further comprises:
randomly selecting the plurality of significant features from the plurality of possible significant features. (Zadeh, end of [1902] teaches random feature selection to discover features that are reliable and unreliable, and then assigning a reliability score to the features that were randomly selected.)
Regarding claim 21, Lui, Kareti, and Zadeh teach,
A system, comprising:
a camera configured to capture images of a person's face;
a memory configured to store computer instructions; and
a processor configured to execute the computer instructions to:
determine a plurality of indexed face-signature features, wherein each corresponding face-signature feature is associated with a unique index;
obtain a plurality of images of the person's face from the camera;
generate a face signature for each of the plurality of images, wherein the face signature includes a plurality of values with each corresponding value representative of the corresponding face-signature feature of the plurality of indexed face-signature features;
generate a feature significance statistic for each corresponding face-signature feature of the plurality of indexed face-signature features from the corresponding values of the face signatures for the plurality of images;
identify a plurality of possible significant features from the plurality of indexed face-signature features having the feature significance statistic above a selected threshold;
select a plurality of significant features from the plurality of possible significant features;
generate a binary cryptographic key corresponding to the plurality of significant features based on the corresponding values of the plurality of significant features for at least one image selected from the plurality of images;
encrypt content based on the binary cryptographic key; and
modify the encrypted content to identify the unique index of each corresponding significant feature of the plurality of significant features.
Claim 21 is rejected using the same basis of arguments used to reject claim 1 above.
Regarding claim 22, Lui, Kareti, and Zadeh teach,
The system of claim 21,
wherein the processor is configured to further execute the computer instructions to:
obtain a second plurality of images of the person's face from the camera;
generate a second face signature for each of the second plurality of images, wherein the second face signature includes a second plurality of values with each corresponding value representative of the corresponding face-signature feature of the
select a second plurality of significant features from the
generate a second binary cryptographic key corresponding to the second plurality of significant features based on the corresponding values of the second plurality of significant features for at least one image selected from the second plurality of images; and
decrypt the encrypted content using the second binary cryptographic key.
Claim 22 is rejected using the same basis of arguments used to reject claim 6 above.
Claims 2, 5, 10, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu, in view of Kareti, in view of Zadeh, in view of US 10635918 to Kim et al. (hereinafter Kim).
Regarding claim 2, Lui, Kareti, and Zadeh teach,
The method of claim 1, comprising:
Lui teaches,
generating a similarity score between each pair of images from the plurality of images; and ([0053] teaches similarity between different features of images in the face sequence (i.e., multiple images).)
for each corresponding image in the plurality of images: ([0053], see above.)
aggregating the similarity scores from each pair of images containing the corresponding image; and ([0053] teaches similarities between the first face feature of the face sequence.)
While, Lui teaches giving greater weight to higher quality images in [0048], Lui, Kareti, and Zadeh fail to explicitly teach discarding images based on similarity score,
However, Kim teaches,
discarding the corresponding image in response to the aggregated similarity score being below a threshold value or identifying the corresponding image as a image in response to the aggregated similarity score being above the threshold value. (Abstract teaches scoring facial images of the same person and assigning quality scores to different facial images, and Col. 8, lines 40-45 (28) teaches deleting facial images based on low quality scores or “lowest similarity score”. See also, Col 2, lines 33-40 (18-19) teaching higher and lower similarities being used for deletion and selection.) (Zadeh, [1924] also teaches using a threshold, and ranking features based on the threshold, but does not explicitly discarding an image based on this threshold.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lui, which teaches using multiple images of a biometric / face with statistical features of the biometric to select specific feature, which are key points in the biometric, (Abstract & [0052]) with Kareti, which also teaches the selection and extraction of biometric features ([0019]) from video [0061], and additionally teaches using biometrics, including facial or iris information, to generate an biometric cryptographic key (Abstract, fig. 2A, & [0028]), with Zadeh, which also teaches image recognition including biometric recognition (Abstract), and additionally teaches labeling (indexing) features and selecting a subset of the features based on thresholds / quality ([1902] & [1924-25]), with Kim, which also teaches multiple facial biometrics being selected (fig. 3 and Abstract), and additionally teaches deletion of biometrics that are not similar enough (Col. 8, lines 40-45). One of ordinary skill in the art would have been motivated to perform such an addition to provide Lui, Kareti, and Zadeh with the added ability to remove / delete facial biometrics that are dissimilar / not similar enough, as taught by Kim, for the purpose of increasing security and increasing computational efficiency by removing inefficient / low quality biometric facial data.
Regarding claim 5, Lui, Kareti, and Zadeh teach,
The method of claim 4, further comprising:
obtaining a second plurality of images of the person's face; (see rejection of claim 1. Lui teaching face sequence. See also, Kareti Abstract which teaches an enrollment face biometric and a second newly acquired face biometric used for authentication.)
generating a second face signature for each of the second plurality of images, wherein the second face signature includes a second plurality of values with each corresponding value representative of the corresponding face-signature feature of the indexed face-signature features;
selecting a second plurality of significant features from the plurality of indexed face-signature features that correspond to the unique index values of the plurality of significant features;
The above features of claim 5 are rejected using the same basis of arguments as in claim 1, as included above.
Kareti teaches,
generating a second binary cryptographic key corresponding to the second plurality of significant features based on the corresponding values of the second plurality of significant features for at least one image selected from the second plurality of satisfactory images; and (Abstract teaches decrypting data using newly captured biometric, that is different than the enrollment data. Fig. 2a and [0052] teach circuit 216 decrypting the data.)
decrypting the encrypted content using the second binary cryptographic key. (Abstract teaches decrypting data using newly captured biometric, that is different than the enrollment data. Fig. 2a and [0052] teach circuit 216 decrypting the data.)
While, Lui teaches giving greater weight to higher quality images in [0048], Lui, Kareti, and Zadeh fail to explicitly teach discarding images based on similarity score,
However, Kim teaches,
discarding images from the second plurality of images having a dissimilar face to result in a second plurality of satisfactory images; (See rejection of claim 2 regarding discarding images.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lui, which teaches using multiple images of a biometric / face with statistical features of the biometric to select specific feature, which are key points in the biometric, (Abstract & [0052]) with Kareti, which also teaches the selection and extraction of biometric features ([0019]) from video [0061], and additionally teaches using biometrics, including facial or iris information, to generate an biometric cryptographic key (Abstract, fig. 2A, & [0028]), with Zadeh, which also teaches image recognition including biometric recognition (Abstract), and additionally teaches labeling (indexing) features and selecting a subset of the features based on thresholds / quality ([1902] & [1924-25]), with Kim, which also teaches multiple facial biometrics being selected (fig. 3 and Abstract), and additionally teaches deletion of biometrics that are not similar enough (Col. 8, lines 40-45). One of ordinary skill in the art would have been motivated to perform such an addition to provide Lui, Kareti, and Zadeh with the added ability to remove / delete facial biometrics that are dissimilar / not similar enough, as taught by Kim, for the purpose of increasing security and increasing computational efficiency by removing inefficient / low quality biometric facial data.
Regarding claim 10, Lui, Kareti, and Zadeh teach,
The method of claim 1, further comprising:
While, Lui teaches giving greater weight to higher quality images in [0048], Lui, Kareti, and Zadeh fail to explicitly teach discarding images based on similarity score,
However, Kim teaches,
discarding images from the plurality of images having a dissimilar face prior to generating the feature significance statistics for each corresponding face-signature feature of the plurality of indexed face-signature features from the corresponding values of the face signatures for the plurality of images. (fig. 2 and Col. 8, lines 30-45 (28) teaches extracting features, determining quality score, and discarding the lowest quality scored images, before further processing. See also, Col 2, lines 33-40 (18-19) teaching higher and lower similarities being used for deletion and selection.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lui, which teaches using multiple images of a biometric / face with statistical features of the biometric to select specific feature, which are key points in the biometric, (Abstract & [0052]) with Kareti, which also teaches the selection and extraction of biometric features ([0019]) from video [0061], and additionally teaches using biometrics, including facial or iris information, to generate an biometric cryptographic key (Abstract, fig. 2A, & [0028]), with Zadeh, which also teaches image recognition including biometric recognition (Abstract), and additionally teaches labeling (indexing) features and selecting a subset of the features based on thresholds / quality ([1902] & [1924-25]), with Kim, which also teaches multiple facial biometrics being selected (fig. 3 and Abstract), and additionally teaches deletion of biometrics that are not similar enough (Col. 8, lines 40-45). One of ordinary skill in the art would have been motivated to perform such an addition to provide Lui, Kareti, and Zadeh with the added ability to remove / delete facial biometrics that are dissimilar / not similar enough, as taught by Kim, for the purpose of increasing security and increasing computational efficiency by removing inefficient / low quality biometric facial data.
Regarding claim 12, Lui, Kareti, and Zadeh teach,
A computing device, comprising:
a memory configured to store computer instructions; and
a processor configured to execute the computer instructions to:
obtain a plurality of images of a person's face;
generate a face signature for each of the plurality of images, wherein the face signature includes a plurality of values with each corresponding value representative of a corresponding face-signature feature of a plurality of indexed face-signature features having corresponding index values;
generate a feature significance statistic for each corresponding face-signature feature of the plurality of indexed face-signature features from the corresponding values of the face signatures for the plurality of satisfactory images;
identify a plurality of possible significant features from the plurality of indexed face-signature features having the feature significance statistic above a selected threshold;
select a plurality of significant features from the plurality of possible significant features, wherein the plurality of significant features is a subset of the plurality of possible significant features; and
generate a binary cryptographic key corresponding to the plurality of significant features .
The above features of claim 12 are rejected using the same basis of arguments used to reject claim 1 above.
Kim teaches,
discard images from the plurality of images having a dissimilar face to result in a plurality of satisfactory images;
The above features of claim 12 are rejected using the same basis of arguments used to reject claim 2 above.
Regarding claim 13, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12, wherein the processor discards images from the plurality of images by being configured to further execute the computer instructions to:
generate a similarity score between each pair of images from the plurality of images; and
for each corresponding image in the plurality of images:
aggregate the similarity scores from each pair of images containing the corresponding image; and
discard the corresponding image in response to the aggregated similarity score being below a threshold value or identifying the corresponding image as a satisfactory image in response to the aggregated similarity score being above the threshold value.
The above features of claim 13 are rejected using the same basis of arguments used to reject claim 2 above.
Regarding claim 14, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12, wherein the processor generates the binary cryptographic key by being configured to further execute the computer instructions to:
generate a first binary cryptographic key corresponding to the plurality of significant features for at least one of the plurality of satisfactory images;
generate a second binary cryptographic key; and
combine the first binary cryptographic key with the second binary cryptographic key.
Claim 14 is rejected using the same basis of arguments used to reject claim 3 above.
Regarding claim 15, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12, wherein the processor is configured to further execute the computer instructions to:
encrypt content using the binary cryptographic key; and
modify the encrypted content to include the index values of the plurality of significant features.
Claim 15 is rejected using the same basis of arguments used to reject claim 4 above.
Regarding claim 16, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 15,
wherein the processor is configured to further execute the computer instructions to:
obtain a second plurality of images of the person's face;
generate a second face signature for each of the second plurality of images, wherein the second face signature includes a second plurality of values with each corresponding value representative of the corresponding face-signature feature of the
discard images from the second plurality of images having a dissimilar face to result in a second plurality of satisfactory images;
select a second plurality of significant features from the
generate a second binary cryptographic key corresponding to the second plurality of significant features based on the corresponding values of the second plurality of significant features for at least one image of the second plurality of satisfactory images; and
decrypt the encrypted content using the second binary cryptographic key.
Claim 16 is rejected using the same basis of arguments used to reject claim 5 above.
Regarding claim 17, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 15,
wherein the processor is configured to further execute the computer instructions to:
obtain at least one second image
generate a second face signature for each of the at least one second image, wherein the second face signature includes a second plurality of values with each corresponding value representative of the corresponding face-signature feature of the plurality of indexed face-signature features;
select a second plurality of significant features from the
generate a second binary cryptographic key corresponding to the second plurality of significant features based on the corresponding values of the second plurality of significant features for the at least one image; and
decrypt the encrypted content using the second binary cryptographic key.
Claim 17 is rejected using the same basis of arguments used to reject claim 6 above.
Regarding claim 18, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12, wherein the processor is configured to further execute the computer instructions to:
compare the binary cryptographic key to a previously generated binary cryptographic key for the person; and
positively authenticate the person in response to a match between the binary cryptographic key and the previously generated binary cryptographic key.
Claim 18 is rejected using the same basis of arguments used to reject claim 7 above.
Regarding claim 19, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12,
wherein identifying the plurality of possible significant features further comprises:
selecting the plurality of possible significant features from the plurality of indexed face- signature features as those face-signature features having the feature significant statistic above a selected threshold value.
Claim 19 is rejected using the same basis of arguments used to reject claim 8 above.
Regarding claim 20, Lui, Kareti, Zadeh, and Kim teach,
The system of claim 12, wherein the processor identifies the plurality of possible significant features by being configured to further execute the computer instructions to:
select the plurality of possible significant features from the plurality of indexed face- signature features as those face-signature features having the feature significant statistic above a selected percentile threshold of the plurality of indexed face-signature features.
Claim 20 is rejected using the same basis of arguments used to reject claim 9 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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