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 .
Applicant(s) Response to Official Action
The response filed on 07/29/2026 has been entered and made of record.
Response to Arguments/Amendments
Presented arguments have been fully considered but are rendered moot in view of the new ground(s) of rejection necessitated by amendment(s) initiated by the applicant(s).
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 62/249,798 15/340,926; 16/240,120; 17/073,247; 17/704,822, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Please refer to the 35 USC § 112 rejection section below.
Claim Interpretation
In light of the original disclosure, the Examiner will interpret the claim element “the set of second image data representing at least a portion of a live face” as image data acquired live during step 204 of stage 202 (i.e. fig. 2) or step 902 (i.e. fig. 9… Once a good quality eye region is detected, both RGB and NIR images of the eye region are acquired… These two images, Iv and Ii are fused to make a hyperspectral image Ih- page 97, section A; provisional specs); while interpreting the claim element “the set of first image data representing at least a portion of a user's face” as image data used to generate and store “first feature vectors” (i.e. The trained model feature vectors may be generated based on images depicting an owner of a mobile device- ¶0027, Application as filed… The training feature vectors for the live user are calculated while the iris enrolling stage- page 98, section C; Provisional Application Specs).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12, 15, 18 and 21-26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. It does not appear to be a written description of the following claim limitations in the parent application(s) as filed:
providing the first set of image data to a trained model, wherein the model is trained to generate a first feature vector based on the first set of image data, … wherein the trained model generates the first feature vector by separately extracting features from each of a plurality of wavebands of the first set of image data (claims 1, 5 and 8);
generating a second feature vector based on the second set of image data using the trained model (claims 1, 5 and 8); and
wherein the at least a portion of the user's face comprises at least one selected from the group consisting of an eye and a nose (claims 12, 15 and 18).
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-12, 15, 18 and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Chen Feng [US 20160019420 A1: and incorporated by reference Tao Sheng US 20130272570 A1, hereafter Tao: all already of record] in view Rahib Hidayat Abiyev et al. [Neural network based biometric personal identification with fast iris segmentation: already of record] and further in view of Darin S. Williams et al. [US 20100208951 A1].
Regarding claim 1, Chen teaches and/or suggests:
1. An electronic device (i.e. Certain aspects relate to systems and techniques for generating high resolution iris templates and for detecting spoofs, enabling more reliable and secure iris authentication- Abstract) comprising:
an image sensor (i.e. front-facing camera 150- ¶0032... camera 212- fig. 2, ¶0036); one or more processors; and one or more computer-readable media storing instructions that (i.e. Another aspect relates to a non-transitory computer-readable medium storing instructions that, when executed, configure at least one processor to perform operations comprising receiving set of image data of an eye- ¶0009), when executed by the one or more processors, cause the electronic device to perform operations comprising:
generating, at a first time and using the image sensor (i.e. In some embodiments, blocks 310 and 315 can be performed independently of some other portions of the process 300, for example during generation of an initial iris template of a user of the system 200 for storage and use in future identity authentication- ¶0050), first set of image data representing at least a portion of a user's (i.e. Accordingly, one aspect relates to a system for multispectral fake iris detection, the system comprising at least one image sensor configured for capture of set of image data of an eye of a user, the eye including an iris region and a sclera region, the set of image data including at least a near-infrared (NIR) channel and a red channel- ¶0007) face, the first set of image data being associated with a first image type (i.e. In some embodiments, camera 212 can capture a number of image frames for each of RGB and NIR set of image data, such as in a video recording mode- ¶0037), the first set of image data (i.e. Pairs of visible light (RGB) and near-infrared (NIR) images can be captured by the iris authentication system for use in iris authentication- ¶0025);
generating a first feature vector based on the first set of image data (i.e. an initial iris template of a user of the system 200 for storage and use in future identity authentication- ¶0050);
storing the first feature vector (i.e. a stored iris template- ¶0054);
generating, at a second time and using the image sensor, second set of image data representing at least a portion of a live face, the second set of image data being associated with the first image type (i.e. In some embodiments, camera 212 can capture a number of image frames for each of RGB and NIR set of image data, such as in a video recording mode- ¶0037), the second set of image data(i.e. Pairs of visible light (RGB) and near-infrared (NIR) images can be captured by the iris authentication system for use in iris authentication- ¶0025);
generating a second feature vector based on the second set of image data (i.e. At block 325, in some embodiments the iris verification module 244 can use the NIR fused iris image (or an NIR image or data from the NIR channel of a four-channel image) to generate an unwrapped and normalized polar image of the feature pattern in the iris, encode the pattern of iris features to generate a template of the iris- ¶0052);
determining that the at least a portion of the live face corresponds to the user's face based at least in part on a comparison of the first feature (i.e. a stored iris template- ¶0054 vector and the second feature (i.e. template generated from the imaged iris- ¶0054) vector (i.e. If the iris is real, then the system can perform iris feature matching to determine whether the iris matches a user iris stored in a template. A real iris that matches a stored template iris can result in user authentication- ¶0027); and
unlocking the electronic device in response to the determination that the at least a portion of the live face corresponds to the user's face (i.e. At block 301 the multispectral iris authentication system 200 can receive an authentication request to authenticate the identity of a user. For example, the authentication request can be triggered in various embodiments by a user request to unlock a digitally locked mobile device, log in to a secure account, enter a secure location, or the like- ¶0047... At block 301 the multispectral iris authentication system 200 can receive an authentication request to authenticate the identity of a user. For example, the authentication request can be triggered in various embodiments by a user request to unlock a digitally locked mobile device, log in to a secure account, enter a secure location, or the like- ¶0055).
However, Chen does not teach explicitly:
providing the first set of image data to a trained model, wherein the model is trained to generate a first feature vector based on the first set of image data; using the trained model.
In the same field of endeavor, Rahib teaches:
providing the first set of image data to a trained model, wherein the model is trained to generate a first feature vector based on the first set of image data; using the trained model (i.e. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns- Abstract... During training the value of the following cost function is calculated Eq. (10)... Here n is the number of output signals of the network and d and Pk Pk are the desired and the current output values of the network, respectively- § 3.2).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Chen with the teachings of Rahib to improve computational power of neural network and to decrease training error (Rahib- page 22 col 1).
However, Chen and Rahib do not teach explicitly:
wherein the trained model generates the first feature vector by separately extracting features from each of a plurality of wavebands of the first set of image data.
In the same field of endeavor, Darin teaches:
wherein the trained model generates the first feature vector by separately extracting features from each of a plurality of wavebands of the first set of image data (i.e. In this application, the features may include inter band features such as the maximum amplitude or energy within each band or may include intra band features such as the difference, ratio or sum of the amplitude or energy between a band and another band. Feature extraction may also include measurement of the hyper-spectral signature of the sclera to compute or calibrate other features to remove the effects of illumination and differences in the effective path transmission between bands- ¶0035 … Referring now to FIG. 9, in an embodiment one or more hyper-spectral sensors 104, an iris data pre-processor 106, one or more feature extractors 108, one or more of the hyper-spectral iris classifiers 89, and reference database 90 are configured to sense a hyper-spectral signature of the iris of an unidentified person from data 110 contained in four or more bands 112 and compare the sensed hyper-spectral signature to the reference hyper-spectral signature to identify the unidentified person- ¶0043).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Chen and Rahib with the teachings of Darin to improve classification performance (Darin, ¶0029).
Regarding claim 2, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches:
wherein:the first image type is near-infrared (i.e. In some embodiments, camera 212 can capture a number of image frames for each of RGB and NIR set of image data, such as in a video recording mode- ¶0037).
Regarding claim 3, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches:
wherein the image sensor comprises a red-green-blue/near-infrared hybrid sensor (i.e. the image capture stage 210 can be accomplished by a camera 212 including an RGB-IR or RGBN image sensor 214 and an NIR flash LED 216. In other embodiments separate NIR and RGB sensors can be used to capture the images for iris authentication- ¶0037).
Regarding claim 4, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches:
wherein determining that the eye at least a portion of the live face corresponds to the user's face based in least in part on the comparison comprises determining that a distance metric generated via the comparison (i.e. Verification module 1048 can use received NIR set of image data to generate a template of the imaged iris for comparison the stored templates. The verification module 1048 can compare the current template and stored templates to generate a quantitative likeness assessment, for example using Hamming distance- ¶0117) is equal to or less than a threshold value (i.e. In some embodiments the iris verification module 244 can use Hamming distance to output a match score representing the level of statistical significance between the current iris template and the stored iris template. Hamming distance is the measurement of the number of bits between two templates which are not the same. Hence match scores based on Hamming distance are dissimilarity score, and the lower the score between two templates, the more likely they are from the same user. Ideally, the Hamming distance between two images of the same iris of the same user would be 0, but due to occlusion and other uncontrollable factors (intra-class variations), even genuine scores can have some dissimilar bits. As discussed above, a threshold of allowable difference between the current template and the stored template can be adjusted based on the objectives of the system 200 as related to security and accessibility, as well as tolerance for false authentication fail determinations and/or false authentication pass determinations. In some embodiments, the threshold can allow the current enrolled iris template and the stored iris template to have a bit shift of plus or minus four bits in both the horizontal and vertical directions- ¶0054).
Regarding claim 5, method claim 5 corresponds to apparatus claim 1 and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 6, method claim 6 corresponds to apparatus claim 2 and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 7, method claim 7 corresponds to apparatus claim 3 and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 8, apparatus claim 8 is drawn to the apparatus using/performing the same method as claimed in claim 5. Therefore, apparatus claim 8 corresponds to method claim 5, and is rejected for the same reasons of obviousness as used above.
Regarding claim 9, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches:
the image sensor is a first type of image sensor; and the electronic device further comprises an additional image sensor, the additional image sensor being a second type that is different than the first type (i.e. In some embodiments, the camera 150 can include separate RGB and NIR sensors)- ¶0032).
Regarding claim 10, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches:
wherein the image sensor is at least a near-infrared image sensor. (i.e. In some embodiments, the camera 150 can include separate RGB and NIR sensors)- ¶0032).
Regarding claim 11, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches:
wherein determining that the at least a portion of the live face corresponds to the user's face based in least in part on the comparison comprises determining that a distance metric generated via the comparison is equal to or less than a threshold value (i.e. Verification module 1048 can use received NIR set of image data to generate a template of the imaged iris for comparison the stored templates. The verification module 1048 can compare the current template and stored templates to generate a quantitative likeness assessment, for example using Hamming distance- ¶0117) is equal to or less than a threshold value (i.e. In some embodiments the iris verification module 244 can use Hamming distance to output a match score representing the level of statistical significance between the current iris template and the stored iris template. Hamming distance is the measurement of the number of bits between two templates which are not the same. Hence match scores based on Hamming distance are dissimilarity score, and the lower the score between two templates, the more likely they are from the same user. Ideally, the Hamming distance between two images of the same iris of the same user would be 0, but due to occlusion and other uncontrollable factors (intra-class variations), even genuine scores can have some dissimilar bits. As discussed above, a threshold of allowable difference between the current template and the stored template can be adjusted based on the objectives of the system 200 as related to security and accessibility, as well as tolerance for false authentication fail determinations and/or false authentication pass determinations. In some embodiments, the threshold can allow the current enrolled iris template and the stored iris template to have a bit shift of plus or minus four bits in both the horizontal and vertical directions- ¶0054).
Regarding claim 12, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches: wherein the at least a portion of the user's face comprises at least one selected from the group consisting of an eye and a nose. (i.e. see figs. 1-2).
Regarding claim 15, Chen, Rahib and Darin teach all the limitations of claim 5 and Chen further teaches: wherein the at least a portion of the user's face comprises at least one selected from the group consisting of an eye and a nose. (i.e. see figs. 1-2).
Regarding claim 18, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches: wherein the at least a portion of the user's face comprises at least one selected from the group consisting of an eye and a nose. (i.e. see figs. 1-2).
Regarding claim 21, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches: wherein at least one of the first set of image data or the second set of image data corresponds to a hyperspectral image comprising the plurality of wavebands. (i.e. Embodiments of the disclosure relate to systems and techniques for multispectral iris authentication including generating high resolution iris images and detecting spoofs. Pairs of visible light (RGB) and near-infrared (NIR) images can be captured by the iris authentication system for use in iris authentication, for example using an NIR LED flash to provide consistent NIR lighting- ¶0025… In the iris tracking stage 220, a tracking module 221 can receive a number of RGB frames 222 and a number of NIR frames 224 from the camera 212- ¶0038).
Regarding claim 22, Chen, Rahib and Darin teach all the limitations of claim 1 and Chen further teaches: wherein the live face corresponds to the user's face (i.e. see fig. 2).
Regarding claim 23, Chen, Rahib and Darin teach all the limitations of claim 5 and Chen further teaches: wherein at least one of the first set of image data or the second set of image data corresponds to a hyperspectral image comprising the plurality of wavebands. (i.e. Embodiments of the disclosure relate to systems and techniques for multispectral iris authentication including generating high resolution iris images and detecting spoofs. Pairs of visible light (RGB) and near-infrared (NIR) images can be captured by the iris authentication system for use in iris authentication, for example using an NIR LED flash to provide consistent NIR lighting- ¶0025… In the iris tracking stage 220, a tracking module 221 can receive a number of RGB frames 222 and a number of NIR frames 224 from the camera 212- ¶0038).
Regarding claim 24, Chen, Rahib and Darin teach all the limitations of claim 5 and Chen further teaches: wherein the live face corresponds to the user's face (i.e. see fig. 2).
Regarding claim 25, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches: wherein at least one of the first set of image data or the second set of image data corresponds to a hyperspectral image comprising the plurality of wavebands. (i.e. Embodiments of the disclosure relate to systems and techniques for multispectral iris authentication including generating high resolution iris images and detecting spoofs. Pairs of visible light (RGB) and near-infrared (NIR) images can be captured by the iris authentication system for use in iris authentication, for example using an NIR LED flash to provide consistent NIR lighting- ¶0025… In the iris tracking stage 220, a tracking module 221 can receive a number of RGB frames 222 and a number of NIR frames 224 from the camera 212- ¶0038).
Regarding claim 26, Chen, Rahib and Darin teach all the limitations of claim 8 and Chen further teaches: wherein the live face corresponds to the user's face (i.e. see fig. 2).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLIFFORD HILAIRE whose telephone number is (571)272-8397. The examiner can normally be reached 5:30-1400.
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
Prosecution Insights