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 .
Information Disclosure Statement
The information disclosure statement (IDS) were submitted on 5/15/2024 and 7/25/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract of the disclosure is objected to because of the use of an implied phrase “Disclosed therein”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 9, with claim 1 being exemplary, recite the following:
“(a) one or more processors; and (b) memory for storing at least one program executed by the one or more processors, wherein the at least one program (c) receives an eye image to be authenticated that captures a region around an eye of a subject, (d) generates segmentation data and eye state information from the eye image to be authenticated, (e) selects a registered eye image based on similarity acquired by comparing the segmentation data and eye state information of the eye image to be authenticated with those of previously registered eye images, and (f) authenticates the subject based on the similarity” [Emphasis added]
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 1 and 9 are directed to an apparatus and a method, respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES. Independent claims 1 and 9 are directed towards a mental process (i.e. an abstract idea).
Regarding claims 1 and 9, limitations (d)-(f), in emphasized claims 1 and 9 above, are mental processes. Limitation (d), under broadest reasonable interpretation, involves determining parts of an eye, such as the pupil and iris, from an image, which is a task commonly performed by ophthalmologists when examining patients. Limitation (e) involves determining the similarity between the segmented image and a reference image and can also be performed by a human mind. For example, a person would determine whether the direction of gaze is the same or different between two images. Limitation (f) involves authenticating the subject. A human mind could do so by comparing features of the eye, such as the color of the iris, between two images.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO. Independent claims 1 and 9 do not recite additional elements that integrate the judicial exception into a practical application.
Regarding claims 1 and 9, limitations (a) and (b), in emphasized claims 1 and 9 above, are mere generic computer elements, a processor and a memory, and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP §2106.05(f)). Limitation (c) is an additional element, receiving an image, that falls under insignificant extra-solution activity since it is merely data gathering and data output (see MPEP §2106.05(g)).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO. Independent claims 1 and 9 do not recite additional elements that amount to significantly more than the judicial exception.
Regarding claims 1 and 9, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claim 1 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP §2106.05(d).
Therefore, independent claims 1 and 9 are directed towards an abstract idea without a practical application or significantly more.
Regarding claim 2 and 10, with claim 2 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the segmentation data is configured such that the eye image capturing the region around the eye is divided into regions of a pupil, an iris, a sclera, and a background falls under mental process as a the human mind could determine and label these parts of the eye (see MPEP §2106.04(a)(2)(III)).
Regarding claim 3 and 11, with claim 3 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one program corrects a tilt of the eye such that left and right ends of the sclera are horizontally aligned in the segmentation data, and corrects a center of the image such that a center of the pupil becomes the center of the eye image falls under a mental process as under broadest reasonable interpretation, the limitation recites a rotation and translation of the image, which can be performed by the human mind (see MPEP §2106.04(a)(2)(III)).
Regarding claims 4 and 12, with claim 4 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one program generates the eye state information about top, bottom, left, and right positions of the pupil and an eye size from the segmentation data falls under a mental process as a human mind could determine the locations of these positions and the size of the eye with a ruler from the image (see MPEP §2106.04(a)(2)(III)).
Regarding claims 7 and 15, with claim 7 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one program extracts feature vectors from the eye image to be authenticated and the selected registered eye image falls under selecting a data type (see MPEP §2106.05(g)).
Regarding claims 8 and 16, with claim 8 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one program authenticates the subject based on a result of comparing the feature vectors of the eye image to be authenticated with those of the selected registered eye image falls under mental process as a human mind could compare the different features from the two images (see MPEP §2106.04(a)(2)(III)).
Finally, it is noted that as per MPEP 2106.04(d)(1), a practical application and thus subject matter eligibility can be found when the following two requirements are met per a two-step analysis: In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Upon review of the present application, the Examiner finds that the improvement to the technical field of biometric authentication using images of eyes, is set forth in the Specification, for example in ¶0096, regarding overcoming difficulties in authenticating images with different poses. Accordingly, claims 5 and 13, and claims 6 and 14, which depend from claims 5 and 13, respectively, are found to recite a practical application, and thus are subject matter eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 7- 10, 15, and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hirsh et al. (US 11,435,820) (hereafter, “Hirsh”).
Regarding claim 1, Hirsh discloses an apparatus for biometric authentication based on an eye image (Fig. 3A, #300; Col. 6 lines 45-48, the eye tracking system 145 may capture two-dimensional images and sensor data describing three-dimensional (3D) depth information (3D depth data) of one or both eyes of the user; Col. 13 line 34-35, FIG. 3A illustrates a structured light eye tracking system 300; Col. 24 line 30, authentication module 908), comprising one or more processors (Fig. 3A Controller 350 (i.e., a processor); Col. 29 line 40, the processor); and memory for storing at least one program executed by the one or more processors (Col. 29 line 19, a random access memory), wherein the at least one program receives an eye image to be authenticated that captures a region around an eye of a subject (Col. 11 lines 37-39, the feature detection module 175 generates the 3D depth profile by combining the 2D images of the eye with the 3D depth data of the eye over time), generates segmentation data (Col. 11 lines 51-53, segment the 3D depth profile into information corresponding to the components of the eye and to determine information corresponding to the properties of the eye) and eye state information (Col. 11, lines 31-36, the specific properties of the eye that may be captured in a 3D depth profile include one or more of a pupil size, a pupil tilt angle, a pupil position, an ambient light level on the eye, an eyelid opening size, a gaze direction, an iris texture or pattern, an eye expression, and eye movement) from the eye image to be authenticated (Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity), selects a registered eye image based on similarity acquired by comparing the segmentation data and eye state information of the eye image to be authenticated with those of previously registered eye images (Col. 25, lines 37-40, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities with corresponding iris patterns. Examiner is interpreting correlate as “similarity”), and authenticates the subject based on the similarity (Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity).
Regarding claim 2, in which claim 1 is incorporated, Hirsh discloses wherein the segmentation data is configured such that the eye image capturing the region around the eye is divided into regions of a pupil, an iris, a sclera, and a background (Col. 11 lines 30-31, the pupil, the eyelid, the cornea, the iris, the sclera, and the eyebrow. Examiner considers the eyelid and eyebrow as part of the “background”).
Regarding claim 7, in which claim 1 is incorporated, Hirsh discloses wherein the at least one program extracts feature vectors (Col. 11, lines 21-23, the 3D depth profile is a profile or model of the user's eye that captures specific biometric features of the eye; Col. 25, lines 8-11, Any suitable machine learning process (e.g., Bayesian networks, neural networks, correlation filter based on trained data, etc.) may be applied to the eye features to determine the interest level. As the eye features are used as input to machine learning models, Examiner considers this to imply the vectorization of the features) from the eye image to be authenticated and the selected registered eye image (Col. 25, lines 37-40, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities with corresponding iris patterns. Examiner considers the unwrapped iris as the ”image to be authenticated” and the image in the database to be the “registered eye image”).
Regarding claim 8, in which claim 7 is incorporated, Hirsh discloses wherein the at least one program authenticates the subject based on a result of comparing the feature vectors of the eye image to be authenticated with those of the selected registered eye image (Col. 25, lines 37-52, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities … The user identification module 906 may provide the user identity to other modules, such as authentication module 908; Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity. Examiner considers the machine learning process to imply using “feature vectors”).
Regarding claim 9, Hirsh discloses a method for biometric authentication based on an eye image, performed by an apparatus for biometric authentication based on an eye image (Fig. 3A, #300; Col. 6 lines 45-48, the eye tracking system 145 may capture two-dimensional images and sensor data describing three-dimensional (3D) depth information (3D depth data) of one or both eyes of the user; Col. 13 line 34-35, FIG. 3A illustrates a structured light eye tracking system 300; Col. 24 line 30, authentication module 908), comprising: receiving an eye image to be authenticated that captures a region around an eye of a subject (Col. 11 lines 37-39, the feature detection module 175 generates the 3D depth profile by combining the 2D images of the eye with the 3D depth data of the eye over time), generating segmentation data (Col. 11 lines 51-53, segment the 3D depth profile into information corresponding to the components of the eye and to determine information corresponding to the properties of the eye) and eye state information (Col. 11, lines 31-36, the specific properties of the eye that may be captured in a 3D depth profile include one or more of a pupil size, a pupil tilt angle, a pupil position, an ambient light level on the eye, an eyelid opening size, a gaze direction, an iris texture or pattern, an eye expression, and eye movement) from the eye image to be authenticated (Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity), selecting a registered eye image based on similarity acquired by comparing the segmentation data and eye state information of the eye image to be authenticated with those of previously registered eye images (Col. 25, lines 37-40, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities with corresponding iris patterns. Examiner is interpreting correlate as “similarity”), and authenticating the subject based on the similarity (Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity).
Regarding claim 10, in which claim 9 is incorporated, Hirsh discloses wherein the segmentation data is configured such that the eye image capturing the region around the eye is divided into regions of a pupil, an iris, a sclera, and a background (Col. 11 lines 30-31, the pupil, the eyelid, the cornea, the iris, the sclera, and the eyebrow. Examiner considers the eyelid and eyebrow as part of the “background”).
Regarding claim 15, in which claim 9 is incorporated, Hirsh discloses extracting feature vectors (Col. 11, lines 21-23, the 3D depth profile is a profile or model of the user's eye that captures specific biometric features of the eye; Col. 25, lines 8-11, Any suitable machine learning process (e.g., Bayesian networks, neural networks, correlation filter based on trained data, etc.) may be applied to the eye features to determine the interest level. As the eye features are used as input to machine learning models, Examiner considers this to imply the vectorization of the features) from the eye image to be authenticated and the selected registered eye image (Col. 25, lines 37-40, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities with corresponding iris patterns. Examiner considers the unwrapped iris as the ”image to be authenticated” and the image in the database to be the “registered eye image”).
Regarding claim 16, in which claim 15 is incorporated, Hirsh discloses wherein authenticating the subject comprises authenticating the subject based on a result of comparing the feature vectors of the eye image to be authenticated with those of the selected registered eye image (Col. 25, lines 37-52, user identification module 906 may apply a machine learning process to the unwrapped iris to correlate the iris to a user identity in a database (not shown) of user identities … The user identification module 906 may provide the user identity to other modules, such as authentication module 908; Col. 26, lines 7-8, authenticates the user if the user identity matches the authorized identity. Examiner considers the machine learning process to imply using “feature vectors”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hirsh et al. (US 11,435,820) (hereafter, “Hirsh”) in view of Shoji et al. (US 2025/0342724) (hereafter, “Shoji”) and further in view of Kishida et al. (JP 2015177881 A) (hereafter, “Kishida”).
Regarding claim 3, Hirsh discloses the apparatus of claim 2.
However, Hirsh fails to explicitly disclose wherein the at least one program corrects a tilt of the eye such that left and right ends of the sclera are horizontally aligned in the segmentation data, and corrects a center of the image such that a center of the pupil becomes the center of the eye image.
Shoji teaches wherein the at least one program corrects a tilt of the eye such that left and right ends of the sclera are horizontally aligned in the segmentation data (Fig. 3; ¶0053, the straight line L1 connecting the point at the outer corner of the eye with the point at the inner corner of the eye is aligned with the horizontal line L2 in the image. The line L1 passes through the left and right ends of the sclera in Fig. 3, so aligning L1 also aligns the sclera).
Both Hirsh and Shoji are analogous to the claimed invention because both Hirsh and Shoji are directed at biometric authentication with eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the alignment of Shoji into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to improve performance, as suggested by Shoji at ¶0081, the recognition performance of targets can be simply increased.
However, neither Hirsh nor Shoji, whether considered individually or in combination, explicitly disclose corrects a center of the image such that a center of the pupil becomes the center of the eye image.
Kishida discloses corrects a center of the image such that a center of the pupil becomes the center of the eye image (Page 4, paragraph 3, the detected pupil center PO is positioned at the image center O).
Hirsh, Shoji, and Kishida are analogous to the claimed invention because both Hirsh and Shoji are directed at biometric authentication with eye images while Kishida is directed at methods for acquiring eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the pupil centering of Kishida into the alignment of Shoji and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to improve imaging speed, as suggested by Kishida at Page 10, last paragraph, it is possible to shorten the time from the start to the end of shooting.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Shoji and Kishida.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Shoji and Kishida to obtain the invention as specified in claim 3.
Regarding claim 11, Hirsh discloses the method of claim 10.
However, Hirsh fails to explicitly disclose wherein generating the segmentation data and the eye state information comprises correcting a tilt of the eye such that left and right ends of the sclera are horizontally aligned in the segmentation data, and correcting a center of the image such that a center of the pupil becomes the center of the eye image.
Shoji teaches wherein generating the segmentation data and the eye state information comprises correcting a tilt of the eye such that left and right ends of the sclera are horizontally aligned in the segmentation data (Fig. 3; ¶0053, the straight line L1 connecting the point at the outer corner of the eye with the point at the inner corner of the eye is aligned with the horizontal line L2 in the image. The line L1 passes through the left and right ends of the sclera in Fig. 3, so aligning L1 also aligns the sclera).
Both Hirsh and Shoji are analogous to the claimed invention because both Hirsh and Shoji are directed at biometric authentication with eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the alignment of Shoji into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to improve performance, as suggested by Shoji at ¶0081, the recognition performance of targets can be simply increased.
However, neither Hirsh nor Shoji, whether considered individually or in combination, explicitly disclose correcting a center of the image such that a center of the pupil becomes the center of the eye image.
Kishida discloses correcting a center of the image such that a center of the pupil becomes the center of the eye image (Page 4, paragraph 3, the detected pupil center PO is positioned at the image center O).
Hirsh, Shoji, and Kishida are analogous to the claimed invention because both Hirsh and Shoji are directed at biometric authentication with eye images and Kishida is directed at methods for acquiring eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the pupil centering of Kishida into the alignment of Shoji and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to improve imaging speed, as suggested by Kishida at Page 10, last paragraph, it is possible to shorten the time from the start to the end of shooting.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Shoji and Kishida.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Shoji and Kishida to obtain the invention as specified in claim 11.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hirsh et al. (US 11,435,820) (hereafter, “Hirsh”) in view of Kang et al. (US 2025/0130638) (hereafter, “Kang”)
Regarding claim 4, in which claim 2 is incorporated, Hirsh discloses [wherein the at least one program generates the eye state information about top, bottom, left, and right positions of the pupil] and an eye size from the segmentation data (Col. 11, lines 31-36, the specific properties of the eye that may be captured in a 3D depth profile include one or more of a pupil size).
However, Hirsh fails to explicitly disclose wherein the at least one program generates the eye state information about top, bottom, left, and right positions of the pupil.
Kang teaches wherein the at least one program generates the eye state information about top, bottom, left, and right positions of the pupil (¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the pupil positions of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang to obtain the invention as specified in claim 4.
Regarding claim 12, in which claim 10 is incorporated, Hirsh discloses [wherein generating the segmentation data and the eye state information comprises generating the eye state information about top, bottom, left, and right positions of the pupil] and an eye size from the segmentation data (Col. 11, lines 31-36, the specific properties of the eye that may be captured in a 3D depth profile include one or more of a pupil size).
However, Hirsh fails to explicitly disclose wherein the at least one program generates the eye state information about top, bottom, left, and right positions of the pupil.
Kang teaches wherein generating the segmentation data and the eye state information comprises generating the eye state information about top, bottom, left, and right positions of the pupil (¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the pupil positions of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang to obtain the invention as specified in claim 12.
Claims 5, 6, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hirsh et al. (US 11,435,820) (hereafter, “Hirsh”) in view of Kang et al. (US 2025/0130638) (hereafter, “Kang”), as applied to claims 4 and 12 above, and further in view of Chaudhary et al. (Chaudhary, Aayush K., et al. "Ritnet: Real-time semantic segmentation of the eye for gaze tracking." 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019) (hereafter, “Chaudhary”).
Regarding claim 5, Hirsh in view of Kang discloses the apparatus of claim 4.
However, Hirsh fails to explicitly disclose wherein the at least one program calculates an eye difference score using a sum of a difference in the top, bottom, left, and right positions of the pupil between the eye image to be authenticated and the registered eye image, a difference in the eye size therebetween, and mean Intersection over Union (mIoU) therebetween.
Kang teaches wherein the at least one program calculates an eye difference score using a [sum] of a difference in the top, bottom, left, and right positions of the pupil between the eye image to be authenticated and the registered eye image (¶0083, measure parameters such as an ellipse aspect ratio of a pupil … compare a pupil position. ¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil), a difference in the eye size therebetween (¶0082, identifying that the pupil size increases), and mean Intersection over Union (mIoU) therebetween (¶0083, mean Intersection Over Union (mIOU)).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the image comparisons of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
However, neither Hirsh nor Kang, whether considered individually or in combination, explicitly disclose a sum of the calculations.
Chaudhary teaches sum of the calculations (Page 3, left column, paragraph 4, The total loss L is given by a weighted combination of these losses as L = LCEL(λ1 + λ2LBAL) + λ3LGDL + λ4LSL).
Hirsh, Kang, and Chaudhary are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang and Chaudhary are directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the weighted sum of Chaudhary into the image comparisons of Kang and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to mitigate effects of distortions when segmenting, as suggested by Chaudhary at Page 4, §Conclusion, mitigate against image distortions.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang and Chaudhary.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang and Chaudhary to obtain the invention as specified in claim 5.
Regarding claim 6, Hirsh in view of Kang and Chaudhary discloses the apparatus of claim 5.
However, Hirsh fails to explicitly wherein the at least one program calculates the eye difference score by setting respective weights for the difference in the top, bottom, left, and right positions of the pupil, the difference in the eye size, and the mIoU.
Kang teaches wherein the at least one program calculates the eye difference score by [setting respective weights] for the difference in the top, bottom, left, and right positions of the pupil (¶0083, measure parameters such as an ellipse aspect ratio of a pupil … compare a pupil position. ¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil), a difference in the eye size therebetween (¶0082, identifying that the pupil size increases), and the mIoU (¶0083, mean Intersection Over Union (mIOU)).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the image comparisons of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
However, neither Hirsh nor Kang, whether considered individually or in combination, explicitly disclose setting respective weights for the calculations.
Chaudhary teaches setting respective weights for the calculations (Page 3, left column, paragraph 4, The total loss L is given by a weighted combination of these losses as L = LCEL(λ1 + λ2LBAL) + λ3LGDL + λ4LSL. Examiner considers the λ values as weights).
Hirsh, Kang, and Chaudhary are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang and Chaudhary are directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the weighted sum of Chaudhary into the image comparisons of Kang and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to mitigate effects of distortions when segmenting, as suggested by Chaudhary at Page 4, §Conclusion, mitigate against image distortions.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang and Chaudhary.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang and Chaudhary to obtain the invention as specified in claim 6.
Regarding claim 13, Hirsh in view of Kang discloses the method of claim 12.
However, Hirsh fails to explicitly disclose wherein selecting the registered eye image comprises calculating an eye difference score using a sum of a difference in the top, bottom, left, and right positions of the pupil between the eye image to be authenticated and the registered eye image, a difference in the eye size therebetween, and mean Intersection over Union (mIoU) therebetween.
Kang teaches wherein selecting the registered eye image comprises calculating an eye difference score using a [sum] of a difference in the top, bottom, left, and right positions of the pupil between the eye image to be authenticated and the registered eye image (¶0083, measure parameters such as an ellipse aspect ratio of a pupil … compare a pupil position. ¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil), a difference in the eye size therebetween (¶0082, identifying that the pupil size increases), and mean Intersection over Union (mIoU) therebetween (¶0083, mean Intersection Over Union (mIOU)).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the image comparisons of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
However, neither Hirsh nor Kang, whether considered individually or in combination, explicitly disclose a sum of the calculations.
Chaudhary teaches sum of the calculations (Page 3, left column, paragraph 4, The total loss L is given by a weighted combination of these losses as L = LCEL(λ1 + λ2LBAL) + λ3LGDL + λ4LSL).
Hirsh, Kang, and Chaudhary are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang and Chaudhary are directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the weighted sum of Chaudhary into the image comparisons of Kang and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to mitigate effects of distortions when segmenting, as suggested by Chaudhary at Page 4, §Conclusion, mitigate against image distortions.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang and Chaudhary.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang and Chaudhary to obtain the invention as specified in claim 13.
Regarding claim 14, Hirsh in view of Kang and Chaudhary discloses the method of claim 13.
However, Hirsh fails to explicitly wherein selecting the registered eye image comprises calculating the eye difference score by setting respective weights for the difference in the top, bottom, left, and right positions of the pupil, the difference in the eye size, and the mIoU.
Kang teaches wherein selecting the registered eye image comprises calculating the eye difference score by [setting respective weights] for the difference in the top, bottom, left, and right positions of the pupil (¶0083, measure parameters such as an ellipse aspect ratio of a pupil … compare a pupil position. ¶0083, measure parameters such as an ellipse aspect ratio of a pupil. Since an ellipse aspect ratio is defined as a ratio of the height and width, Examiner considers this to imply the use of information relating to the “top, bottom, left, and right” positions of the pupil), a difference in the eye size therebetween (¶0082, identifying that the pupil size increases), and the mIoU (¶0083, mean Intersection Over Union (mIOU)).
Both Hirsh and Kang are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang is directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the image comparisons of Kang into the authentication device of Hirsh. The suggestion/motivation for doing so would have been to increase computation efficiency, as suggested by Kang at ¶0076, reduce a computational amount and power consumption of a processor while maintaining accuracy.
However, neither Hirsh nor Kang, whether considered individually or in combination, explicitly disclose setting respective weights for the calculations.
Chaudhary teaches setting respective weights for the calculations (Page 3, left column, paragraph 4, The total loss L is given by a weighted combination of these losses as L = LCEL(λ1 + λ2LBAL) + λ3LGDL + λ4LSL. Examiner considers the λ values as weights).
Hirsh, Kang, and Chaudhary are analogous to the claimed invention because Hirsh is directed at biometric authentication with eye images and Kang and Chaudhary are directed at extracting features from eye images. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the weighted sum of Chaudhary into the image comparisons of Kang and the authentication device of Hirsh. The suggestion/motivation for doing so would have been to mitigate effects of distortions when segmenting, as suggested by Chaudhary at Page 4, §Conclusion, mitigate against image distortions.
This method of improving Hirsh was within the ordinary ability of one of ordinary skill in the art based on the teachings of Kang and Chaudhary.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Hirsh with the teachings of Kang and Chaudhary to obtain the invention as specified in claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Raida et al. 2011 (Raida, Hentati, Moncef Boussellmi, and Mohamed Abid. "Study the iris segmentation method for biometric pattern recognition." 2011 3rd International Conference on Next Generation Networks and Services (NGNS). IEEE, 2011) discloses using feature vectors of the eye to perform biometric authentication (Page 109, §III. Our work, Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration).
Spizhevoy et al. (US 2018/0018451) discloses using machine learning to perform iris identification with segmented images (¶0022, prior to computing the iris code, an eye image needs to be segmented to separate the iris from the pupil and the surrounding sclera; ¶0023, a deep neural network (DNN) can be used to learn an embedding for iris identification).
Mitake (US 2025/0118104) discloses using a difference in position when authenticating facial images (¶0067, the feature point for the same element (for example, eyes, a nose, a mouth edge, a face contour, and the like) as the corresponding feature point for each element, and calculates the distance (difference in position) between the corresponding feature points for each element).
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/XIAOMAO DING/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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