DETAILED ACTION
Claims 1-20 are hereby the present claims under consideration.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 9 reference 910. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In particular paragraph 0146 of the specification references block 908 which is not depicted but seemingly corresponds to block 910 of Fig. 9.
Claim Objections
Claim 1 objected to because of the following informalities:
Claim 1 line 3 it appears that “the images” should read “the plurality of images”
Claim 1 line 6 it appears that “a given one of the images” should read “a given one of the plurality of images”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11, and 20 and their dependents are rejected as each of claims 1, 11, and 20 recite “masking an iris area” and additionally “computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image” but it is unclear what “iris area” is meant to entail. In particular, it is unclear if the “iris area” being masked is meant to entail only the iris (i.e. a ring shape where the pupil is still considered) or if it is meant to entail the entire area bounded by the iris (i.e. a circle where the pupil is not considered). If the iris area is meant to be considered as a ring, then it is unclear if the pupil is considered “outside of the masked iris area” since it is not within the masked area but is surrounded by it. For the purposes of this examination, the limitation of “iris area” will be interpreted as a circle where the pupil is within the mask and not considered. Such an interpretation is based on the recitations of the prediction limitation which recite “outside of the masked iris area” in combination with Figs. 14A and 16A-B of the present specification which appear to indicate that the “iris area” is the area bounded by the iris such that the pupil is within this area and is not considered.
Claim 3 recites “a guided direction of the user’s gaze” in line 4 but it is unclear if this limitation is the same as, related to, or different from “a guided direction corresponding to that image” as recited in claim 1 lines 6-7. In particular, it is unclear if the guiding direction of claim 3 is the same guided direction required by claim 1. For the purposes of this examination, the limitations will be interpreted as referring to the same guided direction. This rejection and interpretation is similarly applied to the similar limitations of claim 13.
Claim Rejections - 35 USC § 112(a)
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, 11, and 20 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.
Claims 1, 11, and 20 each recite “computing a prediction of whether the user has the target health condition by …”. This claim limitation encompasses a large genus of health conditions being predicted by the analysis of the user’s sclera. The specification discloses the assessment of COVID-19 in paragraphs 0058, 0060, 0113, and 0137. The specification further generically recites that other target health conditions may be monitored but fails to provide particular details as to what other health conditions may be monitored, how they manifest in the eye, and/or how the processing the sclera images would differ. In particular, paragraphs 0060-0061 describes how COVID-19 affects particular cells in the eye and paragraphs 0160-0191 describe the particular training method and databases utilized for training the neural network to detect COVID-19. Such particular training details and evaluation metrics are provided only for the detection of COVID-19. Therefore, there is insufficient written description under 35 U.S.C. 112(a), due to only disclosing a non-representative number of species of the large genus of any or all possible health conditions. MPEP 2163(II)(A)(3)(a)(ii) states: The written description requirement for a claimed genus may be satisfied through sufficient description of a representative number of species by actual reduction to practice (see i)(A) above), reduction to drawings (see i)(B) above), or by disclosure of relevant, identifying characteristics, i.e., structure or other physical and/or chemical properties, by functional characteristics coupled with a known or disclosed correlation between function and structure, or by a combination of such identifying characteristics, sufficient to show the inventor was in possession of the claimed genus (see i)(C) above). See Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406. See Juno Therapeutics, Inc. v. Kite Pharma, Inc., 10 F.4th 1330, 1337, 2021 USPQ2d 893 (Fed. Cir. 2021) ( "[T]he written description must lead a person of ordinary skill in the art to understand that the inventor possessed the entire scope of the claimed invention. Ariad, 598 F.3d at 1353–54 ('[T]he purpose of the written description requirement is to ensure that the scope of the right to exclude, as set forth in the claims, does not overreach the scope of the inventor's contribution to the field of art as described in the patent specification.' (internal quotation marks omitted).").” There is not a sufficient number of species disclosed to encompass the broad or large genus of any or all possible health conditions. Health conditions differ greatly from other species that could fall within the genus such as other health conditions that do not affect the eye such as conditions of the gastrointestinal tract. Thus, the specification is considered to provide support for the detection of coronaviruses but not any type of health condition.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a method of processing eye images to diagnose a condition using a computational algorithm, which is an abstract idea. Claims 1-20 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019) and the 2024 Update on Subject Matter Eligibility (Federal Register, Vol 89, No. 137, page 58128, July 17, 2024).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] verifying that the images sufficiently show the user’s sclera, including by estimating a direction of the user’s gaze and confirming that the estimated direction for a given one of the images conforms with a guided direction corresponding to that image
[B1] generating feature-enhanced image data by: extracting an eye image of the user from at least one image from the plurality of images
[C1] masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing
[D1] generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data
[E1] computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image
These elements [A1]-[E1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper. In particular the above abstract idea is considered to be capable of being performed by a human physician who [A1] received images of the user’s eyes and makes sure they are clear, [B1] crops, strikes out, or mentally disregards the portions of the image excluding the eye, [C1] crops, strikes out, or mentally disregards the iris area, [D1] generates any form of data related to the sclera or performs a manipulation of the image of the sclera such as circling/identifying areas of interest, and [E1] uses their judgment to determine if the user has a health condition based on the generated data of the sclera outside of the iris.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] the method being computer-implemented
[B2] receiving a plurality of images from a computing device by way of a network
[C2] the enhanced image data being provided to a convolutional neural network to perform the determining
These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the element [B2] is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, the element [A2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Additionally, the element [C2] is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “receiving a plurality of images from a computing device by way of a network” does not qualify as significantly more because this limitation merely describes the receipt of data from a generic computer connected to a generic computer network and does not incorporate any particular data acquirer as part of the claimed invention.
Further, the elements [A2] and the computing device connected to a network of [B2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Finally, the element [C2] is merely a recitation to implement the decision making and/or judgment of a clinician onto a computer in order to carry out the recited method using the computer as a tool.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-10 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 3: obtain, using the computing device operated by the user, the plurality of images including the user’s sclera
Claim 5: retrieving a signal representing symptom data associated with the user,
Claim 9: transmitting to the computing device operated by the user; and
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 3, 5, and 9 are each merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the limitation of claims 3 and 5 is mere data gathering at a high level of generality with no particular sensor or structure being recited as gathering or retrieving the signal. Additionally, the limitations of claim 9 are merely a generic instruction to share or display the results of the algorithm requiring nothing more than a generic computer to perform generic computer functions (that is, or of data transmission) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
It is further noted that the limitation of claim 3 regarding providing automated guidance to the user to perform the obtaining of data is considered part of the abstract idea as a clinician is well suited to audibly (as required by claim 4) guide a patient though the steps of using a computer to obtain eye images. Providing such guidance in an automated manner is merely a generic recitation to implement the abstract idea onto a computer.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 11 and 20 recite the same abstract idea identified above with respect to claim 1 and are thus rejected on the same grounds as claim 1 above. The additional of claims 11 and 20 that are not addressed in the above rejection of claim 1 are addressed below:
The analysis of claim 11 is as follows:
Step 1: Claim 11 is drawn to a machine
Step 2A – Prong One: Claim 11 recites an abstract idea. In particular, claim 11 recites the same abstract idea as claim 1.
Step 2A – Prong Two: Claim 11 recites the following limitations that are beyond the judicial exception and have not already been addressed in the above rejection of claim 1:
[A2] at least one processor
[B2] memory in communication with the processor
These elements [A2]-[B2] of claim 21 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: The elements [A2] – [B2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually.
Claims 2-19 recite the same abstract idea as claim 11 and only recite additional limitations already addressed in the explanation of claim 2-10 above.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually
The analysis of claim 20 is as follows:
Step 1: Claim 20 is drawn to a machine
Step 2A – Prong One: Claim 20 recites an abstract idea. In particular, claim 20 recites the same abstract idea as claim 1.
Step 2A – Prong Two: Claim 20 recites the following limitations that are beyond the judicial exception and have not already been addressed in the above rejections of claims 1 and/or 11:
[A2] a non-transitory computer readable medium
This element [A2] of claim 21 does not integrate the exception into a practical application of the exception. In particular, the element [A2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: The element [A2] does not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually.
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 1, 3-6, 8-11, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bott US Patent Application Publication Number US 20180125404 A1 hereinafter Bott in view of Huang US Patent Application Publication Number US 20170164830 A1 hereinafter Huang, further in view of Tran US Patent Application Publication Number US 20200405148 A1 hereinafter Tran.
Regarding claim 1 Bott discloses a computer-implemented method for predicting whether a user has a target health condition using eye images (Abstract: detection of cognitive abnormalities from video data including the eyes), the method comprising:
receiving a plurality of images from a computing device by way of a network (Paragraphs 0034 and 0039: the computer used to capture the video and the receipt of the video and metadata from the computer by a remote server);
verifying that the images sufficiently show the user’s sclera, including by estimating a direction of the user’s gaze and confirming that the estimated direction for a given one of the images conforms with a guided direction corresponding to that image (Paragraphs 0036, 0038 and 0042-0044: the positions of the user’s pupil with respect to the whites of the eye, or sclera, is used to determine a gaze model. The gaze model ensures the user is looking at the displayed dot for each image);
generating feature-enhanced image data by: extracting an eye image of the user from at least one image from the plurality of images (Paragraph 0037: the video of the eyes may be a full frame or may be a smaller region of the full frame of the specific area of the user’s eye);
Bott fails to further disclose the method comprising generating feature-enhanced image data by: masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing; and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data; and computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image.
Huang teaches systems, and methods for assessing human health states based on non-shadow imaging of sclera, or whites, of one or both eyes (Abstract). Thus Huang falls within the same field of endeavor as Applicant’s invention.
Huang teaches a method including masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing (Paragraph 0078: during pre-processing a mask is applied to the sclera such that the black portions of the image that are not part of the sclera are removed. It is noted that while Huang teaches the application to of a mask to the area desired to be kept rather than the area to be removed, the teachings of Huang are still considered equivalent as Huang teaches extracting only the sclera from the eye images such that the iris area is not included in downstream processing. Designating the mask as being applied to remove an area versus designating the mask to be applied to the area being kept is considered an obvious variation as the results are the same and designating the removed area as “masked” or “unmasked” is arbitrary.); and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data (Paragraphs 0067 and 0083: a variety of features can be extracted from the sclera images); and computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image (Paragraphs 0065 and 0083-0084: the produced sclera features are processed to determine a health condition ). Huang further teaches that the system may include signs to direct the user to look in particular directions such that all aspects of the sclera including the bottom portion may be imaged (Paragraph 0070)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method of Bott to include the extraction of the user’s sclera for identifying health conditions as taught by Huang because Huang teaches that the sclera can be analyzed to determine information regarding the subject’s health state (Huang: Paragraph 0066) and such information regarding the user’s health state may provide additional context information to the cognitive analysis of Bott such as by providing insight into the presence of other conditions which may affect the user’s cognitive abilities.
Bott in view of Huang fail to further disclose the method wherein the prediction of whether the user has the target health condition is performed by a convolutional neural network.
Tran teaches systems and methods to inspect an eye including capturing an eye image using a mobile device camera; extracting features of the eye; applying a deep learning neural network to detect potential eye damage; and reporting the potential eye damage for treatment, such as those from laser pointers, among others (Abstract). Thus, Tran falls within the same field of endeavor as Applicant’s invention.
Tran teaches the analysis of eye images using a convolutional neural network (CNN) (Paragraphs 0037, 0047-0050).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method of Bott in view of Huang to utilize a CNN for performing the analysis taught by Huang because Tran teaches that CNNs are well suited for analyzing eye images (Tran: Abstract; Paragraphs 0037 and 0047-0050) and the use of a CNN to carry out the determination of Huang may allow the method to become more accurate and adaptable over time as the algorithm may be trained to better identify conditions and to account for a variety of variables in the eye images.
Regarding claim 11, Bott discloses a computer-implemented system for predicting whether a user has a target health condition using eye images (Abstract: detection of cognitive abnormalities from video data including the eyes), the system comprising:
at least one processor (Paragraph 0069: the one or more processors);
memory in communication with the at least one processor (Paragraph 0070: the memory), and
processor-executable instructions stored in the memory that, when executed by the at least one processor, configure the processor (Paragraph 0070: the memory stores software instructions) to:
receive a plurality of images from a computing device by way of a network (Paragraphs 0034 and 0039: the computer used to capture the video and the receipt of the video and metadata from the computer by a remote server);
verify that the images sufficiently show the user’s sclera, including by estimating a direction of the user’s gaze and confirming that the estimated direction for a given one of the images conforms with a guided direction corresponding to that image (Paragraphs 0036, 0038 and 0042-0044: the positions of the user’s pupil with respect to the whites of the eye, or sclera, is used to determine a gaze model. The gaze model ensures the user is looking at the displayed dot for each image);
generate feature-enhanced image data by: extracting an eye image of the user from at least one image of a plurality of images (Paragraph 0037: the video of the eyes may be a full frame or may be a smaller region of the full frame of the specific area of the user’s eye);
Bott fails to further disclose the system wherein the processor is configured to: generate feature-enhanced image data by: masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing; and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data; and compute a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image.
Huang teaches a method including masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing (Paragraph 0078: during pre-processing a mask is applied to the sclera such that the black portions of the image that are not part of the sclera are removed. It is noted that while Huang teaches the application to of a mask to the area desired to be kept rather than the area to be removed, the teachings of Huang are still considered equivalent as Huang teaches extracting only the sclera from the eye images such that the iris area is not included in downstream processing. Designating the mask as being applied to remove an area versus designating the mask to be applied to the area being kept is considered an obvious variation as the results are the same and designating the removed area as “masked” or “unmasked” is arbitrary.); and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data (Paragraphs 0067 and 0083: a variety of features can be extracted from the sclera images); and computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image (Paragraphs 0065 and 0083-0084: the produced sclera features are processed to determine a health condition ). Huang further teaches that the system may include signs to direct the user to look in particular directions such that all aspects of the sclera including the bottom portion may be imaged (Paragraph 0070)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of Bott to include the extraction of the user’s sclera for identifying health conditions as taught by Huang because Huang teaches that the sclera can be analyzed to determine information regarding the subject’s health state (Huang: Paragraph 0066) and such information regarding the user’s health state may provide additional context information to the cognitive analysis of Bott such as by providing insight into the presence of other conditions which may affect the user’s cognitive abilities.
Bott in view of Huang fail to further disclose the system wherein the prediction of whether the user has the target health condition is performed by a convolutional neural network.
Tran teaches the analysis of eye images using a convolutional neural network (CNN) (Paragraphs 0037, 0047-0050).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of Bott in view of Huang to utilize a CNN for performing the analysis taught by Huang because Tran teaches that CNNs are well suited for analyzing eye images (Tran: Abstract; Paragraphs 0037 and 0047-0050) and the use of a CNN to carry out the determination of Huang may allow the method to become more accurate and adaptable over time as the algorithm may be trained to better identify conditions and to account for a variety of variables in the eye images.
Regarding claims 3 and 13, Bott in view of Huang further in view of Tran teaches the method and system of claims 1 and 11 respectively. Modified Bott further teaches the method and system comprising: providing automated guidance to the user to obtain, using the computing device operated by the user, the plurality of images including the user’s sclera, each of the images corresponding to a guided direction of the user’s gaze (Paragraphs 0037-0038: a small dot is moved around the display and the user is instructed to follow the dot with their eyes. Images are captured in various gaze directions; Paragraph 0035: the instructions may be displayed by the system ).
Regarding claims 4 and 14, Bott in view of Huang further in view of Tran teaches the method and system of claims 3 and 13 respectively. Modified Bott further teaches the method and system comprising wherein the automated guidance provides voice guidance in substantially real-time to the user based on the direction of the user’s gaze (Paragraphs 0035-0038 and 0073: the guidance to acquire appropriate eye images may be provided over a series of trial and error periods where the system provides feedback to the user. The system may provide audio status messages, audio response messages, and the like which is considered sufficient to at least suggest that the guidance to the user for acquiring eye images is provided via audio).
Regarding claims 5 and 16, Bott in view of Huang further in view of Tran teaches the method and system of claims 1 and 11 respectively. Modified Bott fails to further teach the method and system comprising: retrieving a signal representing symptom data associated with the user, and wherein computing the prediction of whether the user has the target health condition is based on the feature-enhanced image data and the symptom data associated with the user.
Tran teaches retrieving a signal representing symptom data associated with the user, and wherein computing the prediction of whether the user has the target health condition is based on the feature-enhanced image data and the symptom data associated with the user (Paragraph 0174: the ML algorithm can learn the relationship between various inputs including symptoms in combination with image analysis data and the outputs of disease diagnosis).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of modified Bott to consider additional contextual data such as symptom data in combination with the image analysis data when performing the prediction of Huang because Tran teaches that the CNN used for performing the prediction of Huang may consider additional data such as symptoms when performing the prediction (Tran: paragraph 0174) and the consideration of additional contextual information when diagnosing a patient may produce a more accurate diagnosis since the algorithm receives a more complete representation of the patient’s condition.
Regarding claims 6 and 17, Bott in view of Huang further in view of Tran teaches the method and system of claims 1 and 11 respectively. Modified Bott further teaches the method and system comprising: determining that the plurality of images meet a data quality threshold (Paragraph 0036: the system determines when the user’s pupils can be sufficiently tracked in the images); and
Modified Bott fails to further teach the method or system wherein: in response to determining that the plurality of images meet the data quality threshold, computing the prediction of whether the user has the target health condition.
Huang teaches a method including determining that the plurality of images meet a data quality threshold; and in response to determining that the plurality of images meet the data quality threshold, computing the prediction of whether the user has the target health condition (Paragraph 0063: The images can be checked for a variety of factors to determine if they are useful or should be discarded; Paragraphs 0064-0065: the suitable images and used in the determination of a health condition )
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of modified Bott to include the image quality determination step prior to the prediction step as taught by Huang because determinations made from a poor quality image may yield inaccurate results and thus present the user with an inaccurate or untrue diagnosis.
Regarding claims 8 and 15, Bott in view of Huang further in view of Tran teaches the method and system of claims 1 and 11 respectively. Modified Bott further teaches the method and system wherein the plurality of images include four images corresponding to the user’s gaze being in the up, down, left, and right directions (Paragraphs 0036-0037 and 0042: the positions of the dot which the user follows typically include discrete points or continuous paths along the four corners of the display. Thus following the dot results in images where the user is looking up at the top of the display, down at the bottom of the display, left at the left of the display, and right at the right of the display ).
Regarding claims 9 and 19, Bott in view of Huang further in view of Tran teaches the method and system of claims 1 and 11 respectively. Modified Bott fails to further teach the method and system, further comprising: transmitting to the computing device operated by the user an indication of whether the user has the target health condition based on the prediction.
Huang teaches a method including transmitting to the computing device operated by the user an indication of whether the user has the target health condition based on the prediction (Paragraph 0090: the results of the disease prediction may be displayed locally by the device or may be transmitted via a wired or wireless connection to a display unit or terminal. The display unit may include the user’s mobile phone or computer).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to incorporate the transmittal of the prediction results to a user’s device as taught by Huang into the method and system of modified Bott because transmitting the results to the user’s device allows the user to conveniently see the outcome of the analysis and may allow the user to more easily access their results.
Regarding claim 10, Bott in view of Huang further in view of Tran teaches the method of claim 1. Modified Bott further teaches the method wherein at least one image from the plurality of images contains a face of the user, and generating feature-enhanced image data of the at least one image comprises detecting the face of the user and extracting the eye image of the user from the detected face of the user (Paragraphs 0007 and 0037-0040: a video of the face of the user; the video of the face may be cropped down to regions surrounding the eye based on the location of the pupil).
Regarding claim 20, Bott discloses a non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform (Abstract: detection of cognitive abnormalities from video data including the eyes; paragraphs 0069-0070: the processor and memory):
receiving a plurality of images from a computing device by way of a network (Paragraphs 0034 and 0039: the computer used to capture the video and the receipt of the video and metadata from the computer by a remote server);
verifying that the images sufficiently show the user’s sclera, including by estimating a direction of the user’s gaze and confirming that the estimated direction for a given one of the images conforms with a guided direction corresponding to that image (Paragraphs 0036, 0038 and 0042-0044: the positions of the user’s pupil with respect to the whites of the eye, or sclera, is used to determine a gaze model. The gaze model ensures the user is looking at the displayed dot for each image);
generating feature-enhanced image data by: extracting an eye image of the user from at least one image of the plurality of images (Paragraph 0037: the video of the eyes may be a full frame or may be a smaller region of the full frame of the specific area of the user’s eye);
Bott fails to further disclose the system wherein the processor is configured to: generate feature-enhanced image data by: masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing; and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data; and computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image.
Huang teaches a method including masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing (Paragraph 0078: during pre-processing a mask is applied to the sclera such that the black portions of the image that are not part of the sclera are removed. It is noted that while Huang teaches the application to of a mask to the area desired to be kept rather than the area to be removed, the teachings of Huang are still considered equivalent as Huang teaches extracting only the sclera from the eye images such that the iris area is not included in downstream processing. Designating the mask as being applied to remove an area versus designating the mask to be applied to the area being kept is considered an obvious variation as the results are the same and designating the removed area as “masked” or “unmasked” is arbitrary.); and generating image data including data representing enhanced features corresponding to the user’s sclera in the at least one image to generate the feature-enhanced image data (Paragraphs 0067 and 0083: a variety of features can be extracted from the sclera images); and computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data, wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user’s sclera and outside of the masked iris area in the extracted eye image in the at least one image (Paragraphs 0065 and 0083-0084: the produced sclera features are processed to determine a health condition ). Huang further teaches that the system may include signs to direct the user to look in particular directions such that all aspects of the sclera including the bottom portion may be imaged (Paragraph 0070)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of Bott to include the extraction of the user’s sclera for identifying health conditions as taught by Huang because Huang teaches that the sclera can be analyzed to determine information regarding the subject’s health state (Huang: Paragraph 0066) and such information regarding the user’s health state may provide additional context information to the cognitive analysis of Bott such as by providing insight into the presence of other conditions which may affect the user’s cognitive abilities.
Bott in view of Huang fail to further disclose the system wherein the prediction of whether the user has the target health condition is performed by a convolutional neural network.
Tran teaches the analysis of eye images using a convolutional neural network (CNN) (Paragraphs 0037, 0047-0050).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the system of Bott in view of Huang to utilize a CNN for performing the analysis taught by Huang because Tran teaches that CNNs are well suited for analyzing eye images (Tran: Abstract; Paragraphs 0037 and 0047-0050) and the use of a CNN to carry out the determination of Huang may allow the method to become more accurate and adaptable over time as the algorithm may be trained to better identify conditions and to account for a variety of variables in the eye images.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bott US Patent Application Publication Number US 20180125404 A1 hereinafter Bott in view of Huang US Patent Application Publication Number US 20170164830 A1 hereinafter Huang, in view of Tran US Patent Application Publication Number US 20200405148 A1 hereinafter Tran as applied to claims 6 and 17 respectively above and further in view of Lochner US Patent Number US 12079993 B1 hereinafter Lochner
Regarding claims 7 and 18, Bott in view of Huang further in view of Tran teaches the method and system of claims 6 and 17 respectively. Modified Bott fails to further teach the method and system wherein the data quality threshold is based on at least one of: image data resolution, field of view relating to image data representing the user’s eye, confirmation that the plurality of images is associated with a single user, or image focus associated with the feature-enhanced image data associated with the user’s eye.
Lochner teaches a system for remotely analyzing information, using a computer to, receive eye data from a remote user. The computer processes the eye data to compare the eye data with information indicative of physical impairments. The computer uses the comparing to determine physical impairments in the user based on the eye data received remotely (Abstract). Thus, Lochner falls within the same field of endeavor as Applicant’s invention.
Lochner teaches a method wherein the data quality threshold is based on at least one of: image data resolution, field of view relating to image data representing the user’s eye, confirmation that the plurality of images is associated with a single user, or image focus associated with the feature-enhanced image data associated with the user’s eye (Col 45 line 49 – Col 46 line 52: the quality analysis includes image resolution and position and distance, or field of view, of the eye relative to the camera)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of modified Bott to consider image resolution and the field of view of the camera relative to the eye as image quality parameters as taught by Lochner because Lochner teaches that these parameters are important quality metrics (Lochner: Col 45 line 49 – Col 46 line 52) and analyzing poor quality images may result in inaccurate diagnosis of the patient.
Claims 2 and 12 are not rejected over the prior art as none of the prior art of record is considered to teach or reasonably suggest the system or method “wherein the target health condition is a disease caused by a coronavirus.” In combination with the other claimed limitations.
In particular, claims 1 and 11 each recite that the “iris area” of the image is masked and not considered in downstream processing and that the prediction utilizes the feature-enhanced image data which comprises ocular manifestations in the user’s sclera and outside of the masked iris area. Thus claims 1 and 11 are limited to the analysis of the user’s sclera to perform the target health condition prediction. None of Bott, Huang, Tran, or Lochner consider the prediction being of a condition caused by a corona virus.
Lewis US Patent Application Publication Number US 20220165421 A1, hereinafter Lewis does teach the analysis of an eye image to determine the presence of COVID-19 (Abstract) which is caused by a coronavirus. However Lewis teaches that the retinal patterns are the distinguishing elements of the eye used to identify COVID-19 and requires retinal imaging (Paragraphs 0020 and 0022). Lewis teaches the analysis of the whole eye image through machine learning to perform the analysis (Paragraphs 0022-0025). Thus it would seem that the sclera evaluation of Huang is incompatible with the COVID-19 diagnosis performed by Lewis because Lewis requires eye regions other than the sclera to the considered to perform the analysis and would thus be rendered inoperable when combined with Huang. As such, Lewis cannot be reasonably combined with Huang and Lewis does not serve to anticipate the claim itself because it requires the analysis of eye regions inside of the iris area including the retina which is viewed through the pupil.
Similarly, Gladis Indian Patent Application Publication Number IN 202041019896 A hereinafter Gladis also teaches the diagnosis of COVID-19 through eye images but again requires the analysis of the retina (Page 2: Description paragraph 1; Page 3: Section 2. Segmentation). Thus Gladis similarly cannot be combined with Huang as it would render the detection of COVID-19 inoperable as Gladis requires the processing of eye regions outside of the sclera.
Neither of Lewis or Gladis can be reasonably combined with Huang and neither of Lewis or Gladis serve to anticipate the invention itself since both require the analysis of the iris and retina which is inside the iris area. Thus, claims 2 and 12 are not rejected over the prior art.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-4, 8-15, and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. US 12109025 B2 hereinafter Parent. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of Parent serve to anticipate each of the below presented claims:
Claims 1-3 of the instant application are anticipated by claim 1 of Parent
Claim 4 of the instant application are anticipated by claim 2 of Parent
Claim 8 of the instant application are anticipated by claim 4 of Parent
Claim 9 of the instant application are anticipated by claim 7 of Parent
Claim 10 of the instant application are anticipated by claim 8 of Parent
Claims 11-13 of the instant application are anticipated by claim 10 of Parent
Claim 14 of the instant application are anticipated by claim 11 of Parent
Claim 15 of the instant application are anticipated by claim 12 of Parent
Claim 19 of the instant application are anticipated by claim 15 of Parent
Claim 20 of the instant application are anticipated by claim 18 of Parent
Claims 5 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 10 of U.S. Patent No. US 12109025 B2 hereinafter Parent in view of Tran US Patent Application Publication Number US 20200405148 A1 hereinafter Tran. In particular, claims 1 and 10 of Parent teach all the limitations required by claims 1 and 11 of the present application respectively, Tran further teaches the additional limitations of claims 5 and 16 of the present application as described in the above presented 35 U.S.C. 103 rejection of claims 5 and 16.
Regarding claims 5 and 16, Parent claims 1 and 10 teach the method and system of claims 1 and 11 respectively. Parent fails to further teach the method and system comprising: retrieving a signal representing symptom data associated with the user, and wherein computing the prediction of whether the user has the target health condition is based on the feature-enhanced image data and the symptom data associated with the user.
Tran teaches retrieving a signal representing symptom data associated with the user, and wherein computing the prediction of whether the user has the target health condition is based on the feature-enhanced image data and the symptom data associated with the user (Paragraph 0174: the ML algorithm can learn the relationship between various inputs including symptoms in combination with image analysis data and the outputs of disease diagnosis).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of Parent to consider additional contextual data such as symptom data in combination with the image analysis data when performing the prediction because Tran teaches that the CNN used for performing the prediction of Parent may consider additional data such as symptoms when performing the prediction (Tran: paragraph 0174) and the consideration of additional contextual information when diagnosing a patient may produce a more accurate diagnosis since the algorithm receives a more complete representation of the patient’s condition.
Claims 6 and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 10 of U.S. Patent No. US 12109025 B2 hereinafter Parent in view of Huang US Patent Application Publication Number US 20170164830 A1 hereinafter Huang. In particular, claims 1 and 10 of Parent teach all the limitations required by claims 1 and 11 of the present application respectively, Huang further teaches the additional limitations of claims 6 and 17 of the present application as described in the above presented 35 U.S.C. 103 rejection of claims 6 and 17.
Regarding claims 6 and 17, Parent teaches the method and system of claims 1 and 11 respectively. Parent fails to further teach the method and system comprising: determining that the plurality of images meet a data quality threshold, and in response to determining that the plurality of images meet the data quality threshold, computing the prediction of whether the user has the target health condition.
Huang teaches a method including determining that the plurality of images meet a data quality threshold; and in response to determining that the plurality of images meet the data quality threshold, computing the prediction of whether the user has the target health condition (Paragraph 0063: The images can be checked for a variety of factors to determine if they are useful or should be discarded; Paragraphs 0064-0065: the suitable images and used in the determination of a health condition )
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of Parent to include the image quality determination step prior to the prediction step as taught by Huang because determinations made from a poor quality image may yield inaccurate results and thus present the user with an inaccurate or untrue diagnosis.
Claims 7 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 10 of U.S. Patent No. US 12109025 B2 hereinafter Parent in view of Huang US Patent Application Publication Number US 20170164830 A1 hereinafter Huang as applied to claims 6 and 17 above and further in view of Lochner US Patent Number US 12079993 B1 hereinafter Lochner. In particular, claims 1 and 10 of Parent teach all the limitations required by claims 1 and 11 of the present application respectively, Huang further teaches the additional limitations of claims 6 and 17 of the present application as described above. The additional limitations of claims 7 and 18 are further taught by Lochner as described in the above presented 35 U.S.C. 103 rejection of claims 7 and 18.
Regarding claims 7 and 18, Parent in view of Huang teaches the method and system of claims 6 and 17 respectively. Modified Parent fails to further teach the method and system wherein the data quality threshold is based on at least one of: image data resolution, field of view relating to image data representing the user’s eye, confirmation that the plurality of images is associated with a single user, or image focus associated with the feature-enhanced image data associated with the user’s eye.
Lochner teaches a method wherein the data quality threshold is based on at least one of: image data resolution, field of view relating to image data representing the user’s eye, confirmation that the plurality of images is associated with a single user, or image focus associated with the feature-enhanced image data associated with the user’s eye (Col 45 line 49 – Col 46 line 52: the quality analysis includes image resolution and position and distance, or field of view, of the eye relative to the camera)
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to configure the method and system of modified Parent to consider image resolution and the field of view of the camera relative to the eye as image quality parameters as taught by Lochner because Lochner teaches that these parameters are important quality metrics (Lochner: Col 45 line 49 – Col 46 line 52) and analyzing poor quality images may result in inaccurate diagnosis of the patient.
Conclusion
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