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 .
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: Element 100 in Fig. 2 is not referenced in the specification. 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.
Claim Objections
Claims 6, 8-9, 11, 14-17, and 19 are objected to because of the following informalities: Claim 6 recites “the group” in line 1, but instead should be --a group--. Claim 8 recites “trainedby” in line 1, but instead should be --trained by--. Claim 8 recites “the training” in line 2, but instead should be --the training system--. Claim 8 recites “the level” in line 5, but instead should be --a level--. Claim 8 recites “of the test member” in line 14, but instead should be --to the test member--. Claim 8 recites “a training system” in line 15, but instead should be --the training system--. Claim 8 needs to end in a period instead of a semicolon as recited. Claim 9 recites “a training system” in line 4, but instead should be --a training system--. Claim 9 recites “a subject” in line 6, but instead should be --the subject--. Claim 9 recites “one or more” in line 10, but instead should be --the one or more--. Claim 11 recites “an error rate” in line 2, but instead should be --the error rate--. Claim 14 recites “remove the noise” in line 2, but instead should omit “the.” Claim 15 recites “the IOP value of a patient as a numerical value wherein” in line 2, but instead should be --an IOP value of the patient as a numerical value, and wherein--. The same applies to Claims 16-17 and 19. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-11, 15-17, and 19-20 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.
Claim 8 recites “selecting a tentative architecture” in line 5, but lacks detail in the specification. Clarity on the record is insufficient as to how the architecture is selected or whether the architecture becomes actual or non-tentative? The specification merely recites the same claimed language (¶[0067,0072]) As such, it is unclear as to whether there is possession of the claimed invention. Clarification required.
Claim 8 recites “determine correlations between the level of IOP of the test member and the IOP for each member of the training population” in lines 16-17, but lacks sufficient support in the specification. It is unclear what the limitation encompasses and how the determination is made. The specification merely recites the same claimed language (¶[0067]). Clarification required.
Claims 7-11, 15-17, and 19-20 each recite the use of a neural network, but the specification lacks particulars of an algorithm, formula, or model (¶[0050]). The specification broadly recites the use and results of using neural network but fails to provide the specifics to establish possession of the claimed invention, e.g. no ML classification algorithm, how the ground truths of the IOP are obtained, etc. (¶[0050-62,0074-81]). Clarification required.
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 6-20 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.
Claim 6 recites “but not limited to images of the lids, . . . the cornea, . . . the conjunctiva, . . . the anterior chamber, . . . the iridocorneal angle, . . . the iris, . . . the pupil, . . . the crystalline lens” in lines 2-4, but lack proper antecedent basis. Importantly, “but not limited to” makes the claim indefinite because it is not clear what the interpretation is intended to cover. That is, what are the bounds of the invention? The antecedent basis rejection can be overcome by amending to recite --image of eyelids, . . . cornea, . . . conjunctiva, . . . anterior chamber, . . . iridocorneal angle, . . . iris, . . . pupil, . . . crystalline lens--. The same applies to claim 9.
Claims 7, 13( there’s two claim 13s), and 13-20 recite “wherein the system” in line 1, but is indefinite. Is the system the “system for measurement of an intraocular pressure” or “the optical system” as recited in Claim 1.
Claim 12 recites “the camera” in line 2, but is indefinite. Is the camera referring to a camera of the phone or the at least one camera sensor recited in claim 1? For examination purposes, each of the interpretations is a valid basis for rejection. Clarification required.
There are two recited Claim 13s. Additionally, the second Claim 13 appears to depend on the first Claim 13 instead of Claim 1 because of “the eye picture.” Amendment required. For examination purposes, the claims will be interpreted as being one.
Claims 15-20 recite “computing system” in the preamble, but it is indefinite because no “computing system” has been introduced. Clarification required.
Claim 20 recites “the IOP report” in line 2, but is indefinite and lacks proper antecedent basis. Is the limitation intended to depend on claim 18 that recites “a report” or claim 1? If the former, the preamble must be changed, if the latter, the antecedent basis must be made proper. Clarification required.
Claim 20 recites “the computing system” in line 2, but is indefinite. There is no recited computing system and therefore does not point to an element in Claim 1. Additionally, the limitation logic appears circular because the system transmits and receives to itself. It appears the intended interpretation is to refer to the data processor and will be as such for examination purposes.
Claims not listed are rejected by virtue of claim dependency.
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, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Artsyukhovich et al. (US 20150057524- Cited by applicant), hereinafter Artsyukhovich.
Regarding claim 1, Artsyukhovich teaches A system for measurement of an intraocular pressure (IOP) of at least one eye of a subject comprising, a handheld device ( ¶[0022] and fig.1, the IOP device can be held by the user during operation);
at least one source of light configured to illuminate an anterior aspect of the eye and produce a reflected and refracted light from the anterior aspect of the eye (¶[0006-7,0024,0031,0039] and fig. 1, the light source is directed to the anterior chamber and the data is used to determine the change in in eye pressure);
at least one camera sensor configured to capture the reflected and refracted light from the anterior aspect of the eye (¶[0024,0039], “a system 100 for taking intraoperative biometry and/or refractive measurements”); an optical system mounted in the frame of the device and configured to convey and focus the reflected and refracted light to the at least one camera sensor (fig. 1, the reflected and refracted light is configured to be focused on the camera sensor 106);
at least one data processor; and at least one memory storage device that stores instruction which is executed by the at least one data processor (¶[0025], control unit comprises a processor and memory).
Regarding claim 18, Artsyukhovich teaches wherein the system is further configured to generate a report that contains classified IOP measurement results (¶[0054], “ the control unit 114 communicates information relating to the measurements to a health care provider”).
Regarding claim 20, Artsyukhovich teaches wherein the system is further configured to transmit and display the IOP report received from the computing system of claim 1 to the subject (¶[0050,0054], “the control unit 114 communicates information relating to the measurements to a health care provider.”).
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 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich, as applied to claim 1, and further in view of Samec et al. (US 20170323485- Cited by applicant), hereinafter Samec.
Regarding claim 2 Artsyukhovich fails to teach further comprising an engine selected from the group consisting of a virtual reality engine, an augmented reality engine and a mixed reality engine, the engine including eye-tracking capabilities to collect information from the anterior aspect of the eye.
Samec teaches a system for augmenting the health analysis of a user (abstract). The system is configured to detect and/track physiological behaviors using a virtual reality engine or augmented reality engine to collect information related to “eye position, movement, gaze, or pupil size,” which are from the anterior aspect of the eye (¶0219,0303,0350]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that it comprises a virtual reality engine, an augmented reality engine, or mixed reality engine with eye-tracking capabilities to collect information from the anterior aspect of the eye, as taught by Samec, to aid in producing images that will presented to the user in an effective manner (¶[0004]).
Regarding claim 3, Artsyukhovich fails to teach further comprising infrared sensors.
Samec, on the other hand, teaches the system comprises an infrared camera for monitoring the parameters of the eye (¶[0304). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that it comprises infrared sensors, as taught by Samec, because Artsyukhovich requires a camera, but fails to specify, and Samec teaches that an infrared camera can be used to gather eye diagnostic information.
Regarding claim 4, Artsyukhovich fails to teach wherein least one eye-tracking system is configured to acquire images and videos of the anterior aspect of the eye. It is noted however, Artsyukhovich teaches “The intra-op diagnostics device may also include other eye measurements instruments, like wavefront sensors, video cameras and such” (¶[0024]).
Samec teaches that the system further incorporates acquiring image and video of the anterior aspect of the eye (¶[0358,0384], “detecting eye position and/or movement tracking” and “video tracking”).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that the system further incorporates acquiring image and video of the anterior aspect of the eye, as taught by Samec, to aid in determining whether the user has one or more possible conditions (¶[0382]).
Regarding claim 5, Artsyukhovich-Samec further teach providing a set of 2 or more images (¶[0382] of Samec, images and videos are captured, thereby indicated at least more than 2 images)
Regarding claim 6, Artsyukhovich-Samec further teach wherein the images are images selected from the group consisting of images of the lids, images of the cornea, images of the pupil, and combinations thereof (¶[0350-54] of Samec).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich, as applied to claim 1, and further in view of Tran (US 20200405148- Cited by applicant).
Regarding claim 7, Artsyukhovich fails to teach using at least one type of neural network (NN) or one type of support vector machine (SVM) to measure the IOP.
Tran teaches using a deep neural network for determining IOP to aid in detecting abnormalities of the user’s eyes (abstract and ¶0011]).
As such, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Artsyukhovich, such that the system uses a NN, as taught by Tran, to aid in developing accurate and smart ocular diagnostics (¶[0012] of Tran).
Claims 8-9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich in view of Tran, as applied to claim 7, and further in view of Samec and Ng et al. (US 20220028547), hereinafter Ng.
Regarding claim 8, Artsyukhovich-Tran teach wherein the NN and the SVM are previously trained by a training system, the training comprising: creating a training set of images from images of eyes for each member of a training population, the images of eyes including different levels of IOP (¶[0011] of Tran, “generating historical feature vectors from one or more eye examinations of a patient, training the deep learning neural network with the historical feature vectors along with eye images”), but fail to teach selecting a tentative architecture for a NN to classify the level of IOP in the training set of images through an iterative process; using a training database, wherein the training database includes, for each member of the training population each with an associated IOP test, an assessment dataset that includes at least data relating to the level of IOP for each member of the training population, wherein the training population includes a test member with an associated IOP test; assigning an IOP score of the test member of the training population; configuring an expert system module of a training system including to determine correlations between the level of IOP of the test member and the IOP for each member of the training population.
Samec teaches that the system comprises “various machine learning algorithms (such as e.g., support vector machine, k-nearest neighbors algorithm, Naive Bayes, neural network (including convolutional or deep neural networks), or other supervised/unsupervised models, etc.).” to analyze the eye images (¶[0384]). The NN architecture starts with a base model to classify the eye data that undergoes an iterative process based on the added data (¶[0388], “individual models may be customized for individual data sets. For example, the wearable device may generate or store a base model. The base model may be used as a starting point to generate additional models specific to a data type (e.g., a particular user), a data set (e.g., a set of additional images obtained), conditional situations, or other variations. In some embodiments, the display system may be configured to utilize a plurality of techniques to generate models for analysis of the aggregated data. Other techniques may include using pre-defined thresholds or data values.”).
As such, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich-Tran, such that a tentative architecture for a NN to classify the level of IOP in the training set of images through an iterative process is used, as taught by Samec, to aid in producing images that will presented to the user in an effective manner (¶[0004] of Samec).
Artsyukhovich-Tran-Samec fail to teach using a training database, wherein the training database includes, for each member of the training population each with an associated IOP test, an assessment dataset that includes at least data relating to the level of IOP for each member of the training population, wherein the training population includes a test member with an associated IOP test; assigning an IOP score of the test member of the training population; configuring an expert system module of a training system including to determine correlations between the level of IOP of the test member and the IOP for each member of the training population.
Ng teaches a system that uses machine learning to anlayze medical images (abstract). The machine learning model is trained with a database of a population that includes test scores associated with each member and determined correlations between the test patients and the population to aid in scoring and quantifying diseases (¶[0002,0025], “the process includes receiving registry data for the patient, the registry data including patient data across patient populations, wherein the machine learning model is trained with labeled registry data associating values of for patient populations with respective severity of IBD in particular patients of the patient populations. The process includes extracting one or more values from the registry data to form a registry feature vector. The process includes processing, by the machine learning model or by a second machine learning model in addition to the machine learning model, the registry feature vector. The process includes generating an updated score representing the severity of IBD in the patient indicated by the registry data” (emphasis added)).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich-Tran-Samec, such that a training database is used, wherein the training database includes, for each member of the training population each with an associated test, an assessment dataset that includes at least data relating to the physiological level for each member of the training population, wherein the training population includes a test member with an associated test; assigning a physiological score of the test member of the training population; configuring an expert system module of a training system including to determine correlations between the physiological level of the test member and for each member of the training population, as taught by Ng, to aid in scoring and quantifying disease and predicting disease progression and treatment outcomes (¶[0002] of Ng).
Regarding claim 9, Artsyukhovich-Tran-Samec-Ng teach a user testing platform configured to provide the subject with an IOP test and receive user input regarding responses to the IOP test (¶[0025,0043] and fig. 1 and 7 of Artsyukhovich, “The control unit 114 typically includes a processor and memory, with the processor being, for example only, an integrated circuit with power, input, and output pins capable of performing logic functions”);
an analysis system communicatively coupled to the training system and the user testing platform (¶[0025] of Artsyukhovich, “control unit 114 typically includes a processor”), the analysis system adapted to receive an IOP for the subject generated in response to the a subject IOP test and to assign a lOP score for the subject using the correlations obtained from the training system (¶[0025], “a series of sensed or calculated pressure or IOP readings”);
one or more intermediate NN built using a set of images of the subject in different eye positions (¶[0350] of Samec, the NN are used to for eye tracking, therefore, “detect eye position” changes);
and one or more intermediate NN built using a set of images of the subject to identify different structures of the anterior aspect of the eye including the lids, images of the cornea, images of the pupil, and combinations thereof (¶[0350-54] of Samec).
Regarding claim 11, Artsyukhovich-Tran fail to teach wherein the NN is assigned an error rate relative to a validation set, wherein an error rate of less than 15% identifies the NN as having passed a validation threshold.
Samec teaches that the model is assigned an error rate to determine false positives or false negatives (¶[0294], “ the quantity of data to be collected for an analysis may be the number of data points sufficient to yield an outcome with a reliability higher than chance level (e.g., 50%), with a maximum of 10% type 1 (false positive) error and a maximum of 10% type 2 (false negative) error. In some other embodiments, larger or smaller margins of error may be allowable based on requirements for determination of an outcome in a particular prediction model”(emphasis added)).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich-Tran, such that the NN is assigned an error rate relative to a validation set, wherein an error rate of less than 15% identifies the NN as having passed a validation threshold, as taught by Samec, to aid in determining false positives and false negatives.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich in view of Tran, Samec, and Ng, as applied to claim 9, and further in view of Shi et al. (US 20190244358), hereinafter Shi.
Regarding claim 10, Artsyukhovich-Tran-Samec-Ng fail to teach wherein the number of intermediate NNs is 23.
Shi teaches a system directed to parsing images using a deep neural network (abstract and ¶[0003-5]). The neural network comprises more than 2 intermediate layers (¶[0077]). Shi further teaches that the greater amount of intermediate avoids being too shallow, limited expression ability, and poor training (¶[0078]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich-Tran-Samec-Ng, such that the number of intermediate NNs is 23, as taught by Shi, to aid in providing more depth, expression ability, and better training.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich, as applied to claim 1, and further in view of Gharib (US 20170209046).
Regarding claim 12, Artsyukhovich fails to teach wherein the handheld device is a mobile device and the system is further configured to control the camera of the mobile device to focus on the at least one eye of the subject.
Gharib teaches a non-invasive system for estimating intraocular pressure using a smart phone and its camera (¶[0031] and fig. 2).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that the handheld device is a mobile device and the system is further configured to control the camera of the mobile device to focus on the at least one eye of the subject, as taught by Gharib, to provide a compact, easily accessible, and portable system for monitoring eye parameters (¶[0016] of Gharib).
Regarding claim 13, Artsyukhovich fails to teach wherein the system is further configured to take an eye picture of the at least one eye of the subject which can be used for IOP measurement and the image is sent to a server.
Gharib teaches a non-invasive system for estimating intraocular pressure via an image using a smart phone and its camera (¶[0025,0031] and fig. 2). Additionally, the image data can be sent to a remote server (¶[0030-31]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that the system is further configured to take an eye picture of the at least one eye of the subject which can be used for IOP measurement and the image is sent to a server, as taught by Gharib, to aid in providing a portable non-invasive patient-patient specific system (¶[0006] of Gharib).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich, as applied to claim 1, and further in view of Kanagasingam (US 20170209046).
Regarding claim 14 ,Artsyukhovich fails to teach wherein the system is further configured to receive the eye picture, to remove the noise from the eye picture, and to crop and re-size the eye picture.
Kanagasingam teaches a method for processing images of the eye (abstract). The method requires removing noise from the image and cropping the image (as such, would also re-size as a result) (¶[0026,0160]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that the system is further configured to receive the eye picture, to remove the noise from the eye picture, and to crop and re-size the eye picture, as taught by Kanagasingam, to focus on the relevant regions of interest that are suitable for eye parameter measurement estimation.
Claims 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Artsyukhovich, as applied to claim 1, and further in view of Yang et al. (US 20200074622), hereinafter Yang.
Regarding claims 15-17 and 19, Artsyukhovich fails to teach wherein the system is further configured to run a neural network for measuring the IOP value of a patient as a numerical value wherein convolutional neural networks, or batch normalization, or pooling techniques are used.
Yang teaches a system that takes images of the user’s eyes to identify abnormalities and obtain measurements of intraocular pressure (abstract and ¶[0024]). The system relied on a machine learning model to process the images, wherein the machine learning model is based on convolutional neural network layers, pooling layers, and/or batch normalization (¶[0029]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Artsyukhovich, such that the system is further configured to run a neural network for measuring the IOP value of a patient as a numerical value wherein convolutional neural networks, or batch normalization, or pooling techniques are used, as taught by Yang, to aid in identifying and separating abnormal and normal subjects (¶[0002] of Yang).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. teaches systems for diagnosing glaucoma by combining diagnostic parameters derived from optical coherence tomography images of three different anatomic regions of the eye, including the macular ganglion cell complex (mGCC), the peripapillary nerve fiber layer (ppNFL), and the optic nerve head (ONH). The combined diagnostic parameters form a reduced set of global parameters, which are then fed to pre-trained machine classifiers as input to arrive at a single diagnostic indicator for glaucoma. Also disclosed are methods for training a machine classifier to be used in methods and systems of this invention. US 20100277691
Beyar et al. teaches a method of non-invasive measurement of an intraocular pressure (IOP), the method may include: obtaining an image of at least a portion of a specific element of a subject's eye; detecting at least a portion of the specific element in the obtained image; and determining a value of a geometric property of the specific element based on the obtained image, the determined value of the geometric property is indicative of an IOP value of the subject's eye. US 20220151491 A1
Martorell teaches a system for measuring and assessing intra-ocular pressure (IOP). The processor may cause the system to: capture an image of a user's eye; convert the image to a three-dimensional image; analyze the three-dimensional image; and calculate an IOP measurement. US 20210361160 A1
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791
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