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 .
Claim Status
Claims 1-8 are pending and under exam herein.
Priority
The instant application is a continuation of US 17/067,671, filed 10 October 2020, now US Patent 11,504,186 which is a Continuation of PCT/KR2019/010960 filed 27 August 2019. The Effective Filing Date (EFD) of each of claims 1-8 is 27 August 2019.
Information Disclosure Statement
The Information Disclosure Statement filed 24 October 2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS is included with this Office Action.
Drawings
The Drawings submitted 24 October 2022 are accepted.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1 Analysis: Are claims directed to process, machine, manufacture/composition of matter
With respect to step (1): yes, the claims are directed to methods and a device.
Step 2A, Prong 1 Analysis: Do claims recite abstract idea
With respect to step (2A)(1), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite the abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and in conjunction with mathematical concepts (in particular mathematical relationships and formulas).
Note: The claims elements are italicized herein to highlight the judicial exceptions in the claim steps and underlined to represent the additional claim elements.
Claim 1:
A method for predicting a vaulting value representing a distance between a rear surface of a lens to be inserted into an eyeball of a person to be operated on with lens implant surgery and an anterior surface of a crystalline lens, the method comprising:
inputting examination data of the person to be operated on and one or more lens sizes to a vaulting value prediction model;
predicting vaulting value corresponding to the one or more input lens sizes from the vaulting value prediction model-wherein said operation is a mental process of making a prediction based on data from a model or, alternatively, using a computer as a tool to implement said prediction from the model;
and obtaining, based on the vaulting value corresponding to the one or more input lens sizes, a lens size for the person to be operated on, from among the one or more lens sizes;
wherein the vaulting value prediction model is trained based on examination data of patients who have had lens implant surgery in the past, size information of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients-wherein training a model is a mathematical operation including, as described in the Specification to include supervised learning, unsupervised learning, reinforcement learning, and imitation learning [0084]. Further training algorithmic operations are at [0124].
Claim 2:
wherein the vaulting value is defined as the shortest distance among a plurality of distances between the rear surface of the lens to be inserted into the eyeball of the person to be operated on with the lens implant surgery and the anterior surface of the crystalline lens-wherein said step is a limit to the judicial exception vaulting parameters for the model
Claim 3:
providing information about whether a lens having a lens size corresponding to the predicted vaulting value is suitable for the eyeball of the person to be operated on based on whether the predicted vaulting value satisfies a condition of a predetermined range
Claim 4:
wherein the condition of the predetermined range satisfies the predicted vaulting value being included within a range of 250 to 750 µm
Claim 5:
A method for predicting a vaulting value representing a distance between a rear surface of a lens to be inserted into an eyeball of a person to be operated on with lens implant surgery and an anterior surface of a crystalline lens, the method comprising:
inputting examination data of the person to be operated on to a vaulting value prediction model;
predicting expected lens size and the vaulting value corresponding to the expected lens size from the vaulting value prediction model-wherein the step of making a prediction using a data from a model is an abstract idea that can be performed using pen and paper or alternatively, using a computer as a tool to perform said abstract idea.
determining whether the expected lens size is suitable for the eyeball of the person to be operated on based on the predicted vaulting value-wherein making a determination given data is further an abstract mental process that may be done using pen and paper
wherein the vaulting value prediction model may be trained based on a plurality of examination data of patients who have had lens implant surgery in the past, sizes of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients-further limit to eh abstract recitation of a “model” for prediction herein.
Claim 6:
wherein the expected lens size is one of a plurality of preset lens sizes
Claim 7:
wherein the expected lens size is one of non-standardized lens sizes rather than a plurality of preset lens sizes
Claim 8:
A device for predicting a vaulting value representing a distance between a rear surface of a lens to be inserted into an eyeball of a person to be operated on with lens implant surgery and an anterior surface of a crystalline lens, the device comprising:
a memory, which stores examination data of the person to be operated on; and
a processor,
wherein the processor is configured to:
input examination data of the person to be operated on and one or more lens sizes to a vaulting value prediction model,
predict vaulting value corresponding to the one or more input lens sizes from the vaulting value prediction model-, wherein said operation is a mental process of making a prediction based on data from a model or, alternatively, using a computer as a tool to implement said prediction from the model;
obtain, based on the vaulting value corresponding to the one or more input lens sizes, a lens size for the person to be operated on, from among the one or more lens sizes,
wherein the vaulting value prediction model is trained based on examination data of patients who have had lens implant surgery in the past, sizes of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients-wherein training a model is a mathematical operation including, as described in the Specification to include supervised learning, unsupervised learning, reinforcement learning, and imitation learning [0084]. Further training algorithmic operations are at [0124].
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind (calculations by hand or pen and paper or computer as a tool) and performance by mathematical operation (prediction modeling). There are no specifics as to the methodology involved in “predicting” or in “determining” and thus, under the BRI, one could simply, for example, perform said operation with pen and paper, or, alternatively with the aid of a generic computer as a tool to perform said operations. These recitations are similar to the concepts of collecting information, analyzing it and providing certain results from the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in (Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts.
Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Step 2A, Prong 2 Analysis: Integration to a Practical Application
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the additional elements as underlined above. Specifically said steps are those that include data input (inputting examination data) and getting values (getting output from a prediction) and making determinations. The claims also include a device with processor and memory. The computer memory stores data. Further steps directed to additional elements in the claim are those that further limit the data as in claim 1 and are those that include limitations on the data such as claims 3-4 and 6-7.
As the “system” includes the memory with stored data, said memory with stored data serves as the vehicle for data gathering in the claim. Additional elements in the instant claims directed to data gathering perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further, the system, processor, memory and instructions are part of a general purpose computer system and there are no details herein wherein of how the specific computer structures are used to implement the judicial exceptions beyond generic computing operations, i.e., the computer elements of the claims do not provide improvements to the functioning of the computer itself (see: DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (see: Diamond v. Diehr); nor do they utilize a particular machine (see: Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the judicial exception using a computer, and therefore the claim does not provide integration into a practical application of any judicial exception.
Step 2B Analysis: Do Claims Provide an Inventive Concept
The claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the prior art to, for example, Sramka et al. (IDS reference) recognizes that obtaining examination data and using said data for calculation purposes in lens implantation decisions is a data gathering element that is routine, well-understood and conventional in the art. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide a non-conventional or unconventional step. Data gathering steps constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. Further exemplified prior art to, for example, Sramka et al. teach that computing elements are routine, well-understood and conventional in the art. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. With respect to the “machine learning” aspect recited in the claims, there are no aspects claimed wherein any instructions on how to perform said learning. As such, the “machine learning” is interpreted herein to be mathematical algorithm implementation only, as part of the abstract idea above. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
The dependent claims have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception.
For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
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.
Claims 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 5 recite, “predicting vaulting value corresponding to the one or more input lens sizes from the vaulting value prediction model” and claim 8 recites, similarly, “predict vaulting value corresponding to the one or more input lens sizes from the vaulting value prediction model”, wherein said recitations are unclear with respect to making a prediction of a value that is not defined by the model. It is not clear, for example, the parameters of the model that operate to “predict” a vaulting value. The claim fails to describe any particular process or algorithm by which said operation is performed and thus the scope of intended coverage herein is not clear. Clarification through clearer claim language is requested.
Claim 5 and dependent claims recite, “determining whether the expected lens size is suitable for the eyeball of the person to be operated on based on the predicted vaulting value” wherein the term “suitable” is a relative term which renders the claim indefinite. The term “suitable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Clarification is requested through clearer claim language.
Claim 5 and dependent claims recite, “wherein the vaulting value prediction model may be trained based on a plurality of examination data of patients who have had lens implant surgery in the past, sizes of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients”, wherein the claim recitation of the model that “may be trained” is indefinite with respect to limiting the instant claim, as said operation appears not to be required. It is suggested that the step be amended to recite active, positive limitations such as, “wherein the vaulting value prediction model ” or the like.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (Ophthalmic Res. (2016) Vol. 56:215-221) in view of Nakamura et al. (American Journal of Ophthalmology (2017) Vol. 187:99-107-IDS reference).
Claim 1 is directed to:
A method for predicting a vaulting value representing a distance between a rear surface of a lens to be inserted into an eyeball of a person to be operated on with lens implant surgery and an anterior surface of a crystalline lens, the method comprising-(Zheng et al. disclose vault prediction that accounts for anterior chamber area (ACA) and size of the lens represented by the curvature radius of the anterior surface of the lens (Lenscur) [p.215, cols. 1-2]);
inputting examination data of the person to be operated on and one or more lens sizes to a vaulting value prediction model-(Zheng et al. disclose exam data, including white-to-white diameter (WTW) and Lenscur for input to a regression equation [p. 215, col 2)];
predicting vaulting value corresponding to the one or more input lens sizes from the vaulting value prediction model (Zheng et al. disclose vault prediction that corresponds to the input of Lenscur [p. 215, col. 2]); and
obtaining, based on the vaulting value corresponding to the one or more input lens sizes, a lens size for the person to be operated on, from among the one or more lens sizes (Zheng et al. disclose vault prediction that corresponds to the input of Lenscur [p. 215, col. 2] allowing appropriate choice of ICL size at [p.216, col. 2]; Table 1-Table 2),
wherein the vaulting value prediction model is trained based on examination data of patients who have had lens implant surgery in the past, size information of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients.
Zheng et al. do not disclose a prediction model that is trained based on examination data of previous patients that have had lens implant surgery. However, the prior art to Nakamura et al. disclose using stepwise multiple regression modeling with optimal ICL size as a dependent variable to obtain the NK-formula. This formula was implemented and applied to determine recommended ICL size per patient (page 102, Results).
As such it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the reference to Zheng et al. disclosing vault prediction corresponding to lens size with the methods as disclosed by Nakamura et al. for regression analysis modeling to include for patient-specific optimal collamer lens size as the prior art to Nakamura et al. specifically include lens size as a components in the regression model and uses output for patient-size selections.
It is noted herein that the instant claim includes recitation of “lens” without specific definition for said lens recitations (save for eth preamble of the claim) and thus the “lens” as claimed reads on various types of lenses, including the natural eye crystalline lens.
It is further noted that “training” is not defined in the instant claim, nor is “training” defined beyond the values for training that include “based on examination…size…and vaulting”. As such, the “regression model” as disclosed herein reads on a “trained model”.
With respect to claim 2, the prior art to Nakamura et al. disclose said parameters by describing vault, as understood the the distance between the anterior surface of the crystalline lens and the posterior surface of the ICL (p. 100) and further at p. 102:
Optimal ICL size (mm, in balanced salt solution) = 4.20+0.719x(ACW)(mm)+0.655X(CLR)(mm)
With respect to claims 3 and 4, Nakamura et al. disclose vault value of an average that was equal to 340 +/- 174 µm (p. 104, col. 2).
Claim 5 is directed to:
A method for predicting a vaulting value representing a distance between a rear surface of a lens to be inserted into an eyeball of a person to be operated on with lens implant surgery and an anterior surface of a crystalline lens, the method comprising-(Zheng et al. disclose vault prediction that accounts for anterior chamber area (ACA) and size of the lens represented by the curvature radius of the anterior surface of the lens (Lenscur) [p.215, cols. 1-2]):
inputting examination data of the person to be operated on to a vaulting value prediction model-(Zheng et al. disclose exam data, including white-to-white diameter (WTW) and Lenscur for input to a regression equation [p. 215, col 2)];
predicting expected lens size and the vaulting value corresponding to the expected lens size from the vaulting value prediction model-(Zheng et al. disclose vault prediction that corresponds to the input of Lenscur [p. 215, col. 2]); and
determining whether the expected lens size is suitable for the eyeball of the person to be operated on based on the predicted vaulting value-(Zheng et al. disclose vault prediction that corresponds to the input of Lenscur [p. 215, col. 2] allowing “suitable” choice of ICL size at [p.216, col. 2]; Table 1-Table 2),
wherein the vaulting value prediction model may be trained based on a plurality of examination data of patients who have had lens implant surgery in the past, sizes of lenses inserted into eyeballs of the patients, and vaulting values measured after surgery of the patients.
Zheng et al. do not disclose a prediction model that is trained based on examination data of previous patients that have had lens implant surgery. However, the prior art to Nakamura et al. disclose using stepwise multiple regression modeling with optimal ICL size as a dependent variable to obtain the NK-formula. This formula was implemented and applied to determine recommended ICL size per patient (page 102, Results).
As such it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the reference to Zheng et al. disclosing vault prediction corresponding to lens size with the methods as disclosed by Nakamura et al. for regression analysis modeling to include for patient-specific optimal collamer lens size as the prior art to Nakamura et al. specifically include lens size as a components in the regression model and uses output for patient-size selections.
It is noted herein that the instant claim includes recitation of “lens” without specific definition for said lens recitations (save for eth preamble of the claim) and thus the “lens” as claimed reads on various types of lenses, including the natural eye crystalline lens.
It is further noted that “training” is not defined in the instant claim, nor is “training” defined beyond the values for training that include “based on examination…size…and vaulting”. As such, the “regression model” as disclosed herein reads on a “trained model”.
With respect to claims 6-7, Nakamura et al. disclose pre-set lens sizing from manufacturer recommendations as well-as assessment of specialized values according to the methodology as claimed (p. 99, col. 2).
Claim 8 is directed to the device of as performing the method that includes the steps of claim 1. As such, the automation of the mathematical operations in the prior art to Zheng et al. in view of Nakamura et al. is prima facie obvious, as automation of manual operations accomplish the same result and do not distinguish over the prior art herein. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007).
Prior Art Made of Record
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
1. Abdulhameed et al. (International Journal of Computer Science and Information Security (January 2019) Vol. 17:24-30) disclosing .
2. Grewal et al. (Canadian Journal of Ophthalmology (2018) Vol. 53:309-313) disclosing deep learning applications in the field of ophthalmology.
Conclusion
No claims are allowed.
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