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 .
Status of Claims
Claims 21-40 were previously pending and subject to a non-final Office Action having a notification date of March 30, 2026 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on June 30, 2026 (the “Amendment”), amending claims 21, 22, 25, 29, 31, 34, 35, 38, and 40.
The present Final Office Action addresses pending claims 21-40 in the Amendment.
Claim Objections
Claims 21, 34, and 40 are objected to because of the following informalities:
In claim 1, lines 12 and 17, it is unclear if the number/numbers of "historical records" are referring to the previously recited "historical IOL implantation records." The Examiner will assume they are the same for purposes of examination and the Examiner recommends that Applicant makes appropriate amendments if necessary. Claims 34 and 40 are rejected for similar reasons.
Appropriate correction is required.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On pages 11-13 of the Amendment, Applicant generally discusses how the newly added limitations directed to generating normalized feature vectors corresponding to the historical IOL implantation records by generating numerical feature vectors from the historical pre- operative eye measurements and applying a principal component analysis (PCA)-based linear transform to the numerical feature vectors; generating a normalized feature vector corresponding to the eye based on the one or more pre-operative measurements of the eye and the PCA-based linear transform; determining distance values between the normalized feature vector corresponding to the eye and the normalized feature vectors corresponding to the historical IOL implantation records; and selecting historical IOL implantation records based on the determined distance values, wherein a number of selected historical IOL implantation records corresponds to the first hyper-parameter; improve the accuracy and efficiency of computerized IOL power prediction by facilitating the selection of a prediction model based upon historical implantation records that are similar to a current implantation case. Applicant also asserts how the claimed invention is directed to improving the operation of a computerized ophthalmic prediction engine through a specific computer-implemented processing architecture, rather than merely performing an abstract mental evaluation.
Despite Applicant's above positions regarding the alleged improvements provided by the claimed invention, the Examiner still asserts that the vast majority of the limitations in the independent claims are practically performable in the human mind with pen and paper ("mental processes") or, in the case of the generally-recited PCA-based linear transform, amounts to "mathematical concepts" because it represents mathematical calculations/equations/etc. An improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology. MPEP 2106.05(a)(II). Furthermore, in assessing patent-eligibility of a claim reciting an abstract idea, the paramount consideration is the claimed additional elements. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 217 (2014) (“consider [all claimed] elements . . . to determine whether the additional elements transform the nature of the claim into a patent-eligible application” (internal citation and quotation marks omitted)); see also MPEP § 2106.05(f) (‘[A] consideration . . . [under] Step 2A Prong Two or ... Step 2B is whether the additional elements amount to .. . more than mere instructions to implement an abstract idea or other exception on a computer.”).
In the present case, Applicant's alleged improvement of improving the accuracy of determining an IOL power for use in selecting an IOL for implantation in an eye is an improvement to the abstract idea rather than to computers or technology. Furthermore, most or all of the limitations asserted by Applicant as providing the alleged improvement are part of the abstract idea rather than amount to additional limitations. Still further, the additional limitations in the claims amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)), merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated; transmitting data) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)), and/or amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). See below rejection.
Furthermore, Applicant's attempt to distinguish the present claims from those in Recentive Analytics because the present claims "recite a specific technical workflow that includes PCA-based preprocessing of ophthalmic measurement data, generation of normalized feature vectors, distance-based selection of historical implantation records, hyper-parameter optimization for determining the size of the historical subset used for model evaluation, and evaluation of multiple candidate prediction models using that selected subset" is ineffective. As set forth in the rejection below, Recentive is not cited in relation to the independent claims but instead is cited in relation to certain dependent claims (e.g., claim 22, etc.) that generically call for training the candidate models based on the set of historical IOL implantation records. Like the claims in Recentive, there is no indication that Applicant's manner of training the candidate models provides any technological improvement in the ML execution itself. Instead, Applicant's "improvement" is in the manner of selecting certain historical IOL implantation records for use in training and evaluating ML models which are primarily mentally performable and/or mathematically-related steps as set forth herein.
The 35 USC 101 rejection is maintained.
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 21-40 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 21-33 are directed to a method (i.e., a process), claims 34-39 are directed to a system (i.e., a machine), and claim 40 is directed to a non-transitory computer-readable medium (i.e., a manufacture). Accordingly, claims 21-40 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 34 includes limitations that recite at least one abstract idea. Specifically, independent claim 34 recites:
A system for optimizing intraocular lens (IOL) selection, the system comprising:
one or more memories comprising executable instructions;
one or more processors in data communication with the one or more memories and configured to execute the instructions to:
obtain a set of historical IOL implantation records, each historical IOL implantation record including at least one historical pre-operative eye measurement and a corresponding actual post-operative manifest refraction spherical equivalent (MRSE) value;
generate normalized feature vectors corresponding to the historical IOL implantation records by generating numerical feature vectors from the historical pre- operative eye measurements and applying a principal component analysis (PCA)-based linear transform to the numerical feature vectors;
determine a first hyper-parameter corresponding to a number of historical records to be selected for model evaluation, the first hyper-parameter being determined based evaluations of prediction accuracy using multiple subsets of the set of historical IOL implantation records and corresponding actual post-operative MRSE values associated with the multiple subsets, the multiple subsets having different numbers of historical IOL implantation records;
obtain one or more pre-operative measurements of an eye;
generate a normalized feature vector corresponding to the eye based on the one or more pre-operative measurements of the eye and the PCA-based linear transform;
select, based on the first hyper-parameter, a subset of the set of historical IOL implantation records by:
determining distance values between the normalized feature vector corresponding to the eye and the normalized feature vectors corresponding to the historical IOL implantation records; and
selecting historical IOL implantation records based on the determined distance values, wherein a number of selected historical IOL implantation records corresponds to the first hyper-parameter;
evaluate a plurality of candidate models based on deviations between MRSE values predicted by the plurality of candidate models using the selected subset of historical IOL implantation records and corresponding actual post-operative MRSE values indicated in the selected subset of historical IOL implantation records;
select a prediction model from the plurality of candidate models based on the evaluation of the plurality of candidate models;
use the selected prediction model to determine an IOL power for an eye based on one or more pre-operative measurements of the eye; and
provide the determined IOL power to a user to aid in selection of an IOL for implantation in the eye.
The Examiner submits that the foregoing underlined limitations recite “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). As an example, a user could practically generate normalized feature vectors corresponding to the historical IOL implantation records by generating numerical feature vectors from the historical pre-operative eye measurements (e.g., via converting each historical pre-op eye measurement to a normalized value and compiling the values into a list, table, etc.); determine a first hyper-parameter corresponding to a number of historical records to be selected for model evaluation and based on a prediction accuracy using various subsets of the set of historical IOL implantation records and corresponding actual post-operative MRSE values (each subset including a different number of historical IOL implantation records from the set); generate a normalized feature vector corresponding to the eye based on the one or more pre-operative measurements of the eye (e.g., via converting each pre-op eye measurement to a normalized value and compiling the values into a list, table, etc.); select a subset of the historical IOL implantation records (e.g., each including a pre-operative eye measurement and a corresponding actual post-operative MRSE value) based on the first hyper-parameter via determining distance values between the normalized feature vector corresponding to the eye and the normalized feature vectors corresponding to the historical IOL implantation records (e.g., comparing the values) and selecting a number of historical IOL implantation records based on the determined distance values (e.g., based on the closest distance values) and corresponding to the first hyper-parameter; evaluate a plurality of prediction candidate models based on deviations between MRSE values predicted by the candidate models using the selected subset and the corresponding actual post-operative MRSE values indicated in the subset; select a prediction model from the plurality of candidate models based on the evaluating of the plurality of candidate models (e.g., the model having the smallest deviation(s)); use the selected prediction model to determine an IOL power for an eye based on one or more pre-operative measurements of the eye (which is practically performable in the human mind with pen and paper as the candidate models can be multiple linear regression equations per [0044] of the present application), and provide the determined IOL power to a user to aid in selection of an IOL for implantation in the eye (e.g., via writing it down).
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis found to be "mental processes" in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)). MPEP 2106.04(a)(2)(III).
Furthermore, the high-level limitations reciting how a PCA-based linear transform is applied to generate normalized numerical feature values constitutes "mathematical concepts" because it amounts to mathematical formulates/equations/calculations.
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 25-33, 38, and 39 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 25 and 38 recite how determining the first or second hyper-parameter includes splitting the set of historical IOL implantation records into training and validation sets, evaluating the prediction accuracy of the at least one candidate model using the multiple subsets of the set of historical IOL implantation records derived from the validation set, and selecting the first hyper-parameter based on the evaluated prediction accuracy which amounts to "mental processes." For instance, a person could practically in their mind with pen and paper split the set of historical IOL implantation records into training and validation sets, evaluate the prediction accuracy of the model by comparing the MRSE values predicted using historical measurements from the subset to corresponding actual MRSE values in the subset, and select the first hyper-parameter based on the prediction accuracy (e.g., based on how close the predicted MRSE values are to the actual MRSE values).
-Claims 26 and 39 recite how a) the splitting further includes splitting the set of historical IOL implantation records into at least the training set, the validation set, and a testing set and b) how the method further includes estimating generalization performance of the at least one candidate model using the testing set and the selecting the first hyper-parameter is further based on the estimated generalization performance which amounts to "mental processes." For instance, a person could practically in their mind with pen and paper split the set of historical IOL implantation records into training, validation, and testing sets and "estimate generalization performance" via analyzing predictions made by the model for various types of historical records. The person could also readily select the first hyper-parameter based on the estimated performance.
-Claim 27 recites how the selecting the subset of the set of historical IOL implantation records based on the first hyper-parameter includes selecting a number of historical records based on similarity to the one or more pre-operative measurements of the eye which again is practically performable in the human mind with pen and paper ("mental processes")
-Claim 28 recites how the number of historical records selected is equal to the first hyper-parameter which just further defines the "mental processes" discussed above.
-Claim 29 recites how the determined distance values are Euclidean distance values which just further defines the "mental processes" discussed above.
-Claim 30 recites how the subset of the set of historical IOL implantation records is selected using a K-Nearest Neighbor (KNN) algorithm, wherein a parameter in the KNN algorithm corresponds to the first hyper-parameter. These limitations recite "mathematical concepts" because they relate to mathematical formulas/equations/calculations.
-Claim 31 calls for determining a second hyper-parameter corresponding to a number of hidden layers in a neural network model which is practically performable in the human mind with pen and paper ("mental processes").
-Claim 32 recites how determining the second hyper-parameter includes splitting the set of historical IOL implantation records into at least a training set and a validation set, evaluating prediction accuracy of each of the trained neural network models using the validation set, and selecting the second hyper-parameter based on the evaluated prediction accuracy which amounts to "mental processes." For instance, a person could practically in their mind with pen and paper split the set of historical IOL implantation records into training and validation sets, evaluate the prediction accuracy of each NN by comparing the MRSE values predicted using historical measurements from the subset to corresponding actual MRSE values in the subset, and select the second hyper-parameter based on the prediction accuracy (e.g., based on how close the predicted MRSE values are to the actual MRSE values).
-Claim 33 recites how a) the splitting further includes splitting the set of historical IOL implantation records into at least the training set, the validation set, and a testing set and b) how the method further includes estimating generalization performance of each NN using the testing set and the selecting the second hyper-parameter is further based on the estimated generalization performance which amounts to "mental processes." For instance, a person could practically in their mind with pen and paper split the set of historical IOL implantation records into training, validation, and testing sets and "estimate generalization performance" via analyzing predictions made by the model for various types of historical records. The person could also readily select the first hyper-parameter based on the estimated performance.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for optimizing intraocular lens (IOL) selection, the system comprising:
one or more memories comprising executable instructions;
one or more processors in data communication with the one or more memories and configured to execute the instructions to:
obtain a set of historical IOL implantation records, each historical IOL implantation record including at least one historical pre-operative eye measurement and a corresponding actual post-operative manifest refraction spherical equivalent (MRSE) value;
generate normalized feature vectors corresponding to the historical IOL implantation records by generating numerical feature vectors from the historical pre- operative eye measurements and applying a principal component analysis (PCA)-based linear transform to the numerical feature vectors;
determine a first hyper-parameter corresponding to a number of historical records to be selected for model evaluation, the first hyper-parameter being determined based evaluations of prediction accuracy using multiple subsets of the set of historical IOL implantation records and corresponding actual post-operative MRSE values associated with the multiple subsets, the multiple subsets having different numbers of historical IOL implantation records;
obtain one or more pre-operative measurements of an eye;
generate a normalized feature vector corresponding to the eye based on the one or more pre-operative measurements of the eye and the PCA-based linear transform;
select, based on the first hyper-parameter, a subset of the set of historical IOL implantation records by:
determining distance values between the normalized feature vector corresponding to the eye and the normalized feature vectors corresponding to the historical IOL implantation records; and
selecting historical IOL implantation records based on the determined distance values, wherein a number of selected historical IOL implantation records corresponds to the first hyper-parameter;
evaluate a plurality of candidate models based on deviations between MRSE values predicted by the plurality of candidate models using the selected subset of historical IOL implantation records and corresponding actual post-operative MRSE values indicated in the selected subset of historical IOL implantation records;
select a prediction model from the plurality of candidate models based on the evaluation of the plurality of candidate models;
use the selected prediction model to determine an IOL power for an eye based on one or more pre-operative measurements of the eye; and
provide the determined IOL power to a user to aid in selection of an IOL for implantation in the eye.
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the system including memory with instructions and processor(s) configured to execute the instructions, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of obtaining a set of historical IOL implantation records and obtaining one or more pre-operative measurements of an eye, the Examiner submits that these additional limitations merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated; transmitting data) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
For these reasons, representative independent claim 34 and analogous independent claims 21 and 40 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 34 and analogous independent claims 21 and 40 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 22 and 35 call for training the plurality of candidate models based on the set of historical IOL implantation records such that the evaluation and prediction model selection is of the trained candidate models which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claims 23 and 36 call for obtaining one or more post-operative measurements of the eye, the one or more post-operative measurements including an actual post-operative MRSE value of the eye subsequent to the IOL being implanted in the eye which merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). These claims further call for retraining the selected prediction model based on the actual post-operative MRSE value, wherein subsequent to the retraining, the retrained selected prediction model is configured to be utilized for determining another IOL power based on one or more pre-operative measurements of another eye. These limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” and provide only a result-oriented solution without details as to how the retraining actually occurs (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 24 calls for implanting an IOL having the determined IOL power in the eye which amounts to generic instructions to "apply" the above-noted abstract idea (MPEP 2106.05(f)).
-Claims 25 and 38 call for training at least one of the plurality of candidate models using the training set which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 31 recites how at least one of the plurality of candidate models is an NN model which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 32 calls for training a plurality of neural network models with different numbers of hidden layers using the training set which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 37 recites an IOL having the determined IOL power configured to be implemented in the eye which amounts to generally linking use of the abstract idea to a particular technological environment or field of use without altering/affecting how the process steps of determining the IOL power are performed (see MPEP § 2106.05(h)) as well as generic instructions to "apply" the above-noted abstract idea (MPEP 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 34 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, the additional limitation of the computing device amounts to merely using a computer as a tool to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)) while the limitation specifying how the nutrient profile maps physiological data of the subject to current nutrient levels of the subject merely generally links use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(h)).
Regarding the additional limitations directed to obtaining a set of historical IOL implantation records and obtaining one or more pre-operative measurements of an eye which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea (see MPEP § 2106.05(g)) as discussed above, the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 22 and 35 call for training the plurality of candidate models based on the set of historical IOL implantation records such that the evaluation and prediction model selection is of the trained candidate models which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claims 23 and 36 call for obtaining one or more post-operative measurements of the eye, the one or more post-operative measurements including an actual post-operative MRSE value of the eye subsequent to the IOL being implanted in the eye which merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). Furthermore, the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of receiving/transmitting data over a network. See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); See MPEP 2106.05(d)(II). These claims further call for retraining the selected prediction model based on the actual post-operative MRSE value, wherein subsequent to the retraining, the retrained selected prediction model is configured to be utilized for determining another IOL power based on one or more pre-operative measurements of another eye. These limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” and provide only a result-oriented solution without details as to how the retraining actually occurs (see MPEP § 2106.05(f)). Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 24 calls for implanting an IOL having the determined IOL power in the eye which amounts to generic instructions to "apply" the above-noted abstract idea (MPEP 2106.05(f)).
-Claims 25 and 38 call for training at least one of the plurality of candidate models using the training set which amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 31 recites how at least one of the plurality of candidate models is an NN model which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 32 calls for training a plurality of neural network models with different numbers of hidden layers using the training set which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)) and amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how the training actually occurs. Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13.
-Claim 37 recites an IOL having the determined IOL power configured to be implemented in the eye which amounts to generally linking use of the abstract idea to a particular technological environment or field of use without altering/affecting how the process steps of determining the IOL power are performed (see MPEP § 2106.05(h)) as well as generic instructions to "apply" the above-noted abstract idea (MPEP 2106.05(f)).
Therefore, claims 21-40 are ineligible under 35 USC §101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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