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 .
Response to Amendment
In the amendment filed on July 28, 2026, the following has occurred: claim(s) 1, 10, 19 have been amended. Now, claim(s) 1-19 are pending.
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.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 1
Claims 1-9 are drawn to a system, claims 10-18 are drawn to a method, claim 19 is drawn to a system, and of which are within the four statutory categories (i.e., a machine and a process). Claims 1-19 are further directed to an abstract idea on the grounds set out in detail below.
Claim 1: Step 2A Prong One
Claim 1 recite(s):
process, pre-operatively, the input data to extract a plurality of capsule parameters corresponding to a lens capsule of the eye based on the input data, the plurality of capsule parameters including one or more of: one or more dimensional properties or one or more geometric properties of the lens capsule;
determine, pre-operatively, an axial displacement factor based on the plurality of capsule parameters, the axial displacement factor accounting for a predicted axial shift of the intraocular lens after implantation into the eye; and
utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor
These limitations, as drafted, given the broadest reasonable interpretation but for the
recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded;”, “wherein the controller is configured to selectively execute a finite element model and a machine learning module, execution of the instructions by the one or more processors causing the controller to:” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following instructions to make determinations of a patient’s eye based on data. For example, the claim encompasses a person following instructions to identify and extract a plurality of capsule parameters. Similarly, the claim encompasses a person following instructions to determine an axial displacement factor. Finally, the claim encompasses a person following instructions to pre-operatively recommend an intraocular lens power based on the axial displacement factor. These steps could be accomplished by a person following instructions to make determinations by using obtained information, and therefore encompass Certain Methods of Organizing Human Activity.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claim 1, directly or indirectly, recites the following generic computer components
configured to implement the abstract idea “a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded;”, “wherein the controller is configured to selectively execute a finite element model and a machine learning module, execution of the instructions by the one or more processors causing the controller to:”. As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “obtain pre-operative input data, including one or more biometric parameters of the eye;” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “, using the machine learning module”, “using the finite element model,” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of
the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity
Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Dependent claims 2-9 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 2-3 further describe the generic computer components. Similarly, claims 4-5 further describe the plurality of capsule parameters. Finally, claims 6-9, the capsule skew factor encompasses Mathematical Concepts in the form of a mathematical formula or equation, and this is also an abstract idea. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of
abstract ideas.
Dependent claims recite additional subject matter which amount to limitations consisted
with the additional elements in independent claim 1 (such as claims 2-3 recite additional
limitations that amount to generic computer components).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation
and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims are not patent eligible.
Claims 10-18 recite similar functions to claims 1-9, but in method form. The addition of “selecting the machine learning module to be a neural network” in claim 12 is recited at a high-level of generality that amounts to no more than general purpose computer components programmed to perform the abstract idea.
Claim 19: Step 2A Prong One
Claim 19 recite(s):
extract a plurality of capsule parameters based on the input data;
determine, pre-operatively, an axial displacement factor based in part on the plurality of capsule parameters; and
adjust a lens power of the intraocular lens based in part on the axial displacement factor, the axial displacement factor being a power correction feature accounting for a predicted axial shift of the intraocular lens after implantation into the eye
These limitations, as drafted, given the broadest reasonable interpretation but for the
recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded;”, “wherein the controller is configured to selectively execute a first machine learning module and a second machine learning model trained to emulate a finite element model, execution of the instructions by the one or more processors causing the controller to:” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following instructions to make determinations of a patient’s eye based on data. For example, the claim encompasses a person following instructions to identify and extract a plurality of capsule parameters. Similarly, the claim encompasses a person following instructions to determine, pre-operatively, an axial displacement factor. Finally, the claim encompasses a person following instructions to adjust a lens power of the intraocular lens. These steps could be accomplished by a person following instructions to make determinations by using obtained information, and therefore encompass Certain Methods of Organizing Human Activity.
Claim 19: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claim 19, directly or indirectly, recites the following generic computer components
configured to implement the abstract idea “a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded;”, “wherein the controller is configured to selectively execute a first machine learning module and a second machine learning model trained to emulate a finite element model, execution of the instructions by the one or more processors causing the controller to:”. As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “receive input data, including one or more biometric parameters of the eye;” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “, via the first machine learning module”, “, via the second machine learning model trained to emulate the finite element model” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claim 19: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of
the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h) and Affinity
Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dewey et al. (U.S. Patent Pre-Grant Publication No. 2017/0189233) in view of Hipsley (U.S. Patent Pre-Grant Publication No. 2018/0000339).
As per independent claim 1, Dewey discloses a system for selecting an intraocular lens for implantation into an eye, the system comprising:
a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded (See [0016], [0025]: The controller is programmed to execute a program, and the system comprises a processor; and a memory operable to store data);
wherein the controller is configured to selectively execute a finite element model and a machine learning module (See [0025], [0241]: The controller is programmed to execute a program and the one or more algorithms or models stored in the memory of the laser eye surgery system, which the Examiner is interpreting algorithms or models to encompass a machine learning module), execution of the instructions by the one or more processors causing the controller to:
obtain pre-operative input data, including one or more biometric parameters of the eye (See [0141]-[0148], [0241]-[0242]: In the one or more predictive models, one or more characteristics of the postoperative condition of the patient's eye or vision is modeled based on one or more selected from the group consisting of pre-operative measurements obtained from the laser eye surgery system, a contemplated surgical intervention, and on or more algorithms or models stored in the memory of the laser eye surgery system and executed by the processor, which the Examiner is interpreting pre-operative measurements obtained from the laser eye surgery system to encompass obtain pre-operative input data);
process, pre-operatively using the machine learning module, the input data to extract a plurality of capsule parameters corresponding to a lens capsule of the eye based on the input data (See [0194]-[0196], [0241]-[0242]: In the one or more predictive models, one or more characteristics of the postoperative condition of the patient's eye or vision is modeled based on one or more selected from the group consisting of pre-operative measurements obtained from the laser eye surgery system, a contemplated surgical intervention, and on or more algorithms or models stored in the memory of the laser eye surgery system and executed by the processor, which the Examiner is interpreting the postoperative condition to encompass a plurality of capsule parameters corresponding to a lens capsule of the eye as the anterior capsule and posterior capsule of the lens can be imaged and profiled ([0194]));
determine, pre-operatively using the finite element model, an axial displacement factor based on the plurality of capsule parameters, the axial displacement factor accounting for a predicted axial shift of the intraocular lens after implantation into the eye (See [0016], [0241]-[0245]: The laser eye surgery system and the optical measurements obtained therewith may be used intra-operatively, i.e., during a cataract surgery or other surgical procedure, for, e.g., intraoperative eye diagnostics, determining IOL position and/or orientation, surgical planning, and control/or of a laser surgical system, which the Examiner is interpreting the one or more predictive models to encompass the finite element model, interpreting IOL position and/or orientation to encompass an axial displacement factor as conditions measured during the surgical procedure may be compared to a predicted condition of the patient's eye based on pre-operative measurements, and a difference between the predicted condition and the actual measured condition may be used to undertake additional or corrective actions during the cataract surgery or other surgical procedure, and interpreting the one or more predictive models, one or more characteristics of the postoperative condition of the patient's eye or vision is modeled based on one or more selected from the group consisting of pre-operative measurements obtained from the laser eye surgery system to encompass pre-operatively using the finite element model); and
utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor (See [0016], [0241], [0244]-[0246]: For example, one or more processors of laser eye surgery system 2 may execute an algorithm which includes: accessing the plurality of IOL models stored in, and for each of the IOL models: (1) modeling the subject's eye with an intraocular lens corresponding to the IOL model and the measured characteristics of the subject's eye; (2) simulating the subject's eye based on the plurality of IOL predetermined parameters and the predicted IOL position; (3) performing one of a ray tracing and a power calculation based on said model of the subject's eye; and (4) selecting an IOL for the subject's eye from the plurality of IOL models corresponding to the optimized IOL based on a predetermined criteria and the measured characteristics of the patient's obtained by the laser surgery system, which the Examiner is interpreting the plurality of IOL models to encompass one or more lens constant formulae, and interpreting a power calculation to encompass an intraocular lens power based on the axial displacement factor as the Examiner is interpreting measured characteristics of the subject's eyes to encompass the axial displacement factor, and interpreting pre-operative measurements obtained from a contemplated surgical intervention to encompass pre-operatively recommend when combined with Hipsley’s disclosure in [0385]-[0387] of forward displacement of lens can be measured and simulated according to the model and preoperative measurement of ocular properties can be utilized ([0491]).)
While Dewy discloses obtain pre-operative input data, including one or more biometric parameters of the eye;
determine, pre-operatively using the finite element model, an axial displacement factor based on the plurality of capsule parameters, the axial displacement factor accounting for a predicted axial shift of the intraocular lens after implantation into the eye; and
utilize one or more lens constants formulae to recommend an intraocular lens power based on the axial displacement factor¸ Dewey may not explicitly teach utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor.
Hipsley teaches a method for utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor (See [0232], [0385]-[0387], [0491]: Forward displacement of lens can be measured and simulated according to the model and preoperative measurement of ocular properties can be utilized ([0491]), which the Examiner is interpreting simulations performed using virtual models of ocular structures, such as those used in ocular accommodation processes to encompass pre-operatively recommend.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Dewey to include utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor as taught by Hipsley. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dewey with Hipsley with the motivation of provide improved systems, devices and methods for performing simulations using a multi-component Finite Element Model (FEM) of ocular biomechanics (See Background of the Invention of Hipsley in Paragraph [0012]).
Claim 10 mirrors claim 1 only within a different statutory category, and is rejected for the same reason as claim 1.
As per claim 2, Dewey/Hipsley discloses the system of claim 1 as described above. Dewey further teaches wherein the finite element model is tensor-based, the controller being adapted to employ multi-physics software to execute the finite element model (See [0252], [0257]: Each of the IOL model are associated with a plurality of predetermined parameters, and the model can simulate a geometry of the subject eye with each of the plurality of intraocular lenses (IOL) implanted, in accordance with the plurality of eye characteristics, which the Examiner is interpreting the simulation from the model to encompass multi-physics software to execute the finite element model.)
Claim 11 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Dewey/Hipsley discloses the system of claim 1 as described above. Dewey further teaches wherein the controller is adapted to select the intraocular lens based on the recommended intraocular lens power (See [0250]: An improved system for selecting an intraocular lens (IOL) for implantation, comprises: propose one IOL power for one or more IOL models from the plurality of IOLs corresponding to the optimized IOL(s) based on predetermined criteria, which the Examiner is interpreting selecting an intraocular lens to encompass select the intraocular lens, and interpreting propose one IOL power to encompass recommended intraocular lens power.)
As per claim 4, Dewey/Hipsley discloses the system of claim 1 as described above. Dewey further teaches wherein the plurality of capsule parameters includes a capsule diameter, and a capsule thickness (See [0117]-[0118]: Imaging modalities and techniques described herein, such as those mentioned above, or ultrasound may be used to determine the location and measure the thickness of the lens and lens capsule to provide greater precision to the laser focusing methods, including 2D and 3D patterning, and the spatial disposition of the structures of interest and/or of suitable matching geometric modeling can also be used to determine a wide variety of parameters related to the procedure such as, for example, the upper and lower axial limits of the focal planes used for cutting the lens capsule and segmentation of the lens cortex and nucleus, and the thickness of the lens capsule among others, which the Examiner is interpreting determine a wide variety of parameters related to the procedure to encompass a capsule diameter.)
Claim 13 mirrors claim 4 only within a different statutory category, and is rejected for the same reason as claim 4.
As per claim 5, Dewey/Hipsley discloses the system of claims 1 and 4 as described above. Dewey further teaches wherein the plurality of capsule parameters includes a capsule skew factor that is based on the capsule thickness (See [0207]-[0209]: The image of the eye may comprise one or more structures that can be used to identify one or more treatment axes of the eye and structures and optical tissue surfaces of the eye, which can be combined with data from one or more of the tomography or the tomography system as described herein to determine treatment axis and alignment of the eye, which the Examiner is interpreting alignment of the eye and one or more structures to encompass a capsule skew factor.)
Claim 14 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 6, Dewey/Hipsley discloses the system of claims 1 and 4-5 as described above. Dewey further teaches wherein the capsule skew factor is a ratio of a Y-coordinate of a centroid of a capsule profile divided by the capsule thickness, the capsule profile being a cross-sectional profile of the lens capsule sliced through an anterior pole and a posterior pole of the lens capsule (See [0216]-[0217], [0221], [0226]: The data used to construct the model preferably includes a plurality of ocular biometry information, anterior corneal surface information, posterior corneal surface information, anterior lens surface information, and posterior lens surface information, and lens position information, which the Examiner is interpreting varying target focal points up to three dimensionally, i.e., across one or more of the X-axis 152, Y-axis 154, or Z-axis 156, up to a three-dimensional gradient index of refraction profile of an anatomical structure of the eye EY such as the lens LE may be generated to encompass a ratio of a Y-coordinate of a centroid of a capsule profile divided by the capsule thickness, the capsule profile being a cross-sectional profile of the lens capsule sliced through an anterior pole and a posterior pole of the lens capsule.)
Claim 15 mirrors claim 6 only within a different statutory category, and is rejected for the same reason as claim 6.
As per claim 7, Dewey/Hipsley discloses the system of claims 1 and 4-6 as described above. Dewey further teaches wherein the capsule skew factor is between 0 and 0.2 (See [0214]: Since the full range imaging depth of the OCT measurements are only about 5 mm to 8 mm, then OCT scanning of the invention may provide for OCT scans at different depths of the eye that can be combined together to determine various biometry measurements, including the axial length, to determine corneal shape information and to determine lens shape information, which the Examiner is interpreting determine lens shape information to encompass the capsule skew factor is between 0 and 0.2.)
Claim 16 mirrors claim 7 only within a different statutory category, and is rejected for the same reason as claim 7.
As per claim 8, Dewey/Hipsley discloses the system of claims 1 and 4-6 as described above. Dewey further teaches wherein the capsule skew factor is zero when the Y-coordinate of an equatorial plane is exactly halfway between the anterior pole and the posterior pole (See [0215]: Scans in region “B” may for instance be selected to provide lens surface information regarding the axial position of the anterior surface of the lens, the shape of the anterior surface of the lens, the axial position of the posterior surface of the lens and the shape of the posterior surface of the lens, and the equator of the lens, which the Examiner is interpreting provide lens surface information regarding the equator of the lens to encompass the claimed portion as the capsule skew factor is zero is interpreted as dictating a normal capsule skew factor.)
Claim 17 mirrors claim 8 only within a different statutory category, and is rejected for the same reason as claim 8.
As per claim 9, Dewey/Hipsley discloses the system of claims 1 and 4-6, 8 as described above. Dewey further teaches wherein the capsule skew factor is greater than zero when the Y-coordinate of the equatorial plane is not halfway between the anterior pole and the posterior pole, and a respective mass of a lens of the eye is relatively greater on a posterior side of the equatorial plane (See [0209]: The treatment axis may comprise an axis of an aberration of the eye such as an astigmatism of the eye or a higher order aberration of the eye such as coma or trefoil of the eye, for example, the treatment axis can be identified by the system user such as a physician, and can be defined to have a center corresponding to one or more of the optical axes as described herein such as one or more of the vertex of the cornea, the line of sight of the eye, the visual axis of the eye, or the visual axis of the eye extending from the anterior node of the eye, which the Examiner is interpreting the identification of an aberration of the eye to encompass the claimed portion.)
Claim 18 mirrors claim 9 only within a different statutory category, and is rejected for the same reason as claim 9.
As per claim 12, Dewey/Hipsley discloses the method of claim 10 as described above. Dewey may not explicitly teach further comprising:
selecting the machine learning module to be a neural network.
Hipsley teaches a method further comprising:
selecting the machine learning module to be a neural network (See [0240]-[0241]: The predictive best instruction can be derived from physical structural inputs, neural network simulations, and prospective therapeutic-outcome-influencing.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Dewey to include selecting the machine learning module to be a neural network as taught by Hipsley. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dewey with Hipsley with the motivation of provide improved systems, devices and methods for performing simulations using a multi-component Finite Element Model (FEM) of ocular biomechanics (See Background of the Invention of Hipsley in Paragraph [0012]).
As per independent claim 19, Dewey teaches a system for selecting an intraocular lens for implantation into an eye, the system comprising:
a controller having one or more processors and tangible, non-transitory memory on which instructions are recorded (See [0016], [0025]: The controller is programmed to execute a program, and the system comprises a processor; and a memory operable to store data);
wherein the controller is configured to selectively execute a first machine learning module and a second machine learning model trained to emulate a finite element model, execution of the instructions by the one or more processors (See [0025], [0241]: The controller is programmed to execute a program and the one or more algorithms or models stored in the memory of the laser eye surgery system, which the Examiner is interpreting algorithms or models to encompass a machine learning module) causing the controller to:
receive input data, including one or more biometric parameters of the eye (See [0141]-[0148]: The method for obtaining corneal topography measurements of the patient's eye comprises coupling a corneal topography measurement structure to a patient interface structure to place the topography measurement structure in front of the eye, which the Examiner is interpreting the measurements to encompass one or more biometric parameters of the eye ([0170]: Similar images and biometric information can be obtained with similar maps));
extract a plurality of capsule parameters based on the input data, via the first machine learning module (See [0194]-[0196]: The imaging system can be used to determine one or more optical structures of the eye when the eye fixates naturally without contacting the patient interface in order to determine locations of the one or more optical structures of the eye when the eye contacts the patient interface, which the Examiner is interpreting optical structures to encompass a plurality of capsule parameters based on the input data, via the first machine learning module as the anterior capsule and posterior capsule of the lens can be imaged and profiled ([0194]));
determine, pre-operatively, an axial displacement factor based in part on the plurality of capsule parameters, via the second machine learning model trained to emulate the finite element model (See [0016], [0241]-[0243]: The laser eye surgery system and the optical measurements obtained therewith may be used intra-operatively, i.e., during a cataract surgery or other surgical procedure, for, e.g., intraoperative eye diagnostics, determining IOL position and/or orientation, surgical planning, and control/or of a laser surgical system, which the Examiner is interpreting the one or more predictive models to encompass the finite element model, interpreting IOL position and/or orientation to encompass an axial displacement factor as conditions measured during the surgical procedure may be compared to a predicted condition of the patient's eye based on pre-operative measurements, and a difference between the predicted condition and the actual measured condition may be used to undertake additional or corrective actions during the cataract surgery or other surgical procedure); and
adjust a lens power of the intraocular lens based in part on the axial displacement factor, the axial displacement factor being a power correction feature accounting for a predicted axial shift of the intraocular lens after implantation into the eye (See [0016], [0244]-[0246], [0257]: (3) Performing one of a ray tracing and a power calculation based on said model of the subject's eye; and (4) selecting an IOL from the plurality of IOL models corresponding to the optimized IOL based on a predetermined criteria, which the Examiner is interpreting selecting an IOL from the plurality of IOL models to encompass adjust a lens power of the intraocular lens based in part on the axial displacement factor, and interpreting a power calculation to encompass a power correction feature accounting for a predicted axial shift of the intraocular lens after implantation into the eye.)
While Dewy discloses determine an axial displacement factor based in part on the plurality of capsule parameters, via the second machine learning model trained to emulate the finite element model, Dewey may not explicitly teach determine, pre-operatively, an axial displacement factor based in part on the plurality of capsule parameters, via the second machine learning model trained to emulate the finite element model.
Hipsley teaches a system for determine, pre-operatively, an axial displacement factor based in part on the plurality of capsule parameters (See [0232], [0385]-[0387], [0491]: Forward displacement of lens can be measured and simulated according to the model and preoperative measurement of ocular properties can be utilized ([0491]), which the Examiner is interpreting simulations performed using virtual models of ocular structures, such as those used in ocular accommodation processes to encompass determine, pre-operatively, an axial displacement factor), via the second machine learning model trained to emulate the finite element model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Dewey to include determine, pre-operatively, an axial displacement factor based in part on the plurality of capsule parameters as taught by Hipsley. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dewey with Hipsley with the motivation of provide improved systems, devices and methods for performing simulations using a multi-component Finite Element Model (FEM) of ocular biomechanics (See Background of the Invention of Hipsley in Paragraph [0012]).
Response to Arguments
In the Remarks filed on July 28, 2026, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s), 35 U.S.C. 102 rejection(s), and 35 U.S.C. 103 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the 35 U.S.C. 102 rejection(s). However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) the Examiner characterizes the claims as encompassing ''a person following instructions to make determinations of a patient's eye based on data.'' (See, e.g., Office Action, pp. 3-4). Applicant respectfully submits that this characterization does not account for the claims as a whole. For example, the claims recite specific computational operations that are not practically performable by a human mind, including processing input data using a machine learning module to extract a plurality of capsule parameters corresponding to a lens capsule of the eye, and determining an axial displacement factor using a finite element model that accounts for a predicted axial shift of the intraocular lens after implantation. A human cannot practically execute a finite element model or a machine learning module in the manner recited by the claims. Even assuming arguendo that the claims recite an abstract idea, which Applicant does not concede, the claims integrate any such alleged abstract idea into a practical application. The 2019 PEG instructs that a claim is not ''directed to'' a judicial exception when the additional elements, individually and in combination, integrate the exception into a practical application by imposing a meaningful limit beyond drafting efforts to monopolize the exception. Applicant submits that the claims satisfy such requirements. In particular, the claims recite a specific technological solution to a recognized problem in the field of cataract surgery: the inaccurate prediction of post-implantation intraocular lens position. As disclosed in the specification, ''[a]n important driver of clinical outcome for cataract surgery is the selection of an appropriate intraocular lens power for the best refractive outcomes'' and ''[w]hile remarkable progress has been made in the area of power calculations, challenges remain for eyes falling outside of average biometric parameters.'' (See Specification, para. [0001]). The specification further explains that ''[p]ower calculation formulae generally assume that the effective lens position of the intraocular lens 12 aligns approximately with the pre-operative capsular bag equator'' but that ''the shape and size of the capsular bag varies within the patient population, making achieving the best refractive outcomes for non-average sized, such as long, short and/or myopic eyes, challenging'' and that ''after implantation, the implanted intraocular lens may move either anteriorly or posteriorly along an axial direction based on haptic and mechanical features of the lens, thus impacting the refractive outcome.'' (See Specification, para. [0018]). The claims address this specific technical problem by reciting a particular combination of a machine learning module and a finite element model that work together in a defined pipeline to produce a more accurate intraocular lens power recommendation that accounts for the predicted post-implantation axial shift of the lens, as indicated by an axial displacement prediction. As disclosed in the Specification, ''[b]y incorporating this axial displacement prediction into the lens power calculation, the system may be able to recommend an intraocular lens power that accounts for the expected post-operative position of the lens'' and ''[t]his may allow for more accurate refractive outcomes by adjusting the lens power to compensate for the predicted axial shift after implantation." (See, Specification, para. [0034]). The Specification further discloses that ''the system 10 optimizes, improves, or enhances selection of the intraocular lens 12 for a large patient population." (See, Specification, para. [0024].) Therefore, the integrated pipeline recited by the claims produces a tangible improvement in the technical field of cataract surgery. For at least these reasons, the claims are not ''directed to'' a judicial exception because the alleged abstract idea, if any, is integrated into a practical application that provides a specific improvement in the technical field of intraocular lens selection for cataract surgery. Applicant therefore respectfully requests withdrawal of the rejection of claims 1-19 under 35 U.S.C. § 101;
(2) Applicant respectfully submits that the cited references, alone or in combination, fail to disclose every element of the claims. For example, as discussed and agreed during the Interview, Applicant respectfully submits that Dewey does not disclose determining one or more dimensional or geometric properties of a lens capsule based on processing of input data that includes one or more biometric parameters, in the manner recited by amended claim 1. Additionally as also discussed during the Interview, Applicant submits that Dewey does not disclose pre-operatively determining an axial displacement factor in the manner recited by independent claims 1, 10, and 19. In particular, the Office Action maps this limitation to Dewey's disclosure that ''conditions measured during the surgical procedure may be compared to a predicted condition of the patient's eye based on pre-operative measurements, and a difference between the predicted condition and the actual measured condition may be used to undertake additional or corrective actions during the cataract surgery or other surgical procedure'' (Dewey, para. [0242]). However, Dewey merely indicates that the ''predicted condition'' is a generic postoperative condition of the patient's eye or vision that is ''modeled based on one or more selected from the group consisting of preoperative measurements obtained from the laser eye surgery system 2, a contemplated surgical intervention, and one or more algorithms or models stored in the memory'' (Dewey, para. [0242]). There is no disclosure that such condition is a pre-operatively determined axial displacement factor. Hipsley does not overcome the deficiencies of Dewey. For at least these reasons Applicant submits that the cited references, alone or in combination, do not disclose every element of independent claims 1, 10, and 19. Applicant therefore respectfully requests that the rejections of claims 1-19 be withdrawn.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claimed limitations, as drafted, given the broadest reasonable interpretation but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. The Applicant’s abstract idea that is recited is similar to “iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)” (See MPEP 2106.04(a)(2)(C)), and this recitation is an example of managing personal behavior. The Examiner maintains that “, using the machine learning module”, “using the finite element model,” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The Examiner does not acknowledge the claims recite a specific technological solution as the claims are similar to “ii. Using well-known standard laboratory techniques to detect enzyme levels in a bodily sample such as blood or plasma, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1355, 1362, 123 USPQ2d 1081, 1082-83, 1088 (Fed. Cir. 2017)” and “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (See MPEP 2106.05(a)(II)), which the courts have indicated may not be sufficient to show an improvement to technology. The Applicant’s proposed improvement is to improve recommendation of an intraocular lens, but this does not show an improvement in the treatment using an intraocular lens, the claims recite an immediacy in recommendation of an intraocular lens power, but does not require that the intraocular lens power would be beneficial to the patient. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does find the Applicant’s argument(s) persuasive and has withdrawn the 35 U.S.C. 102 rejection(s). However, the Examiner maintains that the combination of Dewey et al. (U.S. Patent Pre-Grant Publication No. 2017/0189233) and Hipsley (U.S. Patent Pre-Grant Publication No. 2018/0000339) teaches the newly amended claims as described above in the 35 U.S.C. 103 rejection(s). The Examiner maintains that the combination of Dewy and Hipsley teaches the amended recitation of “pre-operative input data” as Dewey describes in [0141]-[0148], [0241]-[0242] that the pre-operative measurements can be obtained from the laser eye surgery system, which the Examiner is interpreting to encompass obtain pre-operative input data, including one or more biometric parameters of the eye as the Applicant’s claims recite that pre-operative input data is obtained, and that the pre-operative input data includes one or more biometric parameters of the eye. The newly amended claimed portion of “34. determine, pre-operatively using the finite element model, an axial displacement factor based on the plurality of capsule parameters, the axial displacement factor accounting for a predicted axial shift of the intraocular lens after implantation into the eye” is taught by Dewey in [0016], [0241]-[0245]: The laser eye surgery system and the optical measurements obtained therewith may be used intra-operatively, i.e., during a cataract surgery or other surgical procedure, for, e.g., intraoperative eye diagnostics, determining IOL position and/or orientation, surgical planning, and control/or of a laser surgical system, which the Examiner is interpreting the one or more predictive models to encompass the finite element model, interpreting IOL position and/or orientation to encompass an axial displacement factor as conditions measured during the surgical procedure may be compared to a predicted condition of the patient's eye based on pre-operative measurements, and a difference between the predicted condition and the actual measured condition may be used to undertake additional or corrective actions during the cataract surgery or other surgical procedure, and interpreting the one or more predictive models, one or more characteristics of the postoperative condition of the patient's eye or vision is modeled based on one or more selected from the group consisting of pre-operative measurements obtained from the laser eye surgery system to encompass pre-operatively using the finite element model. Further, the Examiner maintains that the combination of Dewey and Hipsley describes “utilize one or more lens constants formulae to pre-operatively recommend an intraocular lens power based on the axial displacement factor” in Dewey in [0016], [0241], [0244]-[0246]: For example, one or more processors of laser eye surgery system 2 may execute an algorithm which includes: accessing the plurality of IOL models stored in, and for each of the IOL models: (1) modeling the subject's eye with an intraocular lens corresponding to the IOL model and the measured characteristics of the subject's eye; (2) simulating the subject's eye based on the plurality of IOL predetermined parameters and the predicted IOL position; (3) performing one of a ray tracing and a power calculation based on said model of the subject's eye; and (4) selecting an IOL for the subject's eye from the plurality of IOL models corresponding to the optimized IOL based on a predetermined criteria and the measured characteristics of the patient's obtained by the laser surgery system, which the Examiner is interpreting the plurality of IOL models to encompass one or more lens constant formulae, and interpreting a power calculation to encompass an intraocular lens power based on the axial displacement factor as the Examiner is interpreting measured characteristics of the subject's eyes to encompass the axial displacement factor, and interpreting pre-operative measurements obtained from a contemplated surgical intervention to encompass pre-operatively recommend when combined with Hipsley’s disclosure in [0385]-[0387] of forward displacement of lens can be measured and simulated according to the model and preoperative measurement of ocular properties can be utilized ([0491]). The 35 U.S.C. 103 rejection(s) stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Suzaki (U.S. Patent Pre-Grant Publication No. 2019/0274824), describes an intraocular lens having a novel structure with high utility which is easy to adapt to patients, and can improve quality of vision (QOV).
Rosén et al. (U.S. Patent Pre-Grant Publication No. 2017/0296330), describes devices, systems, and methods for improving or optimizing peripheral vision.
Vacalebre et al. (“Current State of the Art and Next Generation of Materials for a Customized IntraOcular Lens according to a Patient-Specific Eye Power”), describes the evolution of polymeric materials and mainly the current state of the art of the unique properties of some polymeric systems used for IOL design, identifying current limitations for future improvements.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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