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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 11/03rd/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner's Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Step 1 Analysis For All Claims:
Claims 1-6 are directed to a method, which is directed to a process, one of the statutory categories. Claim 7 is directed to a system, which is directed to a machine, one of the statutory categories. Claim 8 is directed to a computer readable storage medium, which is directed to a manufacture, one of the statutory categories.
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
Determining biometric data of the eye from the scan results of an eye, (Mental processes -observation, evaluation, judgment, opinion). A person with a pen and paper could determine the color of an eye. Furthermore, the recitation of “using the physical model” amounts to mere instructions to implement the exception using generic computer components. (See MPEP 2106.05(f)).
using a first, trained machine learning system for determining a final position of an intraocular lens to be inserted, ophthalmological data serving as input data for the first machine learning system (Mental processes -observation, evaluation, judgment, opinion). A person with a pen and paper could determine the position of a lens based on the axial length of the eyeball. Furthermore, the recitation of using a first, trained machine learning system amounts to mere instructions to implement the exception using generic computer components. (See MPEP 2106.05(f)). Furthermore, the recitation of ophthalmological data serving as input data for the first machine learning system amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
determining a first refractive power of the intraocular lens to be inserted, the determination being based on a physical model in which the determined final position of the intraocular lens and the determined biometric data are used as input variables for the physical model (Mental processes -observation, evaluation, judgment, opinion). A person with a pen and paper could determine the refractive power of a lens based on the axial length, corneal curvature, and anterior chamber depth. Furthermore, the recitation of the determination being based on a physical model in which the determined final position of the intraocular lens and the determined biometric data are used as input variables for the physical model amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
determining a final refractive power of the intraocular lens by means of a second, machine learning system, at least one variable from the biometric data and the first refractive power being used as input variables (Mental processes -observation, evaluation, judgment, opinion). A person with a pen and paper could determine the refractive power of a lens based on the axial length, corneal curvature, and anterior chamber depth. Furthermore, the recitation of by means of a second, machine learning system amounts to mere instructions to implement the exception using generic computer components. (See MPEP 2106.05(f)). Furthermore, the recitation of at least one variable from the biometric data and the first refractive power being used as input variables amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
A computer-implemented method for a machine learning supported processing pipeline for determining parameter values for an intraocular lens to be inserted which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
providing a scan result of an eye, the scan result representing an image of an anatomical structure of the eye which is recited at a high level of generality and amounts to extra-solution activity of gathering data, i.e., pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
the second machine learning system being trained in two stages, with a first training step including: producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) as well as insignificant extra-solution activity of outputting data. As described in MPEP 2106.05(g).
training the machine learning system by means of the produced first training data for the purposes of forming a first learning model for determining refractive power which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) as well as insignificant extra-solution activity of outputting data. As described in MPEP 2106.05(g).
with a second training step including: training the machine learning system that was trained with the first training data using clinical ophthalmological training data for the purposes of forming a second learning model for determining refractive power which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
wherein the plurality of first models of data analysis includes a statistical model of data analysis which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B Analysis:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
A computer-implemented method for a machine learning supported processing pipeline for determining parameter values for an intraocular lens to be inserted which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
providing a scan result of an eye, the scan result representing an image of an anatomical structure of the eye which is recited at a high level of generality and amounts to extra-solution activity of gathering data, i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
the second machine learning system being trained in two stages, with a first training step including: producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) as well as insignificant extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data outputting (see MPEP 2106.05(g)). The courts have found limitations directed to outputting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”)
training the machine learning system by means of the produced first training data for the purposes of forming a first learning model for determining refractive power which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) as well as insignificant extra-solution activity of outputting data. As described in MPEP 2106.05(g).
with a second training step including: training the machine learning system that was trained with the first training data using clinical ophthalmological training data for the purposes of forming a second learning model for determining refractive power which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
wherein the plurality of first models of data analysis includes a statistical model of data analysis which amounts to mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below
Dependent claim 2:
Step 2A Prong 1: The claim does not include any mental process elements.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
wherein the biometric data of the eye include at least one selected from the group consisting of a pre operational axial length a pre-operational lens thickness, a preoperative anterior chamber depth, and an intra-operational anterior chamber depth which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the biometric data of the eye include at least one selected from the group consisting of a pre operational axial length a pre-operational lens thickness, a preoperative anterior chamber depth, and an intra-operational anterior chamber depth which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Dependent claim 3:
Step 2A Prong 1: The claim does not include any mental process elements.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
wherein the first machine learning system is a convolutional neural network, a graph attention network or a combination of the two aforementioned networks which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the first machine learning system is a convolutional neural network, a graph attention network or a combination of the two aforementioned networks which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Dependent claim 4:
Step 2A Prong 1: The claim does not include any mental process elements.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
wherein the one variable from the biometric data is the pre-operational axial length which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the one variable from the biometric data is the pre-operational axial length which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h), limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Dependent claim 5:
Step 2A Prong 1: wherein biometric data of the eye are determined from the image manually or by means of a machine learning system from the provided scan results of the eye. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitation of or by means of a machine learning system amounts to mere instructions to implement the exception using generic computer components. (See MPEP 2106.05(f)).
Step 2A Prong 2: The claim does not include additional elements that would integrate the exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Dependent claim 6:
Step 2A Prong 1: wherein further parameters of the eye are determined when determining the final position of the intraocular lens to be inserted. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7: is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1.
Claim 8: is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1.
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 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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over ENDO (EP3491996A1), in view of Matei (US20210081511A1), further in view of Padrick (US20190209242A1).
Regarding claim 1, ENDO teaches A computer-implemented method for a machine learning supported processing pipeline for determining parameter values for an intraocular lens to be inserted ([0010] There is provided an ophthalmologic device for determining a power of an IOL to be inserted in a subject eye, including a parameter acquisition portion that acquires a plurality of eye shape parameters of the subject eye, and a calculation control portion that calculates the IOL power, in which the calculation control portion outputs IOL related information from a mathematic model trained by a machine learning algorithm after inputting the plurality of eye shape parameters into the mathematic model)
providing a scan result of an eye, the scan result representing an image of an anatomical structure of the eye ([0078] In a case where the alignment for the anterior segment is completed, the control unit 80 captures the front face image of the anterior segment of the subject eye by the observation optical system 30 (step S2). The control unit 80 captures a cross sectional image 500 of the subject eye shown in Fig. 5 by using the OCT optical system 100 based on a preset scanning pattern (step 3). The acquired anterior segment image and cross sectional image are stored in the memory 85 or the like).
determining biometric data of the eye from the scan results of an eye ([0079] Subsequently, the control unit 80 acquires the eye shape parameter in step 4. For example, the control unit 80 calculates the corneal shapes of the subject eye based on the ring index images Q1 and Q2 on an anterior segment image 400 stored in the memory 85. For example, the corneal shape specifically is the corneal curvature radius of the corneal anterior surface, astigmatic axis angle of the cornea, or the like in the steep meridian direction and flat meridian direction. The control unit 80 analyzes the cross-sectional image captured by using the OCT device 5. For example, the control unit 80 detects the position of the cornea, the crystalline lens, or the like through edge detection of the cross sectional image, and measures the corneal thickness, the anterior chamber depth, or the crystalline lens thickness based on the position thereof.)
Using a first, trained machine learning system for determining a final position of an intraocular lens to be inserted, ophthalmological data serving as input data for the first machine learning system ([0080] In step S5, the control unit 80 reads out the crystalline lens anterior surface curvature, the crystalline lens posterior surface curvature, the crystalline lens thickness, and the anterior chamber depth acquired by the measurement unit 200 from the memory 85, and inputs the acquired parameters to each node of the input layer M1. The control unit 80 performs the calculation according to the rules of the mathematic model 800, and outputs a value of the predicted postoperative anterior chamber depth from the output layer M3 (step S6). The control unit 80 stores the output predicted postoperative anterior chamber depth in the memory 85.)
Determining a first refractive power of the intraocular lens to be inserted, the determination being based on a physical model in which the determined final position of the intraocular lens and the determined biometric data are used as input variables for the physical model ([0081] In a case where the acquisition of each parameter is completed, the control unit 80 calculates the intraocular lens power by partially using the known SRK/T formula, Binkhorst formula, or the like. For example, the control unit 80 acquires the IOL power by substituting the parameters into the SRK/T formula, the Binkhorst formula, or the like in step S7 (step S8). In a case where the SRK/T formula (the following Expression (3)) is used,\ the intraocular lens power is calculated from the corneal curvature radius, the eye axial length, the predicted postoperative anterior chamber depth, and the like)
determining a final refractive power of the intraocular lens by means of a second, machine learning system, at least one variable from the biometric data and the first refractive power being used as input variables ([0023] A second mathematic model may be a mathematic model that outputs the IOL power after inputting eye shape parameters such as the predicted postoperative anterior chamber depth, the corneal curvature, and the eye axial length.)
However, ENDO is not relied upon to explicitly teach:
producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens.
training the machine learning system by means of the produced first training data for the purposes of forming a first learning model for determining refractive power.
training the machine learning system that was trained with the first training data using clinical ophthalmological training data for the purposes of forming a second learning model for determining refractive power.
On the other hand, Matei teaches producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens ([0042] In another example, as shown by FIG. 1B, in operation 50, it is determined that a component of a first model (e.g., the high-fidelity model) is more complex than other components of the first model. In operation 55, training data is generated from the first model. The examiner notes that ENDO and Matei are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified ENDO’s training data generation to incorporate producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens as taught by Matei [0042] to generate a second model by replacing the component in the first model with the reduced complexity component [0042]).
Furthermore, Padrick teaches training the machine learning system by means of the produced first training data for the purposes of forming a first learning model for determining refractive power ([0021] IOL selection platform 105 includes a prediction engine 120 which may ( as explained in greater detail below) process the received training data, extract measurements of an eye, perform raw data analysis on the training data, train machine learning algorithms and/or models to estimate a post-operative MRSE based on the pre-operative measurements, and iteratively refine the machine learning to optimize the various models used to predict the post-operative MRSE to improve their use with future patients to improve their post-operative vision outcomes (e.g., better optical properties of the eye with the implanted IOL). In some examples, prediction engine 120 may evaluate multiple models ( e.g., one or more neural networks) that are trained to select one model for determining an appropriate IOL power for a patient. The examiner notes that ENDO and Padrick are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified ENDO’s training data generation to incorporate producing first training data for a machine learning system on the basis of a first physical model for a refractive power for an intraocular lens as taught by Padrick [0021] to estimate a post-operative MRSE based on the pre-operative measurements [0021]).
Furthermore, Padrick teaches training the machine learning system that was trained with the first training data using clinical ophthalmological training data for the purposes of forming a second learning model for determining refractive power ([0019] The technology described below involves systems and methods to improve post implantation vision for a patient by determining an appropriate intraocular lens (IOL) power of an intraocular lens for implanting into the patient's eyes based on a target ( e.g., desired) post-operative manifest refraction in spherical equivalent (MRSE). The systems and methods use a two-stage process to determine the appropriate IOL power for the patient. During the first stage, data associated with a current IOL implantation such as one or more pre-operative measurements of the patient's eyes may be obtained. From a database storing multiple historical IOL implantation records associated with previously performed IOL implantations (also referred to as historical IOL implantations), a subset of historical IOL implantation records that are most similar to the current IOL implantation may be selected. During the second stage, multiple prediction models that estimate the post-operative MRSE may be evaluated to identify a prediction model having a smallest deviation based on the selected subset of historical IOL implantation records. The identified prediction model may be used to generate estimated post-operative MRSE values based on a set of available IOL powers. An available IOL power corresponding to an estimated post-operative MRSE value that matches the target post-operative MRSE may be selected. An intraocular lens corresponding to the selected IOL power may then be used for implantation in the patient's eyes. The examiner notes that ENDO and Padrick are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified ENDO’s training data generation to incorporate training the machine learning system that was trained with the first training data using clinical ophthalmological training data for the purposes of forming a second learning model for determining refractive power as taught by Padrick [0019] to iteratively refine the machine learning to optimize the various models used to predict the post-operative MRSE to improve their use with future patients to improve their post-operative vision outcomes (e.g., better optical properties of the eye with the implanted IOL). [0021])
Regarding claim 2, ENDO teaches the biometric data of the eye include at least one selected from the group consisting of a pre operational axial length, a pre-operational lens thickness, a preoperative anterior chamber depth, and an intra-operational anterior chamber depth ([0012] An ophthalmologic device according to a first embodiment will be described. The ophthalmologic device (for example, an ophthalmologic device 10) determines the power of an intraocular lens (also referred to as an IOL) to be inserted in a subject eye. For example, the present device mainly includes a parameter acquisition unit (for example, an OCT device 5, a control unit 80, or an operation unit 84) and a calculation control unit (for example, a control unit 80). For example, the parameter acquisition unit acquires a plurality of eye shape parameters of the subject eye. For example, an eye shape parameter may be a corneal shape parameter such as a corneal anterior surface curvature, a corneal posterior surface curvature, a corneal thickness, a corneal width, or a corneal height, may be a crystalline lens shape parameter such as a crystalline lens anterior surface curvature, a crystalline lens posterior surface curvature, a crystalline lens thickness, a crystalline lens diameter, or a capsule diameter, may be an overall shape parameter such as an eye axial length, an anterior chamber depth, a corner angle, a corner angle distance, or a pupil diameter, or may be a retinal shape parameter such as a retinal thickness. The curvature of the crystalline lens during contraction or the curvature of the crystalline lens during relaxation may be used, or both the curvatures thereof may be used. For example, the parameter acquisition unit acquires the eye shape parameter based on a tomographic image captured by an OCT optical system)
Regarding claim 3, ENDO teaches the first machine learning system is a convolutional neural network, a graph attention network or a combination of the two aforementioned networks ([0015] The neural network is a method of mimicking a behavior of a neuronal network of an organism. The neural network is, for example, a feedforward (forward propagation type) neural network, an RBF network (radial basis function), a spiking neural network, a convolutional neural network, a recursive neural network (a recurrent neural network or a feedback neural network), a probabilistic neural network (a Boltzmann machine or a Bayesian network), or the like)
Regarding claim 4, ENDO teaches the one variable from the biometric data is the pre-operational axial length ([0021] In this case, the trained mathematic model may be a mathematic model that outputs the IOL power after the input of the corneal curvature, the eye axial length, the predicted postoperative anterior chamber depth or the like).
Regarding claim 5, ENDO teaches biometric data of the eye are determined from the image manually or by means of a machine learning system from the provided scan results of the eye ([0079] Subsequently, the control unit 80 acquires the eye shape parameter in step 4. For example, the control unit 80 calculates the corneal shapes of the subject eye based on the ring index images Q1 and Q2 on an anterior segment image 400 stored in the memory 85. For example, the corneal shape specifically is the corneal curvature radius of the corneal anterior surface, astigmatic axis angle of the cornea, or the like in the steep meridian direction and flat meridian direction. The control unit 80 analyzes the cross-sectional image captured by using the OCT device 5. For example, the control unit 80 detects the position of the cornea, the crystalline lens, or the like through edge detection of the cross sectional image, and measures the corneal thickness, the anterior chamber depth, or the crystalline lens thickness based on the position thereof).
Regarding claim 6, ENDO teaches further parameters of the eye are determined when determining the final position of the intraocular lens to be inserted ([0085] The control unit 80 may acquire the toric IOL power by using the mathematic model. In this case, the mathematic model is trained by using the plurality of eye shape parameters as the input training data and using the toric IOL power as the output training data. In a case where the toric IOL power is obtained, the eye shape parameter includes, for example, at least any of corneal anterior surface astigmatic power, corneal anterior surface astigmatic axis, corneal posterior surface astigmatic power, corneal posterior surface astigmatic axis, corneal thickness (CT), anterior chamber depth ACD, postoperative anterior chamber depth (ELP), surgically induced astigmatism (SIA), pupil diameter (PS), crystalline lens anterior surface astigmatic power, crystalline lens anterior surface astigmatic axis, crystalline lens posterior surface astigmatic power, crystalline lens posterior surface astigmatic axis, an incision position, incision width, incision angle, an auxiliary port position, an auxiliary port number, or auxiliary port width. The incision position, the incision width, and the incision angle are the position, width, and angle (a direction of incision) of an incision to be made on the cornea, corneal border, or sclera of the subject eye at the time of inserting the IOL. The auxiliary port is, for example, an incision opening (or a wound) for inserting an auxiliary tool in the eye at the time of inserting the IOL. For example, the astigmatic power of the toric IOL to be inserted in the subject eye is calculated based on information of the crystalline lens anterior surface astigmatism or the crystalline lens posterior surface astigmatism. Toric IOL related information such as a toric angle or rotatability of the IOL after surgery may be obtained in addition to the toric IOL power. In a case where the rotatability of the IOL is acquired, a diameter of the crystalline lens capsule, a minor axis and a major axis of the crystalline lens capsule, the entire length of the IOL, or the like may be used as the input data for the mathematic model.).
Claims 7 and 8 are substantially the same as claim 1, therefore, they are rejected based upon the same rationale as the rejection of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bor (US20200015894A1 )
“Bor teaches a method for intraocular lens selection”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST.
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/KYLE R STORK/Primary Examiner, Art Unit 2128