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 .
Status of Claims
Claims 1, 4-9, 11-13, 15-17 and 20 have been amended.
Claims 1, 2 and 4-20 as presented May 18, 2026 are currently pending and considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/18/26 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention
Claims 16 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites “the subjective outcome score”. There is insufficient antecedent basis for this limitation in the claim. Claim 16 depends on claim 14, which recites “subjective outcome data”. It is unclear whether Applicant has introduced “the subjective outcome score” as a new claim element, or if the Applicant intended “the subjective outcome score” to refer to the subjective outcome data recited in claim 14. For the purposes of compact prosecution, the claim will be interpreted in a manner as best understood by the Examiner, wherein “the subjective outcome score” in claim 16 is treated as the “the subjective outcome data”.
Claim 20 recites “a first diagnostic device…of the patient”. There is insufficient antecedent basis for “the patient” in the preceding lines of claim 20. As best understood by the Examiner, the recitation of “the patient” in claim 33 will be treated as “a patient” for the purposes of compact prosecution.
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, 2 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1, 2 and 4-16 recite a system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, which is within the statutory category of a machine. Claims 17-19 recite a method for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, which is within the statutory category of a process. Claim 20 recites a system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, which is within the statutory category of a machine.
Step 2A - Prong One:
Regarding Prong One of Step 2A , the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they "recite" a judicial exception or in other words whether a judicial exception is "set forth" or "described" in the claims. An "abstract idea" judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Representative independent claim 1 includes limitations that recite at least one abstract idea.
Specifically, independent claim 1 recites: A system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, the system comprising:
a first diagnostic device configured to capture first tear film data of the eye of the patient; a second diagnostic device of a different type from the first diagnostic device, the second diagnostic device configured to capture second tear film data of the eye of the patient; and
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to:
obtain diagnostic data of the eye, including at least the first tear film data and the second tear film data;
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data;
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data;
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye;
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation incorporating an effect of the composite irregularity in the tear film;
obtain historical data composed of respective historical sets of patient data;
analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors based in part on the historical data; and
generate a respective satisfaction metric for the plurality of intraocular lenses based in part the historical data and the visual incorporating an effect of the composite irregularity in the tear film.
The underlined limitations constitute concepts performed in the human mind. That is, other than reciting steps as performed by the generic computer components, nothing in the claim elements precludes the steps from practically being performed in the mind or with pencil and paper. The claim encompasses a mental process of identifying first and second locations with irregularities, correlating the first with the second locations, performing a visual simulation for each intraocular lens, analyzing individual risk factors, obtaining a weighted combination of the individual risk factors and generating a respective satisfaction metric for the plurality of intraocular lenses based on the historical data and visual simulation. The identified abstract idea, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The abstract idea for Claims 17 and 20 are identical as the abstract idea for Claim 1, because the only difference between Claim 1 and 17 is that Claim 1 recites a system, whereas Claim 17 recites a method, and because the only difference between Claims 1 and 20 is that Claim 20 additionally recites the eye being scanned to generate the diagnostic data (which will be later discussed in Step 2A Prong 2) and that the visual simulation incorporates an impact of the tear film data (i.e. part of the abstract idea). Any limitations not identified above as part of the limitation in the mind are deemed “additional elements” and will be discussed further in detail below. Accordingly, independent claims 1, 17 and 20 recite at least one abstract idea.
Similarly, dependent claims 2, 4-16 and 18-19 includes limitations that recite abstract idea(s) as described above. Claim 2 describes selecting the preferred intraocular lens. Claim 4 describes the tear film data. Claim 5 describes identifying irregularities in the tear film. Claims 6-13 and 19 further describe the diagnostic data. Claims 14-16 further describe determining the respective satisfaction metric. Claim 18 describes selecting the preferred intraocular lens. Additionally, claims 10 and 11 recite limitations that constitute an abstract idea that falls under the mathematical concepts grouping because extracting normalized lens oscillation traces, model-fitting a curve and obtaining the one or more wobble parameters, under its broadest reasonable interpretation, represent mathematical calculations and relationships, i.e. mathematical formulas or equations and mathematical calculations (see MPEP 2106.04(a)(2)). Claims 6, 7, 9, 11, 14 and 19 partially narrow the abstract idea as described above, and also introduce additional element(s) which will be discussed in Step 2A Prong 2 and Step 2B.
These limitations only serve to further limit the abstract idea and hence, are directed toward fundamentally the same abstract ideas as independent claims 1, 17 and 20.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As such, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, claims 1, 2 and 4-20 as a whole do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. The additional elements or combination of additional elements, beyond the above-noted at least one abstract idea will be described as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea(s)”).
Specifically, independent claim 1 recites: A system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, the system comprising:
a first diagnostic device configured to capture first tear film data of the eye of the patient; a second diagnostic device of a different type from the first diagnostic device, the second diagnostic device configured to capture second tear film data of the eye of the patient; and
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to:
obtain diagnostic data of the eye, including at least the first tear film data and the second tear film data;
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data;
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data;
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye;
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation incorporating an effect of the composite irregularity in the tear film;
obtain historical data composed of respective historical sets of patient data;
analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors based in part on the historical data; and
generate a respective satisfaction metric for the plurality of intraocular lenses based in part the historical data and the visual incorporating an effect of the composite irregularity in the tear film.
The independent claim recites the additional elements of a system, controller, processor and memory that implement the identified abstract idea. The system, controller, processor and memory are not described by the applicant and are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. See para. [0018] of the specification. Limiting the analysis to ophthalmology, intraocular lens, tear film, cornea and eye of the patient generally links the use of a judicial exception to a particular technological environment or field of use, and thus, does not integrate a judicial exception into a practical application.
Claim 1 further recites the additional elements of capturing first tear film data, capturing second tear film data, obtaining diagnostic data and obtaining historical data. Claim 6, 7, 9, 11, 19 and 20 recite the additional elements of the eye being scanned to generate the diagnostic data, obtaining the lens capsule stability data and acquiring a plurality of images. Under practical application, capturing first tear film data, capturing second tear film data, obtaining diagnostic data, obtaining historical data, scanning the eye to generate the diagnostic data, obtaining the lens capsule stability data and acquiring a plurality of images are forms of extra-solution activity. MPEP 2106.5(g) indicates the term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Therefore, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The dependent claims 11 and 14 recite additional element(s) that implement the identified abstract idea. Claim 11 recites electromagnetic energy, an energy source and a camera. Claim 14 recites machine learning. However, these functions do not integrate a practical application more than the abstract idea because:
the machine learning represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the electromagnetic energy, energy source and camera generally link the use of a judicial exception to a particular technological environment or field of use.
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
When viewed as a whole, claims 1, 2 and 4-20 do not include additional limitations that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art and simply implements the process on a computer(s) is not enough to qualify as "significantly more."
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the system, controller, processor and memory to perform the noted steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). In addition, limiting the analysis to ophthalmology, intraocular lens, tear film, cornea and eye of the patient generally links the use of a judicial exception to a particular technological environment or field of use, and thus, does not amount to significantly more than the judicial exception.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of capturing first tear film data, capturing second tear film data, obtaining diagnostic data, obtaining historical data, scanning the eye to generate the diagnostic data, obtaining the lens capsule stability data and acquiring a plurality of images are considered extra-solution activity. These has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2106.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims also do not recite significantly more than the abstract idea and are not patent eligible.
The dependent claims 11 and 14 recite additional element(s) that implement the identified abstract idea. Claim 11 recites electromagnetic energy, an energy source and a camera. Claim 14 recites machine learning. However, these functions are not deemed significantly more than the abstract idea because:
the machine learning represents mere instructions to apply the abstract idea on a computer (i.e., merely invoking the computer structure as a tool used to execute the limitations); and,
the electromagnetic energy, energy source and camera generally link the use of a judicial exception to a particular technological environment or field of use.
Therefore, claims 1, 2 and 4-20 are rejected under 35 USC §101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
6. 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, 2, 6, 8, 12, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437 (US 2017/0027437 A1) in further view of Neal ‘460 (US 2018/0235460 A1) and Crespo (US 2017/0371178 A1).
Regarding claim 1, Neal ‘437 teaches: A system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient (e.g. see [0016], [0151], [0155]), the system comprising:
a first diagnostic device configured to capture […] tear film data of the eye of the patient; a second diagnostic device of a different type from the first diagnostic device, the second diagnostic device configured to capture […] tear film data of the eye of the patient; and (“Optical measurement instrument 1 includes: an optical coherence tomographer (OCT) subsystem 10, a wavefront aberrometer subsystem 20, and a corneal topographer subsystem 30” that is coupled to a “controller” (The corneal topographer is the first diagnostic device and the wavefront aberrometer is the second diagnostic device.), e.g. see [0055]; “the optical measurement instrument 1” provides an “assessment of the tear film of a patient”; “the tear film assessment comprises a comparison of a wavefront aberrometry map and a corneal topography map or OCT map of the patient's eye…The resulting map…is highly correlated with the tear film”, e.g. see [0107])
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: (“a controller 60, including one or more processor(s) 61 and memory 62”, e.g. see [0055]; “The controller 60 can include any suitable components, such as one or more processor, one or more field-programmable gate array (FPGA), and one or more memory storage devices. ”, e.g. see [0063])
obtain diagnostic data of the eye, including […] tear film data; (controller acquisition of measurement data from the “optical measurement instrument”, e.g. see [0055], [0063], [0107])
correlate the […] locations on the cornea […]; (“the tear film assessment comprises a comparison of a wavefront aberrometry map and a corneal topography map or OCT map of the patient's eye…The resulting map…is highly correlated with the tear film…A determination of whether the tear film is broken…”; “finding or indication that the tear film is disrupted…may be based upon the shape of a spot”, e.g. see [0107])
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation incorporating […]; (“accessing the plurality of IOL models stored in, and for each of the IOL models: (1) modeling 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 “show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0151], [0155]; “The existence of such a disrupted tear film may indicate that K value, and other ocular measurements may not be reliable.”, e.g. see [0107])
obtain historical data composed of respective historical sets of patient data; (“Instrument 1 stores all the biometric data and postoperative information (i.e. historical data) in an embedded database, so that the data contained in this database can be used to further optimize or generate new algorithms to improve future patient's outcomes. In certain embodiments, these algorithms are related to…personalized regressions”, e.g. see [0149]; “the effective treatment transfer function is derived from, for each of a plurality of prior eye treatments, a correlation between a pre-treatment vector characterizing the eye measurement data before treatment, and a post-treatment vector characterizing post-treatment eye measurement data of the associated eye;”, e.g. see [0159])
generate a […] metric for the plurality of intraocular lenses based in part the historical data and the visual simulation […]. (the “effective treatment transfer function” derived from a plurality of prior eye treatments by correlating pre-treatment and post-treatment vectors, with the resulting treatment “improving refraction and/or higher order aberration and/or optical quality”, e.g. see [0159]; storing historical patient “biometric data and postoperative information” to optimize predictive algorithms for subsequent patients, e.g. see [0149]; “calculating from the ray tracing a modulation transfer function (MTF)-based value, and selecting the IOL corresponding to a highest one of the MTF value…This function is closely related to contrast sensitivity measurements, and is also related to visual acuity”; “MTF Volume, Visual Strehl ratio or other suitable optical metrics for predicting the optical quality for each individual IOL model…may be used”, e.g. see [0173])
Neal ‘437 does not teach:
a first diagnostic device configured to capture first tear film data of the eye of the patient; the second diagnostic device configured to capture second tear film data of the eye of the patient;
obtain at least the first tear film data and the second tear film data;
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data;
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data;
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye;
incorporating an effect of the composite irregularity in the tear film
However, Neal ‘460 in the analogous art of ophthalmic diagnostic measurement of tear film breakup (e.g. see [0002]) teaches:
a first diagnostic device configured to capture first tear film data of the eye of the patient; the second diagnostic device configured to capture second tear film data of the eye of the patient; (“optical measurement instrument which includes a wavefront aberrometer and a corneal topographer which may be employed to make measurements for quantifying the tear film breakup of an eye”; the “wavefront aberrometer” and “corneal topographer” each produce separate measurement streams which are processed together into “combined tear film breakup data”, e.g. see [0058]-[0060])
obtain at least the first tear film data and the second tear film data; (the processor receives and processes the “wavefront aberrometry data and the corneal topography data to produce combined tear film breakup data”, e.g. see [0059])
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data; (“corneal topography (CT) spots in a localized corneal region…each CT spot represents a region -0.2 mmx0.2 mm. Locally rough or irregular surfaces result in spot image breakup.” where the processor distinguishes “bad” spots and “good” spots, e.g. see [0079]; “certain topography regions are determined to be "bad"”, e.g. see [0084] (The localized topography regions corresponding to degraded/bad topography spots indicative of tear film break-up are the first locations.))
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data; (“macro-aberrations and micro-aberrations in spots appearing on a Shack-Hartmann wavefront sensor due to tear breakup for an eye”; “smaller lenslets correspond to smaller sample areas on the cornea which help map tear film features”, e.g. see [0039]-[0046]; wavefront irregularity maps and spatially varying tear film defects, e.g. see [0069]-[0072] (The corneal sampling regions corresponding to lenslets/wavefront spots exhibiting wavefront irregularity, enlargement, distortion or other tear film break-up are the second locations.))
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye; (“certain topography regions are determined to be "bad". This information is used to compute the spot divergences from the wavefront corresponding to the topography image…In this way a composite far-field intensity distribution (or composite pointspread-function) can be created that would better represent the image created by the optical system of the eye.” (i.e. composite irregularity in the tear film), e.g. see [0084])
incorporating an effect of the composite irregularity in the tear film (“an optical measurement instrument…provided in U.S. patent application Ser. No. 14/969,264, "OPTICAL IMAGING AND MEASUREMENT SYSTEMS AND METHODS FOR CATARACT SURGERY AND TREATMENT PLANNING,"” (Neal ‘437), e.g. see [0061]; “Measurement and diagnosis of the tear film of an eye…may be used to guide surgery, screen patients”, e.g. see [0003]; a spatially correlated composite optical representation of tear film irregularity capable of determining “Snellen Acuity” and other “optical-acuity metrics”; the tear film effects can be used for “objectively comparing tear film to other visual defects” and to predict how much the tear film limits “visual acuity”; the “composite pointspread-function” represents the image created by the eye and can be used to determine visual-acuity metrics, e.g. see [0083]-[0084])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal 437 to include a first diagnostic device configured to capture first tear film data, a second diagnostic device configured to capture second tear film data, obtain at least the first tear film data and the second tear film data, identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data, identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data, correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye and incorporating an effect of the composite irregularity in the tear film as taught by Neal 460, which identifies the cataract surgery and treatment planning instrument of Neal ‘437 as an example instrument applicable to its tear film measurements (Neal ‘460 [0061]), for the purposes of guiding surgery and screening patients (Neal ‘460 [0003]) and generating an optical representation that “better represents the image created by the optical system of the eye” (Neal ‘460 [0084]).
Neal ‘437 and Neal ‘460 do not teach:
analyze individual risk factors based on the data and
obtain a weighted combination of the individual risk factors based in part on the historical data;
generate a respective satisfaction metric for the plurality of lenses
However, Crespo in the analogous art of generating an expected satisfaction metric for an ophthalmic product for a patient (e.g. see [0031], [0021]) teaches:
analyze individual risk factors based on the data and (analyzing patient/user characteristics, including “biometric” and “optometric” information to identify individual characteristics correlated with “low satisfaction” and to determine whether a new wearer possesses characteristics associated with potential “low satisfaction”, e.g. see [0042]-[0043])
obtain a weighted combination of the individual risk factors based in part on the historical data; (s=hθ(u,x) where θ is the model parameter vector and the expected satisfaction is computed from patient/user features and lens features; the linear formulation is s = θ ∙ y where θ supplies the learned coefficients applied to the individual feature vector, e.g. see [0031]-[0032]; “adjust the parameters θ of the model”, where θ is learned from previous users’ data and satisfaction feedback, e.g. see [0035]-[0036])
generate a respective satisfaction metric for the plurality of lenses based in part on the historical data (an expected satisfaction value for a patient wearing a lens is s=hθ(u,x), e.g. see [0031]; evaluating available lens designs and selecting the “one for which hθ(u,x) is larger” (an expected satisfaction evaluation associated with each candidate lens configuration), e.g. see [0037]; the training data contains prior users’ features, lens features and reported satisfaction, and the fitted model parameters are selected to better predict satisfaction for future users, e.g. see [0036]; the model is continuously updated as further satisfaction feedback becomes available, e.g. see [0039])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437 and Neal ‘460 to include analyze individual risk factors based on the data, obtain a weighted combination of the individual risk factors based in part on the historical data and generate a respective satisfaction metric for the plurality of lenses as taught by Crespo, for the purposes of providing “increased or maximum expected satisfaction to a new user” (Crespo [0031]).
Regarding claim 2, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 further teaches:
wherein the controller is configured to: select the preferred intraocular lens based on the respective […] metric […] (“selecting the IOL corresponding to a highest one of the MTF value for implanting in the subject eye”, e.g. see [0173]).
Neal ‘437 does not teach:
select the preferred lens based on the respective satisfaction metric and the weighted combination of the individual risk factors.
However, Crespo in the analogous art teaches:
select the preferred lens based on the respective satisfaction metric and the weighted combination of the individual risk factors (s=hθ(u,x) where θ is the model parameter vector and the expected satisfaction is computed from patient/user features and lens features; the linear formulation is s = θ ∙ y where θ supplies the learned coefficients applied to the individual feature vector; an expected satisfaction value for a patient wearing a lens is s=hθ(u,x),, e.g. see [0031]-[0032]; “adjust the parameters θ of the model”, where θ is learned from previous users’ data and satisfaction feedback, e.g. see [0035]-[0036]; evaluating available lens designs and selecting the “one for which hθ(u,x) is larger” (an expected satisfaction evaluation associated with each candidate lens configuration), e.g. see [0037])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437 and Neal ‘460 to select the preferred lens based on the respective satisfaction metric and the weighted combination of the individual risk factors as taught by Crespo, for the purposes of providing “increased or maximum expected satisfaction to a new user” (Crespo [0031]).
Regarding claim 6, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 further teaches:
[…] the eye being scanned to generate the diagnostic data; (“A scan of the patient's eye may comprise one or more of a wavefront aberrometry measurement of a patient's eye utilizing the wavefront aberrometry subsystem, a corneal topography measurement of a patient's eye… In a preferred embodiment, the operating sequence includes each of a wavefront aberrometry measurement, a corneal topography measurement and an OCT scan”, e.g. see [0120]; “measure ocular aberrations, preferably including low and high order aberrations”, e.g. see [0058])
Neal ‘437 does not teach:
wherein: the diagnostic data further includes corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations; and the binary result is either a presence of a threshold level of corneal aberrations or an absence of the threshold level of corneal aberrations
However, Neal ‘460 in the analogous art teaches:
wherein: the diagnostic data further includes corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations; and the binary result is either a presence of a threshold level of corneal aberrations or an absence of the threshold level of corneal aberrations (quantitively representing irregular ocular/corneal optic aberrations by fitting measured wavefront data to “Zernike polynomials” and determining residual “fit error” and “Zonal-Modal RMS” (root-mean-square) irregularity; the “Zonal-Modal RMS value reveals the quality of the Zernike fit in wavefront units” and identifies aberrations “not well represented by Zernike polynomials” including irregularities associated with tear film breakup, e.g. see [0044]-[0045]; “produce at least one of tear film breakup corneal topography data, including…corneal topography zonal-modal irregularity data”’; “low and high order wavefront aberrations, and very high order wavefront aberrations, at different points in time”; “RMS corneal topography irregularity for an example eye…is sensitive to tear film variation.” and “Locally rough or irregular surfaces” produce degraded topography spots;” (correlating corneal and wavefront zonal-modal irregularity maps to characterize tear film optical irregularity), e.g. see claim 3, [0069], [0079])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437 to include the diagnostic data further includes corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations as taught by Neal ‘460, for the purposes identifying “significant aberration that is not well represented by Zernike polynomials” and because the Zonal-Modal “irregularity may be more sensitive than the WFFE” (Wavefront Fit Error) when “surfaces have sharp steps” such as during tear film breakup (Neal ‘460 [0045]-[0046]).
Regarding claim 8, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 further teaches:
wherein: the diagnostic data further includes a respective location, orientation, and size of a pupil of the eye in a three-dimensional coordinate system, the pupil being under photopic conditions; and the respective location and respective profile of an anterior corneal surface and a posterior corneal surface of the eye (iris registration processing in which “features that are available include the position, size and shape of the pupil” and “those induced by the measurement, such as changes in the size of the pupil, changes in pupil location”, e.g. see [0117]; “measure the spatial disposition (e.g., three-dimensional coordinates such as X, Y, and Z of points on boundaries) of eye structures in three dimensions. Such structure of interest can include, for example, the anterior surface of the cornea, the posterior surface of the cornea…the pupil,”, e.g. see [0105]; “ray tracing may be performed…at a suitable entrance pupil, such as at or at about a 4 mm entrance pupil…at about six (6) wavelengths weighted according the spectral sensitivity curve in photonic or mesopic conditions”, e.g. see [0173]; acquiring “images…obtained at two or more eye pupil diameters. This can be achieved by changing a target light brightness to control pupil diameter.”, e.g. see [0138]; “anterior corneal surface information, posterior corneal surface information…a pupil diameter (PD)”, e.g. see [0013])
Regarding claim 12, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 further teaches:
wherein the diagnostic data further includes an angle kappa factor (the system integrates the corneal topographer with “an instrument that is meant to measure more characteristics of the eye such as refractive state, gaze angle, angle kappa and iris features”, e.g. see [0127])
Claim 17 recites substantially similar limitations as those already addressed in claim 1, and, as such is rejected for similar reasons as given above.
Claim 18 recites substantially similar limitations as those already addressed in claim 2, and, as such is rejected for similar reasons as given above.
Regarding claim 20, Neal ‘437 teaches: A system for selecting a preferred intraocular lens for implantation into an eye (e.g. see [0016], [0151], [0155]), the system comprising:
a first diagnostic device configured to capture […] tear film data of the eye of the patient; a second diagnostic device of a different type from the first diagnostic device, the second diagnostic device configured to capture […] tear film data of the eye of the patient; and (“Optical measurement instrument 1 includes: an optical coherence tomographer (OCT) subsystem 10, a wavefront aberrometer subsystem 20, and a corneal topographer subsystem 30” that is coupled to a “controller” (The corneal topographer is the first diagnostic device and the wavefront aberrometer is the second diagnostic device.), e.g. see [0055]; “the optical measurement instrument 1” provides an “assessment of the tear film of a patient”; “the tear film assessment comprises a comparison of a wavefront aberrometry map and a corneal topography map or OCT map of the patient's eye…The resulting map…is highly correlated with the tear film”, e.g. see [0107])
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: (“a controller 60, including one or more processor(s) 61 and memory 62”, e.g. see [0055]; “The controller 60 can include any suitable components, such as one or more processor, one or more field-programmable gate array (FPGA), and one or more memory storage devices. ”, e.g. see [0063])
obtain diagnostic data of the eye, including […] tear film data; the eye being scanned by the first diagnostic device and the second diagnostic device to generate the diagnostic data; (controller acquisition of measurement data from the “optical measurement instrument”, e.g. see [0055], [0063], [0107]; “A scan of the patient's eye may comprise one or more of a wavefront aberrometry measurement of a patient's eye utilizing the wavefront aberrometry subsystem, a corneal topography measurement of a patient's eye… In a preferred embodiment, the operating sequence includes each of a wavefront aberrometry measurement, a corneal topography measurement and an OCT scan”, e.g. see [0120])
correlate the […] locations on the cornea […]; (“the tear film assessment comprises a comparison of a wavefront aberrometry map and a corneal topography map or OCT map of the patient's eye…The resulting map…is highly correlated with the tear film…A determination of whether the tear film is broken…”; “finding or indication that the tear film is disrupted…may be based upon the shape of a spot”, e.g. see [0107])
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation incorporating […]; (“accessing the plurality of IOL models stored in, and for each of the IOL models: (1) modeling 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 “show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0151], [0155]; “The existence of such a disrupted tear film may indicate that K value, and other ocular measurements may not be reliable.”, e.g. see [0107])
obtain historical data composed of respective historical sets of patient data; and (“Instrument 1 stores all the biometric data and postoperative information (i.e. historical data) in an embedded database, so that the data contained in this database can be used to further optimize or generate new algorithms to improve future patient's outcomes. In certain embodiments, these algorithms are related to…personalized regressions”, e.g. see [0149]; “the effective treatment transfer function is derived from, for each of a plurality of prior eye treatments, a correlation between a pre-treatment vector characterizing the eye measurement data before treatment, and a post-treatment vector characterizing post-treatment eye measurement data of the associated eye;”, e.g. see [0159])
generate a […] metric for the plurality of intraocular lenses based in part on the visual simulation […] and the historical data. (the “effective treatment transfer function” derived from a plurality of prior eye treatments by correlating pre-treatment and post-treatment vectors, with the resulting treatment “improving refraction and/or higher order aberration and/or optical quality”, e.g. see [0159]; storing historical patient “biometric data and postoperative information” to optimize predictive algorithms for subsequent patients, e.g. see [0149]; “calculating from the ray tracing a modulation transfer function (MTF)-based value, and selecting the IOL corresponding to a highest one of the MTF value…This function is closely related to contrast sensitivity measurements, and is also related to visual acuity”; “MTF Volume, Visual Strehl ratio or other suitable optical metrics for predicting the optical quality for each individual IOL model…may be used”, e.g. see [0173])
Neal ‘437 does not teach:
a first diagnostic device configured to capture first tear film data of the eye of the patient; the second diagnostic device configured to capture second tear film data of the eye of the patient;
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data;
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data;
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye;
incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film
However, Neal ‘460 in the analogous art (e.g. see [0002]) teaches:
a first diagnostic device configured to capture first tear film data of the eye of the patient; the second diagnostic device configured to capture second tear film data of the eye of the patient; (“optical measurement instrument which includes a wavefront aberrometer and a corneal topographer which may be employed to make measurements for quantifying the tear film breakup of an eye”; the “wavefront aberrometer” and “corneal topographer” each produce separate measurement streams which are processed together into “combined tear film breakup data”, e.g. see [0058]-[0060])
identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data; (“corneal topography (CT) spots in a localized corneal region…each CT spot represents a region -0.2 mmx0.2 mm. Locally rough or irregular surfaces result in spot image breakup.” where the processor distinguishes “bad” spots and “good” spots, e.g. see [0079]; “certain topography regions are determined to be "bad"”, e.g. see [0084] (The localized topography regions corresponding to degraded/bad topography spots indicative of tear film break-up are the first locations.))
identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data; (“macro-aberrations and micro-aberrations in spots appearing on a Shack-Hartmann wavefront sensor due to tear breakup for an eye”; “smaller lenslets correspond to smaller sample areas on the cornea which help map tear film features”, e.g. see [0039]-[0046]; wavefront irregularity maps and spatially varying tear film defects, e.g. see [0069]-[0072] (The corneal sampling regions corresponding to lenslets/wavefront spots exhibiting wavefront irregularity, enlargement, distortion or other tear film break-up are the second locations.))
correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye; (“certain topography regions are determined to be "bad". This information is used to compute the spot divergences from the wavefront corresponding to the topography image…In this way a composite far-field intensity distribution (or composite pointspread-function) can be created that would better represent the image created by the optical system of the eye.” (i.e. composite irregularity in the tear film), e.g. see [0084])
incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film (“an optical measurement instrument…provided in U.S. patent application Ser. No. 14/969,264, "OPTICAL IMAGING AND MEASUREMENT SYSTEMS AND METHODS FOR CATARACT SURGERY AND TREATMENT PLANNING,"” (Neal ‘437), e.g. see [0061]; “Measurement and diagnosis of the tear film of an eye…may be used to guide surgery, screen patients”, e.g. see [0003]; a spatially correlated composite optical representation of tear film irregularity capable of determining “Snellen Acuity” and other “optical-acuity metrics”; the tear film effects can be used for “objectively comparing tear film to other visual defects” and to predict how much the tear film limits “visual acuity”; the “composite pointspread-function” represents the image created by the eye and can be used to determine visual-acuity metrics, e.g. see [0083]-[0084])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal 437 to include a first diagnostic device configured to capture first tear film data, a second diagnostic device configured to capture second tear film data, identify first locations on a cornea of the eye with irregularities in the tear film based on the first tear film data, identify second locations on the cornea of the eye with irregularities in the tear film based on the second tear film data, correlate the first locations on the cornea with the second locations on the cornea to identify a composite irregularity in the tear film of the eye and incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film as taught by Neal 460, which identifies the cataract surgery and treatment planning instrument of Neal ‘437 as an example instrument applicable to its tear film measurements (Neal ‘460 [0061]), for the purposes of guiding surgery and screening patients (Neal ‘460 [0003]) and generating an optical representation that “better represents the image created by the optical system of the eye” (Neal ‘460 [0084]).
Neal ‘437 and Neal ‘460 do not teach:
analyze individual risk factors based on the data and
obtain a weighted combination of the individual risk factors based in part on the historical data;
generate a respective satisfaction metric for the plurality of lenses
However, Crespo in the analogous art teaches:
generate a respective satisfaction metric for the plurality of lenses (an expected satisfaction value for a patient wearing a lens is s=hθ(u,x), e.g. see [0031]; evaluating available lens design and selecting the “one for which hθ(u,x) is larger” (an expected satisfaction evaluation associated with each candidate lens configuration), e.g. see [0037; the training data contains prior users’ features, lens features and reported satisfaction, and the fitted model parameters are selected to better predict satisfaction for future users, e.g. see [0036]; the model is continuously updated as further satisfaction feedback becomes available, e.g. see [0039])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437 and Neal ‘460 to include generate a respective satisfaction metric for the plurality of lenses as taught by Crespo, for the purposes of providing “increased or maximum expected satisfaction to a new user” (Crespo [0031]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437, Neal ‘460 and Crespo in further view of Lu (“Tear Film Measurement By Optical Reflectometry Technique”, J Biomed Opt, 2014).
Regarding claim 4, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 and Neal ‘460 teach identifying first locations on a cornea of the eye with irregularities in the tear film as described above.
Neal ‘437, Neal ‘460 and Crespo do not teach:
detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations; and identifying the respective location as a respective irregularity of the tear film in the eye
However, Lu in the analogous art of ophthalmology (e.g. see abstract) teaches:
detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations; and identifying the respective location as a respective irregularity of the tear film in the eye (evaluation of a tear film, e.g. see abstract; an algorithm uses signal-to-noise ratio (SNR) as a metric for analyzing ocular surfaces; a “scanner to search for the best incident position on the tear film that gives reflectance with the highest signal-to-noise ratio (SNR)”, e.g. see pg. 2 para. 3; “the reflectance curve contrast…may attribute to the film thickness uniformity within the measurement spot, since shorter wavelengths are more sensitive to the local film uniformity variation”, e.g. see pg. 4 para. 4 (The ocular surface is scanned and the SNR is analyzed at each position. A high SNR is directly linked to an optimal, regular and uniform spot on the ocular surface. It is understood that the same method of scanning detects spots with low SNR and identifies them as suboptimal, irregular spots.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations and identifying the respective location as a respective irregularity of a tear film in the eye as taught by Lu, for the purposes of “high accuracy evaluations of tear films in an eye” (Lu, pg. 7 para. 2).
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437, Neal ‘460 and Crespo in further view of Arita (“Tear Interferometric Patterns Reflect Clinical Tear Dynamics in Dry Eye Patients”, Invest. Ophthalmol. Vis. Sci, 2016).
Regarding claim 5, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 and Neal ‘460 teach identifying first locations on the cornea of the eye with irregularities in the tear film as described above.
Neal ‘437, Neal ‘460 and Crespo do not teach:
identifying a respective location where the tear film data exhibits at least one of missing information and a varying point distribution; and identifying the respective location as a respective irregularity of the tear film
However, Arita in the analogous art of ophthalmology (e.g. see abstract) teaches:
identifying a respective location where the tear film data exhibits at least one of missing information and a varying point distribution; and identifying the respective location as a respective irregularity of the tear film (“investigat[ing] whether the tear interferometric pattern was able to identify differences in tear film kinetics”, abstract; the measurement of non-invasive tear break up time (NIBUT) by looking for the “time from the last blink to the appearance of a break or discontinuity in the surface of the tear film visualized by interferometry” (It is understood that a “break or discontinuity” would cause missing information or a varying point distribution in a scan.), e.g. see pg. 3929 para. 4)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include identifying a respective location where the tear film data exhibits at least one of missing information and a varying point distribution and identifying the respective location as a respective irregularity of a tear film as taught by Arita, for the purposes of visualization of the layers of the tear film (Arita, pg. 3928 para. 2).
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437, Neal ‘460 and Crespo in further view of Chakrabarti (“IOL Selection for Patients With Age-Related Macular Degeneration", CRST Global, 2015).
Regarding claim 7, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437 teaches the diagnostic data as described above.
Neal ‘437, Neal ‘460 and Crespo do not teach:
macular data represented as at least one of a binary result or as a numerical scale of macular degeneration the eye being scanned to generate the diagnostic data; and the binary result is either a presence of a threshold level of degeneration or an absence of the threshold level of degeneration
However, Chakrabarti in the analogous art of selecting an IOL (e.g. see pg. 1 para. 2) teaches:
macular data represented as at least one of a binary result or as a numerical scale of macular degeneration the eye being scanned to generate the diagnostic data; and the binary result is either a presence of a threshold level of degeneration or an absence of the threshold level of degeneration (guide IOL selection based on the patient’s macula; obtaining and using macular data by categorizing patients based on their clinical signs, e.g. “Category No. 2: Patients with early maculopathy (soft drusen and/or pigmentary changes) with relative sparing of Snellen visual acuity” and “Category No. 3: Patients with moderate to severe AMD (presence of drusen, geographic atrophy, and/or exudative changes)”, e.g. see pg. 1 paras. 2 and 5, pg. 2 para. 1)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include macular data represented as at least one of a binary result or as a numerical scale of macular degeneration the eye being scanned to generate the diagnostic data and the binary result is either a presence of a threshold level of degeneration or an absence of the threshold level of degeneration as taught by Chakrabarti, for the purposes of “maximiz[ing] the patient's visual function postoperatively” (Chakrabarti, pg. 1 para. 2).
Claims 9-11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437, Neal ‘460 and Crespo in further view of He (“Saccadic Lens Instability Increases With Accommodative Stimulus In Presbyopes”, J Vis., 2010).
Regarding claim 9, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437, Neal ‘460 and Crespo teach the diagnostic data as described above.
Neal ‘437, Neal ‘460 and Crespo do not teach:
lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: acquiring a plurality of images of the eye while presenting different accommodative demands to the eye; and generating a motion trace of a lens capsule of the eye using the plurality of images
However, He in the analogous art of analyzing the human lens (e.g. see abstract) teaches:
lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: acquiring a plurality of images of the eye while presenting different accommodative demands to the eye; and generating a motion trace of a lens capsule of the eye using the plurality of images (a method to measure saccadic lens instability in presbyopes and quantify this instability as saccadic lens wobble artifacts, where the instability is quantified by its amplitude (i.e. wobble parameter); subjects were tested at “9 viewing distances ranging from 0.5- to 8-D accommodative demands”; data was captured using an eye tracker or a 60 Hz frame rate infrared sensitive camera to create a video (i.e. plurality of images), e.g. see abstract, pg. 4 para. 2; the eye tracker records position signals over time; the graphs of Figs. 1 and 2A plot these recorded positions over time (i.e. motion trace), e.g. see abstract)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including acquiring a plurality of images of the eye while presenting different accommodative demands to the eye and generating a motion trace of a lens capsule of the eye using the plurality of images as taught by He, for the purposes of “evaluat[ing] the extent to which the ciliary muscle/zonular complex responds during accommodation in presbyopes”(He, pg. 2 para. 2).
Regarding claim 10, Neal ‘437, Neal ‘460, Crespo and He teach the system of claim 9 as described above.
Neal ‘437 does not teach:
extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters as a maximum amplitude and/or a time constant of the curve
However, He in the analogous art teaches:
extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters as a maximum amplitude and/or a time constant of the curve (isolating the lens wobble artifact by subtraction of P1 movement from θH/θV signals; normalizing the data by calculating the ratio of artifact amplitude to saccade amplitude to exclude the effect of saccade size (i.e. normalized lens oscillation traces), abstract, pg. 7 para. 2; Fig. 8 illustrates model fitting a curve to the artifact/saccade ratio data; applying a linear regression fit to the data for each subject, pg. 6 para. 4; calculating the “peak-to-trough distance in the subtracted profiles” to calculate the amplitude of the wobble artifact (i.e. the one or more wobble parameters as a maximum amplitude), e.g. see pg. 7 para. 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include extracting normalized lens oscillation traces based on the motion trace, model-fitting a curve to the normalized lens oscillation traces and obtaining the one or more wobble parameters as a maximum amplitude and/or a time constant of the curve as taught by He, for the purposes of “evaluat[ing] the extent to which the ciliary muscle/zonular complex responds during accommodation in presbyopes” (He, pg. 2 para. 2).
Regarding claim 11, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437, Neal ‘460 and Crespo teach the diagnostic data as described above.
Neal ‘437, Neal ‘460 and Crespo do not teach:
lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: directing electromagnetic energy in a predetermined spectrum onto the eye concurrently with induced eye saccades, via an energy source; acquiring a plurality of images of the eye indicative of the induced eye saccades, via a camera;
generating a motion trace of a lens capsule using the plurality of images and extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters based on the curve
However, He in the analogous art of analyzing the human lens (e.g. see abstract) teaches:
lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: directing electromagnetic energy in a predetermined spectrum onto the eye concurrently with induced eye saccades, via an energy source; acquiring a plurality of images of the eye indicative of the induced eye saccades, via a camera; (a method to measure saccadic lens instability in presbyopes and quantify this instability as saccadic lens wobble artifacts, where the instability is quantified by its amplitude (i.e. wobble parameter), e.g. see abstract; the “eye tracker projects a 930-nm collimated infrared light” into the eye; it performs this measurement while “subjects executed a total of 32 four-degree saccades at 1-s intervals” (i.e. induced eye saccades); subjects were tested at “9 viewing distances ranging from 0.5- to 8-D accommodative demands”; data was captured using an eye tracker or a 60 Hz frame rate infrared sensitive camera to create a video (i.e. plurality of images), e.g. see abstract, pg. 4 para. 2)
generating a motion trace of a lens capsule using the plurality of images and extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters based on the curve (the eye tracker records position signals over time; the graphs of Figs. 1 and 2A plot these recorded positions over time (i.e. motion trace), e.g. see abstract); isolating the lens wobble artifact by subtraction of P1 movement from θH/θV signals; normalizing the data by calculating the ratio of artifact amplitude to saccade amplitude to exclude the effect of saccade size (i.e. normalized lens oscillation traces), abstract, pg. 7 para. 2; Fig. 8 illustrates model fitting a curve to the artifact/saccade ratio data; applying a linear regression fit to the data for each subject, pg. 6 para. 4; calculating the “peak-to-trough distance in the subtracted profiles” to calculate the amplitude of the wobble artifact (i.e. the one or more wobble parameters), e.g. see pg. 7 para. 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include obtaining the lens capsule stability data including directing electromagnetic energy in a predetermined spectrum onto the eye concurrently with induced eye saccades, via an energy source, acquiring a plurality of images of the eye indicative of the induced eye saccades, via a camera, generating a motion trace of a lens capsule using the plurality of images, extracting normalized lens oscillation traces based on the motion trace, model-fitting a curve to the normalized lens oscillation traces and obtaining the one or more wobble parameters based on the curve as taught by He, for the purposes of “evaluat[ing] the extent to which the ciliary muscle/zonular complex responds during accommodation in presbyopes”, (He, pg. 2 para. 2).
Claim 19 recites substantially similar limitations as those already addressed in claim 9, and, as such is rejected for similar reasons as given above.
Claims 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Neal ‘437, Neal ‘460 and Crespo in further view of Bogue (US 2021/0233622 A1).
Regarding claim 13, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437, Neal ‘460 and Crespo teach the diagnostic data as described above.
Neal ‘437, Neal ‘460 and Crespo does not teach:
questionnaire data for the patient with at least one personality trait, the at least one personality trait being represented as at least one of a numerical scale of agreeability or as a binary result, the binary result being either predominantly agreeable or predominantly non-agreeable
However, Bogue in the analogous art teaches:
questionnaire data for the patient with at least one personality trait, the at least one personality trait being represented as at least one of a numerical scale of agreeability or as a binary result, the binary result being either predominantly agreeable or predominantly non-agreeable (“assessing personality type with a validated instrument as a predictor of TKR” and challenges with questionnaire instruments, e.g. see [0233]; using a five factor model (The Five-Factor Model (FFM), or Big Five, is a personality framework that describes five broad dimensions of personality: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism), e.g. see [0235], [0233]; [0233] discusses that certain personality types were “the least likely to express satisfaction post surgery” (This demonstrates the use of personality as a risk factor to predict a subjective outcome. The identification of a patient as an unstable introvert is an example of a binary or categorical classification derived from the questionnaire’s numerical score, and it is understood that of the other dimensions of personality, e.g. agreeableness, can be used to predict outcomes.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include questionnaire data for the patient with at least one personality trait, the at least one personality trait being represented as at least one of a numerical scale of agreeability or as a binary result as taught by Bogue, for the purposes of making “predictions for the various outcome targets” (Bogue [0281]).
Regarding claim 14, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 1 as described above.
Neal ‘437, Neal ‘460 and Crespo does not teach:
wherein: determining the respective satisfaction metric includes selectively executing at least one machine learning model trained with the respective historical sets;
and the respective historical sets include pre-operative objective data, pre- operative personality data, intra-operative data, post-operative objective data, and subjective outcome data
However, Bogue in the analogous art teaches:
wherein: determining the respective satisfaction metric includes selectively executing at least one machine learning model trained with the respective historical sets; and the respective historical sets include pre-operative objective data, pre- operative personality data, intra-operative data, post-operative objective data, and subjective outcome data (“The statistical model may be a Bayesian Network.” (i.e. machine learning model); the model trained on a number of patients, e.g. see [0017], [0313]; patient data includes simulated outcomes, pre-operative and/or post-operative patient monitoring data, sensor data and questionnaire data including personality type, e.g. see [0105], [0154]-[0155], [0233]; “receiving intra-operative data and post-operative data” and “revising a predict a revised predicted satisfaction value based on the intra-operative data and the post-operative data”, e.g. see [0068]-[0069]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include executing at least one machine learning model trained with the respective historical sets including pre-operative objective data, pre- operative personality data, intra-operative data, post-operative objective data and subjective outcome data as taught by Bogue, for the purposes of “constantly improv[ing] accuracy of predictive software” (Bogue [0293]).
Regarding claim 15, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 14 as described above.
Neal ‘437, Neal ‘460 and Crespo does not teach:
wherein: the subjective outcome data in the respective historical sets include a numerical satisfaction scale
However, Bogue in the analogous art teaches:
wherein: the subjective outcome data in the respective historical sets include a numerical satisfaction scale (the predicted satisfaction value is based on patient reported outcome measures such as the Oxford Knee Score and the he Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the Knee injury and Osteoarthritis Outcome Score (KOOS), e.g. see [0018]-[0021])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include the subjective outcome data in the respective historical sets include a numerical satisfaction scale as taught by Bogue, for the purposes of “constantly improv[ing] accuracy of predictive software” (Bogue [0293]).
Regarding claim 16, Neal ‘437, Neal ‘460 and Crespo teach the system of claim 14 as described above.
Neal ‘437, Neal ‘460 and Crespo does not teach:
wherein the controller is configured to quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets and identify the post-operative objective data most strongly correlating with the subjective outcome score
However, Bogue in the analogous art teaches:
wherein the controller is configured to quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets and identify the post-operative objective data most strongly correlating with the subjective outcome score (incorporating preoperative and postoperative patient monitoring data into the statistical model including step-count data, e.g. see [0098]-[0099], [0155]; postoperative monitoring of step count, with additional objective information such as heart rate and blood pressure; receiving postoperative data and using it to determine a revised predicted satisfaction value; postoperative outcome information including range of motion, time to mobilization, ambulation and activity level, e.g. see [0199]-[0200], [0205]-[0208]; “scoring metrics…incorporating objective, directly measurable data such as range of motion (ROM) measurements and subjective, questionnaire based data in order to elucidate and characterize patient dissatisfaction”, e.g. see [0216]; “satisfaction level of the patient, either by directly asking if they're satisfied with the surgical outcome, using a visual analog scale construct or generating a Patient Acceptable Symptom State (PASS) for an existing scoring structure to binarise the patient groups into either satisfied or unsatisfied groups”, e.g. see [0219]; risk-factor analysis using “stepwise multivariate regression” with statistical-significance filtering; factors having predictive importance including “greatest single source predictor” of negative outcomes, e.g. see [0221]-[0222]; “post operative patient data collected at various time points” and “Feedback to the pre-operative database to constantly improve accuracy”, e.g. see [0290], [0293])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Neal ‘437, Neal ‘460 and Crespo to include quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets as taught by Bogue, for the purposes of “constantly improv[ing] accuracy of predictive software” (Bogue [0293]).
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101 of Claims 1, 2, and 4-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive.
Applicant argues the claims are not directed to mental steps.
The Examiner respectfully disagrees. Claim 1 does not require the either diagnostic device to “identify an irregularity” as Applicant asserts. Rather, the first diagnostic device is “configured to capture first tear film data”, and the second diagnostic device is “configured to capture second tear film data”. It is the controller that identifies locations exhibiting irregularities. The diagnostic devices merely supply information that is subsequently evaluated by the controller. As per MPEP 2106.04(a)(2), claims directed to collecting information, analyzing it and displaying certain results of the collection and analysis are directed to an abstract idea. The specific steps of identifying locations with irregularities, correlating first locations with second locations to identify a composite irregularity, analyzing risk factors and generating a satisfaction metric are steps of observation, evaluation and comparison that can be performed in the human mind. Furthermore, the recitation of the involvement of “diagnostic devices” to capture tear film data is merely data gathering or extra-solution activity. The fact that the gathered data (the composite irregularity) is used repeatedly does not convert the underlying mental nature of the analysis into a non-abstract process.
Applicant argues the claims integrate the judicial exception into a practical application.
The Examiner respectfully disagrees. While the MPEP states that an improvement to a computer or technical field can indicate integration into a practical application, the claim itself must recite the components or steps that effectuate this improvement, and the improvement must be in the technology, rather than just an improvement in the abstract idea. Here, the claims do not recite any technical improvements to the devices. Instead, the claims simply use generic diagnostic devices in their routine capacity to collect data, which is fed into the abstract idea. The asserted improvement in fidelity is merely the result of applying a mental process to generate better predictive information. Furthermore, the “tangible result”, which is the “satisfaction metric” in the claims, is essentially a number or score and does not amount to a technological improvement.
Regarding the rejection under 35 U.S.C. § 103 of Claims 1, 2, and 4-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment and/or afforded by the present RCE.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference Schallhorn (Predictors of Patient Satisfaction After Refractive Lens Exchange With an Extended Depth of Focus IOL, Journal of Refractive Surgery, 2020) discloses identifying independent factors associated with postoperative satisfaction after refractive lens exchange with an extended depth of focus intraocular lens. Reference Nichols (US 2020/0154999 A1) discloses multimodal interferometric tear film measurement.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
