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, 5, 17, 18 and 20 have been amended.
Claims 1, 2 and 4-20 as presented November 23, 2025 are currently pending and considered below.
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 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 tear film data of the eye;
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data;
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation based on the locations of the irregularities 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 simulation based on the locations of the irregularities 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. The claim encompasses a mental process of obtaining diagnostic data, identify locations on a cornea with irregularities, performing a visual simulation for each intraocular lens, obtaining historical data, 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. Additionally, claims 10 and 11 describe extracting normalized lens oscillation traces, model-fitting a curve and obtaining the one or more wobble parameters (i.e. mathematical formulas or equations and mathematical calculations). Claims 14-16 further describe determining the respective satisfaction metric. Claim 18 describes selecting the preferred intraocular lens. Claims 11 and 14 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. 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)).
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 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 tear film data of the eye;
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data;
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation based on the locations of the irregularities 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 simulation based on the locations of the irregularities 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 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.
Claim 6, 7 and 20 further recite the additional element of the eye being scanned to generate the diagnostic data. Under practical application, the eye being scanned to generate the diagnostic data is a form 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”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of the eye being scanned to generate the diagnostic data is considered extra-solution activity. This 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 and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Berestka (US 2017/0156591 A1) in further view of Bogue (US 2021/0233622 A1), Fan (US 2020/0138360 A1) and Neal (US 2017/0027437 A1).
Regarding claim 1, Berestka 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]), the system comprising:
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: (e.g. see [0093], [0098])
obtain diagnostic data of the eye, including at least tear film data of the eye; (analyzing an eye of a patient including “capturing images of the eye over time with a plenoptic camera; determining positions of one of more structures of the eye from the captured images”, e.g. see [0016]; “automatically place the focus of the slit beam at…clinically important anatomic structures…These can include the tear film…”, e.g. see [0104])
obtain historical data composed of respective historical sets of patient data; (obtaining and using a database of determined position for historical patients, e.g. see [0016])
Berestka does not teach:
a satisfaction metric
However, Bogue in the analogous art of patient outcomes with a medical implant (e.g. see [0004]) teaches:
a satisfaction metric (“determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation”, e.g. see [0011])
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 Berestka to include a satisfaction metric as taught by Bogue, for the purposes of determining the satisfaction of the patient with the future procedure (Bogue [0011]).
Berestka and Bogue do not teach:
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
However, Fan in the analogous art of predictive analysis based on patient data and historical examples (e.g. see [0152], [0162]) teaches:
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 (the predictive analysis system analyzes a plurality of factors of the patient (i.e. individual risk factors) and assigns a weighted combination of these factors, e.g. the PPG value, MSI value and patient health metric values, e.g. see [0236], [0151]-[0152])
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 Berestka and Bogue to include 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 as taught by Fan, for the purposes of “handl[ing] information in the form of multiple independent variables” (Fan [0150]).
Berestka, Bogue and Fan do not teach:
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation based on the locations of the irregularities in the tear film;
generate a respective […] metric for the plurality of intraocular lenses based in part the historical data and the visual simulation based on the locations of the irregularities in the tear film.
However, Neal in the analogous art of selecting an intraocular lens (e.g. see [0016]) teaches:
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data (“determining the irregular features in either the wavefront aberrometery or corneal topography maps…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…if the spots are not round…oblong or broken up shape”, e.g. see [0107])
perform a visual simulation for each intraocular lens of the plurality of intraocular lenses, the visual simulation based on the locations of the irregularities in the tear film; (“determining the irregular features in…corneal topography maps…The resulting map…is highly correlated with the tear film”, e.g. see [0107]; using the “stored data” including “anterior corneal surface information” (information derived from corneal topography) to “simulate the subject eye by means of ray tracing…and (5) show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0016], [0155])
generate a respective […] metric for the plurality of intraocular lenses based in part the historical data and the visual simulation based on the locations of the irregularities in the tear film. (“show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0016], [0155]; “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]; “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]).
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 Berestka, Bogue and Fan to include identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data, perform a visual simulation for each intraocular lens of the plurality of intraocular lenses based on the locations of the irregularities in the tear film and generate a respective metric for the plurality of intraocular lenses based in part the historical data and the visual simulation as taught by Neal, for the purposes of ensuring reliable ocular measurements (Neal [0107]), “achiev[ing] an accurate three-dimensional model of a patient's eye” and “utiliz[ing] advanced vision modeling techniques” (Neal [0006]).
Regarding claim 2, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach a system whose purpose is to generate predictive metrics to aid a decision and teach the respective satisfaction metric and the weighted combination of the individual risk factors as described above.
Berestka further teaches:
select the preferred intraocular lens (“identifying the first intraocular lens from the library of intraocular lenses”, e.g. see [0016]).
Regarding claim 12, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka does not teach:
the diagnostic data includes an angle kappa factor
However, Neal in the analogous art teaches:
the diagnostic data 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])
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 Berestka to include an angle kappa factor as taught by Neal, for the purposes of providing an integrated instrument that includes a comprehensive measurement and alignment factors (Neal [0127]).
Regarding claim 13, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka 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 Berestka 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, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka 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 Berestka 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, Berestka, Bogue, Fan and Neal teach the system of claim 14 as described above.
Berestka 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 Berestka 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, Berestka, Bogue, Fan and Neal teach the system of claim 14 as described above.
Berestka does not teach:
quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets […]
However, Bogue in the analogous art teaches:
quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets […] (the objective measure of activity level at several recording periods including 6 week, 3 month, 6 month and 12 month after a procedure, e.g. see [0254], [0257]; the “Subjective self reported measures of activity and mobility level”, e.g. see [0253]; “correlate accelerometry data…to patient reported knee instability”, e.g. see [0255])
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 Berestka 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]).
Berestka and Bogue do not teach:
identify the objective data most strongly correlating with the outcome
However, Fan in the analogous art teaches:
identify the objective data most strongly correlating with the outcome (“variable selection simultaneously ranks the importance of each measurement in the model to highlight which optical signals are most critical to the diagnostic accuracy”, e.g. see [0200])
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 Berestka and Bogue to include identify the objective data most strongly correlating with the as taught by Fan, for the purposes of “deliver[ing] high accuracy with minimal redundancy between measurements” (Fan [0200]).
Claim 17 recites substantially similar limitations as those already addressed in claim 1, and, as such is rejected for similar reasons as given above.
Regarding claim 18, Berestka, Bogue, Fan and Neal teach the method of claim 17 as described above.
Berestka, Bogue, Fan and Neal teach the respective satisfaction metric and the weighted combination of the individual risk factors as described above.
Berestka further teaches:
selecting the preferred intraocular lens […], via the controller (“identifying the first intraocular lens from the library of intraocular lenses”, e.g. see [0016]; “controller 300 includes one or more processors 350 configured to execute instructions”, e.g. see [0093])
Berestka does not teach:
selecting based in part on the respective metric and the individual factors
However, Neal in the analogous art teaches:
selecting based in part on the respective metric and the individual factors (“propose the selected IOL power for one or more IOL models from the plurality of IOLs corresponding to the optimized IOL(s) based on predetermined criteria; and (5) show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0016]; “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]; “Instrument 1 stores all the biometric data and postoperative information 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])
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 Berestka to include selecting based in part on the respective metric and the individual factors as taught by Neal, for the purposes of “utiliz[ing] advanced vision modeling techniques” (Neal [0006]).
Claims 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Berestka, Bogue, Fan and Neal in further view of Buhren (US 2016/0302660 A1).
Regarding claim 6, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka, Bogue, Fan and Neal do not teach:
corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations, the eye being scanned to generate the diagnostic data; 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, Buhren in the analogous art teaches:
corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations, the eye being scanned to generate the diagnostic data; 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 (measuring the anterior cornea surface by projecting a discrete number of light points onto the eye and recording their reflection for the purposes of “reconstruction of the topography of the cornea”, e.g. see [0065]-[0067]; “the reconstruction of the anterior cornea surface takes place as a free-form surface, for example by the use of Zernike polynomials”, e.g. see [0075])
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 Berestka, Bogue, Fan and Neal to include corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations, the eye being scanned to generate the diagnostic data; 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 as taught by Buhren, for the purposes of “tak[ing] into consideration all the aspects that have an influence on the selection of the IOL” (Buhren [0045]).
Regarding claim 8, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka, Bogue, Fan and Neal do not teach:
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
However, Buhren in the analogous art teaches:
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 (the patient-specific eye model comprises “free-form surfaces with all degrees of freedom” and their tilting as well as decentering, e.g. see [0109]; “For correct ray tracing, the patient-specific eye model should also contain the location of the pupil.”, e.g. see [0112]; the anterior corneal surface is measured, e.g. see [0065]; “the posterior cornea surface is reconstructed from the B scans, taking into consideration the anterior cornea surface that has already been determined”, e.g. see [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 Berestka, Bogue, Fan and Neal to include 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 as taught by Buhren, for the purposes of “tak[ing] into consideration all the aspects that have an influence on the selection of the IOL” (Buhren [0045]).
Claims 4 are rejected under 35 U.S.C. 103 as being unpatentable over Berestka, Bogue, Fan, Neal in further view of Lu (“Tear Film Measurement By Optical Reflectometry Technique”, J Biomed Opt, 2014).
Regarding claim 4, Berestka, Bogue, Fan and Buhren teach the system of claim 1 as described above.
Neal teaches identifying irregular features of the tear film as described above ([0107]).
Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal in further view of Arita (“Tear Interferometric Patterns Reflect Clinical Tear Dynamics in Dry Eye Patients”, Invest. Ophthalmol. Vis. Sci, 2016).
Regarding claim 5, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Neal teaches identifying locations on the cornea of the eye with irregularities in the tear film based on the tear film data as described above.
Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal in further view of Chakrabarti (“IOL Selection for Patients With Age-Related Macular Degeneration", CRST Global, 2015).
Regarding claim 7, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal 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 Berestka, Bogue and Fan in further view of He (“Saccadic Lens Instability Increases With Accommodative Stimulus In Presbyopes”, J Vis., 2010).
Regarding claim 9, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal 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, Berestka, Bogue, Fan and He teach the system of claim 9 as described above.
Berestka 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 Berestka, Bogue, Fan and Neal 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, Berestka, Bogue, Fan and Neal teach the system of claim 1 as described above.
Berestka, Bogue, Fan and Neal teach the diagnostic data as described above.
Berestka, Bogue, Fan and Neal 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 Berestka, Bogue, Fan and Neal 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.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Buhren in further view of Neal and Bogue.
Regarding claim 20, Buhren teaches: A system for selecting a preferred intraocular lens for implantation into an eye (e.g. see [0002]), the system comprising:
obtain diagnostic data of the eye [...], the eye being scanned to generate the diagnostic data; perform a visual simulation for each of a plurality of intraocular lenses based in part on the diagnostic data […]; (“determining the required biometrical parameters of the eye” and using those parameters to create a “patient-specific eye model”, e.g. see [0047], [0051]; a system for IOL selection that makes available to a user “a matrix of best suitable IOLs, which matrix contains images, the image quality of which corresponds to the vision determined” (i.e. visual simulation), e.g. see [0178], claim 66)
Buhren does not teach:
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 tear film data
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data
the visual simulation incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film;
generate a respective […] metric for the plurality of intraocular lenses based in part on the visual simulation based on the locations of the irregularities in the tear film and the historical data.
However, Neal in the analogous art teaches:
a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: (“Optical measurement instrument 1 may further include a controller 60, including one or more processor(s) 61 and memory 62”, e.g. see [0055]; “tangible media embodying machine readable instructions for implementing the derivation of the treatment”, e.g. see [0021])
obtain tear film data (“the optical measurement instrument 1 of the present invention provides one or more measurements sufficient to provide an assessment of the tear film of a patient”, e.g. see [0107])
identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data (“determining the irregular features in either the wavefront aberrometery or corneal topography maps…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…if the spots are not round…oblong or broken up shape”, e.g. see [0107])
the visual simulation incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film; (“determining the irregular features in…corneal topography maps…The resulting map…is highly correlated with the tear film”, e.g. see [0107]; using the “stored data” including “anterior corneal surface information” (information derived from corneal topography) to “simulate the subject eye by means of ray tracing…and (5) show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0016], [0155])
generate a respective […] metric for the plurality of intraocular lenses based in part on the visual simulation based on the locations of the irregularities in the tear film and the historical data. (“show the simulated optical quality and/or visual performance provided by each of the proposed IOL models”, e.g. see [0016], [0155]; “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]; “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])
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 Buhren to include a controller, tear film data, identify locations on a cornea of the eye with irregularities in the tear film based on the tear film data, the visual simulation incorporating an impact of the tear film data and being based on the locations of the irregularities in the tear film and generate a respective metric for the plurality of intraocular lenses based in part on the visual simulation based on the locations of the irregularities in the tear film and the historical data as taught by Neal, for the purposes of ensuring reliable ocular measurements (Neal [0107]), “achiev[ing] an accurate three-dimensional model of a patient's eye” and “utiliz[ing] advanced vision modeling techniques” (Neal [0006]).
Buhren and Neal do not teach:
obtain historical data composed of respective historical sets of patient data; and
a satisfaction metric
However, Bogue in the analogous art teaches:
obtain historical data composed of respective historical sets of patient data; and (the model trained on a number of patients, e.g. see [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])
a satisfaction metric (“determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation”, e.g. see [0011])
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 Buhren and Neal to include obtain historical data composed of respective historical sets of patient data and a satisfaction metric as taught by Bogue, for the purposes of determining the satisfaction of the patient with the future procedure (Bogue [0011]).
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 because they are “are impossible to perform in the human mind”.
The Examiner respectfully disagrees. The Examiner has modified the grounds of rejection under 35 U.S.C. 101 to correspond with the present claim amendments. Applicant argues the claims rely on “sensor-based analysis”. However, regarding claim 1, there is no recitation of a specific sensor. The claim merely recites “obtain diagnostic data”. Furthermore, the claim lacks specificity regarding the mechanism for identifying irregularities in the tear film. Consequently, the claims recite steps that can be practically performed in the mind rather than being tied to a “specific, medical-technological determination”.
Applicant argues the “concepts of claims 1, 17, and 20 are integrated into a practical application such that the claims are not directed to an abstract idea”.
The Examiner respectfully disagrees. Applicant argues the claims “improve the fidelity of simulating vision” and produce a “tangible result”. However, this alleged improvement is to the abstract idea itself and not to a technology. Improving the accuracy of a mental process by providing it with better data is an improvement to the abstract idea, not an improvement to the technological environment to which the claims are confined (a general-purpose computer). Furthermore, Applicant’s reliance on “sensor-based analysis” to establish a practical application is misplaced. The steps of collecting data, analyzing it to find irregularities and generating a result are abstract concepts. Per MPEP 2106.04(a)(2)(III), “Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”. In addition, the generating of data by being scanned in claim 20 merely adds a data-gathering step and is considered “insignificant extra-solution” activity. 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. Thus, the claims are directed to an abstract idea as described above and fail to provide “significantly more”.
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. Newly cited reference Neal discloses an “assessment of the tear film” ([0107]) and “simulat[ing] the subject eye by means of ray tracing” for IOL selection ([0016]). Neal further teaches performing visual simulation using corneal topography maps, and that these maps capture “irregular features” and spots with an “oblong or broken up shape” which indicate that the “tear film is disrupted” ([0107]). Therefore, Neal in combination with the other references teach the claimed limitations. See details above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.A./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681