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 .
DETAILED ACTION
Status of the Application
Claims 1-23 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Claims filed on 07/25/2024.
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 1-23 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-18 and 21-23 fall within the statutory category of a process. Claims 19-20 fall within the statutory category of an apparatus or system.
Step 2A, Prong One
As per Claims 1 and 19, the limitations of collecting optical information from a patient wherein the optical information includes parameters including eye dimensional measurements, and generating, at a first stage of treatment, a treatment recommendation regarding an ophthalmological treatment, the treatment recommendation including an ophthalmic lens type recommendation and a probability of a predetermined surgical outcome associated with the ophthalmic lens type recommendation, under its broadest reasonable interpretation, covers managing personal behaviors or interactions between people.
As per Claim 21, the limitations of collecting optical information from a patient wherein the optical information includes parameters including eye dimensional measurements, and generating a treatment recommendation regarding an ophthalmological treatment, the treatment recommendation including a custom intraocular lens design recommendation, under its broadest reasonable interpretation, covers managing personal behaviors or interactions between people.
The steps of collecting optical information from a patient, including eye dimensional measurements, and generating a treatment recommendation regarding an ophthalmological treatment are steps which are performed by a physician, specifically an ophthalmologist, in the normal course of treating a patient. This constitutes an interaction between people. The claims describe managing treatment of an ophthalmic patient which is a business interaction for an ophthalmologist in addition to the interaction between the doctor and patient. If a claim limitation, under its broadest reasonable interpretation, covers the management of personal behaviors or interactions between people and commercial interactions such as business relations, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional elements – a processor and memory communicatively coupled to the processor and storing instructions. The processor and memory in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recites the additional elements of receiving optical information as input parameters by an ophthalmic treatment platform hosting a deep-learning model which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the step of receiving optical information is mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). The deep-learning model is described as being trained using training data including historical ophthalmological procedure data associated with a plurality of patients, complication data associated with a plurality of patients, and patient survey data regarding treatment satisfaction of the plurality of patients. The training is not positively recited but merely used to describe the model itself. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a processor and memory communicatively coupled to the processor and storing instructions to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The processor and memory are recited at a high level of generality and are recited as generic computer components by reciting a processor which can be a central processing unit or other processors (Specification [0064]) and a memory which includes RAM, ROM (Specification, [0064]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of receiving optical information as input parameters by an ophthalmic treatment platform hosting a deep-learning model which is a well-understood, routine and conventional computer function in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-18, 20, and 22-23 add further limitations which are also directed to an abstract idea. For example, Claim 2 includes assigning a weight to each input parameter, calculating a probability of a predetermined surgical outcome associated with each ophthalmic lens type of a plurality of different ophthalmic lens types, generating the ophthalmic lens type recommendation for the patient, and creating a mapping model of the recommended ophthalmic lens type. These are steps in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient. The steps of assigning a weight to each parameter and calculating a probability of a predetermined surgical outcome are also activities which describe mathematical calculations.
Claim 3 includes generating a plurality of probabilities of the predetermined surgical outcome associated with each of a plurality of different ophthalmic lens types, and receiving a selection of the ophthalmic lens type from a user. These are steps in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient.
Claim 4 includes generating optimizations for use during the ophthalmic procedure which falls into the abstract grouping of certain methods of organizing human activity for the same reasons as the independent claims. The claim also includes the additional element of a deep-learning model for carrying out the abstract idea of generating optimizations. The use of a commonplace mathematical algorithm applied on a general purpose computer is found to be mere instructions to apply the exception, as per MPEP 2106.05(f). The claim also includes the additional element of receiving optical procedure information, which amounts to insignificant extra-solution activity which is well-understood, routine, and conventional for the same reasons as the independent claims.
Claim 5 includes generating mechanical characteristics used during the ophthalmic procedure to mitigate risk of posterior capsular rupture. These are steps in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient.
Claim 6 includes generating primary care predictions representing a predicted outcome of the ophthalmic procedure based on the post-operative clinical assessment information. These are steps in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient. The claim includes the additional elements of receiving post-operative clinical assessment information and the use of a deep learning model to execute the steps of the abstract idea, which amount to mere instructions to apply the exception and insignificant extra-solution activity which is well-understood, routine, and conventional for the same reasons as Claim 4, as described above.
Claim 7 further describes the model of Claim 1, but this is merely a description of the model and the claim is therefore directed to the same abstract idea as Claim 1.
Claim 8 includes re-training the deep learning model based on updated training data. This is an additional element which amounts to mere instructions to apply the exception, such as use of a mathematical algorithm to apply the abstract idea, as per MPEP 2106.05(f).
Claim 9 includes calculating optical characteristics, and generating primary care predictions by automatically compensating for changes in lens positions without the dependence on any manufacturer provided intraocular calculator constants. These are steps in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient.
Claim 10 includes a description of the ophthalmic procedure. Claim 11 includes a description of the treatment recommendations. Claim 22 includes a description of the custom intraocular lens design recommendation. These claims further specifies or limits the independent claim and the claim is therefore directed to the same abstract idea as Claim 1.
Claims 12-16 include particular mathematical calculations which further specify the elements of the independent claims. Therefore, these claims recite mathematical concepts as mathematical equations, algorithms, calculations.
Claim 17 includes generating a predicted corrected axial length. Which is a step in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient. The claim also includes an optical measurement device or ultrasound device for collecting information which is a device known in its ordinary capacity, and therefore, it amounts to mere instructions to apply the exception, as per MPEP 2106.05(f).
Claim 18 includes determining an estimated difference in vitreoretinal interface in a high dense cataract patient. This is a step in the analysis of patient information and recommendation of lens type in the treatment of a patient by an ophthalmologist which falls into the abstract grouping of certain methods of organizing human activity because they recite interactions between people in the managing of treatment for an ophthalmic patient.
Claim 20 includes presenting a user interface with the recommendation and surgical outcomes to a treatment provider which amounts to insignificant extra-solution activity as mere data outputting. This is well-understood, routine, and conventional activity similar to Presenting offers and gathering statistics, as per MPEP 2106.05(d)(II).
Claim 23 includes generation of alerts or recommendations regarding treatment to optimize a patient outcome, which amounts to insignificant extra-solution activity as mere data outputting. This is well-understood, routine, and conventional activity similar to Presenting offers and gathering statistics, as per MPEP 2106.05(d)(II).
Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4, 6-8, 10, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Campin et al. (US 2022/0331092 A1), hereinafter Campin.
As per Claims 1 and 19, Campin discloses ophthalmic treatment recommendation and guidance platform implemented on a computing system comprising:
a processor ([0081] CPU);
a memory communicatively coupled to the processor, the memory storing instructions that, when executed by the processor ([0081-0082] memory connected to the CPU/processor), cause the platform to:
collect optical information from a patient wherein the optical information includes one or more input parameters including eye dimensional measurements ([0008] receiving data points including measurements of anatomical parameters of an eye, [0028] raw data includes measurements of specific features), the optical information being received as input parameters by an ophthalmic treatment platform hosting a deep-learning model, the deep-learning model being trained using training data including historical ophthalmic procedure data associated with a plurality of patients, complication data associated with the plurality of patients, and patient survey data regarding treatment satisfaction of the plurality of patients ([0022] machine learning models trained with historical patient data, [0023] machine learning models trained using historical patient data of plurality of historical patients, which includes at least treatment data, other information about the treatment, and result data including patient’s satisfaction or dissatisfaction with the treatment result, [0005] the measurements of anatomical parameters provide an indication of complications when the parameters deviate from the known distribution, i.e. are abnormal); and
generate, at a first stage of treatment, a treatment recommendation regarding an ophthalmological treatment ([0007] generate a recommendation including IOL parameters using trained machine learning model), the treatment recommendation including an ophthalmic lens type recommendation and a probability of a predetermined surgical outcome associated with the ophthalmic lens type recommendation ([0007] treatment recommendation generated including IOL parameters such as type of IOL and result parameters indicative of the surgical outcome).
As per Claim 2, Campin discloses the method of Claim 1. Campin also teaches assigning a weight to each of the input parameters ([0035] each parameter of the training dataset has a weight in the function, [0037] predict optimal IOL parameters);
calculating a probability of a predetermined surgical outcome associated with each ophthalmic lens type of a plurality of different ophthalmic lens types ([0035] using different ML algorithms to generate different outputs for each set of inputs, [0042] model determines the recommended IOL type as the IOL type having the highest probability score in the probability distribution of all the IOL types);
generating the ophthalmic lens type recommendation for the patient ([0052-0053] ML models predict IOL parameters for the current patient, [0056] parameters include IOL type); and
creating a mapping model of the recommended ophthalmic lens type ([0063]/[0070] mapping the data points with associated data including measurements mapped to treatment data and results data.
As per Claim 4, Campin discloses the method of Claim 1. Campin also teaches receiving optical procedure information at the ophthalmologic treatment platform, the optical procedure information being associated with an optical procedure selected for the patient ([0053] output the IOL parameters for the procedure and output to a display device of the user console); and
generating, at the deep-learning model, one or more optimizations for use during the ophthalmic procedure, the one or more optimizations being presented to a caregiver via an application exposed by the ophthalmic treatment platform ([0037-0038] using machine learning model to generate or predict optimal IOL parameters for the current patient procedure; [0053] output the IOL parameters for the procedure and output to a display device of the user console).
As per Claim 6, Campin discloses the method of Claim 4. Campin also teaches receiving post-operative clinical assessment information regarding the patient at the ophthalmic treatment platform ([0057] receiving user input at a console of survey data for patient satisfaction post-surgery); and
generating, via the deep-learning model, one or more primary care predictions representing a predicted outcome of the ophthalmic procedure based, at least in part, on the post-operative clinical assessment information ([0058] using the treatment result data which includes the post-surgery survey information from the patient, train a machine learning model to generate parameters, [0060] generating recommendations for the parameters of ophthalmic surgery procedure).
As per Claim 7, Campin discloses the method of Claim 1. Campin also teaches the deep-learning model comprises a deep learning model including a neural network having a plurality of layers ([0043] machine learning model includes plurality of layers of a neural network).
As per Claim 8, Campin discloses the method of Claim 7. Campin also teaches re-training the deep-learning model based on updated training data, the updated training data including ophthalmic procedure data associated with the patient, complication data associated with the patient, and patient survey data regarding treatment satisfaction of the patient ([0039] model trainer adjusts the weights of the machine learning model based on the actual treatments given to the patient and adding each new patient information to the model to continually adjust weights/retrain the model, [0023] machine learning models trained using historical patient data of plurality of historical patients, which includes at least treatment data, other information about the treatment, and result data including patient’s satisfaction or dissatisfaction with the treatment result, [0005] the measurements of anatomical parameters provide an indication of complications when the parameters deviate from the known distribution, i.e. are abnormal).
As per Claim 10, Campin discloses the method of Claim 1. Campin also teaches the ophthalmic procedure includes at least one of a cataract surgery, a retina surgery, a glaucoma surgery, a corneal transplant, and a LASIK surgery (Abstract the ophthalmic procedure is cataract surgery).
As per Claim 20, Campin discloses the method of Claim 19. Campin also teaches an application exposed to a treatment provider (see Fig. 1A/1B user console which received predicted parameters, [0026] user console is used by surgeon), the application presenting a user interface including at least the ophthalmic lens type recommendation and one or more potential surgical outcomes ([0052-0053] predict IOL parameters recommended to optimize surgical outcomes, user console has a user interface which presents IOL parameters).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Hacker et al. (US 2015/0057989 A1), hereinafter Hacker.
As per Claim 3, Campin discloses the method of Claim 1. However, Campin may not explicitly disclose the following which is taught by Hacker: generating, at the first stage of treatment, a plurality of probabilities of the predetermined surgical outcome associated with each of a plurality of different ophthalmic lens types (see Fig. 6/[0047] where the surgical outcome for different IOL types are generated and presented; [0103-0104] the input parameters include different IOL types to determine surgical outcome); and
receiving a selection of the ophthalmic lens type from a user ([0067]/[0075] displaying the different outcomes to the operator for selection, [0077] selection received of an IOL type/parameter based on the displayed outcomes).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining outcomes for each lens type and selecting a lens type to use from Hacker with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to select an appropriate IOL for implanting intraocular lenses (Hacker [0035]).
As per Claim 9, Campin discloses the method of Claim 1. Campin also teaches calculate one or more optical characteristics including lens spherical power, toric power, and aberrations ([0023] IOL parameters include IOL power).
However, Campin may not explicitly disclose the following which is taught by Hacker: wherein the deep-learning model is configured to generate the one or more primary care predictions by automatically compensating for changes in lens positions without the dependence on any manufacturer provided intraocular calculator constants ([0023] model to generate prediction is based on the position of the IOL in the eye based on ray tracing methods, i.e. not depending on manufacturer constants).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining recommendations based on changes in lens position from Hacker with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to select an appropriate IOL for implanting intraocular lenses (Hacker [0035]).
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Sweeney (US 2023/0086252 A1), hereinafter Sweeney.
As per Claim 5, Campin discloses the method of Claim 4. However, Campin may not explicitly disclose the following which is taught by Sweeney: generating one or more mechanical characteristics used during the ophthalmic procedure to mitigate risk of posterior capsular rupture ([0021] increasing the distance between the phacoemulsification tip and the posterior capsule to mitigate the risk of posterior capsule rupture, where the increased distance is the generated mechanical characteristics).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating parameters to decrease risk of posterior capsular rupture from Sweeney with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to minimize complications in cataract surgical procedures which improves surgical outcomes (Sweeney [0006]).
Claims 11 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Weeber (US 2011/0270596 A1), hereinafter Weeber.
As per Claim 11, Campin discloses the method of Claim 1. However, Campin may not explicitly disclose the following which is taught by Weeber: the treatment recommendation includes a lens parameter adjustment ([0045] the optimization of the lens can be a clinical adjustment to the designed lens for implantation during the procedure).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating a recommendation for a lens parameter adjustment from Weeber with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to improve the optimization of clinical implementations of lenses based on the optimized lens design (Weeber [0010]).
As per Claim 21, Campin discloses a computer-implemented method of managing treatment of an ophthalmic patient, the method comprising:
collecting optical information from a patient, wherein the optical information includes one or more input parameters including eye dimensional measurements ([0008] receiving data points including measurements of anatomical parameters of an eye, [0028] raw data includes measurements of specific features), the optical information being received as input parameters by an ophthalmic treatment platform hosting a deep-learning model, the deep-learning model being trained using training data including historical ophthalmic procedure data associated with a plurality of patients, complication data associated with the plurality of patients, and patient survey data regarding treatment satisfaction of the plurality of patients ([0022] machine learning models trained with historical patient data, [0023] machine learning models trained using historical patient data of plurality of historical patients, which includes at least treatment data, other information about the treatment, and result data including patient’s satisfaction or dissatisfaction with the treatment result, [0005] the measurements of anatomical parameters provide an indication of complications when the parameters deviate from the known distribution, i.e. are abnormal); and
generating, at the deep-learning model, a treatment recommendation regarding an ophthalmological treatment ([0007] generate a recommendation including IOL parameters using trained machine learning model).
However, Campin may not explicitly disclose the following which is taught by Weeber: the treatment recommendation including a custom intraocular lens design recommendation (Abstract predictive modeling to design ophthalmic lenses, [0076] custom lens design for individual patients).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating a recommendation for a custom intraocular lens design from Weeber with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to improve the optimization of clinical implementations of lenses based on the optimized lens design (Weeber [0010]).
As per Claim 22, Campin and Weeber discloses the method of Claim 21. Weeber also teaches the custom intraocular lens design recommendation corresponds to at least one of a refractive lens design or a diffractive lens design ([0007] ophthalmic lens may be a diffractive or refractive lens).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating a recommendation for a custom intraocular refractive or diffractive lens design from Weeber with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to improve the optimization of clinical implementations of lenses based on the optimized lens design (Weeber [0010]).
As per Claim 23, Campin and Weeber discloses the method of Claim 21. Campin also teaches in response to selection of the custom intraocular lens design recommendation, generation of one or more alerts or recommendations regarding treatment to optimize a patient outcome ([0050] machine learning model generates optimal IOL parameters for a new patient, i.e. custom recommendations, [0053] generate and transmit electronic messages including the recommended IOL parameters to a destination device).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Weeber (US 2011/0270596 A1), in view of Rice (US 2009/0132445 A1).
As per Claim 12, Campin discloses the method of Claim 1. However, Campin may not explicitly disclose the following which is taught by Weeber: the historical ophthalmic procedure data includes pre-operative decisions and lens parameter adjustments for ophthalmic lenses selected for historical ophthalmic procedures ([0065] data includes historical data including optical parameters, clinical outcomes, recommended actions, [0045] the lens adjustments are part of the collected data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of using historical procedure data including lens parameter adjustments for modelling outcomes from Weeber with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to improve the optimization of clinical implementations of lenses based on the optimized lens design (Weeber [0010]).
However, Campin and Weeber may not explicitly disclose the following which is taught by Rice: the deep-learning model is configured to generate a classification probability associated with each of a plurality of lens parameter adjustments such that
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wherein "v" is a given input variable for a lens parameter, and in case of continuous variables where the values of yv are non-binary, and wherein ki represents a correlating weight that is proportional to the probability that the outcome predicted branch lies within the classes -1 or +1 or an analogous value x ([0040] y is 1 or 0, binary values for features of the individual to predict a classification).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating classification probabilities for the outcomes from Rice with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin and Weeber in order to use the best model with best model performance to result in the most accurate outcome (Rice [0018]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Hall et al. (US 2023/0148321 A1), hereinafter Hall.
As per Claim 13, Campin discloses the method of Claim 1. Campin may not explicitly disclose the following which is taught by Hall: the deep-learning model is adaptable in at least near real-time, and is fine-tuned by computing rate of change of weights for each variable and assigning weighting for probability of each variable based on distances and
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and probability multipliers scaled based on a preset threshold.
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([0013] AI model training includes adjustment and tuning of the model configurations, i.e. weights, to optimize performance of the model according to an accuracy metric, [0125] confidence metrics take into account the distance in the distribution between the score for correct and incorrect classifications).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of fine tuning a learning model from Hall with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin order to generate AI models that perform well on new or unknown datasets (Hall [0021]).
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Hall (US 2023/0148321 A1), in view of Rice (US 2009/0132445 A1).
As per Claim 14, Campin discloses the method of Claim 1. However, Campin may not explicitly disclose the following which is taught by Hall: the deep-learning model includes a plurality of classifiers (see Fig. 1B generating an ensemble model includes using at least two trained AI Models, i.e. classifiers).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of using plurality of classifiers from Hall with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin order to generate AI models that perform well on new or unknown datasets (Hall [0021]).
However, Campin and Hall may not explicitly disclose the following which is taught by Rice: includes an output that is based, at least in part, on dependencies of current and previous information associated with a patient such that P(On) = 1P(Oil|
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1) where (On) is the outcome for the current patient based on the outcome Oi-1 of patient i-1.
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([0028] classification models learn from the successes of the previous classifications where the previous classifications/outcomes are used to determine the outcomes of the current model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating classification probabilities for the outcomes using current and previous data from Rice with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin and Hall in order to use the best model with best model performance to result in the most accurate outcome (Rice [0018]).
As per Claim 15, Campin, Hall, and Rice discloses the method of Claim 14. Campin also teaches the deep-learning model is trained to generate a probability of each of a set of outcomes O={o1, 02 , 03... ot} where oie {good outcome, bad outcome} ([0042] model outputs a probability distribution of the IOL types, i.e. probability of outcomes), wherein each observation of the patient comes from an unknown state and will have an unknown sequence Q={qi, q2,q3... qt} where qi ϵ {monofocal, extended depth of focus, bifocal, trifocal, quadrifocal, or any lens models or variations within any model to account for toricity or aberration profile} ([0036] model can use unsupervised learning where the input data is not labeled, i.e. unknown state).
However, Campin and Hall may not explicitly disclose the following which is taught by Rice:
to calculate P(q1, q2, q3…qt|o1, 02, 03…0t)
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corresponding to a combined probability used for decision making by the deep-learning model for patient k such that Pk-1 for Pk< 0 and +1 for Pk>0 and is not used in the decision-making process if Pk= 0 ([0043] t-value cannot equal zero, if it equals zero it is not used).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating combined classification probabilities for the outcomes from Rice with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin and Hall in order to use the best model with best model performance to result in the most accurate outcome (Rice [0018]).
As per Claim 16, Campin, Hall, and Rice discloses the method of Claim 15. Hall also teaches each classifier is selectively excluded based on whether a confidence level is below a predetermined threshold ([0027] deploying the AI model if the confidence metric exceeds an acceptance threshold, Examiner notes that it would be obvious if the confidence metric does not exceed the threshold that it is not deployed or excluded).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of excluding classifiers which do not meet a minimum threshold confidence level from Hall with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin order to generate AI models that perform well on new or unknown datasets (Hall [0021]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Kashani et al. (US 2023/0031527 A1), hereinafter Kashani, in view of Sweeney (US 2023/0086252 A1).
As per Claim 17, Campin discloses the method of Claim 1. Campin also teaches collecting optical information from a patient includes applying at least one of an optical measurement device or an ultrasound device to a cataract patient (see Fig. 1A/[0007] measurements devices used to collect anatomical measurements of the eye for a patient of cataract surgery).
However, Campin may not explicitly disclose the following which is taught by Kashani:
wherein the deep-learning model is used to generate a predicted corrected axial length ([0047] machine learning model to predict patient-specific threshold distance for values such as axial length).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating a predicted axial length from Kashani with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to prepare a surgical plan for cataract surgery to be performed on a patient with the best outcome (Kashani [0002]).
However, Campin and Kashani may not explicitly disclose the following which is taught by Sweeney: the cataract patient is a low dense cataract patient ([0022]the surgery is to treat a cataract nucleus which is too dense).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of generating recommendations for a low dense cataract patient from Sweeney with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin and Kashani in order to minimize complications in cataract surgical procedures which improves surgical outcomes (Sweeney [0006]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Campin (US 2022/0331092 A1), in view of Debuc (US 2011/0275931 A1), hereinafter Debuc.
As per Claim 18, Campin discloses the method of Claim 1. However, Campin may not explicitly disclose the following which is taught by Debuc: collecting optical information from a patient includes determining an estimated difference in vitreoretinal interface in a high dense cataract patient ([0159] calculate the distance between the vitreoretinal interface which indicates total retinal thickness, i.e. density level of the patient).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining the difference in vitreoretinal interface from Debuc with the known process of treating an ophthalmic patient based on a determined treatment recommendation from Campin in order to use quantitative computing assisted tools to aid in diagnosis of cataracts and improve treatment recommendations (Debuc [0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patton (US 2020/0345431 A1) teaches generating treatment recommendations to treat disease and injury to the eye based on patient specific data entered into a machine learning algorithm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682