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 .
Information Disclosure Statement
Acknowledgement is made of receipt of Information Disclosure Statement (PTO-1449) filed 07/29/2025 and 02/21/2025. An initialed copy is attached to this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4 and 8-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the simulation" in 1. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “the simulation" will be interpreted as “a simulation of one or more treatment scenarios for the patient eye based on the state of the eye condition” as described in claim 1.
Claim 4 recites the limitation "the simulation" in 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “the simulation" will be interpreted as “a simulation of one or more treatment scenarios for the patient eye based on the state of the eye condition” as described in claim 1.
Claim 8 recites the limitation "the simulation" in 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “the simulation" will be interpreted as “a simulation of one or more treatment scenarios for the patient eye based on the state of the eye condition” as described in claim 1.
Claim 9 recites the limitation "the simulation" in 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “the simulation" will be interpreted as “a simulation of one or more treatment scenarios for the patient eye based on the state of the eye condition” as described in claim 1.
Claim 10 recites the limitation "the simulating" in 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes “the simulating " will be interpreted as “a simulation of one or more treatment scenarios for the patient eye based on the state of the eye condition” as described in claim 1.
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 1-7 and 10-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kawczynski et al. (US 20220230300 A1).
Regarding claim 1, Kawczynski discloses in at least figure 13, an ophthalmic system for predictive vision biometrics (network 1300 includes one or more eye imaging systems 1305 and visual function assessment system 1310 fig. 13):
an ophthalmic multispectral imaging device (one or more eye imaging systems 1305 fig. 13) configured to generate ophthalmic multispectral data for a patient eye (eye imaging systems can include color fundus photography and OCT image paragraph [0188] using an interferometer that can split a low coherence near-infrared light beam towards the target tissue and a reference mirror paragraph [0044]);
a memory (a machine-learning model system 1315 may include one or more computing systems with one or more memories paragraph [0190]) comprising executable instructions (machine-learning model system 1315 may be configured to train and/or use a machine-learning model, which may include one or more preprocessing functions paragraph [0191]); and
a processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]) in communication with (a machine-learning model system 1315 may include one or more computing systems having one or more processors and one or more memories paragraph [0190] and is in contact with the one or more eye imaging systems 1305 fig. 13) the memory (a machine-learning model system 1315 may include one or more computing systems with one or more memories paragraph [0190]) and the ophthalmic multispectral imaging device (one or more eye imaging systems 1305 fig. 13) and configured to execute the executable instructions to (machine-learning model system 1315 may be configured to train and/or use a machine-learning model, which may include one or more preprocessing functions paragraph [0191]):
determine a state of an eye condition in a patient eye (multiple visual-acuity metrics are received paragraph [0192]) based on the ophthalmic multispectral data (eye imaging systems can include optical coherence tomography, color fundus photography and a fundus camera paragraph [0188]); and
simulate one or more treatment scenarios for the patient eye (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) based on the state of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) and a model for the one or more treatment scenarios (a model may be trained using a training data set that includes images of subjects' eyes before treatment is administered and that includes a predicted visual acuity paragraph [0095]).
Regarding claim 2, Kawczynski discloses all the limitations of claim 1 and further discloses, wherein the processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]) is further configured to execute the instructions (machine-learning model system 1315 may be configured to train and/or use a machine-learning model, which may include one or more preprocessing functions paragraph [0191]) to at least one of record (not required by claim) or display data (the visual-acuity metric may be presented via a user interface paragraph [0112]) resultant from (based on the image, a visual-acuity metric corresponding to a predicted visual acuity of the eye paragraph [0111]) the simulated one or more treatment scenarios (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]).
Regarding claim 3, Kawczynski discloses all the limitations of claim 1 and further discloses, wherein the simulation (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) comprises a prediction of a resultant state of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in response to hypothetical execution of the one or more treatment scenarios (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]).
Regarding claim 4, Kawczynski discloses all the limitations of claim 1 and further discloses, wherein:
the simulation (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) comprises:
a prediction of a first resultant state (model predictions may be used to inform a decision whether to recommend or use an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab) paragraph [0102]) of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in response to hypothetical execution of a first treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is anti-VEGF injections paragraph [0097]); and
a prediction of a second resultant state (model predictions may be used to inform a decision whether to recommend or use a PDS or PDT for a particular subject and/or subject eye paragraph [0102]) of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in response to hypothetical execution of a second treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is PDS paragraph [0099]); and
the processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]) is further configured to execute the instructions (machine-learning model system 1315 may be configured to train and/or use a machine-learning model, which may include one or more preprocessing functions paragraph [0191]) to generate a comparative output (model predictions may be used to inform a decision whether to recommend or use (for example) an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab), a PDS or PDT for a particular subject and/or subject eye paragraph [0102]) with respect to the first resultant state (model predictions may be used to inform a decision whether to recommend or use an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab) paragraph [0102]) and the second resultant state (model predictions may be used to inform a decision whether to recommend or use a PDS or PDT for a particular subject and/or subject eye paragraph [0102]).
Regarding claim 5, Kawczynski discloses all the limitations of claim 4 and further discloses, wherein:
the first treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is anti-VEGF injections paragraph [0097]) corresponds to a prescribed medication regimen (anti-VEGF injections are injections into the vitreous of the eye to stop the abnormal growth of blood vessels paragraph [0097]); and
the second treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is PDS paragraph [0099]) corresponds to a deviation from the prescribed medication regimen (The PDS is a permanent refillable eye implant that can continuously deliver the anti-VEGF medication to the eye over a period of months paragraph [0099]).
Regarding claim 6, Kawczynski discloses all the limitations of claim 1 and further discloses, wherein the model (machine-learning model system 1315 can train the machine-learning model paragraph [0192]) is based on historical data related to an effectiveness (one metric may correspond to a visual functional test administered approximately 6 months or approximately 1 year after the image of the eye was collected paragraph [0192]) of the one or more treatment scenarios (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) in treating the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in other patients (machine-learning model system 1315 can then receive another image corresponding to another subject paragraph [0193]).
Regarding claim 7, Kawczynski discloses all the limitations of claim 1 and further discloses, wherein the ophthalmic multispectral imaging device (one or more eye imaging systems 1305 fig. 13) operates over a monochromatic wavelength (eye imaging systems can include and OCT image paragraph [0188] using an interferometer that can split a low coherence near-infrared light beam towards the target tissue and a reference mirror paragraph [0044]).
Regarding claim 10, Kawczynski discloses all the limitations of claim 1 and further discloses, further comprising receiving input data (each of the multiple training data elements can include input data of one or more images of at least part of an eye paragraph [0058]) comprising a visual acuity associated with the patient eye (each of the multiple training data elements can further include a label that includes or otherwise indicates a visual acuity associated with the particular subject paragraph [0059]),
wherein the simulating (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) comprises predicting a future visual acuity (a predicted future visual acuity paragraph [0095]) in relation to the patient eye in response to hypothetical execution of the one or more treatment scenarios (a care provider may be able to predict a degree to which visual acuity will change while using a particular treatment paragraph [0096]).
Regarding claim 11, Kawczynski discloses in at least figure 13, a method for predictive vision biometrics (a network 1300 of computing systems that can be configured to perform one or more actions and/or part or all of one more methods disclosed herein paragraph [0188] and network 1300 includes one or more eye imaging systems 1305 and visual function assessment system 1310 fig. 13), the method comprising:
generating, by an ophthalmic multispectral imaging device (one or more eye imaging systems 1305 fig. 13), ophthalmic multispectral data for a patient eye (eye imaging systems can include color fundus photography and OCT image paragraph [0188] using an interferometer that can split a low coherence near-infrared light beam towards the target tissue and a reference mirror paragraph [0044]);
determining, by a processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]) in communication with (a machine-learning model system 1315 may include one or more computing systems having one or more processors and one or more memories paragraph [0190] and is in contact with the one or more eye imaging systems 1305 fig. 13) the ophthalmic multispectral imaging device (one or more eye imaging systems 1305 fig. 13), a state of an eye condition in the patient eye (multiple visual-acuity metrics are received paragraph [0192]) based on the ophthalmic multispectral data (eye imaging systems can include optical coherence tomography, color fundus photography and a fundus camera paragraph [0188]); and
simulating (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]), by the processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]), one or more treatment scenarios for the patient eye based on the state of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) and a model for the one or treatment scenarios (a model may be trained using a training data set that includes images of subjects' eyes before treatment is administered and that includes a predicted visual acuity paragraph [0095]).
Regarding claim 12, Kawczynski discloses all the limitations of claim 11 and further discloses, further comprising at least one of recording (not required by claim) or displaying (the visual-acuity metric may be presented via a user interface paragraph [0112])), by the processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]), data resultant from the simulated one or more treatment scenarios (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]).
Regarding claim 13, Kawczynski discloses all the limitations of claim 11 and further discloses, wherein the simulating (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) comprises predicting a resultant state of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in response to hypothetical execution of the one or more treatment scenarios (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]).
Regarding claim 14, Kawczynski discloses all the limitations of claim 11 and further discloses, wherein:
the simulating (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095]) comprises:
predicting a first resultant state (model predictions may be used to inform a decision whether to recommend or use an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab) paragraph [0102]) of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) in response to hypothetical execution of a first treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is anti-VEGF injections paragraph [0097]); and
predicting a second resultant state (model predictions may be used to inform a decision whether to recommend or use a PDS or PDT for a particular subject and/or subject eye paragraph [0102]) of the eye condition (multiple visual-acuity metrics are received paragraph [0192]) of the eye condition in response to hypothetical execution of a second treatment scenario (a predicted future visual acuity may thus inform whether a given subject will effectively respond to a particular treatment paragraph [0095] one treatment option is PDS paragraph [0099]); and
the method further comprises generating (machine-learning model system 1315 may be configured to train and/or use a machine-learning model, which may include one or more preprocessing functions paragraph [0191]), by the processor (a machine-learning model system 1315 may include one or more computing systems having one or more processors paragraph [0190]), a comparative output (model predictions may be used to inform a decision whether to recommend or use (for example) an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab), a PDS or PDT for a particular subject and/or subject eye paragraph [0102]) with respect to the first resultant state model predictions may be used to inform a decision whether to recommend or use an anti-VEGF agent, an anti-VEGF agent that is delivered via intraocular injection, a particular VEGF agent (e.g., ranibizumab or bevacizumab) paragraph [0102]) and the second resultant state (model predictions may be used to inform a decision whether to recommend or use a PDS or PDT for a particular subject and/or subject eye paragraph [0102]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kawczynski et al. (US 20220230300 A1) as applied to claim 1 above and in view of Tatara et al. (US 12402790 B2).
Regarding claim 8, Kawczynski discloses all the limitations of claim 1.
Kawczynski does not disclose, wherein the processor is further configured to execute the instructions to generate a simulated image of the patient eye based on a result of the simulation.
However Tatara discloses in at least figure 12, wherein the processor (calculating processor 4063 fig. 12) is further configured to execute the instructions (calculating processor 4063 performs a simulation using the ocular characteristic value (and a predetermined ocular parameter value) of the subject's eye to estimate the average depth level of the RPE col. 27 lines 47-51) to generate a simulated image of the patient eye (a virtual image region in a simulation model col. 27 line 58) based on a result of the simulation (calculating processor 4063 performs a simulation col. 27 lines 47-48).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use a simulated image as taught by Tatara in the network of Kawczynski. The RPE shape estimation may be performed using an arbitrary calculation (operation) technique, and is typically performed using an arbitrary simulation (col. 29 lines 21-24).
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kawczynski et al. (US 20220230300 A1) as applied to claim 1 above and in view of Yang et al. (US 20230036463 A1).
Regarding claim 9, Kawczynski discloses all the limitations of claim 1.
Kawczynski does not disclose, wherein:
the determination of a state comprises measuring a surface area of a lesion in an image of the patient eye; and the simulation comprises predicting a future surface area of the lesion in response to hypothetical execution of the one or more treatment scenarios.
However Yang discloses, wherein:
the determination of a state (the determination may be made by evaluating a rule that indicates that an eligibility requirement is that a predicted growth rate or predicted volume exceeds a predetermined lower threshold and/or is below a predetermined upper threshold paragraph [0045]) comprises measuring a surface area of a lesion (the present embodiments include a multi-task 3D neural network for GA lesion size detection paragraph [0039]) in an image of the patient eye (3D OCT images paragraph [0039]); and
the simulation (GA analysis controller 255 may generate an output of the prediction identifying the subsequent size of geographic atrophy or subsequent growth of geographic atrophy in the eye of the subject paragraph [0077]) comprises predicting a future surface area of the lesion (predicted lesion size paragraph [0077]) in response to hypothetical execution of the one or more treatment scenarios (a rule may be configured to be satisfied to recommend a particular type of treatment approach when a predicted lesion size and/or predicted progression is within a predefined open or closed range paragraph [0077]). The GA analysis controller 255 can process the prediction and communicate the results to the client device 210 or other system (paragraph [0077]).
Therefore, it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use a simulated lesion size as taught by Yang in the network of Kawczynski.
Regarding claim 15, Kawczynski discloses all the limitations of claim 11.
Kawczynski does not disclose, wherein:
the determination of a state comprises measuring a surface area of a lesion in an image of the patient eye; and the simulation comprises predicting a future surface area of the lesion in response to hypothetical execution of the one or more treatment scenarios.
However, Yang discloses, wherein:
the determination of a state (the determination may be made by evaluating a rule that indicates that an eligibility requirement is that a predicted growth rate or predicted volume exceeds a predetermined lower threshold and/or is below a predetermined upper threshold paragraph [0045]) comprises measuring a surface area of a lesion (the present embodiments include a multi-task 3D neural network for GA lesion size detection paragraph [0039]) in an image of the patient eye (3D OCT images paragraph [0039]); and
the simulation (GA analysis controller 255 may generate an output of the prediction identifying the subsequent size of geographic atrophy or subsequent growth of geographic atrophy in the eye of the subject paragraph [0077]) comprises predicting a future surface area of the lesion (predicted lesion size paragraph [0077]) in response to hypothetical execution of the one or more treatment scenarios (a rule may be configured to be satisfied to recommend a particular type of treatment approach when a predicted lesion size and/or predicted progression is within a predefined open or closed range paragraph [0077]). The GA analysis controller 255 can process the prediction and communicate the results to the client device 210 or other system (paragraph [0077]).
Therefore, it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use a simulated lesion size as taught by Yang in the network of Kawczynski.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rejman et al. (US 20250099614 A1) discloses an encoded transcription factor with a lesion size measurement.
Lyman et al. (US 20200160980 A1) discloses an ECG interpretation system with a machine learning prediction of lesion size.
Shimizu et al. (US 20150221125 A1) discloses an image processing method with a simulated image from the appearance of a target.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW R WRIGHT whose telephone number is (703)756-5822. The examiner can normally be reached Mon-Thurs 7:30-5 Friday 8-12.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pinping Sun can be reached at 1-571-270-1284. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW R WRIGHT/Examiner, Art Unit 2872
/WILLIAM R ALEXANDER/Primary Examiner, Art Unit 2872
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