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 .
Remarks
In response to communications sent June 26, 2023 claim(s) 1-20 are pending in this application; of these claims 1, 14, and 18 are in independent form. Claims 21 and 22 are cancelled.
Response to Amendment
The preliminary amendments to the claims and specification, filed June 26, 2023, are acknowledged and have been entered into the record.
Priority
The provisional patent application is a slide deck (including footnotes) with less text than the specification of the non-provisional patent application. At the very least, the provisional patent application does not mention a “treatment scar parameter.”
Claims 1-7, 11-13, and 18-19 have a filing date of the provisional patent application, September 23, 2020.
Claims 10, 16, and 20 have a filing date of the non-provisional patent application, September 22, 2021 because these claims recite the element of a “treatment scar parameter” at the least. This element was not located in the provisional patent application.
Claims 8-10 and 14-17 have a filing date of the non-provisional patent application, September 22, 2021 because these claims recite the element of a “demographic data” at the least. This element was not located in the provisional patent application.
Drawings
The drawing(s) filed on March 22, 2023 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: September 22, 2023; April 25, 2025; and November 1, 2025.
Claim Objections
Claim 16 is objected to because of the following informalities: the claims recite the phrase “total area cyst change” in two portions of the claim. However, the specification uses the grammatically preferred phrase: “total area of cyst change”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
"an injection prediction platform configured to receive..." in claim 18.
"a computational model"... "configured to predict" in claim 18.
"a treatment manager configured to generate" in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 18 and 19 invoke U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, no association between the structure and the function can be found in the specification.
Claim 20 is rejected because it depends from claim 18.
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 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 18: Claim limitation "an injection prediction platform configured to receive..." invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
As to claim 18: Claim limitation "a computational model"... "configured to predict" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
As to claim 19: Claim limitation "a treatment manager configured to generate" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
No association between the structure and the function can be found in the specification. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 20 is rejected because it depends from claim 18.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mathematical calculation and a mental process for pre-processing data that is used for the mathematical calculation; claim 20 also recites the mental process of making a recommendation based on the calculation. This judicial exception is not integrated into a practical application because the additional elements are insignificant pre-solution activity that do not meaningfully limit the judicial exception, instead covering all uses of the judicial exception. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the data gathering is well-understood, routine, and conventional because retrieving data over a network or from memory has been found by the courts to be well-understood, routine, and conventional; see 2106.05(d)(II) under “Receiving or transmitting data over a network” and “Storing and retrieving information in memory”).
1. A method for managing a treatment of a subject diagnosed with a macular edema condition, the method comprising:
receiving subject data for a subject, the subject data comprising best corrected visual acuity (BCVA) data for the subject (pre-solution activity which does not meaningfully limit the judicial exception, instead covering all uses of the judicial exception; this is well-understood, routine, and conventional because retrieving data over a network or from memory has been found by the courts to be well-understood, routine, and conventional; see 2106.05(d)(II) under “Receiving or transmitting data over a network” and “Storing and retrieving information in memory”);
generating an input for a computational model using the subject data (mental process of pre-processing data, which can be performed in the human mind or with a pen and paper); and
predicting, via the computational model, an injection frequency for the treatment of the subject diagnosed with the macular edema condition based on the input (Applicant’s specification establishes that the computational model for prediction may be a logistic regression; a logistic regression is a mathematical calculation; a mathematical calculation is an abstract idea and a judicial exception to patent eligibility).
2. The method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that indicates the injection frequency as being above a threshold injection frequency (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
3. The method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that indicates the injection frequency as being below a threshold injection frequency (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
4. The method of claim 3, wherein the threshold injection frequency is two (2) injections during a management period that occurs after an initial treatment period (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
5. The method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that identifies a frequency category from a plurality of frequency categories for the treatment of the subject (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
6. The method of claim 5, wherein the plurality of frequency categories comprises a high frequency category and a low frequency category (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
7. The method of claim 6, wherein the high frequency category corresponds to three (3) or more injections during a management period that occurs after an initial treatment period and wherein the low frequency category corresponds to two (2) or fewer injections during the management period (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
8. The method of claim 1, wherein the generating comprises:
generating the input for the computational model using the BCVA data and at least one of image-derived data or demographic data (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
9. The method of claim 8, wherein the image-derived data includes central thickness data, wherein the central thickness data comprises at least one of a data for a central foveal thickness (CFT) parameter or a central subfield thickness (CST) parameter (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
10. The method of claim 9, wherein the image-derived data comprises data for at least one of a parameter corresponding to a presence of a subretinal fluid, a parameter corresponding to a presence of retinal thickening, a parameter corresponding to a presence of a cystoid space within a selected distance of a center of a retina, a parameter corresponding to a presence of an epiretinal membrane, a parameter corresponding to a presence of a pigment disturbance, a parameter corresponding to a presence of collateral vessels on disc, a parameter corresponding to a presence of retinal collateral vessels, a parameter corresponding to a presence of retinal hemorrhage, a total area of leakage in the central subfield, a total area of leakage in a central inner outer subfield, a total area of cyst change in the central subfield, a total area of cyst change in the central inner outer subfield, or a treatment scar parameter (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
11. The method of claim 1, further comprising:
generating a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency predicted for the treatment (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
12. The method of claim 1, wherein the computational model comprises a trained logistic regression model (this is a mathematical calculation, which is a judicial exception).
13. The method of claim 1, wherein the computational model comprises a machine learning model and further comprising:
training the machine learning model using training data that comprises BCVA training data, wherein the BCVA training data comprises a mean BCVA score for each of a plurality of training subjects corresponding to a selected period of time (this is training of a parameters of a logistic regression, which is a mathematical calculation).
14. A method for managing a treatment of a subject diagnosed with a macular edema condition, the method comprising
receiving subject data for a subject diagnosed with the macular edema condition, the subject data comprising best corrected visual acuity (BCVA) data for the subject and at least one of image-derived data or demographic data for the subject (pre-solution activity which does not meaningfully limit the judicial exception, instead covering all uses of the judicial exception; this is well-understood, routine, and conventional because retrieving data over a network or from memory has been found by the courts to be well-understood, routine, and conventional; see 2106.05(d)(II) under “Receiving or transmitting data over a network” and “Storing and retrieving information in memory”);
generating an input for a computational model using the subject data (mental process of pre-processing data, which can be performed in the human mind or with a pen and paper);
predicting, via the computational model, an injection frequency for the treatment of the subject diagnosed with the macular edema condition based on the input by generating an injection frequency output (Applicant’s specification establishes that the computational model for prediction may be a logistic regression; a logistic regression is a mathematical calculation; a mathematical calculation is an abstract idea and a judicial exception to patent eligibility); and
generating a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency output (this is an output from the computational model, which may be the output of a regression model, which is a mathematical calculation).
15. The method of claim 14, wherein the image-derived data comprises central thickness data (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
16. The method of claim 14, wherein the image-derived data comprises at least one of a treatment scar parameter, a total area cyst change central subfield, or a total area cyst change central inner outer subfield (this limits the outputting from the computational model, which may be the output of a regression model, which is a mathematical calculation).
17. The method of claim 14, wherein the computational model comprises a machine learning model (the logistic regression, as described in the specification, learns parameters for model optimization).
18. A computer system comprising:
an injection prediction platform configured to receive subject data for a subject and to generate an input using the subject data, wherein the subject data comprises best corrected visual acuity (BCVA) data for the subject (Applicant’s specification establishes that the computational model for prediction may be a logistic regression; a logistic regression is a mathematical calculation; a mathematical calculation is an abstract idea and a judicial exception to patent eligibility); and
a computational model that is part of the injection prediction platform and configured to predict an injection frequency for a treatment of the subject diagnosed with a macular edema condition based on the input (Applicant’s specification establishes that the computational model for prediction may be a logistic regression; a logistic regression is a mathematical calculation; a mathematical calculation is an abstract idea and a judicial exception to patent eligibility).
19. The computer system of claim 18, further comprising:
a treatment manager configured to generate a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency predicted (a recommendation is a mental process that can be performed in the human mind, and is an abstract idea and a judicial exception).
20. The computer system of claim 18, wherein the subject data further comprises data for at least one of a central foveal thickness parameter, a central subfield thickness parameter, a treatment scar parameter, a total area cyst change central subfield, or a total area cyst change central inner outer subfield (this limits the computational model, which may be a regression model, which is a mathematical calculation).
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US-20230157533-A1 (“Chang”).
As to claim 1, Chang teaches a method for managing a treatment of a subject diagnosed with a macular edema condition (Chang Para [0134]: treating a patient diagnosed with macular edema, which is at once envisaged as a disclosed embodiment in Chang), the method comprising:
receiving subject data for a subject (Chang Para [0152]: step 402, receive data for a subject), the subject data comprising best corrected visual acuity (BCVA) data for the subject (Chang Para [0032]: the subject data including BCVA data);
generating an input for a computational model using the subject data (Chang Para [0158]: use the subject data to generate a dataset that is inputted into the machine-learning model); and
predicting, via the computational model, an injection frequency for the treatment of the subject diagnosed with the macular edema condition based on the input (Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another; Chang Para [0229], combined with Para [0172], teaches that the dosing may be a pro re nata injection schedule).
As to claim 2, Chang teaches the method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that indicates the injection frequency as being above a threshold injection frequency (Chang Para [0194]: a high frequency regimen; a high value is inherently above at least one threshold frequency value).
As to claim 3, Chang teaches the method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that indicates the injection frequency as being below a threshold injection frequency (Chang Para [0194]: a low frequency regimen; a low value is inherently below at least on threshold frequency).
As to claim 4, Chang teaches the method of claim 3, wherein the threshold injection frequency is two (2) injections during a management period that occurs after an initial treatment period (Chang Para [0194]: a low frequency regimen; the management period is unspecified; therefore, any injection frequency is below 2 injections divided by management period duration, for some duration, as long as the duration is not zero; this is because 2/x can be any positive number for at least some value of x; here, x is the management period, and it is not specified in the claim; therefore, the broadest reasonable interpretation of the claim encompasses a value for the management period that is sufficient for the two injections per management period to be a threshold value for the injection frequency).
As to claim 5, Chang teaches the method of claim 1, wherein the predicting comprises:
generating, via the computational model, an injection frequency output that identifies a frequency category from a plurality of frequency categories for the treatment of the subject Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another.
As to claim 6, Chang teaches the method of claim 5, wherein the plurality of frequency categories comprises a high frequency category and a low frequency category (Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another; if the dosing frequencies are different, one is higher than the other).
As to claim 7, Chang teaches the method of claim 6, wherein the high frequency category corresponds to three (3) or more injections during a management period that occurs after an initial treatment period and wherein the low frequency category corresponds to two (2) or fewer injections during the management period (Chang Para [0231]: “administering to the patient three individual doses of the VEGF antagonist at 4-week intervals, and thereafter administering to the patient an additional dose every 4, 8 or 12 weeks”).
As to claim 8, Chang teaches the method of claim 1, wherein the generating comprises:
generating the input for the computational model using the BCVA data (Chang Para [0032]: BCVA as input data) and at least one of image-derived data or demographic data (Chang Para [0034]: input data captured from images).
As to claim 9, Chang teaches the method of claim 8, wherein the image-derived data includes central thickness data (Chang: [0032]: thickness data), wherein the central thickness data comprises at least one of a data for a central foveal thickness (CFT) parameter or a central subfield thickness (CST) parameter (Chang Para [0034]: central subfield foveal thickness).
As to claim 10, Chang teaches the method of claim 9, wherein the image-derived data comprises data for at least one of a parameter corresponding to a presence of a subretinal fluid (Chang Para [0183]: subretinal fluid imaging for the modeling), a parameter corresponding to a presence of retinal thickening (Chang Para [0183]: retinal thickness imaging for the modeling), a parameter corresponding to a presence of a cystoid space (Chang Para [0183]: cyst volume) within a selected distance of a center of a retina (this element is in the alternative and does not need to be mapped), a parameter corresponding to a presence of an epiretinal membrane (Chang Para [0183]: epiretinal membrane probability), a parameter corresponding to a presence of a pigment disturbance (Chang Para [0183]: pigment epithelium detachment probability), a parameter corresponding to a presence of collateral vessels on disc (this element is in the alternative and does not need to be mapped), a parameter corresponding to a presence of retinal collateral vessels, a parameter corresponding to a presence of retinal hemorrhage (this element is in the alternative and does not need to be mapped), a total area of leakage in the central subfield (this element is in the alternative and does not need to be mapped), a total area of leakage in a central inner outer subfield (this element is in the alternative and does not need to be mapped), a total area of cyst change in the central subfield (this element is in the alternative and does not need to be mapped), a total area of cyst change in the central inner outer subfield (the broadest reasonable interpretation of “central subfield” or “central inner outer subfield” is the entire macula, including the central, inner, and outer locations of the macula; hence the total area of cyst change of the macula is mapped to Chang Para [0053], which teaches “change in anatomical variable measurements, over a period of time,” which refers to variables like cyst volume in predetermined areas within 1mm, 3mm, and 6mm in Chang Para [0032]; the Examiner interprets “within 1mm, 3mm, and 6mm area” to refer to the macular map, which is what the claimed central, inner, and outer subfields are defined as), or a treatment scar parameter (this element is in the alternative and does not need to be mapped).
As to claim 11, Chang teaches the method of claim 1, further comprising:
generating a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency predicted for the treatment (Chang Para [0062]-[0068]: the drug administration recommendation determines scheduling with a health care provider).
As to claim 12, Chang teaches the method of claim 1, wherein the computational model comprises a trained logistic regression model (Chang Para [0167]: logistic regression algorithm as the machine learning algorithm).
As to claim 13, Chang teaches the method of claim 1, wherein the computational model comprises a machine learning model and further comprising:
training the machine learning model using training data that comprises BCVA training data, wherein the BCVA training data comprises a mean BCVA score for each of a plurality of training subjects corresponding to a selected period of time (Chang Para [0032]: training using longitudinal patient data of BCVA scores).
As to claim 14, Chang teaches a method for managing a treatment of a subject diagnosed with a macular edema condition (Chang para [0134]: treating a patient diagnosed with macular edema, which is at once envisaged as a disclosed embodiment in Chang), the method comprising
receiving subject data for a subject (Chang Para [0152]: step 402, receive data for a subject) diagnosed with the macular edema condition (Chang para [0134]: treating a patient diagnosed with macular edema condition), the subject data comprising best corrected visual acuity (BCVA) data for the subject (Chang Para [0032]: the subject data including BCVA data) and at least one of image-derived data or demographic data for the subject (Chang Para [0034]: input data captured from images);
generating an input for a computational model using the subject data (Chang Para [0158]: use the subject data to generate a dataset that is inputted into the machine-learning model);
predicting, via the computational model, an injection frequency for the treatment of the subject diagnosed with the macular edema condition based on the input (Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another; Chang Para [0229], combined with Para [0172], teaches that the dosing may be a pro re nata injection schedule) by generating an injection frequency output (Chang Para [0194]: generating regimen frequency); and
generating a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency output (Chang Para [0062]-[0068]: the drug administration recommendation determines scheduling with a health care provider).
As to claim 15, Chang teaches the method of claim 14, wherein the image-derived data comprises central thickness data (Chang: [0032]: thickness data; e.g., Chang Para [0034]: central subfield foveal thickness).
As to claim 16, Chang teaches the method of claim 14, wherein the image-derived data comprises at least one of a treatment scar parameter (this element is in the alternative and does not need to be mapped), a total area cyst change central subfield (this element is claimed in the alternative and does not need to be mapped), or a total area cyst change central inner outer subfield (the broadest reasonable interpretation of “central subfield” or “central inner outer subfield” is the entire macula, including the central, inner, and outer locations of the macula; hence the total area of cyst change of the macula is mapped to Chang Para [0053], which teaches “change in anatomical variable measurements, over a period of time,” which refers to variables like cyst volume in predetermined areas within 1mm, 3mm, and 6mm in Chang Para [0032]; the Examiner interprets “within 1mm, 3mm, and 6mm area” to refer to the macular map, which is what the claimed central, inner, and outer subfields are defined as).
As to claim 17, Chang teaches the method of claim 14, wherein the computational model comprises a machine learning model (Chang Para [0167]: a machine learning algorithm).
As to claim 18, Chang teaches a computer system comprising:
an injection prediction platform (Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another; Chang Para [0229], combined with Para [0172], teaches that the dosing may be a pro re nata injection schedule) configured to receive subject data for a subject (Chang Para [0152]: step 402, receive data for a subject) and to generate an input using the subject data (Chang Para [0158]: use the subject data to generate a dataset that is inputted into the machine-learning model), wherein the subject data comprises best corrected visual acuity (BCVA) data for the subject (Chang Para [0032]: the subject data including BCVA data); and
a computational model that is part of the injection prediction platform and configured to predict an injection frequency for a treatment of the subject diagnosed with a macular edema condition based on the input (Chang Para [0172]: using the machine learning algorithm to predict the appropriateness of switching from one dosing regimen to another; Chang Para [0229], combined with Para [0172], teaches that the dosing may be a pro re nata injection schedule).
As to claim 19, Chang teaches the computer system of claim 18, further comprising:
a treatment manager configured to generate a schedule recommended for performing a set of medical evaluations for the subject based on the injection frequency predicted (Chang Para [0062]-[0068]: the drug administration recommendation determines scheduling with a health care provider).
As to claim 20, Chang teaches the computer system of claim 18, wherein the subject data further comprises data for at least one of a central foveal thickness parameter (this element is claimed in the alternative and does not need to be mapped), a central subfield thickness parameter (Chang Para [0034]: central subfield foveal thickness), a treatment scar parameter (this element is claimed in the alternative and does not need to be mapped), a total area cyst change central subfield (this element is claimed in the alternative and does not need to be mapped), or a total area cyst change central inner outer subfield (the broadest reasonable interpretation of “central subfield” or “central inner outer subfield” is the entire macula, including the central, inner, and outer locations of the macula; hence the total area of cyst change of the macula is mapped to Chang Para [0053], which teaches “change in anatomical variable measurements, over a period of time,” which refers to variables like cyst volume in predetermined areas within 1mm, 3mm, and 6mm in Chang Para [0032]; the Examiner interprets “within 1mm, 3mm, and 6mm area” to refer to the macular map, which is what the claimed central, inner, and outer subfields are defined as).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh, Rajeev Kumar, and Rohan Gorantla. "DMENet: diabetic macular edema diagnosis using hierarchical ensemble of CNNs." Plos one 15.2 (2020): e0220677.
US 20190326002 A1: Updating AI based regimens
Intervening reference between the provisional patent application filing date and the filing date of the non-provisional patent application:
Gallardo, Mathias, et al. "Machine learning can predict anti–VEGF treatment demand in a treat-and-extend regimen for patients with neovascular AMD, DME, and RVO associated macular edema." Ophthalmology retina 5.7 (2021): 604-624.
(Published in July 2021.)
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 June 18, 2026
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