DETAILED ACTION
Oath/Declaration
1. Oath and declaration filed on 7/26/2024 is accepted.
Information Disclosure Statement
2. The prior art documents submitted by application in the Information Disclosure Statement filed on 4/24/2024,8/26/2024,3/4/2024 and 4/19/2024 have all been considered and made of record (note the attached copy of form PTO – 1449).
Claim Rejections - 35 USC § 103
3. 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.
Claim(s) 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Li, Zhihuan (WO2020/083382A1) in view of Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) further in view of Drobe ,Bjorn (WO2020/126513A1) (note: applicant provided).
Regarding claim 1, Li, Zhihuan discloses ,a processing device for determining data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the processing device being configured to (abstract and electronic device to create an application comprising a software module obtaining data of the individual ; a software module evaluating data using a machine learning algorithm to generate a prediction of myopia progression and claim 33): receive at least one input file containing data related to the person, the data including: a refractive status of the person, age, gender, and ethnicity of the person, and at least one risk factor related to the person (page 10, para0012); and provide at least one output file containing data related to a progression of refractive values of the person determined with at least one machine learning algorithm, wherein the at least one machine learning algorithm is configured to determine the data related to the progression of the refractive values of the person from the data related to the person by deploying the data from the at least one input file in a determining step, wherein the at least one machine learning algorithm includes at least one prediction model for determining a relationship between the data related to the person and the progression of the refractive values of the person ( claim 65-70 and a machine learning model to generate a prediction of myopia onset or myopia progression wherein the machine learning model has a sensivity at least about 90% and specifically at least about 90% when evaluated against an indepdent data set of at least 200 samples and providing the prediction to the individual or the third party , the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data) .
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe, Bjorn discloses the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe, Bjorn (Abstract).
Regarding claim 2, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the at least one risk factor is selected from data related to at least one of :the refractive status of at least one parent of the person; and at least one parameter related to a behavior of the person.
Regarding claim 3, combination of Zhihuan in view of Tang et al further in view of Drobe, Bjorn discloses wherein the at least one parameter related to the behavior of the person is selected from data related to at least one of: a first amount of time spent by the person on near vision working; and a second amount of time spent outdoors by the person.
Regarding claim 4, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the data related to the person further comprises at least one type of myopia treatment, the at least one type of myopia treatment being selected from an application of at least one of: an optical lens selected from a contact lens or a spectacle lens, a dose of a drug, and refractive surgery; and wherein the refractive status is selected from at least one of: at least one refractive value of at least one eye, at least one biometric value of the at least one eye.
Regarding claim 5, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses, wherein the first prediction model deploys longitudinal data, wherein the longitudinal data includes a plurality of first pieces of data which are related to a particular person, wherein the second prediction model deploys cross-sectional data, and wherein the cross-sectional data includes at least one second piece of data related to a plurality of different persons.
Regarding claim 6, combination of Zhihuan in view of Tang et al further in view of Drobe, Bjorn discloses wherein a total data input into the at least one machine learning algorithm comprises a first amount of longitudinal data input and a second amount of cross-sectional data input, wherein the first amount is from 30 % to 70 % and the second amount is from 30 % to 70 %, wherein the first amount and the second amount add up to 100 %.
Regarding claim 7, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the processing device is further configured to determine at least one of: a ranking of the person compared to a plurality of further persons; a risk of myopia for the person; and a risk of high myopia for the person.
Regarding claim 8, Li, Zhihuan discloses a system for providing data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the system comprising: at least one input interface configured to receive at least one input file containing data related to a person ; a processing device being configured to: receive the at least one input file containing the data related to the person, including: a refractive status of the person, age, gender, and ethnicity of the person(page 10, para0012).
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except risk factor the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe ,Bjorn discloses risk factor and the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe ,Bjorn (Abstract).
Regarding claim 9, combination of Zhihuan in view of Tang et al discloses further in view of Drobe ,Bjorn discloses, wherein the at least one output interface is further configured to provide at least one of: at least one percentile referencing for the refractive values, wherein the at least one percentile referencing is provided for population-based data covering a range of ages; and a modified progression of refractive values of the person considering an implementation of the at least one type of myopia treatment.
Regarding claim 10, Li, Zhihuan discloses, a computer-implemented method for determining data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the method comprising: receiving at least one input file containing data related to a person including: a refractive status of the person, age, gender, and ethnicity of the person(page 10, para0012), and at least one risk factor related to the person; providing at least one output file containing data related to a progression of refractive values of the person determined with at least one machine learning algorithm, wherein the at least one machine learning algorithm is configured to determine the data related to the progression of the refractive values of the person from the data related to the person from the data from the at least one input file in a determining step, wherein the at least one machine learning algorithm includes at least one prediction model for determining a relationship between the data related to the person and the progression of the refractive values of the person, wherein the at least one machine learning algorithm includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data containing a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR).
( claim 65-70 and a machine learning model to generate a prediction of myopia onset or myopia progression wherein the machine learning model has a sensivity at least about 90% and specifically at least about 90% when evaluated against an indepdent data set of at least 200 samples and providing the prediction to the individual or the third party , the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data) .
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe, Bjorn discloses the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe, Bjorn (Abstract).
Regarding claim 11, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the first prediction model generates intermediate prediction data, and wherein the intermediate prediction data are deployed as input for the second prediction model.
Regarding claim 12, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses a computer-implemented method for providing data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the method comprising: receiving at least one input file containing data related to the person , determining data related to the progression of the refractive values of the person with at least one processing device; and providing the data related to the progression of the refractive values of the person with at least one output interface.
Regarding claim 13, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method.
Regarding claim 14, Li, Zhihuan discloses, a processing device for determining data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the processing device being configured to :receive at least one input file containing data related to a person, comprising a refractive status of the person, age, gender, and ethnicity of the person, and at least one risk factor related to the person; provide at least one output file containing data related to a progression of refractive values of the person ( claim 65-70 and a machine learning model to generate a prediction of myopia onset or myopia progression wherein the machine learning model has a sensivity at least about 90% and specifically at least about 90% when evaluated against an indepdent data set of at least 200 samples and providing the prediction to the individual or the third party , the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data) .
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe, Bjorn discloses the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe, Bjorn (Abstract).
Regarding claim 15, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the at least one risk factor is selected from data related to at least one of: the refractive status of at least one parent of the person ;at least one parameter related to a behavior of the person.
Regarding claim 16, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the at least one parameter related to the behavior of the person is selected from data related to at least one of: a first amount of time spent by the person on near vision working; and a second amount of time spent outdoors by the person.
Regarding claim 17, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the data related to the person further comprises at least one type of myopia treatment, wherein the at least one type of myopia treatment is selected from an application of at least one of: an optical lens selected from a contact lens or a spectacle lens, a dose of a drug, and refractive surgery; and wherein the refractive status is selected from at least one of: at least one refractive value of at least one eye; and at least one biometric value of the at least one eye.
Regarding claim 18, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein a total data input into the at least one machine learning algorithm comprises a first amount of longitudinal data input and a second amount of cross-sectional data input, wherein the first amount is from630 % to 70 % and the second amount is from 30 % to 70 %, and wherein the first amount and the second amount add up to 100 %.
Regarding claim 19, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the processing device is further configured to determine at least one of a ranking of the person compared to a plurality of further persons; a risk of myopia for the person; and a risk of high myopia for the person.
Regarding claim 20, Li, Zhihuan discloses a system for providing data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at [east one eye of the person over a period of time, the system comprising: at least one input interface configured to receive at least one input file comprising data related to a person ; a processing device being configured to: receive the at least one input file comprising the data related to the person including: a refractive status of the person; age, gender, and ethnicity of the person; and at least one risk factor related to the person; provide at least one output file comprising data related to a progression of refractive values of the person determined by deploying at least one machine learning algorithm, wherein the at least one machine learning algorithm is configured to determine the data related to the progression of the refractive values of the person from the data related to the person by deploying the data from the at least one input file in a determining step.
( claim 65-70 and a machine learning model to generate a prediction of myopia onset or myopia progression wherein the machine learning model has a sensivity at least about 90% and specifically at least about 90% when evaluated against an indepdent data set of at least 200 samples and providing the prediction to the individual or the third party , the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data) .
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe, Bjorn discloses the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe, Bjorn (Abstract).
Regarding claim 21, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the at least one output interface is further configured to provide at least one of: at least one percentile referencing for the refractive values, wherein the at least one percentile referencing is provided for population-based data covering a range of ages; and a modified progression of refractive values of the person considering an implementation of the at least one type of myopia treatment.
Regarding claim 22, Li, Zhihuan discloses a computer-implemented method for determining data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the method comprising: receiving at least one input file containing data related to a person including: a refractive status of the person; age, gender, and ethnicity of the person; and at least one risk factor related to the person; providing at least one output file containing data related to a progression of refractive values of the person determined by deploying at least one machine learning algorithm, wherein the at least one machine learning algorithm is configured to determine the data related to the progression of the refractive values of the person from the data related to the person from the data from the at least one input file in a determining step, wherein the at least one machine learning algorithm includes at least one prediction model for determining a relationship between the data related to the person and the progression of the refractive values of the person.
( claim 65-70 and a machine learning model to generate a prediction of myopia onset or myopia progression wherein the machine learning model has a sensivity at least about 90% and specifically at least about 90% when evaluated against an indepdent data set of at least 200 samples and providing the prediction to the individual or the third party , the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data) .
Zhihuan discloses all of the claimed limitations except wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR),
the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
Tang et al (eye and vision, a machine learning based algorithm used to estimate the physiological elongation of ocular axial length in myopic children) wherein the at least one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model deploying Gaussian Process Regression (GPR).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching one machine learning algorithm further includes a first prediction model and a second prediction model, wherein, in a first prediction step of the determining step, the first prediction model generates intermediate prediction data including a ratio of an axial length divided by a corneal radius for the person by deploying Support Vector Regression (SVR), deploying Gaussian Process Regression (GPR) in to the Li, Zhihuan ,a processing device correcting myopia as taught by Tang et al .
Li, Zhihuan (WO2020/083382A1) in view of Tang et al discloses all of the claimed limitations except the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model.
Drobe, Bjorn discloses the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model (abstract).
It would have been obvious to one of ordinary skill in the art at the time of invention was made to provide of teaching the second prediction model predicts the progression of the refractive values of the person, and wherein the second prediction model is a second linear prediction model in to the Li, Zhihuan in view of Tang et al a processor device process to vision related prediction model can be created as taught by Drobe, Bjorn (Abstract).
Regarding claim 23, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses wherein the first prediction model generates intermediate prediction data, and wherein the intermediate prediction data are deployed as input for the second prediction model.
Regarding claim 24, combination of Zhihuan in view of Tang et al further in view of Drobe ,Bjorn discloses a computer-implemented method for providing data related to a progression of refractive values of a person, the progression of the refractive values being a forecast of a temporal alteration of the refractive values of at least one eye of the person over a period of time, the method comprising: receiving at least one input file containing data related with least one input interface; determining data related to a progression of refractive values of with at least one processing device; and providing the data related to the progression of the refractive values of the person with at least one output interface.
Regarding claim 25, combination of Zhihuan in view of Tang et al further in view of Drobe, Bjorn discloses a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method.
Conclusion
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/MOHAMMED A HASAN/Primary Examiner, Art Unit 2872 4/8/2026
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