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 .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Priority
Acknowledgement is made of applicant’s claim for priority based on CN202410596505.2 dated 05/14/2024.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
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 acquisition module, configured to acquire eyeball data,” “a calculation module, configured to calculate retinal optical characteristic data information,” “an analysis module, configured to analyze and process the retinal optical characteristic data information,” and “an output module, configured to output the results” in claim 8.
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 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:
Claims 6-7 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 6 recites the limitation "the stored eyeball data information" in line 6. There is insufficient antecedent basis for this limitation in the claim. The term “stored eyeball data information” in claim 6 is likely in reference to “measured data information about an eyeball to be monitored” from claim 5. However, as presently written, there is insufficient antecedent basis for this limitation in the claim. For examination purposes, ‘the stored eyeball data information’ will be interpreted as ‘measured data information about an eyeball to be monitored.’
Claim 7 is rejected for its dependence on claim 6.
Claim Objections
Claim 2, 4, and 6 are objected to because of the following informalities:
Claims 2 and 6 recite the phrase “acquiring, from the stored eyeball data information, data information needs to be monitored currently about the eyeball to be monitored.” This limitation is grammatically incorrect and makes the scope is unclear. The spec reproduces this exact error at [0008] and [0044]. The office suggests the correction as follows to enhance clarity of the claim:
“acquiring, from the stored eyeball data information, that the data information needs to be monitored currently about the eyeball to be monitored.”
Claims 2 and 6 recites the phrase “storing the measured eyeball data information on the basis of user information about the eyeball to be monitored” however eyeballs do not contain “user information.” The intended meaning is presumably patient/user information associated with the eyeball to be monitored, but the phrasing as presently written is ambiguous.
Claims 4 recites the phrase “wherein the analyzing and processing the retinal optical characteristic data information about the eyeball to be monitored” however the phrase is not grammatically correct and the phrasing as presently written renders the scope unclear. The office suggests the correction as follows to enhance clarity of the claim:
“wherein the analyzing and processing of the retinal optical characteristic data information about the eyeball to be monitored”
Appropriate correction is required.
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.
(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.
Claims 1-2, 5-6, and 8-10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Marin (US 20240428946 A1).
Regarding claim 1, Marin teaches: a monitoring method for an eyeball emmetropization process (the eyeball emmetropization process is mainly monitored by means of detecting individual's eye axial variation and refractive status (see para [0003]-[0004] of the instant application). Myopia is a failure or disruption of this process, specifically axial disruption/elongation that causes the eye to focus images in front of the retina rather than on the retina. Therefore, the monitorization of myopia can be mapped to the monitoring method of emmetropization since they both describe a way to analyze the progression/deterioration of the eye) (“method 100 for determining a risk of an onset or progression of myopia over a timeframe”; [0047]), comprising:
acquiring eyeball data information about an eyeball to be monitored (“step 110, determining a value of at least one parameter associated with the vision condition of a subject”; [0047]);
outputting retinal optical characteristic data information about the eyeball to be monitored on the basis of the eyeball data information about the eyeball to be monitored (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096], see para [0007] which explains that a ‘parameter’ includes at least one of “a dioptric optical parameter, a parameter relating to the subject's lifestyle, activity or behavior, a parameter relating to the subject's genetic history, an optical biometric parameter.” See also para [0057] which describes that “Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope.”); and
analyzing and processing the retinal optical characteristic data information about the eyeball to be monitored (“at step 620, calculating a value of a risk ratio, using the at least one predictive model based on the machine learning algorithm”; [0097]),
and outputting, for the eyeball to be monitored, an equation representing individual emmetropization characteristics as well as related parameters (“the machine learning algorithm may include a multifactorial equation correlating the risk of myopia onset or progression over the timeframe and the respective at least one parameter associated with vision condition”; [0097]).
Regarding claim 2, Marin teaches the monitoring method according to claim 1. Marin further teaches: wherein the acquiring eyeball data information about an eyeball to be monitored (“step 110, determining a value of at least one parameter associated with the vision condition of a subject”; [0047]) comprises the steps of:
measuring the eyeball to be monitored by means of a medical measuring instrument (“the subject's dioptric optical parameter may include an objective measurement of a subject's refractive error, e.g. spherical power obtained during an eye examination. Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”; [0057]);
storing the measured eyeball data information on the basis of user information about the eyeball to be monitored (“one or more neural networks may be trained by inputting a series of determined values of the parameter associated with vision condition of numerous individuals, e.g. population sample, which may have the same or similar profile, e.g. sensitivity parameter SRx, age, genetic history, dioptric optical parameter, and building a correlation table or any database containing information on the relationship between the risk of myopia onset or progression and the at least one parameter associated with vision condition. In an embodiment, said database or correlation table may include or be the longitudinal study which established the correlation of the risk of myopia onset or progression over a timeframe and the at least one parameter associated with vision condition”; [0077], longitudinal data is stored on the database and used to establish the correlation of myopia progression); and
acquiring, from the stored eyeball data information, data information needs to be monitored currently about the eyeball to be monitored (“step 640, which includes comparing the calculated value of the risk ratio with a second predetermined threshold value. The second predetermined threshold value may be established on the basis of the database including information on the risk of myopia onset or progression over the timeframe for individuals”; [0099], data information is retrieved from the database and used to assess the risk ratio about the eyeball to be monitored).
Regarding claim 5, Marin teaches: a monitoring method for an eyeball emmetropization process (the eyeball emmetropization process is mainly monitored by means of detecting individual's eye axial variation and refractive status (see para [0003]-[0004] of the instant application). Myopia is a failure or disruption of this process, specifically axial disruption/elongation that causes the eye to focus images in front of the retina rather than on the retina. Therefore, the monitorization of myopia can be mapped to the monitoring method of emmetropization since they both describe a way to analyze the progression/deterioration of the eye) (“method 100 for determining a risk of an onset or progression of myopia over a timeframe”; [0047]), comprising:
acquiring measured data information about an eyeball to be monitored (“step 110, determining a value of at least one parameter associated with the vision condition of a subject”; [0047]);
processing the measured data information about the eyeball to be monitored, and outputting retinal optical characteristic data information about the eyeball to be monitored (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096], see para [0007] which explains that a ‘parameter’ includes at least one of “a dioptric optical parameter, a parameter relating to the subject's lifestyle, activity or behavior, a parameter relating to the subject's genetic history, an optical biometric parameter.” See also para [0057] which describes that “Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”); and
analyzing and processing the retinal optical characteristic data information and eye axial data information (“the optical biometric parameter may include an objective measurement of a subject's axial length (in mm)”; [0060]) about the eyeball to be monitored (“at step 620, calculating a value of a risk ratio, using the at least one predictive model based on the machine learning algorithm”; [0097]),
and outputting, for the eyeball to be monitored, an equation representing individual emmetropization characteristics as well as related parameters (“the machine learning algorithm may include a multifactorial equation correlating the risk of myopia onset or progression over the timeframe and the respective at least one parameter associated with vision condition”; [0097]).
Regarding claim 6, Marin teaches the monitoring method according to claim 5. Marin further teaches: the acquiring measured data information about an eyeball to be monitored (“step 110, determining a value of at least one parameter associated with the vision condition of a subject”; [0047]) comprises the steps of:
measuring the eyeball to be monitored by means of a medical measuring instrument (“the subject's dioptric optical parameter may include an objective measurement of a subject's refractive error, e.g. spherical power obtained during an eye examination. Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”; [0057]);
storing the measured data information on the basis of user information about the eyeball to be monitored (“one or more neural networks may be trained by inputting a series of determined values of the parameter associated with vision condition of numerous individuals, e.g. population sample, which may have the same or similar profile, e.g. sensitivity parameter SRx, age, genetic history, dioptric optical parameter, and building a correlation table or any database containing information on the relationship between the risk of myopia onset or progression and the at least one parameter associated with vision condition. In an embodiment, said database or correlation table may include or be the longitudinal study which established the correlation of the risk of myopia onset or progression over a timeframe and the at least one parameter associated with vision condition”; [0077], longitudinal data is stored on the database and used to establish the correlation of myopia progression); and
acquiring, from the stored eyeball data information, data information needs to be monitored currently about the eyeball to be monitored (“step 640, which includes comparing the calculated value of the risk ratio with a second predetermined threshold value. The second predetermined threshold value may be established on the basis of the database including information on the risk of myopia onset or progression over the timeframe for individuals”; [0099], data information is retrieved from the database and used to assess the risk ratio about the eyeball to be monitored).
Regarding claim 8, Marin teaches: monitoring system for an eyeball emmetropization process (the eyeball emmetropization process is mainly monitored by means of detecting individual's eye axial variation and refractive status (see para [0003]-[0004] of the instant application). Myopia is a failure or disruption of this process, specifically axial disruption/elongation that causes the eye to focus images in front of the retina rather than on the retina. Therefore, the monitorization of myopia can be mapped to the monitoring method of emmetropization since they both describe a way to analyze the progression/deterioration of the eye) (“method 100 for determining a risk of an onset or progression of myopia over a timeframe”; [0047]), comprising:
an acquisition module (This element is interpreted under 35 U.S.C. 112(f) as a medical measuring instrument that measures the eyeball data, see para [0044] of the instant application) (“the subject's dioptric optical parameter may include an objective measurement of a subject's refractive error, e.g. spherical power obtained during an eye examination. Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”; [0057]), configured to acquire eyeball data information about an eyeball to be monitored (“computer-controlled machine used during an eye examination to provide an objective measurement of the subject's refractive error, e.g. phoropter, refractor, autorefractor and/or a retinoscope”; [0101]);
a calculation module (This element is interpreted under 35 U.S.C. 112(f) as a processor, see para [0084] of the instant application) (“at step 620, calculating a value of a risk ratio, using the at least one predictive model based on the machine learning algorithm”; [0097]), configured to calculate retinal optical characteristic data information about the eyeball to be monitored on the basis of the eyeball data information (“step 620, provides a probability indicative of the subject's risk of the onset or progression of myopia over the timeframe, and is carried out using the at least one predictive model based on the machine learning algorithm”; [0097]);
an analysis module (This element is interpreted under 35 U.S.C. 112(f) as a processor, see para [0084] of the instant application), configured to analyze and process the retinal optical characteristic data information (“At step 630, method 600 may include determining the subject's risk of the onset or progression of myopia over the timeframe based on the calculated value of the risk ratio”; [0098]) to obtain an equation representing individual emmetropization characteristics as well as related parameters (“the machine learning algorithm may include a multifactorial equation correlating the risk of myopia onset or progression over the timeframe and the respective at least one parameter associated with vision condition”; [0097]), and
to monitor and predict the refractive development of the eyeball to be monitored on the basis of the equation representing individual emmetropization characteristics as well as the related parameters (“the machine learning algorithm may include a multifactorial equation correlating the risk of myopia onset or progression over the timeframe and the respective at least one parameter associated with vision condition, for individuals within a population sample. Accordingly, the calculated value of the risk ratio determined at step 620, provides a probability indicative of the subject's risk of the onset or progression of myopia over the timeframe, and is carried out using the at least one predictive model based on the machine learning algorithm”; [0097]); and
an output module (This element is interpreted under 35 U.S.C. 112(f) as a mobile network connected to a mobile terminal, see para [0057] of the instant application), configured to output the results of monitoring and predicting the refractive development of the eyeball to be monitored (“the determined value of the at least one parameter associated with vision condition of the subject, obtained at step 110, may be transmitted according to a pre-defined wireless communication protocol to the circuit to determine the subject's risk of the onset or progression of myopia over the timeframe 720”; [0108], “Examples of the pre-defined wireless communication protocols include: global system for mobile communication (GSM), enhanced data GSM environment (EDGE), wideband code division multiple access (WCDMA), code division multiple access (CDMA), time division multiple access (TDMA), wireless fidelity (Wi-Fi), voice over Internet protocol (VoIP), worldwide interoperability for microwave access (Wi-MAX), Wi-Fi direct (WFD), an ultra-wideband (UWB), infrared data association (IrDA), Bluetooth, ZigBee, SigFox, LPWan, LoRaWan, GPRS, 3G, 4G, LTE, and 5G communication systems. Accordingly, each of the circuits may include the necessary hardware required to support the wireless transmission”; [0108]).
Regarding claim 9, Marin teaches: a computer-readable storage medium, having at least one computer program stored thereon (“A digital circuit may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus, in various embodiments, a “circuit” may be a digital circuit, e.g. a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g. a microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A “circuit” may also include a processor executing software, e.g. any kind of computer program, e.g. a computer program using a virtual machine code such as e.g. Java”; [0104]), wherein the computer program is employed to implement a monitoring method for an eyeball emmetropization process according to any one of claims 1-4 and/or claims 5-7 (“a computer program product may include instructions to cause the system 700 to execute the steps of methods 100, 300, 400, 500A, 500B and 600. The steps of methods 100, 300, 400, 500A, 500B and 600 may be used to determine the subject's risk of onset or progression of myopia over the timeframe”; [0109]).
Regarding claim 10, Marin teaches: a computer program product, which, when runs on a computer, causes the computer to implement a monitoring method for an eyeball emmetropization process according to any one of claims 1-4 and/or claims 5-7 (“a computer program product may include instructions to cause the system 700 to execute the steps of methods 100, 300, 400, 500A, 500B and 600. The steps of methods 100, 300, 400, 500A, 500B and 600 may be used to determine the subject's risk of onset or progression of myopia over the timeframe”; [0109]).
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 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Marin (US 20240428946 A1) as applied to claims 1 and 5 above, and further in view of Kubota (US 20230284897 A1).
Regarding claim 3, Marin teaches the monitoring method according to claim 1. Marin further teaches: the outputting retinal optical characteristic data information about the eyeball to be monitored on the basis of the eyeball data information about the eyeball to be monitored (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096], see para [0007] which explains that a ‘parameter’ includes at least one of “a dioptric optical parameter, a parameter relating to the subject's lifestyle, activity or behavior, a parameter relating to the subject's genetic history, an optical biometric parameter.” See also para [0057] which describes that “Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”) comprises the steps of:
inputting the data information about the eyeball to be monitored into a preset mathematical model (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096]). However, Marin fails to explicitly teach: generating and outputting, after computational processing by the preset mathematical model, volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored.
In a related invention in the field of myopia prediction diagnosis, Kubota teaches: generating and outputting, after computational processing by the preset mathematical model (“the processor or computing devices and systems described and/or illustrated herein broadly represent any type or form of computing device or system capable of executing computer-readable instructions”; [0095]),
volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored (“the refractive properties of the eye for the foveal location of the retina comprise one or more of a first sphere, a first cylinder, a first axis, a first spherical equivalent, first coefficients of a wavefront map or a first wavefront map and the refractive properties of the eye for the non-foveal location of the retina”; [0112]). Furthermore, Kubota teaches this configuration such that “Accordingly, this offset of the spots shows that there are refractive errors corresponding to the locations of the spots on the patient's retina and corresponding wavefront errors for the corresponding locations of the pupil of the eye” (Kubota, [0060]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Marin to incorporate the teachings of Kubota to provide a device capable of generating and outputting, after computational processing by the preset mathematical model, volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored, for the purpose of analyzing refractive errors corresponding to the locations of the spots on the patient's retina and corresponding wavefront errors (Kubota, [0060]).
Regarding claim 7, Marin teaches the monitoring method according to claim 6. Marin further teaches: the outputting retinal optical characteristic data information about the eyeball to be monitored on the basis of the eyeball data information about the eyeball to be monitored (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096], see para [0007] which explains that a ‘parameter’ includes at least one of “a dioptric optical parameter, a parameter relating to the subject's lifestyle, activity or behavior, a parameter relating to the subject's genetic history, an optical biometric parameter.” See also para [0057] which describes that “Said parameter may be obtained using any ophthalmic testing device, e.g. phoropter, refractor, autorefractor and/or a retinoscope”) comprises the steps of:
inputting the data information into a preset mathematical model (“at step 610, entering the determined value of the at least one parameter (at step 110) into the at least one predictive model based on the machine learning algorithm”; [0096]).
However, Marin fails to explicitly teach: generating and outputting, after computational processing by the preset mathematical model, volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored.
In a related invention in the field of myopia prediction diagnosis, Kubota teaches: generating and outputting, after computational processing by the preset mathematical model (“the processor or computing devices and systems described and/or illustrated herein broadly represent any type or form of computing device or system capable of executing computer-readable instructions”; [0095]),
volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored (“the refractive properties of the eye for the foveal location of the retina comprise one or more of a first sphere, a first cylinder, a first axis, a first spherical equivalent, first coefficients of a wavefront map or a first wavefront map and the refractive properties of the eye for the non-foveal location of the retina”; [0112]). Furthermore, Kubota teaches this configuration such that “Accordingly, this offset of the spots shows that there are refractive errors corresponding to the locations of the spots on the patient's retina and corresponding wavefront errors for the corresponding locations of the pupil of the eye” (Kubota, [0060]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Marin to incorporate the teachings of Kubota to provide a device capable of generating and outputting, after computational processing by the preset mathematical model, volumetric strain data and related values for a retinal photofocal plane or a retinal wavefront of the eyeball to be monitored, for the purpose of analyzing refractive errors corresponding to the locations of the spots on the patient's retina and corresponding wavefront errors (Kubota, [0060]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Marin (US 20240428946 A1) and Kubota (US 20230284897 A1) as applied to claim 3, and further in view of Abitbol (US 20220125303 A1).
Regarding claim 4, Marin teaches the monitoring method according to claim 3. The combination of Marin and Kubota fail to explicitly teach: wherein the analyzing and processing the retinal optical characteristic data information about the eyeball to be monitored, and outputting, for the eyeball to be monitored, an equation representing individual emmetropization characteristics as well as related parameters comprises the steps of: mathematically analyzing the volumetric strain data of the retinal photofocal plane or the retinal wavefront of the eyeball to be monitored and eye axial variation data, and obtaining the equation representing individual emmetropization characteristics as well as the related parameters; and monitoring and predicting the refractive development of the eyeball to be monitored on the basis of the equation representing individual emmetropization characteristics as well as the related parameters.
In a related invention in the field of myopia prediction diagnosis, Abitbol teaches: wherein the analyzing and processing the retinal optical characteristic data information about the eyeball to be monitored, and outputting, for the eyeball to be monitored, an equation representing individual emmetropization characteristics as well as related parameters comprises the steps of:
mathematically analyzing the volumetric strain data of the retinal photofocal plane or the retinal wavefront of the eyeball to be monitored (“features of the wavefront of rays 162 may be used to derive corresponding aberrations of eye 114 and the refraction of the eye 114 calculated based thereon, in accordance with wavefront analysis methods well known in the art”; [0035]) and eye axial variation data (“A possible mathematical formulaic technique that may be carried out by axial length calculation subsystem 130 for deriving the axial length of eye 114”; [0051], “Axial length as derived by system 100 may be useful for a variety of applications, particularly preferably including monitoring of myopia progression”; [0050]), and
obtaining the equation representing individual emmetropization characteristics as well as the related parameters (“It is understood that equations (1)-(11) serve to calculate the individual powers of various elements in eye 114 as well as the various distances therein”; [0057]); and
monitoring and predicting the refractive development of the eyeball to be monitored on the basis of the equation representing individual emmetropization characteristics as well as the related parameters (“Axial length as derived by system 100 may be useful for a variety of applications, particularly preferably including monitoring of myopia progression”; [0050], “first axial length calculation sub-step 440 may involve the performance of calculations set forth in equations (1)-(3) hereinabove”; [0070]).
Furthermore, Abitbol teaches this configuration such that “It is appreciated that system 100 is particularly well suited for performance of repeated, follow up measurements due to the simplicity and cost-effectiveness thereof” (Abitbol, [0050]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Marin and Kubota to incorporate the teachings of Abitbol to provide a device capable of mathematically analyzing the volumetric strain data of the retinal photofocal plane or the retinal wavefront of the eyeball to be monitored and eye axial variation data, and obtaining the equation representing individual emmetropization characteristics as well as the related parameters; and monitoring and predicting the refractive development of the eyeball to be monitored on the basis of the equation representing individual emmetropization characteristics as well as the related parameters, for the purpose of providing system capable of repeated follow up cost-effective measurements (Abitbol, [0050]).
Conclusion
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/RUBY L KAUFFMAN/Examiner, Art Unit 2872
/PINPING SUN/Supervisory Patent Examiner, Art Unit 2872
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