DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment filed on 3/9/26. Claims 16-32 and 34 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17, 19, and 21 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.
Claims 17 and 19 recite: “wherein the at least one parameter associated with vision condition of the subject further comprises: 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, and/or a parameter relating to personal information about the subject.”
It is unclear whether the “at least one parameter associated with vision condition” in claim 17 is describing/defining an additional parameter to the at least one parameter of claim 16 (a second parameter), or whether the parameters listed in claim 17 are intended to further define the same “at least one parameter” recited in claim 16. More specifically, it is unclear how the parameter options listed in claim 17 further define the “[the] sensitivity parameter being relative to the sensitivity of said subject to a variation of at least one dioptric optical feature.”
A similar analysis applies to the language of claim 19. Additionally, claim 21 inherits the deficiencies of claim 19 through dependency, and is therefore also rejected.
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 16-32 and 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e, a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these “four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of Section 101 even if the subject matter is otherwise new and useful.” In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007). Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter? Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 16-31, are drawn to a method; claims 32-33 are drawn to a system; claim 34 is drawn to a product/article of manufacture.
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. While the terms “set forth” and “describe” are thus both equated with “recite”, their different language is intended to indicate that there are different ways in which an exception can be recited in a claim. For instance, the claims in Diehr set forth a mathematical equation in the repetitively calculating step, while the claims in Mayo set forth laws of nature in the wherein clause, meaning that the claims in those cases contained discrete claim language that was identifiable as a judicial exception. The claims in Alice Corp., however, described the concept of intermediated settlement without ever explicitly using the words “intermediated” or “settlement.” A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
Claims 16-32 and 34 recite(s) a method, system, and product (with instructions for performing a method) as explained. In the instant case, the language of claims 16 and 32 encompasses performance of the limitations(s) in the mind. Additionally, the language of claim 34 encompasses performance of the limitations(s) in the mind but for the recitation of generic computer components.
In particular, the limitation of determining a value of at least one parameter associated with vision condition of a subject, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (but for the recitation of generic computer components in claim 34). That is, other than reciting “computer executable instructions…when executed by a computer, cause the computer to perform,” in claim 34, nothing in the claim element precludes the step from practically being performed in the mind. “Determining” in the context of this claim encompasses a practitioner/user assessing how an individual is performing on a vision test.
Similarly, the limitation of “determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (but for the recitation of generic computer components in claim 34) The “determining” step in the context of this claim encompasses the user making a decision regarding the subject’s risk based upon the assessment. Additionally, claims 16 and 32 have been amended to further recite “said sensitivity parameter…being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation.” The amended claim language includes a “determination” and encompasses a user making a judgement or evaluation regarding the subject’s certainty.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
As explained in MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). (emphasis added) As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Moreover, courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Also, regarding the amendments to Claims 16 and 32 to further recite: “being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation…”. The additional limitation is not sufficient to render the claim patent eligible, as the new limitation recites a mathematical relationship (i.e. determining a certitude probability function of the subject. )
This judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)). Thus, the second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Id. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute Sections 102, 103, and 112 inquiries for the better established inquiry under Section 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the Section 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp.,838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9).
As described in MPEP 2106.05, Step 2B of the Office’s eligibility analysis is the second part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. _, 134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. 66, 101 USPQ2d 1961 (2012)). Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 34 recites additional limitation(s), including “non-transitory computer-readable storage medium including computer executable instructions” and “a computer.” The additional claim language recites a computer components/ structure that are and were routine and conventional at the time of Applicant’s invention was filed and that amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “ 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" (par. 104 of PG-Pub US 20240428946 A1) The disclosure also states: “System 700 includes means for determining a value of at least one parameter associated with vision condition of the subject 710, which may include at least one circuit, e.g. microprocessor(s). For example, the means for determining a value of at least one parameter associated with vision condition of the subject 710 may include any ophthalmic testing device, such as a 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.” (par. 101) The language describing the system components underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of generic system components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in claims 16-34:
- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
- Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
- Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claims 17-31 are dependent from Claim 16 and include(s) all the limitations of claim(s) 16. However, the additional limitations of the claims 17-31 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or additional steps which amount to insignificant extra solution activities. Therefore, claim(s) 17-31 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
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.
Claim(s) 16-24; 27; and 30-32, 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US 20210375460 A1) in view of Marin et al (US 20210311325 A1)
Claims 16, 32, and 34 Li discloses a method, system, non-transitory computer readium medium for determining a risk of an onset or progression of myopia over a timeframe, the system including means (par. 3-5; par. 43-45) for performing the method comprising:
determining a value of at least one parameter associated with vision condition of a subject, said at least one parameter (par. 9-the data comprises an age and refraction under cycloplegia of one or both eyes of the individual measured at one or more time points. In some embodiments, the one or more time points comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 time points. In some embodiments, the method further comprises processing the data to calculate a spherical equivalent based on the refraction) and
determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter. (par. 10: a software module evaluating the data using a machine learning algorithm to generate a prediction of myopia progression; and iii) a software module providing the prediction to the individual or a third party. In some embodiments, the machine learning algorithm comprises a linear model providing a relationship between myopia progression and two or more features… the prediction comprises a likelihood of progression to a higher degree of myopia… In some embodiments, the prediction comprises an age of onset of myopia in an individual who does not have myopia. In some embodiments, the prediction comprises a rate of progression of myopia. In some embodiments, the rate of progression comprises a predicted change in degree of myopia within a time period.)
Claim 1 further recites that the “at least one parameter” comprises a sensitivity parameter of said subject, said sensitivity parameter being relative to the sensitivity of said subject to a variation of at least one dioptric optical feature of at least one ophthalmic lens placed in front of at least one eye of said subject.
Li does not expressly disclose, but Marin teaches a method and system for determining the value of assessing determining a sensitivity parameter, said sensitivity parameter being relative to the sensitivity of said subject to a variation of at least one dioptric optical feature of at least one ophthalmic lens placed in front of at least one eye of said subject. (par. 54-63; par. 118-121- to determine said value of the global sensitivity parameter taking into account a combination of said single values of the first and second sensitivity parameters (block 120 of FIG. 1))
At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the method and system of Li with the teaching of Marin to include a sensitivity parameter reflecting the subject’s sensitivity to changes in at least one dioptric optical feature. One would have been motivated to include this feature to more accurately assess factors impacting the vision acuity of a subject.
Claims 16 (34) and 32 have been further amended to recite: a sensitivity parameter…. being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation”
Li does not disclose, but Marin teaches a sensitivity parameter…. being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation.” (par. 152-156: The value of each specific sensitivity parameter is for example evaluated with a probability function of certitude of the subject's answers (par. 0152)) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the method and system of Li with the teaching of Marin to include a sensitivity parameter reflecting the subject’s sensitivity to changes in at least one dioptric optical feature and the subject’s certainty (certitude
) regarding their answers/selections. One would have been motivated to include this feature to more accurately assess factors impacting the vision acuity of a subject.
Claim 17 Li teaches the method of claim 16, wherein the at least one parameter associated with vision condition of the subject further comprises: 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, and/or a parameter relating to personal information about the subject. (par. 13- the data comprises average exposure to sunlight or natural lighting, intensity of sunlight exposure, average UV index, latitude, or any combination thereof. In some embodiments, the input data is obtained from an eye examination. In some embodiments, the input data comprises age, gender, nationality, ethnicity, spherical equivalent (SE) measurement of one or both eyes)
Claim 18 Li teaches the method, wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter, further comprises:
assigning to each of the at least one parameter, a respective risk category selected from a plurality of predetermined risk categories, based on the determined value of the respective parameter (par. 51: the algorithms and models disclosed herein can provide clinically meaningful predictions of refractive error over various time spans, while accurately predicting the onset of high myopia in internal and external validations. Improved predictive models can help identify high-risk patients who would benefit from earlier preventive interventions. The systems and methods disclosed herein offer a new avenue for prediction of visual outcomes in populations with high myopia prevalence while identifying individuals at risk for developing high myopia, potentially mitigating vision loss while combating both economic and healthcare burdens. In some embodiments, the algorithms and models disclosed herein are tailored to specific population cohorts; par. 53; par. 56); and determining the subject's risk of the onset or progression of myopia over the timeframe, based on the respective risk category assigned to each of the at least one parameter. (par. 51; par. 53-56: using classifier algorithms to identify features, generating a prediction model based on the data analysis to predict risk of myopia.)
Claim 19. Li and Marin in combination teach the method of claim 16, wherein the parameter comprises a sensitivity value, as explained in the rejection of claim 16.
Furthermore, Li teaches a method wherein the at least one parameter associated with vision condition of the subject further comprises: 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, and/or a parameter relating to personal information about the subject, and wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter, further comprises: assigning to each of the at least one parameter, a respective risk category selected from a plurality of predetermined risk categories, based on the determined value of the respective parameter; (par. 13- the data comprises average exposure to sunlight or natural lighting, intensity of sunlight exposure, average UV index, latitude, or any combination thereof. In some embodiments, the input data is obtained from an eye examination. In some embodiments, the input data comprises age, gender, nationality, ethnicity, spherical equivalent (SE) measurement of one or both eyes); par. 51-54)
and determining the subject's risk of the onset or progression of myopia over the timeframe, based on the respective risk category assigned to each of the at least one parameter. (par. 13; par. 51-54)
Claim 20 Li teaches a method, wherein assigning the respective risk category to each of the at least one parameter, based on the determined value of the respective parameter comprises :determining if the determined value of the respective parameter matches a corresponding reference value or lies within a corresponding reference value range, for the respective parameter associated with the respective risk category. (par. 13-14; par. 56-the classifier comprises two or more feature spaces. The two or more feature spaces may be distinct from one another. In some embodiments, a feature space comprises information such as age and a corresponding refraction or refractive error for measured for one or both eyes of an individual…the classification is the myopic progression such as rate of progression, amount of progression, age of onset of progression, or other suitable metric for assessing myopia and its progression; par. 137)
Claim 21 Li discloses a method, wherein assigning the respective risk category to each of the at least one parameter, based on the determined value of the respective parameter comprises: determining if the determined value of the respective parameter matches a corresponding reference value or lies within a corresponding reference value range, for the respective parameter associated with the respective risk category. (par. 50-56; par. 58-using a classifier to determine/identify groups and risk categories; )
Claim 22 Li discloses a method, wherein the reference value or reference value range for the respective parameter, is established on the basis of a database comprising information of the risk of myopia onset or progression over the timeframe, for individuals within a population sample. (par. 52-56)
Claim 23 Li discloses a method, wherein the reference value or reference value range for the respective parameter, is established on the basis of a database comprising information of the risk of myopia onset or progression over the timeframe, for individuals within a population sample. (database records: par. 50-external validation was confirmed through the use of two separate independent cohorts. In some embodiments, prediction accuracy is assessed via area under the receiver operating characteristics curve (AUC)… records of individuals in hospital-based databases and follow-up records of participants in population-based cohorts are included in the data. In some embodiments, the machine learning algorithm accurately predicts the presence of high myopia for external validations of one or more cohorts. In some embodiments, the algorithm is validated using at least 2 cohorts. In one embodiment, with respect to the prediction of high myopia by 20 years of age as a surrogate of high myopia in adulthood, the algorithm provides clinically meaningful predictions over a period of years.)
Claim 24 Li discloses a method, wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the respective risk category assigned to each of the at least one parameter comprises: determining for each risk category of the plurality of predetermined risk categories, a number of parameters among the at least one parameter, to which the respective risk category has been assigned; (par. 5-a predictive algorithm can comprise one or more features corresponding to ethnicity and/or nationality of an individual in predicting myopia onset and/or progression for the individual. Alternatively, or in combination, a predictive algorithm can be trained on a specific population such that an individual can be screened and then evaluated by the appropriate algorithm trained on the appropriate population data; par. 9- the machine learning algorithm comprises a linear model providing a relationship between myopia progression and two or more features…prediction comprises a likelihood of progression to a higher degree of myopia. In some embodiments, the higher degree of myopia is low myopia, moderate myopia, or high myopia. In some embodiments, the low myopia corresponds to a spherical equivalent of −3.00 diopters or less.…, the moderate myopia corresponds to a spherical equivalent between −3.00 diopters to −6.00 diopters. In some embodiments, the high myopia corresponds to a spherical equivalent of −6.00 diopters or more); and determining as the subject's risk of the onset or progression of myopia over the timeframe, that risk category of the plurality of predetermined risk categories, that has been assigned to a highest number of parameters. (par. 9-11);
Claim 27 Li discloses a method wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter, is established based on at least one predictive model based on a machine learning algorithm, which provides a relationship between the risk of myopia onset or progression and the at least one parameter. (par. 9-12: the machine learning model is a linear model providing a relationship between myopia progression and two or more features corresponding to the input data. In some embodiments, the machine learning model is generated using a machine learning procedure comprising multivariate linear regression analysis. In some embodiments, the machine learning model comprises a logistic regression model. In some embodiments, the machine learning model comprises a support vector machine model. In some embodiments, the prediction comprises a likelihood of progression to a higher degree of myopia. In some embodiments, the higher degree of myopia is low myopia, moderate myopia, or high myopia. In some embodiments, the low myopia corresponds to a spherical equivalent of −3.00 diopters or less)
Claims 30-31 Li teaches a method wherein the at least one parameter associated with vision condition of said subject, comprises: a first parameter indicating a near refraction of said subject; a third parameter indicating a far refraction of said subject;; and a fifth parameter indicating an overlap degree of a far comfort range and a near comfort range of said subject; wherein the determining the value of the fifth parameter comprises: determining the far comfort range and the near comfort range based on the determined values of the first to fourth parameters, determining the overlap degree of the far and near comfort ranges, and assigning a value to the fifth parameter based on the determined overlap degree of the far and near comfort ranges. (par. 9-12; par. 37-confidence intervals)
Li does not expressly disclose, but Marin teaches a method and system for determining the value of assessing determining a sensitivity parameter including
a second parameter indicating a near refraction sensitivity of said subject ; a fourth parameter indicating a far refraction sensitivity of said subject (par. 12-21) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the method and system of Li with the teaching of Marin to include a sensitivity parameter reflecting the subject’s sensitivity to changes in at least one dioptric optical feature. One would have been motivated to include this feature to more accurately assess factors impacting the vision acuity of a subject.
Claim(s) 25-26 and 28-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US 20210375460 A1) in view of Marin et al (US 20210311325 A1), as applied to claims 16, 32 and 34, and in further view of Brennan et al (US 20230081566 A1)
Claim 25 Li and Marin in combination teach a method of claim 18 as explained in the rejection of claim 18, wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the respective risk category assigned to each of the at least one parameter.:
Li and Marin do not disclose, but Brennan teaches
assigning a weight to each one of the plurality of predetermined risk categories; (par. 18-25- determining based on one or more of the demographic or behavioral information, a progression factor for the subject by weighting, according to a predetermined progression formula, the demographic and behavioral information, wherein the predetermined progression formula and weighting is derived from progression data associated with a population),
determining for each risk category of the plurality of predetermined risk categories, a number of parameters among the at least one parameter, to which the respective risk category has been assigned; (par. 22-29)
calculating for each risk category of the plurality of predetermined risk categories, a score by multiplying the weight assigned to the respective risk category with the number of parameters, to which said risk category has been assigned; (par. 139-154)
calculating a final score by summing the scores calculated for the plurality of predetermined risk categories; (par. 139-154)
and determining the subject's risk of the onset or progression of myopia over the timeframe based on the final score. (par. 150)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Li and Marin in combination, with the teaching of Brennan to include the steps of risk category weighting and a cumulative risk score. One would have been motivated to include these features to clearly convey an overall patient risk and how the various factors contribute to disease.
Claim 26 Li and Marin in combination teach a method of claim 18 as explained in the rejection of claim 18, wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the respective risk category assigned to each of the at least one parameter.
Li and Marin do not disclose, but Brennan teaches
assigning a weight to each one of the plurality of predetermined risk categories; determining for each risk category of the plurality of predetermined risk categories, a number of parameters among the at least one parameter, to which the respective risk category has been assigned (par. 18-29- determining based on one or more of the demographic or behavioral information, a progression factor for the subject by weighting, according to a predetermined progression formula, the demographic and behavioral information, wherein the predetermined progression formula and weighting is derived from progression data associated with a population);
calculating for each risk category of the plurality of predetermined risk categories, a score by multiplying the weight assigned to the respective risk category with the number of parameters, to which said risk category has been assigned; (par. 139-154)
calculating a final score by summing the scores calculated for the plurality of predetermined risk categories; and (par. 139-154)
determining the subject's risk of the onset or progression of myopia over the timeframe based on the final score, (par. 150) and wherein determining the subject's risk of the onset or progression of myopia over the timeframe based on the final score comprises, comparing the final score with at least one first predetermined threshold value. (Fig. 18-19; par. 126-129)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Li and Marin in combination, with the teaching of Brennan to include the steps of risk category weighting and a cumulative risk score. One would have been motivated to include these features to clearly convey an overall patient risk and how the various factors contribute to disease.
Claim 28 Li discloses a method, wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter, is established based on at least one predictive model based on a machine learning algorithm, which provides a relationship between the risk of myopia onset or progression and the at least one parameter, (par. 9-12) and wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter further comprises:
entering the determined value of the at least one parameter into the at least one predictive model based on the machine learning algorithm; (par. 12-13- the input data is obtained from an eye examination. In some embodiments, the input data comprises age, gender, nationality, ethnicity, spherical equivalent (SE) measurement of one or both eyes, In some embodiments, the machine learning model is a classifier…i) a software module obtaining input data of the individual; ii) a software module evaluating the input data using a machine learning model to generate a prediction of myopia onset or myopia progression).
Li and Marin in combination do not disclose, but Brennan teaches
calculating a value of a risk ratio, using the at least one predictive model based on the machine learning algorithm; and (Fig. 9; Fig. 13; par. 102- The hazard ratio (HR) comparing subjects who had one myopic parent with those with none was 1.48 (95% CI, 1.09-1.99, p=0.01). Children of two myopic parents had an increased hazard ratio of eventual myopia compared with children who had no myopic parents (HR, 2.38; 95% CI, 1.66-3.41; P<0.0001))
determining the subject's risk of the onset or progression of myopia over the timeframe based on the calculated value of the risk ratio; (par. 102; par. 109-113)
wherein the calculated value of the risk ratio 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. (par. 81-85; par. 94-96-models for risk)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Li and Marin in combination, with the teaching of Brennan. One would have been motivated to include these features to clearly convey an overall patient risk and how the various factors contribute to disease.
Claim 29 Li teaches a method wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter, is established based on at least one predictive model based on a machine learning algorithm, which provides a relationship between the risk of myopia onset or progression and the at least one parameter, (par. 9-12) and
wherein determining the subject's risk of the onset or progression of myopia over the timeframe, based on the determined value of the at least one parameter further comprises: entering the determined value of the at least one parameter into the at least one predictive model based on the machine learning algorithm; (par. 12-13- the input data is obtained from an eye examination. In some embodiments, the input data comprises age, gender, nationality, ethnicity, spherical equivalent (SE) measurement of one or both eyes, In some embodiments, the machine learning model is a classifier…i) a software module obtaining input data of the individual; ii) a software module evaluating the input data using a machine learning model to generate a prediction of myopia onset or myopia progression)
Li and Marin in combination do not disclose, but Brennan teaches
calculating a value of a risk ratio, using the at least one predictive model based on the machine learning algorithm; (Fig. 9; Fig. 13; par. 102- The hazard ratio (HR) comparing subjects who had one myopic parent with those with none was 1.48 (95% CI, 1.09-1.99, p=0.01). Children of two myopic parents had an increased hazard ratio of eventual myopia compared with children who had no myopic parents (HR, 2.38; 95% CI, 1.66-3.41; P<0.0001)) and
determining the subject's risk of the onset or progression of myopia over the timeframe based on the calculated value of the risk ratio; (par. 102; par. 109-113)
wherein the calculated value of the risk ratio 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, (par. 81-85; par. 94-96-models for risk)
and wherein determining the subject's risk of the onset or progression of myopia over the timeframe based on the risk ratio comprises, comparing the calculated value of the risk ratio with at least one second predetermined threshold value. (Fig. 18-19; par. 126-129)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Li and Marin in combination, with the teaching of Brennan. One would have been motivated to include these features to clearly convey an overall patient risk and how the various factors contribute to disease.
Response to Arguments
Applicant's arguments filed 3/9/26 have been fully considered but they are not persuasive.
(A) Applicant argues that the claim rejections under 35 USC 112(b).
In response, the examiner disagrees with applicant’s remarks. The claim rejections under 35 USC 112(b) have been maintained.
(B) Applicant argues that the claim rejections under 35 USC 101. Applicant argues that the claim cannot be performed practically inside the mind, especially in light of the claim amendments.
In response, the Examiner has updated the 101 rejection to reflect the current claim language. Claims 16 and 32 have been further amended to recite: “being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation…”. The additional limitation is not sufficient to render the claim patent eligible, as the new limitation recites a mathematical relationship (i.e. determining a certitude probability function of the subject. )
Moreover, Applicant is reminded that , the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. It is respectfully submitted that the claim language, even as amended
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
It is noted that claims 16 and 32 have been amended to further recite “said sensitivity parameter…being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation.” The amended claim language includes a “determination” and encompasses a user making a judgement or evaluation regarding the subject’s certainty.
Morvover, the additional limitation is not sufficient to render the claim patent eligible, as the new limitation recites a mathematical relationship (i.e. determining a certitude probability function of the subject. ) It should be further noted that as drafted, it is not clear that the “being determined” step is actively performed as a function of the claimed method/ system. (passively recited limitation)
For all the reasons explained, the rejection of the claims under 35 USC 101 has been maintained.
(C) The Applicant argues that the claimed invention reflects an improvement to the accuracy of risk prediction.
In response, consideration of improvements is relevant to the integration analysis regardless of the technology of the claimed invention. That is, the consideration applies equally whether it is a computer-implemented invention, an invention in the life sciences, or any other technology. See, e.g., Rapid Litigation Management Ltd. v. CellzDirect, Inc., in which the court noted that a claimed process for preserving hepatocytes could be eligible as an improvement to technology because the claim achieved a new and improved way for preserving hepatocyte cells for later use, even though the claim is based on the discovery of something natural.(See 827 F.3d 1042, 1048 (Fed. Cir. 2016)) Notably, the court did not distinguish between the types of technology when determining that the invention improved technology.
However, it is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. (921 F.3d 1084, 1093-94 (Fed. Cir. 2019).
It is respectfully submitted that applicant’s argued improvement reflects an improvement to the abstract idea (i.e. improvement to mental process of rendering more better judgements or evaluations.)
(D) Applicant argues that the prior art does not disclose the amended features.
In response, the examiner disagrees and has provided additional citations to address the claim language as amended.
Marin teaches a sensitivity parameter…. being determined based on a certitude probability function of the subject representing a certainty of choices made by the subject relative to said variation.” (par. 152-156: The value of each specific sensitivity parameter is for example evaluated with a probability function of certitude of the subject's answers (par. 0152)) At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the method and system of Li with the teaching of Marin to include a sensitivity parameter reflecting the subject’s sensitivity to changes in at least one dioptric optical feature and the subject’s certainty (certitude) regarding their answers/selections. One would have been motivated to include this feature to more accurately assess factors impacting the vision acuity of a subject.
(E ) Applicant argues that the Li and Marin references should not be combined.
In response to applicant's argument that the Marin and Li references cannot be combined, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Drobe et al (US 20220084687 A1)- discloses a method and device for predicting evolution over time of at least one vision-related parameter of at least one person.
Sankaridurg et al (US 20200183185 A1) discloses an ophthalmic lens system for reducing the risk of progression of a myopic eye by selectively maintaining, inducing or creating asymmetry of the peripheral retinal profile for the eye.
Ho et al (US 20100296058 A1) teaches a lens system for treatments customised to the needs of the patient and which allow for extended treatment periods involving repeated refinement of lenses and treatments to control the progression of myopia and match changing need.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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RACHEL L. PORTER
Primary Examiner
Art Unit 3684
/Rachel L. Porter/Primary Examiner, Art Unit 3684
Prosecution Insights