DETAILED ACTION
This Office Action is responsive to the claims filed on February 2, 2026. Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are under examination.
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 USC 112(a).
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 USC 112(b).
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 USC 101.
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 USC 103 over Fujii, Korb, Altheimer, and Kasaragod.
Response To Arguments/Amendments
35 USC 112(a) Rejections: The Examiner maintains that the claims are not linked to any potentially substantive descriptive or enabling content in the specification beyond a superficial recitation of the claim language in prose. The Applicant has responded to this by referring to the claim language in prose in the spec. Possession is demonstrated not merely by stating claim language but demonstrating that conception has occurred. The conception has to be sufficient to make and use the invention without undue experimentation. This is also complicated by a rejection presented herein that questions the clarity and meaning of the claim term, “wherein determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability or probability density of the individual data for given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model for the calculation of optimization of the ophthalmic lens.” Please see the specific 35 USC 112(b) rejection of the quoted feature for a more detailed explanation for why it is not clear that the inventor has possession based on the specification. The Applicant referred to an explanation at page 58, but the elements of the claim do not match the descriptions stated there. The claim is sufficiently broad that it is unclear what the difference is between “the probability or probability density of the individual data for given parameters of the individual eye model” and “the probability or probability density of the parameters of the individual eye model for the calculation of optimization of the ophthalmic lens.” Accordingly, the rejections are maintained.
35 USC 112(b) Rejections: The Applicant’s amendments have been considered and are partially persuasive. The rejections of claims 24, 28, and 31 that are allegedly mooted by the amendment and claim cancelations is the limitation that was incorporated into the independent claims from claims 24 and 28, “calculating or optimizing an ophthalmic lens according to the inventive method for calculating or optimizing an ophthalmic lens” and the analogous “a calculator or optimizer configured to calculate or optimize the ophthalmic lens according to a method for calculating or optimizing an ophthalmic lens.” The Applicant provided no other arguments other than to state that the rejections are moot in light of the amendments. Therefore, the other existing 35 USC 112(b) rejections for which no arguments or amendments have been presented are maintained.
35 USC 112(d) Rejections: The amendments and arguments have been considered and are persuasive. The rejections have been withdrawn.
35 USC 101 Rejections: The Applicant’s amendments and arguments have been considered but are not persuasive. The Applicant argues that the amended claims represent “concrete operations go beyond mental processes or mathematical concepts.” The Applicant then amended the independent claims to incorporate further mental processes and mathematical concepts. The Applicant states that the claim as a whole “now applies such calculations physical context of lens design and optimization rather than claiming the mathematical relationships themselves.” The designs may relate to physical parameters, but any improvement is still entirely claimed within elements that are abstract ideas, which fails to integrate the abstract ideas into practical applications at Step 2A, Prong 2 and Step 2B.
The Applicant then attempts to analogize claim 16 to Example 47, Claim 3. Claim 16 is not analogous for several reasons. For one, the improvement to claim 16 is an improvement to machine learning training, so that is inherently contained entirely within the software realm. Further, machine learning has been given special attention and status as a technology in this context. By contrast, the Applicant’s claim 16 merely inputs data and outputs data, a pure data inference, like Electric Power Group or Example 47, claim 2. The alleged invention in claim 16 is not directed to computer technology or how the computer is incorporated. It is, at best, an alleged improvement to lens creation. However, the improvements, as discussed are entirely contained in the abstract ideas, like in EPG and Example 47, claim 2.
If the Applicant wishes to integrate the abstract idea into a practical application of lens creation, the Applicant is recommended to, with support for the specification, provide a specific mechanism for applying the data determinations of the current independent claims. MPEP 2106.05(f)(1) states,
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
In the context of the Applicant’s claims, a mechanism that could qualify as an additional limitation (e.g., other than a mental process or mathematical concept) and could include a valid mechanism is a manufacturing procedure describing a mechanism of how the lens is manufactured based on the determined data.
The Applicant continues to argue that the claims are an inventive collection of elements that are not WURC and are distinct from the methods used prior to the alleged invention. However, the examiner has identified all of these elements as either mental processes or mathematical concepts, abstract ideas. To confer eligibility, the claim must include additional limitations (outside of the abstract ideas) that contribute to the improvement. As detailed in the rejection presented below, there are no such additional limitations.
To reiterate, a mechanism that could qualify as an additional limitation (e.g., other than a mental process or mathematical concept) and could include a valid mechanism is a manufacturing procedure describing a mechanism of how the lens is manufactured based on the determined data. The Applicant is advised to so amend the independent claims.
Accordingly, the claims as amened are ineligible. The rejections are maintained.
35 USC 103: The Applicant’s amendments and arguments have been considered and are persuasive. New art rejections have been presented herein.
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:
“Modelling module” in claims 25 and 26.
“Main ray identification module,” “evaluation module,” and “optimization module” of claim 27.
“Calculator or optimizer” of claim 28.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure, if any, 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
35 USC 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Written Description
Lack of Written Description
MPEP 2161.01(I): “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV. […] When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002).”
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 16-19, 21-22 and 29-30 recite methods and included steps that are not presented in the body of the written description clearly enough to associate the methods and steps with the evaluations of mathematical equations presented in the body of the written description. Under MPEP 2161.01(I), for each method and each step, a computer and algorithm for executing the method or step must be disclosed. The Applicant has failed to do so. A person of ordinary skill in the art would not interpret the specification as showing that the Applicant is in possession of the methods and their respectively recited steps.
Claims 25-26 recite devices and modules that are never described in the specification beyond the claim language restated in prose. Under MPEP 2161.01(I), for each device and each module, a computer and algorithm for executing the device or module must be disclosed. The Applicant has failed to do so. A person skilled in the art would not interpret the specification as showing that the Applicant is in possession of the devices and their respectively recited modules.
Means-Plus Function Limitations
Claim 25 is written in means-plus function form. As previously indicated, a computer and algorithm for the devices and modules claimed are not disclosed under MPEP 2161.02. Means-plus-function claims rely on the disclosure for structure, so a person skilled in the art would interpret that claim 25 was not in the Applicant’s possession. For at least this reason, the claims lack written description.
Enablement
Claims 16-19, 21-22, and 25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
MPEP 2161.01(III): “To satisfy the enablement requirement of 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, the specification must teach those skilled in the art how to make and use the full scope of the claimed invention without "undue experimentation." See, e.g., In re Wright, 999 F.2d 1557, 1561, 27 USPQ2d 1510, 1513 (Fed. Cir. 1993); In re Wands, 858 F.2d 731, 736-37, 8 USPQ2d 1400, 1402 (Fed. Cir. 1988). In In re Wands, the court set forth the following factors to consider when determining whether undue experimentation is needed: (1) the breadth of the claims; (2) the nature of the invention; (3) the state of the prior art; (4) the level of one of ordinary skill; (5) the level of predictability in the art; (6) the amount of direction provided by the inventor; (7) the existence of working examples; and (8) the quantity of experimentation needed to make or use the invention based on the content of the disclosure. Wands, 858 F.2d at 737, 8 USPQ2d 1404. The undue experimentation determination is not a single factual determination; rather, it is a conclusion reached by weighing all the factual considerations. Id. […] When a claim is not limited to any particular structure for performing a recited function and does not invoke 35 U.S.C. 112(f), any claim language reciting the ability to perform a function per se would typically be construed broadly to cover any and all embodiments that perform the recited function. Because such a claim encompasses all devices or structures that perform the recited function, there is a concern regarding whether the applicant's disclosure sufficiently enables the full scope of protection sought by the claim. In re Swinehart, 439 F.2d 210, 213, 169 USPQ 226, 229 (CCPA 1971); AK Steel Corp. v. Sollac, 344 F.3d 1234, 1244, 68 USPQ2d 1280, 1287 (Fed. Cir. 2003); In re Moore, 439 F.2d 1232, 1236, 169 USPQ 236, 239 (CCPA 1971). Applicants who present broad claim language must ensure the claims are fully enabled. Specifically, the scope of the claims must be less than or equal to the scope of the enablement provided by the specification. Sitrick v. Dreamworks, LLC, 516 F.3d 993, 999, 85 USPQ2d 1826, 1830 (Fed. Cir. 2008) ("The scope of the claims must be less than or equal to the scope of the enablement to ensure that the public knowledge is enriched by the patent specification to a degree at least commensurate with the scope of the claims." (quotation omitted)).”
Claims 16-19 and 21-22 recite methods and included steps that are not presented in the body of the written description clearly enough to associate the methods and steps with the evaluations of mathematical equations presented in the body of the written description. A person of ordinary skill in the art would not be able to interpret which elements of the body of the specification correspond to the methods and steps defined solely by their claims in prose equivalents without proper cross references to other elements of the specification. Therefore, it is unclear whether the scope of the claims is enabled because it is unclear what elements of the claims correspond to what elements of the allegedly enabling disclosure.
Similarly, Claim 25 recites devices and modules that are never described in the specification beyond the claim language restated in prose. A person ordinarily skilled in the art would not interpret the specification as showing that the Applicant is in possession of the devices and their respectively recited modules. A person of ordinary skill in the art would not be able to interpret which elements of the body of the specification correspond to the devices and modules defined solely by their claims in prose equivalents without proper cross references to other elements of the specification. Therefore, it is unclear whether the scope of the claims is enabled because it is unclear what elements of the claims correspond to what elements of the allegedly enabling disclosure.
When assessing claims 16-19, 21-22, 25-26, 29-30, and 32-33 under the Wands factors, with emphasis and the final three and most important factors:
(1/A) The breadth of the claims is unclear because it is unclear what elements of the written description correspond to which methods, steps, devices, modules, or CRMs of the claims.
(6/F) The amount of direction provided by the “inventor” is limited. It is unclear, based on the “inventor’s” disclosure which elements of the claims, which are merely repeated in the specification in prose and not clearly associated with the specific mathematical operations in the body of the specification, so it is unclear which direction from the written description is applied to which element of the claims.
(7/G) There exist no clear working examples because it is not clear to what elements of the claims any examples presented in the specification refer.
(8/H) The quantity of experimentation to make and use the “invention” could be infinite because a person of ordinary skill in the art could never be entirely certain that the person so skilled had accomplished the or even ascertained the elements of the claims, which are not clearly presented in the specification.
Accordingly, the Applicant has failed to enable claims 16-19, 21-22, 25-26, 29-30, and 32-33. If the Applicant disagrees, the Applicant is invited to, without presenting any new matter, associate, on the record, each method and each method step in each method claim; each device and module of each device claim; and each CRM and each method step of each CRM claim with the alleged specific enabling disclosure in the specification for each of those elements. If the Applicant fails to include any portion of the specification in association with the specific claim elements, that element of the specification will be considered other than an element of the enabling disclosure. Without adequate association between the allegedly enabling disclosure and each claim element, the rejection will be maintained.
35 USC 112(b)
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.
Claim 16-19, 21-22, 25-26, 29-30, and 32-33 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.
Indefiniteness
Claims 16 and 25 recite, “and wherein determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability or probability density of the individual data for given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model;” It is unclear what this means. Specifically, the two quantities being multiplied appear to be the same thing. That is, there does not appear to be any distinction between the parameters of the individual eye model and the given parameters of the individual eye model. The claim does not clarify what the distinction between the parameters and the given parameters is. For purposes of examination, this feature will be interpreted to mean that the model containing parameters are consistency checked using a probability determination.
Claim 30 depends from canceled claim 23. This makes the scope of claim 30 indefinite.
Means-Plus-Function With No Corresponding Structure
MPEP 2181(II)(A): “The proper test for meeting the definiteness requirement is that the corresponding structure (or material or acts) of a means- (or step-) plus-function limitation must be disclosed in the specification itself in a way that one skilled in the art will understand what structure (or material or acts) will perform the recited function.”
MPEP 2181(II)(B): “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). […] The sufficiency of the algorithm is determined in view of what one of ordinary skill in the art would understand as sufficient to define the structure and make the boundaries of the claim understandable. For example, in Williamson, the Federal Circuit found that the term "distributed learning control module" is a means-plus- function limitation that performs three specialized functions (i.e., "receiving,", "relaying," and "coordinating"), which "must be implemented in a special purpose computer." Williamson, 792 F.3d at 1351-52, 115 USPQ2d at 1113. The Federal Circuit explained that "[w]here there are multiple claimed functions, as we have here, the [specification] must disclose adequate corresponding structure to perform all of the claimed functions." Id., 115 USPQ2d at 1115. Yet the Federal Circuit determined that the specification "fails to disclose any structure corresponding to the ‘coordinating’ function." Id. at 1354, 115 USPQ2d at 1115. Specifically, the Federal Circuit found no "disclosure of an algorithm corresponding to the claimed ‘coordinating’ function," concluding that the figures in the specification relied upon by patentee as disclosing the required algorithm, instead describe "a presenter display interface" and not an algorithm corresponding to the claimed "coordinating" function. Id. at 1353-54, 115 USPQ2d at 1114-15. Accordingly, the Federal Circuit affirmed the district court’s judgment that claims containing the "distributed learning control module" limitation are invalid for indefiniteness under 35 U.S.C. 112(b). Id. at 1354, 115 USPQ2d at 1115. See also Noah, 675 F.3d at 1319, 102 USPQ2d at 1421 (holding that "[c]omputer- implemented means-plus- function claims are indefinite unless the specification discloses an algorithm to perform the function associated with the limitation[,]" and that "[w]hen the specification discloses an algorithm that only accomplishes one of multiple identifiable functions performed by a means-plus- function limitation, the specification is treated as if it disclosed no algorithm.").”
Claim 25
Claim 25 recites devices and modules as placeholders with associated functions. However, there is nothing in the Applicant’s specification “corresponding” to these devices and placeholders that would qualify as (1) a physical computer device to execute these devices or modules and (2) an algorithm by which the functions of the devices or modules execute. That is, the written description need not only provide the structure but also a sufficient link between the structure and the claim elements at issue. The claims elements have only been recited in the body of the specification without any correspondence to the mathematical operations and models expressed in the body of the written description (e.g., outside of repetition of the claim language itself in prose). Because the metes and bounds of means-plus-function claims are determined based entirely on the disclosure of the specification, and because the written description fails to identify the sections of the description associated with the recited claim terms, the scope of claim 25 is indefinite. In order to overcome this rejection, the Applicant must demonstrate that the specification provides (1) a computer device structure and (2) an algorithmic structure that, under the terms of the specification, corresponds to the specific claim limitations.
Missing Essential Element
MPEP 2172.01: “Depending on the specific facts at issue, a claim which omits subject matter disclosed to be essential to the invention as described in the specification or in other statements of record may be rejected under […] under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as failing to claim the subject matter that the inventor or a joint inventor (or, for applications subject to pre-AIA 35 U.S.C. 112, the applicant) regards as the invention (see, e.g., In re Collier, 397 F.2d 1003, 158 USPQ 266 (CCPA 1968)). Such essential matter may include missing elements, steps or necessary structural cooperative relationships of elements described by the applicant(s) as necessary to practice the invention.”
The Applicant’s specification states on Page 54, Lines 11-13, “[t]he central idea of the invention is to calculate at least the length parameter dLR (or DLR) from other measurement data and a priori assumptions about other degrees of freedom and not to assume it a priori itself, as is conventional.” This feature is described as essential (e.g., central), but this feature is not explicitly reflected in the independent claims. Applicant is instructed to include this feature in independent claims 16 and 25 to overcome this rejection.
The Applicant’s specification states on Page 52, Lines 13-16, “[a] central aspect of the invention relates precisely to the aim of not having to measure all parameters directly. In particular, it is significantly easier to measure the refraction of the relevant eye or to determine it objectively and/or subjectively than to measure all parameters of the model eye individually.” This feature is described as essential (e.g., central), but this feature is not explicitly reflected in the independent claims. Applicant is instructed to include this feature in independent claims 16 and 25 to overcome this rejection.
All of the dependent claims are rejected based on their dependency from the independent claims that lack the essential elements.
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 16-19, 21-22, 25-26, 29-30, and 32-33 are rejected under 35 U.S.C. 101 as directed to abstract ideas without significantly more.
Independent Claims
Claim 25 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical concept, which are abstract ideas.
MPEP 2106.04(a)(2)(Ill): “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.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 25 recites (claim features in italics, paragraph references are to the Applicant’s specification):
[…] model and/or construct an individual eye model by defining a set of parameters of the individual eye model and determining a probability distribution of values of the parameters of the individual eye model on the basis an initial probability distribution of the parameters of the eye model and of the individual data for the calculation or optimization of the ophthalmic lens, wherein the initial probability distribution is based on information about the probability distribution of the parameters of the eye model in a population of persons, and wherein determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability or probability density of the individual data for given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model; (Evaluation, Mathematical Concept- Page 62, lines 4-9: “Description of the methods Two possible methods for calculating an ophthalmic lens will be presented below (Bayes A and Bayes B). In the Bayes A method, the available information is used to set up a (single) individual eye model, with the help of which an ophthalmic lens optimal for this eye model is calculated.”)
The model or construct and determine steps are evaluations of mathematical parameters to mathematically create and/or solve a mathematical construct (e.g., a machine learning model) that merely represents mathematical relationships. The evaluation to create and/or use the model is a mental process. The mathematical operations on mathematical relationships to create and/or use the model represent a mathematical concept. This is illustrated by the excerpt presented above stating that Bayes statistical methods are used to create the model and use the model for the optimization of a lens. Mental processes and mathematical concepts are abstract ideas.
[…] identify the course of a main ray through at least one visual point of at least one surface of the ophthalmic lens to be calculated or optimized into the model eye; (Mental Process – Identifying a trajectory of a ray through a modeled lens is practically performable in the mind or with the aid of pen and paper (e.g., using optical equations), so it is an evaluation, a mental process, an abstract idea.)
[…] evaluate an aberration of a wavefront resulting from a spherical wavefront incident on the first surface of the ophthalmic lens along the main ray on an evaluation surface compared to a wavefront converging in one point on the retina of the eye model: and (Mental Process The comparison of wavefronts to evaluate an aberration is practically performable mentally or with the aid of pen and paper. This was done manually prior to the advent of computers. Therefore, this is an evaluation, a mental process, an abstract idea.)
[…] iteratively vary the at least one surface of the ophthalmic lens to be calculated or optimized until the evaluated aberration corresponds to a predetermined target aberration; (Mental Process Iterative modification of variables until a target output is reached is practically performable mentally or with the aid of pen and paper. This was done manually prior to the advent of computers. Therefore, this is an evaluation, a mental process, an abstract idea.)
wherein the [mind] uses the probability distribution of values of the parameters of the individual eye model and is configured to improve lens performance by minimizing optical aberrations for the individual eye (Mental Process Iterative modification of variables using a probability distribution until a target output (e.g., maximum or zero aberration) is reached is practically performable mentally or with the aid of pen and paper. This was done manually prior to the advent of computers. Therefore, this is an evaluation, a mental process, an abstract idea.)
identifying relevant individual parameters of at least one eye of a spectacle wearer for the calculation or optimization of an ophthalmic lens for the at least one eye of the spectacle wearer, comprising:
This is an element of the preamble describing a result to be achieved by the claimed device, so it should not affect the eligibility analysis. However, if, arguendo, the preamble were interpreted to have been incorporated by cross reference into the body of the claim, the identifying […] parameters is an evaluation of mathematical parameters to mathematically create and/or solve a mathematical construct (e.g., a machine learning model) that merely represents mathematical relationships. This is true at least inasmuch as the identifying, by virtue of the claim construction, includes the model or construct and determining steps. Accordingly, the identifying is an abstract idea.
Claim 25 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. 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. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
The additional limitations:
A device for […]
at least one data interface configured to […]
a modeling module configured to […] wherein the modeling module is configured to […]
a surface model database configured to […]
a main ray identification module configured to […]
an evaluation module configured to […]
an optical optimization module configured to […]
an output interface configured to […]
There is no specific detail in the specification regarding the device, data interface, or modeling module other than what is claimed (and repeated in the written description in prose form). That is, device, data interface, and modeling module are described based on their function. A person skilled in the art could reasonably interpret, based on the poor definition of the scope of the terms, device, data interface, and modeling module, that the modules are merely software elements executable by a computer. Because the device, data interface, and modules are not described otherwise, the broadest reasonable interpretation includes that the device, data interface, and modules are generic elements of a generic computer. Under MPEP 2106.05(f) recitation of a general purpose computer does not integrate an abstract idea into a practical application under Step 2A, Prong 2.
provide individual data on properties of the at least one eye of the spectacle wearer; and
generate optimized surface data defining the ophthalmic lens for manufacturing or display,
The provide and generate steps merely gather existing information (individual data) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), providing data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The provide step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong Two.
[…] for the calculation or optimization of the ophthalmic lens.
This along with the ophthalmologic data in the claim merely limits the abstract idea to a particular technological environment of lens optimization. Therefore, under MPEP 2106.05(h), it does not integrate the abstract idea into a practical application at Step 2A, prong 2.
Claim 25 does not integrate the abstract idea into a practical application and is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, 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.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following 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. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
The additional limitations:
A device for […]
at least one data interface configured to […]
a modeling module configured to […] wherein the modeling module is configured to […]
a surface model database configured to […]
a main ray identification module configured to […]
an evaluation module configured to […]
an optical optimization module configured to […]
an output interface configured to […]
There is no specific detail in the specification regarding the device, data interface, or modules other than what is claimed (and repeated in the written description in prose form). That is, device, data interface, and modules are described based on their function. A person skilled in the art could reasonably interpret, based on the poor definition of the scope of the terms, device, data interface, and modules, that the device, data interface, and modules can be software elements executable by a computer. Because the device, data interface, and modules are not described otherwise, the broadest reasonable interpretation includes that the device, data interface, and modules are generic elements of a generic computer. Under MPEP 2106.05(f) recitation of a general purpose computer does not combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept under Step 2B.
provide individual data on properties of the at least one eye of the spectacle wearer; and
generate optimized surface data defining the ophthalmic lens for manufacturing or display,
The provide and generate steps are storing and retrieving information from memory and also indicative of sending or reciting data, so they are analogous to the i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] and iv. Storing and retrieving information in memory examples cited in MPEP 2106.05(d)(II)(i) representing well-understood, routine, and conventional functions.
Because the additional limitations of the storing and generating steps are insignificant extra-solution activity (as illustrated under Step 2A Prong 2) and a well-understood, routine, and conventional function, the provide and generate steps do not combine with the elements of the claim to provide significantly more than the abstract idea and would not render the claim an inventive concept.
[…] for the calculation or optimization of the ophthalmic lens.
This along with the ophthalmologic data merely limits the abstract idea to a particular technological environment of lens optimization. Therefore, under MPEP 2106.05(h), it does not combine with other elements of the claim to provide significantly more than the abstract idea at Step 2B.
Therefore, none of the additional limitations can combine with the other elements of the claim to provide significantly more than the abstract idea and would not render the combination of the additional limitations with the other elements of the claims an inventive concept, under MPEP 2106.05(d), 2106.05(f), and MPEP 2106.05(g).
Claim 25 is ineligible.
Claim 16 (Statutory Category – Process)
Claim 16 recites the method steps executed by the device of claim 25 and the eligibility analysis of claim 25 is applied to claim 16 mutatis mutandis.
Claim 16 is ineligible
Dependent Claims
Dependent claims 17-24 and 26-31 are also ineligible for at least the following reasons. Because it is unclear which elements of the written description, other than the claims restated in prose, correspond to the claims, references to the Applicant’s written description are omitted. The classification of elements in this section as abstract ideas and additional limitations that do not contribute to eligibility rely on the ordinary meaning of the claim terms.
Claim 17
Claim 17 recites,
wherein: constructing an individual eye model comprises providing an initial probability distribution of the parameters of the eye model; and
determining a probability distribution of values of the parameters of the individual eye model further takes place on the basis of the initial probability distribution of parameters of the eye model.
These elements are evaluations, that merely qualify the evaluations of the constructing and determining steps of claim 16. Evaluations are mental processes, which are elements of an abstract idea. Therefore, the elements merge with the abstract idea of claim 16 and do not provide any additional limitations that can integrate the abstract idea into a practical application under Step 2A, Prong 2 or can combine with the other features of the claims to provide significantly more than the abstract idea or confer an inventive concept.
Claim 17 is ineligible.
Claim 18
Claim 18 recites,
wherein the step of constructing an individual eye model and/or determining a probability distribution of values of the parameters of the individual eye model is carried out using Bayesian statistics.
This limitation merely qualifies the evaluations of mathematical relationships of the constructing or determining steps. Also, Bayesian statistics are mathematical evaluations. Therefore, this limitation is an element of the mental process, the mathematical concept, and, consequently, the abstract idea of claim 16. The features of claim 16 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 18 is ineligible.
Claim 19
wherein determining a probability distribution of values of the parameters of the individual eye model for use in lens calculation or optimization comprises calculating a consistency measure, wherein the product of the probability or probability density of the individual data with given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model with given background knowledge is used as the consistency measure.
This limitation merely qualifies the evaluations of mathematical relationships of the constructing or determining steps. Also, calculating a consistency measure, evaluating products, and using probability densities are mathematical evaluations. Therefore, this limitation is an element of the mental process, the mathematical concept, and, consequently, the abstract idea of claim 16. The features of claim 16 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 19 is ineligible.
Claims 21 and 26
Claim 26 recites,
wherein the step of providing individual data comprises providing individual refraction data on the at least one eye of the spectacle wearer; and
This merely qualifies the data gathered in the providing step. This is insignificant extra-solution activity for the same reasons as the providing step, under MPEP2106.05(g), so it does not integrate the abstract idea into a practical application under Step 2A, Prong 2. Also, for the same reasons as the providing step, this is a well-understood, routine, and conventional function. Therefore, this limitation does not combine with the other elements of the claim to provide significantly more than the abstract idea and would not confer an inventive concept under Step 2B.
constructing an individual eye model comprises defining an individual eye model in which at least:
a shape and/or power of a corneal front surface of a model eye; and/or
a cornea-lens distance; and/or parameters of the lens of the model eye; and/or
a lens-retina distance; and/or
a size of the entrance pupil; and/or
a size and/or position of a physical aperture diaphragm
is configured to be determined on the basis of individual measurement values for the eye of the spectacle wearer and/or standard values and/or on the basis of the provided individual refraction data,
This merely qualifies the parameters of the mathematical evaluation of the modeling step, so it is an element of the abstract idea, and does not provide any additional limitations.
[…] carry out a consistency check of the defined eye model with the provided individual refraction data and to solve any inconsistencies with the aid of analytical and/or probabilistic methods.
The carrying out of a consistency check using analytical or probabilistic methods is a mathematical evaluation of mathematical relationships (e.g., a model). The evaluation is a mental process and the mathematical operations on the mathematical relationships represent a mathematical concept. Mental processes and mathematical concepts are abstract ideas. Therefore, this element is an element of an abstract idea that merges with the abstract idea of claim 1. Because the carrying out step is an abstract idea, this element does not provide an additional limitation.
wherein the modeling module is configured to […]
The modeling module is still a generic computing element, as demonstrated, and, under MPEP 2106.05(f), neither integrates the abstract idea into a practical application under Step 2A, Prong 2, nor does it combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
The features of claim 26 do not provide additional limitations that integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept. Claim 21 recites the method features of claim 26 and is treated the same under the eligibility analysis, mutatis mutandis. Claims 21 and 26 are ineligible.
Claim 22
Claim 22 recites,
wherein any inconsistencies are solved by:
adapting one or more parameters of the eye model, wherein several parameters of the eye model are adapted and the adaptation is divided among the several parameters of the eye model; and/or
adding at least one new parameter to the eye model and defining it such that the eye model becomes consistent; and/or
adapting a target power of the ophthalmic lens.
The alternative or cumulative adapting steps and adding steps merely qualify the input and output parameters of the model, which is an evaluation of a mathematical calculation or modification of mathematical relationships, which is a mental process and/or a mathematical concept, an element of an abstract idea. The features of claim 22 are elements of an abstract idea that merge with the abstract idea of claims 16 and 21. The features of claim 22 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 22 is ineligible.
Claim 23
Claim 23 recites,
a method for identifying relevant individual parameters of the at least one eye of the spectacle wearer according to claim 21;
See the eligibility analysis of claim 21.
specifying a first surface and a second surface for the ophthalmic lens to be calculated or optimized;
The specifying step is mere data gathering analogous and is insignificant extra solution activity under MPEP 2106.05(g). An analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g). The storing adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A Prong 2.
The specifying step, under the broadest reasonable interpretation, includes storing and retrieving information from memory and is also indicative of sending or receiving data, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) representing well-understood, routine, and conventional functions. Because the specifying step is a well-understood, routine, and conventional function, and because the specifying step is insignificant extra-solution activity, the specifying step is not an additional limitation that can combine with the other elements of the claim to provide significantly more than the abstract idea or confer an inventive concept under Step 2B.
identifying the course of a main ray through at least one visual point of at least one surface of the ophthalmic lens to be calculated or optimized into the model eye;
evaluating an aberration of a wavefront resulting from a spherical wavefront incident on the first surface of the ophthalmic lens along the main ray on an evaluation surface compared to a wavefront converging in one point on the retina of the eye model; and
iteratively varying the at least one surface of the ophthalmic lens to be calculated or optimized until the evaluated aberration corresponds to a predetermined target aberration.
The identifying, evaluating, and iteratively varying steps are all evaluations. For example, even the iteratively varying, under the broadest reasonable interpretation, could reasonably include varying the surface in a model or numerical specification prior to fabrication. Evaluations are mental processes, which are abstract ideas.
(NOTE: In the alternative, the abstract idea of claim 16 and its additional limitations define a solution for creating a model, and the features of claim 23 are largely separate operations directed to a different solution, which is constructing a lens. Accordingly, all of the features of claim 23 could be considered insignificant extra-solution activity that neither integrates the abstract idea into a practical application under Step 2A, Prong 2, nor combines with the other elements of the claim to provide significantly more than the abstract idea under Step 2B.)
The features of claim 23 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 23 is ineligible.
Claim 29
Claim 29 recites,
A non-transitory computer program product including program code configured to, when loaded and executed on a computer, perform […]
A non-transitory computer program product is a generic storage medium of a generic computer for storing software, such as memory. Under MPEP 2106.05(f), recitation of a general purpose computer does not integrate the abstract idea into a practical application under Step 2A, Prong 2 or combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept under Step 2B.
[…] a method for identifying relevant individual parameters of at least one eye of a spectacle wearer according to claim 16.
See the eligibility analysis of claim 16.
The features of claim 29 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 29 is ineligible.
Claim 30
Claim 30 recites,
A non-transitory computer program product including program code configured to, when loaded and executed on a computer, perform the computer-implemented […]
A non-transitory computer program product is a generic storage medium of a generic computer for storing software, such as memory. Under MPEP 2106.05(f), recitation of a general purpose computer does not integrate the abstract idea into a practical application under Step 2A, Prong 2 or combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept under Step 2B.
[…] method of claim 16.
See the eligibility analysis of claim 16.
The features of claim 30 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claim 30 is ineligible.
Claims 32 and 33
Claim 32 recites,
The computer-implemented method according to claim 16, wherein the consistency measure corresponds to a posterior of Bayesian statistics. (Mental Process, Mathematical Concept – Determining a consistency measure corresponding to a posterior of Bayesian statistics is practically performable in the mind or with aid of pen and paper, so it is an evaluation, mental process, abstract idea. Also, determining consistency based on correspondence to a posterior of Bayesian statistics is a mathematical calculation, which is a mathematical concept, an abstract idea.)
The features of claims 32 and 33 do not provide any additional limitations that could integrate the abstract idea into a practical application or combine with the other elements of the claim to contribute significantly more than the abstract idea to render the combination an inventive concept.
Claims 32 and 33 are ineligible.
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 factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 16-19, 21-22, 25-26, 29-30, and 32-33: Fujii, Korb, Altheimer, and Kasaragod
Claims 16-19, 21-22, 25-26, 29-30, and 32-33 rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0082957 A1 to Fujii et al. (Fujii) in view of NPL: “Bayesian Artificial Intelligence, Chapter 2” by Korb (Korb), US 2015/0002810 A1 to Altheimer et al. (Altheimer), and NPL: “Bayesian maximum likelihood estimator of phase retardation for quantitative polarization-sensitive optical coherence tomography” by Kasaragod et al. (Kasaragod).
Claims 16, 25, and 29
Regarding Claim 25, Fujii Teaches:
A device for identifying relevant individual parameters of at least one eye of a spectacle wearer for the calculation or optimization of an ophthalmic lens for the at least one eye of the spectacle wearer, comprising: (Fujii [0046] “As shown in FIG. 3, the ophthalmic apparatus 1 includes the fundus camera unit 2, the OCT unit 100 and the arithmetic and control unit 200. The fundus camera unit 2 is provided with an optical system and a mechanism for acquiring front images of the subject's eye. The OCT unit 100 includes part of an optical system and part of mechanisms for performing OCT. Another part of the optical system and another part of the mechanisms for performing OCT are provided in the fundus camera unit 2. The arithmetic and control unit 200 includes one or more processors that execute various calculations and controls.)
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at least one data interface configured to provide individual data on properties of the at least one eye of the spectacle wearer; and (Fujii [0115] “The magnification corrector 233 changes at least one of the size of the distribution data 300 generated by the distribution data generator 231 and the size of the normative data 400, based on the magnification correction value calculated by the correction value calculator 232. In other words, the magnification corrector 233 changes the relative size between the normative data 400 and the distribution data 300, based on the magnification correction value calculated by the correction value calculator 232.” See FIG. 6A – The Distribution data is passed via a data interface within the data processor 230 from the distribution data generator 231 to the magnification corrector 233.)
a modeling module configured to model and/or construct an individual eye model by defining a set of parameters of the individual eye model and (Fujii [0092] “The distribution data generator 231 generates distribution data of a predetermined measurement value in the fundus Ef based on the three dimensional image data constructed. As described above, the predetermined measurement value is referred to in diagnostic imaging using OCT and image analysis, and is of the same kind as the measurement value represented by the standard distribution data. The present example will describe a case where the predetermined measurement value is the thickness of a predetermined layer tissue (e.g., nerve fiber layer, ganglion cell layer) of retina and the standard distribution data is the normative data 400 will be described. However, the predetermined measurement value is not limited to the layer thickness. The distribution data generated by the distribution data generator 231 is used as the distribution data 300 shown in FIG. 6A and FIG. 6B.” – Eye data specific to the person for whom the lens will be modeled is used to make an eye model.)
determining a probability distribution of values of the parameters of the individual eye model on the basis an initial probability distribution of the parameters of the eye model and of the individual data for the calculation or optimization of the ophthalmic lens , wherein the initial probability distribution is based on information about the probability distribution of the parameters of the eye model in a population of persons, and (Fujii [0080] “In the present embodiment, standard distribution data generated in advance is stored in the storage 212.” [0081] “The standard distribution data includes, for example, values obtained by statistically processing a sample of the predetermined measurement values acquired from a large number of normal eyes. Typically, the standard distribution data represents a distribution of normal ranges calculated from a sample of layer thickness values obtained by applying OCT to the funduses of a large number of normal eyes. Each of the normal ranges can be set to include, for example, an average value derived from the sample. The standard distribution data generated based on the normal eyes in this way is called normative data. Note that the standard distribution data may be generated based on a plurality of eyes suffering from a specific disease.” – The elements of the model eye that are not measured are inferred from known distributions of the parameters of eyes from different populations based on probabilistic/statistical measurements, such as mean. [0242] “The knowledge acquisition processor is configured to acquire knowledge by executing at least one of machine learning and data mining based on data collected in advance. The knowledge to be acquired includes knowledge about the distribution of a predetermined measurement value on the eye fundus (e.g., the layer thickness value).” [0246] “In machine learning, by analyzing the data collected as described above (mainly in a statistical manner), the knowledge acquisition processor extracts laws, rules, knowledge representations, judgment criteria, etc. from the data analyzed, and develop an algorithm of inference (described later) based on the information extracted.” [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.” – Machine learning techniques are used to infer unmeasured parameters. One example inference model is a Bayesian network. [0114] “In the calculation of the magnification correction value, an eye characteristic value different from the one or more estimated values described above can be used. The corneal curvature radius and the intraocular lens power are examples of the eye characteristic value different from the aforesaid estimated values. The eye characteristic value may be, for example, a value acquired by the ophthalmic apparatus 1, a standard value from a model eye etc., or other default values. As an example, when an OCT scan has been applied to the anterior eye segment of the subject's eye E by the ophthalmic apparatus 1, the corneal curvature radius can be determined from the data obtained by the anterior eye segment OCT scan. As another example, the value of the corneal curvature radius in the Gullstrand eye model can be used. As still another example, the power of the intraocular lens implanted in the subject's eye E can be acquired from an electronic medical record or another source.” – The determinations are for the calculation or optimization of the ophthalmic lens.)
wherein the optical optimization module uses the probability distribution of values of the parameters of the individual eye model and is configured to improve lens performance by minimizing optical aberrations for the individual eye. (Fujii [0080] “In the present embodiment, standard distribution data generated in advance is stored in the storage 212.” [0081] “The standard distribution data includes, for example, values obtained by statistically processing a sample of the predetermined measurement values acquired from a large number of normal eyes. Typically, the standard distribution data represents a distribution of normal ranges calculated from a sample of layer thickness values obtained by applying OCT to the funduses of a large number of normal eyes. Each of the normal ranges can be set to include, for example, an average value derived from the sample. The standard distribution data generated based on the normal eyes in this way is called normative data. Note that the standard distribution data may be generated based on a plurality of eyes suffering from a specific disease.” – The elements of the model eye that are not measured are inferred from known distributions of the parameters of eyes from different populations based on probabilistic/statistical measurements, such as mean. [0242] “The knowledge acquisition processor is configured to acquire knowledge by executing at least one of machine learning and data mining based on data collected in advance. The knowledge to be acquired includes knowledge about the distribution of a predetermined measurement value on the eye fundus (e.g., the layer thickness value).” [0246] “In machine learning, by analyzing the data collected as described above (mainly in a statistical manner), the knowledge acquisition processor extracts laws, rules, knowledge representations, judgment criteria, etc. from the data analyzed, and develop an algorithm of inference (described later) based on the information extracted.” [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.” – Machine learning techniques are used to infer unmeasured parameters. One example inference model is a Bayesian network. In Bayesian network training, this will be optimized using the posterior of Bayesian statistics. That is how Bayesian networks are trained. [0114] “In the calculation of the magnification correction value, an eye characteristic value different from the one or more estimated values described above can be used. The corneal curvature radius and the intraocular lens power are examples of the eye characteristic value different from the aforesaid estimated values. The eye characteristic value may be, for example, a value acquired by the ophthalmic apparatus 1, a standard value from a model eye etc., or other default values. As an example, when an OCT scan has been applied to the anterior eye segment of the subject's eye E by the ophthalmic apparatus 1, the corneal curvature radius can be determined from the data obtained by the anterior eye segment OCT scan. As another example, the value of the corneal curvature radius in the Gullstrand eye model can be used. As still another example, the power of the intraocular lens implanted in the subject's eye E can be acquired from an electronic medical record or another source.” – The determinations are for the calculation or optimization of the ophthalmic lens.)
Fujii suggests (Fujii [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.”) but appears to fail to explicitly teach, but Korb teaches:
wherein the initial probability distribution is based on information about the probability distribution of the parameters of the eye model in a population of persons, and wherein determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability or probability density of the individual data for given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model;
(Korb Page 29, 2.2 Bayesian network basics “A Bayesian network is a graphical structure that allows us to represent and reason about an uncertain domain. The nodes in a Bayesian network represent a set of random variables, X = X1, ..Xi, ...Xn, from the domain. A set of directed arcs (or links) connects pairs of nodes, Xi → Xj, representing the direct dependencies between variables. Assuming discrete variables, the strength of the relationship between variables is quantified by conditional probability distributions associated with each node.” – Bayesian networks determine conditional probabilities that link random variables, each of which have a probability distribution.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the Bayesian network as taught in Fujii with the specifics of how the Bayesian network functions in Korb because a person of ordinary skill in the art would be motivated, based on the generic mention of the use of Bayesian network as an inference model in Fujii, to look to Korb, which provides specific details on how Bayesian networks are constructed and to use the Bayesian network to infer parameter distributions of the eye model. (Fujii “[0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.”; Korb “In this chapter we will describe how Bayesian networks are put together (the syntax) and how to interpret the information encoded in a network (the semantics). We will look at how to model a problem with a Bayesian network and the types of reasoning that can be performed.)
Fujii suggests (Fujii [0059] “The diopter correction lenses 70 and 71 can be selectively inserted into the photographing optical path between the aperture mirror 21 and the dichroic mirror 55. The diopter correction lens 70 is a positive lens (convex lens) for correcting high hyperopia. The diopter correction lens 71 is a negative lens (concave lens) for correcting high myopia.”) but appears to not explicitly teach, but Altheimer teaches:
a surface model database configured to specify a first surface and a second surface for the ophthalmic lens to be calculated or optimized; (Altheimer [0118] “a surface model database for specifying a first surface and a second surface for the spectacle lens to be calculated or optimized;” – A first and second surface are specified.)
a main ray identification module configured to identify the course of a main ray through at least one visual point of at least one surface of the ophthalmic lens to be calculated or optimized into the model eye; (Altheimer [0119] “a main ray determination module for determining the path of a main ray through at least one visual point (i) of at least one surface of the spectacle lens to be calculated or optimized;” – A path of a main ray is determined through a point.)
an evaluation module configured to evaluate an aberration of a wavefront resulting from a spherical wavefront incident on the first surface of the ophthalmic lens along the main ray on an evaluation surface compared to a wavefront converging in one point on the retina of the eye model; and (Altheimer [0120]-[0121] “an object model modelling module for specifying a spherical wavefront (w0) incident on the first surface of the spectacle lens along the main ray; a wavefront calculation module for determining a wavefront (we) in the at least one eye, which results from the spherical wavefront in a surrounding of the main ray due to the power of at least the first and second surfaces of the spectacle lens, the corneal front surface, and the lens of the at least one eye;” - An aberration of a wavefront resulting from a spherical wavefront incident on the first surface of the ophthalmic lens along the main ray on an evaluation surface compared to a wavefront converging in one point on the retina of the eye model is evaluated.)
an optical evaluation module configured to iteratively vary the at least one surface of the ophthalmic lens to be calculated or optimized until the evaluated aberration corresponds to a predetermined target aberration. (Altheimer [0122] “an optimization module adapted to iteratively vary the at least one surface of the spectacle lens to be calculated or optimized until an aberration of the resulting wavefront corresponds to a specified target aberration.” - The at least one surface of the ophthalmic lens to be calculated or optimized is iteratively varied until the evaluated aberration corresponds to a predetermined target aberration.)
an output interface configured to generate optimized surface data defining the ophthalmic lens for manufacturing or display, (Altheimer [0167] “As soon as the target function is minimized sufficiently in step ST34, the spectacle lens surfaces can be output e.g. in the form of vertex depths and the spectacle lens can be manufactured accordingly.”
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the detection of an ophthalmic correction by Fujii with the corrective lens production of Altheimer because a person of ordinary skill in the art would be motivated to use the eye model and vision correction determined in Fujii for one of the few purposes it has, to correct vision, and Altheimer demonstrates how to use the vision correction and eye model to manufacture/machine a lens to correct the vision of a spectacle wearer. (Fujii [0059] “The diopter correction lenses 70 and 71 can be selectively inserted into the photographing optical path between the aperture mirror 21 and the dichroic mirror 55. The diopter correction lens 70 is a positive lens (convex lens) for correcting high hyperopia. The diopter correction lens 71 is a negative lens (concave lens) for correcting high myopia.”; Altheimer [0012] “It is the object of the invention to provide an improved method for calculating or optimizing a spectacle lens, preferably a progressive spectacle lens, wherein the spectacle lens is adapted to the individual needs of the spectacle wearer in an improved way.” [0014] “Other than the conventional methods, the method according to the invention now comprises a step of specifying an individual eye model, which at least specifies certain specifications on the geometric and optical properties of the eye.” [0125] “calculating or optimizing a spectacle lens according to the method for calculating or optimizing a spectacle lens according to the present invention, particularly in a preferred embodiment thereof; manufacturing the thus calculated or optimized spectacle lens.”)
Fujii in view of Korb and Altheimer do not appear to explicitly teach but Fujii in view of Korb, Altheimer, and Kasaragod teach (NOTE: Please see interpretation of this feature determined in the corresponding 35 USC 112(b) rejection of this feature):
wherein determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability or probability density of the individual data for given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model; (Kasaragod Abstract “This paper presents the theory and numerical implementation of a maximum likelihood estimator for local phase retardation (i.e., birefringence) measured using Jones-matrix-based polarization sensitive optical coherence tomography. Previous studies have shown conventional mean estimations of phase retardation and birefringence are significantly biased in the presence of system noise. Our estimator design is based on a Bayes’ rule that relates the distributions of the measured birefringence under a particular true birefringence and the true birefringence under a particular measured birefringence.” Page 7, 2.3 Distributions of measured and true birefringence “The first distribution is the probability distribution of the measured local birefringence b at a specific true birefringence value β and a specific ESNR γ. And it is represented by a distribution function of f(b;β,γ). Since this is a probability distribution, therefore it is normalized so that its integral on b is unity. Note that this distribution is characterized by two parameters, β and γ, as discussed in Section 2.2. In practice, b is a function of z, as shown in Eq. (4). However, we have omitted it for simplicity without sacrificing generality. Practically, this distribution function can be determined using a histogram of the measured birefringence obtained by multiple measurements at a single point. The second distribution is the probability distribution of the true birefringence when a specific birefringence and ESNR has been measured. And it is expressed by a distribution function p(β;b,γ). This distribution is also referred to as a posterior distribution, because it is a probability distribution determined only after the measurement was performed. The purpose of a birefringence measurement is to know the true birefringence, so this probability distribution is our main interest.” - Determining the probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure using the product of the probability of the individual data for given parameters of the individual eye model with the probability of the parameters of the individual eye model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the Bayesian network determinations of Fujii by the birefringence optimization of Kasaragod because the person of ordinary skill in the art would be motivated by the aim of Fujii to model an eye for lens correction to look to Kasaragod, which provides simplified, detailed, and unbiased estimations of birefringence for better eye modeling. (Fujii [0004] “The accuracy of fundus structural analysis such as layer thickness distribution analysis is influenced by a magnification error caused by the subject's eye. For example, normative data comparative analysis addresses layer thickness distribution in a predetermined area of an eye fundus. As a specific example, glaucoma diagnosis deals with thickness distribution of a layer (e.g., nerve fiber layer, ganglion cell layer) in a predetermined area around the optic nerve head and thickness distribution of a layer (e.g., nerve fiber layer, ganglion cell layer) in a predetermined area including the fovea centralis. Here, the predetermined areas have preset sizes. A typical size of the areas are 6 mm×6 mm or 9 mm×9 mm.”; Kasaragod Abstract “Our estimator design is based on a Bayes’ rule that relates the distributions of the measured birefringence under a particular true birefringence and the true birefringence under a particular measured birefringence. We used a Monte-Carlo method to calculate the likelihood function that describes the relationship between the distributions and numerically implement the estimator. Our numerical and experimental results show that the proposed estimator was asymptotically unbiased even with low signal-to-noise ratio and/or for the true phase retardations close to the edge of the measurement range. The estimator revealed detailed clinical features when applied to the in vivo anterior human eye.” Page 20, 6. Conclusions “We have presented a Bayesian MLE for birefringence measurements using JM-OCT. This estimator is based on a Bayesian rule that associates the distribution of the measured birefringence and the probability distribution of the true birefringence. This MLE rationally provides an estimation of the true birefringence and is asymptotically unbiased. A Monte-Carlo method was utilized to obtain a likelihood function, which was then used to describe the association between the measured and true birefringence distributions. This numerical derivation of the likelihood function is an integral part of the estimator. It simplifies the implementation and accounts for the complex noise properties of JM-OCT. The performance of the Bayesian MLE was examined using numerical simulations and experiments on samples with known birefringence. The results demonstrated that the Bayesian MLE performed well. We also applied the Bayesian MLE to in vivo anterior eyes. The Bayesian mle images provided an improved contrast when compared to mean birefringence images, both for the anterior eye angle and a trabeculectomy bleb structure. In particular, trabecular meshwork was clearly visible at the anterior eye angle, and the high birefringence that is associated with fibrosis was visible in the trabeculectomy bleb. Further clinical studies would further emphasize the clinical utilities of this estimator.”)
Regarding claim 16, claim 16 is merely the method executed by the device of claim 25. Claim 16 is rejected for at least the same reason as claim 25.
Regarding claim 29, claim 29 is a CRM configured to execute the method steps of the device of claim 25 and is taught by the storage 212 of Fujii.
Claim 17
Regarding claim 17, Fujii in view of Korb, Altheimer, and Kasaragod teaches the features of claim 16, and further teaches:
wherein: constructing an individual eye model comprises providing an initial probability distribution of the parameters of the eye model; and determining a probability distribution of values of the parameters of the individual eye model further takes place on the basis of the initial probability distribution of parameters of the eye model. (Korb Page 32, 2.2.3 Conditional probabilities: “Once the topology of the BN is specified, the next step is to quantify the relationships between connected nodes – this is done by specifying a conditional probability distribution for each node. As we are only considering discrete variables at this stage, this takes the form of a conditional probability table(CPT).First, for each node we need to look at all the possible combinations of values of those parent nodes. Each such combination is called an instantiation of the parent set. For each distinct instantiation of parent node values, we need to specify the probability that the child will take each of its values.” – An initial probability distribution is established for each variable. Conditional probabilities are applied at edges of the graph to modify the probability distribution to be specific to the person/situation for whom/which the lens is being modeled.)
Claim 18
Regarding claim 18, Fujii in view of Korb, Altheimer, and Kasaragod teaches the features of claim 16, and further teaches:
wherein constructing an individual eye model and/or determining a probability distribution of values of the parameters of the individual eye model is carried out using Bayesian statistics. (Fujii [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.” – The Bayesian network is based on and functions using Bayesian statistics. Here, the Bayesian network is used to construct the eye model and to determine conditional probability distributions.)
Claim 19
Regarding claim 19, Fujii in view of Korb, Altheimer, and Kasaragod teaches the features of claim 16 and further teaches:
wherein determining (Fujii [0218] “The data comparator 734 is configured to compare the distribution data 760 generated by the distribution data generator 731 and the standard distribution data (the normative data 770) generated in advance for the predetermined measurement value represented by the distribution data 760 with each other. This process may be executed in the same manner as the data comparator 234 described above.” [0253]-[0253] “For example, the inference processor draw inference based on any of the three dimensional data acquired by the data acquisition device 1100, the three dimensional image data constructed based on the three dimensional data, the distribution data generated based on the three dimensional image data, and based on the knowledge acquired by the knowledge acquisition processor. The inference is a process of determining data not included in the distribution data. For example, the inference is a process of determining a predetermined measurement value (e.g., the layer thickness value) at a position outside the area of the eye fundus for which distribution data has been obtained, from the distribution data. Furthermore, the inference may be a process of determining a predetermined measurement value for a position that is outside the area of the eye fundus for which the distribution data has been obtained and is within the definition area of the standard distribution data, from the distribution data. By such inference, supplementation processing for the distribution data can be carried out. In other words, the estimation of measurement values can be realized for positions outside the area of the OCT scan for acquiring the distribution data. With this, distribution data corresponding to the entire definition area of the standard distribution data can be generated from distribution data only including data corresponding to part of the definition area of the standard distribution data, for example.” – The inference engine (e.g., Bayes network) infers missing variables based on data taken from others. The values are sanity checked based on the population data.)
Fujii suggests (Fujii [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.”) but appears to fail to explicitly teach, but Korb teaches:
wherein determining a probability distribution of values of the parameters of the individual eye model comprises calculating a consistency measure, wherein the product of the probability or probability density of the individual data with given parameters of the individual eye model with the probability or probability density of the parameters of the individual eye model with given background knowledge is used as the consistency measure. (Korb Page 29, 2.2 Bayesian network basics “A Bayesian network is a graphical structure that allows us to represent and reason about an uncertain domain. The nodes in a Bayesian network represent a set of random variables, X = X1, ..Xi, ...Xn, from the domain. A set of directed arcs (or links) connects pairs of nodes, Xi → Xj, representing the direct dependencies between variables. Assuming discrete variables, the strength of the relationship between variables is quantified by conditional probability distributions associated with each node.” – Bayesian networks determine conditional probabilities that link random variables, each of which have a probability distribution. Page 32, 2.2.3 Conditional probabilities: “Once the topology of the BN is specified, the next step is to quantify the relationships between connected nodes – this is done by specifying a conditional probability distribution for each node. As we are only considering discrete variables at this stage, this takes the form of a conditional probability table(CPT).First, for each node we need to look at all the possible combinations of values of those parent nodes. Each such combination is called an instantiation of the parent set. For each distinct instantiation of parent node values, we need to specify the probability that the child will take each of its values.” – An initial probability distribution is established for each variable. Conditional probabilities are applied at edges of the graph to modify the probability distribution to be specific to the person/situation for whom/which the lens is being modeled. When the conditional probabilities have been applied (e.g., multiplied as a product for variables assumed to be independent for Markov propagation) to the initial parameter probability distribution, a probable range of values for the eye will be established and will be ready for comparison with the standard/normative distributions as a consistency measure.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the Bayesian network as taught in Fujii with the specifics of how the Bayesian network functions in Korb because a person of ordinary skill in the art would be motivated, based on the generic mention of the use of Bayesian network as an inference model in Fujii, to look to Korb, which provides specific details on how Bayesian networks are constructed and to use the Bayesian network to infer parameter distributions of the eye model. (Fujii “[0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.”; Korb “In this chapter we will describe how Bayesian networks are put together (the syntax) and how to interpret the information encoded in a network (the semantics). We will look at how to model a problem with a Bayesian network and the types of reasoning that can be performed.)
Claims 21 and 26
Regarding claims 26 and 21, Fujii in view of Korb, Altheimer, and Kasaragod teaches the features of the features of claims 25 and 16, respectively, and further teaches:
Wherein the step of providing individual data comprises providing individual refraction data on the at least one eye of the spectacle wearer; and (Fujii [0050] “Then, the observation illumination light is reflected on the peripheral part (i.e., the surrounding area of the aperture part) of the aperture mirror 21, penetrates the dichroic mirror 46, and refracted by the objective lens 22, thereby illuminating the subject's eye E (the fundus Ef thereof). The returning light of the observation illumination light from the subject's eye E is refracted by the objective lens 22, penetrates the dichroic mirror 46, passes through the aperture part formed in the center area of the aperture mirror 21, passes through the dichroic mirror 55, travels through the photography focusing lens 31, and is reflected by the mirror 32.” – Fujii discloses providing measured refractometer data. Also, see reception parts 640, 740, and 840.)
constructing an individual eye model comprises defining an individual eye model in which at least: a shape and/or power of a corneal front surface of a model eye; and/or a cornea-lens distance; and/or parameters of the lens of the model eye; and/or a lens-retina distance; and/or a size of the entrance pupil; and/or a size and/or position of a physical aperture diaphragm is determinable on the basis of individual measurement values for the eye of the spectacle wearer and/or standard values and/or on the basis of the provided individual refraction data; (Fujii [0096] “The correction value calculator 232 includes the first calculator 2321 and the second calculator 2322. The first calculator 2321 is configured to calculate an estimated value of the axial length of the subject's eye E. The second calculator 2322 is configured to calculate an estimated value of the diopter (refractive power) of the subject's eye E. The correction value calculator 232 is configured to determine a magnification correction value based on at least one of the estimated value of the axial length and the estimated value of the diopter.” – Inferred parameters include axial length and a refractive power of the subject’s eye.
and wherein the method further comprises: carrying out a consistency check of the defined eye model with the provided individual refraction data, and solving any inconsistencies with the aid of analytical and/or numerical and/or probabilistic methods. (Fujii [0118] “The data comparator 234 compares the normative data 400 and the distribution data 300 with each other, at least one of whose sizes has been changed by the magnification corrector 233. For example, when only the size of the normative data 400 has been changed by the magnification corrector 233, the magnification corrector 233 compares the normative data 400 whose size has been changed and the distribution data 300 with each other.” [0124]-[0127] “Next, […] the data comparator 234 calculates a statistical value from a plurality of layer thickness values included in the concerned section of the distribution data 300. The […] data comparator 234 compares the average value for the concerned section of the distribution data 300, with the normal range (or the abnormal range) assigned to the section of the normative data 400 corresponding to the concerned section of the distribution data 300. When the average value belongs to the normal range, the layer thickness value in the concerned section of the distribution data 300 is determined to be normal. On the other hand, when the average value does not belong to the normal range, the layer thickness value in the concerned section of the distribution data 300 is determined to be abnormal. In addition to this, the degree of abnormality of the layer thickness value in the concerned section of the distribution data 300 may be determined by comparing the average value with the preset degrees of abnormality. In other words, it is possible to determine to which of the two or more abnormal ranges the average value belongs.” – The eye model parameters are compared with normal parameters. If the data appears inconsistent, a degree of abnormality (solved inconsistency) is determined.)
Regarding claim 21, claim 21 recites features similar to claim 26 and is rejected for at least the same reasons.
Claim 22
Regarding claim 22, Fujii and Korb teach the features of claim 21. Fujii further teaches:
wherein any inconsistencies are solved by: adapting one or more parameters of the eye model, wherein several parameters of the eye model are adapted and the adaptation is divided among the several parameters of the eye model; and/or adding at least one new parameter to the eye model and defining it such that the eye model becomes consistent; and/or adapting a target power of the ophthalmic lens. (Fujii [0129]-[0130] “The determination result obtained by the data comparator 234 is displayed, for example, as a comparison map. The comparison map shows the normality, the abnormality, the degree of abnormality, and the like of the layer thickness values in the distribution data 300 in a color-coded manner. When changing the size of the distribution data 300, the size of the data from which the distribution data 300 is generated may be changed in order to change the size of the distribution data 300. FIG. 6B shows an example of the configuration of the data processor 230 applicable in such a case. In the example shown in FIG. 6B, the magnification corrector 233 changes the size of the three dimensional image data 500 constructed by the data processor 230. The distribution data generator 231 constructs the distribution data 300 based on the three dimensional image data 500 whose size has been changed. The data comparator 234 compares the distribution data 300 based on the three dimensional image data 500 whose size has been changed, with the normative data 400. – In response to determining a deviation, the system can change the size/magnification of the data, which is a change to parameters of image data represented in different dimensions.)
Claim 30
Regarding claim 30, Fujii, Korb, and Altheimer teach the features of claim 23. Therefore, Fujii, Korb, and Altheimer teach:
[…] perform a method for calculating or optimizing an ophthalmic lens according to claim 23.
Fujii further teaches:
A non-transitory computer program product including program code configured to, when loaded and executed on a computer (Fujii [0206] “The type of the recording medium may be optional and may be any non-transitory recording medium such as a magnetic tape, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, a solid state drive, or another type of recording medium.” – CRM for doing the things.)
So does Altheimer: (Altheimer [0124] “Moreover, the invention provides a storage medium with a computer program stored thereon, the computer program being adapted, when loaded and executed on a computer, to perform a method for calculating or optimizing a spectacle lens according to the present invention, particularly in a preferred embodiment thereof.”)
Claims 32-33
Regarding claim 33, Fujii, Korb, and Altheimer teach the features of claim 16. Therefore, Fujii, Korb and Altheimer teach
by a method according to claim 25 (See analysis of claim 25)
Altheimer further teaches:
wherein the consistency measure corresponds to a posterior Bayesian statistics (Fujii [0080] “In the present embodiment, standard distribution data generated in advance is stored in the storage 212.” [0081] “The standard distribution data includes, for example, values obtained by statistically processing a sample of the predetermined measurement values acquired from a large number of normal eyes. Typically, the standard distribution data represents a distribution of normal ranges calculated from a sample of layer thickness values obtained by applying OCT to the funduses of a large number of normal eyes. Each of the normal ranges can be set to include, for example, an average value derived from the sample. The standard distribution data generated based on the normal eyes in this way is called normative data. Note that the standard distribution data may be generated based on a plurality of eyes suffering from a specific disease.” – The elements of the model eye that are not measured are inferred from known distributions of the parameters of eyes from different populations based on probabilistic/statistical measurements, such as mean. [0242] “The knowledge acquisition processor is configured to acquire knowledge by executing at least one of machine learning and data mining based on data collected in advance. The knowledge to be acquired includes knowledge about the distribution of a predetermined measurement value on the eye fundus (e.g., the layer thickness value).” [0246] “In machine learning, by analyzing the data collected as described above (mainly in a statistical manner), the knowledge acquisition processor extracts laws, rules, knowledge representations, judgment criteria, etc. from the data analyzed, and develop an algorithm of inference (described later) based on the information extracted.” [0247] “In addition, examples of techniques applicable to machine learning executed by the knowledge acquisition processor include decision tree learning, association rule learning, neural network, genetic programming, inductive logic programming, support vector machine, clustering, Bayesian network, reinforcement learning, feature learning, and other techniques.” – Machine learning techniques are used to infer unmeasured parameters. One example inference model is a Bayesian network. In Bayesian network training, this will be optimized using the posterior of Bayesian statistics. That is how Bayesian networks are trained. [0114] “In the calculation of the magnification correction value, an eye characteristic value different from the one or more estimated values described above can be used. The corneal curvature radius and the intraocular lens power are examples of the eye characteristic value different from the aforesaid estimated values. The eye characteristic value may be, for example, a value acquired by the ophthalmic apparatus 1, a standard value from a model eye etc., or other default values. As an example, when an OCT scan has been applied to the anterior eye segment of the subject's eye E by the ophthalmic apparatus 1, the corneal curvature radius can be determined from the data obtained by the anterior eye segment OCT scan. As another example, the value of the corneal curvature radius in the Gullstrand eye model can be used. As still another example, the power of the intraocular lens implanted in the subject's eye E can be acquired from an electronic medical record or another source.” – The determinations are for the calculation or optimization of the ophthalmic lens.)
Regarding claim 32, claim 32 recites features similar to the features of claim 33 and is rejected for at least the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From This Action)
NPL: “Design and analysis of Bayesian adaptive crossover trials for evaluating contact lens safety and efficacy” by Zhang et al. (Teaches using Bayesian methods to assess lens parameters for safety reasons)
(From Previous Action)
NPL: “Visual Binary Stars with Partially Missing Data: Introducing Multiple Imputation in Astrometric Analtysis” by Claveria et al. (Teaches multiple imputation, the method the Applicant appears to use to determine eye parameters)
NPL: “Multiple Imputation: A Flexible Tool for Handling Missing Data” by Li et al. (Teaches multiple imputation, the method the Applicant appears to use to determine eye parameters)
US 2004/0246440 A1 to Andino et al. (Teaches making an intraocular lens)
US 2009/0009717 A1 to Barrett et al. (Teaches many different parameters for modeling the eye and making intraocular lenses)
US 2016/0302660 A1 to Buhren et al. (Teaches using ray tracing to make an intraocular lens)
US 2010/0145489 A1 to Esser et al. (Teaches various parameters for an eye model)
US 2007/0268453 A1 to Hong et al. (Teaches using an eye model to determine features of a lens)
US 2012/0069298 A1 to Ng (Teaches modeling a lens of an eye using refractive index and further correction for aberration)
US 2003/0063254 A1 to Piers et al. (Teaches intraocular lens design)
US 2011/0242482 A1 to Olsen (Teaches using statistical probabilities to determine eye parameters for a model)
US 2019/0293967 A1 to Qi (Teaches Astigmatism correction in lenses)
US 2019/0164647 A1 to Rosen et al. (Teaches using bayes for predicting spectacle dependence)
US 2018/0153681 A1 to Rosen et al. (Teaches iterative improvement of an intraocular lens)
US 2015/0103313 A1 to Sarver et al. (Teaches intraocular lens corrections using estimates from population data)
US 2014/0125949 A1 to Shea et al. (Teaches astigmatism correction based on refractive components)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY MICHAEL WHITE whose telephone number is (571)272-7073. The examiner can normally be reached Mon-Fri 11:00-7:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at 571-272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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