Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/20/2026 has been entered.
Notice to Applicant
This communication is in response to the amendment filed 04/20/2026. Claims 1, 11 have been amended. Claims 6, 16 have been canceled. Claims 1-4, 7-14, 17-20 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-4, 7-14, 17-20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “generating a degree of variance as a function of the ocular vector and an ocular utopia, wherein the ocular profile is further generated as a function of the ocular attribute datum and an ocular utopia using an ocular machine-learning model which comprises: receiving ocular training data, wherein the ocular training data correlates a plurality of ocular attribute data and a plurality of ocular utopia data as inputs to a plurality of ocular profile data as outputs; training, iteratively, the ocular machine-learning model, using the ocular training data with feedback from previous iterations of the ocular machine-learning model, wherein training the ocular machine-learning model comprises: training the ocular machine-learning model using the ocular training data as input; adjusting one or more connections and one or more weights between nodes in adjacent layers of the ocular machine-learning model; and adjusting the ocular machine-learning model as a function of the adjusted connections to produce the output layer of nodes; and generating the ocular profile using the trained ocular machine-learning model.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 11, claims 1, 11 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-4, 7-10, 12-14, 17-20 incorporate the allowable features of originally numbered independent claims 1, 11, through dependency, respectively.
However, the claims are still rejected under 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 7-14, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a system which is within the four statutory categories (i.e., machine). Claim 11 is drawn to a method which is within the four statutory categories (i.e., method).
Independent claim 1 (which is representative of independent claim 11) recites…determine an ocular attribute datum as a function of receiving an ocular assessment; generate an ocular profile by determining at least an ocular vector as a function of the ocular attribute datum; and generating a degree of variance as a function of the ocular vector and the ocular utopia, wherein the ocular profile is further generated as a function of the ocular attribute datum and an ocular utopia using an ocular…model which comprises: receiving ocular training data, wherein the ocular training data correlates a plurality of ocular attribute data and a plurality of ocular utopia data as inputs to a plurality of ocular profile data as outputs; and generating the ocular profile using the trained ocular…model; and identify at least an edible as a function of the ocular profile, a nourishment composition, and an edible [model], wherein the identification includes: determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction; and develop a profile outcome including a treatment outcome and a prevention outcome as a function of the edible, wherein the treatment outcome is configured to at least eliminate the ocular attribute datum associated with the ocular dysfunction.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers concepts performed in the human mind, but for the recitation of generic computer components. That is, other than reciting generic computer components (discussed infra), nothing in the claim precludes the step from practically being performed in the mind. For example, the claims encompass a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 1 (which is representative of independent claim 11) further recites…training, iteratively, the ocular machine-learning model, using the ocular training data with feedback from previous iterations of the ocular machine-learning model, wherein training the ocular machine-learning model comprises: training the ocular machine-learning model using the ocular training data as input; adjusting one or more connections and one or more weights between nodes in adjacent layers of the ocular machine-learning model; and adjusting the ocular machine-learning model as a function of the adjusted connections to produce the output layer of nodes…; training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. When given its broadest reasonable interpretation in light of the disclosure, the claim encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra. For example, with regards to training machine learning models, the specification mentions: “K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples” (¶ 0023) and “Connections between nodes may be created via the process of "training" the network, in which elements from a training data 504 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes” (¶ 0042). If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components (discussed infra), then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
This judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements (i.e., a computing device; an ocular machine-learning model; an edible classifier; an edible machine-learning model). Claim 11 recites additional elements (i.e., a computing device; an ocular machine-learning model; an edible classifier; an edible machine-learning model). Looking to the specification, a computing device is described at a high level of generality (¶ 0009; ¶ 0054), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, the “machine-learning [models]” and “classifier” (which is defined in ¶ 0021 of the specification as “a machine-learning model”) is only used to generally apply the abstract idea without placing any limits on how the model actually functions (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., computing device) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, the “machine-learning [models]” and “classifier” (which is defined in ¶ 0021 of the specification as “a machine-learning model”) is only used to generally apply the abstract idea without placing any limits on how the model actually functions (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-4, 7-10, 12-14, 17-20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-4, 8-10, 12-14, 18-20 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 7, 17 further recites the additional elements of “wherein the computing device generates the edible classifier using a K-nearest neighbors (KNN) algorithm,” which is described in the specification as “a machine-learning model.” However, using a machine learning model amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the aforementioned abstract idea groupings and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 04/20/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 04/20/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“the linear sequencing nature of the system architecture is not akin to a mental process that can cover concepts performed in the human mind. Instead, claim 1 as amended recites steps of computer pipeline architecture, where multiple machine learning models are connected sequentially for data processing designed to improve the prediction quality of an updated edible using nourishment composition and the ocular dysfunction of the ocular profile as inputs. As such, just like in Synopsys, except in its most simplistic form, the limitations recited in amended claim 1 could not conceivably be performed in the human mind or with pencil and paper… Similarly [to Example 39], claim 1 as amended recites limitation “training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible,” which does not recite a judicial exception… Applicant respectfully submits that the ocular machine-learning and the steps for training an edible machine-learning model that are recited in amended claim 1 “do not recite process steps which are themselves mathematical calculations, formulae, or equations”…although any form of neural network would necessarily “involve” mathematical calculations, including minimization of error as in other machine-learning algorithms, a claim reciting training a neural network does not itself recite those mathematical operations. Claim 1 as amended recites a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model”;
“Analogous to Example 47 and claims 1 and 3, in the present application, claim 1 as amended also teaches technological improvement that is integrated into a practical application as Claim 1 as amended recites a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model. The background section of the instant application further explains that current edible suggestion systems do not account for ocular measurements for an individual and it leads to inefficiency of an edible suggestion system and a poor nutrition plan for the individual. This is further complicated by a lack of uniformity of nutritional plans”; and
“claim 1 as amended recites details of a particular way to generate an edible as it teaches a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model. Claim 1 as amended purports to improve existing computing technology by integrating the daisy chain-like structure to reduce the inefficiency and taking into account the ocular measurements of an individual.”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 101 rejections,
“the linear sequencing nature of the system architecture is not akin to a mental process that can cover concepts performed in the human mind. Instead, claim 1 as amended recites steps of computer pipeline architecture, where multiple machine learning models are connected sequentially for data processing designed to improve the prediction quality of an updated edible using nourishment composition and the ocular dysfunction of the ocular profile as inputs. As such, just like in Synopsys, except in its most simplistic form, the limitations recited in amended claim 1 could not conceivably be performed in the human mind or with pencil and paper… Similarly [to Example 39], claim 1 as amended recites limitation “training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible,” which does not recite a judicial exception… Applicant respectfully submits that the ocular machine-learning and the steps for training an edible machine-learning model that are recited in amended claim 1 “do not recite process steps which are themselves mathematical calculations, formulae, or equations”…although any form of neural network would necessarily “involve” mathematical calculations, including minimization of error as in other machine-learning algorithms, a claim reciting training a neural network does not itself recite those mathematical operations. Claim 1 as amended recites a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model”:
It is respectfully submitted that Applicant argues “the linear sequencing nature of the system architecture is not akin to a mental process that can cover concepts performed in the human mind. Instead, claim 1 as amended recites steps of computer pipeline architecture, where multiple machine learning models are connected sequentially for data processing designed to improve the prediction quality of an updated edible using nourishment composition and the ocular dysfunction of the ocular profile as inputs.” However, it is noted that the features upon which applicant relies (i.e., “The machine learning pipeline first produces a precisely encoded output (e.g., an ocular vector of ocular profile) using the ocular machine-learning model that the edible machine learning consumes”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Furthermore, the claim limitations to which Applicant refer (i.e., “generating the ocular profile using the trained ocular…model; and identify at least an edible as a function of the ocular profile, a nourishment composition, and an edible [model], wherein the identification includes: determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction”) are interpreted as a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra, which is the abstract idea of concepts performed in the human mind within the “Mental Processes” grouping, but for the recitation of generic computer components; and the “training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible” is interpreted as the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which is the abstract idea of mathematical relationships within the “Mathematical Concepts” grouping, but for the recitation of generic computer components. Also, the “machine-learning [models]” and “classifier” (which is defined in ¶ 0021 of the specification as “a machine-learning model”) are not interpreted as part of the abstract idea, but as additional elements to be interpreted in Step 2A, Prong Two, which is only used to generally apply the abstract idea without placing any limits on how the model actually functions (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “just like in Synopsys, except in its most simplistic form, the limitations recited in amended claim 1 could not conceivably be performed in the human mind or with pencil and paper.” However, Applicant fails to specify how “the limitations recited in amended claim 1 could not conceivably be performed in the human mind or with pencil and paper” and how the aforementioned claim limitations are “above [the] benchmark” set in Synopsys.
Applicant argues “Similarly [to Example 39], claim 1 as amended recites limitation “training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible,” which does not recite a judicial exception.” However, Applicant fails to specify how the aforementioned claim limitations are similar to Example 39. Regardless, the claim limitations of the present invention are different from the claim limitations of Example 39. Even if the claim limitations of the present invention are similar to that of the claims found eligible (and they are not similar), the claimed inventions are fundamentally different in scope and examples should be interpreted based on the asserted fact patterns; as previously stated above, other fact patterns may have different eligibility outcomes, as is the case with the claims of the present invention. Unlike the claims found eligible in Example 39, the claims of the present invention recite “training an edible machine-learning model using the nourishment composition and the ocular dysfunction of the ocular profile as inputs to output the at least an edible,” which is described in the specification as “Connections between nodes may be created via the process of "training" the network, in which elements from a training data 504 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes” (¶ 0042), which encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers the sub-grouping of mathematical relationships in the “Mathematical Concepts” grouping of abstract ideas, as stated previously in Office Action dated 10/20/2025 and above.
Applicant argues “the ocular machine-learning and the steps for training an edible machine-learning model that are recited in amended claim 1 “do not recite process steps which are themselves mathematical calculations, formulae, or equations”…although any form of neural network would necessarily “involve” mathematical calculations, including minimization of error as in other machine-learning algorithms, a claim reciting training a neural network does not itself recite those mathematical operations. Claim 1 as amended recites a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model.” However, it is noted that the features upon which applicant relies (i.e., “training a neural network”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Furthermore, as stated previously above, the claim limitations to which Applicant seem to refer are interpreted as a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra, which is the abstract idea of concepts performed in the human mind within the “Mental Processes” grouping, but for the recitation of generic computer components; and the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which is the abstract idea of mathematical relationships within the “Mathematical Concepts” grouping, but for the recitation of generic computer components, respectively. The “machine-learning [models]” and “classifier” (which is defined in ¶ 0021 of the specification as “a machine-learning model”) are not interpreted as part of the abstract idea, but as additional elements to be interpreted in Step 2A, Prong Two, which is only used to generally apply the abstract idea without placing any limits on how the model actually functions (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claims are directed to an abstract idea.
“Analogous to Example 47 and claims 1 and 3, in the present application, claim 1 as amended also teaches technological improvement that is integrated into a practical application as Claim 1 as amended recites a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model. The background section of the instant application further explains that current edible suggestion systems do not account for ocular measurements for an individual and it leads to inefficiency of an edible suggestion system and a poor nutrition plan for the individual. This is further complicated by a lack of uniformity of nutritional plans”:
Applicant argues “Analogous to Example 47 and claims 1 and 3.” However, Applicant fails to specify how the claims of the present invention are “Analogous to Example 47 and claims 1 and 3.” Regardless, the claim limitations of the present invention are different from the claim limitations of Example 47. Even if the claim limitations of the present invention are similar to that of the claims found eligible in Example 47 (and they are not similar), the claimed inventions are fundamentally different in scope and examples should be interpreted based on the asserted fact patterns; as previously stated above, other fact patterns may have different eligibility outcomes, as is the case with the claims of the present invention. The claims found eligible in Example 47 are directed to an improvement to computer functionality; unlike Example 47, the claims of the present invention do not recite any technological computational efficiency, solution, or improvement, but are directed to a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra, and the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which is the abstract idea. Furthermore, unlike Example 47, the disclosure of the present invention does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a). For example, “account for ocular measurements for an individual…of an edible suggestion system and a poor nutrition plan for the individual…a lack of uniformity of nutritional plans" does not address a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined (i.e., a well-known, general purpose computer). Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea of a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra. However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement.
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“claim 1 as amended recites details of a particular way to generate an edible as it teaches a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model. Claim 1 as amended purports to improve existing computing technology by integrating the daisy chain-like structure to reduce the inefficiency and taking into account the ocular measurements of an individual”:
Applicant argues “claim 1 as amended recites details of a particular way to generate an edible as it teaches a daisy chain-like system architecture such that two machine learning models are structured in sequence to develop a profile outcome – the ocular profile outputted by the first trained ocular machine-learning model is utilized as part of the inputs for the edible machine-learning model after an ocular dysfunction is determined as function of the ocular profile to generate a second output of at least an edible using the trained edible machine-learning model.” However, Applicant fails to specify the claim limitations to which Applicant refer. Regardless, as stated previously above, the claim limitations to which Applicant seem to refer are interpreted as a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra, which is the abstract idea of concepts performed in the human mind within the “Mental Processes” grouping, but for the recitation of generic computer components; and the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which is the abstract idea of mathematical relationships within the “Mathematical Concepts” grouping, but for the recitation of generic computer components, respectively. The “machine-learning [models]” and “classifier” (which is defined in ¶ 0021 of the specification as “a machine-learning model”) are only used to generally apply the abstract idea without placing any limits on how the model actually functions (i.e., no description of the mechanism for accomplishing the result), such that using machine learning models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “Claim 1 as amended purports to improve existing computing technology by integrating the daisy chain-like structure to reduce the inefficiency and taking into account the ocular measurements of an individual.” However, Applicant fails to specify how “existing computing technology” is improved (i.e., what “inefficiency” is reduced). Regardless, as stated previously above, the disclosure of the present invention does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a). For example, “taking into account the ocular measurements of an individual " does not address a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined (i.e., a well-known, general purpose computer). Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea of a person thinking about ocular data, converting the data into numbers, and determining the next steps (i.e., what is the most appropriate item to eat) based on a person’s numbers and goals in the manner described in the identified abstract idea, supra. However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement.
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1-4, 7-14, 17-20, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
Conclusion
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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