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 .
Status of Claims
Claims 1-20 were previously pending and subject to a non-final Office Action having a notification date of July 3, 2025 (“non-final Office Action”). Following the non-final Office Action, Applicant filed an amendment on January 5, 2026 (the “Amendment”), amending claims 1 and 16.
The present Final Office Action addresses pending claims 1-20 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §112
While Applicant generically asserts that all of the rejections under 35 USC 112 have been addressed in the Amendment, the Examiner disagrees and maintains most of the rejections as set forth below. Furthermore, additional rejections are set forth below as necessitated by the Amendment.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
At page 12 of the Amendment after presenting most of the limitations of independent claim 1, Applicant asserts that the claims involves analysis of information through a specific technological framework that cannot be replicated through human thought alone. To the extent Applicant is asserting that independent claim 1 does not recite any limitations that can be practically performed in the human mind, the Examiner disagrees.
For instance, a medical professional could practically in their mind with pen and paper review first user data (e.g., diet data, geographical data, product usage data, etc.); determine a "user toxin profile" as a function of the first user data and including toxic element data (e.g., sources/types/amounts of toxic elements such as heavy metals, radioactive elements, etc.) correlated to user biological attributes (e.g., blood pressure, red blood cell count, platelet count, etc.) and environmental attributes (e.g., air pollution, drinking water chemicals, etc.); generate a query result based on user input/first user data (e.g., conducting a search for an article of interest based on the diet data, product usage data, etc.); review and filter various training data to identify sets of filtered training data that each classify toxic load elements (e.g., aspartame levels, paraben levels, etc.) to corresponding categories of articles of interest (e.g., shampoos, mouthwashes, etc.); select one of the filtered training sets based on the particular article of interest (e.g., selecting the "shampoos" filtered training set when the article of interest is a shampoo); determine a toxic load of the query result based on a toxic load of the article of interest; map/link/correlate extracted ingredient identifiers for the article of interest to quantitative toxic load values (e.g., x% thickeners, y% fragrant oils, etc.); generate "a toxic load quantifier" representing an aggregate toxicity score computed from toxic element classifications (e.g., pollutants, heavy metals, etc.), user-specific absorption characteristics (e.g., gut-wall permeability/malabsorption, etc.), and usage frequency parameters (e.g., how often the article of interest is used and how much); and display the toxic load of the article of interest.
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis that were found to be equivalent to mental processes in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)).
On pages 12-13 of the Amendment, Applicant takes the position that generating a training data classifier as a function of unfiltered training data using a classification algorithm, filtering elements of the unfiltered training data to generate multiple filtered training data sets that classify toxic element data into toxicity categories associated with articles of interest, and training a toxic load machine learning model with a selected filtered training data set in the context of the present claims, like the training of the artificial neural network in Example 47, define a specific machine learning workflow that improves the operation of a toxic load determination system rather than merely performing abstract analysis.
The Examiner initially notes that the present claims no longer recite the training data to be "unfiltered." Furthermore, the training of the ANN in claim 3 of Example 47 was not found to "define a specific machine learning workflow that improves the operation of a toxic load determination system rather than merely performing abstract analysis" as asserted by Applicant but instead was found to amount to "mathematical calculations" performed on a computer.
Applicant then asserts the following:
Furthermore, just as the artificial neural network in Example 47 improves network security by enabling real time, automatic responses to detected anomalies, the present claims improve toxic exposure assessment by automatically acquiring image data using a scanner module, extracting ingredient identifiers using an optical character recognition model, mapping extracted ingredient identifiers and user specific toxin profile data to quantitative toxic load values using a trained machine learning model, and generating an aggregate toxicity score that reflects toxic element classifications, user specific absorption characteristics, and usage frequency parameters. This direct application of machine learning and image processing to determine toxic load aligns with the principles established in Example 47.
The Examiner disagrees with Applicant's position that the ANN in (claim 3 of) Example 47 improved network security by generically "enabling" real-time automatic responses to detected anomalies. In contrast, Example 47 specifically describes how actually proactively taking such automatic real-time responses via dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f) provides for the improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger as described in the background thereby improving the functioning of a computer or technical field. See MPEP 2106.04(d)(1) and 2106.05(a).
The present claims do not include any additional, proactive, non-mentally performable steps equivalent to the manner in which the above additional limitations of claim 3 of Example 47 remediate danger from malicious network packets. The above limitations directed to automatically acquiring image data using a scanner module and extracting ingredient identifiers using an optical character recognition model merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). Furthermore, the above limitations reciting the mapping of the extracted ingredient identifiers and user specific toxin profile data to quantitative toxic load values and generation of the aggregate toxicity score are practically performable in the human mind with pen and paper. Finally, the high level recitation of the trained ML model just amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the toxic load ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
In fact, the limitations of present claim 1 generally reciting the receiving/acquiring of the user input data/article of interest/ingredient identifiers, filtering of the training data using a training data classifier to obtain filtered training data, selecting a filtered training set, training the ML model with the selected filtered training set, and generating a toxic load quantifier using the trained ML model is more similar to claim 2 (ineligible) of Example 47 than to claim 3 (eligible) of Example 47 because the limitations of present claim 1 are concerned with ultimately generating a toxic load quantifier of an article of interest (similar to ultimately generating anomaly data in claim 2 of Example 47) rather than taking non-mentally performable proactive actions (dropping potentially malicious packets in step and blocking future traffic from the source address in claim 3 of Example 47).
At pages 13-14 of the Amendment, Applicant take the position that the present claims are similar to claim 2 of Example 48 because they improve upon traditional toxic exposure assessment systems by integrating trained machine learning models into a specific technical process. Specifically, Applicant asserts "Just as (claim 2 of) Example 48's deep neural network leverages an embedding space to cluster speech sources and improve output quality, the present claims utilize iterative machine learning training to generate an aggregate toxicity score that reflects multiple computational factors rather than relying on static thresholds or manual evaluation."
However, the generic training of the ML model to allow for generation of an aggregate toxicity score for an article of interest as recited in the present claims is distinct from and has nothing to do with generating masked clusters, synthesizing separate speech waveforms, and combining them into a new audio signal that excludes undesired sources as called for in claim 2 of Example 48. More specifically, while the steps of generating masked clusters, synthesizing separate speech waveforms, and combining them into a new audio signal that excludes undesired sources in claim 2 of Example 48 are proactive, additional limitations that actually separate speech in a mixed speech signal solving the problem of separating speech from different speech sources belonging to the same class while not requiring prior knowledge of the number of speakers or speaker-specific training, the present claims in contrast are ultimately concerted with the mental process of generating a toxic load quantifier of an article of interest rather than performing non-mentally performable limitations that improve technology.
On pages 14-15 of the Amendment, Applicant takes the position that the "determining," "generating," "acquiring," and "processing" limitations of claim 1 presents a non-conventional and specific arrangement that provides a technical improvement in the field of computer implemented toxic load determination systems aligning with the principles set forth in Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018)). While the Examiner has considered the novelty of the claims (as evidenced by the prior art rejections being withdrawn), the Examiner has ultimately determined that the claim limitations either recite an abstract idea or recite additional limitations that that do not provide a "practical application" of the abstract idea or provide "significantly more" than the abstract idea as set forth in the rejection below.
The 35 USC 101 rejections are maintained.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 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.
The specification also does not appear to provide support for "[generating] a plurality of filtered training data sets each containing a plurality of data entries classifying toxic load elements to categories of articles of interest" as now recited in claims 1 and 16. While [0121] generically notes how training data can correlate articles of interest to toxic load quantifiers and [0122] notes how the training data classifier can classify training data elements "to categories of articles of interest, toxic load impact levels, toxic element categories, and the like," there is no disclosure of generating a plurality of filtered training data sets each containing a plurality of data entries classifying toxic load elements to categories of articles of interest" as now recited in claims 1 and 16.
The specification also does not appear to provide support for "[selecting] at least one filtered training data set of the plurality of training data sets as a function of the at least an article of interest using the training data classifier." While [0122] appears to disclose how the training data classifier can filter/sort training data, it does not appears to disclose selecting one of a plurality of filtered training sets, much less as a function of an article of interest as now recited in claims 1 and 16.
The specification also does not appear to provide support for "wherein the toxic load machine learning model is configured to map extracted ingredient identifiers and toxin profile data to quantitative toxic load values" as now recited in independent claims 1 and 16. For instance, while [0078] discloses how toxic load machine learning model 132 may be configured to input articles of interest and output one or more toxic loads (which amounts to a mapping between articles of interest and toxic loads), neither this paragraph nor any other paragraph appears to disclose mapping extracted ingredient identifiers and toxin profile data to toxic load values as now recited in claims 1 and 16.
The also does not appear to provide support for "generating, using the trained toxic load machine learning model, a toxic load quantifier representing an aggregate toxicity score computed from toxic element classifications, user-specific absorption characteristics, and usage frequency parameters." While [0078] generally discloses how apparatus 100 may determine a toxic load based on absorption rates of toxic elements, ratios of toxic elements to non-toxic elements of articles of interest, frequency of use of toxic elements, and the like, neither this paragraph nor any other paragraph appears to disclose using the trained toxic load machine learning model to generate a toxic load quantifier representing an aggregate toxicity score computed from toxic element classifications, user-specific absorption characteristics, and usage frequency parameters as now recited in claims 1 and 16.
The remaining claims are rejected due to their dependency from the above claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 6, 8, 15, 17, 18, and 20 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.
The limitations of claims 5 and 6 directed to using a scanner device including a camera to scan the article of interest appear to already be disclosed in independent claim 1.
The limitations of claim 8 appear to already be disclosed in independent claim 1.
It is unclear whether or not the "toxin profile" in claim 15 is referring to the same "toxin profile" from independent claim 1.
The limitations of claims 17 and 18 directed to using a scanner device including a camera to scan the article of interest appear to already be disclosed in independent claim 16.
It is unclear whether and/or how the "toxic load impact" of claim 20 is different than the "toxic load quantifier" of independent claim 16.
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-15 are directed to an apparatus (i.e., a machine) and claims 16-20 are directed to a method (i.e., a process). Accordingly, claims 1-20 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
An apparatus for determining a toxic load, comprising:
at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive user input comprising first user data;
determine, using the at least a processor, a toxin profile associated with a user as a function of the first user data, wherein the toxin profile is stored in a toxin profile database comprising toxic element data correlated to user biological and environmental attributes;
generate at least a query result as a function of user input, wherein the at least a query result comprises at least an article of interest related to the first user data;
acquire, using a scanner module comprising a camera device, image data associated with the article of interest;
process the image data using an optical character recognition model to extract ingredient identifiers corresponding to the article of interest;
generate a training data classifier as a function of training data using a classification algorithm;
filter elements of the training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying toxic load elements to categories of articles of interest, wherein each filtered training data set classifies toxic element data into toxicity categories associated with articles of interest;
select at least one filtered training data set of the plurality of training data sets as a function of the at least an article of interest using the training data classifier;
determine, as a function of the at least an article of interest related to the first user data, a toxic load of the at least a query result, wherein determining the toxic load comprises:
training, using the at least a processor, a toxic load machine learning model with the selected filtered training data set, wherein the toxic load machine learning model is configured to map extracted ingredient identifiers and toxin profile data to quantitative toxic load values; and
generating, using the trained toxic load machine learning model, a toxic load quantifier representing an aggregate toxicity score computed from toxic element classifications, user-specific absorption characteristics, and usage frequency parameters; and
display the toxic load related to the at least an article of interest at a display device.
The Examiner submits that the foregoing underlined limitations recite “mental processes” because they are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper). For instance, a medical professional could practically in their mind with pen and paper review first user data (e.g., diet data, geographical data, product usage data, etc.); determine a "user toxin profile" as a function of the first user data and including toxic element data (e.g., sources/types/amounts of toxic elements such as heavy metals, radioactive elements, etc.) correlated to user biological attributes (e.g., blood pressure, red blood cell count, platelet count, etc.) and environmental attributes (e.g., air pollution, drinking water chemicals, etc.); generate a query result based on user input/first user data (e.g., conducting a search for an article of interest based on the diet data, product usage data, etc.); review and filter various training data to identify sets of filtered training data that each classify toxic load elements (e.g., aspartame levels, paraben levels, etc.) to corresponding categories of articles of interest (e.g., shampoos, mouthwashes, etc.); select one of the filtered training sets based on the particular article of interest (e.g., selecting the "shampoos" filtered training set when the article of interest is a shampoo); determine a toxic load of the query result based on a toxic load of the article of interest; map/link/correlate extracted ingredient identifiers for the article of interest to quantitative toxic load values (e.g., x% thickeners, y% fragrant oils, etc.); generate "a toxic load quantifier" representing an aggregate toxicity score computed from toxic element classifications (e.g., pollutants, heavy metals, etc.), user-specific absorption characteristics (e.g., gut-wall permeability/malabsorption, etc.), and usage frequency parameters (e.g., how often the article of interest is used and how much); and display the toxic load of the article of interest.
These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis that were found to be equivalent to mental processes in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 2-4, 6-15, and 18-20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
Claim 2 recites how a toxic element of the toxic load includes carcinogens which just further defines the at least one abstract idea discussed above.
Claim 3 recites how a toxic element of the toxic load includes toxins that affect a user's immune system and/or toxins that affect DNA of a user which just further defines the at least one abstract idea discussed above.
Claim 4 recites how the article of interest is a consumable item which just further defines the at least one abstract idea discussed above.
Claim 6 calls for calculating a toxic load value, displaying educational content, and suggesting alternative articles of interest which can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 7 calls for generating a cumulative toxin exposure model which can be practically performed in the human mind with pen and paper at such claimed high level of generality (e.g., a user could readily develop a model/algorithm that maps toxic loads, impacts, etc. of products to relative scores).
Claim 8 calls for determining the toxic load specific to a user which can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 9 calls for generate a detoxing protocol which can be practically performed in the human mind with pen and paper at such claimed high level of generality (e.g., a person could easily develop/determine foods, products, strategies, etc. for detoxing the user).
Claim 10 calls for receiving second user data and adjusting the detoxing protocol as a function of the second user data which again can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 11 calls for generating a prophylaxis protocol as a function of the toxic load which can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 12 calls for classifying environmental exposures to toxic element exposure levels using an environmental exposure classifier (e.g., algorithm) which can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 13 recites how the first user data includes an article of interest and further calls for displaying alternative articles of interest as a function of the toxic load which just further defines the at least one abstract idea discussed above.
Claim 14 recites how first user data further includes an article of interest and further calls for determining a toxic load impact of the article of interest which just further defines the at least one abstract idea discussed above.
Claim 15 calls for generating a toxin profile as a function of the user input and the toxic load which just further defines the at least one abstract idea discussed above.
Claim 18 calls for calculating a toxic load value, displaying educational content, and suggesting alternative articles of interest which can be practically performed in the human mind with pen and paper at such claimed high level of generality.
Claim 19 calls for displaying alternative articles of interest to a user as a function of the toxic load of the at least a query result which just further defines the at least one abstract idea discussed above.
Claim 20 calls for determining a toxic load impact of the article of interest of the at least a query result which just further defines the at least one abstract idea discussed above.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
An apparatus for determining a toxic load, comprising:
at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)):
receive user input comprising first user data (extra-solution activity (data gathering), see MPEP § 2106.05(g));
determine, using the at least a processor (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)), a toxin profile associated with a user as a function of the first user data, wherein the toxin profile is stored in a toxin profile database (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)) comprising toxic element data correlated to user biological and environmental attributes;
generate at least a query result as a function of user input, wherein the at least a query result comprises at least an article of interest related to the first user data;
acquire, using a scanner module comprising a camera device, image data associated with the article of interest (extra-solution activity (data gathering), see MPEP § 2106.05(g); using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f));
process the image data using an optical character recognition model to extract ingredient identifiers corresponding to the article of interest (extra-solution activity (data gathering), see MPEP § 2106.05(g));
generate a training data classifier as a function of training data using a classification algorithm (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
filter elements of the training data using the training data classifier (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) to generate a plurality of filtered training data sets each containing a plurality of data entries classifying toxic load elements to categories of articles of interest, wherein each filtered training data set classifies toxic element data into toxicity categories associated with articles of interest;
select at least one filtered training data set of the plurality of training data sets as a function of the at least an article of interest using the training data classifier (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
determine, as a function of the at least an article of interest related to the first user data, a toxic load of the at least a query result, wherein determining the toxic load comprises:
training, using the at least a processor, a toxic load machine learning model with the selected filtered training data set, wherein the toxic load machine learning model is configured to (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) map extracted ingredient identifiers and toxin profile data to quantitative toxic load values; and
generating, using the trained toxic load machine learning model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), a toxic load quantifier representing an aggregate toxicity score computed from toxic element classifications, user-specific absorption characteristics, and usage frequency parameters; and
display the toxic load related to the at least an article of interest at a display device (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)).
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the processor, memory with instructions, database, scanner module with camera device, and display device, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of acquiring image data associated with the article of interest and processing the image data using an OCR model to extract ingredient identifiers, the Examiner submits that these additional limitations merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of generating a training data classifier as a function of training data using a classification algorithm and then using the training data classifier to perform the (mental processes of) filtering elements of the training data and selecting at least one of the filtered training data sets based on the article of interest, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how generation of the training data classifier actually occurs.
Regarding the additional limitations of training a toxic load ML model using the selected at least one training data set and using the toxic load ML model to perform the (mental processes of) mapping the ingredient identifiers and toxin profile data to the quantitative toxic load values and generating the toxic load quantifier, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the toxic load ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claim 16 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 1 and analogous independent claim 16 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 5 and 6 call for a scanner device and camera device which just amounts to using computers as tools to perform an existing process at such high level of generality (see MPEP § 2106.05(f)).
-Claim 10 calls for receiving second user data which merely represents insignificant extra-solution activity (data gathering) (see MPEP § 2106.05(g)).
-Claims 17 and 18 recite how generating the query result includes use of a scanner device and camera device which just amounts to using computers as tools to perform an existing process at such high level of generality (see MPEP § 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the processor, memory with instructions, database, scanner module with camera device, and display device, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of acquiring image data associated with the article of interest and processing the image data using an OCR model to extract ingredient identifiers, the Examiner submits that these additional limitations merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Regarding the additional limitations of generating a training data classifier as a function of training data using a classification algorithm and then using the training data classifier to perform the (mental processes of) filtering elements of the training data and selecting at least one of the filtered training data sets based on the article of interest, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how generation of the training data classifier actually occurs.
Regarding the additional limitations of training a toxic load ML model using the selected at least one training data set and using the toxic load ML model to perform the (mental processes of) mapping the ingredient identifiers and toxin profile data to the quantitative toxic load values and generating the toxic load quantifier, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). These additional limitations provide only a result-oriented solution and lack details as to how training and execution of the toxic load ML model actually occurs.
Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id. Claims that do not delineate steps through which the machine learning technology achieves an alleged improvement do not render the claims patent eligible. Id., p. 13. Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Id., p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Regarding the additional limitations of acquiring image data associated with the article of interest and processing the image data using an OCR model to extract ingredient identifiers which the Examiner submits merely add insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)), the Examiner has reevaluated such limitations and determined such limitations to not be unconventional as they merely consist of receiving/transmitting data over a network (See Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016)) and electronically scanning/extracting data from a physical document through OCR (See Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343,1348 (Fed. Cir. 2014). MPEP 2106.05(d)(II).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 5 and 6 call for a scanner device and camera device which just amounts to using computers as tools to perform an existing process at such high level of generality (see MPEP § 2106.05(f)).
-Claim 10 calls for receiving second user data which merely represents insignificant extra-solution activity (data gathering) (see MPEP § 2106.05(g)).
-Claims 17 and 18 recite how generating the query result includes use of a scanner device and camera device which just amounts to using computers as tools to perform an existing process at such high level of generality (see MPEP § 2106.05(f)).
Therefore, claims 1-20 are ineligible under 35 USC §101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686