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 ("RCE") was filed in this application after a decision by the Patent Trial and Appeal Board, but before the filing of a Notice of Appeal to the Court of Appeals for the Federal Circuit or the commencement of a civil action. 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examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on November 26, 2025, has been entered.
Status of Claims
Claims 1-10, 12-23, and 25-28 were previously pending and subject to a Final Office Action dated April 22, 2024 (“Final Office Action”). Following the Final Office Action, Applicant filed a Notice of Appeal on August 1, 2024, and an Appeal Brief on September 25, 2024, after which an Examiner’s Answer was mailed on October 25, 2024. After Applicant filed a Reply Brief on December 16, 2024, the Board issued a Decision on October 1, 2025 (“Decision”), affirming the rejection of all rejections under 35 USC 101 and 103. Applicant then filed the RCE along with an amendment on November 26, 2025 (“the Amendment”), amending claims 1, 13, and 14.
The present non-final Office Action addresses pending claims 1-10, 12-23, and 25-28 in the Amendment.
Response to Arguments
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §101
On pages 12-14 of the Amendment (corresponding to the second occurrence of pages 1-3 of the Amendment as indicated by Applicant), Applicant takes the position that the newly added limitations include features demonstrating computer improvements that integrate the abstract idea into a "practical application" such that the claims as a whole are not directed to an abstract idea.
Specifically, Applicant asserts that applying multi-dimensional scaling to the plurality of visual content features in order to reduce data complexity of the plurality of visual content features, applying the hierarchical rule base to the reduced complexity visual content features, and mapping between semantic concepts based on the snippets conserves computing resources and improves computer efficiency because each subsequent processing step is performed on less complex data.
However, in assessing patent-eligibility of a claim reciting an abstract idea, the paramount consideration is the claimed additional elements. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 217 (2014) (“consider [all claimed] elements . . . to determine whether the additional elements transform the nature of the claim into a patent-eligible application” (internal citation and quotation marks omitted)); see also MPEP § 2106.05(f) (‘[A] consideration . . . [under] Step 2A Prong Two or ... Step 2B is whether the additional elements amount to .. . more than mere instructions to implement an abstract idea or other exception on a computer.”).
In the present case, applying multi-dimensional scaling to the plurality of visual content features to reduce data complexity of visual content features constitutes “mathematical concepts” because it is a set of statistical techniques used to reduce the complexity of a data set (as evidenced by NPL "Multidimensional scaling" to Hout et al. at page 1). This limitation is similar to performing a resampled statistical analysis to generate a resampled distribution. SAP America, Inc. v. Investpic, LLC, (898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018)). MPEP 2106.04(a)(2)(I)(C).
Furthermore, a medical professional could review data (e.g., imaging and textual data) of any appropriate population (e.g., at a city level, state level, etc.) and identify attributes and care practices from the defined semantic concepts (e.g., based on matching or similarity of data objects). As an example, the person could readily visually review visual content of the population (e.g., images) and extract/determine visual content features from the images (e.g., various hair features such as color, length, degree of waviness, texture, density, etc.), identify unimportant ones of the features (e.g., based on their experience) to reduce the complexity of the features, and apply a hierarchical rule set to the features via aggregating biases for/against inclusion of each of a plurality of "snippets" using an aggregation rule. For instance, the person could consider/aggregate one or more biases for inclusion of a particular set of features as indicating a particular disease/disorder with one or more other biases against inclusion of the set of features as indicating the particular disease/disorder.
Thereafter, the medical professional could identify/map connections/correlations between the semantic attribute concepts and the care practices (e.g., based on proximity in the medical literature) in text content features and the snippets which would include identifying a connection between a first attribute semantic concept and a first care practice component semantic concept when mapping between the semantic concepts, draw/create a knowledge graph of the attribute concepts with nodes and edges, identify a subset of the attribute concepts for a particular user based on “visual content” of the user, and review/query the knowledge graph based on the subset to determine a medication/treatment/therapy (“care practice component concept”) related to the subset.
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 in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)).
As the present claims are thus directed to at least one abstract idea and do not include additional limitations that provide a "practical application" of or amount to "significantly more" than the at least one abstract idea, the 35 USC 101 rejection is maintained.
Response to Applicant’s Arguments Regarding Claim Rejections Under 35 USC §103
These rejections are withdrawn in view of the Amendment.
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 1-10, 12-23, and 25-28 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.
Each of independent claims 1, 13, and 14 has been amended to recite, inter alia:
wherein identifying the plurality of attributes and the plurality of care practices indicated within the population data further comprises:
performing image processing on visual content of the population data in order to extract a plurality of visual content features;
applying multi-dimensional scaling to the plurality of visual content features in order to reduce data complexity of the plurality of visual content features; and
applying a hierarchical rule base to the plurality of visual content features in order to determine a plurality of snippets among the population data, wherein applying the hierarchical rule base further comprises aggregating a plurality of biases for and against inclusion of each snippet using an aggregation rule;
mapping between semantic concepts of the plurality of semantic concepts based on the plurality of snippets and a plurality of text content features extracted from text among the population data, wherein mapping between the semantic concepts further comprises applying a first machine learning model to the plurality of text content features,
Applicant asserts that at least [0110], [0115], [0120]-[0123], and Figure 6 provide support for the above newly-added limitations. The Examiner disagrees.
Initially, none of the these portions makes any reference to population data, much less performing image processing on visual content of the population data in order to extract a plurality of visual content features as recited in the present claims. For instance, while [0117] generally discloses "feature extraction," it does not do so on visual content of population data as claimed. Furthermore, while [0120] discloses performing image processing to extract image features from visual content, it appears such processing is performed in the content of a user seeking a product recommendation rather than on population data as called for in the claims.
In relation to "applying multi-dimensional scaling to the plurality of visual content features in order to reduce data complexity of the plurality of visual content features" as recited in the claims, [0115] discloses "[the] feature assembly may include the application of multi-dimensional scaling to reduce the data complexity for subsequent inferencing. However, there is no indication that the "feature assembly" in this paragraph is referring to the "plurality of visual content features" of the population data as called for in the claims.
In relation to "applying a hierarchical rule base to the plurality of visual content features in order to determine a plurality of snippets among the population data, wherein applying the hierarchical rule base further comprises aggregating a plurality of biases for and against inclusion of each snippet using an aggregation rule" as recited in the claims, none of the above portions of the specification disclose use of a hierarchical rule base applied in the manner recited. While [0126] discloses inputting extracted features to a hierarchical rule base to generate concepts/relationships to create a mapping to create a knowledge base, this paragraph does not disclose applying a hierarchical rule base to visual content features to determine snippets among the population data via aggregating biases for/against snippet inclusion using an aggregation rule as claimed. Furthermore, while [0116]-[0118] generally disclose using a bias-based aggregation rule to determine what "snippets" should be included in a recommendation, this paragraph does not disclose using such bias-based aggregation rule in the context of a hierarchical rule base that is applied to visual content features of population data to determine snippets among the population data via aggregating biases for/against inclusion of each snippet in the manner recited in the present claims.
The Examiner also cannot identify any portions of the present specification supporting mapping between semantic concepts of the plurality of semantic concepts "based on the plurality of snippets and a plurality of text content features extracted from text among the population data" as called for in the present claims. In contrast, the snippets are disclosed as being included in a "recommendation" ([0116]-[0118]) rather than being used with text content features extracted from text among the population data to facilitate mapping between semantic concepts of the plurality of semantic concepts as recited.
The remaining claims are rejected based on their dependencies from claims 1, 13, or 14.
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 1-10, 12-23, and 25-28 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 1-10, 12, 26, and 28 are directed to a method (i.e., a process), claim 13 is directed to a non-transitory computer readable medium (i.e., a manufacture), and claims 14-23 and 25 are directed to a system (i.e., a machine). Accordingly, claims 1-10, 12-23, and 25-28 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 update issued by the USPTO as now 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 14 includes limitations that recite at least one abstract idea. Specifically, independent claim 14 recites:
A system for discretizing connections of semantically defined attributes using multi- stage machine learning, comprising:
a processing circuitry; and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
define a plurality of semantic concepts including a plurality of attribute semantic concepts representing known discrete attributes of conditions and a plurality of care practice component semantic concepts representing known discrete components of care practices, wherein the plurality of semantic concepts is defined such that each of the attribute semantic concepts is a data object including at least one first term collectively representing a discrete attribute of a respective condition and each of the care practice component semantic concepts is a data object including at least one second term collectively representing a discrete component of a care practice;
identify a plurality of attributes and a plurality of care practices indicated within population data, wherein the attributes and the care practices are defined via the plurality of semantic concepts, wherein the system is further configured to:
perform image processing on visual content of the population data in order to extract a plurality of visual content features;
apply multi-dimensional scaling to the plurality of visual content features in order to reduce data complexity of the plurality of visual content features; and
apply a hierarchical rule base to the plurality of visual content features in order to determine a plurality of snippets among the population data, wherein applying the hierarchical rule base further comprises aggregating a plurality of biases for and against inclusion of each snippet using an aggregation rule;
map between semantic concepts of the plurality of semantic concepts based on the plurality of snippets and a plurality of text content features extracted from text among the population data, wherein mapping between the semantic concepts further comprises applying a first machine learning model to the plurality of text content features, wherein the first machine learning model is trained to identify correlations between the plurality of semantic concepts with respect to the attributes and care practices identified within the population data, wherein the system is further configured to identify a connection between a first attribute semantic concept of the plurality of semantic concepts and a first care practice component semantic concept of the plurality of care practice semantic concepts when mapping between the semantic concepts, wherein the first machine learning model is trained to identify correlations between semantic concepts among the plurality of semantic concepts indicated in textual content;
create a knowledge graph including a plurality of nodes representing the plurality of semantic concepts and a plurality of edges connecting the plurality of nodes based on the mapping;
apply a second machine learning model to visual content for a user in order to identify a subset of the attribute semantic concepts for the user, wherein the second machine learning model is trained to identify attribute semantic concepts of the plurality of attribute semantic concepts shown in the visual content; and
query the knowledge graph based on the identified subset of the attribute semantic concepts output for the user, wherein the knowledge graph returns at least one care practice component semantic concept connected to the queried subset of the attribute semantic concepts.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” 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). As an example, a medical professional could practically in their mind with pen and paper review medical literature (e.g., journals, websites, medical notes, guidelines, etc.) to define attribute sematic concepts defining known discrete attributes of conditions (e.g., vomiting, visual auras, etc. of migraines) and care practice semantic concepts defining known discrete components of care practices (e.g., drinking water to avoid dehydration, avoiding caffeine, improving sleep quality to reduce the likelihood or limit the duration of migraines). For instance, “vomiting” is a first term that can be written down as a “data object” that collectively represents a discrete attribute of a migraine (condition) and “drinking water to avoid dehydration” is a second term that can be written down as a “data object” that collectively represents a discrete component of a diet modification (care practice).
The medical professional could then review data of any appropriate population (e.g., at a city level, state level, etc.) and identify attributes and care practices from the defined semantic concepts (e.g., based on matching or similarity of data objects). As an example, the person could readily visually review visual content of the population (e.g., images) and extract/determine visual content features from the images (e.g., various hair features such as color, length, degree of waviness, texture, density, etc.), identify unimportant ones of the features (e.g., based on their experience) to reduce the complexity of the features, and apply a hierarchical rule set to the features via aggregating biases for/against inclusion of each of a plurality of "snippets" using an aggregation rule. For instance, the person could consider/aggregate one or more biases for inclusion of a particular set of features as indicating a particular disease/disorder with one or more other biases against inclusion of the set of features as indicating the particular disease/disorder.
Thereafter, the medical professional could identify/map connections/correlations between the semantic attribute concepts and the care practices (e.g., based on proximity in the medical literature) in text content features and the snippets which would include identifying a connection between a first attribute semantic concept and a first care practice component semantic concept when mapping between the semantic concepts, draw/create a knowledge graph of the attribute concepts with nodes and edges, identify a subset of the attribute concepts for a particular user based on “visual content” of the user, and review/query the knowledge graph based on the subset to determine a medication/treatment/therapy (“care practice component concept”) related to the subset.
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 in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)).
Furthermore, the Examiner submits that applying multi-dimensional scaling to the plurality of visual content features to reduce data complexity of visual content features constitutes “mathematical concepts” because it is a set of statistical techniques used to reduce the complexity of a data set (as evidenced by NPL "Multidimensional scaling" to Hout et al. at page 1). This limitation is similar to performing a resampled statistical analysis to generate a resampled distribution. SAP America, Inc. v. Investpic, LLC, (898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018)). MPEP 2106.04(a)(2)(I)(C).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 2-4, 6-10, 15-17, and 19-23 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract).
In relation to claims 2-4 and 15-17, these claims call for generating recommendations based on care practice component semantic concepts in the knowledge graph and logging user progress using user inputs which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claims 6 and 19, these claims call for determining a user target by applying an interaction rule based on at least one portion of content viewed by the user during an exploration and data indicating user interactions with the at least one portion of content during the exploration which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claims 7 and 20, these claims call for updating the knowledge graph based on the logged progress which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claims 8 and 21, these claims call for determining whether each of a number of potential recommendations is in line with a user preference and selecting a recommendation based on the determination which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claims 9 and 22, these claims call for generating a user profile based on the plurality of attribute semantic concepts for the user and the knowledge graph which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claims 10 and 23, these claims call for determining a confidence level for the subset of the attribute semantic concepts for the user; determining that the confidence level is below a threshold; updating the subset of the attribute semantic concepts for the user; and querying the knowledge graph using the updated subset which again can be practically performed in the human mind with pen and paper (“mental processes”).
In relation to claim 28, this claim calls for identifying a portion of the attributes associated with the plurality of attribute semantic concepts show in the visual content which again can be practically performed in the human mind with pen and paper (“mental processes”).
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”):
A system for discretizing connections of semantically defined attributes using multi- stage machine learning (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)), comprising:
a processing circuitry (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)); and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)):
define a plurality of semantic concepts including a plurality of attribute semantic concepts representing known discrete attributes of conditions and a plurality of care practice component semantic concepts representing known discrete components of care practices, wherein the plurality of semantic concepts is defined such that each of the attribute semantic concepts is a data object including at least one first term collectively representing a discrete attribute of a respective condition and each of the care practice component semantic concepts is a data object including at least one second term collectively representing a discrete component of a care practice;
identify a plurality of attributes and a plurality of care practices indicated within population data, wherein the attributes and the care practices are defined via a plurality of semantic concepts, wherein the system is further configured to (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f)):
perform image processing on visual content of the population data in order to (using computers as tools to perform an existing process at such high level of generality, see MPEP § 2106.05(f))extract a plurality of visual content features;
apply multi-dimensional scaling to the plurality of visual content features in order to reduce data complexity of the plurality of visual content features; and
apply a hierarchical rule base to the plurality of visual content features in order to determine a plurality of snippets among the population data, wherein applying the hierarchical rule base further comprises aggregating a plurality of biases for and against inclusion of each snippet using an aggregation rule;
map between semantic concepts of the plurality of semantic concepts based on the plurality of snippets and a plurality of text content features extracted from text among the population data, wherein the system is further configured to apply a first machine learning model to the plurality of text content features, wherein the first machine learning model is trained 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)) identify correlations between the plurality of semantic concepts with respect to the attributes and care practices identified within the population data, wherein the system is further configured to identify a connection between a first attribute semantic concept of the plurality of semantic concepts and a first care practice component semantic concept of the plurality of care practice semantic concepts when mapping between the semantic concepts, wherein the first machine learning model is trained to identify correlations between semantic concepts among the plurality of semantic concepts indicated in textual content;
create a knowledge graph including a plurality of nodes representing the plurality of semantic concepts and a plurality of edges connecting the plurality of nodes based on the mapping;
apply a second machine learning model to visual content for a user in order 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)) identify a subset of the attribute semantic concepts for the user, wherein the second machine learning model is trained 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)) identify attribute semantic concepts of the plurality of attribute semantic concepts shown in the visual content; and
query the knowledge graph based on the identified subset of the attribute semantic concepts output for the user, wherein the knowledge graph returns at least one care practice component semantic concept connected to the queried subset of the attribute semantic concepts.
Regarding the additional limitations of the system including processing circuitry, memory, and instructions and the generic image processing, 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 using a first ML model trained to identify the correlations and a second ML model trained to process user visual content to identify a sematic attribute subset, 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)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 14 and analogous independent claims 1 and 13 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 14 and analogous independent claims 1 and 13 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 18: These claims recite how the second recommendation is generated based on a user target defined with respect to one of the attribute semantic concepts which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Claims 10 and 23: These claims call for requesting confirmation/rejection user input and therefore merely represent insignificant extra-solution activity (see MPEP § 2106.05(g)).
Claims 12 and 25: These claims recite how the attribute semantic concepts and care practices respectively include hair attributes and hair care product ingredients which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Claim 26: This claim recites how the first machine learning model is trained based on instances of the plurality of attribute semantic concepts and of the plurality of care practice semantic concepts in training textual content, wherein the first machine learning model is trained such that the first machine learning model indicates connections between attributes represented by the plurality of attribute semantic concepts and the plurality of care practice semantic concepts. However, 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 (see MPEP § 2106.05(f)). More specifically, as claim 1 already recites how the first ML model is applied to identify correlations between the attribute and care practice semantic concepts, then reciting how the first ML model is trained based on the same type of data from which it is configured to identify correlations/connections after training merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished. Simply reciting that the first ML model is trained on the same generic type of data from which it is configured to identify correlations/connections does not recite any details of how a solution to a problem is accomplished.
Claim 27: This claim recites how the second ML model is trained by applying a machine learning algorithm to labeled visual content having labels indicating respective attributes shown in the labeled visual content. However, similar to claim 26, 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 (see MPEP § 2106.05(f)). Specifically, as claim 1 already recites how the second ML model is applied to visual content of a user to identify a subset of the attribute semantic concepts in the visual content, then reciting how second ML model is trained by applying a machine learning algorithm to labeled visual content having labels indicating respective attributes shown in the labeled visual content (the same generic type of data on which the second ML model is configured to be applied) merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished.
Claim 28: This claim recites how the second ML model is further trained to identify the portion of the attributes associated with the plurality of attribute semantic concepts show in the visual content. However, as identifying the portion of the attributes associated with the plurality of attribute semantic concepts show in the visual content is already part of the abstract idea as noted above, then merely reciting how the second ML model is generically trained to perform such identification amounts 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)).
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. 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. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id., p. 13.
Thus, 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 14 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 system including processing circuitry, memory, and instructions and the generic image processing, 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 using a first ML model trained to identify the correlations and a second ML model trained to process user visual content to identify a sematic attribute subset, 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)).
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 18: These claims recite how the second recommendation is generated based on a user target defined with respect to one of the attribute semantic concepts which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Claims 10 and 23: These claims call for requesting confirmation/rejection user input and therefore merely represent insignificant extra-solution activity (see MPEP § 2106.05(g)).
Claims 12 and 25: These claims recite how the attribute semantic concepts and care practices respectively include hair attributes and hair care product ingredients which does no more than generally link use of the abstract idea to a particular technological environment or field of use without adding an inventive concept to the abstract idea (see MPEP § 2106.05(h)).
Claim 26: This claim recites how the first machine learning model is trained based on instances of the plurality of attribute semantic concepts and of the plurality of care practice semantic concepts in training textual content, wherein the first machine learning model is trained such that the first machine learning model indicates connections between attributes represented by the plurality of attribute semantic concepts and the plurality of care practice semantic concepts. However, 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 (see MPEP § 2106.05(f)). More specifically, as claim 1 already recites how the first ML model is applied to identify correlations between the attribute and care practice semantic concepts, then reciting how the first ML model is trained based on the same type of data from which it is configured to identify correlations/connections after training merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished. Simply reciting that the first ML model is trained on the same generic type of data from which it is configured to identify correlations/connections does not recite any details of how a solution to a problem is accomplished.
Claim 27: This claim recites how the second ML model is trained by applying a machine learning algorithm to labeled visual content having labels indicating respective attributes shown in the labeled visual content. However, similar to claim 26, 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 (see MPEP § 2106.05(f)). Specifically, as claim 1 already recites how the second ML model is applied to visual content of a user to identify a subset of the attribute semantic concepts in the visual content, then reciting how second ML model is trained by applying a machine learning algorithm to labeled visual content having labels indicating respective attributes shown in the labeled visual content (the same generic type of data on which the second ML model is configured to be applied) merely recites the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished.
Claim 28: This claim recites how the second ML model is further trained to identify the portion of the attributes associated with the plurality of attribute semantic concepts show in the visual content. However, as identifying the portion of the attributes associated with the plurality of attribute semantic concepts show in the visual content is already part of the abstract idea as noted above, then merely reciting how the second ML model is generically trained to perform such identification amounts 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)).
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. 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. Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. Id., p. 13.
Therefore, claims 1-10, 12-23, and 25-28 are ineligible under 35 USC §101.
Conclusion
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686