DETAILED ACTION
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 . This action is made final.
This Office Action is in response to the amendments filed on September 8th, 2025.
Claims 1, 3, 8, 9, 15, and 16 have been amended.
Response to Amendment
The amendment filed September 8th, 2025 has been entered. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant's arguments filed September 8th, 2025 for the 35 U.S.C. § 101 rejections have been fully considered but they are not persuasive.
Regarding the 103 Arguments
Applicant argues, in respect to claim 1, 8 and 15;As the Examiners appeared to agree during the interview, Dirac, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest each limitation recited by independent claims 1, 8, and 15. For example, Dirac, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest "receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system, wherein the feature family repository stores groups of reusable machine learning features for implementation by machine learning models performing related action requests within the inter-network facilitation system," as recited by currently amended independent claim 1 and as similarly recited by currently amended independent claims 8 and 15. Emphasis added.
Dirac describes a machine learning service (MILS) that determines whether to perform "an analysis to detect whether at least a portion of contents of one or more observation records of a first data set are duplicated in a second set of observation records." Dirac at Abstract. In addition, Dirac describes using "recipes" that "allow[]MILS users to indicate various feature processing steps that they have applied on data sets." Id. at [0085]. The recipes "may be specified in text format and then compiled into executable formats that can be re-used with different data sets on different resource sets as needed." Id. Furthermore, Dirac describes "[a]s part of developing the processing plan for a job, the MLS may select a workload distribution strategy for the job." Id. at [0092]. However, Dirac fails to suggest "receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter- network facilitation system, wherein the feature family repository stores groups of reusable machine learning features for implementation by machine learning models performing related action requests within the inter-network facilitation system," as recited by currently amended independent claim 1 and similarly currently amended independent claims 8 and 15. Emphasis added.
Examiners Response
Applicants’ argument is directed towards Dirac and that it fails to teach “receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system, wherein the feature family repository stores groups of reusable machine learning features for implementation by machine learning models performing related action requests within the inter-network facilitation system”. However, Dirac does disclose this, specifically at, [0090-0096], [0143-0145] A client device (external user/system) sends an API request that includes a recipe specifying which features (via group definitions/selection) to se. Those groups are stored as part of recipes/artifacts within the MLS artifact repository, [0094], [0115], and [0143-0144]. The recipes output/destination binds those groups to model execution, i.e., implementation by ML models upon action requests from clients operating across provider/inter-network environment, [0097-0105], and [0146-0147]. These together map to the newly amended language of claims 1, 8 and 15.
Applicant’s arguments filed September 8th, 2025 have been fully considered but they are not persuasive.
Regarding the 101 Arguments
Applicant argues:
Applicant respectfully requests consideration of the § 101 rejections in light of the above amendments, including amendments to claims 3, 9, and 16, which further add eligible subject matter to the claims, based on the Examiners' suggestions. Indeed, Applicant submits that, as amended, the currently amended independent claims are eligible at least because the claims, as amended, cannot practically be performed in the human mind (as a mental process) pursuant to Step 2A Prong One of the eligibility analysis. As stated in the August 4, 2025 USPTO memorandum to Technology Centers 2100, 2600, and 3600, "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)." USPTO Reminders On Evaluating Subject Matter Eligibility of Claims Under 35 U.S.C. 101, p. 2 (August 4, 2025) (Section IIA. Step 2A Prong One); see also MPEP § 2106.04(a)(2).
Further, Applicant submits that the claims as amended are eligible at least because the claims integrate any alleged judicial exception into a practical application under Prong Two of Step 2A because the amended claims above recite additional elements that, when considered in combination, integrate the abstract idea into a practical application. See MPEP § 2106.04; see also USPTO Reminders On Evaluating Subject Matter Eligibility of Claims Under 35 U.S.C. 101, p. 4 ("The claim itself does not need to explicitly recite the improvement described in the specification.").
Examiner’s Response to the mental process
The Applicant argues that the claim does not recite a mental process, as it cannot be practically preformed in the human mind. However, their amendments are merely defining the storage and implementation by a machine learning model for the abstract idea. There is no clear and definite distinction in the claims as to why the features cannot be performed in the human mind, “generate a feature family to store…and comprising feature references indicating respective network locations”, a human can create a data table (like in excel) with various information, based on the location of that information. For example, in Excel, a human can create numerous pages based on a location, and that page contains relevant data of the reference location. Applicant’s arguments do not provide an indication that the claimed invention does not recite a mental process, as required by the MPEP. Therefore, Applicant’s arguments are not persuasive.
Examiner’s Response to the integration into a practical application
The Applicant argues that if a claim recites any additional element that integrates a judicial exception into a practical application or amounts to significantly more, than it is patent eligible. However, their amendment to claim 1, in view of their argument is merely implementing the abstract idea with generic, well known computer components, and methodology. MPEP 2106.05(a) indicates that “[i]f it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification,” and that “[a]n indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” Applicant’s arguments do not provide an indication that the claimed invention provides an improvement nor do they show where in the specification a technical problem and explanation of an unconventional solution, as required by the MPEP. Therefore, adding that the family feature repository stores reusable machine learning features, implemented by models, is just implementing the abstract idea using generic computer components. Applicant’s arguments are not persuasive.
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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a system (a machine), Claims 8-14 are directed to a method (a process), and Claims 15-20 are directed to a non-transitory computer-readable medium (a manufacture). Therefore, claims 1-20 are directed to a process, machine, manufacture or composition of matter.
Regarding Claim 1
Step 2A, Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processor”, “non-transitory computer readable medium”, “machine learning feature”) [see MPEP 2106.04(a)(2)(III)].
“generate a feature family to store…and comprising feature references indicating respective network locations” (e.g., a human can generate a list comprising of features which can include locations)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a “processor”, “non-transitory computer readable medium” and “machine learning model”, which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). In particular, the recited “machine learning model” is merely a generic computer component, because it is merely recited to perform the function of implementing “the feature family” and “the plurality of machine learning features” the claims do not recite any particular structure for how such “machine learning model” is implemented.
Regarding the “receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system, wherein the feature family repository stores groups of reusable machine learning features for implementation by machine learning models performing related action requests within the inter-network facilitation system” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “ implement the feature family with a machine learning model” and “retrieve the plurality of machine learning features from the respective network locations indicated by the feature references of the feature family to provide the plurality of machine learning features to the machine learning model” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” and “non-transitory computer readable medium” and “machine learning model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system, wherein the feature family repository stores groups of reusable machine learning features for implementation by machine learning models performing related action requests within the inter-network facilitation system” limitation, as discussed above, the additional element of receiving data from other devices, which is recited at a high-level of generality and amounts to extra-solution activity of recevining data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “ implement the feature family with a machine learning model” and “retrieve the plurality of machine learning features from the respective network locations indicated by the feature references of the feature family to provide the plurality of machine learning features to the machine learning model” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Regarding Claim 2
Step 2A, Prong 1
Claim 2 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receive the request to implement the feature family by receiving a request to verify an action associated with a second client device, the action comprising a login, a funds transfer, an account registration, a credit request, a transaction dispute, or an online payment” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving user verification data, i.e. pre-solution activity of gathering data for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receive the request to implement the feature family by receiving a request to verify an action associated with a second client device, the action comprising a login, a funds transfer, an account registration, a credit request, a transaction dispute, or an online payment” limitation, as discussed above, the additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving user verification data, i.e. pre-solution activity of gathering data for use in the claimed system. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Regarding Claim 3
Step 2A, Prong 1
Claim 3 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “determine that the request indicates the feature family by comparing a stored entity name associated with the feature family within the feature family repository and a requested entity name indicated within the request,” limitation, the additional element is recited at a high-level of generality and amounts to insignificant extra-solution activity of comparing entity names to select a feature group, i.e. pre-solution activity of selecting a particular data source of type of data to be manipulated for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “retrieve, from the feature family repository, the feature references for the plurality of machine learning features; and retrieve, utilizing the feature references associated with the requested entity name, the plurality of machine learning features from the respective network locations” limitations, these additional elements are recited at a high-level of generality and amount to extra-solution of obtaining data for a model, i.e., pre-solution activity of data gathering (See MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts a to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “determine that the request indicates the feature family by comparing a stored entity name associated with the feature family within the feature family repository and a requested entity name indicated within the request”, and “retrieve, from the feature family repository, the feature references for the plurality of machine learning features; and retrieve, utilizing the feature references associated with the requested entity name, the plurality of machine learning features from the respective network locations” limitations, as discussed above, the additional elements of selecting types of data to manipulate and obtaining data, which are recited at a high level of generality and amounts to extra-solution activity of data manipulation and receiving data i.e. pre-solution activity of selecting a particular data source of type of data to be manipulated for use in the claimed system, and pre-solution activity of gathering data for use in the claimed process, respectively. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 4
Step 2A, Prong 1
Claim 4 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “update feature values corresponding to each of the plurality of machine learning features within the feature family by requesting updated feature value data from the respective network locations on a periodic basis.” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving updated values from network sources, i.e., pre-solution activity of data gathering (e.g., updating activity logs and checking for data as data is gathered) for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “update feature values corresponding to each of the plurality of machine learning features within the feature family by requesting updated feature value data from the respective network locations on a periodic basis” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving updated input values from network sources, i.e. pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
Claim 5 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “provide the plurality of machine learning features to the machine learning model without generating a new feature family corresponding to the request” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of providing existing feature data to a model, i.e. post-solution activity of data outputting (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “provide the plurality of machine learning features to the machine learning model without generating a new feature family corresponding to the request” limitation, as discussed above, the additional element is recited at a high-level of generality and amounts to extra-solution activity of providing existing feature data to a model, i.e. post-solution activity of data outputting. The courts have found limitations directed to outputting or transmitting data to another component or process, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
Claim 6 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “to include offline machine learning features stored at network locations updated on a periodic basis and online machine learning features stored at network locations updated concurrently with network activity within the inter-network facilitation system” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of categorizing and selecting machine learning features based on update frequency, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “generate the feature family to include offline machine learning features stored at network locations updated on a periodic basis and online machine learning features stored at network locations updated concurrently with network activity within the inter-network facilitation system” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of categorizing and selecting machine learning features based on update frequency, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed system. The courts have found selecting and sorting data based on known characteristics, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 7
Step 2A, Prong 1
Claim 7 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “processor” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receive the request to implement the feature family for one or more of training the machine learning model or applying the machine learning model for generating a prediction” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving a request to implement the feature family for machine learning training or prediction, i.e. pre-solution activity of insignificant application for use in the claimed system (see MPEP 2106.05(g)). Courts have found similar activity (e.g., outputting content after ad verification or executing predefined logic on a computer) to be insignificant application of an abstract idea (see Ultramercial, 772 F.3d at 716; Fort Properties, 671 F.3d at 1323-24; Bancorp, 687 F.3d at 1280-81)
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “processor” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receive the request to implement the feature family for one or more of training the machine learning model or applying the machine learning model for generating a prediction” limitation, the additional elements are recited at a high level of generality and amount to extra-solution activity of receiving a request to implement the feature family for machine learning training or prediction, i.e. pre-solution activity of insignificant application for use in the claimed system. The courts have found limitations directed to receiving or transmitting data over a network, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 8
Claims 8 recites a method. which corresponds directly to the systems recited in claim 1, respectively, with the addition of method instructions and computer-executable instructions which are insufficient to render the claims subject matter eligible for the same reasons described above.
Specifically:
Claim 8 corresponds to claim 1, with the added recitation of a generic method instructions to perform the same abstract system of claim 1.
Regarding Claim 9
Step 2A, Prong 1
Claim 9 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “receiving an additional request to implement a different feature family not stored within the feature family repository” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving additional values from stored data, i.e., pre-solution activity of mere data gathering for use in the claimed system (see MPEP 2106.05(g)), e.g., updating activity logs and checking for data as data is gathered).
Regarding the “generating an additional feature family corresponding to the additional request to store within the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of data construction, i.e. post-solution activity of insignificant application for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “designating server space within the inter-network facilitation system to store feature families of the feature family repository; selecting a centralized server location within the server space of the inter-network facilitation system; creating and storing the additional feature family within the centralized server location; and within the additional feature family, generating features references indicating the respective network locations for machine learning features associated with the additional feature family” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “receiving an additional request to implement a different feature family not stored within the feature family repository” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving additional values from stored data, i.e. pre-solution activity of mere data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “generating an additional feature family corresponding to the additional request to store within the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of data construction, i.e. post-solution activity of insignificant application. The courts have found applying known data construction methods, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Regarding the “designating server space within the inter-network facilitation system to store feature families of the feature family repository; selecting a centralized server location within the server space of the inter-network facilitation system; creating and storing the additional feature family within the centralized server location; and within the additional feature family, generating features references indicating the respective network locations for machine learning features associated with the additional feature family” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 10
Step 2A, Prong 1
Claim 10 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “updating feature values associated with the plurality of machine learning features associated with the feature family based on detecting a trigger event associated with the plurality of machine learning features from network activity within the inter-network facilitation system” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving updated values from network sources, i.e., pre-solution activity of data gathering (e.g., updating activity logs and checking for data as data is gathered) for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “updating feature values associated with the plurality of machine learning features associated with the feature family based on detecting a trigger event associated with the plurality of machine learning features from network activity within the inter-network facilitation system” limitation, the additional element is recited at a high-level of generality and amounts to extra-solution activity of retrieving updated input values from network sources, i.e. pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 11
Step 2A, Prong 1
Claim 11 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “identifying a machine learning feature associated with the feature family that is not required to perform an action associated with the request to implement the feature family” and “in response to identifying the machine learning feature that is not required, providing a modified subset of machine learning feature within the feature family to the machine learning model that does not include the machine learning feature that is not required.” limitations, these additional elements are recited at a high level of generality and amount to extra-solution activity of filtering data or modifying data before applying it to a model, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “identifying a machine learning feature associated with the feature family that is not required to perform an action associated with the request to implement the feature family” and “in response to identifying the machine learning feature that is not required, providing a modified subset of machine learning feature within the feature family to the machine learning model that does not include the machine learning feature that is not required.”, the additional elements are recited at a high-level of generality and amounts to extra-solution activity of filtering data or modifying data before applying it to a model, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed system. The courts have found selecting and sorting data based on known characteristics, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 12
Step 2A, Prong 1
Claim 12 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “generating, based on the plurality of machine learning features associated with the feature family indicated by the request and based on the machine learning model, predicted weights for the plurality of machine learning features for implementation via the machine learning model” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of applying a model function, i.e. post-solution activity of an insignificant application for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “generating, based on the plurality of machine learning features associated with the feature family indicated by the request and based on the machine learning model, predicted weights for the plurality of machine learning features for implementation via the machine learning model” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of applying a model function, i.e. post-solution activity of an insignificant application for use in the claimed system. The courts have found applying known model functions to input data, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 13
Step 2A, Prong 1
Claim 13 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “receiving a prediction query requesting feature information regarding the feature family used to generate a prediction via the machine learning model at a particular point in time” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data for use in the model, i.e. pre-solution activity of mere data gathering for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “determining, in response to receiving the prediction query, feature values for the plurality of machine learning features associated with the feature family at the particular point in time” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting a value and determine the type of information it represents, i.e. pre-solution activity of selecting a particular data type to be manipulated for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “receiving a prediction query requesting feature information regarding the feature family used to generate a prediction via the machine learning model at a particular point in time” limitations, this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data for use in the model, i.e. pre-solution activity of mere data gathering for use in the claimed system (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “determining, in response to receiving the prediction query, feature values for the plurality of machine learning features associated with the feature family at the particular point in time” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting a value and determine the type of information it represents, i.e. pre-solution activity of selecting a particular data type to be manipulated for use in the claimed system (see MPEP 2106.05(g)). The courts have found selecting and sorting data based on known characteristics, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 14
Step 2A, Prong 1
Claim 14 recites the same abstract idea as Claim 1.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “determining feature names for machine learning features indicated within a plurality of requests for the feature family, wherein each of the plurality of requests indicate less than all stored feature names associated with the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of evaluating data, i.e. pre-solution activity of mere data gathering for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “identifying, based on comparing the feature names indicated within the plurality of requests with the stored feature names, a machine learning feature that is named in less than a threshold percent of the plurality of requests for the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting a value and determine the type of information it represents, i.e. pre-solution activity of selecting a particular data type to be manipulated for use in the claimed system (see MPEP 2106.05(g)).
Regarding the “generating an additional feature family to exclude a feature reference indicating a network location where the machine learning feature that is named in less than a threshold percent of the plurality of requests is stored” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of modifying data structures to generate a new feature family, i.e. post-solution activity of insignificant application for use in the claimed system (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “determining feature names for machine learning features indicated within a plurality of requests for the feature family, wherein each of the plurality of requests indicate less than all stored feature names associated with the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of evaluating data, i.e. pre-solution activity of mere data gathering for use in the claimed system (see MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “identifying, based on comparing the feature names indicated within the plurality of requests with the stored feature names, a machine learning feature that is named in less than a threshold percent of the plurality of requests for the feature family” limitation, this additional element is recited at a high level of generality and amounts to extra-solution activity of selecting a value and determine the type of information it represents, i.e. pre-solution activity of selecting a particular data type to be manipulated for use in the claimed system (s