DETAILED ACTION
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 responsive to the Amendment filed on 02/27/2026. Claims 1-20 are pending in the case. Claim 20 is has been added. Claims 1 and 15 are independent claims.
Response to Arguments
Applicant's amendments and arguments regarding the 35 U.S.C. § 112 rejections are persuasive. Accordingly, these rejections are hereby withdrawn.
Applicant’s 35 U.S.C. § 101 arguments have been fully considered but they are not persuasive. Applicant argues that the claims are not directed to an abstract idea. (Amendment at page 8). Specifically, Applicant analogizes to Ex parte Desjardins and asserts that improvements to machine learning itself are patent eligible. (Id. at page 9). While Applicant is correct on the legal principle, the training step is recited at such a high level of generality that it falls within an “apply it” step under MPEP § 2106.05(f). Applicant further argues that no part of the claims is directed to a mental process. Examiner respectfully disagrees. In short, the providing step can be done by manually coming up with at least two labeling functions/methods that can be shared. The selecting step can be done by a user looking at the available labeling functions and choosing one or more based on their profiles. And, the grouping step can be done by manually grouping the unlabeled data considering how much of the data each labeling function can cover. Accordingly, these rejections are hereby maintained.
Applicant's amendments and prior art arguments are persuasive. Accordingly, these rejections are hereby withdrawn.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-14, 16-18, and 20 are directed towards the statutory category of a process. Claims 15 and 19 are directed towards the statutory category of a machine.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
A data programming method for supporting artificial intelligence (AI) systems, wherein shareable labeling functions for labeling data are used, wherein the data programming method comprises (mental process):
providing or publishing-at least two shareable labeling functions with their profile across different domains, wherein each of the at least two shareable labeling function profiles includes at least one training-related performance metric and/or weight (mental process – manually coming up with at least two labeling functions/methods that can be shared);
selecting at least one of these at least two shareable labeling functions by a selecting domain, wherein the selecting is based on respective at least one training-related performance metric and/or weight of the at least two shareable labeling functions generated by one of the domains that is different from the selecting domain (mental process – a user looks at the available labeling functions and chooses one or more based on their profiles);
grouping unlabeled data of the selecting domain for providing at least one group, wherein the grouping is based on a definable degree of coverage of the selected at least one shareable labeling function per unlabeled data and/or on a definable degree of coverage of unlabeled data per shareable labeling function (mental process – manually group the unlabeled data considering how much of the data each labeling function can cover).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
training a machine learning model of the selecting domain per at least one group with the respective at least one training-related performance metric and/or weight for producing labeled data of the selected at least one shareable labeling functions (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training a machine learning model of the selecting domain per at least one group with the respective at least one training-related performance metric and/or weight for producing labeled data of the selected at least one shareable labeling functions (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2.
The data programming method according to claim 1, wherein.
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
a profile of the labeling function includes a semantically annotated data dependency and/or is a semantically annotated profile (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
a profile of the labeling function includes a semantic type of input and output data and/or estimated performance metrics and/or an estimated computation time and/or a partitioning granularity and/or a provider profile and/or third-party data sources and/or a labeled data set (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
the at least one training-related performance metric comprises an estimated capability to produce correct labels for a certain size of data (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
the at least one training-related performance metric and/or weight is generated from one or more domains other than the selecting domain (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
an initial selecting of the at least one of these shareable labeling functions by a selecting domain is carried out based on a matching between a provided data schema and the annotated input of all labeling functions (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
the selecting step is additionally based on labeled data of the selecting domain and/or a ground-truth data set of the selecting domain (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
each of the at least two shareable labeling functions, will be selected and estimated by all other domains (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception.
the grouping step comprises a production of a probabilistic label for one or more or all unlabeled data (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 10:
2A Prong 1: This claim is directed to a judicial exception.
at least one estimated performance metric and/or weight of the selected labeling functions in each group and/or the number of samples in the group is or are reported to other domains (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a discriminative and/or local machine learning model of the selecting domain is trained using the produced labeled data or produced labels (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a discriminative and/or local machine learning model of the selecting domain is trained using the produced labeled data or produced labels (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 12:
2A Prong 1: This claim is directed to a judicial exception.
low-quality labeling functions are filtered out from provided or published or shared labeling functions (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 13:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
published labeling functions are maintained by a function catalog or function catalog server, the function catalog or function catalog server (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
published labeling functions are maintained by a function catalog or function catalog server, the function catalog or function catalog server (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 14:
2A Prong 1: This claim is directed to a judicial exception.
at least one domain comprises or runs an agent that comprises a function publisher and/or a function selector and/or a label producer and/or a local model learner (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 15:
2A Prong 1: This claim is directed to a judicial exception.
A system for carrying out a data programming method for supporting artificial intelligence (AI) systems, wherein shareable labeling functions for labeling data are used, the system comprising (mental process):
provide or publish at least two of shareable labeling functions with their profile across different domains, wherein each of the at least two shareable labeling function profiles includes at least one training-related performance metric and/or weight (mental process – manually coming up with at least two labeling functions/methods that can be shared);
selecting at least one of the at least two shareable labeling functions by a selecting domain, wherein their selecting is based on respective at least one training-related performance metric and/or weight of the at least two shareable labeling functions generated by one of the domains that is different from the selecting domain (mental process – a user looks at the available labeling functions and chooses one or more based on their profiles);
group unlabeled data of the selecting domain for providing at least one group, wherein the grouping is based on a definable degree of coverage of the selected at least one shareable labeling function per unlabeled data and/or on a definable degree of coverage of unlabeled data per shareable labeling function (mental process – manually group the unlabeled data considering how much of the data each labeling function can cover).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
one or more memories storing program steps (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
one or more processors configured to execute the program steps so as to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
train a machine learning model of the selecting domain per at least one group with the respective at least one training- related performance metric and/or weight for producing labeled data of at least one selected at least one shareable labeling functions (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)),
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
one or more memories storing program steps (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
one or more processors configured to execute the program steps so as to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
train a machine learning model of the selecting domain per at least one group with the respective at least one training- related performance metric and/or weight for producing labeled data of at least one selected at least one shareable labeling functions (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)),
With respect to claim 16:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the Al system is a machine learning (ML) system (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the Al system is a machine learning (ML) system (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 17:
2A Prong 1: This claim is directed to a judicial exception.
the correct labels are produced in terms of different types of machine learning measures, including at least one of accuracy, precision, recall and F1-score (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 18:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the function catalog or the function catalog server comprises a global ontology and/or a function repository and/or a propagator (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the function catalog or the function catalog server comprises a global ontology and/or a function repository and/or a propagator (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 19:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the Al system is a machine learning (ML) system that carries out the data programming method (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the Al system is a machine learning (ML) system that carries out the data programming method (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 20:
2A Prong 1: This claim is directed to a judicial exception.
the machine learning model is a generative machine learning model, and wherein the selecting domain is located on a device that is geographically remote from the other domains, is hosted by a different entity and has access to data that is not accessible by the other domains (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Casey R. Garner/Primary Examiner, Art Unit 2123
Prosecution Insights