CTNF 18/325,629 CTNF 73033 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are directed to either a process, machine, manufacture or composition of matter. With respect to claims 1, 9, 16: 2A Prong 1 : transforming customer content data to anonymized system metadata (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); performing k-aggregation of the data samples by: randomly selecting an enterprise from the multiple enterprise; randomly selecting a user associated with the selected enterprise from the users of the remote computing systems; randomly selecting a data sample corresponding to the selected user; repeating the random selection of the enterprise, the random selection of the user, and the random selection of the data sample a first predetermined number (k) of times, wherein the repetition of the random selection of the enterprise and the random selection of the user is performed without replacement; and aggregating the randomly-requested data samples by position to generate an aggregated data sample; repeating the performance of the k-aggregation of the data samples a second predetermined number (N) of times with replacement to generate N aggregated data samples; concatenating the N aggregated data samples (mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); wherein the data samples for each user are sorted by position; (mental process – a user can manually track or observe changes or perform math) ; generate an anonymized dataset that is classified as system metadata; (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data); Modeling (mental process of modeling with assistance of pen and paper); detecting (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data); 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: implemented via a computing system comprising a processor ( computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358 ); logging customer content data comprising data samples corresponding to each user’s interactions with the enterprise application (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); training a machine learning model using the anonymized dataset; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data ); deploying the trained machine learning model via the enterprise application (reads on transmitting/receiving, adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); Claim 16 storage medium for storing programs (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); machine learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see 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: implemented via a computing system comprising a processor ( computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358 ); logging customer content data comprising data samples corresponding to each user’s interactions with the enterprise application (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); training a machine learning model using the anonymized dataset; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data ); deploying the trained machine learning model via the enterprise application (reads on transmitting/receiving, adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); Claim 16 storage medium for storing programs (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); machine learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). Further, the logging steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer . The claim is not patent eligible. 2. The method of claim 1, deploying the trained machine learning model via the enterprise application comprises utilizing the trained machine learning model to perform at least one of feed ranking or content recommendation via the enterprise application (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction). 3. The method of claim 2, comprising deploying the trained machine learning model across multiple enterprise applications within a suite of enterprise applications comprising the enterprise application, without regard for privacy boundaries between different enterprises (further expand mental process user can perform math) . 4. The method of claim 2, comprising: logging additional customer content data comprising additional data samples corresponding to each user’s interactions with the enterprise application with respect to the at least one of the feed ranking or the content recommendation; and utilizing the additional customer content data during a subsequent iteration of the method (further expand mental process user can perform math) . 5. The method of claim 1, comprising, prior to performing the k-aggregation of the data samples: cleaning the data samples; detecting outliers within the cleaned data samples; and performing correction or removal of each detected outlier (further expand mental process user can perform math) . 6. The method of claim 1, comprising aggregating the randomly-requested data samples by calculating a mean of the randomly-requested data samples in each position (further expand mental process user can perform math) . 7. The method of claim 1, wherein each data sample comprises at least one numeric value associated with an interaction of one of the users with the enterprise application via a corresponding one of the remote computing systems (further expand mental process user can perform math) . 8. The method of claim 1, wherein the first predetermined number is equal to a whole number that is between 4 and 6, inclusive; and wherein the second predetermined number is equal to a whole number that is between 250 and 1000, inclusive (further expand mental process user can perform math) . 10. The application service provider server of claim 9, wherein the computer-readable storage medium comprises computer-executable instructions that, when executed by the processor, cause the processor to deploy the trained machine learning model via the enterprise application by utilizing the trained machine learning model to perform at least one of feed ranking or content recommendation via the enterprise application (further expand mental process user can perform math) . 11. The application service provider server of claim 10, wherein the computer-readable storage medium comprises computer-executable instructions that, when executed by the processor, cause the processor to deploy the trained machine learning model across multiple enterprise applications within a suite of enterprise applications comprising the enterprise application, without regard for privacy boundaries between different enterprises (further expand mental process user can perform math) . 12. The application service provider server of claim 10, wherein the computer-readable storage medium comprises computer-executable instructions that, when executed by the processor, cause the processor to: log additional customer content data comprising additional data samples corresponding to each user’s interactions with the enterprise application with respect to the at least one of the feed ranking or the content recommendation; and combine the additional data samples with the data samples (further expand mental process user can perform math) . 13. The application service provider server of claim 9, wherein the computer-readable storage medium comprises computer-executable instructions that, when executed by the processor, cause the processor to, prior to performing the k-aggregation of the data samples: clean the data samples; detect outliers within the cleaned data samples; and perform correction or removal of each detected outlier (further expand mental process user can perform math) . 14. The application service provider server of claim 9, wherein the computer-readable storage medium comprises computer-executable instructions that, when executed by the processor, cause the processor to aggregate the randomly-requested data samples by calculating a mean of the randomly-requested data samples in each position (further expand mental process user can perform math) . 15. The application service provider server of claim 9, wherein each data sample comprises at least one numeric value associated with an interaction of one of the users with the enterprise application via a corresponding one of the remote computing systems (further expand mental process user can perform math) . 17. The computer-readable storage medium of claim 16, wherein the computer-executable instructions, when executed by the processor, cause the processor to deploy the trained machine learning model via the enterprise application by utilizing the trained machine learning model to perform at least one of feed ranking or content recommendation via the enterprise application (further expand mental process user can perform math) . 18. The computer-readable storage medium of claim 16, wherein the computer-executable instructions, when executed by the processor, cause the processor to deploy the trained machine learning model across multiple enterprise applications within a suite of enterprise applications comprising the enterprise application, without regard for privacy boundaries between different enterprises (further expand mental process user can perform math) . 19. The computer-readable storage medium of claim 16, wherein the computer-executable instructions, when executed by the processor, cause the processor to, prior to performing the k-aggregation of the data samples: clean the data samples; detect outliers within the cleaned data samples; and perform correction or removal of each detected outlier (further expand mental process user can perform math) . 20. The computer-readable storage medium of claim 16, wherein the computer-executable instructions, when executed by the processor, cause the processor to aggregate the randomly-requested data samples by calculating a mean of the randomly-requested data samples in each position (further expand mental process user can perform math) . 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Andrade (US 2018/0316571) teaches aggregation (0211, 0228, 0235) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30. 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at 5712703428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DAVID R VINCENT/Primary Examiner, Art Unit 2123 Application/Control Number: 18/325,629 Page 2 Art Unit: 2123 Application/Control Number: 18/325,629 Page 3 Art Unit: 2123 Application/Control Number: 18/325,629 Page 4 Art Unit: 2123 Application/Control Number: 18/325,629 Page 5 Art Unit: 2123 Application/Control Number: 18/325,629 Page 6 Art Unit: 2123 Application/Control Number: 18/325,629 Page 7 Art Unit: 2123 Application/Control Number: 18/325,629 Page 8 Art Unit: 2123 Application/Control Number: 18/325,629 Page 9 Art Unit: 2123 Application/Control Number: 18/325,629 Page 10 Art Unit: 2123 Application/Control Number: 18/325,629 Page 11 Art Unit: 2123 Application/Control Number: 18/325,629 Page 12 Art Unit: 2123 Application/Control Number: 18/325,629 Page 13 Art Unit: 2123 Application/Control Number: 18/325,629 Page 14 Art Unit: 2123
Prosecution Insights