CTNF 18/422,104 CTNF 90409 DETAILED ACTION 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of mental judgements without significantly more. In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.) Regarding claims 1, 9, and 16, taking claim 1 as exemplary: Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 a device, claim 10 a method. Step 2A, prong one: Do the claims recite an abstract idea, law of nature or natural phenomenon? Yes. Regarding claims 1, 9, and 16, taking claim 1 as exemplary, claim 1 recites “A computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train a machine learning model to predict a type of computing system to process requests, wherein training the machine learning model includes training the model to identify, based on historical data related to request volume, number of active users, and frequency of requests, a type of computing system to process a request; receive monitoring data, wherein the monitoring data includes current availability data of a plurality of computing resources associated with different types of computing systems; receive a first request for processing, wherein the first request for processing includes parameters of the first request; execute the machine learning model, wherein executing the machine learning model includes inputting the monitoring data of the plurality of computing resources and the parameters of the first request to output a particular type of computing system to process the first request; determine, based on the received monitoring data, whether a delay exists in computing resources associated with the particular type of computing system output by the machine learning model; responsive to determining that a delay does not exists, send the first request for processing to the computing resources associated with the particular type of computing system for processing; responsive to determining that a delay does exist: evaluate the delay to identify a cause of the delay; identify, from historical data, a remediation action to address the cause of the delay; automatically execute the remediation action to resolve the delay; and send the first request for processing to the computing resources associated with the particular type of computing system for processing. ” This all recites the mental process of making judgments based on human intelligence which can be performed in the human mind and/or with the aid of pen and paper without significantly more. Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No – Although claims 1, 9, and 16, taking claim 1 as exemplary recites “computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train a machine learning model to predict a type of computing system to process requests, wherein training the machine learning model includes training the model” the use of “computing systems” and/or “computing platforms” and claim 16 further recites “One or more non-transitory computer-readable media” they are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf models which is no more than extra solution activity (see MPEP 2106.05 (f)). The receiving of input data and the sending output data amounts to no more than extra solution activity. The use of processor, memory, computing system, and computing platform is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer components. (See MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No – Although claims 1, 9, and 16, taking claim 1 as exemplary recites “computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train a machine learning model to predict a type of computing system to process requests, wherein training the machine learning model includes training the model” the use of “computing systems” and/or “computing platforms” and claim 16 further recites “One or more non-transitory computer-readable media” they are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf models which is no more than extra solution activity (see MPEP 2106.05 (f)). The receiving of input data and the sending output data amounts to no more than extra solution activity. The use of processor, memory, computing system, and computing platform is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer components. (See MPEP 2106.05 (f)). The receiving of input data and the sending output data amounts to no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 134 S. Ct. 2347, 2360 (2014). For the reasons above, claims 1, 9, and 16 are rejected as being directed to non-patentable subject matter under §101. The additional limitations of the dependent claims are addressed briefly below: Regarding dependent claims 2, 10, and 17, taking claim 2 as exemplary: “wherein the particular type of computing system for processing the first request output by the machine learning model is one of: quantum computing, classical computing or hybrid computing.” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 1, 9, and 16. The use of a machine learning model and quantum computing, classical computing or hybrid computing is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claims 3, 11, and 18, taking claim 3 as exemplary: “wherein processing the first request using hybrid computing includes processing a first portion of the first request using quantum computing techniques and a second portion of the first request using classical computing techniques.” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 2, 10, and 17. The use of hybrid computing and classical computing is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claims 4, 12, and 19, taking claim 4 as exemplary: “wherein the quantum computing includes photonic-based quantum computing hardware” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 2, 10, and 17. The use of quantum computing includes photonic-based quantum computing hardware is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claims 5, 13, and 20, taking claim 5 as exemplary: “further including instructions that, when executed, cause the computing platform to update the machine learning model based on processing the first request” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 1, 9, and 16. The use of a machine learning model is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claims 6 and 14, taking claim 6 as exemplary: “wherein the monitoring data further includes current network status” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 1 and 9. The use of a network is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claims 7 and 15, taking claim 7 as exemplary: “wherein executing the machine learning model to output the particular type of computing system to process the first request includes analyzing a complexity of computations in the first request” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claims 1 and 9. The use of a machine learning model, computing system, and computations is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Regarding dependent claim 8: “wherein the identifying the remediation action to address the cause of the delay is performed using machine learning” – which continues to recite the abstract idea of making judgments based on human intelligence without significantly more of claim 1. The use of a machine learning is recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using a generic machines that gather data and input into readily available routine into an off the shelf computer component. (see MPEP 2106.05 (f)) which is no more than extra solution activity (see MPEP 2106.05(g)); 2106.05(d)(II)(iii) update log, electronic record keeping; 2106.05(d)(II)(iv) storing and retrieving data in memory). Taken alone, the additional elements of the dependent claims above do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Would Be Allowable Subject Matter Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action. 13-03-01 The following is a statement of reasons for the indication of would be allowable subject matter: As per claims 1, 9, and 16: Though Koike et al., (US 2023/0368065 A1), part of the prior art made of record, teaches the monitoring of data for use of computational tasks for different computing platforms of claims 1, 9, and 16 in paragraph [0009] through the load balancing of classical deep neural network and quantum neural networks. Though Sunwood et al., (US 2022/0035679 A1), part of the prior art made of record, teaches monitoring of data for computational tasks with machine learning of claims 1, 9, and 16 in paragraph [0041] through processing of monitored data through a trained machine learning model and that the monitoring data is based on workloads from multiple different processing cores. The primary reason for marking of would be allowable subject matter of independent claims 1, 9, and 16, taking claim 1 as exemplary, in the instant application, is the combination with the inclusion in these claims of the limitations of a computing platform, method, and non-transitory computer-readable media comprising: “ train a machine learning model to predict a type of computing system to process requests, wherein training the machine learning model includes training the model to identify, based on historical data related to request volume, number of active users, and frequency of requests, a type of computing system to process a request ; receive monitoring data, wherein the monitoring data includes current availability data of a plurality of computing resources associated with different types of computing systems; receive a first request for processing, wherein the first request for processing includes parameters of the first request; execute the machine learning model, wherein executing the machine learning model includes inputting the monitoring data of the plurality of computing resources and the parameters of the first request to output a particular type of computing system to process the first request; determine, based on the received monitoring data, whether a delay exists in computing resources associated with the particular type of computing system output by the machine learning model; responsive to determining that a delay does not exists, send the first request for processing to the computing resources associated with the particular type of computing system for processing; responsive to determining that a delay does exist: evaluate the delay to identify a cause of the delay; identify, from historical data, a remediation action to address the cause of the delay; automatically execute the remediation action to resolve the delay; and send the first request for processing to the computing resources associated with the particular type of computing system for processing. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach monitoring of data for use of computational tasks for different computing platforms and monitoring of data for computational tasks with machine learning, it does not teach monitoring data based on a data type and determining whether a delay exists associated with that type for a computing system for processing that data type, responsive to determining that a delay does exist, evaluating the delay to identify a cause of the delay identifying from historical data a remediation action to address the cause of the delay and automatically resolving the delay all as part of executing a machine learning model as claimed and specified above— and that training a machine learning model is trained to identify , based on historical data related to request volume, number of active users, and frequency of requests, a type of computing system to process a request. Dependent claim(s) 2-8, 10-15, 17-20 are marked as would be allowable at least for the reasons recited above as including all of the limitations of the would be allowable independent base claim 1, 9, and 16 upon which claims 2-8, 10-15, 17-20 depend. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Koike et al., (US 2023/0368065 A1), part of the prior art made of record, teaches the monitoring of data for use of computational tasks for different computing platforms of claims 1, 9, and 16 in paragraph [0009] through the load balancing of classical deep neural network and quantum neural networks. Sunwood et al., (US 2022/0035679 A1), part of the prior art made of record, teaches monitoring of data for computational tasks with machine learning of claims 1, 9, and 16 in paragraph [0041] through processing of monitored data through a trained machine learning model and that the monitoring data is based on workloads from multiple different processing cores. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE D WOOLWINE whose telephone number is (571)272-4138. The examiner can normally be reached M-F 9:30-6:00 PM. 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, MIRANDA HUANG can be reached at (571) 270-7092. 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. SHANE D. WOOLWINE Primary Examiner Art Unit 2124 /SHANE D WOOLWINE/Primary Examiner, Art Unit 2124 Application/Control Number: 18/422,104 Page 2 Art Unit: 2124 Application/Control Number: 18/422,104 Page 3 Art Unit: 2124 Application/Control Number: 18/422,104 Page 4 Art Unit: 2124 Application/Control Number: 18/422,104 Page 5 Art Unit: 2124 Application/Control Number: 18/422,104 Page 6 Art Unit: 2124 Application/Control Number: 18/422,104 Page 7 Art Unit: 2124 Application/Control Number: 18/422,104 Page 8 Art Unit: 2124 Application/Control Number: 18/422,104 Page 9 Art Unit: 2124 Application/Control Number: 18/422,104 Page 10 Art Unit: 2124 Application/Control Number: 18/422,104 Page 11 Art Unit: 2124 Application/Control Number: 18/422,104 Page 12 Art Unit: 2124
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