DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 12/29/2025.
Claims 1, 14, and 18 have been amended.
Claims 1-5, 7-11, 14-15, 17-18, and 21-22 are currently pending and have been examined.
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 .
Priority
This application claims priority of Provisional Application 63/389822 filed on 7/15/2022. Applicant's claim for the benefit of this prior-filed application is acknowledged.
Response to Amendments
The previously pending 35 USC 103 rejection has been withdrawn in response to Applicant’s amendments. Please see below for reasoning.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
The Examiner asserts the claimed “an apparatus comprising: an agglomerate network comprising a sequencing circuit, a mimicking circuit, a connector circuit, a scheduler circuit, and a sequence data provisioning circuit” appears to be general purpose computer components (See MPEP 2106), where a sequence is just a schedule.
With regard to the limitations of claims 1-5, 7-11, 14-15, 17-18, and 21-22, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that the Applicant’s claims are determining trends for schedules, where these trends are then used for commercial purposes such as managing a schedule to mitigate issues that occur within the schedule, which is managing how humans interact for commercial purposes. The Examiner asserts that implementing the abstract idea on a general purpose computer does not make the claims eligible (See MPEP 2106). The Examiner has taken the claims in light of there broadest reasonable interpretation (See Applicant’s specification Figure 1 and related text). Applicant’s arguments are not persuasive.
Applicant further argues the claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. The Examiner asserts adding weighted values and bias to the input values does not integrate the abstract idea into a practical application, but rather further narrows the abstract idea as these are just input values into an analysis. Yes, the human input values (e.g. weights) change how the calculations are run and may improve the results of the analysis, but do not improve the functioning of the computer, the computer is still merely being used as a tool for implementing the abstract idea. In addition, there is only generic machine learning claimed, which is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106). Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible under 2B. The Examiner respectfully disagrees. The Examiner asserts that implementing the abstract idea on a general purpose computer does not make the claims eligible. In addition, the mimic command / mimicking circuit, is a generic piece of computer equipment as shown in Paragraph 0540 of Applicant’s specification, which states “Referring to Fig. 75, a method 130700 for schedule mimicking, in accordance with embodiments of the current disclosure, is provided. The method 130700 may be performed via apparatus 130500 and/or any other computing device disclosed herein”. Also, there is only generic machine learning claimed, such as wherein the machine learning model comprises a neural network. The Examiner notes that if this agglomerate network is a specific arrangement of hardware components, please claim what this specific arrangement is because it appears to all be general purpose computer components (See Paragraph 0314-0315 of Applicant’s specification). Applicant’s arguments are not persuasive.
The Examiner further points to MPEP 2106.05 which states “the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter”, where a narrow abstract idea is still an abstract idea. 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.
Claims 1-5, 7-11, 14-15, 17-18, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-5, 7-11, 14-15, 17-18, and 21-22 are directed toward a process and a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims 1, 14, and 18 are directed toward an apparatus for generating a new schedule with attributes from different sequence data using machine-learning, comprising: an agglomerate network comprising a sequencing circuit, a mimicking circuit, a connector circuit, a scheduler circuit, and a sequence data provisioning circuit: the sequencing circuit configured to interpret sequence data corresponding to a first sequence associated with a first user and a second sequence associated with a second user, wherein the first sequence and the second sequence are schedules; and the mimicking circuit configured to: extract a sequence trend from the sequence data, using a machine learning model trained on historical sequence data and feedback indicative of favorable attributes in the historical sequence data, wherein the machine learning model comprises a neural network, and wherein the sequence trend determined by the machine learning model is indicative of attributes of the first sequence inferred to be favorable by the first user and attributes of the second sequence inferred to be favorable by the second user; identify portions of the first sequence and the second sequence corresponding to the extracted sequence trend; and generate a mimic command value, based at least in part on the identified portions of the first sequence and the second sequence and the extracted sequence trend, said mimic command value being structured to adjust the connector circuit of the agglomerate network by setting or changing a bias parameter stored in memory and used by the connector circuit to control a weight, between the first sequence and the second sequence for contributing to new sequence data, wherein the scheduler circuit is configured to generate the new sequence data comprising a new schedule based on the identified portions and the bias parameter of the connector circuit; wherein the mimicking circuit is further configured to receive feedback data corresponding to the new sequence data, and update the mimic command value to adjust the bias parameter of the connector circuit based on the feedback data to modify the weight for subsequent generation of the new sequence data; and the sequence data provisioning circuit configured to transmit the new sequence data (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing sequence data (e.g. events/tasks) at least partially done by a human to determine trends to determine new sequence data for a purpose (e.g. mitigation) by adding weights to the input values and modifying the schedule (e.g. sequence) based on the analysis to transmit to a human for interpretation and feedback, which is managing how humans interact for commercial purposes.
Dependent claims 2-5, 7-11, 15, 17, and 21-22 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “an apparatus comprising: an agglomerate network comprising a sequencing circuit, a mimicking circuit, a connector circuit, a scheduler circuit, and a sequence data provisioning circuit: the sequencing circuit configured to; and the mimicking circuit configured to: using a machine learning model, wherein the machine learning model comprises a neural network, stored in memory and used by the connector circuit; wherein the mimicking circuit is further configured to (claim 1)”; “using machine learning; via a sequence circuit; via a mimicking circuit; using a machine learning model; wherein the machine learning model comprises a neural network; stored in memory and used by a connector circuit; via a scheduler circuit; via a sequence data provisioning circuit (claim 14)”; “agglomerate network; using machine-learning, the agglomerate network comprising: a sequence mimicker circuit structured to; using a machine learning model; wherein the machine learning model comprises a neural network; stored in memory and used by a connector circuit; a scheduler circuit (claim 18)” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
The Examiner notes the claims “using machine learning” is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106).
In addition, dependent claims 2-5, 7-11, 15, 17, and 21-22 further narrow the abstract idea and dependent claims 2-3, 15, and 17 additionally recite “by a manager (claims 2-3 and 15); “via a mitigation circuit; a mitigation action provisioning circuit (claim 17)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; and System Independent claims 1, 14, and 18 recite “an apparatus comprising: an agglomerate network comprising a sequencing circuit, a mimicking circuit, a connector circuit, a scheduler circuit, and a sequence data provisioning circuit: the sequencing circuit configured to; and the mimicking circuit configured to: using a machine learning model, wherein the machine learning model comprises a neural network, stored in memory and used by the connector circuit; wherein the mimicking circuit is further configured to (claim 1)”; “using machine learning; via a sequence circuit; via a mimicking circuit; using a machine learning model; wherein the machine learning model comprises a neural network; stored in memory and used by a connector circuit; via a scheduler circuit; via a sequence data provisioning circuit (claim 14)”; “agglomerate network; using machine-learning, the agglomerate network comprising: a sequence mimicker circuit structured to; using a machine learning model; wherein the machine learning model comprises a neural network; stored in memory and used by a connector circuit; a scheduler circuit (claim 18)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Figure 1 and Paragraphs 0314-0315 and 0818-0819. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-5, 7-11, 15, 17, and 21-22 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2-3, 15, and 17 additionally recite “by a manager (claims 2-3 and 15); “via a mitigation circuit; a mitigation action provisioning circuit (claim 17)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-5, 7-11, 14-15, 17-18, and 21-22 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1, 14, and 18 disclose a system and method for analyzing sequence data (e.g. events/tasks) at least partially done by a human to determine trends to determine new sequence data for a purpose (e.g. mitigation) by adding weights to the input values and modifying the schedule (e.g. sequence) based on the analysis to transmit to a human for interpretation and feedback using machine learning.
Regarding a possible 103 rejection: The closest prior art of record is:
Crabtree et al. (US 2022/0078210 A1) – which discloses collaborative cybersecurity defense strategy analysis.
Williams et al. (US 2021/0209544 A1) – which discloses managing shipped object and schedules.
Vijayaraghavanet al. (US 2021/0165708 A1) – which discloses predictive system failure monitoring.
Billings al. (US 2020/0279334 A1) – which discloses machine learning risk factor identification and mitigation.
Stenning al. (US 2019/0108747 A1) – which discloses augmented industrial management.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1, 14, and 18, such as analyzing sequence data (e.g. events/tasks) at least partially done by a human to determine trends to determine new sequence data for a purpose (e.g. mitigation) by adding weights to the input values and modifying the schedule (e.g. sequence) based on the analysis to transmit to a human for interpretation and feedback using machine learning. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “an apparatus for generating a new schedule with attributes from different sequence data using machine-learning, comprising: an agglomerate network comprising a sequencing circuit, a mimicking circuit, a connector circuit, a scheduler circuit, and a sequence data provisioning circuit: the sequencing circuit configured to interpret sequence data corresponding to a first sequence associated with a first user and a second sequence associated with a second user, wherein the first sequence and the second sequence are schedules; and the mimicking circuit configured to: extract a sequence trend from the sequence data, using a machine learning model trained on historical sequence data and feedback indicative of favorable attributes in the historical sequence data, wherein the machine learning model comprises a neural network, and wherein the sequence trend determined by the machine learning model is indicative of attributes of the first sequence inferred to be favorable by the first user and attributes of the second sequence inferred to be favorable by the second user; identify portions of the first sequence and the second sequence corresponding to the extracted sequence trend; and generate a mimic command value, based at least in part on the identified portions of the first sequence and the second sequence and the extracted sequence trend, said mimic command value being structured to adjust the connector circuit of the agglomerate network by setting or changing a bias parameter stored in memory and used by the connector circuit to control a weight, between the first sequence and the second sequence for contributing to new sequence data, wherein the scheduler circuit is configured to generate the new sequence data comprising a new schedule based on the identified portions and the bias parameter of the connector circuit; wherein the mimicking circuit is further configured to receive feedback data corresponding to the new sequence data, and update the mimic command value to adjust the bias parameter of the connector circuit based on the feedback data to modify the weight for subsequent generation of the new sequence data; and the sequence data provisioning circuit configured to transmit the new sequence data (as required by independent claims 1, 14, and 18)”, thus rendering claims 1, 14, 18 and their dependent claims as allowable over the prior art.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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