DETAILED ACTION
This correspondence is responsive to the Preliminary Amendment filed September 6, 2023. Claims 51-72 are pending in the case, with claims 51, 68, and 71 in independent form.
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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Summary of Detailed Action
Claim 68 is objected to regarding informalities.
Claims 51- 71 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite
Claim 63 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite.
Claim 65 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite.
Claims 51-72 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Objections
Claim 68 is objected to because of the following informalities:
Claim 68, line 7: change the two commas “,,” to one comma “,”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
I. Claims 51- 71 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claim 51 recites “events” to be predicted, each event of the one or more events is associated with one or more “input events.” The claim language regarding the events and the input events is not clear because an event term is usually used to describe a physical phenomenon happening to the physical system. The input event in claim 1 represents data as time series because they are sent to the model. Thus, such data cannot be a phenomenon specified as an event, but only comprise patterns representing (i.e., originated by..) such a physical phenomenon in the time series. It is not clear how or an event to be predicted is associated with an input event. It is further unclear what an event associated with an input event is or is not. It is further unclear what an input event is or is not. For example, is an input event any input at all? Or is an input event a time-series data point capture or recording or state? It is yet further unclear if the event and associated input event are referring to the same event or are referring to different events. For example, is a system failure/anomaly event that is input into the model considered both the event and the input event? Or are system events completely different from an associated input event? Applicant may cancel claim 51 or amend claim 51 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 52-67 depend directly or indirectly from claim 51 and are rejected for the same reasons discussed above with respect to their parent claim 51.
Claim 68 recites a method that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 68 is rejected for the same reasons discussed above with respect to claim 51. Claims 69-70 depend directly from claim 68 and are rejected for the same reasons discussed above with respect to parent claim 68.
Claim 71 recites a non-transitory computer readable storage medium that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 71 is rejected for the same reasons discussed above with respect to claim 51. Claim 72 depends directly from claim 71 and is rejected for the same reasons discussed above with respect to parent claim 71.
II. Claims 51- 71 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claim 51 recites indications of occurrence of the one or more input events generated using the anomaly detection model. Claim 51 does not clearly specify the type of the indications of occurrence of the one or more input events. It is not clear what type of indications the indications of occurrence are or are not or what type of indications of occurrence include or do not include. The specification provides guidance as to what the indications of occurrence are or are not or include or do not include. The specification only discloses the indications of occurrence as Boolean values. Applicant may cancel claim 51 or amend claim 51 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 52-67 depend directly or indirectly from claim 51 and are rejected for the same reasons discussed above with respect to their parent claim 51.
Claim 68 recites a method that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 68 is rejected for the same reasons discussed above with respect to claim 51. Claims 69-70 depend directly from claim 68 and are rejected for the same reasons discussed above with respect to parent claim 68.
Claim 71 recites a non-transitory computer readable storage medium that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 71 is rejected for the same reasons discussed above with respect to claim 51. Claim 72 depends directly from claim 71 and is rejected for the same reasons discussed above with respect to parent claim 71.
III. Claims 51- 71 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claim 51 recites to generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data. It is not clear how to generate labels for unlabeled data using quantitative indications of events. It is not clear how to label which unlabeled data with which quantitative indications of events. For example, is there a mapping or matching process that is used to label certain unlabeled data points with certain quantitative indications of events to be predicted? Or, is a time matching or mapping process used to label unlabeled data with quantitative indications of events to be predicted? Or, is a time range or time interval used to label unlabeled data with quantitative indications of events to be predicted? Or, is a particular sensor or input source mapped or matched to quantitative indications of events to be predicted? Or is there an entirely different process used to somehow label unlabelled data with quantitative indications of events to be predicted. Applicant may cancel claim 51 or amend claim 51 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 52-67 depend directly or indirectly from claim 51 and are rejected for the same reasons discussed above with respect to their parent claim 51.
Claim 68 recites a method that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 68 is rejected for the same reasons discussed above with respect to claim 51. Claims 69-70 depend directly from claim 68 and are rejected for the same reasons discussed above with respect to parent claim 68.
Claim 71 recites a non-transitory computer readable storage medium that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 71 is rejected for the same reasons discussed above with respect to claim 51. Claim 72 depends directly from claim 71 and is rejected for the same reasons discussed above with respect to parent claim 71.
IV. Claims 51- 71 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claim 51 recites A computerized system “configured to perform” training of machine learning models “to enable” prediction of occurrence of one or more events “to be” predicted, the one or more events “to be” predicted being associated with a system “to be” analyzed, the computerized system comprising: a processing circuitry configured to perform the following: a. “provide” one or more trained Machine Learning Anomaly Detection Models; b. “provide” one or more Engineering Analysis Tools, “configured to provide” quantitative indications of the one or more events “to be” predicted, wherein each event “to be” predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events “to be” predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are “configured to utilize” at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems; c. receive first unlabelled data … d. input the first unlabelled data to the one or more trained Machine Learning Anomaly Detection Models; e. generate, using the one or more trained Machine Learning Anomaly Detection Models, the indications of occurrence of the one or more input events, based on the first unlabelled data; f. input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools; g. generate, using the one or more Engineering Analysis Tools, the quantitative indications of the one or more events “to be” predicted, based at least on the indications of the occurrence of the one or more input events; and h. generate, using the quantitative indications of the one or more events “to be” predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data, whereby the first labeled data “is usable to enable” training one or more Machine Learning Event Prediction Models associated with the system “to be” analyzed, wherein the one or more trained Machine Learning Event Prediction Models are “configured to” predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events “to be” predicted, wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabeled data comprises at least additional condition parameters data. Thus, claim 51 recites numerous instances of terms “to be”, “configured to”, “useable to enable” and such that it does not clearly specify whether certain actions are actually performed or are just to be performed or just configured to be performed or are simply enabled or useable to be performed. Claim 51 recites subject matter filled with terms that suggest performing an act, but do not actually appear to require performing the acts. For example, it is not clear if claim 51 actually predicts an event, actually analyzes a system, actually trains a model, or provides tools that actually provide quantitative indications of events. The examiner suggests amending the claims to clearly recite performing the specified actions. Applicant may cancel claim 51 or amend claim 51 to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 52-67 depend directly or indirectly from claim 51 and are rejected for the same reasons discussed above with respect to their parent claim 51.
Claim 68 recites a method that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 68 is rejected for the same reasons discussed above with respect to claim 51. Claims 69-70 depend directly from claim 68 and are rejected for the same reasons discussed above with respect to parent claim 68.
Claim 71 recites a non-transitory computer readable storage medium that parallels the system of claim 51, with the same limitations and subject matter recitations of events” to be predicted, each event of the one or more events is associated with one or more “input events.” Therefore, claim 71 is rejected for the same reasons discussed above with respect to claim 51. Claim 72 depends directly from claim 71 and is rejected for the same reasons discussed above with respect to parent claim 71.
V. Claim 63 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 63 depends from claim 51 and recites the limitation "the second unlabeled data.” There is insufficient antecedent basis for this limitation in the claim.
VI. Claim 65 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 65 depends from claim 51 and recites the limitation (ii) the performance of “the repetition” is after at least one of a defined time interval, a system repair, a system overhaul and a system failure. There is insufficient antecedent basis for this limitation “the repetition” in the claim.
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 51-72 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) subject matter at a general, high-level to provide one or more Engineering Analysis Tools, configured to provide quantitative indications of the one or more events to be predicted, wherein each event to be predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events to be predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are configured to utilize at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems (mental processes, human with pen and paper, also mathematical concepts); generate, indications of occurrence of the one or more input events, based on the first unlabelled data (mental processes); input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools (mental processes, human with pen and paper, also mathematical concepts); generate, the quantitative indications of the one or more events to be predicted, based at least on the indications of the occurrence of the one or more input events (mental processes, human with pen and paper, also mathematical concepts); generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data (mental processes), predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events to be predicted (mental processes), wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabeled data comprises at least additional condition parameters data (mental processes), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 51-72 recite one of the four statutory categories of patent able subject matter and belong to the statutory class(es) of a process (method claims 68-70), a machine (system/apparatus claims 51-67), and an article of manufacture (non-transitory computer readable media claims 71-72).
Claim 51 recites a system, thus a machine and one of the four statutory categories of patentable subject matter. However, claim 51 further recites to b provide one or more Engineering Analysis Tools, configured to provide quantitative indications of the one or more events to be predicted, wherein each event to be predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events to be predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are configured to utilize at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems (mental processes, human with pen and paper, also mathematical concepts); e. generate, indications of occurrence of the one or more input events, based on the first unlabelled data (mental processes); f. input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools (mental processes, human with pen and paper, also mathematical concepts); g. generate, using the one or more Engineering Analysis Tools, the quantitative indications of the one or more events to be predicted, based at least on the indications of the occurrence of the one or more input events (mental processes, human with pen and paper, also mathematical concepts); h. generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data (mental processes), predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events to be predicted (mental processes), wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabeled data comprises at least additional condition parameters data (mental processes), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
A computerized system configured to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
perform training of machine learning models to enable prediction of occurrence of one or more events to be predicted, the one or more events to be predicted being associated with a system to be analyzed, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
the computerized system comprising: a processing circuitry configured to perform the following: (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
a. provide one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
c. receive first unlabelled data associated with the system to be analyzed, wherein the first unlabelled data comprises at least condition parameters data, the condition parameters data comprising at least one of: sensor data associated with one or more sensors; recorded data; state data; (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
d. input the first unlabelled data to the one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
using the one or more trained Machine Learning Anomaly Detection Models, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
whereby the first labeled data is usable to enable training one or more Machine Learning Event Prediction Models associated with the system to be analyzed, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
wherein the one or more trained Machine Learning Event Prediction Models are configured to (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more, and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d), and generally linking the use of the judicial exception to a particular technological field of use does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 52, dependent on claim 51, recites only additional abstract ideas for wherein at least one of the following is true: (I) the indications of the occurrence of the one or more input events comprise Boolean values (mental processes, human with pen and paper, also mathematical concepts); (II) the indications of the occurrence of the one or more input events are associated with indications of anomalies in the first unlabeled data (mental processes, human with pen and paper, also mathematical concepts); (III) the first unlabelled data is associated with a timestamp, and the probabilities of occurrence of the one or more events to be predicted are associated with the timestamp; (IV) a single indication of occurrence of the one or more input events is associated with a plurality of timestamps, wherein a single quantitative indication of the one or more events to be predicted is associated with the plurality of timestamps; or (V) the training of the one or more Machine Learning Anomaly Detection Models comprises unsupervised training, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Examiner notes that, while the claim 52 only requires one of options I-V to be true and options I and II are abstract ideas as discussed above, option (V) the training of the one or more Machine Learning Anomaly Detection Models comprises unsupervised training is an additional element that amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 53, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein each input event of the one or more input events is associated with a trained Machine Learning Anomaly Detection Model of the one or more trained Machine Learning Anomaly Detection Models (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 54, dependent on claim 51, recites only additional abstract ideas of mental processes, including human with pen and paper and mathematical concepts for
wherein at least one of the following is true: (i) the first unlabeled data comprises condition parameters data, associated with at least one of characteristics of the system to be analyzed and characteristics of system operation (mental processes, human with pen and paper, also mathematical concepts); or (ii) the first unlabeled data comprises condition parameters data, associated with at least one of characteristics of the system to be analyzed and characteristics of system operation, wherein the condition parameters data comprises data deriving from within the system to be analyzed and data deriving from without the system, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 55, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein the one or more trained Machine Learning Anomaly Detection Models are configured such that an indication of the occurrence of each input event of the one or more input events is based on sensor data associated with a sub-set of the one or more sensors (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 56, dependent on claim 55, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein the configuration of the one or more trained Machine Learning Anomaly Detection Models is based on the one or more Engineering Analysis Tools (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 57, dependent on claim 51, recites only additional abstract ideas of mental processes, including human with pen and paper and mathematical concepts for
wherein the one or more Engineering Analysis Tools comprise default first probabilities of occurrence of the one or more input events, wherein said step (g) is further based at least on the on the default first probabilities of occurrence of the one or more input events, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 58, dependent on claim 57, recites only additional abstract ideas of mental processes, including human with pen and paper and mathematical concepts for
wherein the default first probabilities of occurrence of the one or more input events are input into the one or more Engineering Analysis Tools, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 59, dependent on claim 57, recites only additional abstract ideas of mental processes, including human with pen and paper and mathematical concepts for
wherein the step (e) further comprises generating, based on the indications of occurrence of the one or more input events and the first unlabeled data, data-based factors corresponding respectively with the indications of occurrence of the one or more input events, wherein the step (g) comprises modifying the default first probabilities of occurrence of the one or more input events, based on corresponding data-based factors, thereby deriving updated first probabilities of occurrence of the one or more input events, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 60, dependent on claim 51, recites only additional abstract ideas of mental processes, including human with pen and paper and mathematical concepts for
wherein at least one of the following is true: (1) the one or more Engineering Analysis Tools are further configured to provide qualitative indications of the one or more events to be predicted (mental processes, human with pen and paper, also mathematical concepts); (2) the one or more Engineering Analysis Tools are further configured to provide qualitative indications of the one or more events to be predicted, wherein the qualitative indications of the one or more events to be predicted comprise indications of occurrence of the one or more events to be predicted, wherein the step (g) comprises:(i) generating, using the one or more Engineering Analysis Tools, the indications of occurrence of the one or more events to be predicted; (ii) inputting the indications of occurrence of the one or more events to be predicted into the one or more Engineering Analysis Tools; and (iii) performing the generating of the quantitative indications of the one or more events to be predicted in respect of events to be predicted that are associated with positive indications of occurrence of the one or more events to be predicted (mental processes, human with pen and paper, also mathematical concepts); or (3) the one or more Engineering Analysis Tools are further configured to provide qualitative indications of the one or more events to be predicted, wherein the qualitative indications of the one or more events to be predicted comprise indications of occurrence of the one or more events to be predicted, wherein the step (g) comprises: (i) generating, using the one or more Engineering Analysis Tools, the indications of occurrence of the one or more events to be predicted; (ii) inputting the indications of occurrence of the one or more events to be predicted into the one or more Engineering Analysis Tools; and (iii) performing the generating of the quantitative indications of the one or more events to be predicted in respect of events to be predicted that are associated with positive indications of occurrence of the one or more events to be predicted, wherein the indications of occurrence of the one or more events to be predicted comprise Boolean values (mental processes, human with pen and paper, also mathematical concepts), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 61, dependent on claim 51, recites only additional abstract ideas of mental processes, including human with pen and paper for wherein each predicted third probability of the predicted third probabilities is associated with a given time of the occurrence, which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
Claim 62, dependent on claim 51, recites additional abstract ideas of mental processes, including human with pen and paper, and mathematical concepts for wherein at least one of the following is true: (A) the one or more Machine Learning Event Prediction Models comprises one or more Machine Learning Failure Prediction Models, wherein the one or more trained Machine Learning Event Prediction Models comprises one or more trained Machine Learning Failure Prediction Models; (B) the one or more Machine Learning Anomaly Detection Models comprises at least one of a One Class Classification Support Vector Machine (OCC SVM), a Local Outlier Factor (LOF), and a One Class Classification Random Forest (OCC RF); (C) the one or more Machine Learning Event Prediction Models comprises at least one of a Bayesian network and a Deep Neural Network; (D) the one or more events to be predicted comprise one or more failures (mental processes, including human with pen and paper); (E) the one or more events to be predicted are based on logic combinations of input events (mental processes, human with pen and paper, also mathematical concepts); (F) the one or more events to be predicted comprise one or more Top Events (mental processes, human with pen and paper, also mathematical concepts); or (G) the one or more input events comprise one or more Basic Events (mental processes, human with pen and paper, also mathematical concepts), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Examiner notes that, while the claim 62 only requires one of options A-G to be true and options D-G are abstract ideas as discussed above, options A-C also do not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of: (A) the one or more Machine Learning Event Prediction Models comprises one or more Machine Learning Failure Prediction Models, wherein the one or more trained Machine Learning Event Prediction Models comprises one or more trained Machine Learning Failure Prediction Models (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)); (B) the one or more Machine Learning Anomaly Detection Models comprises at least one of a One Class Classification Support Vector Machine (OCC SVM), a Local Outlier Factor (LOF), and a One Class Classification Random Forest (OCC RF) (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)); (C) the one or more Machine Learning Event Prediction Models comprises at least one of a Bayesian network and a Deep Neural Network (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 63, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein at least one of the following is true: (I) the step (a) comprises: training one or more Machine Learning Anomaly Detection Models, utilizing second unlabeled data, wherein the second unlabeled data comprises at least further condition parameters data, thereby generating the one or more trained Machine Learning Anomaly Detection Models (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)); or (II) the first unlabeled data, the second unlabeled data and the third unlabeled data are distinct portions of a single data set (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
Claim 64, dependent on claim 51, recites additional abstract ideas of mathematical concepts for wherein at least one of the following is true: (i) the one or more Engineering Analysis Tools comprise Event Tree Analysis (mathematical concepts); (ii) the one or more Engineering Analysis Tools comprise one or more Reliability, Availability, Maintainability and Safety (RAMS) Analysis Tools (mathematical concepts); or (iii) the one or more Engineering Analysis Tools comprise Failure Tree Analysis (mathematical concepts), which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 65, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein at least one of the following is true: (i) the processing circuitry further configured to perform a repetition of steps (a) to (h), or (ii) the performance of the repetition is after at least one of a defined time interval, a system repair, a system overhaul and a system failure (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 66, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
Claim 67, dependent on claim 51, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein the system to be analyzed is one of an aircraft system and a spacecraft system (This additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 68 recites a method, thus a process and one of the four statutory categories of patentable subject matter. However, claim 68 further recites to b. provide one or more Engineering Analysis Tools, configured to provide quantitative indications of the one or more events to be predicted,, wherein each event to be predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events to be predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are configured to utilize at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems (mental processes, human with pen and paper, also mathematical concepts); e. generate, the indications of occurrence of the one or more input events, based on the first unlabeled data (mental processes); f. input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools (mental processes, human with pen and paper, also mathematical concepts); g. generate, using the one or more Engineering Analysis Tools, quantitative indications of the one or more events to be predicted, based at least on the indications of the occurrence of the one or more input events (mental processes, human with pen and paper, also mathematical concepts); and h. generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabeled data, thereby deriving first labeled data from the first unlabeled data (mental processes), predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events to be predicted (mental processes), wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabeled data comprises at least additional condition parameters data (mental processes), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
training machine learning models to enable prediction of occurrence of one or more events to be predicted, the one or more events to be predicted being associated with a system to be analyzed, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
using a processing circuitry to perform the following: (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
a. provide one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
c. receive first unlabeled data associated with the system to be analyzed, wherein the first unlabeled data comprises at least condition parameters data, the condition parameters data comprising at least one of: sensor data associated with one or more sensors; recorded data; state data; (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
d. input the first unlabeled data to the one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
using the one or more trained Machine Learning Anomaly Detection Models, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
whereby the first labeled data is usable to enable training one or more Machine Learning Event Prediction Models associated with the system, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
wherein the one or more trained Machine Learning Event Prediction Models are configured to (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more, and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d), and generally linking the use of the judicial exception to a particular technological field of use does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 69, dependent on claim 68, does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
wherein the step (a) comprises: training one or more Machine Learning Anomaly Detection Models, utilizing second unlabeled data, thereby generating the one or more trained Machine Learning Anomaly Detection Models (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Claim 70, dependent on claim 68, recites additional abstract ideas of mathematical concepts for wherein at least one of the following is true: (i) the one or more Engineering Analysis Tools comprise Event Tree Analysis (mathematical concepts); (ii) the one or more Engineering Analysis Tools comprises one or more Reliability, Availability, Maintainability and Safety (RAMS) Analysis Tools (mathematical concepts); or (iii) the one or more Engineering Analysis Tools comprise Failure Tree Analysis (mathematical concepts), which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 71 recites A non-transitory computer readable storage medium, thus an article of manufacture and one of the four statutory categories of patentable subject matter. However, claim 70 further recites to b. provide one or more Engineering Analysis Tools, configured to provide quantitative indications of the one or more events to be predicted, wherein each event to be predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events to be predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are configured to utilize at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems; (mental processes, human with pen and paper, also mathematical concepts); e. generate, the indications of occurrence of the one or more input events, based on the first unlabelled data (mental processes); f. input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools; (mental processes, human with pen and paper, also mathematical concepts); g. generate, using the one or more Engineering Analysis Tools, the quantitative indications of the one or more events to be predicted, based at least on the indications of the occurrence of the one or more input events (mental processes, human with pen and paper, also mathematical concepts); and h. generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data (mental processes), predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events to be predicted (mental processes), wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabelled data comprises at least additional condition parameters data (mental processes), which are mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III) and mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
A non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method of (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
training machine learning models to enable prediction of occurrence of one or more events to be predicted, the one or more events to be predicted being associated with a system to be analyzed, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
the method being performed by a processing circuitry and comprising performing the following: (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
a. provide one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
c. receive first unlabelled data associated with the system to be analyzed, wherein the first unlabelled data comprises at least condition parameters data, the condition parameters data comprising at least one of: sensor data associated with one or more sensors; recorded data; state data; (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
d. input the first unlabelled data to the one or more trained Machine Learning Anomaly Detection Models; (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
using the one or more trained Machine Learning Anomaly Detection Models, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
whereby the quantitative indications of the one or more events to be predicted are usable as a diagnostic tool for the first unlabelled data (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
whereby the first labeled data is usable to enable training one or more Machine Learning Event Prediction Models associated with the system to be analyzed, (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
wherein the one or more trained Machine Learning Event Prediction Models are configured to (This additional element amounts to merely the words to “apply it” (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f).) Also, this additional element amounts to no more than generally linking the use of the judicial exception to a particular technologic environment or field of use - The application or use of the judicial exception in this manner does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment. MPEP 2106.05(h)).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more, and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d), and generally linking the use of the judicial exception to a particular technological field of use does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 72, dependent on claim 71, recites additional abstract ideas of mathematical concepts for wherein at least one of the following is true: (i) the one or more Engineering Analysis Tools comprise one or more Reliability, Availability, Maintainability and Safety (RAMS) Analysis Tools (mathematical concepts); (ii) the one or more Engineering Analysis Tools comprise Event Tree Analysis (mathematical concepts); or (iii) the one or more Engineering Analysis Tools comprise Failure Tree Analysis (mathematical concepts), which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Allowable Subject Matter
Claims 51-72 would be allowable if the objection regarding informalities is overcome, the rejections under 35 U.S.C. 112(b) as being indefinite are overcome, and the rejections under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
The closest known prior arts of record are Ambichl et al. (US 2020/0042426), Fattu et al. (US 2020/0162342), Harutyunyan et al. (US 2020/0264965), Prasanna et al. (US 2022/0036199), Ben-Itzhak et al. (US 2022/0012626), Carullo et al. (US 2020/0272112), Gu (US 2019/0324831), Lu et al. (US 2020/0110181), Singh et al. (US 10,248,490), Zheng et al. (US 11,657,300), Chu et al. (US 2018/096261), Srinivasan et al. (US 2020/210854), Oliner et al. (US 2018/0219889), Ristovski et al. (US 2019/0235484), Zhang et al. (US 2019/0384257), Sartorius Stedim Data Analytics AB (WO 2020/049087), Huawei Technologies Co. LTD (WO2020/043267). The examiner notes that Singh et al., Chu et al., Srinivasan et al., Oliner et al., Ristovski et al., Zhang et al., Sartorius Stedim Data Analytics AB, Huawei Technologies Co. LTD are cited on applicants Information Disclosure Statements filed May 14, 2025 and June 6, 2023.
Ambichl et al. (US 2020/0042426) teaches A system and method is disclosed for the automated identification of causal relationships between a selected set of trigger events and observed abnormal conditions in a monitored computer system. On the detection of a trigger event, a focused, recursive search for recorded abnormalities in reported measurement data, topological changes or transaction load is started to identify operating conditions that explain the trigger event. The system also receives topology data from deployed agents which is used to create and maintain a topological model of the monitored system. The topological model is used to restrict the search for causal explanations of the trigger event to elements of that have a connection or interact with the element on which the trigger event occurred. This assures that only monitoring data of elements is considered that are potentially involved in the causal chain of events that led to the trigger event. Abstract.
Fattu et al. (US 2020/0162342) teaches An apparatus for cognitive data center management is disclosed. A computer-implemented method and computer program product also perform the functions of the apparatus. According to an embodiment of the present invention, the apparatus includes a performance module that determines performance metrics over a predetermined time interval at a device coordinate in a three-dimensional (“3D”) coordinate system for each replaceable device of a plurality of replaceable devices within a data center. The apparatus maps the performance metrics to environmental sensor measurements taken in the 3D coordinate system. The apparatus further includes an input analysis module that uses discovery analytics to determine a predicted time to failure for each replaceable device. The apparatus further includes a preventative action module that determines recommended actions to prevent failure of the replaceable devices and a tradeoff learning module that provides updated weighting factors based on changes to performance metrics in response to taking recommended actions. Abstract.
Harutyunyan et al. (US 2020/0264965) teaches Computational processes and systems are directed to detecting abnormally behaving objects of a distributed computing system. An object can be a physical or a virtual object, such as a server computer, application, VM, virtual network device, or container. Processes and systems identify a set of metrics associated with an object and compute an indicator metric from the set of metrics. The indicator metric is used to label time stamps that correspond to outlier metric values of the set of metrics. The metrics and outlier time stamps are used to compute rules by machine learning. Each rule corresponds to a subset or combination of metrics and represents specific threshold conditions for metric values. The rules are applied to run-time metric data of the metrics to detect run-time abnormal behavior of the object. Abstract.
Prasanna et al. (US 2022/0036199) teaches An exemplary system and method are disclosed for identifying anomalies relating to distribution power line disturbances and faults indicative of foliage impingement and potential equipment failure. The exemplary system and method employ neural network-based models such as generative adversarial networks models that can continuously monitor for electrical-signal anomalies to locate faults, predict power outages and safety hazards, thereby reducing the likelihood of wildfires. The exemplary system and method can beneficially learn and update its neural network models in a continuous and unsupervised manner using a live stream of sensor inputs. Abstract.
Ben-Itzhak et al. (US 2022/0012626) teaches Techniques for implementing unsupervised anomaly detection by self-prediction are provided. In one set of embodiments, a computer system can receive an unlabeled training data set comprising a plurality of unlabeled data instances, where each unlabeled data instance includes values for a plurality of features. The computer system can further train, for each feature in the plurality of features, a supervised machine learning (ML) model using a labeled training data set derived from the unlabeled training data set, receive a query data instance, and generate a self-prediction vector using at least a portion of the trained supervised ML models and the query data instance, where the self-prediction vector indicates what the query data instance should look like if it were normal. The computer system can then generate an anomaly score for the query data instance based on the self-prediction vector and the query data instance. Abstract.
Carullo et al. (US 2020/0272112) teaches Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device. Abstract.
Gu (US 2019/0324831) teaches An unsupervised pattern extraction system and method for extracting user interested patterns from various kinds of data such as system-level metric values, system call traces, and semi-structured or free form text log data and performing holistic root cause analysis for distributed systems. The distributed system includes a plurality of computer machines or smart devices. The system consists of both real time data collection and analytics functions. The analytics functions automatically extract event patterns and recognize recurrent events in real time by analyzing collected data streams from different sources. A root cause analysis component analyzes the extracted events and identifies both correlation and causality relationships among different components to pinpoint root cause of a networked-system anomaly. Furthermore, an anomaly impact prediction component estimates the impact scope of the detected anomaly and raises early alarms about impending service outages or application performance degradations based on the identified correlation and causality relationships. Abstract.
Lu et al. (US 2020/0110181) teaches An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data. Abstract.
Singh et al. (US 10,248,490) teaches that Systems and methods for predictive reliability mining are provided that enable predicting of unexpected emerging failures in future without waiting for actual failures to start occurring in significant numbers. Sets of discriminative Diagnostic Trouble Codes (DTCs) from connected machines in a population are identified before failure of the associated parts. A temporal conditional dependence model based on the temporal dependence between the failure of the parts from past failure data and the identified sets of discriminative DTCs is generated. Future failures are predicted based on the generated temporal conditional dependence and root cause analysis of the predicted future failures is performed for predictive reliability mining. The probability of failure is computed based on both occurrence and non-occurrence of DTCs. The root cause analysis enables identifying a subset of the population when an early warning is generated and also when an early warning is not generated. Abstract.
Zheng et al. (US 11,657,300) teaches A method for predicting a time-to-failure of a target storage device may include training a machine learning scheme with a time-series dataset, and applying the telemetry data from the target storage device to the machine learning scheme which may output a time-window based time-to-failure prediction. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include applying a data quality improvement framework to a time-series dataset of operational and failure data from multiple storage devices, and training the scheme with the pre-processed dataset. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include training the scheme with a first portion of a time-series dataset of operational and failure data from multiple storage devices, testing the machine learning scheme with a second portion of the time-series dataset, and evaluating the machine learning scheme. Abstract.
While the prior arts of record disclose trained machine learning anomaly detection models, engineering analysis tools, quantitative indications of events, Boolean values, input events, indications of occurrence of input events, sensor data, unlabeled raw data, labeled data, events to be predicted, machine learning even prediction models, analyzed systems, analyzed aircraft systems, condition parameters, prediction probabilities, predicted time of occurrence of event, the prior arts of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious, all of the following limitations of independent claims 51, 68 and 70, when considered as a whole, of a computerized system configured to perform training of machine learning models to enable prediction of occurrence of one or more events to be predicted, the one or more events to be predicted being associated with a system to be analyzed, the computerized system comprising: a processing circuitry configured to perform the following: a. provide one or more trained Machine Learning Anomaly Detection Models; b. provide one or more Engineering Analysis Tools, configured to provide quantitative indications of the one or more events to be predicted, wherein each event to be predicted of the one or more events is associated with one or more input events, the one or more input events comprising at least one of a physical event and occurrence of a physical phenomenon, wherein the quantitative indications of the one or more events to be predicted are based on indications of occurrence of the one or more input events, wherein the one or more Engineering Analysis Tools are configured to utilize at least engineering knowledge of one or more of: system behavior; system components, architecture and function; relations between system components and sub-systems; events associated with components, sub-systems, and/or systems; c. receive first unlabelled data associated with the system to be analyzed, wherein the first unlabelled data comprises at least condition parameters data, the condition parameters data comprising at least one of: sensor data associated with one or more sensors; recorded data; state data; d. input the first unlabelled data to the one or more trained Machine Learning Anomaly Detection Models; e. generate, using the one or more trained Machine Learning Anomaly Detection Models, the indications of occurrence of the one or more input events, based on the first unlabelled data; f. input the indications of the occurrence of the one or more input events into the one or more Engineering Analysis Tools; g. generate, using the one or more Engineering Analysis Tools, the quantitative indications of the one or more events to be predicted, based at least on the indications of the occurrence of the one or more input events; and h. generate, using the quantitative indications of the one or more events to be predicted, labels for the first unlabelled data, thereby deriving first labelled data from the first unlabelled data, whereby the first labeled data is usable to enable training one or more Machine Learning Event Prediction Models associated with the system to be analyzed, wherein the one or more trained Machine Learning Event Prediction Models are configured to predict, based on third unlabeled data, predicted third probabilities of occurrence of the one or more events to be predicted, wherein each predicted third probability of the third probabilities is associated with a predicted time of the occurrence of the event, wherein the third unlabeled data comprises at least additional condition parameters data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20220012625-A1, US-20210034994-A1, US-20200272947-A1, US-20050281456-A1, US-20250086045-A1, US-20250067252-A1, US-11042145-B2.
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/BARBARA M LEVEL/ Examiner, Art Unit 2142
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