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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Preliminary Amendment
Preliminary Amendment filed on 02/28/2023 noted by the examiner, claims 1-19 and 22 are pending.
Claim Rejections - 35 USC § 112
2. 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.
Claim 1-19 and 22 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.
Regarding claims 1-19 and 22, the terms “anomalies” “untrained” “trained” are vague and a relative term that renders the claim indefinite. The terms “anomalies” “untrained” “trained” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “anomalies” “untrained” “trained” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “anomalies” “untrained” “trained” occur.
Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood.
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 1-19 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a method of monitoring a milling machine, the method comprising comprising: deploying an untrained machine learning model for determining one or more anomalies in time series data which is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtaining, by the untrained machine learning model, during operation of the milling machine, first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine; training the untrained machine learning model, during operation of the milling machine, based on the obtained first time series data are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of obtaining, by the trained machine learning model, during operation of the milling machine, second time series data the rotational speed of the milling head of the milling machine and the further operating parameter; and determining, by the trained machine learning model, during operation of the milling machine, one or more anomalies in the second time series data appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible.
Claim 22 Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related deploy an untrained machine learning model for determination of one or more anomalies in time series data which is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of obtain, by the untrained machine learning model, during operation of the milling machine, first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine; train the untrained machine learning model, during operation of the milling machine, based on the obtained first time series data are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of obtain, by the trained machine learning model, during operation of the milling machine, second time series data representing the rotational speed of the milling head of the milling machine and the further operating parameter; and determine, by the trained machine learning model, during operation of the milling machine, one or more anomalies in the second time series data, a processor; and memory
wherein the processor is configured to monitor a milling machine, the processor being configured to monitor the milling machine comprising the processor being configured to appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 22 not eligible.
Claim 2, determining a ramp up phase ramp down phase, or the ramp up phase and the ramp down phase of the milling head, the ramp up phase the ramp down phase, or the ramp up phase and the ramp down phase comprising one or more data points in the first time series data below a first threshold of the rotational speed of the milling head removing the determined data points of the ramp up phase ramp down phase, or the ramp up phase and the ramp down phase, as well as corresponding data points of the further operating parameter from the first time series data and ]training the untrained machine learning model based on the remaining data points in the first time series data.
Claim 3, removing data points in the first time series data between a ramp down phase and a consecutive ramp up phase of the milling head.
Claim 4, wherein training the untrained machine learning model based on the first time series data comprises: predicting based on a first subset of the first time series data one or more data points.
Claim 5, wherein training the untrained machine learning model based on the first time series data comprises: determining a first deviation between the one or more data points predicted and the one or more data points in a second subset of the first time series data.
Claim 6, determining a first threshold based on the first deviation, wherein the first threshold serves for comparing the data points of the second time series data to the first threshold
Claim 7, determining a probability distribution of the first deviation between the one or more data points predicted and the one or more data points in the second subset of the first time series data.
Claim 8, removing data points in the second time series data corresponding to a ramp up ramp down, or the ramp up and the ramp down of the milling head removing data points in the second time series data between a ramp down and a consecutive ramp up of the milling head; or a combination thereof.
Claim 9, predicting one or more data points based on the first time series data; determining a second deviation between the data points predicted and the data points in the second time series data.
Claim 10, comparing the second deviation with a measure of dispersion of the probability distribution.
Claim 11, dividing the second time series data into multiple subsets and determining, for each subset of the multiple subsets, a third deviation between the data points predicted and the data points in the respective subset and comparing the respective third deviation of the subsets with the measure of dispersion of the probability distribution; and updating the probability distribution based on the comparison.
Claim 12, identifying an anomaly in case the second deviation exceeds the measure of dispersion.
Claim 13, training a support vector machine (SVM) based on one or more data points of the first subset of the first time series determining a first deviation between one or more hyperplanes of the SVM and the data points in the second subset of the first time series data.
Claim 14, determining a second threshold based on the first deviation and comparing the data points of the second time series data to the second threshold.
Claim 15, determining a probability distribution of the first deviations between the one or more hyperplanes and the data points in the second subset of the first time series data
Claim 16, determining a second deviation between the one or more hyperplanes of the SVM and the data points in the second time series data
Claim 17, comparing the second deviation with a measure of dispersion of the probability distribution.
Claim 18, dividing the second time series data into multiple sub-sets and determining, for each subset of the multiple subsets, a third deviation between the data points predicted and the data points in the subsets comparing the respective third deviation of the subsets with the measure of dispersion of the probability distribution, and updating the probability distribution based on the comparison.
Claim 19, identifying an anomaly in case the second deviation exceeds the measure of dispersion.
The above dependent claims merely recite further data characterization and mathematical concepts that are part of the abstract idea, claims 2-19 not eligible as well
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-19 and 22 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Kummar et al. (US Patent Application Publication 20190152011, Pub, Date: May 23, 2019)
Regarding claim 1:
Kummar described a method of monitoring a milling machine, the method comprising: deploying an untrained machine learning model for determining one or more anomalies in time series data (0004-0005 real-time, before learning start: ie. untrained, real-time anomaly detection); obtaining, by the untrained machine learning model, during operation of the milling machine, first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool spindle speed) training the untrained machine learning model, during operation of the milling machine, based on the obtained first time series data (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool) obtaining, by the trained machine learning model (0004, learning software program), during operation of the milling machine (0006, operating characteristic), second time series data the rotational speed of the milling head of the milling machine and the further operating parameter (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool); and determining, by the trained machine learning model, during operation of the milling machine, one or more anomalies in the second time series data (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool spindle speed).
Regarding claim 22:
Kummar described an apparatus, comprising- a processor; and memory (fig. 2, 210, host server) wherein the processor is configured to monitor a milling machine, the processor being configured to monitor the milling machine comprising the processor being configured to (abstract, health of milling machine): deploy an untrained machine learning model for determination of one or more anomalies in time series data (0004-0005, real-time anomaly detection); obtain, by the untrained machine learning model, during operation of the milling machine (0004-0005 real-time, before learning start: ie. untrained, real-time anomaly detection), first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool spindle speed); train the untrained machine learning model, during operation of the milling machine, based on the obtained first time series data (0004-0005 real-time, before learning start: ie. untrained, real-time anomaly detection);
obtain, by the trained machine learning model, during operation of the milling machine, second time series data representing the rotational speed of the milling head of the milling machine and the further operating parameter (0004, 0024, 0025 real-time during operation of milling machine rotation of the tool); and
determine, by the trained machine learning model, during operation of the milling machine, one or more anomalies in the second time series data 0004, 0024, 0025 real-time during operation of milling machine rotation of the tool), predicting, one or more data points based on, determining a second deviation between the data points predicted and the data points in the second time series data (0004, 0005, 0024, 0025 real-time during operation of milling machine rotation of the tool spindle speed, real-time anomaly detection).
Regarding claim 2, Kummar further described determining a ramp up phase ramp down phase (0020, in real-time), or the ramp up phase and the ramp down phase of the milling head, the ramp up phase the ramp down phase, or the ramp up phase and the ramp down phase comprising one or more data points in the first time series data below a first threshold of the rotational speed of the milling head removing the determined data points of the ramp up phase ramp down phase (0004, 0005, 0024, 0025 real-time during operation of milling machine rotation of the tool spindle speed, real-time anomaly detection), or the ramp up phase and the ramp down phase, as well as corresponding data points of the further operating parameter from the first time series data and ]training the untrained machine learning model based on the remaining data points in the first time series data.
Regarding claim 3, Kummar further described removing data points in the first time series data between a ramp down phase and a consecutive ramp up phase of the milling head (0004-0005, 0027, real-time exchange data).
Regarding claim 4, Kummar further described wherein training the untrained machine learning model based on the first time series data comprises: predicting based on a first subset of the first time series data one or more data points (0031, predicting function over time).
Regarding claim 5, Kummar further described wherein training the untrained machine learning model based on the first time series data comprises: determining a first deviation between the one or more data points predicted and the one or more data points in a second subset of the first time series data (fig. 3, over time).
Regarding claim 6, Kummar further described determining a first threshold based on the first deviation, wherein the first threshold serves for comparing the data points of the second time series data to the first threshold (0042, threshold comparison new data?)
Regarding claim 7, Kummar further described determining a probability distribution of the first deviation between the one or more data points predicted and the one or more data points in the second subset of the first time series data (0037, statistics deviation).
Regarding claim 8, Kummar further described removing data points in the second time series data corresponding to a ramp up ramp down (0004-0005, any exchange real-time data), or the ramp up and the ramp down of the milling head removing data points in the second time series data between a ramp down and a consecutive ramp up of the milling head; or a combination thereof.
Regarding claim 9, Kummar further described predicting one or more data points based on the first time series data; determining a second deviation between the data points predicted and the data points in the second time series data (0004-0005, predicts when a cutting tool failure in real-time).
Regarding claim 10, Kummar further described comparing the second deviation with a measure of dispersion of the probability distribution (0037, statistics deviation).
Regarding claim 11, Kummar further described dividing the second time series data into multiple subsets and determining, for each subset of the multiple subsets, a third deviation between the data points predicted and the data points in the respective subset and comparing the respective third deviation of the subsets with the measure of dispersion of the probability distribution; and updating the probability distribution based on the comparison (0037-0039, Data may be divided in to train, statistics deviation, 0049, compare to pattern).
Regarding claim 12, Kummar further described identifying an anomaly in case the second deviation exceeds the measure of dispersion (0045, fig. 6, anomalies may be detected from real-time high frequency sensor signals, deviation from mean).
Regarding claim 13, Kummar further described training a support vector machine (SVM) based on one or more data points of the first subset of the first time series determining a first deviation between one or more hyperplanes of the SVM and the data points in the second subset of the first time series data (0004-0005, 0039, any vector machine in real-time).
Regarding claim 14, Kummar further described determining a second threshold based on the first deviation and comparing the data points of the second time series data to the second threshold (0042, specified threshold indicating that model should be retained on the new data).
Regarding claim 15, Kummar further described determining a probability distribution of the first deviations between the one or more hyperplanes and the data points in the second subset of the first time series data (0039, 0042, accuracy model change?)
Regarding claim 16, Kummar further described determining a second deviation between the one or more hyperplanes of the SVM and the data points in the second time series data (0004-0005, 0039, any vector machine in real-time).
Regarding claim 17, Kummar further described comparing the second deviation with a measure of dispersion of the probability distribution (0037, statistics deviation).
Regarding claim 18, Kummar further described dividing the second time series data into multiple sub-sets and determining, for each subset of the multiple subsets (0056, combination of subset data), a third deviation between the data points predicted and the data points in the subsets comparing the respective third deviation of the subsets with the measure of dispersion of the probability distribution (0037, statistics deviation), and updating the probability distribution based on the comparison (0004-0005, 0045, update in real-time).
Regarding claim 19, Kummar further described , identifying an anomaly in case the second deviation exceeds the measure of dispersion (0049, anomalies may be detected from real-time, 0042, beyond threshold).
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
November 30, 2025
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