DETAILED ACTION
Non-Final Rejection
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 .
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 9-14 are rejected under 35 USC § 101 because they are directed to non-statutory subject matter.
The descriptions or expressions of the computer program product (element ) are not physical “things”. They are neither computer components non statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer, which permit the computer program’s functionality to be realized. In contrast, a claimed a non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. Accordingly, it is important to distinguish claims that define descriptive material per se from claims that define statutory inventions.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims 1-20 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1-8 fall within category process. For example, each of claims 15-20 fall within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)) and For example, each of claims 9-14 fall within category of non statutory category.
Regarding Claims 1-9
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of recommending a machine learning model.
The abstract idea is set forth or described by the following italicized limitations:
1. A computer-implemented method comprising:
training, by one or more processors, a first set of machine learning models on a first combination of a first set of data features;
measuring, by the one or more processors, a sub-set of a set of training samples to provide a second set of data features;
combining, by the one or more processors, the first set of data features and the second set of data features to obtain a third set of data features, wherein the third set of data features is a preferred set of data features; and
recommending, by the one or more processors, for use by a multi-sensor system, a first machine learning model employing the preferred set of data features..
The italicized limitations above represent a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) . Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “training[..]; measuring[..]; combining[..]; and recommending[..]” are a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea), and/or mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
The only additional element is “by the one or more processors; a multi-sensor system”. This element amounts to mere use of a generic sensor system with computer component, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic computer components with computer software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea..
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For examples, “processors; multi-sensor system” , which are well understood, routine and convention (see background of current discloser, IDS and PTO892) and MPEP 2106.05(d)) The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
Dependent Claims 2-8
Dependent claims 2-8 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-8 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For example, the limitations of Claims 2-8 are a mental step a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment).
Claims 9-20
Claims 9-20 contains language similar to claims 1-8 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 1-8 are also rejected under 35 U.S.C. § 101(abstract idea).
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s)1-2,6, 9-10 and 15-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cantwell et al. (US 20230280736).
Regarding Claims 1, 9 and 15. Cantwell teaches a computer-implemented method comprising(fig.3):
training, by one or more processors, a first set of machine learning models on a first combination of a first set of data features(302: fig. 3; [0106]);
measuring, by the one or more processors, a sub-set of a set of training samples to provide a second set of data features(304: fig.3; [0107]-[0108]);
combining, by the one or more processors, the first set of data features and the second set of data features to obtain a third set of data features, wherein the third set of data features is a preferred set of data features(318: fig. 3; [0107]- [0109]); and
recommending, by the one or more processors, for use by a multi-sensor system, a first machine learning model employing the preferred set of data features(320: fig.4; [0110]).
Regarding Claims 2, 10 and 16. Cantwell further teaches prior to training the first set of machine learning models on the first combination of the first set of data features, gathering, by the one or more processors, the set of training samples using the multi-sensor system at time t0, wherein said gathering step further comprises([0102]):
measuring, by the one or more processors, one or more data features of the set of training samples(observing its outputs:[0102]);
ranking, by the one or more processors, each data feature of the one or more data features according to a degree of importance of each data feature(labeled inputs: [0102]); and
extracting, by the one or more processors, the first set of data features(training set: [0102]).
Regarding Claim 6. Cantwell further teaches measuring the sub-set of training samples to provide the second set of data features further comprises(304: fig.3; [0101]):
comparing, by the one or more processors, the second set of data features to the first set of data features ([0101], [0107]);
ranking, by the one or more processors, each data feature of the second set of data features according to a deviation of the second set of data features from the first set of data features ([0101], [0107]); and
extracting, by the one or more processors, the second set of data features([0101], [0107]).
Examiner Notes
Although there are no prior art rejections for Claims 3-5, 7-8,11-14 and 17-20 the Examiner cannot comment on their allowability until all the rejections under 35 U.S.C 101 is satisfactorily addressed. However, closest prior art fail to teach the limitation of claim 3,11 and 17, e.g. ” subsequent to training the first set of machine learning models on the first combination of the first set of data features, creating, by the one or more processors, a pool of trained machine learning models, wherein the pool of trained machine learning models includes one or more machine learning models that have achieved a desired performance metric, and wherein each machine learning model uses a different combination of the first set of data features to achieve the desired performance metric”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) US 20200379454: discloses a predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured values of the vitals. The server may assign an anomaly score based on the differences between the predicted values and the measured values. In one embodiment, the machine learning model may be an autoencoder that generates a distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation. In one embodiment, the server may use a histogram approach to predict anomaly.
b) US 20240311650: disclose systems and methods for using machine learning for time series forecasting are disclosed. According to certain aspects, a set of time series data may be prepared and a plurality of features extracted therefrom. A feature vector based on the plurality of features may be generated and input into a classifier model to assess how well each of a plurality of available machine learning models is equipped to analyze the set of time series data and output a time series forecast. In embodiments, a stacking machine learning model may improve the time series forecast by accounting for multiple machine learning models as well as a set of covariates.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 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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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2857
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