DETAILED ACTION
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 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:
According to the first part of the analysis, in the instant case, claims 1-12 and 15-20 are directed to a method, claim 13 is directed to using an apparatus comprising: a memory section comprising computer executable program code; and a processing section configured to cause the apparatus to perform, when executing the program code to perform the method, and claim 14 is directed to a non-transitory computer readable medium having stored there on a computer program comprising computer executable program code which when executed in an apparatus causes the apparatus to perform the method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A computer implemented method for analyzing a target system for the purpose of controlling the target system, wherein the target system is a mobile communication network, an industrial process, a life science application, or an asset performance optimization system, the method comprising:
obtaining a dataset comprising observations related to the target system;
computing alignment score for the dataset using a linear kernel to obtain a linear alignment score;
computing alignment score for the dataset using a non-linear kernel to obtain a non-linear alignment score;
comparing the linear alignment score and the non-linear alignment score;
if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures;
performing the selected anomaly detection on the dataset; and
providing results of the anomaly detection for detecting problems and taking corrective actions.
Step 2A Prong 1:
“obtaining a dataset comprising observations related to the target system” is directed to mental step of data gathering.
“computing alignment score for the dataset using a linear kernel to obtain a linear alignment score” is directed to math.
“comparing the linear alignment score and the non-linear alignment score” is directed to metal step of comparing the linear alignment score and the non-linear alignment score.
“if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures” is directed to math.
“performing the selected anomaly detection on the dataset” is directed to a mental step of analyzing the dataset.
“providing results of the anomaly detection for detecting problems and taking corrective actions” is directed to a mental step of displaying results of analyzing.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “sensor and measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of “computing alignment score for the dataset using a linear kernel to obtain a linear alignment score; comparing the linear alignment score and the non-linear alignment score; if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A computer implemented method for analyzing a target system for the purpose of controlling the target system, wherein the target system is a mobile communication network, an industrial process, a life science application, or an asset performance optimization system, the method comprising” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining a dataset comprising observations related to the target system” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“computing alignment score for the dataset using a linear kernel to obtain a linear alignment score” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“comparing the linear alignment score and the non-linear alignment score” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“performing the selected anomaly detection on the dataset” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“providing results of the anomaly detection for detecting problems and taking corrective actions” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A computer implemented method for analyzing a target system for the purpose of controlling the target system, wherein the target system is a mobile communication network, an industrial process, a life science application, or an asset performance optimization system, the method comprising” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining a dataset comprising observations related to the target system” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“computing alignment score for the dataset using a linear kernel to obtain a linear alignment score” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“comparing the linear alignment score and the non-linear alignment score” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“performing the selected anomaly detection on the dataset” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“providing results of the anomaly detection for detecting problems and taking corrective actions” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 13 is similar to claim 1 but recites a comprising: a memory section comprising computer executable program code; and a processing section configured to cause the apparatus to perform, when executing the program code to perform the method. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 13 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 13.
Claim 14 cites a non-transitory computer readable medium having stored there on a computer program comprising computer executable program code which when executed in an apparatus causes the apparatus to perform the method. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 14 for the same reason discussed above with regard to the rejection of claim 1.
Regrading claim 2, “wherein the non-linear kernel is a radial kernel or a polynomial kernel” is directed to mental step of definition data.
Regrading claim 3, “wherein the dataset comprises unlabeled observations related to the target system” is directed to mental step of definition data.
Regrading claim 4, “wherein centered kernel target alignment method is applied for computing the alignment scores” is directed to math.
Regrading claim 5, “wherein the alignment scores are computed by maximizing alignment score relative to initially unknown label-vector” is directed to math.
Regrading claim 6, “wherein the maximization of the alignment score is formulated as an optimization problem with respect to a target vector” is directed to math.
Regrading claim 7, “wherein the maximization of the alignment score is performed using a process that iteratively updates the target vector until objective converges and that returns the target vector and the alignment score” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 8, “wherein non-Euclidean space measures comprise one or more of robust principal component analysis, kernel principal component analysis and neural network-based methods” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 9, “wherein Euclidean space measures comprise one or more of principal component analysis, isolation forest and local outlier factor” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 10, “wherein the target system is a mobile communication network, and the observations relate to network performance” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 11, “wherein the target system is an industrial process, and the observations comprise sensor data from the industrial process” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 12, “wherein the target system is a life science application, and the observations comprise measurement results” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 15, “wherein the dataset comprises unlabeled observations related to the target system” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regrading claim 16, “wherein centered kernel target alignment method is applied for computing the alignment scores” is directed to math.
Regrading claim 17, “wherein centered kernel target alignment method is applied for computing the alignment scores” is directed to math.
Regrading claim 18, “wherein the alignment scores are computed by maximizing alignment score relative to initially unknown label-vector” is directed to math..
Regrading claim 19, “wherein the alignment scores are computed by maximizing alignment score relative to initially unknown label-vector” is directed to math.
Regrading claim 20, “wherein the alignment scores are computed by maximizing alignment score relative to initially unknown label-vector” is directed to math.
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, none of the prior art of record teaches or suggests if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures. It is these limitations as they are claimed in the combination with other limitations of claim, which have not been found, taught or suggested in the prior art of record, that make these claims allowable over the prior art.
Oliner et al. (US 2018/0219889 A1) disclose a computer implemented method for analyzing a target system for the purpose of controlling the target system, wherein the target system is a mobile communication network, an industrial process, a life science application, or an asset performance optimization system, the method comprising (see para 63-66, Oliner et al. disclose analyzing data of telecommunication network) : obtaining a dataset comprising observations related to the target system (see Fig. 13, para. 240-243); computing alignment score for the dataset using a linear kernel to obtain a linear alignment score; computing alignment score for the dataset using a non-linear kernel to obtain a non-linear alignment score; comparing the linear alignment score and the non-linear alignment score (see para. 260-262, Figs. 14A, 14B, Oliner et al. disclose using linear and non-linear models for analyzing the dataset); selecting anomaly detection (see para. 271-275, Oliner et al. disclose evaluating and selecting an anomaly detection model); performing the selected anomaly detection on the dataset; and providing results of the anomaly detection for detecting problems and taking corrective actions (see para. 294-295, 228, 299, 302, Figs. 16, 17). However, Oliner et al. fail to disclose if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures.
Yuan et al. (US 7,930,122) disclose a method for evaluating an anomaly measurement x' in a machine condition monitoring system wherein measurements xi are evaluated in a one-class classifier having a decision region R1 for the class C1 such that an evaluation function f(x) is greater than or equal to a threshold T for a measurement x within the region, and less the T outside the region. The method includes the steps of training the one-class classifier to establish the decision region R1 from a set of training samples {x1, x2, . . . , xN}; receiving the anomaly measurement xi; determining that the anomaly measurement is outside the region R1; determining a distance from the measurement x' to a boundary of the region R1; and evaluating the anomaly measurement x' based on the distance. However, Yuan et al. fail to disclose if linear alignment score>non-linear alignment score, selecting anomaly detection that uses Euclidean space measures, and else selecting anomaly detection that uses non-Euclidean space measures.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner 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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/JOHN H LE/Primary Examiner, Art Unit 2857
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