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 30-49 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 30-38 are directed to a method, claim 39-44 are directed to using a system to perform the method, and claims 45-49 are directed to a non-transitory storage media storing instructions. 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 method, comprising:
obtaining, with at least one processor, sensor data associated with operation of industrial equipment;
inputting, with the at least one processor, the sensor data to a trained machine learning model, wherein the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model; and
predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models.
Step 2A Prong 1:
“obtaining, with at least one processor, sensor data associated with operation of industrial equipment” is directed to mental step of data gathering.
“predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models” is directed to math because it is simply running a highly sophisticated, rapid-fire sequence of mathematical calculations. The invention of the algorithms, the design of the models, and the interpretation of the results are all deeply rooted in various branches of mathematics.
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, body part, 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 " a) recording a plurality of chronologically sequential measured values of a measured variable, b) acquiring a change between a previous measured value of the measured values and a chronologically subsequent measured value of the measured values” 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:
“obtaining, with at least one processor, sensor data associated with operation of industrial equipment” 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)).
“inputting, with the at least one processor, the sensor data to a trained machine learning model, wherein the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model” 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)).
“predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models” 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.
Claim 30 recites the additional element(s) of using generic AI/ML technology, i.e. “the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of “the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
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:
“obtaining, with at least one processor, sensor data associated with operation of industrial equipment” 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)).
“inputting, with the at least one processor, the sensor data to a trained machine learning model, wherein the trained machine learning model comprises a physics based feature extraction model and a deep learning based automatic feature extraction model” 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)).
“predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models” 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 39 is similar to claim 30 but recites a system comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the steps as in claim 30. 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 39 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 39.
Claim 45 cites at least one non-transitory storage media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps as in claim 30. 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 45 for the same reason discussed above with regard to the rejection of claim 30.
Regarding claim 31, 40, and 46, “wherein the physics based feature extraction model is built using supervised learning with labeled data comprising labels that correspond to the operating conditions, the labeled data comprising of sensor data from a sensor hub” 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)).
Regarding claim 32, 41, and 47, “re-shaping the sensor data into intermediate buckets to form bucketed data; dividing the bucketed data into sub-sample windows; extracting features from the sub-sampled windows; and predicting the operating conditions for each respective sub-sample window according to the extracted features, wherein an operating condition associated with the intermediate buckets is determined according to a number of predictions associated with the sub-sample windows” 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)).
Regarding claim 33, 42, and 48, “wherein the deep learning based automatic feature extraction model is trained using unsupervised learning with thresholds calculated from signal distributions in additional sensor data, wherein the thresholds are associated with the operating conditions” 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)).
Regarding claim 34, 43, and 49, “wherein the deep learning based automatic feature extraction model is trained using unsupervised learning with labeled data comprising labels that correspond to a normal operating condition, the labeled data comprising additional sensor data and additional temperature data” 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)).
Regarding claim 35, 44, and 50, “detecting a drift from a normal operating condition, wherein the trained machine learning model is actively trained in response to determining a cause of the drift is a modified configuration of the industrial equipment, wherein active training uses the determined cause of the drift” 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)).
Regarding claim 36, “wherein the trained machine learning model is actively trained based on unlabeled input data by identifying patterns in the unlabeled input data, and predicting the operating conditions is based on, at least in part, the identified patterns” 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)).
Regarding claim 37, “obtaining additional sensor data, additional temperature data, operational parameters, or a combination thereof from [[the]] sensor hubs at two or more time intervals, wherein the additional sensor data is associated with the industrial equipment, and wherein the two or more time intervals include at least a first time interval and a second time interval, the first time interval spanning a first amount of time during a given day, and the second time interval spanning a second amount of time during the given day, the second amount of time being shorter than the first amount of time and being separated from the first amount of time during the given day; labeling the additional sensor data, the additional temperature data, and the operational parameters as corresponding to at least one operating condition; and training the machine learning model using a training dataset comprising the labeled additional sensor data, the labeled additional temperature data, and the labeled operating parameters” 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)).
Regarding claim 38, “training the machine learning model using additional sensor data, additional temperature data, infrared heat maps of a product being produced by the industrial equipment, images of the product being produced by the industrial equipment, or any combinations thereof” 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)).
Hence the claims 30-49 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 30-31, 33-34, 36-40, 42-43, 45-46, and 48-49 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al. ("A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing") in view of Chen et al. (CN 107121926 A).
Regarding claims 30, 39, and 45, Shao et al. disclose a method, comprising:
obtaining, with at least one processor, sensor data associated with operation of industrial equipment (a least implicitly disclosed in the Abstract and the Introduction: sensor data from a motor is acquired and processed);
inputting, with at least one processor, the sensor data to a trained machine learning model, wherein the trained machine learning model comprises a physics based feature extraction model (see Remark 1 ) and a deep learning based automatic feature extraction model (see Remark 2 ); and
Remark 1
See page 1350, right column: Therefore, in this study the vibration signals are transformed from time domain to frequency domain using FFT, and the frequency distribution of each signal is used as input of the DBN architecture. The feature "FFT" is considered to equate the feature "physic based feature extraction model". This interpretation is in line with §147 of the present application.
Remark 2
See Fig.5: the block "Trained DBN" represents a deep learning network architecture which according to page 1348, right column, lines 19-21 can extract "features"); and predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models (see page 1351, Fig. 5 and the left column: Classification process is then followed to predict the fault category.
Shao et al. fail to disclose predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models.
Chen et al. teaches predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models (abstract: an industrial robot reliability modelling method based on deep learning, comprising the following steps: constructing deep neural network DNN by limiting Boltzmann machine RBM; training RBM and DNN by using contrast dispersion fast learning algorithm, and evaluating the training result; inputting the accelerated degradation original data of the evaluation object industrial robot, constructing the acceleration degradation model, predicting the expected working life and reliability under the normal working condition). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate predicting, with the at least one processor, operating conditions associated with operation of the industrial equipment using the trained machine learning models of Chen et al. with the system and method of Shao et al. for the purposes of providing an improved industrial robot reliability modelling method based on deep learning (Chen et al., abstract).
Regarding claims 31, 40, and 46, Shao et al. disclose wherein the physics based feature extraction model is built using supervised learning with labeled data comprising labels that correspond to the operating conditions, the labeled data comprising of sensor data from a sensor hub (p.1350, right column, lines 14-18).
Regarding claims 33, 42, and 48, Shao et al. disclose the deep learning based automatic feature extraction model is trained using unsupervised learning with thresholds calculated from signal distributions in additional sensor data, wherein the thresholds are associated with the operating conditions (page 1251, left column: "The unsupervised..”) .
Regarding claim 34, 43, and 49, Shao et al. disclose the deep learning based automatic feature extraction model is trained using unsupervised learning with labeled data comprising labels that correspond to a normal operating condition, the labeled data comprising additional sensor data and additional temperature data (page 1251, left column: "The unsupervised..”).
Regarding claim 36, “wherein the trained machine learning model is actively trained based on unlabeled input data by identifying patterns in the unlabeled input data, and predicting the operating conditions is based on, at least in part, the identified patterns“ is considered to be a well-known procedure.
Regarding claim 37, “obtaining additional sensor data, additional temperature data, operational parameters, or a combination thereof from sensor hubs at two or more time intervals, wherein the additional sensor data is associated with the industrial equipment, and wherein the two or more time intervals include at least a first time interval and a second time interval, the first time interval spanning a first amount of time during a given day, and the second time interval spanning a second amount of time during the given day, the second amount of time being shorter than the first amount of time and being separated from the first amount of time during the given day; labeling the additional sensor data, the additional temperature data, and the operational parameters as corresponding to at least one operating condition; and training the machine learning model using a training dataset comprising the labeled additional sensor data, the labeled additional temperature data, and the labeled operating parameters” is considered to represent obvious design options the skilled person would choose under the dictate of circumstances.
Regarding claim 38, “training the machine learning model using additional sensor data, additional temperature data, infrared heat maps of a product being produced by the industrial equipment, images of the product being produced by the industrial equipment, or any combinations thereof” is considered to represent obvious design options the skilled person would choose under the dictate of circumstances.
Claim(s) 32, 41, and 47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al. ("A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing") in view of Chen et al. (CN 107121926 A)as applied to claim 30 above, and further in view of Nadeem et al.("Outlier Detection in Sensor Data using Ensemble Learning").
Regarding claim 32 , 41, and 47, the combination of Shao et al. and Chen et al. fail to disclose re-shaping the sensor data into intermediate buckets to form bucketed data; dividing the bucketed data into sub-sample windows; extracting features from the sub-sampled windows; and predicting the operating conditions for each respective sub-sample window according to the extracted features, wherein an operating condition associated with the intermediate buckets is determined according to a number of predictions associated with the sub-sample windows.
Nadeem et al. teach re-shaping the sensor data into intermediate buckets to form bucketed data; dividing the bucketed data into sub-sample windows; extracting features from the sub-sampled windows; and predicting the operating conditions for each respective sub-sample window according to the extracted features, wherein an operating condition associated with the intermediate buckets is determined according to a number of predictions associated with the sub-sample windows (abstract). Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Nadeem et al. with the teaching of Shao et al. in view of Chen et al. in order to provide outlier detection in sensor data using ensemble learning.
Claim(s) 35 and 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shao et al. ("A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing") in view of Chen et al. (CN 107121926 A) as applied to claim 30 above, and further in view of Umer et al. (""Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi ").
Regarding claims 35 and 44, the combination of Shao et al. and Chen et al. fail to disclose detecting a drift from a normal operating condition, wherein the trained machine learning model is actively trained in response to determining a cause of the drift is a modified configuration of the industrial equipment, wherein active training uses the determined cause of the drift.
Umer et al. teach detecting a drift from a normal operating condition, wherein the trained machine learning model is actively trained in response to determining a cause of the drift is a modified configuration of the industrial equipment, wherein active training uses the determined cause of the drift (abstract). Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Umer et al. with the teaching of Shao et al. in view of Chen et al. in order to provide machine learning-based real-time sensor drift fault detection.
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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN H LE/Primary Examiner, Art Unit 2857