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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-10, 16-17 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 7, 16 and 20 each recite “the machine learning model is trained with a machine learning algorithm”, however, the specification is silent with regards to the type(s) of algorithm that are used for the training. At best, the background recites “a broad array of machine learning algorithms are available, with new algorithms the subject of active research” ([0003]), which appears to be an expressed statement to use or apply existing algorithms and algorithms that do not presently exist at the time the application was filed. Claims that are directed to the use of new or not yet discovered algorithms contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 8-10 and 17 depend from claims 7 and 16 respectively and fail to remedy the deficiencies of the claims from which they depend.
Claims 7-10, 16-17 and 20 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.
Claims 7, 16 and 20 each recite “the machine learning model is trained with a machine learning algorithm”, however, the specification is silent with regards to the type(s) of algorithm that are used for the training. At best, the background recites “a broad array of machine learning algorithms are available, with new algorithms the subject of active research” ([0003]), which appears to be an expressed statement to use or apply existing algorithms and algorithms that do not presently exist at the time the application was filed. The specification fails to identify the algorithms that are utilized to train the model. For example, do the algorithms include supervised learning algorithms, unsupervised learning algorithms, reinforcement learning algorithms or some other type of algorithms? One of ordinary skill in the art would not be able to readily ascertain the meets and bounds of the claim limitations, therefore the limitations renders the claims indefinite.
Claims 8-10 and 17 depend from claims 7 and 16 respectively and fail to remedy the deficiencies of the claims from which they depend.
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.
Claim 1 is a method that recites identifying a sound signature for the at least one component from the sound data, wherein the sound signature includes at least one of a sound type or a frequency range for the noise; classifying the sound signature for the at least one component with a machine learning model to predict a remaining operating life for the at least one component; and determining an operating status of the at least one component based on the remaining operating life predicted.
In the context of the claim, identifying a sound signature, classifying the sound signature and determining an operating status encompass reviewing and analyzing the sound signature, then thinking and assigning an operating status represented by the signature. The limitations cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion, thus the limitations fall within the Mental Process grouping of abstract ideas. See MPEP 2106.04(a)(2)III. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim element of receiving the sound data, wherein the sound data represents a noise generated by at least one component during operation. The claim limitation is recited with a high level of generality and does not add any meaningful limitations to practicing the abstract idea. Therefore, the additional limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim element is insignificant extra-solution activity. Additionally, the prior art (see e.g. Cella et al. in PGPub 2018/0284758) establishes that the additional claim element is known in the art. Thus, the limitations represent well-understood, routine, conventional activity. See MPEP 2106.05(d)II. The claim is not patent eligible.
Claims 2-11 depend from claim 1 and recite the same abstract idea as claim 1. The additional claim element recited in claims 2-11 server merely to add additional steps or details to the abstract idea (claims 2 and 4-10), merely tie the abstract idea to a technical field of endeavor (claims 3) or add additional insignificant extra-solution activity (claims 5 and 11). The limitations recited in claims 2 and 4-10 encompass limitations that either can performed in the human mind, including observation, evaluation, judgment, and opinion, thus fall within the Mental Process grouping of abstract ideas (see MPEP 2106.04(a)(2)III) or can be performed using a mathematical tool such as an algorithm, thus fall within the Mathematical Concepts grouping of abstract ideas (see MPEP 2106.04(a)(2)I).
The additional claim elements recited in claim 3 are a mere indication of the field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). These elements are also insignificant extra-solution activity because it merely gathers data for use in identifying and classifying a sound signature. See MPEP 2106.05(g). The additional claim elements recited in claims 5 and 11 merely add additional data gathering steps therefore, the additional limitation are insignificant extra-solution activity. See MPEP 2106.05(g). Additionally, the prior art (see e.g. Cella et al. in PGPub 2018/0284758) establishes that the additional claim elements are known in the art. Thus, the limitations represent well-understood, routine, conventional activity. See MPEP 2106.05(d)II.
The additional claim limitations neither integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 12 is an article of manufacture that recites identifying a sound signature for the at least one component from the sound data, wherein the sound signature includes at least one of a sound type or a frequency range for the noise; classifying the sound signature for the at least one component with a machine learning model to predict a remaining operating life for the at least one component; and determining an operating status of the at least one component based on the remaining operating life predicted.
In the context of the claim, identifying a sound signature, classifying the sound signature and determining an operating status encompass reviewing and analyzing the sound signature, then thinking and assigning an operating status represented by the signature. The limitations cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion, thus the limitations fall within the Mental Process grouping of abstract ideas. See MPEP 2106.04(a)(2)III. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim element of receiving the sound data, wherein the sound data represents a noise generated by at least one component during operation. The claim limitation is recited with a high level of generality and does not add any meaningful limitations to practicing the abstract idea. Therefore, the additional limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim element is insignificant extra-solution activity. Additionally, the prior art (see e.g. Cella et al. in PGPub 2018/0284758) establishes that the additional claim element is known in the art. Thus, the limitations represent well-understood, routine, conventional activity. See MPEP 2106.05(d)II. The claim is not patent eligible.
Claims 13-17 depend from claim 12 and recite the same abstract idea as claim 12. The additional claim element recited in claims 2-11 server merely to add additional steps or details to the abstract idea (claims 2 and 4-10), merely tie the abstract idea to a technical field of endeavor (claims 3) or add additional insignificant extra-solution activity (claims 5 and 11). The limitations recited in claims 2 and 4-10 encompass limitations that either can performed in the human mind, including observation, evaluation, judgment, and opinion, thus fall within the Mental Process grouping of abstract ideas (see MPEP 2106.04(a)(2)III) or can be performed using a mathematical tool such as an algorithm, thus fall within the Mathematical Concepts grouping of abstract ideas (see MPEP 2106.04(a)(2)I).
The additional claim elements recited in claim 3 are a mere indication of the field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). These elements are also insignificant extra-solution activity because it merely gathers data for use in identifying and classifying a sound signature. See MPEP 2106.05(g). The additional claim elements recited in claims 5 and 11 merely add additional data gathering steps therefore, the additional limitation are insignificant extra-solution activity. See MPEP 2106.05(g). Additionally, the prior art (see e.g. Cella et al. in PGPub 2018/0284758) establishes that the additional claim elements are known in the art. Thus, the limitations represent well-understood, routine, conventional activity. See MPEP 2106.05(d)II.
The additional claim limitations neither integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 18 is an apparatus that recites identify a sound signature for the at least one component from the sound data, wherein the sound signature includes at least one of a sound type or a frequency range for the noise; classify the sound signature for the at least one component with a machine learning model to predict a remaining operating life for the at least one component; and determine an operating status of the at least one component based on the remaining operating life predicted.
In the context of the claim, identify a sound signature, classify the sound signature and determine an operating status encompass reviewing and analyzing the sound signature, then thinking and assigning an operating status represented by the signature. The limitations cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion, thus the limitations fall within the Mental Process grouping of abstract ideas. See MPEP 2106.04(a)(2)III. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim elements of a plurality of microphones; a controller in electrical communication with the plurality of microphones, wherein the controller is configured to: receive the sound data, wherein the sound data represents a noise generated by at least one component during operation. The recitation of a microphone is a mere indication of the field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). This element is also insignificant extra-solution activity because it merely gathers data for use in identifying and classifying a sound signature. See MPEP 2106.05(g). The recitation of a controller is merely as a computer is used as a tool to perform generic computer function of receiving and analyzing data. See MPEP 2106.05(f). The claim limitation of receive the sound data is recited with a high level of generality and does not add any meaningful limitations to practicing the abstract idea. Therefore, the additional limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim elements are generic computer components performing generic computer functions, a mere indication of the field of use or technological environment and insignificant extra-solution activity. Additionally, the prior art (see e.g. Cella et al. in PGPub 2018/0284758) establishes that the additional claim element is known in the art. Thus, the limitations represent well-understood, routine, conventional activity. See MPEP 2106.05(d)II. The claim is not patent eligible.
Claims 19-20 depend from claim 19 and recite the same abstract idea as claim 19. The additional claim element recited in claims 19-20 server merely to add additional steps or details to the abstract idea The limitations recited in the claims encompass limitations that can performed using a mathematical tool such as an algorithm, thus fall within the Mathematical Concepts grouping of abstract ideas (see MPEP 2106.04(a)(2)I). The additional claim limitations neither integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
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.
(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) 1-5, 7 and 9-20 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Cella et al. in U.S. Patent Publication 2018/0284758.
Regarding claim 1, Cella et al. teaches:
receiving the sound data (e.g. Figs. 2-4), wherein the sound data represents a noise generated by at least one component during operation (see “Ambient noise, or the overall sound environment of the area and/or overall vibration of the device of interest, optionally in conjunction with other ambient sensed conditions, may be used in detecting or predicting events, outcomes, or states. Ambient noise may be measured ”, [1033]; see “sound sensor”, [0997]);
identifying a sound signature for the at least one component from the sound data (see “identify patterns, sounds types, noise types, etc.”, [1033]), wherein the sound signature includes at least one of a sound type or a frequency range for the noise (see “identify patterns, sounds types, noise types, etc.”, [1033]; see “ identify the presence of high frequency torsional vibration”, [0876]);
classifying the sound signature for the at least one component with a machine learning model (see “employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations” , [0317]; see “analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like)”, [0321]) to predict a remaining operating life for the at least one component (see “indicating successful predictions of anticipated states”, [0322]; see “ recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]); and
determining an operating status of the at least one component based on the remaining operating life predicted (see “wherein the state of the at least one bearing is at least one of an operating state, a health state, a predicted lifetime state and a fault state”, [0805]; see “recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]; [0876]).
Regarding claim 2, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the noise generated by the at least one component captured by the sound data includes sound waves in an ultrasonic frequency range (see “continuous ultrasonic monitoring”, [0010]; [0237]).
Regarding claim 3, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. wherein the sound data is collected from a microphone system having a plurality of microphones positioned in predetermined relative locations (see “sensors may be…microphone”, [0097], [1390]; see “directional and omnichannel microphones”, [1272]).
Regarding claim 4, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches including determining a location of origin of the sound signature relative to the microphone system (see “a set of indicators of fault conditions of one or more industrial machines may include indicators…ultrasonic signature”, [1168]).
Regarding claim 5, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches including receiving image data from a camera system and identifying the location of origin of the sound signature on an image from the image data (see “detecting or measuring ambient noise, such as a sound sensor or microphone…and an optical vibration sensor (e.g., using a camera to see oscillations that produce noise)”, [0097], [1033]; see “a camera 11788 may have object recognition capabilities”, [1243]; [1272]).
Regarding claim 7, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the machine learning model is trained with a machine learning algorithm on at least one training dataset (see “machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms”, [0318]; see “an expert system or neural network may be trained…based on a data set, model, or the like.”, [0910]), the at least one training dataset includes a collection of sample sound signatures for each of a plurality of sample components (see “a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments”, [0319]; see “one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data”, [0910]).
Regarding claim 9, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the at least one training dataset includes a service history for each of the plurality of sample components associated with a time when each of the sample sound signatures were obtained (see “repair histories’, [2055]).
Regarding claim 10, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the collection of sample sound signatures includes at least one of a sound type or a frequency range for each of the plurality of sample components (see “identify patterns, sounds types, noise types, etc.”, [1033]; see “identify the presence of high frequency torsional vibration”, [0876]).
Regarding claim 11, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the at least one component includes a plurality of components and the sound data includes a plurality of noises generated by a corresponding one of the plurality of components (see “a number of noise sources such as vibrational noise and electro-magnetic (EM) noise sources”, [0005]; see “the use of ambient, local and vibration noise for prediction”, [0377]).
Regarding claim 12, Cella et al. teaches:
receiving the sound data (e.g. Figs. 2-4), wherein the sound data represents a noise generated by at least one component during operation (see “Ambient noise, or the overall sound environment of the area and/or overall vibration of the device of interest, optionally in conjunction with other ambient sensed conditions, may be used in detecting or predicting events, outcomes, or states. Ambient noise may be measured ”, [1033]; see “sound sensor”, [0997]);
identifying a sound signature for the at least one component from the sound data (see “ identify patterns, sounds types, noise types, etc.”, [1033]), wherein the sound signature includes at least one of a sound type or a frequency range for the noise (see “identify patterns, sounds types, noise types, etc.”, [1033]; see “ identify the presence of high frequency torsional vibration”, [0876]);
classifying the sound signature for the at least one component with a machine learning model (see “employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations” , [0317]; see “analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like)”, [0321]) to predict a remaining operating life for the at least one component (see “indicating successful predictions of anticipated states”, [0322]; see “ recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]); and
determining an operating status of the at least one component based on the remaining operating life predicted (see “wherein the state of the at least one bearing is at least one of an operating state, a health state, a predicted lifetime state and a fault state”, [0805]; see “recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]; [0876]).
Regarding claim 13, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the noise generated by the at least one component captured by the sound data includes sound waves in an ultrasonic frequency range (see “continuous ultrasonic monitoring”, [0010]; [0237]).
Regarding claim 14, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. wherein the sound data is collected from a microphone system having a plurality of microphones positioned in predetermined relative locations (see “sensors may be…microphone”, [0097], [1390]; see “directional and omnichannel microphones”, [1272]; see “a set of indicators of fault conditions of one or more industrial machines may include indicators…ultrasonic signature”, [1168]).
Regarding claim 15, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches including determining a location of origin of the sound signature relative to the microphone system (see “detecting or measuring ambient noise, such as a sound sensor or microphone…and an optical vibration sensor (e.g., using a camera to see oscillations that produce noise)”, [0097], [1033]; see “a camera 11788 may have object recognition capabilities”, [1243]; [1272]).
Regarding claim 16, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the machine learning model is trained with a machine learning algorithm on at least one training dataset (see “machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms”, [0318]; see “an expert system or neural network may be trained…based on a data set, model, or the like.”, [0910]), the at least one training dataset includes a collection of sample sound signatures for each of a plurality of sample components (see “a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments”, [0319]; see “ one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data”, [0910]).
Regarding claim 17, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the at least one training dataset includes a service history for each of the plurality of sample components associated with a time when each of the sample sound signatures were obtained (see “repair histories’, [2055]).
Regarding claim 18, Cella et al. teaches:
a plurality of microphones (see “sensors may be…microphone”, [0097], [1390]; see “directional and omnichannel microphones”, [1272]);
a controller in electrical communication with the plurality of microphones (see “controller”, [1004], [1011]), wherein the controller is configured to:
receive the sound data (e.g. Figs. 2-4), wherein the sound data represents a noise generated by at least one component during operation (see “Ambient noise, or the overall sound environment of the area and/or overall vibration of the device of interest, optionally in conjunction with other ambient sensed conditions, may be used in detecting or predicting events, outcomes, or states. Ambient noise may be measured ”, [1033]; see “sound sensor”, [0997]);
identify a sound signature for the at least one component from the sound data (see “ identify patterns, sounds types, noise types, etc”, [1033]), wherein the sound signature includes at least one of a sound type or a frequency range for the noise (see “identify patterns, sounds types, noise types, etc”, [1033]; see “ identify the presence of high frequency torsional vibration”, [0876]);
classify the sound signature for the at least one component with a machine learning model (see “employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations” , [0317]; see “analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like)”, [0321]) to predict a remaining operating life for the at least one component (see “indicating successful predictions of anticipated states”, [0322]; see “ recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]); and
determine an operating status of the at least one component based on the remaining operating life predicted (see “wherein the state of the at least one bearing is at least one of an operating state, a health state, a predicted lifetime state and a fault state”, [0805]; see “recognize various operating state, health states (e.g., lifetime predictions) and fault states”, [0692]; [0876]).
Regarding claim 19, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the noise generated by the at least one component captured by the sound data includes sound waves in an ultrasonic frequency range (see “continuous ultrasonic monitoring”, [0010]; [0237]).
Regarding claim 20, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches wherein the machine learning model is trained with a machine learning algorithm on at least one training dataset (see “machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms”, [0318]; see “an expert system or neural network may be trained…based on a data set, model, or the like.”, [0910]), the at least one training dataset includes a collection of sample sound signatures for each of a plurality of sample components (see “a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments”, [0319]; see “ one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data”, [0910]) and the at least one training dataset includes a service history for each of the plurality of sample components associated with a time when the sample sound signature was obtained for each of the plurality of sample components (see “repair histories’, [2055]).
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) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. in U.S. Patent Publication 2018/0284758 as applied to claim 4 above, and further in view of Jayaraman et al. in U.S. Patent Publication 2021/0304587.
Regarding claim 6, Cella et al. teaches the limitations as disclosed above. Further, Cella et al. teaches sound signature received by a plurality of microphones. Cella et al. differs from the claimed invention in that it is silent regarding the location of the origin of the sound signature is determined based on a triangulation.
Jayaraman et al. teaches “The multi-modal input includes acoustic signal generated by or reflected off the physical asset and captured by a set of acoustic sensors. The method further includes generating a digital asset corresponding to the physical asset by determining an identification, a location, a shape, a size, and a behavior of the physical asset based on the multi-modal input and the overall layout, detecting one or more events of interest involving the digital asset based on the behavior of the physical asset” (Abstract) wherein “The main aspect for the location identification may be a sound pressure as the sound is detected. The location identification may be performed by employing at least one of…a triangulation technique” ([0040] the origin of the sound signature is determined based on a triangulation). Jayaraman et al. relates to “detect a faulty section from a complete infrastructure before recognizing a real time problem” ([0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combine the teachings of Jayaraman et al. with Cella et al. to improve Cella et al. with a reasonable expectation that it would facilitate the system being able to identify the location of the fault or problem and thereby improving the ability to determining the operating status of the system.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. in U.S. Patent Publication 2018/0284758 as applied to claim 7 above.
Regarding claim 8, Cella et al. teaches the limitations as disclosed above. Cella et al. differs from the claimed invention in that it does not expressly recite wherein the plurality of sample components are identical to the at least one component.
Cell et al. does disclose “the sourced data from legacy instruments can be easily compared to newly acquired data” ([0390]; [0436]). Cella et al. is directed to comparison of the components to that of a trained model where the model is trained from a sample of components in order to detect or determine faults or other issues within the system. It would be reasonable for one of ordinary skill in the art to use and/or infer the use of sample components identical to the components to facilitate accuracy in detecting such faults. Thus, the prior art to Cella et al. suggests the plurality of sample components are identical to the at least one component. See MPEP 2144.01.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Streichert et al. in Foreign Patent Document CN 115840095 A teaches “The invention claims a continuous state monitoring for electric system (100), especially for early fault detection technology. the state monitoring unit (114) is formed with an interface to the memory (116), in the memory (116), the trained normal state model is stored as a model, the model has been trained in the normal state data representing the normal state of the electric system in the training stage. In addition, the state monitoring unit (114) comprises: a data interface (112; 302) for continuously obtaining the sensor data of the electric system through a group of sensors (m1, m2), an extractor (304) for extracting features from the obtained sensor data, a differentiator (310) for passing through the distance metric (e.g., Euclidean norm, sum norm, the maximum norm) determines the deviation of the learned characteristic of the extracted characteristic and the normal state model, a scoring unit (318), for calculating the abnormal score according to the determined deviation, and an output unit (106; 320)” (Abstract).
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/MI'SCHITA' HENSON/ Primary Examiner, Art Unit 2857
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