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 .
Information Disclosure Statement
The IDS’ filed on 06/02/23, 08/04/23, 07/04/24, 01/30/25, and 10/02/25 have been considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “10” has been used to designate both actuator (such as in paragraph 00065 of the applicant’s original specification) and a pump (such as in paragraph 00067 of the applicant’s original specification). Furthermore, reference character “60” has been used to designate both first machine learning system (such as in paragraph 00057 of the applicant’s original specification) and classifier (such as in paragraph 00072 of the applicant’s original specification). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 9, and 11 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.
Claim 6 states, “the second machine learning system is initially trained by way of the plurality of second representations and the remaining service lives respectively assigned to the second representations such that it can determine a remaining service life for the first representations.”
Here, “first representations” is expressed in the plural. However, claim 6 depends on claim 1, and claim 1 only provides antecedent basis for “a first representation” in singular form. It will be construed that claim 6 should state, “the second machine learning system is initially trained by way of the plurality of second representations and the remaining service lives respectively assigned to the second representations such that it can determine a remaining service life for the first representation.”
Claim 9 states, “wherein the first representation is held available by the technical system and/or the second device together with a measurement time for the input signal and, at an end of life of at least the component of the technical system, the first representation is included as a second representation in the plurality of the second representations, and wherein the remaining service life corresponding to the first representation is determined by a difference of a time of the end of life and the measurement time.” The presence of the “and/or” operator, along with multiple “and” clauses render the scope of the claims indefinite.
For example, one way of interpreting the claims is:
wherein the first presentation is held available by the technical system OR (emphasis mine)
the second device together with a measurement time for the input signal and, at an end of life of at least the component of the technical system, the first representation is included as a second representation in the plurality of the second representations, and wherein the remaining service life corresponding to the first representation is determined by a difference of a time of the end of life and the measurement time
In such an interpretation, there are two clauses, and only one of them must be taught by the art, in order for the claim to be anticipated. In such an interpretation, each of the clauses after the “and” occurrences (i.e. “at an end of life” and “wherein the remaining service life”) merely serve as sub-clauses that modify the second main clause of “the second device together with a measurement time for the input signal.”
Another way of interpreting the claims is:
wherein the first presentation is held available by the technical system AND
the second device together with a measurement time for the input signal AND
at an end of life of at least the component of the technical system, the first representation is included as a second representation in the plurality of the second representations AND
wherein the remaining service life corresponding to the first representation is determined by a difference of a time of the end of life and the measurement time (emphasis mine)
In such an interpretation, there are four independent clauses, and all four clauses must be present. However, please note that under such an interpretation, the second clause of “the second device together with a measurement time for the input signal” may not make grammatical sense, as it lacks a verb.
For the purposes of examination, the first way of interpreting, as discussed above, will be adopted, where there is an OR between two clauses.
Claim 11 states, “The method according to claim 1, wherein at least the component of the technical system is replaced if the determined remaining service life reaches or falls below a predefined minimum remaining service life.”
In the preliminary amendment of 06/02/23, claim 1 was amended to state, “A computer-implemented method …” On initial glance, replacing a component of a technical system would appear to be a real-world, structural action. However, it is not clear how a real-world, structural action could be performed by a computer-implemented method that lies entirely in the realm of data processing. It is possible that replacing a component, in a computer-implemented, data processing context, refers to making a note about the internal state of the technical system. This appears to be supported by paragraph 00034 of the applicant’s original specification, which states, “This is advantageous, because the operator and/or user of the technical system can thus be informed about the internal state of the technical system and enabled, for example, to decide whether the component and/or the technical system should be replaced or taken out of service.” The scope of the claims for a physical replacement is different than the scope of the claims for a data processing, computer-implemented notice of replacement. For the purposes of examination, the examiner will construe “wherein at least the component of the technical system is replaced if the determined remaining service life reaches or falls below a predefined minimum remaining service life” to refer to informing an operator and/or user about the internal state of the technical system, such as through some sort of alert or display.
The examiner does note that paragraph 00036 of the applicant’s original specification appears to give a basis for a more structural replacement, by stating, “The replacement can preferably take place automatically, for example by means of a robot …” However, the claims do not mention or positively recite a robot; the current claims appear limited to a “computer-implemented method.”
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a computer-implemented method, which is a process. Claims 2-15 depend on claim 1. As such, claims 1-15 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
determining a remaining service life of at least one component of a technical system (Determination is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. The claimed “determining” therefore recites an abstract mental process. Furthermore, as seen below, the claimed determining process also recites abstract mathematical concepts.)
determining a first representation of the first input signal by way of an encoder of a first machine learning system (Paragraph 0002 of the applicant’s original specification states, “A method for determining a representation by means of an auto encoder is known from Hinton and Salakhutdinov, ‘Reducing the Dimensionality of Data with Neural Networks’, 07/28/2006, https://www.cs.toronto.edu/~hinton/science.pdf.” This article was included with the 06/02/23 IDS. An inspection of this article shows encoders to be defined by specific mathematical equations and calculations. The limitation therefore recites abstract mathematical concepts. Also, general “determining” is an abstract mental process that can be performed in the human mind.)
determining the remaining service life on the basis of the first representation and on the basis of a provided plurality of second representations, wherein the plurality of second representations is determined on the basis of a plurality of second input signals by way of the encoder and a corresponding remaining service life is assigned to each second representation (As discussed above, the actions of the encoder are defined by abstract mathematical concepts. Also, general “determining” is an abstract mental process that can be performed in the human mind.)
Dependent claims 2-15 depend on independent claim 1 and also recite its abstract limitations by virtue of their dependence. In addition, some of the dependent claims also recite their own abstract mathematical concepts and/or mental processes.
Claim 2 discloses similarity of one of the second representations to the first representation. This recites a mathematical relationship between the two representations.
Claim 3 discloses determining remaining service life depending on the remaining service lift of the one of the second representations. This recites an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.
Claim 4 discloses the second representation being the one that is most similar to the first representation. Similarity recites a mathematical relationship between the first and second representations.
Claim 5 discloses concepts, such as average, median, minimum, maximum, and similarity, which recites mathematical relationships.
Claim 6 discloses training the second machine learning system. As seen in the Hinton reference presented by the applicant’s original specification as “Prior art,” the training of machine learning models is defined by mathematical relationships, equations, and/or calculations.
Claim 7 discloses training the first machine learning system. As seen in the Hinton reference presented by the applicant’s original specification as “Prior art,” the training of machine learning models is defined by mathematical relationships, equations, and/or calculations.
Claim 9 discloses the remaining service life corresponding to the first representation is determined by a difference of a time of the end of life and the measurement time. This difference is a mathematical relationship between time of the end of life and measurement time. It can also be expressed as a particular mathematical equation.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A computer-implemented method (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
determining a first input signal by way of at least one sensor, wherein the first input signal characterizes an operating state of at least the component of the technical system (As discussed above, general “determining” is considered an abstract mental process under step 2A, prong one. However, here, the determining a first input signal by way of at least one sensor appears to be less analogous to a mental process and more analogous to using a sensor to collect data. In that context, it is not indicative of integration into a practical application because using a generic sensor to collect data that is then processed by the data processing “solution” of the claims merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Furthermore, the mention here of “the component of the technical system” is also not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Here, there is no mention of what the component or technical system is. In the applicant’s disclosure, multiple application contexts are discloses, such as that of a motor vehicle (figure 3), a manufacturing machine (figure 4), and a household appliance (figure 5). However, the claims do not identify any specific application context or component. The claims, as a whole, are therefore considered to merely be a drafting effort designed to monopolize the exception of the data processing technique that is claimed, through all possible technological environments or fields of use.)
Dependent claims 2-15 depend on independent claim 1 and also recite its limitations that are not indicative of integration into a practical application by virtue of their dependence. In addition, some of the dependent claims also recite their own limitations that are not indicative of integration into a practical application.
Claim 8 discloses transmitting data to a second device by way of a network connection. This limitation is not indicative of integration into a practical application because it appears to merely use a computer as a tool to perform an abstract idea. Furthermore, the claim merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. No details are given as to the nature of the second device or the nature of the network connection.
Claim 9 discloses that the first representation is held available by the technical system and/or the second device together with a measurement time for the input signal. This limitation appears to be a data processing limitation that merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.
Claim 10 discloses communicating information to an operator and/or a user of the technical system by way of a display device. A general and generic displaying of the results of data processing merely serves to add insignificant extra-solution activity to the judicial exception.
Claim 11 discloses that the component of the technical system is replaced if the determined remaining service life reaches or falls below a predefined minimum remaining service life. As discussed in the above 112 rejection, this limitation is construed to be a computer processing action to inform a user or operator of a status of a component. Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application. Furthermore, merely informing a user or operator of data results merely serves to add insignificant extra-solution activity to the judicial exception. Finally, even if the replacement here was a structural replacement, the claims do not give any context to the nature of the component, the technical system, or the means of replacement. Generally linking the use of the judicial exception to a particular technological environment or field of use (in a way that monopolizes the exception) is not indicative of integration into a practical application.
Claim 12 discloses a system for data processing, which merely uses a computer as a tool to perform an abstract idea.
Claim 13 discloses a system for data processing, which merely uses a computer as a tool to perform an abstract idea.
Claim 14 discloses a computer program, which merely uses a computer as a tool to perform an abstract idea.
Claim 15 discloses a machine-readable storage medium, which merely uses a computer as a tool to perform an abstract idea.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
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) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al NPL (Yu, Wennian; Kim, Il Yong; and Mechefske, Chris – “An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme”; Reliability Engineering and System Safety 199 (2020).) (see attached version).
With respect to claim 1, Yu et al discloses:
A method for determining a remaining service life of at least one component of a technical system (abstract states, “Remaining useful life (RUL) estimation of a degrading system is the major prognostic activity in many industry applications. This paper presents an improved version of the similarity-based curve matching method for the remaining useful life estimation of a mechanical system …”)
determining a first representation by way of an encoder of a first machine learning system (figures 1-2; page 2, column 2, first paragraph in section 2. Methodology states, “In the first step, an RNN autoencoder is trained on the available run-to-failure training instances in an unsupervised manner. The trained RNN autoencoder serves as a feature (embedding) extractor for the input time series, which will be employed to map the original multi-sensor readings of training instances into one-dimensional health index (HI) curves via embeddings. These HI curves represent various degradation patterns of the training instances and will be stored in a library in the off-line stage. In the second step, the similarity-based curve matching technique is adopted to match the HI curve of a test instance obtained in the on-line stage with each training HI curve in the library, from which the top few training HI curves that have the similar degradation pattern with the test HI curve are selected for the final RUL estimation of the test instance …” The HI curve of a test instance can be broadly and reasonably construed to serve as the claimed first representation.)
determining the remaining service life on the basis of the first representation and on the basis of a provided plurality of second representations, wherein the plurality of second representations is determined on the basis of a plurality of second input signals by way of the encoder and a corresponding remaining service life is assigned to each second representation (figures 1-2; page 2, column 2, first paragraph in section 2. Methodology states, “In the first step, an RNN autoencoder is trained on the available run-to-failure training instances in an unsupervised manner. The trained RNN autoencoder serves as a feature (embedding) extractor for the input time series, which will be employed to map the original multi-sensor readings of training instances into one-dimensional health index (HI) curves via embeddings. These HI curves represent various degradation patterns of the training instances and will be stored in a library in the off-line stage. In the second step, the similarity-based curve matching technique is adopted to match the HI curve of a test instance obtained in the on-line stage with each training HI curve in the library, from which the top few training HI curves that have the similar degradation pattern with the test HI curve are selected for the final RUL estimation of the test instance …” The various training HI curves that are compared to the test instance can be broadly and reasonably construed to serve as the claimed plurality of second representations.)
With respect to claim 1, Yu et al NPL differs from the claimed invention in that it does not explicitly disclose:
a computer-implemented method (The word “computer” was not found in the immediate text of Yu et al NPL.)
determining a first input signal by way of at least one sensor, wherein the first input signal characterizes an operating state of at least the component of the technical system (The phrase “input signal” was not found in the immediate text of Yu et al NPL.)
of the first input signal
With respect to claim 1, the following limitations are obvious in view of the total teachings of Yu et al NPL:
a computer-implemented method (Since Yu et al discloses data processing via machine learning techniques (abstract discloses neural network based autoencoder scheme), computer-implementation is implied and would be obvious to one of ordinary skill in the art.)
determining a first input signal by way of at least one sensor, wherein the first input signal characterizes an operating state of at least the component of the technical system (page 1, column 2, first paragraph states, “data-driven methods are the most popular to tackle the RUL estimation problem as they are easier to implement and rely mainly on routinely collected monitoring data via various types of sensors …” An input signal would be obvious, in view of Yu’s sensor teachings. A first input signal is implied by the presence of the sensors, as data collected by sensor must be processed electronically, such as through signals.)
of the first input signal (obvious for reasons discussed above)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the total teachings of Yu et al NPL. The motivation for the skilled artisan in doing so is to gain the benefit of efficiently processing sensor data.
With respect to claim 2, Yu et al NPL, as modified, discloses:
in the step of determining the remaining service life, the remaining service life is determined depending on at least one similarity of one of the second representations to the first representation (Yu abstract states, “We propose a zero-centering rule to tackle the varying initial health across instances (systems) when using the similarity-based health index curve matching technique to identify the training instances that share a similar degradation pattern with the test instance whose RUL needs to be determined.”)
With respect to claim 3, Yu et al NPL, as modified, discloses:
in the step of determining the remaining service life, the remaining service life is determined depending on the remaining service life of the one of the second representations (Yu page 2, column 2, first paragraph in section 2. Methodology states, “the similarity-based curve matching technique is adopted to match the HI curve of a test instance obtained in the on-line stage with each training HI curve in the library, from which the top few training HI curves that have the similar degradation pattern with the test HI curve are selected for the final RUL estimation of the test instance …” “Similar degradation pattern” is construed to anticipate “depending on the remaining service life of the one of the second representations.” Please also note figure 4, which shows one training HI curve being compared to one test HI curve.)
With respect to claim 4, Yu et al NPL, as modified, discloses:
in the step of determining the remaining service life, the remaining service life assigned to a second representation is provided as the determined remaining service life (Yu figure 4; page 2, column 2, first paragraph after section 2. Methodology; see also page 3, column 1, first paragraph in section 2.2, which states, “The basic idea behind the similarity-based HI curve matching method is to find the top few run-to-failure training instances which have similar degradation trends (HI curves) with the test instance and use them to estimate the RUL of the test instance.”)
the second representation is the one of the plurality of second representations that is most similar to the first representation (Yu figure 4)
With respect to claim 5, Yu et al NPL, as modified, discloses:
in the step of determining the remaining service life, an average or a median or a minimum or a maximum of remaining service lives corresponding to a subset of the plurality of second representations is provided as the determined remaining service life (page 2, column 2, first paragraph in section 2. Methodology states, “from which the top few training HI curves that have the similar degradation pattern with the test HI curve are selected for the final RUL estimation of the test instance using a weighted average strategy …” Page 6, column 2, lines 1-2 state, “The median and its 95% confidence interval …” Figure 6 on page 6 states, “the horizontal red line indicates the median value whereas the red dot indicates the mean value …”)
wherein the subset includes a predefined number of second representations most similar to the first representation (figure 3; page 2, column 2, first paragraph after section 2. Methodology states, “the similarity-based curve matching technique is adopted to match the HI curve of a test instance obtained in the on-line stage with each training HI curve in the library, from which the top few training HI curves that have the similar degradation pattern with the test HI curve are selected for the final RUL estimation of the test instance …”)
With respect to claim 6, Yu et al NPL, as modified, discloses:
the remaining service life is determined by way of a second machine learning system (Yu, page 2, column 2, first paragraph of section 2. Methodology; The first step that trains the various HI curves can broadly and reasonably be construed as a second machine learning system to the system in the second step that performs the similarity-based curve matching technique.)
wherein the second machine learning system is initially trained by way of the plurality of second representations and the remaining service lives respectively assigned to the second representations such that it can determine a remaining service life for the first representation (Yu, page 2, column 2, first paragraph of section 2. Methodology; The first step that trains the various HI curves can broadly and reasonably be construed as a second machine learning system to the system in the second step that performs the similarity-based curve matching technique.)
With respect to claim 7, Yu et al NPL, as modified, discloses:
wherein the encoder of the first machine learning system is trained by way of the plurality of second input signals (Yu, page 2, column 2, first paragraph of section 2. Methodology)
With respect to claim 8, Yu et al NPL, as modified, discloses:
the first representation is transmitted to a second device by way of a network connection of the technical system (obvious in view of total teachings of Yu et al NPL. Page 2, column 1, last paragraph of Yu states, “In this work, we propose a novel zero-centering rule to address the issue of varying initial health across instances when matching the on-line test HI curve with the off-line training HI curve …” Neither the claims, nor the applicant’s specification give detail about the nature of the claimed “second device” or “network connection”. In view of the broad claims, the examiner construes the claimed terms to be obvious in view of any “Internet” related terms, such as on-line and off-line.)
the step of determining the remaining service life is carried out by the second device (obvious in view of on-line and off-line teachings of Yu. It would be obvious to one of ordinary skill in the art for on-line actions to be performed by one device and off-line actions to be performed by a second device, especially since the claims do not define what constitutes the second device.)
With respect to claim 9, Yu et al NPL, as modified, discloses:
wherein the first representation is held available by the technical system and/or the second device together with a measurement time for the input signal and, at an end of life of at least the component of the technical system, the first representation is included as a second representation in the plurality of the second representations, and wherein the remaining service life corresponding to the first representation is determined by a difference of a time of the end of life and the measurement time (Yu et al NPL discloses, “wherein the first representation is held available by the technical system.” (see page 2, column 2, first paragraph after section 2. Methodology.))
With respect to claim 10, Yu et al NPL, as modified, discloses:
wherein the determined remaining service life is communicated to an operator and/or a user of the technical system by way of a display device (Although Yu et al NPL does not explicitly mention a display device, it discloses multiple figures that show graphs/charts of pertinent data (see figures 3-11). Displaying such data for an operator or user would be obvious to one of ordinary skill in the art.)
With respect to claim 11, Yu et al NPL, as modified, discloses:
wherein at least the component of the technical system is replaced if the determined remaining service life reaches or falls below a predefined minimum remaining service life (Yu, page 2, column 1, paragraph 1 states, “represent the health degradation of the monitored system from the healthy condition … to the failed condition …” It would be obvious to one of ordinary skill in the art to determine that a component in failed condition needs to be replaced. Also, as discussed in claim 10 above, it would be obvious to one of ordinary skill in the art to alert an operator/user that there is a failed condition and that a component needs to be replaced.)
With respect to claims 12, Yu et al NPL, as modified, discloses:
A system for data processing, comprising means for carrying out the method according to claim 1 (figure 1 represents a system)
With respect to claim 13, Yu et al NPL, as modified, discloses:
A system for data processing, comprising means for training the machine learning system according to claim 7 (figure 1)
With respect to claim 14, Yu et al NPL, as modified, discloses:
A computer program configured to carry out the method according to claim 1 when executed by a processor (obvious to disclosure of Yu et al NPL, which discloses a prognostic algorithm. A computer program to run a machine learning algorithm would be obvious to one of ordinary skill in the art.)
With respect to claim 15, Yu et al NPL, as modified, discloses:
A machine-readable storage medium on which the computer program according to claim 14 is stored (obvious to disclosure of Yu et al NPL, which discloses a prognostic algorithm. A machine-readable storage medium on which the computer program to run a machine learning algorithm is stored would be obvious to one of ordinary skill in the art.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kocberber et al (US PgPub 20200104200) discloses disk drive failure prediction with neural networks.
Yuan et al (CN111289250A) (with machine translation) discloses a method for predicting the remaining service life of rolling bearings in servo motors.
Mohammadi et al NPL (Mohammadi, Mehdi; Al-Fuqaha, Ala; Sorour, Sameh; and Guizani, Mohsen – “Deep Learning for IoT Big Data and Streaming Analytics: A Survey”; IEEE Communications Surveys & Tutorials, VOL. 20, NO. 4, Fourth Quarter 2018.)
Zhang et al NPL (Zhang, Liangwei; Lin, Jing; Liu, Bin; Zhang, Zhigong; Yan, Xiaohui; and Wei, Muheng – “A Review on Deep Learning Applications in Prognostics and Health Management”; Special Section on Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams; Volume 7, 2019.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD S LIANG/Examiner, Art Unit 2857 03/18/26