DETAILED ACTION
This action is in response to the Applicant Response filed 02 March 2026 for application 17/957,609 filed 30 September 2022.
Claim(s) 1-3, 6 is/are currently amended.
Claim(s) 5 is/are cancelled.
Claim(s) 1-4, 6-14 is/are pending.
Claim(s) 1-4, 6-14 is/are rejected.
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 .
Examiner’s Remarks
The Examiner notes that the instant application was previously examined by a different examiner. As such, the Examiner proceeds with prosecution giving full faith and credit to the search and action of the previous examiner per MPEP § 704.01:
When an examiner is assigned to act on an application which has received one or more actions by some other examiner, full faith and credit should be given to the search and action of the previous examiner unless there is a clear error in the previous action or knowledge of other prior art. In general the second examiner should not take an entirely new approach to the application or attempt to reorient the point of view of the previous examiner, or make a new search in the mere hope of finding something. See MPEP §719.05.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claims 1-4, 6-14 have been fully considered but are not persuasive. Applicant argues that, similar to Desjardins, the recited claims provide an improvement which integrates the judicial exception into a practical application. Examiner respectfully disagrees. Desjardins includes an improvement related to catastrophic forgetting while the claims in the instant application only recite machine learning at a high level to perform an abstract idea, as detailed below. While Desjardins provides a specific training strategy that allows models to preserve performance on earlier tasks even as it learns new ones, the current claims merely employ generic machine learning at a high level to organize and analyze data. Further as stated in the MPEP, it is important to note, the judicial exception alone cannot provide the improvement, and an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a). As noted below, the claims recite an abstract idea and therefore do not recite an improvement which integrates the abstract idea into a practical application. For similar reasons, the claims do not recite an improvement under step 2B. Therefore, claims 1-4, 6-14 stand rejected under 35 U.S.C. 101.
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of claims 1-4, 6-14 have been fully considered but are not persuasive. Applicant first argues that Campbell does not teaches the claims topological model structure as recited in the claims. Examiner respectfully disagrees. Campbell teaches a hierarchical structure where the bottom level represents controller data from a factory floor and the data is aggregated up the structure hierarchically (Figure 6; column 10, lines 38-48). Applicant next argues that Campbell does not teach a machine learning algorithm for the representations. Examiner respectfully disagrees. As acknowledged by applicant, Campbell teaches a machine learning model for determining trends in the data, including aggregate data (column 5, lines 20-35). Applicant next argues that Campbell fails to teach training of the machine learning model, but provides no support for this assertion. However, as noted below, Campbell does, in fact, teach training the model. The remainder of applicant’s arguments are moot because as they do not rely on any portion of the references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Therefore, the 35 U.S.C. 103 rejections of claims 1-4, 6-14 are maintained.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Claim 1 recites:
receiving, by a machine learning unit, a topology model comprising structural information on hierarchical relations between components of the industrial plant, wherein the components comprise data signals of sensors of the industrial plant and hierarchical units, wherein the hierarchical units comprise assets, plant sub-units, plant units and plant sections of the industrial plant;
determining, by the machine learning unit, a representation hierarchy comprising a plurality of levels using the received data signals on a bottom level and the received topology model, wherein the representation hierarchy comprises a signal representation for each of the plurality of received data signals and a hierarchical representation for each of the hierarchical units on one or more levels above the bottom level;
wherein each representation on a higher level represents a group of representations on a lower level such that there are less representations on the higher level than the lower level;
wherein each of the signal representation and the hierarchical representation comprise a machine learning model;
training, by the machine learning unit, an output machine learning model of the machine learning unit using the determined hierarchical representations and without the signal representations; and
wherein training the output machine learning model comprises training the output machine learning model using the hierarchical representations of the one or more levels of the representation hierarchy that have fewer representations than the bottom level with the signal representations.
This is a mental process because the claims are directed towards data retrieval (receiving.. a topology model, determining.. a representation) and data analysis steps (training.. a model). A person of ordinary skill in the art equipped with a generic computer is capable of performing the above steps.
This judicial exception is not integrated into a practical application because there are no additional elements in the claimed subject matter that appear to improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological problem as defined in the specification. The claimed “receiving… a model …” element is a step of sending query values to a database system and is thus adding insignificant extra-solution activity to the judicial exception as a form of mere data gathering (MPEP 2106.05(g)). The remaining steps are mental process steps of identifying data or executing a query against data. As such, the judicial exception is not integrated into a practical application.
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The “receiving …” element and “determining” or “training” data elements are recognized as a well understood, routine, and conventional activity within the field of computer functions as elements of electronic recordkeeping and storing and retrieving information in memory (MPEP 2106.05(d)(II)(i)). The remaining steps of the claims, as outlined above, appear to be mental process steps of analysis and judgment. None of the claimed elements appears to, in part or in whole, improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological problem.
Dependent claims 2-4, and 6-14 are similarly rejected as mental processes. The claims merely include insignificant extra-solution activity (claim 2-4, and 11-14, list how the data representation is shown) or data training steps (claim 6-10, list using a generic learning mechanism). None of the dependent claims appear to provide a practical application because none of the dependent claims appear to include additional elements that improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological problem as defined in the specification. Similarly, none of the dependent claims contains additional claimed elements that appear to, in part or in whole, improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological problem.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-8, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Campbell, Jr et al. (U.S. Pat. No. 7,827,122 B1 – Data Mining of Unfiltered Controller Data, hereinafter referred to as “Campbell”) in view of Wang et al. (Deep Learning for Smart Manufacturing: Methods and Applications, hereinafter referred to as “Wang”).
Regarding claim 1, Campbell teaches a computer-implemented method of hierarchical machine learning for an industrial plant machine learning system, comprising:
receiving, by a machine learning unit, a topology model comprising structural information on hierarchical relations between components of the industrial plant, wherein the components comprise data signals of sensors of the industrial plant and hierarchical units, wherein the hierarchical units comprise assets, plant sub-units, plant units and plant sections of the industrial plant (Campbell Figure 6, column 10, lines 38-48, aggregating up);
determining, by the machine learning unit, a representation hierarchy comprising a plurality of levels using the received data signals and the received topology model, wherein the representation hierarchy comprises a signal representation for each of the plurality of received data signals on a bottom level and a hierarchical representation for each of the hierarchical units on one or more levels above the bottom level (Campbell Figure 6, column 10, lines 38-48, aggregating up);
wherein each representation on a higher level represents a group of representations on a lower level such that there are less representations on the higher level than the lower level (Campbell Figure 6; column 10, lines 38-48; column 11, lines 1-10);
wherein each of the signal representation and the hierarchical representation comprise a machine learning model (Campbell column 5, lines 20-35);
training, by the machine learning unit, an output machine learning model of the machine learning unit using the determined hierarchical representations and without the signal representations (Campbell column 11, lines 30-60).
However, Campbell does not explicitly teach wherein training the output machine learning model comprises training the output machine learning model using the hierarchical representations of the one or more levels of the representation hierarchy that have fewer representations than the bottom level with the signal representations.
Wang teaches wherein training the output machine learning model comprises training the output machine learning model using the hierarchical representations of the one or more levels of the representation hierarchy that have fewer representations than the bottom level with the signal representations (Wang page 10, right column, section 5.5 taking a result and training/applying the next asset (level), also see page 9, left column, bottom paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the combined Campbell’s topology hierarchy to include Wang’s ability to learn from previous levels because it provides faster and more efficient way to identify issues or structures of industrial plant.
Regarding claim 2, Campbell as modified discloses The method of claim 1, wherein the representation hierarchy comprises at least the bottom level and a target level, wherein the bottom level comprises the signal representations, and wherein the target level comprises the hierarchical representations (Campbell column 11, lines 26-45).
Regarding claim 3, Campbell as modified discloses The method of claim 2, wherein the representation hierarchy comprises at least one intermediate level, wherein the at least one intermediate level comprises the hierarchical representations with a lower level than the target level (Campbell column 11, lines 30-60, and Wang page 6, left column, continuous learning methodology is taught at each layer including hidden ones).
Regarding claim 4, Campbell as modified discloses The method of claim 2, wherein the target level comprises only one hierarchical representation that contains information about all lower level representations (Campbell column 11, lines 30-60, and Wang page 6, left column, continuous learning methodology is taught at each layer including hidden ones).
Regarding claim 6, Campbell as modified discloses The method of claim 2, wherein training the output machine learning model comprises training the output machine learning model using solely the hierarchical representations of the target level (Campbell column 11, lines 16-35).
Regarding claim 7, Campbell as modified discloses The method of claim 1, wherein determining the representation hierarchy comprises learning, for each data signal, a signal representation and learning, for each hierarchical unit, a hierarchical representation, and wherein each hierarchical representation is learned based on corresponding representations of a previous level (Campbell column 11, lines 16-35).
Regarding claim 8, Campbell as modified discloses The method of claim 7, wherein learning the signal representation and the hierarchical representation comprises using a dimensionality reduction method (Wang page 5, right column, second paragraph, when using dimensionality reduction technique a final maximum dimension to be reached must be set).
Regarding claim 13, Campbell as modified discloses The method of claim 1, wherein the topology model and the data signals of sensors of the industrial plant are provided by the industrial plant and/or by an industrial plant simulation (Campbell column 11, lines 16-35).
Regarding claim 14, Campbell as modified discloses The method of claim 1, wherein the topology model comprises structural information on hierarchical relations between process steps in a process recipe processed by the industrial plant (Campbell column 11, lines 16-35).
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Campbell in view of Wang and further in view of Bacciu et al. (Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model, hereinafter referred to as “Bacciu”).
Regarding claim 9, Campbell as modified does not specifically disclose distance matrix as recited here but does disclose the use of various algorithmic rules for topology optimization (Campbell Figure 9, and does teach aggregating up in Figure 6, column 10, lines 38-48).
BACCIU discloses
determining a distance matrix between the representations using the received topology model;
identifying hierarchical representations as parent representations and its corresponding children representations on a lower level using the determined distance matrix; and
learning the parent representations using the identified children representations (Bacciu page 234, col 2, representing data in tree-structure, best suited distance metric).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the combined Campbell’s topology hierarchy to include ability to utilize distances to optimize the topology model as it is well known technique in the art for optimization.
Regarding claim 10, Campbell as modified discloses The method of claim 9, wherein learning the parent representations using the identified children representations comprises:
determining reconstructed children data by decoding the identified children representations; and learning the parent representations using the reconstructed children data (walking the tree from either direction to follow the representation as well as decoding children representations and reconstructing denoising the data are algorithmic steps common in the field related to mathematical model with no special purpose or special way recited for its operation, Bacciu page 234, col 2, representing data in tree-structure, best suited distance metric, also see Wang page 6, section 3.3).
Regarding claim 11, Campbell as modified discloses The method of claim 10, wherein decoding the identified children representations comprises reconstructing and/or de-noising the data of the identified children representations (Wang page 5, right column, second paragraph, denoising techniques is taught).
Regarding claim 12, Campbell as modified discloses the method of claim 9, further comprising repeating identifying hierarchical representations as parent representations and its corresponding children representations from a lower level to a higher level until the target level is reached (Wang page 5, right column, second paragraph, when using dimensionality reduction technique a final maximum dimension to be reached must be set).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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