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 . Claims 1-10 are presented in the case.
Information Disclosure Statement
The information disclosure statements submitted on 05/18/2023, 07/15/2025 and 01/26/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application EP22176499.6 filed in Europe on 05/31/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”)
Claim 1 and 8 have the following abstract idea analysis.
Step 1: The claim is directed to “a method, system and crm”. The claims are directed to the statutory categories accordingly.
Step 2A Prong 1: claims recite the abstract idea limitations of "computing a difference between the predicted time series values and the observed time series values," and "detecting an anomaly if the difference exceeds a threshold.". These limitations include mental concepts (act of evaluating. Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2)). "comparing collected information to a predefined threshold, which is an act of evaluating information that can be practically performed in the human mind. "calculating the difference between local and average data values" is also recited in the MPEP as a mathematical calculation. Thus, these steps are an abstract idea in the “mental process” and "mathematical calculations" grouping". The specification also provides example operations performed such as anomaly frequency thresholds. See USPGPUB ¶79. Other sections of the claims such as "forecasting, by a machine learning model", "predicted time series values for all nodes,", "receiving current sensor measurements from sensors placed in the industrial system," and "extracting observed time series values for at least some or all of the nodes from the current sensor measurements," are advanced processes, too generic or high level to be listed as a judicial exception given the available descriptions and MPEP comparisons.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "a model", "sensors", "a processor" or "memory" does not yield eligibility. Claims are still in line with mental concepts such as claim 1, 8 and 15 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(f).
Claim 1 and 8 do not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. Processors, memory and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-7 and 9-10, they merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1 and 9 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-7 and 9-10.
With respect to step 2B These claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts. Claims 2-7 and 9-10 recite the additional elements of "wherein the extracting operation is performed by a material flow tracking system that is processing the sensor measurements. wherein the machine learning processes previous sensor measurements when executing the forecasting operation. with the additional operation of automatically halting at least a part of the industrial system after detecting the anomaly. with the additional operation of outputting, by a user interface, an alert to an operator after detecting the anomaly. wherein the machine learning model has been initially trained by a Gradient-based Reconciling Propagation algorithm in order to learn trainable parameters of a projection matrix, wherein the projection matrix is used to project base forecasts to coherent forecasts in a hierarchically-coherent solution space, and wherein the coherent forecasts contain the predicted time series values. wherein the Gradient-based Reconciling Propagation algorithm ensures that information propagation between forecasts is restricted to nodes who are connected through an ancestral and descendant relation, by masking entities of the projection matrix by a second matrix, thereby constraining the effects of the projection matrix." These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-7 and 9-10 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Regarding claim 9, the claim limitation recites “a computer program product, comprising a computer readable hardware storage device”. However, the usage of the phrase “machine-readable storage medium” is broad enough to include both “non-transitory” and “transitory” media. The specification further explicitly does not limit the utilization of a non-transitory computer-readable medium (See specification, ¶ [0089] where “storage medium" transitory and non-transitory mediums are discussed, however, readable medium is not defined). When the specification is silent, the BRI of a CRM and a computer readable storage media (CRSM) in view of the state of the art covers a signal per se. See Ex parte Mewherter, 2012-007962 (PTAB, 2013). Therefore, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
Claims 1-3, 5 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Haji Soleimani (US 20210272027 A1 hereinafter Haji) in view of Trinh et al. (US 11307570 B2 hereinafter Trinh)
As to independent claim 1, Haji teaches a computer implemented method for detecting sensor anomalies, comprising the following operations, wherein the operations are performed by components, and wherein the components are software components executed by one or more processors and/or hardware components: [demand sensing system with instructions, processors and memory ¶45-47]
forecasting, by a machine learning model, [forecasts via an algorithm including OLS, NNLS (require models) ¶59, ¶83]
wherein the machine learning model models a material flow in an industrial system, as a hierarchical time series, [forecasts demand in a supply chain across different time ¶43, ¶55 "hierarchy is an event time-based hierarchy, where the different levels of the hierarchy represent different time periods of demand"]
wherein the hierarchical time series represents a structure of the material flow using a directed acyclic graph with a set of nodes and a set of edges, [Fig. 3 illustrates a hierarchy of nodes (level 0 to 2) in a tree without cycles (DAG) with nodes (A, A1) and edges (lines) ¶51-55 " hierarchy 300 is a hierarchy of parts and customers, with three levels: Level 0 302 (the Root or entire data); Level 1 304 (Part/SKU level); and Level 2 306 (Part-Customer level). Eight nodes are labelled as: A1, A2, A3, B1, B2, A, B, Data)."]
wherein each node is associated to a time series, and wherein the edges represent parent-child relations where each value of a time series at a parent node equals the sum of the respective values of its child nodes, [summation matrix and forecasts Fig. 5 504 ¶62-63 " reconciled forecasts 502 of each of the eight nodes is the sum of the child forecast nodes below, with the bottom-level nodes having an optimal bottom-level forecast 506"]
predicted time series values for all nodes, [forecast values for nodes Fig. 4, ¶57 "forecast values: A1=20, A2=30 and A3=50; B1=80 and B2=70; A=100; B=150; and Data=250"]
Haji does not specifically teach receiving current sensor measurements from sensors placed in the industrial system, extracting observed time series values for at least some or all of the nodes from the current sensor measurements, computing a difference between the predicted time series values and the observed time series values, and detecting an anomaly if the difference exceeds a threshold.
However, Trinh teaches receiving current sensor measurements from sensors placed in the industrial system,[receives sensor data from sensors at a facility Fig. 1 140, Col. 1 ln. 40-56 "receiving a set of sensor data generated from sensors associated with equipment"]
extracting observed time series values for at least some or all of the nodes from the current sensor measurements, [time--series and nodes Col. 11-12 ln. 57-11, Col. 6 ln. 63-67 "data generated by a sensor 154 may be in any suitable format such as a time-series format"]
computing a difference between the predicted time series values and the observed time series values, and [measures the difference Col. 16 ln. 5-21 " machine learning model may measure the difference between the predicted historical measurements of the vital (e.g. outputs of the machine learning model) and the actual historical measurements of the vital. The training reduces or minimizes the value of the objective function. The difference may be measured in L1 norm, L2 norm"]
detecting an anomaly if the difference exceeds a threshold. [detect anomaly and compares to a score threshold Col. 7 ln. 50-67 "When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair. "]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the forecasting process disclosed by Haji by incorporating the receiving current sensor measurements from sensors placed in the industrial system, extracting observed time series values for at least some or all of the nodes from the current sensor measurements, computing a difference between the predicted time series values and the observed time series values, and detecting an anomaly if the difference exceeds a threshold disclosed by Trinh because both techniques address the same field of machine learning and by incorporating Trinh into Haji improve predictions and detection using more cost effective operations [Trinh Col. 1 ln. 21-36]
As to dependent claim 2, the rejection of claim 1 is incorporated, Haji and Trinh further teach wherein the extracting operation is performed by a material flow tracking system that is processing the sensor measurements. [Trinh maintenance systems that monitors sensors tracking physical or chemical properties Col. 5-6 ln. 63-62 "predictive maintenance system 100… Sensors 154 may also be various chemical or biosensors that detect chemical molecules, concentration, and biological components"]
As to dependent claim 3, the rejection of claim 1 is incorporated, Haji and Trinh further teach wherein the machine learning processes previous sensor measurements when executing the forecasting operation. [Trinh training data (previous sensor data) Col. 8 ln. 33-51, Col. 11 ln. 16-36 "collect data at various stages to train different types of machine learning models as the data develop"]
As to dependent claim 5, the rejection of claim 1 is incorporated, Haji and Trinh further teach with the additional operation of outputting, by a user interface, an alert to an operator after detecting the anomaly. [Trinh alerts in a user interface Col. 14 ln. 25-62 "The alert may be sent as a message or may be displayed on a user interface"]
As to independent claim 8, Haji teaches a system for detecting sensor anomalies, comprising: [demand sensing system with instructions, processors and memory ¶45-47]
a machine learning model, [forecasts via an algorithm including OLS, NNLS (require models) ¶59, ¶83]
wherein the machine learning model models a material flow in an industrial system, as a hierarchical time series, [forecasts demand in a supply chain across different time ¶43, ¶55 "hierarchy is an event time-based hierarchy, where the different levels of the hierarchy represent different time periods of demand"]
wherein the hierarchical time series represents a structure of the material flow using a directed acyclic graph with a set of nodes and a set of edges, [Fig. 3 illustrates a hierarchy of nodes (level 0 to 2) in a tree without cycles (DAG) with nodes (A, A1) and edges (lines) ¶51-55 " hierarchy 300 is a hierarchy of parts and customers, with three levels: Level 0 302 (the Root or entire data); Level 1 304 (Part/SKU level); and Level 2 306 (Part-Customer level). Eight nodes are labelled as: A1, A2, A3, B1, B2, A, B, Data)."]
wherein each node is associated to a time series, and wherein the edges represent parent-child relations where each value of a time series at a parent node equals the sum of the respective values of its child nodes, [summation matrix and forecasts Fig. 5 504 ¶62-63 " reconciled forecasts 502 of each of the eight nodes is the sum of the child forecast nodes below, with the bottom-level nodes having an optimal bottom-level forecast 506"]
predicted time series values for all nodes, [forecast values for nodes Fig. 4, ¶57 "forecast values: A1=20, A2=30 and A3=50; B1=80 and B2=70; A=100; B=150; and Data=250"]
one or more processors, [processors and memory ¶45-47]
Haji does not specifically teach an interface, configured for receiving current sensor measurements from sensors placed in the industrial system,, extracting observed time series values for at least some or all of the nodes from the current sensor measurements, computing a difference between the predicted time series values and the observed time series values, and detecting an anomaly if the difference exceeds a threshold.
However, Trinh teaches an interface, configured for receiving current sensor measurements from sensors placed in the industrial system, [receives sensor data from sensors at a facility Fig. 1 140, Col. 1 ln. 40-56 "receiving a set of sensor data generated from sensors associated with equipment"]
extracting observed time series values for at least some or all of the nodes from the current sensor measurements, [time--series and nodes Col. 11-12 ln. 57-11, Col. 6 ln. 63-67 "data generated by a sensor 154 may be in any suitable format such as a time-series format"]
computing a difference between the predicted time series values and the observed time series values, and [measures the difference Col. 16 ln. 5-21 " machine learning model may measure the difference between the predicted historical measurements of the vital (e.g. outputs of the machine learning model) and the actual historical measurements of the vital. The training reduces or minimizes the value of the objective function. The difference may be measured in L1 norm, L2 norm"]
detecting an anomaly if the difference exceeds a threshold. [detect anomaly and compares to a score threshold Col. 7 ln. 50-67 "When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair. "]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the forecasting process disclosed by Haji by incorporating the an interface, configured for receiving current sensor measurements from sensors placed in the industrial system, extracting observed time series values for at least some or all of the nodes from the current sensor measurements, computing a difference between the predicted time series values and the observed time series values, and detecting an anomaly if the difference exceeds a threshold disclosed by Trinh because both techniques address the same field of machine learning and by incorporating Trinh into Haji improve predictions and detection using more cost effective operations [Trinh Col. 1 ln. 21-36]
As to dependent claim 9, the rejection of claim 1 is incorporated, Haji and Trinh further teach a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method with program instructions for carrying out a method according to claim 1. [Haji instructions, processors and memory ¶45-47]
As to dependent claim 10, the rejection of claim 9 is incorporated, Haji and Trinh further teach wherein the provision device stores and/or provides the computer program product. [Haji instructions, processors and memory ¶45-47]
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Haji and Trinh, as applied in the rejection of claim 1 above, and further in view of Didari et al. (US 20200104639 A1 hereinafter Didari)
As to dependent claim 4, Haji and Trinh, teach the rejection of claim 1 that is incorporated.
Haji and Trinh, do not specifically teach with the additional operation of automatically halting at least a part of the industrial system after detecting the anomaly.
However, Didari teaches with the additional operation of automatically halting at least a part of the industrial system after detecting the anomaly. [shuts downs (halts) when anomaly is detected ¶18 "provide alerts to users and shut down manufacturing equipment responsive to detecting an anomaly"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Haji and Trinh by incorporating the with the additional operation of automatically halting at least a part of the industrial system after detecting the anomaly disclosed by Didari because all techniques address the same field of machine learning systems and by incorporating Didari into Haji and Trinh reduces down time, better finds root causes of anomalies and saves energy consumption [Didari ¶18]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Tawari et al. (US 20230004805 A1) teaches forecasting hierarchical time series data (see Fig. 5)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143