DETAILED ACTION
Claims 1-14 are pending. Claim 14 is new.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to German Patent Application No. 10202113338.5, filed on 12/15/2021.
Response to Arguments
Applicant’s arguments, filed 12/1/25, have been fully considered but are not persuasive.
Applicant’s comments on the claim objections (page 6) are noted and the claims are no longer objected to.
Applicant argues, with regard to 35 U.S.C. § 101, that ‘Applicant's model is used to obtain more reliable information about the state of the IOLink system and/or at least one IO-Link device… Unlike the prior art, which monitors the technical parameters by way of field devices such as sensors, Applicant's recited method monitors the technical parameters by way of the current Im, the voltage Um, etc. measured at the port of an IO-Link master… Applicant's claims recite the specific technical implementation of the model… The above-mentioned features are not known to the skilled person… this method… processes is novel as well as unexpected and surprising. While the recording of current, voltage, and/or electrical power measurement values for IO-Link systems is already known, the use of the specific model being claimed here is not. Use of the model to generate monitoring of a condition and/or detection of anomalies, errors, deviations, and/or maintenance indicators and/or a prediction of maintenance requirements, errors, and or failures of the IO-Link system and/or at least one IO-Link device and/or the plant from the measured values, cannot be regarded as a conventional model based analysis, but, as already explained, as a specific technical implementation’ (pages 6-7).
It is respectfully submitted that ensuring that information is reliable is an abstract concept and that issues of novelty/obviousness are a separate enquiry under 35 U.S.C. § 102 and 103. In addition, as detailed below in the current rejection under 35 U.S.C. § 103, the claimed invention is considered obvious and unsurprising. Further, as indicated by Applicant, ‘recording of current, voltage, and/or electrical power measurement values for IO-Link systems is already known’ and Applicant indicates that ‘the specific model being claimed here is not’, i.e. the alleged inventive concept is defined by the model, but eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) as cited in MPEP 2106.04, i.e. an improvement to the abstract idea itself (a model) is still merely an abstract idea. Applicant’s arguments are therefore not persuasive.
Applicant states that ‘Applicant believes that Lenz does not disclose an IO-Link system and therefore, also does not disclose an IO-Link master at whose at least one port electrical quantities such as current, voltage, and power are measured’ (page 8).
It is respectfully submitted that this moot because Lenz is not cited as teaching a ‘port’. Furthermore, Lenz teaches the other limitations because Lenz describes that a system 100 includes one or more field devices 102 (IO link device) shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N, an auxiliary processing module 118, and a fieldbus 124 [0036], and a field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power [0037-0038] and the field processing module 106 (IO link master) [0049]. Note that the claims are given their broadest reasonable interpretation in light of the specification, see MPEP 2111, e.g. in terms of interpreting IO links. Applicant’s arguments are therefore not persuasive.
Applicant argues that ‘However, the present application goes beyond Lens. Applicant's invention does not claim the monitoring of a plant, a plant component, and/or a process using field operational characteristics provided by field devices 102. Instead, the current Im, the voltage Um, etc. are monitored at the port of an IO-Link master. The fact that this method (measuring the current Im, the voltage Um, etc., at the port of an IO-Link master) is advantageous for monitoring the status of industrial processes is surprising and was not known to Lenz… In contrast to Lenz' s direct method based on temperature, pressure, etc., the present invention could be described as an indirect method that does not require additional sensors’ (pages 8-9).
It is respectfully submitted that, as detailed below in the current rejection under 35 U.S.C. § 103, the claimed invention is considered obvious over the combination of Lenz and Sidener and the above comments do not provide any reasoned argument or evidence to rebut that rejection. As indicated above, the claims are given their broadest reasonable interpretation in light of the specification, see MPEP 2111, but it is improper to import claim limitations from the specification. Features described in the specification that may potentially differentiate the instant invention from the prior art could be claimed. Applicant’s arguments are therefore not persuasive.
Applicant’s arguments regarding the dependent claims (page 9) are unpersuasive given the continued rejection of claim 1.
For at least these reasons, the rejection of the claims is 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.
Claim(s) 1, 6-11 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental process) of monitoring/identify a condition based on data.
Claim 1 recites a method for monitoring an IO link system and/or at least one IO link device (S1, S2, S3, S4, A1) of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system, i.e. a process, which is a statutory category of invention. The claim recites:
monitoring of a condition (Z) and/or a detection of anomalies (30-33), errors (50-52), deviations and/or maintenance indicators and/or a prediction of a maintenance requirement, an error and/or an outage of the IO link system and/or of the at least one IO link device (S1, S2, S3, S4, A1) and/or of the plant, of the plant part and/or of the process occurs in the IO link master (1) that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. an IO link system and/or at least one IO link device (S1, S2, S3, S4, A1) of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), recording a current (Im), voltage (Um) and/or electrical power (Pm) at at least one port (11) of an IO link master (1) of the IO link system (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) using generic computer technology) and usage of a model (M) for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) previously learned via machine learning (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C) do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Note that IO links/systems and plants are well-understood, routine and conventional, see for example the instant specification [pages 1-2], Kitamura et al. U.S. Patent Publication No. 20180196410 [0001-0003, Fig. 7] and the references cited below in the rejection under 35 U.S.C. § 103.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, an IO link system and/or at least one IO link device (S1, S2, S3, S4, A1) of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), recording a current (Im), voltage (Um) and/or electrical power (Pm) at at least one port (11) of an IO link master (1) of the IO link system (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) using generic computer technology) and usage of a model (M) for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) previously learned via machine learning (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C) are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 6 recites ‘an evaluation and/or correction of the result of the usage of the model (M) and/or a confirmation or characterisation of the current condition (Z), anomaly (30-33), error (50-52), deviation and/or maintenance indicator can be undertaken by a user via an interface’ (mental process). Thus this claim recites an abstract idea.
Claim 7 recites ‘a temporal course of the current (Im), of the voltage (Um) and/or of the electrical power (Pm) are recorded (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) using generic computer technology), and the temporal course and/or variables derived from the latter are used in the monitoring’ (mental process). Thus this claim recites an abstract idea.
Claim 8 recites ‘the current (Im), the voltage (Um) and/or the electrical power (Pm) are measured at several ports (11) of the IO link master (1) (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)), and the values, their temporal course and/or variables derived from them are used in combination in the monitoring’ (mental process). Thus this claim recites an abstract idea.
Claim 9 recites ‘one of the following variants of machine learning is used: artificial neural networks decision-tree based methods; margin-based methods; cluster methods; ensemble methods; nearest neighbour methods; linear and/or non-linear regression methods’ (linear regression is a mathematical process). Thus this claim recites an abstract idea.
Claim 10 recites ‘a pattern recognition using the measured values for the current (Im), the voltage (Um) and/or the electrical power (Pm) is undertaken on the basis of the model (M) in the event of a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and/or when a maintenance requirement, an error and/or an outage is predicted’ (mental process). Thus this claim recites an abstract idea.
Claim 11 recites ‘a statistical test method using measured values for the current (Im), the voltage (Um) and/or the electrical power (Pm) is undertaken when using the model (M) in the event of a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and/or when a maintenance requirement, an error and/or an outage is predicted’ (mental/mathematical process). Thus this claim recites an abstract idea.
Claim 13 recites ‘the monitoring is provided by the IO link master (1) having an electronic computing device that is equipped to carry out the monitoring’ (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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) 1-3, 5, 7-11 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenz et al. U.S. Patent Publication No. 20060058847 (hereinafter Lenz) in view of Sidener et al. U.S. Patent Publication No. 20210192384 (hereinafter Sidener).
Regarding claim 1, Lenz teaches a method for monitoring an IO link system and/or at least one IO link device (S1, S2, S3, S4, A1) of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system [0021-0023, Figs. 1-5 — a distributed operations system having integrated diagnostics includes a field device such as a sensor or an actuator for generating a field operating characteristic… The method also includes generating field operating data, including a field diagnostic parameter as a function of the field operating characteristic.; 0036 — a distributed monitoring, controlling and diagnostics system 100 according to one embodiment of the invention is illustrated. The system 100 can be any type of system requiring monitoring and controlling that can include, by way of example, a fabrication system, a manufacturing system, an assembly system, a processing system, an energy management system, a predictive maintenance system, a test system, a packaging system, and an operational control system. The system 100 includes one or more field devices 102 (IO link device) shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N, an auxiliary processing module 118, and a fieldbus 124.], the method comprising:
recording a current (Im), the-voltage (Um) and/or the electrical power (Pm) at an IO link master (1) of the IO link system [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)], and
monitoring of a condition (Z) and/or a detection of anomalies (30-33), errors (50-52), deviations and/or maintenance indicators and/or a prediction of a maintenance requirement, an error and/or an outage of the IO link system and/or of the at least one IO link device (S1, S2, S3, S4, A1) and/or of the plant, of the plant part and/or of the process occurs in the IO link master (1) by means of usage of a model (M) for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) [0036-0039 — system 100 can be any type of system requiring monitoring… system 100 includes one or more field devices 102 shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N… a plurality of field devices 102A-N are deployed for sensing, actuating, and generating one or more field operational or operating characteristics 105A to 105N via one or more field device facilities 104A or communication links. The field device 102 and the field device facility 104A can be analog or digital… field processing module 106 includes a field device communication module 108 with an interface to communicate with the field device 102 and receive field operating characteristic 105; 0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning)… field processing module 106 can perform an operational function, that can include by way of example, a device powering, a diagnostics, trouble shooting method, a statistical process control (SPC) computed parameter, fault detection, fault isolation, a root cause, a setting, a limit, a threshold, a calibration, a failure prediction].
But Lenz fails to clearly specify input at at least one port and a model previously learned via machine learning.
However, Sidener teaches input at at least one port [0028 — an input apparatus that can be generally stated as including a number of input ports that are structured to receive a number of sets of signals that are representative of a number of operational parameters of the system and to provide them as input signals to the processor apparatus] and a model previously learned via machine learning [0005 – An improved portable real-time system monitoring device is advantageously capable of autonomously learning the normal behavior of any system… the device uses a combination of machine learning and statistical process control to autonomously develop its own model of the monitored system and then autonomously monitor the system for unexpected behavior; 0027 — using a preexisting learned model and data from the previous system].
Lenz and Sidener are analogous art. They relate to monitoring systems, particularly systems that detect anomalies/faults using machine learning.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Lenz, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to utilize ports for inputs in order to transmit data to the internal processing apparatus, as suggested by Sidener [0028, 0038] and to use a previously learned model in order to accelerate the prediction/learning process, as suggested by Sidener [0027]. Furthermore, it would be obvious to simply substitute a known device with input ports for inputs for a known device with inputs for the predictable result of a method utilizing a device with input ports.
Regarding claim 2, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches the IO link master (1) and data being recorded for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)].
Further, Sidener teaches the model (M) is learned in the IO device via training data being recorded for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) and the corresponding conditions (Z), anomalies (30-33), errors (50-52), deviations and/or maintenance indicators, and the model (M) being calculated from these [0007-0008 — the device of the disclosed and claimed concept begins autonomously learning the normal behavior of the system. It learns the normal ranges of operation, for example if the pump were to have high-speed and low-speed normal operating modes.; 0038 — IO link device 4 has input/output ports; 0040-0041 — operational parameters of the system 8, when in the exemplary form of the aforementioned pump, might include, by way of example, motor current… primary input port 20A might be a conventional set of terminals to which wires would be attached such that the primary input port 20A is analog in nature and detects a voltage; 0054 — Since the model 84 was calculated based upon only a subset of the series of learning signals, i.e., it was based upon only the learning data set, the model 84 may optionally be recalculated based upon the entire series of learning signals, i.e., the learning data set and the validation set together to result in a recalculated or updated version of the model which is then stored as the model 84 in the storage 36; 0063-0067, Fig. 4 — an improved method in accordance with the disclosed and claimed concept. Processing can begin, as at 102, with a learning operation. As noted, the routines 40 cause the device 4 to autonomously learn the normal behavior of the system 8, and this is done by, for instance, receiving on a subset of the input ports 20 one or more series of first learning signals that are representative of one or more operational parameters of the system 8… Processing can then continue, as at 118, with the obtaining of an error signal that is based at least in part upon a difference between the predicted operational signal, which is a predicted value of the second operational signal, and the observed value of the second operational signal…. the routines 40 additionally advantageously permit further learning of normal system behavior in other fashions.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to do this modification in order for the monitoring method/device to automatically learn normal/abnormal behavior, as suggested by Sidener [0005-0008].
Regarding claim 3, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches the IO link master (1) and at least one other IO link master [0036 — one or more field processing modules 106A and 106B; 0049 — the field processing module 106 can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)].
Further, Sidener teaches the model (M) learned in the IO device is transferred to at least one other device [0038 — IO link device 4 has input/output ports; 0027 — Another aspect of the disclosed and claimed concept is to provide such an improved device and method which can be removed from one system and can be subsequently reapplied to a completely different new system in which such autonomous learning is repeated for the new system, or, if put onto a similar system, can be used to predict that system in an accelerated fashion using a preexisting learned model and data from the previous system].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to do this modification in order to use a previously learned model in order to accelerate the prediction/learning process, as suggested by Sidener [0027].
Regarding claim 5, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches measured values (Im, Um, Pm) [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power].
Further, Sidener teaches the model (M) is updated via the measured values (Im, Um) while being used [0016 — The device then initiates a continuous loop where it records incoming data, fits systems models based on machine learning, calculates prediction error, and identifies whether the system is unpredictable; 0040-0041 — operational parameters of the system 8, when in the exemplary form of the aforementioned pump, might include, by way of example, motor current… primary input port 20A might be a conventional set of terminals to which wires would be attached such that the primary input port 20A is analog in nature and detects a voltage; 0056 — the routines 40 can periodically perform a retraining or reevaluation operation in the background to determine whether any model generated by any other algorithm, whether or not previously employed, generates a candidate model with a better, i.e., lower, RSME].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to do this modification in order to continuously improve the model without interrupting system operation, as suggested by Sidener [0056].
Regarding claim 7, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches a temporal course of the current (Im), of the voltage (Um) and/or of the electrical power (Pm) are recorded [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)], and the temporal course and/or variables derived from the latter are used in the monitoring [0048 — field processing module 106 is configured to determine the occurrence of at least one operating event… Examples of field operating events include, by way of example, a change of a state, a change of a mode, a change of a status, a failure, a change of a field parameter, a change of the field operating characteristic, a time rate of change of a temperature characteristic (first derivative),; 0079 — The field processing module 106 compares the energy flow to the rate of temperature change of the container — Note that a change of state implies a temporal change. Additionally, given the explicit temporal temperature monitoring, it would at least be obvious to one having ordinary skill in the art to utilize temporal voltage/current/power monitoring in order to detect changing trends in conditions or to predict future abnormalities].
Regarding claim 8, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches the current (Im), the voltage (Um) and/or the electrical power (Pm) are measured at several ports (11) of the IO link master (1), and the values, their temporal course and/or variables derived from them are used in combination in the monitoring [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power… one or more of these operating characteristics can be representative of one or more other operating characteristics; 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning)… field processing module 106 can perform an operational function, that can include by way of example, a device powering, a diagnostics, trouble shooting method, a statistical process control (SPC) computed parameter, fault detection, fault isolation, a root cause, a setting, a limit, a threshold, a calibration, a failure prediction].
Further, Sidener teaches current (Im), the voltage (Um) and/or the electrical power (Pm) are measured at several ports (11) of the IO device, and the values, their temporal course and/or variables derived from them are used in combination in the monitoring [0038 — IO link device 4 has input/output ports; 0040-0041 — operational parameters of the system 8, when in the exemplary form of the aforementioned pump, might include, by way of example, motor current… primary input port 20A might be a conventional set of terminals to which wires would be attached such that the primary input port 20A is analog in nature and detects a voltage; 0063-0037, Fig. 4 — processing by the routines 40 can continue, as at 106, with the receipt on each of the subset of input ports of a first operational signal that is representative of the one or more of the operational parameters. Such processing can also include, as at 110, at least substantially simultaneously receiving on the another input port 20 a second operational signal that is representative of the another operational parameter… Processing can then continue, as at 118, with the obtaining of an error signal that is based at least in part upon a difference between the predicted operational signal, which is a predicted value of the second operational signal, and the observed value of the second operational signal. Processing then continues by inputting this error signal into a known statistical process control algorithm, as at 122. It is then determined, as at 126, whether the statistical process control algorithm has determined that the error signal exceeds a predetermined threshold].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to utilize multiple ports for multiple inputs in order to more easily transmit data to the internal processing apparatus, as suggested by Sidener [0028, 0038]. In addition, it would be obvious to one having ordinary skill in the art to utilize whatever combination of data best defines an error condition to detect the error. Furthermore, it would be obvious to simply substitute a known device with multiple input ports for inputs for a known device with inputs for the predictable result of a method utilizing a device with multiple input ports.
Regarding claim 9, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches that one of the following variants of machine learning is used: artificial neural networks decision-tree based methods; margin-based methods; cluster methods; ensemble methods; nearest neighbour methods; linear and/or non-linear regression methods [0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network].
Regarding claim 10, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches using the measured values for the current (Im), the voltage (Um) and/or the electrical power (Pm) is undertaken on the basis of the model (M) in the event of a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and/or when a maintenance requirement, an error and/or an outage is predicted [0036-0039 — system 100 can be any type of system requiring monitoring… system 100 includes one or more field devices 102 shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N… a plurality of field devices 102A-N are deployed for sensing, actuating, and generating one or more field operational or operating characteristics 105A to 105N via one or more field device facilities 104A or communication links. The field device 102 and the field device facility 104A can be analog or digital… field processing module 106 includes a field device communication module 108 with an interface to communicate with the field device 102 and receive field operating characteristic 105; 0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning)… field processing module 106 can perform an operational function, that can include by way of example, a device powering, a diagnostics, trouble shooting method, a statistical process control (SPC) computed parameter, fault detection, fault isolation, a root cause, a setting, a limit, a threshold, a calibration, a failure prediction].
Further, Sidener teaches pattern recognition using the measured values for the current (Im), the voltage (Um) and/or the electrical power (Pm) is undertaken on the basis of the model (M) in the event of a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and/or when a maintenance requirement, an error and/or an outage is predicted [0029 — receiving on the another input port a second operational signal that is representative of the another operational parameter, inputting at least a representation of each first operational signal into the at least first model to obtain from the at least first model a predicted operational signal that is a prediction of the another operational parameter, obtaining an error signal based at least in part upon a difference between the predicted operational signal and the second operational signal, subjecting the error signal to a statistical process control algorithm, and outputting an alarm when the statistical process control algorithm determines that the error signal exceeds a predetermined threshold. Such a threshold may be self-determined, wherein the device determines the threshold instead of the user. Such a predetermined threshold may also be a pattern of errors and not just a threshold of error.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to make this modification to more accurately/reliably define an error condition that is based on a pattern rather than using a single measurement/criterion, as suggested by Sidener [0028].
Regarding claim 11, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches a statistical test method using measured values for the current (Im), the voltage (Um) and/or the electrical power (Pm) is undertaken when using the model (M) in the event of a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and/or when a maintenance requirement, an error and/or an outage is predicted [0036-0039 — system 100 can be any type of system requiring monitoring… system 100 includes one or more field devices 102 shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N… a plurality of field devices 102A-N are deployed for sensing, actuating, and generating one or more field operational or operating characteristics 105A to 105N via one or more field device facilities 104A or communication links. The field device 102 and the field device facility 104A can be analog or digital… field processing module 106 includes a field device communication module 108 with an interface to communicate with the field device 102 and receive field operating characteristic 105; 0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0042-0045 — a statistical function… field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning)… field processing module 106 can perform an operational function, that can include by way of example, a device powering, a diagnostics, trouble shooting method, a statistical process control (SPC) computed parameter, fault detection, fault isolation, a root cause, a setting, a limit, a threshold, a calibration, a failure prediction; 0056-0061 — a statistical function… The operational function can be any operational function including a diagnostics, a trouble shooting method, a statistical process control (SPC) computed parameter].
Regarding claim 13, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches the monitoring is provided by the IO link master (1) having an electronic computing device that is equipped to carry out the monitoring [0082-0087 — Generally, the field processing module 106 and the auxiliary processing module 118 include an operating environment that can include a computer system with a computer that comprises at least one central processing unit (CPU)… ] The CPU can be of familiar design and includes an arithmetic logic unit (ALU) for performing computations, a collection of registers for temporary storage of data and instructions, and a control unit for controlling operation of the system. Any of a variety of processors, including at least those from Digital Equipment, Sun, MIPS, Atmel, Motorola, NEC, Intel, Cyrix, AMD, HP, and Nexgen, is equally preferred for the CPU].
Regarding claim 14, Lenz teaches a method for monitoring an IO link system and/or at least one IO link device (S1, S2, S3, S4, A1) of the IO link system and/or a plant, a plant part and/or a process that works together with the IO link system [0021-0023, Figs. 1-5 — a distributed operations system having integrated diagnostics includes a field device such as a sensor or an actuator for generating a field operating characteristic… The method also includes generating field operating data, including a field diagnostic parameter as a function of the field operating characteristic.; 0036 — a distributed monitoring, controlling and diagnostics system 100 according to one embodiment of the invention is illustrated. The system 100 can be any type of system requiring monitoring and controlling that can include, by way of example, a fabrication system, a manufacturing system, an assembly system, a processing system, an energy management system, a predictive maintenance system, a test system, a packaging system, and an operational control system. The system 100 includes one or more field devices 102 (IO link device) shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N, an auxiliary processing module 118, and a fieldbus 124.], the method comprising:
recording a current (Im), the-voltage (Um) and/or the electrical power (Pm) at an IO link master (1) of the IO link system [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)], and
monitoring of a condition (Z) and/or a detection of anomalies (30-33), errors (50-52), deviations and/or maintenance indicators and/or a prediction of a maintenance requirement, an error and/or an outage of the IO link system and/or of the at least one IO link device (S1, S2, S3, S4, A1) and/or of the plant, of the plant part and/or of the process occurs in the IO link master (1) by means of usage of a model (M) for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) [0036-0039 — system 100 can be any type of system requiring monitoring… system 100 includes one or more field devices 102 shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N… a plurality of field devices 102A-N are deployed for sensing, actuating, and generating one or more field operational or operating characteristics 105A to 105N via one or more field device facilities 104A or communication links. The field device 102 and the field device facility 104A can be analog or digital… field processing module 106 includes a field device communication module 108 with an interface to communicate with the field device 102 and receive field operating characteristic 105; 0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning)… field processing module 106 can perform an operational function, that can include by way of example, a device powering, a diagnostics, trouble shooting method, a statistical process control (SPC) computed parameter, fault detection, fault isolation, a root cause, a setting, a limit, a threshold, a calibration, a failure prediction];
the IO link master (1) and data being recorded for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) [0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)].
But Lenz fails to clearly specify input at at least one port and a model previously learned via machine learning; wherein the model (M) is learned in the IO device via training data being recorded for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) and the corresponding conditions (Z), anomalies (30-33), errors (50-52), deviations and/or maintenance indicators, and the model (M) being calculated from these.
However, Sidener teaches input at at least one port [0028 — an input apparatus that can be generally stated as including a number of input ports that are structured to receive a number of sets of signals that are representative of a number of operational parameters of the system and to provide them as input signals to the processor apparatus] and a model previously learned via machine learning [0005 – An improved portable real-time system monitoring device is advantageously capable of autonomously learning the normal behavior of any system… the device uses a combination of machine learning and statistical process control to autonomously develop its own model of the monitored system and then autonomously monitor the system for unexpected behavior; 0027 — using a preexisting learned model and data from the previous system];
wherein the model (M) is learned in the IO device via training data being recorded for the current (Ie), the voltage (Ue) and/or the electrical power (Pe) and the corresponding conditions (Z), anomalies (30-33), errors (50-52), deviations and/or maintenance indicators, and the model (M) being calculated from these [0007-0008 — the device of the disclosed and claimed concept begins autonomously learning the normal behavior of the system. It learns the normal ranges of operation, for example if the pump were to have high-speed and low-speed normal operating modes.; 0038 — IO link device 4 has input/output ports; 0040-0041 — operational parameters of the system 8, when in the exemplary form of the aforementioned pump, might include, by way of example, motor current… primary input port 20A might be a conventional set of terminals to which wires would be attached such that the primary input port 20A is analog in nature and detects a voltage; 0054 — Since the model 84 was calculated based upon only a subset of the series of learning signals, i.e., it was based upon only the learning data set, the model 84 may optionally be recalculated based upon the entire series of learning signals, i.e., the learning data set and the validation set together to result in a recalculated or updated version of the model which is then stored as the model 84 in the storage 36; 0063-0067, Fig. 4 — an improved method in accordance with the disclosed and claimed concept. Processing can begin, as at 102, with a learning operation. As noted, the routines 40 cause the device 4 to autonomously learn the normal behavior of the system 8, and this is done by, for instance, receiving on a subset of the input ports 20 one or more series of first learning signals that are representative of one or more operational parameters of the system 8… Processing can then continue, as at 118, with the obtaining of an error signal that is based at least in part upon a difference between the predicted operational signal, which is a predicted value of the second operational signal, and the observed value of the second operational signal…. the routines 40 additionally advantageously permit further learning of normal system behavior in other fashions.].
Lenz and Sidener are analogous art. They relate to monitoring systems, particularly systems that detect anomalies/faults using machine learning.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Lenz, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to utilize ports for inputs in order to transmit data to the internal processing apparatus, as suggested by Sidener [0028, 0038], to use a previously learned model in order to accelerate the prediction/learning process, as suggested by Sidener [0027] and in order for the monitoring method/device to automatically learn normal/abnormal behavior, as suggested by Sidener [0005-0008]. Furthermore, it would be obvious to simply substitute a known device with input ports for inputs for a known device with inputs for the predictable result of a method utilizing a device with input ports.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Lenz and Sidener in view of Ramsoy et al. U.S. Patent Publication No. 20170308802 (hereinafter Ramsoy).
Regarding claim 4, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches the model (M) and the IO link master (1) [0042-0045 — field module processor 116 can include a microprocessor for processing the field operating characteristic and a field diagnostic component including at least one of an algorithm, a program, an artificial intelligence module, a neural network (machine learning); 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format].
Further, Sidener teaches he model (M) is pre-learned [0005 – An improved portable real-time system monitoring device is advantageously capable of autonomously learning the normal behavior of any system… the device uses a combination of machine learning and statistical process control to autonomously develop its own model of the monitored system and then autonomously monitor the system for unexpected behavior; 0027 — using a preexisting learned model and data from the previous system].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Sidener.
One of ordinary skill in the art would have been motivated to use a previously learned model in order to accelerate the prediction/learning process, as suggested by Sidener [0027].
But the combination of Lenz and Sidener fails to clearly specify the model (M) is pre-learned in an external system (PC) and transferred to the device.
However, Ramsoy teaches the model (M) is pre-learned in an external system (PC) and transferred to the device [0005 — a computer-implemented method includes the steps of: receiving, at one or more central site servers from one or more data sources, historical data associated with a plurality of outcomes; generating, by the central site servers, a plurality of datasets from the historical data; training, by the central site servers (external system) and using the datasets, a set of models to predict an outcome, wherein a particular model in the set of models comprises a plurality of sub-models corresponding to a hierarchy of components of an industrial asset; combining, by the central site servers, the set of models into an ensemble model; and transmitting, from the central site servers, the ensemble model to one or more remote sites].
Lenz, Sidener and Ramsoy are analogous art. They relate to monitoring systems, particularly systems using machine learning.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Ramsoy.
One of ordinary skill in the art would have been motivated to do this modification to enable the model training to take place more efficiently/quickly on a more powerful remote computer system with more available resources than the local (IO link) device.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Lenz and Sidener in view of Krishnan et al. U.S. Patent Publication No. 20220351744 (hereinafter Krishnan).
Regarding claim 6, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
But the combination of Lenz and Sidener fails to clearly specify an evaluation and/or correction of the result of the usage of the model (M) and/or a confirmation or characterisation of the current condition (Z), anomaly (30-33), error (50-52), deviation and/or maintenance indicator can be undertaken by a user via an interface.
However, Krishnan teaches an evaluation and/or correction of the result of the usage of the model (M) and/or a confirmation or characterisation of the current condition (Z), anomaly (30-33), error (50-52), deviation and/or maintenance indicator can be undertaken by a user via an interface [0028 — This alert may be presented at a user interface of computing device coupled to factory server 110A or cloud platform 180. This alert may also serve as a label for the sensor data, such that the sensor data associated with the time the anomaly alert can be used to further train the local ML model 106A. In some instances, the user interface presenting the anomaly alert may request a confirmation that the anomaly alert is indeed accurate or that the anomaly alert is false (e.g., a user at the user interfaces selects a user interface element to confirm or reject the anomaly alert for the machine). This confirmation may also be provided with a label to further train the local ML model. Although training node 105A and factory server 110A are depicted as separate devices, the training node 105A may be implemented as part of the factory server 110A].
Lenz, Sidener and Krishnan are analogous art. They relate to monitoring systems, particularly systems that detect anomalies/faults using machine learning.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Krishnan.
One of ordinary skill in the art would have been motivated to enable a user to confirm an anomaly/condition, as suggested by Krishnan [0028], that may be particularly useful when significant uncertainty exists regarding identification of the anomaly/condition.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Lenz and Sidener in view of Mopur et al. U.S. Patent Publication No. 20200151619 (hereinafter Mopur).
Regarding claim 12, the combination of Lenz and Sidener teaches all the limitations of the base claims as outlined above.
Further, Lenz teaches IO link data and IO link devices [0036 — a distributed monitoring, controlling and diagnostics system 100 according to one embodiment of the invention is illustrated. The system 100 can be any type of system requiring monitoring and controlling that can include, by way of example, a fabrication system, a manufacturing system, an assembly system, a processing system, an energy management system, a predictive maintenance system, a test system, a packaging system, and an operational control system. The system 100 includes one or more field devices 102 (IO link device) shown as field devices 102A to 102N, one or more field processing modules 106A and 106B, one or more bitbuses 112A to 112N, an auxiliary processing module 118, and a fieldbus 124; 0037-0038 —field device 102 can include, by way of example, a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a force sensor, a liquid detection sensor, a stress sensor, a motion sensor, a position sensor, a voltage sensor, a current sensor… field operating characteristics 105 can include by way of example, in one form or another, a resistance, a current, a voltage, a Hall effect voltage, an energy, a mass, a power, including an electrical power; 0049 — the field processing module 106 (IO link master) can include one or more other operational components such as an analog-to-digital conversion component (not shown). In such a case, the field device 102 can generate the field operating characteristic 105 in an analog format. The field processing module 106 receives the analog format and generates the field operating data 111 in a digital format.; 0042 — the field memory 114 can store the received field operating characteristic 105 (recording)].
But the combination of Lenz and Sidener fails to clearly specify that additional data that is transferred from one or several of the devices is used during learning (41) of the model (M), and/or in the usage of the learned model (M), and/or in a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and or when a maintenance requirement, an error and/or an outage are predicted.
However, Mopur teaches that additional data that is transferred from one or several of the devices is used during learning (41) of the model (M), and/or in the usage of the learned model (M), and/or in a classification (44) of a condition (Z) and/or when anomalies (30-33), errors (50-52), deviations and/or maintenance indicators are detected, and or when a maintenance requirement, an error and/or an outage are predicted [0015 — One example of such data processing and ML (machine learning) analytics inference may include predictive analytics of sensors data from machines in factories to monitors such as vibration, temperature for potential anomalies and predictive maintenance; 0024-0028, Fig. 2 — intelligent model updating system 200 includes an IoT edge 240 comprising a plurality of sensors 202. Sensors 202 may include any device configured to collect data and send such collected data to through a data stream to a computing edge device 204… the transmitted data (i.e., the aggregated model metadata and sensor data) can be saved as historical data for future use in comparison and training of ML models.].
Lenz, Sidener and Mopur are analogous art. They relate to monitoring systems, particularly systems using machine learning.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Lenz and Sidener, by incorporating the above limitations, as taught by Mopur.
One of ordinary skill in the art would have been motivated to do this modification to enable the model to be trained using relevant data that may not be available in a single local device (IO link), as suggested by the teachings of Mopur that describe collecting related data from different sensors [e.g. 0026].
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference 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. See MPEP 2123.
Conclusion
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119