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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an Indian application 2023-41066857 filed on October 5, 2023.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 5, 9, and 20 are rejected under 35 U.S.C. 102(a)(1) or 102(a)(2) as being anticipated by Schmidt et al. (US PG Pub. No. 20200116170), herein “Schmidt.”
Regarding claim 1,
Schmidt teaches a method comprising: monitoring a plurality of operating characteristics of a pneumatic control system; (Received/measured variables, data, measurements or metrics 60, or input/stored variables, metrics, information or data 60, whether received to the microprocessor 23 by user-input or feedback from any of the sensors 25, includes at least: electric actuator motor current, pneumatic actuator air pressure, valve stem torque, valve vibration, valve temperature, valve stem emissions, media type, media flow, media pressure, and actuator dwell time amongst others.
logging a plurality of instances of the operating characteristics; (Par. 0034: “The storage device 50 may be any suitable storage device for storing data. The data collection unit 51 may collect, gather, manipulate, and/or categorize the data 60 transmitted by the sensors 25 about the smart or intelligent valve 10 as well as process system 70 and/or media 18 and received by the microprocessor 23 or electronics module 20. Each sensor 25 contributes metrics or data 60 which lead to a partial view of the underlying smart valve 10 and actuator 15 condition. When combining the metrics 60 of a group or plurality of sensors 25 using real-time analytical techniques, an accurate evaluation of the valve 10 and actuator 15 condition may be obtained. The data collection unit 51 may manipulate the collected data into a format that allows the operator and/or the microprocessor 23 to take appropriate action during the operations. The risk assessment or analysis unit 52 may receive the categorized data 60 from the data collection unit 51 in order to determine if there is any present or future risk likely at the smart valve 10 and may make predictions not limited to remaining valve life…”
creating a baseline based on the plurality of instances of the operating characteristics; (Par. 0031: “Additional information used by the microprocessor 23 in its algorithms may include one or more stored control schedules, algorithms, immediate control inputs received through a control or display interface 55a, and data, commands, commissioning, and other information received from other processing systems (including the data communication between the computing units 23, 16a and 17a), remote data-processing systems, including cloud based data-processing systems (not illustrated) and may further include statistical analysis of mean, deviation, deviation of baseline, Bayesian, and FFT (including other analyses) of data 60.”)
comparing a trend in the plurality of instances of the operating characteristics to the baseline; (Par. 0031: “The data 60 (for example, from the accelerometer 31, flow sensor 43, temperature sensor 32, strain gauge 30, and/or torque sensors 44) may be collected and analyzed both singularly and collectively to determine faults, predicted faults, comparison to base line readings, and others using statistical models such as Bayesian decision making and fine analysis of raw data 60 using Fast Fourier Transforms (hereinafter, also "FFT').” Par. 0034, last portion: “show shifts in baseline performance measured at commissioning compared to long term operation. These changes can be directly correlated to actuator 15 and valve 10 performance and lead to predictive methods that indicate potential actuator 15 and valve 10 failure or predict the need for service. When this analysis is correlated with direct torque measurements 60 of the valve stem 11, the statistical significance of the correlated data results in accurate predictive assertions.”)
and predicting a component failure based on the comparison. (Par. 0031: “Analog and digital interfaces 55a of the microcontroller 23 may process the sensor data 60 and perform real-time analysis of the collected data 60. The microprocessor 23 can extract and deduce from the raw real-time sensor data 60 information or predictions regarding (and not limited to): remaining valve 10 life, remaining actuator 15 life, service intervals, potential pending failure or loss of service, and preventative maintenance.” See also claim 16.)
Regarding claim 2,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Schmidt also teaches monitoring the plurality of operating characteristics comprises monitoring a cycle time, a flow rate, and a pressure associated with a pneumatically controlled component. (Par. 0031: “…the microprocessor 23 may monitor and record the valve 10 vibration, and valve stem 11 torque data 60 over several periods of time into the physical data storage component 50, and adjust the position of the smart valve 10 accordingly to account for wear/deterioration for a necessary media flow 18 volume or amount and/or alert the operator when the sensed data or metric 60 exceeds a stored desired data value or set of parameters for the corresponding sensed data 60.” Par. 0031: “The data 60 (for example, from the accelerometer 31, flow sensor 43, temperature sensor 32, strain gauge 30, and/or torque sensors 44) may be collected…” Par. 0028: “An optional second pressure sensor 30 may sense, record, measure or obtain and transmit a metric or data 60 of a pneumatic actuator 15 air pressure.” “…pneumatic actuator air pressure, valve stem torque, valve vibration, valve temperature, valve stem emissions, media type, media flow, media pressure, and actuator dwell time amongst others.”)
Regarding claim 4,
The previously cited reference(s) teach the limitations of claim 1 which claim 4 depends. Schmidt also teaches wherein the baseline comprises a mathematical model of the pneumatic control system. (Par. 0031: “…predicted faults, comparison to base line readings, and others using statistical models such as Bayesian decision making and fine analysis of raw data 60 using Fast Fourier Transforms (hereinafter, also "FFT').
Regarding claim 5,
The previously cited reference(s) teach the limitations of claim 1 which claim 5 depends. Schmidt also teaches wherein the baseline comprises a mathematical model of each monitored component of the pneumatic control system. (Par. 0031: “The data 60 (for example, from the accelerometer 31, flow sensor 43, temperature sensor 32, strain gauge 30, and/or torque sensors 44) may be collected and analyzed both singularly and collectively to determine faults, predicted faults, comparison to base line readings, and others using statistical models such as Bayesian decision making and fine analysis of raw data 60 using Fast Fourier Transforms (hereinafter, also “FFT”). The computations may be distributed between the microprocessors 23 and other computing units or electronics within the actuator 15 (such as microprocessors 16a or 17a). Received/measured variables, data, measurements or metrics 60, or input/stored variables, metrics, information or data 60, whether received to the microprocessor 23 by user-input or feedback from any of the sensors 25…”)
Regarding claim 9,
The previously cited reference(s) teach the limitations of claim 1 which claim 9 depends. Schmidt also teaches predicting the component failure comprises recognizing a pattern in the trend. (Par. 0031: “data, commands, commissioning, and other information received from other processing systems (including the data communication between the computing units 23, 16a and 17a), remote data-processing systems, including cloud-based data-processing systems (not illustrated) and may further include statistical analysis of mean, deviation, deviation of baseline,”)
Regarding claim 20, it is directed to a method of steps to implement the system or apparatuses set forth in claim 1. Schmidt teaches the claimed system or apparatuses in claim 1. Therefore, Schmidt teaches the method of steps in claims 20.
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.
Claims 6, 7, 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Chinese patent document Patnaik et al. (CN 112977437 A), herein “Patnaik.”
Regarding claim 6,
The previously cited reference(s) teach the limitations of claim 1 which claim 6 depends. Schmidt does not teach that the baseline is fixed or not updated. However, Patnaik teaches that wherein the baseline is fixed once created. (Page 2, last paragraph: 0027: “…updating the dynamics model of the group of tires based on the baseline information and the received sensor data; receiving, by one or more processors, information on at least one of (i) a road condition of a partial road or (ii) an environmental condition; determining, by the one or more processors based on the updated dynamics model and the received information, whether the probability of the tire fault of at least one tire in the set exceeds a threshold probability; and when the determination exceeds a threshold probability, one or more processors cause the autonomous vehicle to take corrective action.” Thus, the model is updated for the tires given the sensor data; and the baseline is fixed.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the pneumatic system that consists of at least a valve wherein sensor data related to flow and pressure is stored and a baseline is established for the component(s) wherein a failure can be predicted by comparing data with the baseline as in Schmidt with an air pressure monitoring system that creates a baseline that does not vary and the model varies based on sensor data with a fixed baseline as in Patnaik in order to model a particular time and predict when a possibility of failure is high. (Page 14, Par. 3).
Regarding claim 7,
The previously cited reference(s) teach the limitations of claim 1 which claim 7 depends. Schmidt does not teach that the baseline is moving (variable). However, Patnaik teaches the baseline includes fixed elements and moving elements, and wherein the moving elements are modified with subsequent instances of the operating characteristics. (Page 17, Par. 4: “The baseline information can be used as a reference for comparison with future tire inspection, and input model to predict tire fault. any one or all of the additional inspection of these factors can be performed during the travel period, such as periodically (e.g., each 1-5 inch or every 1-10 minutes, or more or less) or satisfies some standard or threshold value (e.g., tyre or external temperature exceeds 100; the tyre pressure exceeds the recommended pressure by more than 5-10 % and so on). In addition, data from a plurality of previous travel inspection can be used for updating the baseline information, such as considering weather conditions (e.g., warmer or cooler temperature) and other factors. For example, before the late afternoon check in summer months later, before the early morning check in winter, the check may start at a lower temperature and/or a lower pressure.”)
Regarding claim 8,
The previously cited reference(s) teach the limitations of claim 1 which claim 7 depends. Schmidt does not teach that the baseline is modified with operating conditions. However, Patnaik teaches the portion of monitor the baseline is modified with subsequent instances of the operating characteristics. (Page 17, last paragraph: “In addition, data from a plurality of previous travel inspection can be used for updating the baseline information, such as considering weather conditions (e.g., warmer or cooler temperature) and other factors. For example, before the late afternoon check in summer months later, before the early morning check in winter, the check may start at a lower temperature and/or a lower pressure. The data with respect to these different environmental conditions can be used to correspondingly adjust the baseline, for example, the pressure before the stroke in the winter morning can be in the allowable range, even if it is lower than the nominal pressure of some other time in one year (e.g., 32 pounds per square inch or 34 pounds per square inch).”)
Regarding claim 10,
The previously cited reference(s) teach the limitations of claim 9 which claim 10 depends. Schmidt does not teach a previous component failure. However, Patnaik teaches wherein the pattern is based on a previous component failure. (Page 19, Par. 2: “The machine learning model for such sensor information may be used to detect a burst or other fault condition, in addition to capturing information about the state of the
tire in real time by the sensor. In one example, the system (onboard or remote) may
construct an anomaly detection model. This method will find out an event as an
outliers from a common mode/event. In this case, it may include finding a tire that is
currently different from the expected condition. In another example, the monitoring
machine learning model can be trained on the still image or short video segment of
the tire.”
Regarding claim 11,
The previously cited reference(s) teach the limitations of claim 1 which claim 11 depends. Schmidt does not teach a trend of one component associated with a plurality of components. However, Patnaik teaches predicting the component failure comprises comparing the trend associated with one component with the baseline, which is associated with a plurality of components. (Page 3, Last Paragraph: “The control system is configured to: acquire baseline information of a set of tyres of the vehicle; during the driving period of the autonomous vehicle, receiving sensor data about at least one tire in the group of tires from the sensing system; updating the dynamics model of the group of tires based on the baseline information and the received sensor data; receiving information about at least one of (i) a road condition of a partial road or (ii) an environmental condition; determining whether the probability of a tire fault of at least one tire in the set exceeds a threshold probability based on the updated kinetic model and the received information; and when the determination exceeds a threshold probability, the autonomous vehicle to take corrective action. Page 18, Par. 4: “…described above, the model can be used for predicting the possibility of burst or other fault. This may be the possibility of a general burst, or the possibility of a particular tire may fail. In any case, if the possibility of failure exceeds a threshold prediction level, the vehicle can take one or more actions. If the vehicle is not starting or stopped in the stroke, it can request the tyre to replace or modify the freight weight distribution, or it can change the route and/or departure time, so as to avoid the expected road or environmental condition of the fault.”)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Kriss et al. (US PG Pub. No. 20190178680), herein “Kriss.”
Regarding claim 12,
The previously cited reference(s) teach the limitations of claim 1 which claim 12 depends. Schmidt does not teach that predicting a failure based on cycles. However, Kriss teaches that predicting the component failure comprises predicting when a pneumatically controlled component will malfunction based on a trend of increased cycle times of the pneumatically controlled component. (Par. 0033: “Predictive algorithms may be configured to identify and characterize trends and/or unexplained increases in replenishment events. The predictive algorithms may compare a current characterization of a system with a benchmark or baseline characteristic. In some implementations, the system operation and health may be characterized using monitored parameters and/or replenishment events and cycles. The predictive algorithms may detect and analyze trends and/or episodes that deviate from benchmark or baseline operations. The trends may be gradual, indicating deterioration of the system that may lead to system failure and/or inefficiency. Benchmark and baseline characteristics may be derived from operations of the system over a period of time in which the system is performing nominally. Benchmark and baseline characteristics may be generated using a population of comparable or peer systems.” Par. 0063, 0064, and 0065.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the pneumatic system that consists of at least a valve wherein sensor data related to flow and pressure is stored and a baseline is established for the component(s) wherein a failure can be predicted by comparing data with the baseline as in Schmidt with a system that has pneumatic components a predicting deterioration of a system which may lead to a system failure based on the number of cycles as in Kriss in order to anticipate and prevent equipment failures. (Par. 0057)
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Adams et al. (US Patent No. 6,035,878), herein “Adams.”
Regarding claim 13,
The previously cited reference(s) teach the limitations of claim 1 which claim 13 depends. Schmidt does not teach that predicting a failure based on increasing or decreasing flow rate(s). However, Adams teaches that predicting the component failure comprises predicting when a pneumatically controlled component will malfunction based on a trend of decreased flow rate through the pneumatically controlled component. (Col. 8, line 66: “…provides pneumatic pressure …” Col. 9, lines 40 – 51: “Baseline diagnostic data can be used to develop a "signature" for a specific regulator, which may be stored in the controller's memory or in the memory of an external system. Performance information provided to the diagnostics section 49 from the sensing section 50 and the alternate inputs section 53 may then be processed and compared to the baseline data, or signature, and the diagnostics section 49 can provide alarms, actual and predicted failures, and other diagnostic information to the system operator if regulator characteristics and performance deviate from the expected signature performance by more than some predetermined amount.” Col. 10, lines 21 – 52: “Inlet pressure sensitivity: FIGS. 9A and 9B each show three plots of control pressure vs. flow rate at various inlet pressures, labeled a, b, and c. This illustrates a regulator's sensitivity to varying inlet pressures. For a given flow rate, the difference between control pressures for different inlet pressures defines inlet sensitivity. The curves in FIG. 9A illustrate inlet sensitivity for a pressure reducing regulator, while FIG. 9B illustrates inlet sensitivity curves for a back pressure regulator. As with offset, the inlet sensitivity can be compared to baseline information to provide diagnostic and failure prediction information from the electronic controller to a user. Hysteresis and Deadband: Hysteresis is defined as the tendency of an instrument to give a different output for a given input, depending on whether the input resulted from an increase or decrease from the previous value. FIG. 10 illustrates a measure of hysteretic error which includes hysteresis and deadband. The curve labeled "a" shows control pressure plotted against flow rate for a decreasing flow demand. The curve labeled "b" shows a similar curve for increasing flow demand. In other words, curve "a" plots control pressure for given flow rates when the throttling element is moving in a first direction, and curve "b" plots control pressure for corresponding flow rates when the throttling element is moving in the opposite direction. The difference between the two curves is referred to as "deadband." Monitoring the slope of a hysteresis curve can provide information regarding spring constant, for example. A change in deadband or in the slope of a hysteresis curve may indicate or be used to predict problems with the spring, actuator, throttling element or other component of the regulator.” See figures 9A and 9B.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the pneumatic system that consists of at least a valve wherein sensor data related to flow and pressure is stored and a baseline is established for the component(s) wherein a failure can be predicted by comparing data with the baseline as in Schmidt with a pneumatic system that uses a decreasing flow rate comparing a baseline to predict a failure as in Adams in order to have a pressure vs flow rate comparison when predicting a failure using the baseline of the flow rate. (Col. 10, lines 21 – 52)
Regarding claim 14,
The previously cited reference(s) teach the limitations of claim 1 which claim 14 depends. Schmidt does not teach that predicting a failure based on increasing or decreasing flow rate(s). However, Adams teaches that predicting the component failure comprises predicting when a pneumatically controlled component will malfunction based on a trend of increased flow rate to the pneumatically controlled component. (Col. 10, lines 21 – 52. See rejection for claim 13 above.)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt in view of Nistane et al. (US Patent No. 20190081813), herein “Nistane.”
Regarding claim 15,
The previously cited reference(s) teach the limitations of claim 1 which claim 15 depends. Schmidt does not teach that predicting a failure based decreasing pressure. However, Nistane teaches that predicting the component failure comprises predicting when a pneumatically controlled component will malfunction based on a trend of decreased pressure associated with the pneumatically controlled component. (Par. 0070: “In case of any discrepancy in working of the system 100, the device 105, 106, 107 and 108 functioning may be impacted. Therefore, in such a situation, only the working of the primary server 103 may be switched off. The system thus provides better security and fall back over any potential problem after implementation of the system 100. This is possible as the system 100 is non-intrusive and non-disruptive, which means the system 100 does not alter the electric circuitry of devices 105, 106, 107 and 108 hence it if operationally very easy to connect or disconnect system 100 from devices 105, 106, 107 and 108. In one embodiment, the sensor unit 104 and/or switch unit 111 may be configured to capture error codes and operating status from devices 105, 106, 107 and 108. The sensor unit 104 and/or switch unit 111 may monitor operating mechanical and electrical parameters related to high and low pressure of refrigerant or any other liquid or gas, vibrations, temperature, humidity, air quality, flow rate, current, voltage, power factor, load factor, active power, reactive power, time of the day, frequency, phase reversal, single phasing of the devices 105, 106, 107 and 108. The analysis of these captured parameters and benchmark or reference operating parameters may give the system 100 ability to forecast any equipment failure. Said feature may be called as Asset Management. Said asset management may be enabled via wired or wireless network and computer implemented platform.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the pneumatic system that consists of at least a valve wherein sensor data related to flow and pressure is stored and a baseline is established for the component(s) wherein a failure can be predicted by comparing data with the baseline as in Schmidt with a pneumatic system that senses air pressure and predict a failure using a benchmark when the pressure is trending lower as in Nistane in order to have a system to raise alerts due to network failures, device failures, sensor or switch failure. (Par. 0078)
Allowable Subject Matter
Claim 3 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues above. Reasons for allowance will be held in abeyance pending final recitation of the claims. Schmidt teaches the portion of monitor a flow rate, a pressure, a torque associated with a pneumatically controlled component. (Par. 0027: “The sensor interface 42 receives or obtains one or more data or metric measurements 60 from the sensors 25 which may include one or more of the following types of sensors 25 (and may include multiples of each type of sensor 25): a force, strain, or pressure sensor 30; a vibration sensor or accelerometer 31; a temperature sensor 32 or thermocouple 35; a flow sensor 43; a torque sensor 44…”) Schmidt or other prior art does not teach with regard to a pneumatic control device and creating a baseline based on data from monitoring the plurality of operating characteristics comprises an opening speed, a closing speed, and a cycle count.
Claim 16 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues above. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art (see cite reference of Bader et al. below) does not teach all the elements of claim 16.
Claim 17 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues above. Reasons for allowance will be held in abeyance pending final recitation of the claims. Claim 17 is similar to claim 16 and the prior art does not teach: predicting the component failure comprises predicting when a first pneumatically controlled component will malfunction based on a trend of decreased flow rate through the first pneumatically controlled component compared with a second pneumatically controlled component.
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues above. Reasons for allowance will be held in abeyance pending final recitation of the claims. Claim 18 is similar to claim 16 and the prior art does not teach: predicting the component failure comprises predicting when a first pneumatically controlled component will malfunction based on a trend of increased flow rate to the first pneumatically controlled component compared with a second pneumatically controlled component.
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues above. Reasons for allowance will be held in abeyance pending final recitation of the claims. Claim 18 is similar to claim 16 and the prior art does not teach: predicting the component failure comprises predicting when a first pneumatically controlled component will malfunction based on a trend of decreased pressure associated with the first pneumatically controlled component compared with a second pneumatically controlled component.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Butler et al. (US PG Pub. No. 20070012052) teaches a pneumatic system (HVAC system) that senses and controls individual components of the system and creates a baseline for all components in the system. Par. 0040: “The thermostat 30 preferably comprises an initial set-up mode that will prompt scheduled operation periods of all of the various controllers and components upon installation, to speed the process of obtaining base line parameter information for the various controllers and components within the system.”)
Brown et al. (US PG Pub. No. 20200182377) also teaches many of the same elements as Schmidt. See paragraphs 0026 and 0030.
Kriss et al. (US PG Pub. No. 20190178680) also is relevant to the instant application and teaches a benchmark or baseline of a derived from operations of the system over a period of time in which the system is performing nominally. (Par. 0033) Kriss may also teach many or all of the elements of claim 1. Paragraph 0041 teaches that the system includes pneumatic motors used in manufacturing production line equipment.
Bader et al. (US Patent No. DE 102004028557 A1) teaches some elements of claim 16 such as predicting the component failure comprises predicting when a first pneumatically controlled component will malfunction based on a trend of increased cycle times (work cycle) (Page Par. “…pneumatic or hydraulic or electro-hydraulic or electric drives…” Page 13, Par. 2: “The work cycle defined by the working sections is first referred to as a torque curve on the display device of the PC 90 shown. Each torque profile section leaving a predetermined torque band, ie permissible minimum and maximum values for the torque band of this axis, are analyzed as such and evaluated in a subsequent method step. In a simple evaluation step, only the frequency of leaving the torque band within a specific time, predetermined by the work cycle, is used as the benchmark for the evaluation. Another possibility is that the curve in an analyzed torque profile section is used for the evaluation. overall, the torque profile sections are optionally additionally provided with an empirically determined factor from the frequency and / or the curve profile, and the current axis wear due to such a work cycle is estimated. The simplest axle wear which can be estimated using the method according to the invention is thus an axle wear per work cycle. With the knowledge of the previously completed working cycle of the robot 62 According to the invention then also the current state of wear of the robot 62 or the relevant first axis. On the basis of this estimate, a statement is then made possible which relates to the period during which this robot axis can still be operated during the current working cycle.” Page 7, Par. 3: “This common value is now compared to a comparison value matrix that is empirical For this special robot type and for the respective axle joint was determined empirically. The result The comparison is a qualitative or quantitative statement about the Condition of the relevant axle joint.” See also Page 4, Par. 2.) However, Bader does not teach a comparison of cycle times with another pneumatically controlled component; (Bader does not teach the first pneumatically controlled component compared with a second pneumatically controlled component.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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