Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending. This Office Action is responsive to the amendment filed on 08/05/2025, which has been entered into the above identified application.
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 (i.e., changing from AIA to pre-AIA ) 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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 8-10, 12, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (US 20220138625 A1, Japanese application filed 02/21/2019); in view of McIntyre et al. (US 6137112 A, filed 09/10/1998), hereinafter McIntyre; in further view of Wu et al. (“Covariance intersection-based fusion algorithm for asynchronous multirate multisensor system with cross-correlation”, published 12/13/2016), hereinafter Wu. Kobayashi, McIntyre and Wu were cited in the previous Office Action.
Regarding Claim 1, Kobayashi teaches a sensor system comprising:
a first sensor (Kobayashi: “The first sensor data according to the present embodiment is sensor data as a target for which labeling is to be performed, and may be, for example, chronological data that is collected by various motion sensors” [0044]);
a second sensor (Kobayashi: “The second sensor data according to the present embodiment is sensor data that improves efficiency of labeling operation with respect to the first sensor data… Further, the first sensor data and the second sensor data according to the present embodiment are different kinds of sensor data that are collected by different sensors.” [0050]); and
a master unit, wherein the master unit includes a processor (Kobayashi: “the information processing system according to the present embodiment includes a sensor data collection apparatus 10, a labeling data collection apparatus 20, and the information processing apparatus 30” [0042]; “As illustrated in FIG. 12, the information processing apparatus 30 includes, for example, a processor 871, a read only memory (ROM) 872, a random access memory (RAM) 873, a host bus 874, a bridge 875, an external bus 876, an interface 877, an input device 878, an output device 879, a storage 880, a drive 881, a connection port 882, and a communication device 883.” [0123]) configured to:
acquire data measured by the first sensor and data measured by the second sensor (Kobayashi: “A sensor data collection unit 110 according to the present embodiment collects the first sensor data by using various motion sensors.” [0046]; “A labeling data collection unit 210 according to the present embodiment collects the second sensor data.” [0052]), and
generate learning data which is used for machine learning of a learning model and in which acquired data of the first sensor is regarded as input data and acquired data of the second sensor is regarded as label data indicating a property of the input data (Kobayashi: “The information processing apparatus 30 is able to estimate a candidate label and generate learning data through the flows as described above, on the basis of the input first sensor data and the input second sensor data” [0118]; “The second sensor data according to the present embodiment is sensor data that improves efficiency of labeling operation with respect to the first sensor data.” [0050]; “determine whether to adopt collected first sensor data as learning data for machine learning, wherein the controlling includes presenting, on the user interface, the first sensor data, second sensor data that is collected together with the first sensor data, and a label candidate that is estimated from the second sensor data” [0006]).
However, Kobayashi fails to expressly disclose a first sensor configured to measure a workpiece; and a second sensor configured to measure the workpiece in a relatively longer cycle than the first sensor.
In the same field of endeavor, McIntyre teaches a first sensor configured to measure a workpiece (McIntyre: “An ion implanter including a time of flight energy measurement apparatus for measuring and controlling the energy of an ion beam includes an ion source for generating the ion beam, an ion acceleration assembly for accelerating the beam resulting in the beam comprising a series of ion pulses having a predetermined frequency and beam forming and directing structure for directing the ion beam at workpieces supported in an implantation chamber of the implanter. The time of flight energy measurement apparatus includes spaced apart first and second sensors” [Abstract]); and
a second sensor configured to measure the workpiece (McIntyre: “An ion implanter including a time of flight energy measurement apparatus for measuring and controlling the energy of an ion beam includes an ion source for generating the ion beam, an ion acceleration assembly for accelerating the beam resulting in the beam comprising a series of ion pulses having a predetermined frequency and beam forming and directing structure for directing the ion beam at workpieces supported in an implantation chamber of the implanter. The time of flight energy measurement apparatus includes spaced apart first and second sensors” [Abstract]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a first sensor configured to measure a workpiece; and a second sensor configured to measure the workpiece, as taught by McIntyre, to the system of Kobayashi because both of these systems are directed towards data collection using a multi-sensor system. In making this combination and applying the multi-sensor system of Kobayashi to workpieces, it would allow the system of Kobayashi to “achiev[e] desired implantation” in manufactured products by providing “accurate control, measurement and monitoring” (McIntyre: [Col. 1, Lines 48-50]).
However, Kobayashi and McIntyre still fail to expressly disclose a second sensor configured to measure the workpiece in a relatively longer cycle than the first sensor.
In the same field of endeavor, Wu teaches a second sensor configured to measure the workpiece in a relatively longer cycle than the first sensor (Wu: “The l sensors observe the common target during the same period with different sampling rates asynchronously.” [Section 2. Problem Formulation]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a second sensor configured to measure the workpiece in a relatively longer cycle than the first sensor, as taught by Wu, to the system of Kobayashi and McIntyre because both of these systems are directed towards data collection using a multi-sensor system. Wu teaches that “in practice, the observation of sensors may be performed using asynchronous samples at different sampling rates in many cases. When different types of sensors are used for synergy, the sampling rate of each sensor uniform is unreasonable, for example, GPS will not have the same sampling rate as INS in the navigation system” [Section 1. Introduction]. When using multiple sensors, it is more likely than not that the different sensors will naturally have different sampling rates, thereby making one of the sensor’s measurement cycle longer than the other. In light of this, it would have been obvious to try an orientation in which the second sensor has the longer measurement cycle, as there are only two options for an orientation with two sensors. One of ordinary skill in the art would have reasonably been able to recognize this phenomenon, as taught by Wu, and make this combination by applying the two-sensor set up in which the second sensor has the longer cycle to the system of Kobayashi and McIntyre.
Regarding Claim 2, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 1, wherein the processor generates the learning data by matching the input data to the label data based on a time difference calculated from a movement speed of the workpiece and a distance between the first sensor and the second sensor, a measurement cycle of the first sensor, and a measurement cycle of the second sensor (McIntyre: “As explained above, the time period, T, between pulses is known because the frequency of pulses is known from the rf ion accelerator. The number of pulses, N, between the first and second sensors is known based on the FEM energy approximation. The timing circuitry accurately determines .DELTA.t using a cross correlation calculation of the digitized waveforms received from the first and second sensors. Once the time, t, of the pulse traversing the distance, d, between the first and the second sensors is accurately known, conversion circuitry calculates the velocity of an ion pulse as follows: v(pulse)=d/t, where d is the distance between the first and second sensors.” [Col. 3, Lines 20-31]; See [Col. 10, Lines 23-46]; BRI in light of [Fig.7] and Paragraphs [0120]-[0122] of the specification would support that “matching the input data with the label data” is achieved by determining an offset between the two sensors).
Regarding Claim 3, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 1, wherein the first sensor is installed upstream from the second sensor in a line in which the workpiece is moving (McIntyre: “The first sensor and a second sensor are disposed adjacent the ion beam and spaced a predetermined distance apart, the second sensor being downstream of the first sensor” [Abstract]).
Regarding Claim 4, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 1, wherein the processor is further configured to perform machine learning of the learning model using the learning data to generate a learned model (Kobayashi: “an exemplary case is illustrated in which the first sensor data according to the present embodiment is used to train a recognizer that recognizes a gesture using a hand of the user from motion data” [0070]).
Regarding Claim 6, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 1, wherein a plurality of the first sensors is included, and wherein the processor is further configured to calculate, for one of the plurality of first sensors, a correlation coefficient between the acquired data of the first sensor and the acquired data of the second sensor (McIntyre: “The timing circuitry accurately determines .DELTA.t using a cross correlation calculation of the digitized waveforms received from the first and second sensors.” [Col. 3, Lines 24-26]).
Regarding Claims 8-10 and 12, they are product claims that correspond to the system of Claims 1-2, 4 and 6. Therefore, they are rejected for the same reasons as Claims 1-2, 4 and 6 above.
Regarding Claim 16, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 2, wherein the first sensor is installed upstream from the second sensor in a line in which the workpiece is moving (McIntyre: “The first sensor and a second sensor are disposed adjacent the ion beam and spaced a predetermined distance apart, the second sensor being downstream of the first sensor” [Abstract]).
Regarding Claim 17, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 2, wherein processor is further configured to perform machine learning of the learning model using the learning data to generate a learned model (Kobayashi: “an exemplary case is illustrated in which the first sensor data according to the present embodiment is used to train a recognizer that recognizes a gesture using a hand of the user from motion data” [0070]).
Regarding Claim 18, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 3, wherein the processor is further configured to perform machine learning of the learning model using the learning data to generate a learned model (Kobayashi: “an exemplary case is illustrated in which the first sensor data according to the present embodiment is used to train a recognizer that recognizes a gesture using a hand of the user from motion data” [0070]).
Regarding Claim 19, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 2, wherein a plurality of the first sensors is included, and wherein the processor is configured to calculate, for one of the plurality of first sensors, a correlation coefficient between the acquired data of the first sensor and the acquired data of the second sensor (McIntyre: “The timing circuitry accurately determines .DELTA.t using a cross correlation calculation of the digitized waveforms received from the first and second sensors.” [Col. 3, Lines 24-26]).
Regarding Claim 20, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 3, wherein a plurality of the first sensors is included, and wherein the processor is further configured to calculate, for one of the plurality of first sensors, a correlation coefficient between the acquired data of the first sensor and the acquired data of the second sensor (McIntyre: “The timing circuitry accurately determines .DELTA.t using a cross correlation calculation of the digitized waveforms received from the first and second sensors.” [Col. 3, Lines 24-26]).
Claims 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi, in view of McIntyre, in further view of Wu, as applied to Claims 4 and 10; in further view of Jain et al. (US 11238370 B2; filed 12/31/2018), hereinafter Jain. Jain was cited in the previous Office Action.
Regarding Claim 5, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 4. However, they fail to expressly disclose wherein the processor is further configured to input the acquired data of the first sensor to the learned model and causes the learned model to output a predicted value.
In the same field of endeavor, Jain teaches wherein the master unit further includes a prediction unit that inputs the acquired data of the first sensor to the learned model and causes the learned model to output a predicted value (Jain: “Information describing the first sensor data and the second sensor data can be provided to a machine learning model trained to predict whether a pair of sensors are calibrated or mis-calibrated based on sensor data captured by the pair of sensors” [Abstract]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the master unit further includes a prediction unit that inputs the acquired data of the first sensor to the learned model and causes the learned model to output a predicted value, as taught by Jain, to the system of Kobayashi, McIntyre and Wu, because both of these systems are directed to data collection using a multi-sensor system for training a predictive machine learning model. In making this combination and making a prediction, it would allow the system of Kobayashi, McIntyre and Wu to utilize the machine learning model that it trained to make determinations in real-time (Jain: [Col. 4, Lines 19-24]).
Regarding Claim 11, it is a product claim that correspond to the system of Claim 5. Therefore, it is rejected for the same reason as Claim 5 above.
Claims 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi, in view of McIntyre, in further view of Wu, as applied to Claims 4 and 10; in further view of Jones (US 20220092346 A1; provisional application filed 09/18/2019). Jones was cited in the previous Office Action.
Regarding Claim 7, Kobayashi, McIntyre and Wu teach the sensor system according to Claim 1, wherein a plurality of the first sensors is included (Kobayashi: “The first sensor data according to the present embodiment is sensor data as a target for which labeling is to be performed, and may be, for example, chronological data that is collected by various motion sensors” [0044]), and
wherein the processor generates learning data in which data acquired from at least one of the plurality of first sensors is regarded as input data (Kobayashi: “The information processing apparatus 30 is able to estimate a candidate label and generate learning data through the flows as described above, on the basis of the input first sensor data and the input second sensor data” [0118]).
However, Kobayashi, McIntyre and Wu fail to expressly disclose wherein the processor is further configured to calculate a learning progress value indicating a ratio of learning progress of the learned model based on the acquired data of the second sensor and a predicted value output by inputting the input data to the learned model generated by performing the machine learning of the learning model using the learning data.
In the same field of endeavor, Jones teaches wherein the processor is further configured to calculate a learning progress value indicating a ratio of learning progress of the learned model based on the acquired data of the second sensor and a predicted value output by inputting the input data to the learned model generated by performing the machine learning of the learning model using the learning data (Jones: “As a simple example, assume the input sensory data set includes ten rows, that the input sensory data set includes two columns denoted A and B, and that the exemplary trained neural network model outputs a predicted value of B given an input value of A. In this example, testing the exemplary trained neural network model may include inputting each of the ten values of A from the input sensor data set, comparing the predicted values of B to the corresponding actual values of B from the input sensor data set, and determining if and/or by how much the two predicted and actual values of B differ. To illustrate, if a particular neural network correctly predicted the value of B for nine of the ten rows, then the exemplary fitness function may assign the corresponding model a fitness value of 9/10=0.9.” [0334]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the processor is further configured to calculate a learning progress value indicating a ratio of learning progress of the learned model based on the acquired data of the second sensor and a predicted value output by inputting the input data to the learned model generated by performing the machine learning of the learning model using the learning data, as taught by Jones, to the system of Kobayashi, McIntyre and Wu, because both of these systems are directed to using a trained machine learning model to make a prediction based on input data from a multi-sensor system. In making this combination and comparing the predicted values output by the model to the actual measured values, it would allow the system of Kobayashi, McIntyre and Wu to “indicate how well the exemplary trained neural network model models the input sensory data set” (Jones: [0334]).
Regarding Claim 13, it is a product claim that correspond to the system of Claim 7. Therefore, it is rejected for the same reason as Claim 7 above.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (US 20220138625 A1, Japanese application filed 02/21/2019); in view of McIntyre et al. (US 6137112 A, filed 09/10/1998), hereinafter McIntyre; in further view of Jain et al. (US 11238370 B2; filed 12/31/2018), hereinafter Jain, in further view of Wu et al. (“Covariance intersection-based fusion algorithm for asynchronous multirate multisensor system with cross-correlation”, published 12/13/2016), hereinafter Wu. Kobayashi, McIntyre, Jain and Wu were cited in the previous Office Action.
Regarding Claim 14, Kobayashi teaches a prediction device comprising a processor (Kobayashi: “the information processing system according to the present embodiment includes a sensor data collection apparatus 10, a labeling data collection apparatus 20, and the information processing apparatus 30” [0042]; “As illustrated in FIG. 12, the information processing apparatus 30 includes, for example, a processor 871, a read only memory (ROM) 872, a random access memory (RAM) 873, a host bus 874, a bridge 875, an external bus 876, an interface 877, an input device 878, an output device 879, a storage 880, a drive 881, a connection port 882, and a communication device 883.” [0123]) configured to:
acquire data measured by a first sensor and a second sensor (Kobayashi: “A sensor data collection unit 110 according to the present embodiment collects the first sensor data by using various motion sensors.” [0046]);
wherein the learned model is generated by performing machine learning of a learning model using learning data generated when the data of the first sensor is regarded as input data and data of the second sensor is regarded as label data indicating a property of the input data (Kobayashi: “an exemplary case is illustrated in which the first sensor data according to the present embodiment is used to train a recognizer that recognizes a gesture using a hand of the user from motion data” [0070]; “The information processing apparatus 30 is able to estimate a candidate label and generate learning data through the flows as described above, on the basis of the input first sensor data and the input second sensor data” [0118]; “The second sensor data according to the present embodiment is sensor data that improves efficiency of labeling operation with respect to the first sensor data.” [0050]; “determine whether to adopt collected first sensor data as learning data for machine learning, wherein the controlling includes presenting, on the user interface, the first sensor data, second sensor data that is collected together with the first sensor data, and a label candidate that is estimated from the second sensor data” [0006]); and
control a display to display the predicted value and determination result (Kobayashi: “As described above, the user interface according to the present embodiment may allow the user to determine whether to adopt the first sensor data. Further, the user may be allowed to select or modify the label candidate estimated by the label estimation unit 310 via the user interface.” [0097]).
However, Kobayashi fails to expressly disclose a prediction device predicting an abnormality or a sign of an abnormality of a workpiece; acquire data measured by a first sensor measuring the workpiece and a second sensor measuring the workpiece in a relatively longer cycle than the first sensor; input acquired data of the first sensor to a learned model and cause the learned model to output a predicted value; and determine whether the output predicted value is greater than an upper limit threshold or less than a lower limit threshold to set a determination result.
In the same field of endeavor, McIntyre teaches a prediction device predicting an abnormality or a sign of an abnormality of a workpiece (McIntyre: “Accuracy in both: a) the quantity of ions implanted in a semiconductor wafer workpiece during the implantation process; and b) the implantation depth of ion implantation in the workpiece surface are of critical importance in producing an acceptable end product. The allowable tolerances on implantation depth and total ion implantation quantity or dose in the manufacturing of many semiconductor devices are now at the 1% level in many applications.” [Col. 1, Lines 37-45]); and
acquire data measured by a first sensor measuring the workpiece and a second sensor measuring the workpiece (McIntyre: “An ion implanter including a time of flight energy measurement apparatus for measuring and controlling the energy of an ion beam includes an ion source for generating the ion beam, an ion acceleration assembly for accelerating the beam resulting in the beam comprising a series of ion pulses having a predetermined frequency and beam forming and directing structure for directing the ion beam at workpieces supported in an implantation chamber of the implanter. The time of flight energy measurement apparatus includes spaced apart first and second sensors” [Abstract]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a prediction device predicting an abnormality or a sign of an abnormality of a workpiece; and acquire data measured by a first sensor measuring the workpiece and a second sensor measuring the workpiece, as taught by McIntyre, to the system of Kobayashi because both of these systems are directed towards data collection using a multi-sensor system. In making this combination and applying the multi-sensor system of Kobayashi to predicting abnormalities in workpieces, it would allow the system of Kobayashi to “achiev[e] desired implantation” in manufactured products by providing “accurate control, measurement and monitoring” (McIntyre: [Col. 1, Lines 48-50]).
However, Kobayashi and McIntyre fail to expressly disclose a second sensor measuring the workpiece in a relatively longer cycle than the first sensor; input acquired data of the first sensor to a learned model and cause the learned model to output a predicted value; determine whether the output predicted value is greater than an upper limit threshold or less than a lower limit threshold to set a determination result.
In the same field of endeavor, Jain teaches to input acquired data of the first sensor to a learned model and cause the learned model to output a predicted value (Jain: “Information describing the first sensor data and the second sensor data can be provided to a machine learning model trained to predict whether a pair of sensors are calibrated or mis-calibrated based on sensor data captured by the pair of sensors” [Abstract]); and
determine whether the output predicted value is greater than an upper limit threshold or less than a lower limit threshold to set a determination result (Jain: “the prediction module 308 can apply a regression analysis to the inputted sensor data to determine an amount of mis-calibration between the first sensor and the second sensor” []; “The machine learning model can also output an amount of offset (or error) between the mis-calibrated pair of sensors. In various embodiments, at block 464, the amount of offset (or error) outputted by the model can be used to automatically adjust calibration parameters corresponding to the pair of sensors.” []; BRI of offset indicates deviation from central value which implies as upper and lower bound threshold at an offset distance from the central value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated to input acquired data of the first sensor to a learned model and cause the learned model to output a predicted value; determine whether the output predicted value is greater than an upper limit threshold or less than a lower limit threshold to set a determination result, as taught by Jain, to the system of Kobayashi and McIntyre, because both of these systems are directed to data collection using a multi-sensor system for training a predictive machine learning model. In making this combination and making a prediction with the model trained on sensor data, it would allow the system of Kobayashi and McIntyre to utilize the machine learning model that it trained to make determinations in real-time (Jain: [Col. 4, Lines 19-24]).
Kobayashi, McIntyre and Jain still fail to expressly disclose a second sensor measuring the workpiece in a relatively longer cycle than the first sensor.
In the same field of endeavor, Wu teaches a second sensor in a relatively longer cycle than the first sensor (Wu: “The l sensors observe the common target during the same period with different sampling rates asynchronously.” [Section 2. Problem Formulation]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated a second sensor in a relatively longer cycle than the first sensor, as taught by Wu, to the system of Kobayashi, McIntyre and Jain because both of these systems are directed towards data collection using a multi-sensor system. Wu teaches that “in practice, the observation of sensors may be performed using asynchronous samples at different sampling rates in many cases. When different types of sensors are used for synergy, the sampling rate of each sensor uniform is unreasonable, for example, GPS will not have the same sampling rate as INS in the navigation system” [Section 1. Introduction]. When using multiple sensors, it is more likely than not that the different sensors will naturally have different sampling rates, thereby making one of the sensor’s measurement cycle longer than the other. In light of this, it would have been obvious to try an orientation in which the second sensor has the longer measurement cycle, as there are only two options for an orientation with two sensors. One of ordinary skill in the art would have reasonably been able to recognize this phenomenon, as taught by Wu, and make this combination by applying the two-sensor set up in which the second sensor has the longer cycle to the system of Kobayashi, McIntyre and Jain.
Regarding Claim 15, it is a method claim that correspond to the system of Claim 14. Therefore, it is rejected for the same reason as Claim 14 above.
Response to Arguments
Examiner acknowledges the Applicant’s amendments to Claims 1-2, 4-14 and 17-20.
Applicant’s arguments, filed on 08/05/2025, regarding the rejection of Claims 14 and 15 under 35 U.S.C. § 101 have been fully considered and are persuasive. The rejections have been withdrawn.
Applicant’s arguments, filed on 08/05/2025, traversing the rejection of Claims 1-20 under 35 U.S.C. § 103 have been fully considered but are not persuasive.
Applicant alleges, on Pages 14-15 of the Remarks, that the combination of Kobayashi, McIntyre and Wu fail to teach or render obvious the feature of “a first sensor configured to measure a workpiece; a second sensor configured to measure the workpiece in a relatively longer cycle than the first sensor” of the independent claim 1 because:
McIntyre does not disclose different sampling cycles, and Wu does not provide any specific motivation, teaching, or implementation to modify McIntyre’s pulse triggered system to introduce differing measurements cycles for the two sensors.
McIntyre’s sensors are not configured to measure the workpiece itself, but rather properties of the ion beam used to process the workpiece.
Regarding point (a), while McIntyre on its own does not disclose different sampling cycles, Wu suggests that, in multi-sensor systems, in practice, there is a reasonable expectation of non-uniform sampling rates in multi-sensor systems, especially when multiple types of sensors are used. Coupled with Kobayashi, which teaches using multiple sensors for measurement for machine learning prediction, it is reasonable to assume that the two sensors are naturally asynchronous. Then, given that in a two-sensor system there can only be two orientations for the order in which the sensors are situated on the line taught by McIntyre, it would be obvious to try the orientation in which the second sensor has the longer sampling rate, in the same way that it would be obvious to try the orientation in which the first sensor has the longer sampling rate.
Regarding point (b), the present claims do not describe how or what the sensors are measuring, merely that they are measuring the workpiece. The specification provides an example in which the amount of light received by the sensor is used to determine the thickness of the workpiece by measuring how much light the workpiece is blocking. BRI in light of the specification would then support that “measuring a workpiece” would encompass measuring something (e.g. light blockage, ion energy) that indicates a quality of a workpiece (e.g. thickness, ion implantation depth).
Thus, Examiner asserts that the 35 U.S.C. § 103 rejection set forth above is proper and establishes a prima facie case of patent ineligibility.
Claims 8 and 14-15 are similarly ineligible as they correspond to the ineligible independent claim 1. Claims 2-7, 9-13 and 16-20 are ineligible for their dependency on an ineligible claim, as well as for their own deficiencies as noted in the 35 U.S.C. § 103 analysis above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Botcha et al. (“Process-machine interactions and a multi-sensor fusion approach to predict surface roughness in cylindrical plunge grinding process”) discusses a multi-sensor system to predict the surface quality of a workpiece in a plunge grinding process.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.E.H./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143