DETAILED ACTION
Claims 1-17 (filed 10/11/2023) have been considered in this action. Claims 1-17 are newly filed.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 17/481,611, filed on 09/15/2023.
Claim Objections
Claim 11 is objected to because of the following informalities: a typographical error exists in that the limitation “at least one processor or circuit configured to function as:” is recited twice in a row in the claim, offering a duplicate claiming of such structure in claim 11. It is considered for the sake of claim interpretation that this limitation was intended to be recited only once, and this was merely a scrivener’s error.
Appropriate correction is required.
Claim Objections
Applicant is advised that should claim 1 be found allowable, claim 13 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). While claim 13 is directed to “A processing device using a feedback control device”, the body of the claim contains identical subject as claim 1 relating to the claimed feedback control device. The differences are primarily in the preamble and are insignificant towards technological improvement of the invention. Accordingly, the examiner considers these claimed differences lacking in any substantive manner and are directed towards the same invention.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites the limitation "the feedback control device" in the preamble of claim 16. There is insufficient antecedent basis for this limitation in the claim because a previously established “feedback control device” is not recited.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 5-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 requires that the control unit takes information regarding the control deviation as input in order to output ‘a control amount’ from the control unit, while claim 5, which is dependent upon claim 1, requires that ‘the control amount’ is input to the second control unit. These statements are in contradiction with what is described in the specification, as the specification never makes any reference or teaching to ‘the control amount’ being an input or being inputted to input nodes of the control unit. The specification makes repeated and explicit mention that ‘a control deviation’ which is determined according to a measured and target value are what is being input to the input nodes of the second control unit (i.e. the machine learning algorithm), not ‘the control amount’. The examiner considers this new matter because it has not been explained in a clear and concise way to one of ordinary skill in the art how the control amount relates to the inputs/input nodes of the second control unit. This is seemingly a typographical error in claim 5, that should be amended to explicitly claim that the control deviation is what is input at the input nodes, not the control amount.
Claims 6-10 are dependent upon claim 5, and thus inherit the rejection of claim 5 under 35 U.S.C. 112(a).
Allowable Subject Matter
Claim 9 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, and if the above rejection of claim 5 under 35 U.S.C. 112(a) is remedied in such rewritten form.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5, 6, 8 and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Published Japanese patent document JP2019071405 (hereinafter, Asano) in view of Goldman (US 5598508, hereinafter Goldman).
In regards to Claim 1, Asano teaches “A feedback control device that takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object, comprising:” ([page 4] The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) “at least one processor or circuit configured to function as:” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning;” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 2, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 1 above. Goldman further teaches “The feedback control device according to claim 1, wherein the sampling unit is configured to select, at a predetermined period, the predetermined number of control deviation data from a plurality of control deviation data included in the information regarding the history of the control deviation” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
In regards to Claim 3, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 1 above. Goldman further teaches “The feedback control device according to claim 1, wherein the sampling unit is configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that a time length of the predetermined number of control deviation data increases” ([col 7 line 22] Another situation which must be accounted for occurs if the waves are arriving at a relatively low frequency, and the buffer is partially filled with zeros waiting for a wave to begin. This situation is illustrated in the curve 82 shown in FIG. 7. If this happens, the end point determined by neural network 2 will never fall after the last collected data point. For example, they both may be at data point 300. Further, any time the end point is found in the last 20 nodes, it will be desirable to collect additional data. This will permit neural network 2 to get a good look at the terminal portion of the wave. To handle both these situations, the earliest 20 points 84 are deleted from the buffer so that the wave is "slid" to the left to make room for additional data collection. Deletion is pedormed by moving the 0 index value 20 points later, (to the right), and changing the 20 data point valves to -1. This will result in the wave 86 shown in FIG. 7. The assumption is that if no valid end point has yet been identified, (or if it found in the last 20 nodes), it is unlikely that the earliest data points are part of a valid wave and can be removed. Note that the number of neural network input points was chosen to be significantly larger than the anticipated maximum wave duration. Further, this shifting procedure insures that neural network 3 will have sufficient information about the terminal portion of the wave to make accurate classifications; wherein the sliding of values means that more than the initial 300 inputs (15 sec) are considered and thus the period increases and is over the entire 2 minute period).
In regards to Claim 5, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 1 above. Asano further teaches “The feedback control device according to claim 1, wherein the control unit includes a plurality of input nodes, and the control amount is input to the input nodes via a memory” ([page 4] The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer). Goldman further teaches “and the control amount is input to the input nodes via a memory capable of storing a plurality of the control deviations that is greater than the number of the input nodes” ([col 2 line 32] Initially, a first portion of the signal is transmitted to a neural network so that N consecutive samples of said signal are transmitted to N input nodes of the neural network; [col 4 line 65] In particular, a memory storage area in the program 38 comprises a circular input buffer 40 which temporarily stores the capnogram signal 26 before transferring it to a pair of neural networks, designated neural network No. 1, 42 and neural network No. 2, 44. These neural networks are trained to locate the start point and end point of an individual capnogram 28. Once the start point and end point are defined by neural network No. 1, 42 and No. 2, 44 outputs, the capnogram waveform 28 defined by these points is transmitted to neural network No. 3, 46 which has been trained to recognize specific abnormal waveform features; [col 5 line 44] Referring now to FIG. 3 a representation of a neural network 62 having 300 input nodes 64 and one output node 66 is shown. Each of the 300 input nodes receive data from the capnogram signal 26 contained in the input buffer 40 where data in each corresponding index number in the buffer is transmitted to one of the 300 input nodes having a corresponding index number. [col 8 line 12] Neural network 3, 46 is a three layer perceptron having 50 number of input nodes, so that it is sufficiently wide to accept the expected waves between start points and end points; wherein the memory is capable of storing more than the number of input nodes, because it can store the 300 nodes of each of the first two neural networks, while also 50 for the third neural network, thus the memory is capable of storing more than what is input to any of the neural networks individually).
In regards to Claim 6, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 1 above. Goldman further teaches “The feedback control device according to claim 5, wherein the sampling unit provides a plurality of the control deviations stored in the memory to the input nodes by thinning them out at the predetermined period.” ([col 4 line 57] The analog to digital conversion is pedormed at 20 hertz which is sufficiently fast to capture all the features of the capnogram 26 with a minimum amount superfluous data. A waveform analysis program 38 in accordance with the present invention is stored in the PC 36 memory and performs the steps of waveform analysis as described hereinafter. In particular, a memory storage area in the program 38 comprises a circular input buffer 40 which temporarily stores the capnogram signal 26 before transferring it to a pair of neural networks, designated neural network No. 1, 42 and neural network No. 2, 44; wherein a circular buffer is operated at the 20hz analog to digital conversion rate to fill the buffer with new data, while discarding old data in a circular manner at the 20hz rate (i.e. 1/20hz = 0.05s period)).
In regards to Claim 8, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 5 above. Goldman further teaches “The feedback control device according to claim 5, wherein the control deviation input to the memory is updated every certain predetermined interval” ([col 4 line 57] The analog to digital conversion is pedormed at 20 hertz which is sufficiently fast to capture all the features of the capnogram 26 with a minimum amount superfluous data. A waveform analysis program 38 in accordance with the present invention is stored in the PC 36 memory and performs the steps of waveform analysis as described hereinafter. In particular, a memory storage area in the program 38 comprises a circular input buffer 40 which temporarily stores the capnogram signal 26 before transferring it to a pair of neural networks, designated neural network No. 1, 42 and neural network No. 2, 44; wherein 20hz is the predetermined interval for inputting data to memory using circular buffer).
In regards to Claim 10, the combination of Asano and Goldman teach the feedback control device as incorporated by claim 1 above. Goldman further teaches “The feedback control device according to claim 5, further comprising a hold unit configured to hold the control deviation input to the memory for the predetermined interval” ([col 5 line 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; wherein the memory is stored for two minutes as a predetermined interval).
In regards to Claim 11, Asano teaches “A lithography device using a feedback control device, wherein the feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object” ([page 2] The present invention relates to a controller, a lithographic apparatus, a measuring apparatus, a processing apparatus, a planarization apparatus, and an article manufacturing method….In the present embodiment, an imprint apparatus will be described as an example of a lithography apparatus that forms a pattern on a substrate. [page 4] FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment.... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) “wherein the feedback control device comprises: at least one processor or circuit configured to function as” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning) “wherein the lithography device comprises a forming unit configured to form a pattern for lithography using a controlled object that is controlled by the feedback control device” ([page 7] The position control device described above can also be applied to an exposure apparatus having a forming unit that illuminates the mask and transfers the pattern of the mask onto the substrate by the projection optical system to form the pattern on the substrate.).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 12, Asano teaches “A measurement device using a feedback control device, wherein the feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object” ([page 7] the present invention can be applied to measurement devices other than the imprint device and processing devices. The measuring device includes the above-described position control device to control the position of the target object, and also includes a measuring unit that measures an object whose position is controlled by the position control device. The measurement unit may, for example, be a contact probe or a non-contact interferometer; [page 4] FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment.... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) “wherein the feedback control device comprises: at least one processor or circuit configured to function as:” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning;” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning) “wherein the measurement device comprises a measurement unit configured to measure the position of the controlled object that is controlled by the feedback control device.” ([page 7] The measuring device includes the above-described position control device to control the position of the target object, and also includes a measuring unit that measures an object whose position is controlled by the position control device. The measurement unit may, for example, be a contact probe or a non-contact interferometer).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 13, Asano teaches “A processing device using a feedback control device, wherein the feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object;” ([page 7] The processing apparatus also includes the above-described position control device to control the position of the target object, and also includes a processing unit that processes an object whose position is controlled by the position control device. As the processing unit, for example, a cutting tool or a laser may be mentioned.; [page 4] FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment.... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) ““wherein the feedback control device comprises: at least one processor or circuit configured to function as:” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning;” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning) “wherein the processing device comprises a processing unit configured to process the controlled object that is controlled by the feedback control device.” ([page 7] The processing apparatus also includes the above-described position control device to control the position of the target object, and also includes a processing unit that processes an object whose position is controlled by the position control device. As the processing unit, for example, a cutting tool or a laser may be mentioned).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 14, Asano teaches “A planarizing device using a feedback control device, wherein the feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object;” ([page 7] In the above embodiment, a mold having a pattern portion is used as the imprint apparatus, but a planarization apparatus (molding apparatus) for forming a resin on a substrate so as to be planarized using a mold having no pattern portion is also described above; [page 4] FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment.... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16.) “wherein the feedback control device comprises: at least one processor or circuit configured to function as” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning) “wherein the planarizing device comprises a planarizing unit configured to planarize a composition using a controlled object that is controlled by the feedback control device.” ([page 7] In the above embodiment, a mold having a pattern portion is used as the imprint apparatus, but a planarization apparatus (molding apparatus) for forming a resin on a substrate so as to be planarized using a mold having no pattern portion is also described above. A position control device can be applied. For example, it can be applied to position control of a mold or a stage of a substrate.).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 15, Asano teaches “An article manufacturing method using a lithography device, wherein the lithography device comprises a feedback control device;” ([page 2] The present invention relates to a controller, a lithographic apparatus, a measuring apparatus, a processing apparatus, a planarization apparatus, and an article manufacturing method….In the present embodiment, an imprint apparatus will be described as an example of a lithography apparatus that forms a pattern on a substrate) “wherein the feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object;” [page 4] FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment.... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) “wherein the feedback control device comprises: at least one processor or circuit configured to function as” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) ““a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning) “wherein the lithography device comprises a forming unit configured to form a pattern for lithography using a controlled object that is controlled by a feedback control device; and” ([page 2] an imprint apparatus will be described as an example of a lithography apparatus that forms a pattern on a substrate. FIG. 1 is a schematic view of an imprint apparatus. The imprint apparatus forms a pattern of a cured product to which a concavo-convex pattern of a mold is transferred by bringing an imprint material supplied on a substrate into contact with the mold and applying energy for curing to the imprint material; [page 7] The position control device described above can also be applied to an exposure apparatus having a forming unit that illuminates the mask and transfers the pattern of the mask onto the substrate by the projection optical system to form the pattern on the substrate.) “wherein the manufacturing method comprises: a step for forming a pattern on a substrate using the lithography device” ([page 2] The imprint apparatus forms a pattern of a cured product to which a concavo-convex pattern of a mold is transferred by bringing an imprint material supplied on a substrate into contact with the mold and applying energy for curing to the imprint material) “a step for processing a substrate on which the pattern is formed; and” ([page 2] the imprint apparatus supplies an imprint material on a substrate, and cures the imprint material in a state in which a mold (mold) on which a pattern of concavities and convexities is formed is in contact with the imprint material on the substrate) “a manufacturing step for manufacturing an article from a processed substrate” ([page 7] , a method of manufacturing an article (semiconductor IC element, liquid crystal display element, color filter, MEMS, etc.) using the above-described exposure apparatus will be described. The article includes the steps of exposing a substrate (wafer, glass substrate, etc.) coated with a photosensitive agent using the exposure apparatus described above, developing the substrate (photosensitive agent), and developing the substrate. It is manufactured by processing in the well-known processing process of Other known processes include etching, resist stripping, dicing, bonding, packaging and the like. According to this manufacturing method, it is possible to manufacture an article of higher quality than in the past).
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the method of forming a substrate device using a feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 16, Asano teaches “A non-transitory computer-readable storage medium configured to store a computer program to control each unit of the feedback control device;” ([page 4]FIG. 2 is a schematic view of a control system 200 (feedback controller) in the present embodiment. The dotted line is the control system 200, and a digital computer is used to perform complicated calculations. The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA, and a storage device such as a memory; wherein a memory is a storage medium) “he feedback control device takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object;” ([page 4] .... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16.) “wherein the feedback control device comprises: at least one processor or circuit configured to function as” ([page 4] The control system 200 includes an arithmetic processing unit such as a CPU or an FPGA) “a control unit configured to take a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation unit configured to operate the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning)
Asano fails to teach “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and”.
Goldman teaches “a sampling unit configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control unit is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
In regards to Claim 17, Asano teaches “A feedback control method that takes information regarding a history of a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object, the feedback control method comprising the following steps” (([page 2] The present invention relates to a controller, a lithographic apparatus, a measuring apparatus, a processing apparatus, a planarization apparatus, and an article manufacturing method….In the present embodiment, an imprint apparatus will be described as an example of a lithography apparatus that forms a pattern on a substrate. [page 4] .... In the control unit 1, the difference between the stage position measured value) sent from the position measurement unit 2 by the deviation calculation unit 13 and the target value of the stage position sent from the position command unit 3 (control deviation, hereinafter called stage deviation) ) And sends the stage deviation to the controller 11 and the controller 12....The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16) “a control step for taking a predetermined number of control deviation data included in information regarding the history of the control deviation as input and to output a control amount for the controlled object, and in which a parameter for calculating the control amount is determined by machine learning” ([page 4] FIG. 3 shows the configuration of the controller 12. The controller 12 comprises a deviation memory 15 for storing a history of stage deviations, and a neural network 16. The deviation memory 15 has a predetermined number (N, N is a natural number), and stores the stage deviation for the last N steps. The neural network 16 outputs the value corresponding to the correction value of the force instruction value (output value) of the controller 11 when the neural network 16 inputs N stages of stage deviations in the deviation memory 15 to the input layer, Parameters such as network weights have been adjusted; wherein a neural network is a form of machine learning) “an operation step for operating the controlled object using the control amount output from the control unit; and” ([page 4] The controller 11 receives the information on the stage deviation and outputs the operation amount to the wafer stage 7….The second control unit receives information on the stage deviation, and a parameter for outputting an operation amount to the wafer stage 7 is determined by machine learning)
Asano fails to teach “a sampling step for thinning out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control step is selected; and”.
Goldman teaches “a sampling step for thinning out, at a predetermined period, the information regarding the history of the control deviation so that the predetermined number of control deviation data input to the control step is selected; and” ([page 5 col 34] the circular input buffer 40 is over-sized so that about two minutes of data can be stored. Nevertheless, the goal of the preferred embodiment is to be able to recognize a capnogram 28 observation in 15 seconds or less. Data is sampled at 20 hertz and fed to neural networks one and two, each having 300 input nodes. Thus, since there is a one to one correspondence of collected data points to input nodes a maximum of 300 data points 15 seconds can be analyzed (300.div.20=15). A given location in the buffer 40 is defined as an index point; [col 11 line 22] Referring now to FIG. 21, a summary of the steps performed by the waveform analysis system 30 are shown. In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2; wherein because the buffer is circular, it is constantly having new data replace the old data at the rate of 20Hz as the old data is discarded).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the feedback control device that uses a neural network that inputs a plurality of historical data deviation data to determine a control amount as taught by Asano, with the use of the neural network that takes a plurality of historical data and thins out the data by using a circular ring buffer that takes in new information and discards old information at a set frequency/period as taught by Goldman, because both inventions are in the same field of using neural-network controllers, and thus their combination can be considered taking known structures that would functions the same way in a similar invention. For example, Asano makes no mention of how the data is received by the neural network, but Goldman teaches such a structure through the ring/circular buffer used to store a preset number of data values for being fed into a neural network, thus their combination is just the use of the known ring buffer applied to the neural network of Asano in a known way that achieves predictable results.
Claim(s) 4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Asano and Goldman as applied to claims 1 and 5 respectively above, and further in view of James et al. (“Synthesis, Stability Analysis, and Implementation of a Multirate Repetitive Learning Controller”, hereinafter James).
In regards to Claim 4, the combination of Asano and Goldman teaches the feedback control device as incorporated by claim 1 above.
Goldman further teaches “The feedback control device according to claim 1, wherein the sampling unit is configured to thin out, at a predetermined period, the information regarding the history of the control deviation” ([col 4 line 57] The analog to digital conversion is pedormed at 20 hertz which is sufficiently fast to capture all the features of the capnogram 26 with a minimum amount superfluous data. A waveform analysis program 38 in accordance with the present invention is stored in the PC 36 memory and performs the steps of waveform analysis as described hereinafter. In particular, a memory storage area in the program 38 comprises a circular input buffer 40 which temporarily stores the capnogram signal 26 before transferring it to a pair of neural networks, designated neural network No. 1, 42 and neural network No. 2, 44. [col 11 line 24] In the first step 114 the entire contents of the buffer 40 at an arbitrary point in time is shown. To the left of the vertical dotted line the wave which was last identified is shown and is about to be discarded from the buffer. To the right of the vertical dotted line is the newly collected raw data which is to be transmitted to the inputs of neural networks number 1 and number 2).
The combination of Asano and Goldman fail to teach “…so that a sampling pitch of the predetermined number of control deviation data increases”.
James teaches “wherein the sampling unit is configured to thin out, at a predetermined period, the information regarding the history of the control deviation so that a sampling pitch of the predetermined number of control deviation data increases.” ([page 653 col 1] This paper introduces a multirate repetitive learning controller with an adjustable sampling rate that can be used as an “add-on” module to further enhance the performance of a feedback control system. As a result of its multirate characteristics, the user can choose a sampling rate to achieve the required performance based on a trade-off between the accuracy and the complexity of the controller. The controller learns the plant input based on the tracking error down-sampled; [page 658 col 1] For our multirate repetitive learning control experiments, we have used a down-sampling ratio of 5; wherein a down sampling ratio of 5 means for every 5 samples, one is accepted by the neural network which is an increase in sampling pitch compared to 1:1 ratio).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system which uses a constant sampling rate as taught by Goldman, with the use of a modified sampling rate that can be selected by a user and is 1:5 of the input data as taught by James, because it would gain the stated benefit of James, namely having a similar stability while using less memory to store the sampled values for a complete period. By combining these elements, it can be considered taking the known sampling unit of Goldman that samples and thins out data at a predetermined period of sampling, and improving it with the use of a modifiable sampling rate such that an input to a neural network which is downsampled at a particular sampling ratio is utilized by the control system in a known way that achieves predictable results.
In regards to Claim 7, the combination of Asano and Goldman teaches the feedback control device as incorporated by claim 5 above.
The combination of Asano and Goldman fail to teach “The feedback control device according to claim 5, wherein the predetermined period is a value that is set according to the period of a disturbance noise”.
James teaches “The feedback control device according to claim 5, wherein the predetermined period is a value that is set according to the period of a disturbance noise” ([page 653 col 1] The order of a discrete-time repetitive learning controller is directly proportional to both its sampling rate and the period length of the fundamental periodic task/disturbance. If the repetitive learning controller is made to sample at the same rate as the plant compensation (which is often chosen to be as fast as possible/practical), then the resulting order of the repetitive learning controller may be very large. Practically speaking, a repetitive learning controller with too high an order may be unstable, have undesirable high frequency characteristics, and require inhibitively large (and wasteful) memory storage. In cases such as these, it is usually necessary to implement a control architecture in which the learning controller samples at a slower rate corresponding to an integer multiple of the plant’s sampling period; we will refer to this multiple as the “sampling ratio.” This arrangement is called multirate repetitive learning control and is a special case of multirate control; [page 654 col 1] In the configuration shown in Figure 1, the disturbances, d,, are shown entering the system at the output. However, since the plant is assumed to be linear and the magnitude and phase of the disturbances are unknown, the exact point of injection is not important. T, is the closed-loop plant sampling period and we assume for now that the sampling ratio m = 1; let K denote the discrete-time index. The error signal, e,.(K), is fed into the repetitive learning controller through a gain, KL, and the filter Q(z). The signal then enters a N-step delay positive feedback loop. Note that Q(z) can be non-causal, with relative degree up to -N. [page 655 col 1] In this controller, the error signal, e,(n), is first down-sampled, as shown in Figure 2 for linear weighted average down-sampling; wherein the filtered disturbances are canceled by the selection of Q based upon the downsampled learning controller and its selected downsampling ratio).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system which uses a predetermined period to thin out sampled values as taught by Goldman, with the use of an adjustable sampling rate that sets a sampling rate for filtering out values to stabilize the system from disturbances as taught by James, because it would gain the stated benefit of James, namely similar performance as a 1:1 sampling ratio, but with less memory requirements for a downsampled data with the same stability and disturbance rejection capabilities. By combining these elements, it can be considered taking the known use of a downsampled input that is selected by a user to be effective at reducing a disturbance, and using it in place of the sampler of Goldman that has a fixed sampling period in a known way that achieves predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lu et al. (US 5159660) – teaches a neural network with sampler that has sampling rate determined by the nature of the process being controlled
Stewart et al. (US 4893066) – teaches a servo control loop with adjustable sampling rates
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