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 .
Continuity/reexam data
Parent data
18820857 filed 08/30/2024 Claims Priority from Provisional Application 63536234 , filed 09/01/2023
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None
Last updated by sbekurezion on 09/11/2024 10:51:29
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(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-20
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a method, a system and a tangible, non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recite “generating based on at least one of the samples of the observation data, processed by a machine learning template derivation system, a filtering template to separate the signal component from the noise component, wherein the machine learning template derivation system comprises one or more trainable layers, with at least one layer of the one or more trainable layers implementing a respective one of one or more iterations of an unrolled optimization process to determine optimized template parameters for the filtering template; and applying the filtering template to one or more of the samples of the observation data to
obtain the signal component of the observation data” which are processes that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims recite memory storage devices, processor and instructions to perform the signal detection. These are generic computer components and program which use to perform abstract ideas.
The limitation:
“Obtaining samples of observation data comprising a signal component produced by a
source object, and a noise component” is the process of getting the information for retrieval or analysis.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 2, the limitation: “The observation data is representative of signals measured in a high-dimensional signal space, and wherein the signal component produced by the source object occupies a low-dimensional submanifold of the high-dimensional signal space” is further defined what the observation data is and insignificantly to amount significantly more.
As to claims 3 and 15, the limitation:
“The unrolled optimization process comprises a gradient descent optimization process” is the pro computer process to enhance routine for better result.
As to claims 4 and 16, the limitation:
“Determining, in response to the at least one of the samples of the observation data, layer output parameters representing a resultant respective Jacobian matrix and step size information for a respective one of the one or more iterations of the gradient descent optimization process implemented by the one or more layers of the machine learning template derivation system” which is process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As to claim 5, the limitation:
“The resultant respective Jacobian matrix and step size for the respective one of the one or more iterations of the gradient descent optimization process are represented by a collection of resultant layer output lookup matrices, W” is mathematical result from a computer algorithm.
As to claims 6 and 17, the limitation:
“Determining the layer output parameters comprises: applying the trained parameter values of the at least one layer of the machine learning template derivation system to the at least one of the samples of the observation data, and further to a previously determined set of values of the template parameters, gk-1, to generate the layer output parameters representing the resultant respective Jacobian matrix and the step size information for the respective one of the one or more iterations of the gradient descent optimization process” which is process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As to claims 7 and 18, the limitation:
“Combining the layer output parameters with the previously determined set of values of the template parameters to produce a next set of values of template parameters, &” is the mathematical calculation” is a mathematical computer processing of adding layer output and determined set of value.
As to claim 8, the limitation:
“The next set of values of the template parameters, sk, is produced by a last layer of the machine learning, with & representing the final template parameters for the filtering template” is the mathematical computer algorithm to calculate the set of value.
As to claims 10 and 19, the limitation:
“The next set of values of the template parameters, sk, is produced by a last layer of the machine learning, with & representing the final template parameters for the filtering template” is the process calculating a set of values for the template parameters” is the mathematical computer algorithm to calculate the set of value.
As to claim 9, the limitation:
“The next set of values of template parameters, &, is produced by a first layer of the machine learning template derivation system, with the previously determined set of values of the template parameters, gk-1 representing an initial estimate, C, of the template parameters for the filtering template” are further defined what template parameter is and produced” is the mathematical computer algorithm to calculate the set of value.
As to claim 10,
“Providing the next set of values of template parameters, &, and the at least one of the
samples of the observation data to a next at least one layer of the one or more layers of the
machine learning template derivation system to determine a further next set of values of template parameters, gk+1” is the computer component to generate value for next processing computer step.
As to claim 11, the limitation:
“The samples of observation data comprise samples of gravitationally-produced observation data comprising a gravitational waves data component” is further defined what samples of observation data is and insignificantly to amount significantly more.
As to claim 12, the limitation:
“The machine learning template derivation system comprises a neural-network-based machine learning template derivation system” is further defined what machine learning template deviation is and insignificantly to amount significantly more.
As to claim 13, the limitation:
“Training, prior to obtaining the samples of observation data, the one or more layers of the machine learning template derivation system with training data, the training data comprising
input data representing observation samples, and output data representing ground truth data
associated with the input data, wherein the ground truth data includes one or more of: template
parameters computed in response to the training data using a matched filtering technique, or
previously determined template parameters that were used for the input data” is a computer based
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
3. Claim(s) 1 and 3-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Woergoetter et al. (Pub. No. US 2005/0027373 A1) in view of Cammerer et al. (Pub. No. US 2025/0173545 A1).
Zhou et al. (Pub. No. US 2024/0290011 A1) and in view of Smith et al. (Pub. No. US 2019/0120932).
As to claim 1, Woergoetter discloses a method for signal detection, the method comprising:
obtaining samples of observation data (sample pattern) (paragraph 0009) comprising a signal component produced by a source object (signal) (paragraph 0004), and a noise component (signal component, called noise) (paragraph 0004);
generating based on at least one of the samples of the observation data, processed by a
machine learning template derivation system (… is learned form a number of sample patterns…) (paragraph 0009), a filtering template to separate the signal component from the noise component (the goal of all electronic filter is to separate the designed signal form all other signal components, called noise…) (paragraph 0004); and
Woergoetter does not disclose wherein the machine learning template derivation system comprises one or more trainable layers, with at least one layer of the one or more trainable layers implementing a respective one of one or more iterations of an unrolled optimization process to determine optimized template parameters for the filtering template and applying the filtering template to one or more of the samples of the observation data to obtain the signal component of the observation data.
However, Cammerer discloses wherein the machine learning template derivation system comprises one or more trainable layers, with at least one layer of the one or more trainable layers implementing a respective one of one or more iterations of an unrolled optimization process to determine optimized template parameters for the filtering template (training starts with the highest modulation order, then frees the parameters input to the NRX unrolled loops layer and 620 and continuation training parameters of the MCS-specific…) (paragraph 0098) and applying the filtering template to one or more of the samples of the observation data to obtain the signal component of the observation data (the first output layer use a firs set of learned parameters that is specific to the first MCS to generate the first reliability estimates and the second output layer uses a second set of learned parameters that is specific to the second MCS to generate the second reliability estimate) (paragraph 0110). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include wherein the machine learning template derivation system comprises one or more trainable layers, with at least one layer of the one or more trainable layers implementing a respective one of one or more iterations of an unrolled optimization process to determine optimized template parameters for the filtering template and applying the filtering template to one or more of the samples of the observation data to obtain the signal component of the observation data as disclosed by Cammerer in order for filtering data.
As to claim 3, Cammerer discloses the method of claim 1, wherein the unrolled optimization process comprises a gradient descent optimization process (gradient descent) (paragraph 0058).
As to claim 4, Cammerer discloses the method of claim 3, wherein generating the filtering template comprises: determining, in response to the at least one of the samples of the observation data, layer output parameters representing a resultant respective Jacobian matrix (matrix) (paragraph 0031) and step size information for a respective one of the one or more iterations of the gradient descent optimization process implemented by the one or more layers of the machine learning template derivation system (gradient descent) (paragraph 0058).
As to claim 5, Cammerer the method of claim 4, wherein the resultant respective Jacobian matrix and step size for the respective one of the one or more iterations of the gradient descent (gradient descent) (paragraph 0058) optimization process are represented by a collection of resultant layer output lookup matrices, W (matrix) (paragraph 0031)
As to claim 6, Cammerer discloses the method of claim 4, wherein determining the layer output parameters comprises: applying the trained parameter values of the at least one layer of the machine learning template derivation system to the at least one of the samples of the observation data (retrain if any parameters changed) (paragraph 0002), and further to a previously determined set of values of the template parameters, gk-1, to generate the layer output parameters representing the resultant respective Jacobian matrix (state matrix) (paragraph 0031) and the step size information for the respective one of the one or more iterations of the gradient descent optimization process (gradient descent) (paragraph 0058).
As to claim 7, Cammerer discloses the method of claim 6, further comprising:
combining the layer output parameters with the previously determined set of values of the template parameters to produce a next set of values of template parameters, & (an inter-stream interference aggregation function 250 may combine inter-stream interference feature output…) (paragraph 0051).
As to claim 8, Cammerer discloses the method of claim 7, wherein the next set of values of the template parameters, sk, is produced by a last layer of the machine learning, with & representing the final template parameters for the filtering template (further iteration may be stopped once the set of state tensors (s.bu.k(n.sub.T)) output by the most recent iteration differs from output the preceding…) (paragraph 0053)
As to claim 9, Cammerer discloses the method of claim 7, wherein the next set of values of template parameters, &, is produced by a first layer of the machine learning template derivation system (machine-based signal processing algorithm) (paragraph 0003), with the previously determined set of values of the template parameters, gk-1 representing an initial estimate, C, of the template parameters for the filtering template (the receiver neural network further comprises a first output layer for process first state tensors to generate first reliability for projection bit values…) (paragraph 0006).
As to claim 10, Cammerer discloses the method of claim 7, further comprising:
providing the next set of values of template parameters, &, and the at least one of the
samples of the observation data to a next at least one layer of the one or more layers of the
machine learning template derivation system to determine a further next set of values of template parameters, gk+1 (a second output layer projecting the processed second state tensors to generate second reliability estimate for bit value…) (paragraph 0006).
As to claim 11, Cammerer discloses the method of claim 1, wherein the samples of observation data comprise samples of gravitationally-produced observation data comprising a gravitational waves data component.
As to claim 12, Cammerer discloses the method of claim 1, wherein the machine learning template derivation system comprises a neural-network-based machine learning template derivation system (neural network based) (paragraph 0003).
As to claim 13, Cammerer discloses the method of claim 1, further comprising:
training, prior to obtaining the samples of observation data, the one or more layers of the
machine learning template derivation system with training data, the training data comprising
input data representing observation samples (… the readout stage is replaced with reconfigurable components which are trained to support multiple MCS-specific input and output layers (paragraph 0027), and output data representing ground truth data associated with the input data, wherein the ground truth data includes one or more of: template parameters computed in response to the training data using a matched filtering technique, or
previously determined template parameters that were used for the input data (ground truth data) (paragraph 0038).
Claim 14 is rejected under the same reason as to claim 1, Cammerer discloses a signal detection system comprising: one or more memory storage devices (memory device) (paragraph 0059); and a processor-based device (processor) (paragraph 0080) in electrical communication with the one or more memory storage devices (a processor executing instructions stored in memory).
Claim 15 is rejected under the same reason as to claim 3.
Claim 16 is rejected under the same reason as to claim 4.
Claim 17 is rejected under the same reason as to claim 6.
Claim 18 is rejected under the same reason as to claim 7.
Claim 19 is rejected under the same reason as to claim 10.
Claim 20 is rejected under the same reason as to claim 1, Cammerer discloses a non-transitory computer readable media (memory) (paragraph 0080) comprising computer instructions executable on a processor-based device (processor executing instructions) (paragraph 0080).
4. Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Woergoetter et al. (Pub. No. US 2005/0027373 A1) in view of Cammerer et al. (Pub. No. US 2025/0173545 A1) and in view of Kaufhold et al. (Patent No. US 9,990,687 B1).
As to claim 2, Woergoetter discloses the method of claim 1 excepting for wherein the observation data is representative of signals measured in a high-dimensional signal space, and wherein the signal component produced by the source object occupies a low-dimensional submanifold of the high-dimensional signal space. However, Kaufhold discloses the observation data is representative of signals measured in a high-dimensional signal space (most of the high dimensional objects will have to be placed such too far away) (col. 10, lines 7-9), and wherein the signal component produced by the source object occupies a low-dimensional submanifold of the high-dimensional signal space (low-dimensional embedding) (col. 10, line 10). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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