DETAILED ACTION
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 .
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.
Claims 1-9, 18-26 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-9, 18-26 are directed to a system and a method of normalization in ML models. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1 and 18 these claims
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0022]-[0031] etc. Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 18 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 18: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites “receive…”, “normalize…”, “generate…”, take…”. etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 19
Claim 2, 19 merely recite other additional elements that define the characteristics which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 20
Claim 3, 20 merely recite other additional elements that define the ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 21
Claim 4, 21 merely recite other additional elements that define the constants which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 22
Claim 5, 22 merely recite other additional elements that define the ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 23
Claim 6, 23 merely recite other additional elements that define the instance data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 24
Claim 7, 24 merely recite other additional elements that define the generated inference which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 25
Claim 8, 25 merely recite other additional elements that define the actions which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9, 26
Claim 9, 26 merely recite other additional elements that define the defined constants which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 7, 9 and 18-20, 22, 24, 26 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Sohl-Dickstein et al. (Sohl-Dickstein) US 2022/0108149.
In regard to claim 1, Sohl-Dickstein disclose A processing system, ([0004] a system) comprising:
at least one memory having executable instructions stored thereon; and
one or more processors configured to execute the executable instructions in order to cause the processing system to: ([0105]-[0112] memory, processors and instructions)
receive an input for processing using a machine learning model; ([0016]-[0029] receive an input for processing using a neural network (ML model))
normalize instance data associated with the input based on one or more defined constants associated with characteristics learned from a training data set used in training the machine learning model; ([0017]-[0030] [0040]-[0045][0047]-[0059] normalization during processing of the input based on trained values of the parameters of the neural network during training the neural network from training data used, such as a standard deviation of the transformed elements which is a constant value associated with the characteristics from the training data used, or a mean squared error loss, etc. )
generate an inference for the input based on the normalized instance data; ([0019]-[0029] [0047]-[0059] [0115] compute a network output during inferencing for the input based on the normalized input data) and
take one or more actions based on the generated inference. ([0019]-[0029][0047]-[0059] [0115] perform tasks based on the network output from the inferencing)
In regard to claim 2, Sohl-Dickstein disclose The processing system of Claim 1,
Sohl-Dickstein disclose wherein the characteristics comprise a standard deviation associated with the training data set. ([0017]-[0030] [0040]-[0045][0047]-[0059] [0078] a mean of the elements and a standard deviation of the transformed elements associated with the training data)
In regard to claim 3, Sohl-Dickstein disclose The processing system of Claim 1,
Sohl-Dickstein disclose wherein the machine learning model comprises a transformer neural network, and wherein the one or more defined constants comprise constants associated with a self-attention layer of the transformer neural network. ([0034] [0047]-[0057] [0066] a transformer neural network and the constants are associated with a self-attention layer of the transformer neural network)
In regard to claim 5, Sohl-Dickstein disclose The processing system of Claim 1,
Sohl-Dickstein disclose wherein the machine learning model does not include a normalizing block that processes an intermediate output of the machine learning model to generate the inference. ([0047]-[0059] the pre-normalized layer process the input and generate output and does not across multiple layers. Note: please do not use negative language to describe the invention since the negative language has not much patent weight)
In regard to claim 7, Sohl-Dickstein disclose The processing system of Claim 1,
Sohl-Dickstein disclose wherein the generated inference comprises an identification of one or more objects within an image input into the machine learning model. ([0019]-[0029] [0047]-[0059] [0115] identifying an object within an image input into the neural network)
In regard to claim 9, T Sohl-Dickstein disclose he processing system of Claim 1,
Sohl-Dickstein disclose wherein the defined constants comprise a scaling constant and a shifting constant learned from the training data set. ([0017]-[0030] [0040]-[0045][0047]-[0059] shifting and scaling constants from the training data)
In regard to claims 18-20, 22, 24, 26, claims are method claims corresponding to the system claims 1-3, 5, 7, 9 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 5, 7, 9.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4, 6, 21, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Sohl-Dickstein et al. (Sohl-Dickstein) US 2022/0108149 in view of Fayyaz et al. (Fayyaz) US 2023/0153379
In regard to claim 4, Sohl-Dickstein disclose The processing system of Claim 3,
But Sohl-Dickstein fail to explicitly disclose “wherein the one or more defined constants comprise: a first mean and a first standard deviation associated with keys of the transformer neural network, and a second mean and a second standard deviation associated with queries serving as inputs into the transformer neural network.”
Fayyaz disclose wherein the one or more defined constants comprise:
a first mean and a first standard deviation associated with keys of the transformer neural network, and a second mean and a second standard deviation associated with queries serving as inputs into the transformer neural network. ([0038]-[0058] calculate means and standard deviations associated with keys and queries of the transformer neural network)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Fayyaz’s adaptive tokens sampling for efficient transformer into Sohl-Dickstein’s invention as they are related to the same field endeavor of machine learning and training models. The motivation to combine these arts, as proposed above, at least because Fayyaz’s method of normalization associated with the transformer would help to provide more normalization method into Sohl-Dickstein’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more normalization method used for the transformer network would help to improve efficiency of the transformer in training and inferencing.
In regard to claim 6, Sohl-Dickstein disclose The processing system of Claim 1,
But Sohl-Dickstein fail to explicitly disclose “wherein the instance data associated with the input comprises tokens derived from an input image to be processed using the machine learning model.”
Fayyaz disclose wherein the instance data associated with the input comprises tokens derived from an input image to be processed using the machine learning model. ([0003]-[0005] [0025]-[0034] tokens derived from input image to be inputted using the transformer)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Fayyaz’s adaptive tokens sampling for efficient transformer into Sohl-Dickstein’s invention as they are related to the same field endeavor of machine learning and training models. The motivation to combine these arts, as proposed above, at least because Fayyaz’s method of normalization associated with the transformer would help to provide more normalization method into Sohl-Dickstein’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more normalization method used for the transformer network would help to improve efficiency of the transformer in training and inferencing.
In regard to claims 21, 23, claims 21, 23 are method claims corresponding to the system claims 4, 6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 4, 6.
Claims 8, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Sohl-Dickstein et al. (Sohl-Dickstein) US 2022/0108149 in view of Chen et al. (Chen) US 2020/0285899
In regard to claim 8, Sohl-Dickstein disclose The processing system of Claim 7,
But Sohl-Dickstein fail to explicitly disclose “wherein the one or more actions comprises controlling an autonomous vehicle to avoid a collision with the one or more objects within the image.”
Chen disclose wherein the one or more actions comprises controlling an autonomous vehicle to avoid a collision with the one or more objects within the image. [0059]-([0066] make corrective actions to avoid collision for a vehicle with objects in the images)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen’s ML based on correlations of the training data into Sohl-Dickstein’s invention as they are related to the same field endeavor of machine learning and training models. The motivation to combine these arts, as proposed above, at least because Chen’s ML based on correlations of the training data with performance trends would help to provide more preventive actions into Sohl-Dickstein’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more preventive actions would help to improve performance of the system and therefore improve user experience using the system.
In regard to claim 25, claim 25 is a method claim corresponding to the system claim 8 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 2024/0273873 2024-08-15 Cai et al.
DYNAMIC TEMPORAL NORMALIZATION FOR DEEP LEARNING IN VIDEO UNDERSTANDING APPLICATIONS
Cai et al. disclose application of deep neural networks to video for video recognition and understanding are discussed. A feature map of a deep neural network for a current time stamp of input video is standardized to a standardized feature map and pooled to a feature vector. The feature vector and transform parameters for a prior time stamp are used to generate transform parameters for the current time stamp based on application of a meta temporal relay. The resultant current time stamp transform parameters, such as a hidden state and a cell state of the meta temporal relay, are used to transform the standardized feature map to a normalized feature map for use by a subsequent layer of the deep neural network… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
