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 .
Status of Claims
Claims 21-40 are presented for examination.
Claims 1-20 are preliminarily cancelled.
Claims 21-40 are rejected.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 21 is directed to “A method…”, claim 28 is directed to “One or more non-transitory computer-accessible storage media…”, and claim 35 is directed to “…A system...”. Therefore, claims 21, 28, and 35 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 21 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claims 28, 35 are rejected for the same reasons as the representative claim 1 as discussed here. Claim 21 recites:
“A method, comprising: training, by a machine learning system comprising at least one processor and a memory, a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.”
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determines…”, and “determines…” all the various data in the context of this claim encompasses a person looking at data collected (received, determined, estimates, detected, identified, and analyzed etc.) and forming a simple judgement (determination, analysis, comparison, and judgement etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
“A method, comprising: training, by a machine learning system comprising at least one processor and a memory, a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.”
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the “One or more non-transitory computer-accessible storage media…processors…” steps from / using sensor system(s) are recited at a high level of generality (i.e. as a general means of a processor; and a computer readable media, and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The “generating a training data set…training the classifier…” steps are also recited at a high level of generality and amounts to mere post solution action, which is a form of insignificant extra-solution activity. Lastly, claims 21, 28, and 35 further recite “generating a training data set comprising samples of labeled data and unlabeled data…training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data…” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps. In order to expedite prosecution, Examiner also notes that the mere recitation of “…generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data” in claims 21, 28, and 35, are not significant enough to integrate the judicial exception into a practical application since the claims do not include a positive recitation (if supported by the specification, such limitation is an example of a significant enough limitation to integrate the judicial exception into a practical application).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 21 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to implement a machine learning system to perform…” the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of “…generating…training…labelling…classification…” steps are well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “…generating the training data set, comprises labeling additional unlabeled data according to the specified amount of bias to generate the labeled data…labeling the additional unlabeled data comprises labeling data points of the additional unlabeled data…the training of the classifier comprises semi-supervised training…” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017). Hence, the claim is not patent eligible.
Dependent claim(s) 22-27, 29-34, and 36-40 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 22-27, 29-34, and 36-40 do are not patent eligible under the same rationale as provided for in the rejection of claims 21, 28, and 35.
Therefore, claim(s) 21-40 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nachum et al. (US Pub. No.: 2022/0036203 A1: hereinafter “Nachum”).
Consider claims 21, 28, and 35:
Nachum teaches a system (Fig. 2A elements 100-180, “…computing system 100 that performs techniques to reduce bias in machine-learned models…”), one or more non-transitory computer-accessible storage media (Fig. 2A elements 100-180, “…The memory 114 can include one or more non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof…”), and a method (See Nachum, e.g., “…systems and methods for identifying and correcting label bias in machine learning via intelligent re-weighting of training examples. In particular, aspects of the present disclosure leverage a problem formulation which assumes the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases towards certain groups. Despite the fact that a biased training dataset provides only observations of the biased labels, the systems and methods described herein can nevertheless correct the bias by re-weighting the data points without changing the labels…” of Abstract, ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312) comprising: training, by a machine learning system (e.g., Fig. 2A elements 100-180, “…systems and methods for identifying and correcting label bias in machine learning via intelligent re-weighting of training examples included in a biased training dataset…”) comprising at least one processor and a memory (Fig. 2A elements 100-180, “…The user computing device 102 includes one or more processors 112 and a memory 114…”), a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets (See Nachum, e.g., “…a training dataset might include example images and each image might include an example label that indicates whether or not the image depicts a cat. Thus, a classifier model can be trained on the training dataset to classify an input image…a classification model may be incorporated into other systems, such as a reinforcement learning system in which an agent interacts with an environment by performing actions that are selected by the reinforcement learning system in response to receiving sensor inputs that characterize the current state of the environment…include a classifier having a classification model trained according to techniques described herein and use the classifier to process received sensor inputs…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312), the training comprising: generating a training data set comprising samples of labeled data and unlabeled data (See Nachum, e.g., “…identifying and correcting label bias in machine learning via intelligent re-weighting of training examples included in a biased training dataset…leverage a problem formulation which assumes the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases towards certain subgroups…training the classification model under the resulting weighted objective leads to an unbiased classifier on the original un-weighted dataset…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312), the labeled data comprising a specified amount of bias (See Nachum, e.g., “…compute the weight for each training example based on the re-weighting control values and according to the closed form expression that expresses the biased label function y.sub.bias in terms of the unbiased label function y.sub.true in combination with one or more re-weighting control values…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312); and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data (See Nachum, e.g., “…training a classifier on examples with biased labels weighted by w(x, y) is equivalent to training a classifier on examples labelled according to the true, unbiased labels…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0095]-¶ [0101], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 22, 29, and 36:
Nachum teaches everything claimed as implemented above in the rejection of claims 21, 28, and 35 above. In addition, Nachum teaches wherein the specified amount of bias comprises one or more of a specified amount of label bias (See Nachum, e.g., “…compute the weight for each training example based on the re-weighting control values and according to the closed form expression that expresses the biased label function y.sub.bias in terms of the unbiased label function y.sub.true in combination with one or more re-weighting control values…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312) and a specified amount of selection bias in at least one dimension of a plurality of label dimensions (See Nachum, e.g., “…training with the weighted dataset based on these values will guarantee that the final classifier will be approximately unbiased. However, the above rate has a dependence on the dimension D, which may be unattractive in high-dimensional settings. However, if the data lies on a d-dimensional submanifold, then Theorem 3 below says that without any changes to the procedure, a rate that depends on the manifold dimension and independent of the ambient dimension will be enjoyed. Interestingly, these rates are attained without knowledge of the manifold or its dimension…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 23, 30, and 37:
Nachum teaches everything claimed as implemented above in the rejection of claims 21, 28, and 35 above. In addition, Nachum teaches wherein generating the training data set, comprises labeling additional unlabeled data according to the specified amount of bias to generate the labeled data (See Nachum, e.g., “…compute the weight for each training example based on the re-weighting control values and according to the closed form expression that expresses the biased label function y.sub.bias in terms of the unbiased label function y.sub.true in combination with one or more re-weighting control values…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 24, 31, and 38:
Nachum teaches everything claimed as implemented above in the rejection of claims 23, 30, and 37 above. In addition, Nachum teaches wherein the specified amount of bias comprises a specified amount of label bias (See Nachum, e.g., “…compute the weight for each training example based on the re-weighting control values and according to the closed form expression that expresses the biased label function y.sub.bias in terms of the unbiased label function y.sub.true in combination with one or more re-weighting control values…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312), and wherein labeling the additional unlabeled data comprises labeling data points of the additional unlabeled data (See Nachum, e.g., “…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312), the data points comprising unobserved ground truths, according to the specified amount of label bias (See Nachum, e.g., “…a training dataset might include example images and each image might include an example label that indicates whether or not the image depicts a cat. Thus, a classifier model can be trained on the training dataset to classify an input image…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 25, 33, and 39:
Nachum teaches everything claimed as implemented above in the rejection of claims 21, 28, and 35 above. In addition, Nachum teaches wherein the specified amount of bias comprises a specified amount of selection bias (See Nachum, e.g., “…compute the weight for each training example based on the re-weighting control values and according to the closed form expression that expresses the biased label function y.sub.bias in terms of the unbiased label function y.sub.true in combination with one or more re-weighting control values…identify the amount of bias in the training data and correct this bias by assigning appropriate weights to each example in the training data…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312), and wherein generating the training data set comprises sampling the labeled data according to the specified amount of selection bias (See Nachum, e.g., “…identifying and correcting label bias in machine learning via intelligent re-weighting of training examples included in a biased training dataset…leverage a problem formulation which assumes the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases towards certain subgroups…training the classification model under the resulting weighted objective leads to an unbiased classifier on the original un-weighted dataset…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 26, 33:
Nachum teaches everything claimed as implemented above in the rejection of claims 21, 28 above. In addition, Nachum teaches wherein the training of the classifier comprises semi-supervised training (See Nachum, e.g., “…training a classifier on examples with biased labels weighted by w(x, y) is equivalent to training a classifier on examples labelled according to the true, unbiased labels…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0095]-¶ [0101], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Consider claim 27, 34, and 40:
Nachum teaches everything claimed as implemented above in the rejection of claims 21, 28, and 35 above. In addition, Nachum teaches wherein the semi-supervised training comprises a metric to promote unbiased classification of one or more labels based, at least in part, on the additional unlabeled data points (See Nachum, e.g., “…training a classifier on examples with biased labels weighted by w(x, y) is equivalent to training a classifier on examples labelled according to the true, unbiased labels…” of ¶ [0009]-¶ [0012], ¶ [0023]-¶ [0050], ¶ [0095]-¶ [0101], ¶ [0103]-¶ [0123], ¶ [0129]-¶ [0141], and Fig. 1 elements “…a graphical diagram of an example problem formulation for training an unbiased classifier…”, Figs. 2A-C elements 1-180, and Fig. 3 steps 302-312).
Obviousness Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-19 of US Patent No. 11,775,863 B2. Although the claims at issue are not identical, they are not patentably distinct from each other. This is a nonprovisional nonstatutory double patenting rejection because the patentably indistinct claims have in fact been patented/issued, take an example of claims 21, 28, and 35 of the instant application and claims 1, 6, and 13 of the US Patent No. 11,775,863 B2 (Please see the Table below):
Claims of pending Application 18/942116
Claims of US Pat. No. 11,775,863 B2 (hereinafter ‘863)
Reasoning
21. (New) A method, comprising: training, by a machine learning system comprising at least one processor and a memory, a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
28. (New) One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to implement a machine learning system to perform: training a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
35. (New) A system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system configured to train a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, wherein to train the classifier the machine learning system is configured to: generate a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and train the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
1. A system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system configured to train an unbiased classifier, the unbiased classifier configured to determine unbiased classifications of one or more labels for the one or more data sets, wherein to generate the unbiased classifier the machine learning system is configured to: generate a training data set from samples of input data points of a feature space comprising a plurality of dimensions according to a plurality of training parameters, wherein the training data set comprises an amount of label bias and an amount of selection bias, and wherein the training parameters comprise: a value indicating the amount of label bias; another value indicating the amount of selection bias; and a control for discrepancy between rarity of features; and train the unbiased classifier using the generated training data set and the plurality of training parameters.
6. A method, comprising: training, by a machine learning system comprising at least one processor and a memory, an unbiased classifier that, when applied to one or more data sets determines unbiased classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set from samples of input data of a feature space comprising a plurality of dimensions according to a plurality of training parameters, wherein the training data set comprises an amount of label bias and an amount of selection bias, and wherein the training parameters comprise: a value indicating the amount of label bias; another value indicating the amount of selection bias; and a control for discrepancy between rarity of features; and training the unbiased classifier using the generated training data set.
13. One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to implement a machine learning system performing: training an unbiased classifier that, when applied to one or more data sets determines unbiased classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set from samples of input data of a feature space comprising a plurality of dimensions according to a plurality of training parameters, wherein the training data set comprises an amount of label bias and an amount of selection bias, and wherein the training parameters comprise: a value indicating the amount of label bias; another value indicating the amount of selection bias; and a control for discrepancy between rarity of features; and training the unbiased classifier using the generated training data set.
Claims of ‘863 only differ from the instant application, in that the claims of ‘863 specify “…a disconnect positioned between the energy storage device and the electric power take-off system and configured to selectively decouple the electric power take-off system from the energy storage device…wherein when the motor is decoupled from the energy storage device by the disconnect, the hydraulic pump is disabled”. Nonetheless, the removal of said limitations from claims of the instant application made claims a broader version of claims of ‘863. Therefore, since omission of an element and its function in combination is an obvious expedient if the remaining elements perform the same function as before (In re Karlson (CCPA) 136 USPQ 184 (1963)), claims are not patentably distinct from claims of '863.
Claims 21-40 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,175,344 B2. Although the claims at issue are not identical, they are not patentably distinct from each other. This is a nonprovisional nonstatutory double patenting rejection because the patentably indistinct claims have in fact been patented/issued, take an example of claims 21, 28, and 35 of the instant application and claims 1, 8, and 15 of the US Patent No. 12,175,344 B2 (Please see the Table below):
Claims of pending Application 18/942116
Claims of US Pat. No. 12,175,344 B2 (hereinafter ‘344)
Reasoning
21. (New) A method, comprising: training, by a machine learning system comprising at least one processor and a memory, a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
28. (New) One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to implement a machine learning system to perform: training a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: generating a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and training the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
35. (New) A system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system configured to train a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, wherein to train the classifier the machine learning system is configured to: generate a training data set comprising samples of labeled data and unlabeled data, the labeled data comprising a specified amount of bias; and train the classifier according to the generated training data set and a statistical parity of respective selection rates of a plurality of partitions of the unlabeled data.
1. A method, comprising: training, by a machine learning system comprising at least one processor and a memory, a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: labeling unlabeled data according to a specified amount of bias to generate labeled data comprising the specified amount of bias, wherein the specified amount of bias comprises one or more of a specified amount of label bias and a specified amount of selection bias in at least one dimension of a plurality of label dimensions; generating a training data set comprising samples of the labeled data and additional unlabeled data; and training the classifier according to the generated training data set and training parameters comprising an indication the specified amount of bias.
8. One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to implement a machine learning system to perform: training a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, the training comprising: labeling unlabeled data according to a specified amount of bias to generate labeled data comprising the specified amount of bias, wherein the specified amount of bias comprises one or more of a specified amount of label bias and a specified amount of selection bias in at least one dimension of a plurality of label dimensions; generating a training data set comprising samples of the labeled data and additional unlabeled data; and training the classifier according to the generated training data set and training parameters comprising an indication the specified amount of bias.
15. A system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a machine learning system configured to train a classifier that, when applied to one or more data sets determines classifications of one or more labels for the one or more data sets, wherein to train the classifier the machine learning system is configured to: label unlabeled data according to a specified amount of bias to generate labeled data comprising the specified amount of bias, wherein the specified amount of bias comprises one or more of a specified amount of label bias and a specified amount of selection bias in at least one dimension of a plurality of label dimensions; generate a training data set comprising samples of the labeled data and additional unlabeled data; and train the classifier according to the generated training data set and training parameters comprising an indication the specified amount of bias.
Claims of ‘344 only differ from the instant application, in that the claims of ‘344 specify “wherein rotation of the first motor selectively drives at least one of the plurality of wheels… a second motor configured to drive a hydraulic pump to convert electrical power received from the battery into hydraulic power…and a disconnect positioned between the battery and the electric power take-off and configured to selectively decouple the electric power take-off system from the battery to disable the lifting system and the compactor”. Nonetheless, the removal of said limitations from claims of the instant application made claims a broader version of claims of ‘344. Therefore, since omission of an element and its function in combination is an obvious expedient if the remaining elements perform the same function as before (In re Karlson (CCPA) 136 USPQ 184 (1963)), claims are not patentably distinct from claims of '344.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Farrar et al. (US Pub. No.: 2022/0156646 A1) teaches “A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.”
Nourian et al. (US Pub. No.: 2021/0049503A1) teaches “Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667
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