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 1-20 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 1-10, 11-19, 20 are directed to a method, system and medium of conformal predicting. 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, 11 and 20 these claims recite
selecting, from a memory storing one or more online radius predictors for generating a prediction set in response to a real time input variable, an active set of online radius predictors based on lifetimes of the set of online radius predictors;
generating, by the active set of online radius predictor that are neural network based models implemented on one or more hardware processors, a predicted radius based on a weighted sum of respective predicted radiuses generated from the active set of online radius predictors;
computing a ground-truth radius based on a ground-truth prediction corresponding to the real-time input variable and a prediction set generated by a conformal predictor according to the predicted radius;
computing a quantile loss between the ground-truth radius and the predicted radius according to a target coverage level; and
for the online radius predictors in the active set: training the online radius predictors based on the quantile loss, and generating, by the trained respective online radius predicator, a next predicted radius.
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, [0056-0065], Fig. 4. 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, 11 and 20 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, 11 and 20: 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 “selecting…”, “generating…”, “computing…”,”computing…”, “training…”, “generating…” 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 2,12
Claim 2, 12 merely recite other additional elements that define configuring the radius predictors 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 3,13
Claim 3, 13 merely recite other additional elements that define computing a lifetime 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 4,14
Claim 4, 14 merely recite other additional elements that define respective predicted radiuses 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 5,15
Claim 5, 15 merely recite other additional elements that define normalized probabilities 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 6,16
Claim 6, 16 merely recite other additional elements that define a quantile loss 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 7,17
Claim 7, 17 merely recite other additional elements that define generating predicted radius 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 8,18
Claim 8, 18 merely recite other additional elements that define receiving input data to generate the predicted radius 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 9,19
Claim 9, 19 merely recite other additional elements that define computing a quantile loss data to generate the predicted radius 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 10
Claim 10 merely recite other additional elements that computing a gradient 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8-9, 11-15, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pipelidis et al. (Pipelidis) US 2022/0113366 in view of Venkatraman et al. (Venkatraman) US 2014/0192658 and Romano et al. (Romano) “Conformalized Quantile Regression” May, 2019, Methodology (stat.ME); Machine Learning (stat.ML)arXiv:1905.03222 [stat.ME]
https://doi.org/10.48550/arXiv.1905.03222
In regard to claim 1, Pipelidis disclose A method for adaptive online conformal predicting, comprising: [0008]-[0016] [0121]-[0133] mobile device motion prediction)
Pipelidis disclose the active set of online radius predictor that are neural network based models implemented on one or more hardware processors, ([0043]-[0048] [0069]-[0072][0088]-[0089] [0121]-[0130][0138] various prediction models which are neural network based models)
computing a ground-truth radius based on a ground-truth prediction corresponding to the real-time input variable and a prediction set generated by a conformal predictor according to the predicted radius; (Fig. 8 [0049]-[0062][0171]-[0175] compute the real location of the mobile with a radius based on the prediction corresponding to the input measurement data and the prediction data sets generated according to the predicted radius. Note: please further define various predictors to help move forward the prosecution.)
computing the quantile loss between the ground-truth radius and the predicted radius according to the target coverage level; (Fig. 8 [0035]-[0041] [0049]-[0061] [0071][0092]-[0095] [0132] [0171]-[0175] compute the error between the radius and the predicted radius a valuation of reliability until correctly identified matching geographic context location 48 is sufficiently close or distance threshold, etc.)
training the online radius predictors based on the quantile loss, ([0021] [0041]-[0062] [0070] [0121] [0132] [0138][0171]-[0175] training the estimations based on the error) and
generating, by the trained respective online radius predicator, a next predicted radius. ([0021] [0041]-[0061] [0070] [0121] [0132] [0138][0171]-[0175] generate the next predicted radius with iteration)
But Pipelidis fail to explicitly disclose “selecting, from a memory storing one or more online radius predictors for generating a prediction set in response to a real time input variable, an active set of online radius predictors based on lifetimes of the set of online radius predictors; generating, by the active set of online radius predictor, a predicted radius based on a weighted sum of respective predicted radiuses generated from the active set of online radius predictors; generating, by the active set of online radius predictor, a predicted radius based on a weighted sum of respective predicted radiuses generated from the active set of online radius predictors;”
Venkatraman disclose selecting, from a memory storing one or more online radius predictors for generating a prediction set in response to a real time input variable, an active set of online radius predictors based on lifetimes of the set of online radius predictors; (Fig. 11-16, [0055]-[0061] in response to the input data of the mobile device, selecting various uncertainties estimation, such as annulus position estimation, GNSS position estimation, Urban LAN position estimation, etc. and generating uncertainty radii based on the signal of the mobile device received, the uncertainty estimations are stored at the memory)
generating, by the active set of online radius predictor, a predicted radius based on a weighted sum of respective predicted radiuses generated from the active set of online radius predictors; (Fig. 11-16, [0055]-[0061] 740, Fig. 15, generating an estimated radius based on the weighted sum of each predicted radiuses generated from the annulus position estimation, GNSS position estimation, Urban LAN position estimation, etc.)
and for the online radius predictors in the active set: (Fig. 11-16, [0055]-[0061] 740, Fig. 15, for the annulus position estimation, GNSS position estimation, Urban LAN position estimation, etc.)
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 Venkatraman‘s dynamic position estimation into Pipelidis’s invention as they are related to the same field endeavor of position prediction. The motivation to combine these arts, as proposed above, at least because Venkatraman‘s dynamic position estimation with weighted sum of different predictors would help to provide more prediction mechanism into Pipelidis’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 various prediction mechanism would help to improve accuracy of prediction.
But Pipelidis and Venkatraman fail to explicitly disclose “computing a quantile loss between the first value and the second value according to a target coverage level;”
Ramano disclose computing a quantile loss between the first value and the second value according to a target coverage level; (2. Quantile regression, page 2-3, 9, 6 Experiments, 8-10, quantile loss with difference between the two values with low and up level)
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 Ramano‘s conformalized quantile regression into Venkatraman and Pipelidis’s invention as they are related to the same field endeavor of method of prediction. The motivation to combine these arts, as proposed above, at least because Ramano‘s quantile loss with a normal level would help to provide more loss control mechanism into Venkatraman and Pipelidis’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 provide loss control mechanism in training would help to improve accuracy of prediction.
In regard to claim 2, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
Pipelidis disclose further comprising:
configuring the one or more online radius predictors based on the target coverage level for predicting the prediction set in response to the real-time input variable, (Fig. 8 [0021] [0049]-[0062][0071][0092]-[0095] [0171]-[0175] configure the
wherein each online radius predictor generates a respective predicted radius in response to the real-time input variable. ([0021] [0045]-[0062][0071][0092]-[0095] [0171]-[0175] various uncertainty radius is in response to the input measurement data, distance uncertainty radius, direction uncertainty radius, etc.)
In regard to claim 3, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
Pipelidis disclose wherein the active set of online radius predictors is selected by:
computing, for each online radius predictor, a respective lifetime based on a current time instance; ([0123]-[0130] computer the time passed with time step 0-k, etc. based on the current time instance for distance or direction uncertainty radius, for example)
But Pipelidis and Ramano fail to explicitly disclose “and selecting the active set of online radius predictors at the current time instance based on lifetimes of the set of online radius predictors from the current time instance.”
Venkatraman disclose and selecting the active set of online radius predictors at the current time instance based on lifetimes of the set of online radius predictors from the current time instance. (Fig. 11-16, [0055]-[0061] in response to the input data of the mobile device, selecting various uncertainties estimation, such as annulus position estimation, GNSS position estimation, Urban LAN position estimation, etc. and generating uncertainty radii based on the signal of the mobile device received at the current time)
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 Venkatraman‘s dynamic position estimation into Ramano and Pipelidis’s invention as they are related to the same field endeavor of position prediction. The motivation to combine these arts, as proposed above, at least because Venkatraman‘s dynamic position estimation with weighted sum of different predictors would help to provide more prediction mechanism into Ramano and Pipelidis’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 various prediction mechanism would help to improve accuracy of prediction.
In regard to claim 4, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
Pipelidis disclose wherein the respective predicted radiuses are weighed by respective normalized probabilities indicating respective online radius predictors in the active set are active at a current time instance. ([0021] [0039]-[0054][0057] [0126]-[0132] the uncertainty radiuses are weighted based on the probabilities distribution in the set of estimators (distance or direction, etc.) at the current time)
In regard to claim 5, Pipelidis and Venkatraman, Ramano disclose The method of claim 4, wherein the respective normalized probabilities are computed by:
Pipelidis disclose for each online radius predictor in the active set:
computing a prior probability that the respective online radius predictor is active at the current time instance, computing an un-normalized probability based at least part on the prior probability and weights of the respective conformal predictor at the current time instance, ([0021] [0039]-[0054][0057] [0126]-[0132] compute probabilities with the uncertainty radiuses around the initial estimated position and computer probabilities based on the prior probabilities and weight based on the probabilities distribution in the set of estimators (distance or direction, etc.) at the current time with displacement with distance or direction, etc.) and
computing, from the un-normalized probability, a normalized probability indicating that the respective online radius predictor is active at the current time instance. ([0021] [0039]-[0054][0057] [0126]-[0132] compute normalized probabilities with the uncertainty radiuses at the current time with displacement with distance or direction, etc. indicating active)
In regard to claim 8, Pipelidis and Venkatraman, Ramano disclose The method of claim 1, further comprising:
Pipelidis receiving, via a communication interface, a first time series comprising at least the real time input variable at the current time instance; ([0124]-[0134] receive the input data at the current time over a sampling time window)
generating, by trained online radius predicators and the conformal predictor, predicted intervals for one or more future time instances, wherein each predicted interval corresponds to a future time instance and has a width based on the predicted radius at the current time instance. ([0124]-[0134] generating the intervals n for the next sampling time over a sampling time window and has a time window length based on the uncertainty radius related to the direction or distance, etc. at the moment)
In regard to claim 9, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
Pipelidis disclose wherein the quantile loss is computed based at least in part on a difference between the predicted radius and the ground-truth radius, weighed by the target coverage level. (Fig. 8 [0035]-[0041] [0049]-[0061] [0071][0092]-[0095] [0129]-[0132][0156]-[0157] [0171]-[0175] compute the error between the radius and the predicted radius based on a different comparison and weighted based on the threshold or level)
In regard to claims 11-15, 18-19, claims 11-15, 18-19 are system claims corresponding to the method claims above 1-5, 8-9 and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5, 8-9.
In regard to claim 20, claim 20 is a medium claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claims 6-7, 10, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Pipelidis et al. (Pipelidis) US 2022/0113366 in view of Venkatraman et al. (Venkatraman) US 2014/0192658 and Romano et al. (Romano) “Conformalized Quantile Regression” May, 2019, Methodology (stat.ME); Machine Learning (stat.ML)arXiv:1905.03222 [stat.ME]
https://doi.org/10.48550/arXiv.1905.03222 in view of Dasgupta et al. (Dasgupta) uS 2022/0180413
In regard to claim 6, Pipelidis and Venkatraman, Ramano disclose The method of claim 1, further comprising:
Pipelidis disclose computing a respective predictor quantile loss between the ground-truth radius and a respective predicted radius from the respective online radius predictor according to the target coverage level; (Fig. 8 [0035]-[0041] [0049]-[0061] [0071][0092]-[0095] [0132] [0171]-[0175] compute the error between the radius and the predicted radius a valuation of reliability until correctly identified matching geographic context location 48 is sufficiently close or distance threshold, etc.)
But Pipelidis and Venkatraman, Ramano fail to explicitly disclose “computing a gradient based on a difference between the quantile loss and the respective predictor quantile loss; and updating parameters of the respective online radius predictor based on the computed gradient.”
Dasgupta disclose computing a gradient based on a difference between the quantile loss and the respective predictor quantile loss; and updating parameters of the respective online radius predictor based on the computed gradient. ([0051]-[0068][0075]-[0078] computing a gradient based on the difference between the current quantile and the respective quantile loss and updating parameters of the prediction based on the gradient)
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 Dasgupta ‘s indicating forecasts into Venkatraman, Ramano and Pipelidis’s invention as they are related to the same field endeavor of method of prediction. The motivation to combine these arts, as proposed above, at least because Dasgupta ‘s indicating forecasts with gradient calculation would help to provide more prediction mechanism into Venkatraman, Ramano and Pipelidis’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 gradient loss prediction mechanism would help to improve accuracy of prediction.
In regard to claim 7, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
Pipelidis computing a respective ground-truth radius based on the ground-truth prediction and the respective prediction set; (Fig. 8 [0049]-[0062][0171]-[0175] compute the real location of the mobile with a radius based on the prediction corresponding to the input measurement data and the prediction data sets generated according to the predicted radius)
computing a respective quantile loss between the respective ground-truth radius and the respective predicted radius according to the target coverage level; (Fig. 8 [0035]-[0041] [0049]-[0061] [0071][0092]-[0095] [0132] [0171]-[0175] compute the error between the radius and the predicted radius a valuation of reliability until correctly identified matching geographic context location 48 is sufficiently close or distance threshold, etc.)
and updating the respective predicted radius for a next time instance based on the respective predicted radius at the current time instance ([0021] [0041]-[0061] [0070] [0121] [0132] [0138][0171]-[0175] generate the next predicted radius with iteration based on the uncertainty radius at the current time)
But Pipelidis and Ramano fail to explicitly disclose “wherein each respective predicted radius is generated by a respective online radius predictor in the active set at a current time instance by: receiving, at a current time instance, the real-time input variable; generating, by the respective online radius predictor and the conformal predictor, a respective prediction set in response to the real-time input variable;”
Venkatraman disclose wherein each respective predicted radius is generated by a respective online radius predictor in the active set at a current time instance by: receiving, at a current time instance, the real-time input variable; generating, by the respective online radius predictor and the conformal predictor, a respective prediction set in response to the real-time input variable; (Fig. 11-16, [0055]-[0061] in response to the input data of the mobile device, selecting various uncertainties estimation, such as annulus position estimation, GNSS position estimation, Urban LAN position estimation, etc. and generating uncertainty radii for annulus position estimation, GNSS position estimation, Urban LAN position estimation based on the signal of the mobile device received)
But Pipelidis and Venkatraman, Ramano fail to explicitly disclose
“and updating the value for a next time instance based on a gradient of the respective quantile loss.”
Dasgupta disclose and updating the value for a next time instance based on a gradient of the respective quantile loss ([0051]-[0068][0070]-[0078] updating parameters for the future time interval based on the gradient of respective quantile loss)
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 Dasgupta ‘s indicating forecasts into Venkatraman, Ramano and Pipelidis’s invention as they are related to the same field endeavor of method of prediction. The motivation to combine these arts, as proposed above, at least because Dasgupta ‘s indicating forecasts with gradient calculation would help to provide more prediction mechanism into Venkatraman, Ramano and Pipelidis’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 gradient loss prediction mechanism would help to improve accuracy of prediction.
In regard to claim 10, Pipelidis and Venkatraman, Ramano disclose The method of claim 1,
But Pipelidis and Venkatraman, Ramano fail to explicitly disclose “wherein the training the online radius predictors based on the quantile loss comprises: computing a gradient based on a difference between a first quantile loss corresponding to the predicted radius and a second quantile loss corresponding to a respective predicted radius generated by a particular online radius predictor from the active set; and updating parameters of the particular online radius predictor based on the gradient.”
Dasgupta disclose wherein the training the online radius predictors based on the quantile loss comprises: computing a gradient based on a difference between a first quantile loss corresponding to the predicted radius and a second quantile loss corresponding to a respective predicted radius generated by a particular online radius predictor from the active set; and updating parameters of the particular online radius predictor based on the gradient. (([0051]-[0068][0075]-[0078] computing a gradient based on the difference between respective quantile losses generated by the prediction and updating parameters of the prediction based on the gradient)
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 Dasgupta ‘s indicating forecasts into Venkatraman, Ramano and Pipelidis’s invention as they are related to the same field endeavor of method of prediction. The motivation to combine these arts, as proposed above, at least because Dasgupta ‘s indicating forecasts with gradient calculation would help to provide more prediction mechanism into Venkatraman, Ramano and Pipelidis’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 gradient loss prediction mechanism would help to improve accuracy of prediction.
In regard to claims 16-17 claims 16-17 are system claims corresponding to the method claims above 6-7 and, therefore, are rejected for the same reasons set forth in the rejections of claims 6-7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20150185995 A1 2015-07-02 Shoemaker et al.
SYSTEMS AND METHODS FOR GUIDED USER ACTIONS
Shoemaker et al. disclose systems and methods for guided user actions are described, including detecting a first action performed by a user; gathering information associated with the first action; retrieving a predictive model based on the information; determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action… 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
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