DETAILED ACTION
This action is in response to the Applicant Response filed 14 May 2024 for application 18/664,202 filed 14 May 2024.
Claim(s) 1-20 is/are pending.
Claim(s) 1-9, 16-20 is/are rejected.
Claim(s) 10-15 is/are allowed.
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 .
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 conflicting claims 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); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-9, 16-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 28-29, 31-33 of U.S. Patent No. 11,989,633 (16/258,116) in view of Wan et al. (Privacy-Preservation for Gradient Descent Methods, hereinafter referred to as “Wan”). Although the claims at issue are not identical, they are not patentably distinct from each other, because, as noted in the table below, claims 1-9, 16-20 of the instant application have similar limitations as recited in U.S. Patent No. 11,989,633 (16/258,116).
Application 18/664,202
U.S. Pat. No. 11,989,633 (16/258,116)
Claim 1
Claim 1
A method comprising:
A computer-implemented method, the method comprising:
training, by a first device, a first machine model that produces for an event corresponding to a training record of a set of training records:
training, by a first party using a set of training records, a first model that produces for a respective event corresponding to a respective training record of the set of training records:
a first score related to a label classification of the event,
a first-party score related to a label classification of the respective event,
wherein at least one first feature from a set of features for the event is not shared with a second device,
wherein at least one first feature from a set of features for the respective event is not shared and at least one other feature from the set of features for the respective event is shared with a second party,
wherein the first device and second device are controlled by different entities,
training, by a first party … wherein at least one first feature … is not shared … with a second party,
wherein the training record corresponds to, if yet available, a second score, generated previously by the second device using at least a set of second features comprising at least one second feature that is not shared with the first device, and
wherein the respective training record corresponds to, if available, a second-party score, generated previously by the second party using at least a set of second party features comprising at least one third feature that is not shared and at least one fourth feature that is shared with the first party, and
wherein, when an iteration of the training is a first iteration, the second score is not yet available in the first iteration, and
wherein the second-party score is not available in a first iteration corresponding to an initial round of performing the set of steps for the training of the model that produces the first-party score,
when the iteration of the training is a subsequent iteration, the second score is available in the subsequent iteration;
and the second-party score is available in a second iteration corresponding to a subsequent round of performing the set of steps for the training of the model that produces the first-party score;
sending the first score to the second device by the first device, wherein the second device uses the first score to train a second machine model to produce a next second score related to a label classification of the event;
communicating the first-party score to the second party, for use by the second party to train a second model to produce another second-party score related to a label classification of the respective event; and
receiving, by the first device, the next second score from the second device;
receiving, by the first party, the other second-party score from the second party;
repeating iteratively the training the first machine model, the sending the first score, and the receiving the next second score for each event in each training record of the set of training records until a training stop condition has been reached; and
responsive to a training stop condition having not been reached, repeating a set of steps, the set of steps corresponding to a particular iteration for training a set of models, the set of steps comprising:
providing one or more trained models including the first machine model.
responsive to the training stop condition having been reached, outputting one or more trained models.
Claim 2
Claim 2
wherein the training stop condition includes that a number of iterations have been reached or that a prediction error between iterations is less than a threshold.
wherein the training stop condition comprises at least one of: a number of iterations have been reached; a sufficient prediction quality level tested on a test data set having been achieved; having reached a model's ability to improve; or a prediction error between iterations is less than a threshold.
Claim 3
Claim 2
wherein the training stop condition includes that a sufficient prediction quality level tested on a test data set had been achieved or that a model’s ability to improve had been reached.
wherein the training stop condition comprises at least one of: a number of iterations have been reached; a sufficient prediction quality level tested on a test data set having been achieved; having reached a model's ability to improve; or a prediction error between iterations is less than a threshold.
Claim 4
Claim 3
training, by the first device, a third machine model that produces for the event corresponding to the training record, a third score related to a particular label classification of the event, the training of the third machine model using the set of training records and the second scores;
training, by the first party, a third model that produces for the respective event corresponding to the respective training record, a second first-party score related to a particular label classification of the respective event, the training of the third model using the set of training records and the second-party scores;
sending the third score to the second device, wherein the second device uses the third score to train a fourth model to produce a fourth score related to a particular label classification of the event; and
communicating the second first-party score to the second party, for use by the second party to train a fourth model to produce a third second-party score related to a particular label classification of the respective event;
receiving, by the first device, the fourth score from the second device.
receiving, by the first party, the third second-party score from the second party.
Claim 5
Claim 4
predicting a label for an unlabeled event using at least one of the one or more trained models.
predicting a label for an unlabeled event using at least one of the one or more trained models.
Claim 6
Claim 5
wherein predicting the label for the unlabeled event comprises:
wherein predicting a label for the unlabeled event using the one or more trained models comprises:
generating a first prediction score by a first trained model of the one or more trained models based on event data associated with the unlabeled event and a second prediction score of a prior iteration, when available;
given associated event data associated with the unlabeled event, using a trained first-party model, at least some of the associated event data, and a second-party score of a prior iteration, if such exists and is available, to generate a first-party score;
sending the first prediction score to the second device, wherein the second device generates a second prediction score for a current iteration;
communicating the first-party score to the second party, in which the second party uses the first-party score to produce for the unlabeled event a second-party score related to a label classification of the unlabeled event;
receiving from the second device, the second prediction score for the current iteration;
receiving the second-party score; and
repeating the generating, the sending, and the receiving until reaching a prediction stop condition; and
responsive to a prediction stop condition having not been reached, repeating steps comprising:
when the prediction stop condition is reached outputting the label for the unlabeled event.
responsive to the prediction stop condition having been reached, outputting the label for the unlabeled event.
Claim 7
Claim 6
wherein the second device generates the second predication score based on the first prediction score and the event data associated with the unlabeled event.
wherein the second party produces the second-party score for the unlabeled event by performing the steps comprising: using a trained second-party model, at least some of the associated event data of the unlabeled event, and the first-party score of a prior iteration to generate the second-party score.
Claim 8
Claim 7
comparing the label predicted for the unlabeled event with a label predicted by the second device to determine whether the labels match.
comparing the label predicted for the unlabeled event with a label predicted by the second party to determine whether the labels match.
Claim 9
wherein the first machine model corresponds to a logistic regression classifier, random forest classifier, decision tree classifier, or a neural network classifier.
Claim 16
Claim 28
A non-transitory computer-readable medium comprising:
A non-transitory computer-readable medium comprising:
computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform one or more operations comprising:
computer-readable instructions that, when executed by a processor, cause the processor to perform one or more operations comprising:
training, by a first device, a first machine model that produces for an event corresponding to a training record of a set of training records:
training, by a first party using a set of training records, a first model that produces for a respective event corresponding to a respective training record of the set of training records:
a first score related to a label classification of the event,
a first-party score related to a label classification of the respective event,
wherein at least one first feature from a set of features for the event is not shared with a second device,
wherein at least one first feature from a set of features for the respective event is not shared and at least one other feature from the set of features for the respective event is shared with a second party,
wherein the first device and second device are controlled by different entities,
training, by a first party … wherein at least one first feature … is not shared … with a second party
wherein the training record corresponds to, if yet available, a second score, generated previously by the second device using at least a set of second features comprising at least one second feature that is not shared with the first device, and
wherein the respective training record corresponds to, if available, a second-party score, generated previously by the second party using at least a set of second party features comprising at least one third feature that is not shared and at least one fourth feature that is shared with the first party,
wherein, when an iteration of the training is a first iteration, the second score is not yet available in the first iteration, and
wherein the second-party score is not available in a first iteration corresponding to an initial round of performing the set of steps for the training of the model that produces the first-party score,
when the iteration of the training is a subsequent iteration, the second score is available in the subsequent iteration;
the second-party score is available in a second iteration corresponding to a subsequent round of performing the set of steps for the training of the model that produces the first-party score;
sending the first score to the second device by the first device,
communicating the first-party score to the second party,
wherein the second device uses the first score to train a second machine model to produce a next second score related to a label classification of the event;
or use by the second party to train a second model to produce another second-party score related to a label classification of the respective event;
receiving, by the first device, the next second score from the second device;
receiving, by the first party, the other second-party score from the second party;
repeating iteratively the training the first machine model, the sending the first score, and the receiving the next second score for each event in each training record of the set of training records until a training stop condition has been reached; and
responsive to a training stop condition having not been reached, repeating a set of steps, the set of steps corresponding to a particular iteration for training a set of models, the set of steps comprising:
providing one or more trained models including the first machine model.
responsive to the training stop condition having been reached, outputting one or more trained models.
Claim 17
Claim 29
wherein the training stop condition includes that a number of iterations had been reached, that a prediction error between iterations is less than a threshold, that a sufficient prediction quality level tested on a test data set had been achieved or that a model’s ability to improve had been reached.
wherein the training stop condition comprises at least one of: a number of iterations have been reached; a sufficient prediction quality level tested on a test data set having been achieved; having reached a model's ability to improve; or a prediction error between iterations is less than a threshold.
Claim 18
Claim 31
predicting a label for an unlabeled event using at least one of the one or more trained models.
predicting a label for an unlabeled event using at least one of the one or more trained models.
Claim 19
Claim 32
wherein predicting the label for the unlabeled event comprises:
wherein predicting the label for the unlabeled event using the one or more trained models comprises:
generating a first prediction score by a first trained model of the one or more trained models based on event data associated with the unlabeled event and a second prediction score of a prior iteration, when available;
given associated event data associated with the unlabeled event, using a trained first-party model, at least some of the associated event data, and a second-party score of a prior iteration, if such exists and is available, to generate a first-party score;
sending the first prediction score to the second device, wherein the second device generates a second prediction score for a current iteration;
communicating the first-party score to the second party, in which the second party uses the first-party score to produce for the unlabeled event a second-party score related to a label classification of the unlabeled event;
receiving from the second device, the second prediction score for the current iteration;
receiving the second-party score; and
repeating the generating, the sending, and the receiving until reaching a prediction stop condition; and
responsive to a prediction stop condition having not been reached, repeating steps comprising:
when the prediction stop condition is reached outputting the label for the unlabeled event.
responsive to the prediction stop condition having been reached, outputting the label for the unlabeled event.
Claim 20
Claim 33
wherein the second device generates the second predication score based on the first prediction score and the event data associated with the unlabeled event.
wherein the second party produces the second-party score for the unlabeled event by performing the steps comprising: using a trained second-party model, at least some of the associated event data of the unlabeled event, and the first-party score of a prior iteration to generate the second-party score.
Regarding claim 9, U.S. Patent No. 11,989,633 teaches all of the limitations of claim 1 as stated in the chart. However, U.S. Patent No. 11,989,633 does not explicitly teach wherein the first machine model corresponds to a logistic regression classifier, random forest classifier, decision tree classifier, or a neural network classifier.
Wan teaches wherein the first machine model corresponds to a logistic regression classifier, random forest classifier, decision tree classifier, or a neural network classifier (Wan, section 3.3 – teaches linear regression and neural networks).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify U.S. Patent No. 11,989,633 with the teachings of Wan in order to extend the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques in the field of privacy-preserving classification on vertically partitioned data (Wan, Abstract – “Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.”).
Allowable Subject Matter
Claims 10-15 are allowed.
The following is an examiner’s statement of reasons for allowance: in view of claim 10 and further search, claim 10 is considered allowable since when reading the claim in light of the specification, as per MPEP 2111.01, none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in the claim.
Regarding the cited limitations of claim 10 which do not appear to be taught by the prior art: Wan et al. teaches two parties together using vertically partitioned data. Yu teaches a multi-party privacy preserving model using vertically partitioned data.
However, claim 10 is deemed to be directed to a nonobvious improvement over the prior art of record. As noted above, the claim comprises iterative processing between two parties using iteration scores and vertically partitioned data.
Therefore, the cited/applied prior art fails to teach or suggest each and every feature of each of the combination of features recited in independent claim 10.
When taken into context, the claim as a whole was not uncovered in the prior art; i.e., all dependent claims which depend from claim 10 are allowed as they depend upon an allowable independent claim.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled "Comments on Statement of Reasons for Allowance."
Conclusion
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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