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 . Claims 1-20 are pending.
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 a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 13 to a device (i.e., a machine), and claim 20 to a non-transitory computer-readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 13, and 20 is (claim 1 being representative):
generating a multi-classification label set corresponding to an object set based on a binary classification label set corresponding to the object set;
receiving an embedding vector set from a non-label party model, wherein an embedding vector in the embedding vector set is generated based on a feature of an object in the object set; and
generating a label party model based on the embedding vector set and the multi-classification label set.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level: generating multi-classification labels; receiving vectorized, non-labeled input from a model; generating a label model based on the vectorized, non-labeled input and muti-classifications labels. These are steps an administrator could accomplish mentally and/or with pen and paper, in order to ensure data privacy during/prior to training. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to generating a label model that ensures data privacy during/prior to training, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0003]-[0004] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites no additional elements.
Claim 13 recites the following additional elements: an electronic device; a processor; a memory coupled to the processor, wherein the memory has instructions stored therein.
Claim 20 recites the following additional elements: a non-transitory computer-readable storage medium, storing computer-executable instructions.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0069] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-12 and 14-19 further narrow the abstract idea with additional abstract steps and/or information. This narrowing of the abstract idea does not integrate it into practical application and/or add significantly more either, and the groupings highlighted above still apply. Some of the dependent claims recite further additional elements, beyond those highlighted above:
Claims 5 and 17: wherein the label party model comprises a first network and a second network.
Claim 6: applying the first network.
Claims 7 and 19: applying the first network; training the first network.
Claim 9: wherein the second network is a second parallel network; training the second parallel network.
Claim 10: applying the second parallel network.
Claim 11: wherein the second network is a second serial network; training the second serial network.
Claim 12: applying the second serial network.
Similar to above, these are generic computing elements and functions claimed in a results-oriented manner and applied to the tasks of the abstract idea. Per MPEP 2106.05(f), whether viewed alone or in combination, this does not integrate the narrowed abstract idea into practical application and/or add significantly more.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 7-8, 13, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (CN 115688882 A; page numbers correspond to those of attached translation) in view of Yi (CN 112529100 A; page numbers correspond to those of attached translation).
Claims 1, 13, and 20
Zheng discloses:
[A method for split learning {Page 2: The invention claims a method and device for multi-party joint model training for protecting privacy, using the method and device, training member in the process of splitting and learning, further using contrast learning algorithm for unsupervised training local model, without idle waiting gradient return, so as to effectively improve the whole training efficiency.}, comprising:]
[An electronic device, comprising: a processor; and a memory coupled to the processor, wherein the memory has instructions stored therein, and the instructions, when being executed by the processor {Page 4: According to a sixth aspect, there is provided a computing device, comprising a memory and a processor, wherein the memory is stored with executable code, the processor executes the executable code, realizing the first aspect or the second aspect of the method.}, cause the electronic device to:]
[A non-transitory computer-readable storage medium, storing computer-executable instructions, wherein the computer-executable instructions, when being executed by a processor {Page 4: According to a fifth aspect, there is provided a computer readable storage medium, on which a computer program is stored, when the computer program is executed in the computer, the computer executes the method of the first aspect or the second aspect. Regarding processor, see previous citation to page 4.}, cause an electronic device to:]
receiving an embedding vector set from a non-label party model, wherein an embedding vector in the embedding vector set is generated based on a feature of an object in the object set {Page 2: According to the first aspect, there is provided a method for multi-party joint model training, the multi-party comprises a label side with a sample label and a plurality of data sides with sample features; The method is applied to any one of the data parties, including: using the first model of the local deployment to process the first batch local feature corresponding to the first batch sample identification appointed by the multi-party, obtaining the first output, and sending the first output to the label party; based on multiple second batch local features, using contrast learning to perform the first update to the first model; receiving the return gradient from the label party, based on the first output of each data party, and the first batch sample identification corresponding to the sample label and the label party deployed in the target model determined; based on the return gradient, performing a second update to the first model through the first update.
Examiner notes that Zheng describes receiving data from a non-label party, where the data corresponds to processed features.
Examiner further notes that “embedding vector” is described in page 8: It should be understood that the mathematical form of the representation output is generally a vector, and the characteristic set corresponding to the output set.};
generating a label party model based on the embedding vector set and the classification label set {Pages 11-12: [N]ext, at least using the first model processing comprises the sample set of the augmented sample, obtaining the representation output of each sample. In one embodiment, the sample set further comprises the training sample of any one of the batch. In one embodiment, it can use the first model to process each sample, obtaining the first output of each sample as the corresponding representation output. In another embodiment, the first output of each sample can be further processed by the second model of the local deployment, obtaining the second output of each sample as the corresponding representation output. [T]hen calculating contrast loss according to the representation output. It should be understood that the comparison loss negative correlation with respect to the corresponding different training sample representation output pair vector distance between the positive phase corresponding to the same training sample of the representation output pair vector distance.
Examiner notes that classification label set further described in page 11: [F]or the training sample under the horizontal scene, in the image classification scene, the training sample can be the original image, the corresponding sample label is image category label. Further, in the image classification scene of more fine granularity, such as target identification scene, comprising sample label marking frame and object marking type (such as vehicle, tree, pedestrian and so on) aiming at the target object in the original image; Also, in the face identification scene, the original image is a face image, the image type tag is a user identification (such as mobile phone number and so on). [I]n the text classification scene, training sample can be original text, corresponding to the text sample label. For example, in the content recommendation scene, text category may include science and technology, music, social news and so on.}.
Zheng doesn’t explicitly disclose, however, Yi, in a similar field of endeavor directed to a training method of multi-classification model, teaches: generating a multi-classification label set corresponding to an object set based on a binary classification label set corresponding to the object set; multi-classification label set {Page 2: The embodiment of the invention claims a training method of multi-classification model, device, electronic device, storage medium and computer program, capable of converting the two classification problem into multi-classification problem, ensuring the data security in the model training process. The technical solution of the embodiment of the present invention is implemented as follows. The embodiment of the present invention provides a multi-classification model training method, comprising: obtaining the training sample carrying two classification labels; the second classification label is used for indicating the result of the second classification corresponding to the training sample; performing label conversion on the training sample, so that the training sample carries multiple classification label; the multi-classification label is used for indicating the result of the multi-classification corresponding to the training sample; obtaining the multi-classification prediction result sent by the second participant device; the multi-classification prediction result is obtained by prediction of training sample carrying the multi-classification label by multi-classification model; performing result conversion to the multi-classification prediction result, obtaining the multi-classification model corresponding to the second classification prediction result; based on the difference between the result of the second classification of the second classification of the prediction result and the second classification, updating the model parameter of the multi-classification model.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Zheng to include the features of Yi. Given that Zheng is directed to multi-party joint model training used in assessing risk, one of ordinary skill would have been motivated to look to Yi, in order to combine multiple prediction results to facilitate risk evaluation (i.e., implement or do not implement) {Page 6 of Yi}.
Claims 5 and 17
Zheng further discloses: wherein the label party model comprises a first network and a second network {Page 3: In one specific embodiment, at least using the first model processing comprises the sample set of the augmented sample, obtaining the representation output of each sample, comprising: using the first model to process the each sample, obtaining the first output of each sample; processing the first output of each sample by using the second model of the local deployment, obtaining the second output of each sample, as the characteristic output; according to the comparison loss, at least updating the first model, comprising: updating the first model and the second model by using the contrast loss.}.
Claims 7 and 19
Yi further teaches: wherein generating the label party model comprises: applying the first network to embedding vectors in the embedding vector set to obtain a multi-classification result of the first network {Page 9: Step 302: performing label conversion on the training sample, so that the training sample carries multi-classification label. [W]herein the multi-classification label is used for indicating the multi-classification result corresponding to the training sample; The multi-classification refers to three or more types, namely the multi-classification label is used for indicating one type of at least three categories to which the training sample belongs.}; generating a first loss function for the first network based on the multi-classification result and corresponding multi-classification labels in the multi-classification label set {Page 12: The step 305 is as follows: based on the difference between the result of two classification of the prediction result and the second classification label indication, updating the model parameter of the multi-classification model.}; and training the first network based on the first loss function {See previous citation to page 12, where updating corresponds to training.}.
The motivation and rationale to include the additional features of Yi is the same as set forth previously.
Claim 8
Zheng further discloses: wherein generating the label party model further comprises: determining a feedback gradient vector for the embedding vectors based on the embedding vectors, the classification result, and the classification labels {Pages 6-7: Returning to FIG. 1, wherein the label side Py and the m (m is a positive integer) of the sample feature are shown and any one of the data parties is recorded as Pi; In the Py party, the target model Mt is deployed, and the first model Mi is deployed in the Pi party. [B]ased on the above, in the joint training process, the Pi party inputs the local feature corresponding to the sample identification of the batch (1 in FIG. 1 as batch b1) into the first model Mi, to obtain the first output oi, and sends it to the Py party, and the Pi party before receiving the gradient of the Py party back, using the comparison learning algorithm, using multiple batches (in FIG. 1 as batch b2 to bn) of the local feature of the first model Mi for several times of iterative training, realizing the first model Mi model parameter (or weighing parameter) of several times of iterative update, the iteration change process of the schematic parameter in FIG. 1: w0, w1, ..., wn-1; after that, Pi can use the received return gradient gi to further update the model parameter wn-1 in the first model Mi, updating is wn. so that the Pi side calculates the time period between the first output oi and receiving return gradient gi, not in the idle state, but using contrast learning to train the first model Mi continuously, so as to effectively improve the whole training efficiency.}; and sending the feedback gradient vector to the non-label party model {See previous citation to pages 6-7}.
Yi further teaches: multi-classification result; multi-classification labels {See previous citation to page 2.}.
The motivation and rationale to include the additional features of Yi is the same as set forth previously.
Claims 2-4 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zheng and Yi, further in view of “Deep Learning with Label Differential Privacy” by Ghazi et al. (NPL attached; hereinafter Ghazi).
Claims 2 and 14
Yi further teaches: wherein generating the multi-classification label set corresponding to the object set comprises: generating a multi-classification label for each classification label in the binary classification label set to obtain the multi-classification label set {See previous citation to page 2}.
The motivation and rationale to include the additional features of Yi is the same as set forth previously.
The combination of Zheng and Yi, while teaching the features above, doesn’t explicitly teach, however, Ghazi, in a similar field of endeavor directed to deep learning with label differential privacy, teaches: flipping a binary classification label subset in the binary classification label set to obtain a flipped binary classification label set; flipped label set {“4 Application of RRWithPrior: Multi-Stage Training” on Page 6: Specifically, we assume that we have a training algorithm A that outputs a probabilistic classifier which, on a given unlabeled sample x, can assign a probability to each class. We partition our dataset into subsets S(1),...,S(T), and we start with a trivial model M(0) that outputs equal probabilities for all classes. At each stage we use the most recent model M(t−1) to assign the probabilities (p1,...,pK) for each sample from S(t). Applying RRWithPrior with this prior on the true label, we get a randomized label. We then use all the samples with randomized labels obtained so far to train the model M(t).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Zheng and Yi to include the features of Ghazi. Given that Zheng is directed to multi-party joint model training, one of ordinary skill would have been motivated to look to Ghazi, in order to improve model accuracy {“1.1 Applications to Learning with Label Differential Privacy” on page 2 of Ghazi}.
Claims 3 and 15
Ghazi further teaches: wherein flipping the binary classification label subset in the binary classification label set comprises: randomly selecting binary classification labels from the binary classification label set at a predetermined probability to form the binary classification label subset; and flipping each label in the binary classification label subset to a further label of a binary classification label {See previous citation to “4 Application of RRWithPrior: Multi-Stage Training” on Page 6}.
The motivation and rationale to include the additional features of Ghazi is the same as set forth previously.
Claims 4 and 16
Yi further teaches: wherein generating the multi-classification label for each classification label in the binary classification label set comprises: uniformly dividing the binary classification label set into a plurality of binary classification label subsets, wherein each of the plurality of binary classification label subsets comprises one classified label {Pages 9-10: In some embodiments, it can realize the conversion between the two classification label of the training sample and the multi-classification label by the following way: when the number of training samples is multiple, dividing the sample set composed of a plurality of training samples, obtaining at least two sample subsets; respectively determining label conversion relation corresponding to each sample subset; label conversion relation is used for converting the two classification label into multi-classification label; based on the label conversion relation corresponding to each sample subset, respectively performing label conversion to the two classification label carried by the training sample in each sample set, so that each training sample in the sample set carries the multi-classification label. In practical implementation, the number of sample subset can be set according to the actual need, and for the sample subset of the target number, the dividing mode of the sample set may include random division or equal amount division; wherein, for random division, the training sample in the sample set is randomly divided into the sample subset of the target number; the number of each sample sub-set training sample is the same or different; For equal division, the training sample in the sample set is divided into sample subsets of the target number with the same number of samples.; and generating the multi-classification label for each binary classification label in the plurality of binary classification label subsets, wherein a multi-classification label is generated for labels in a same binary classification label subset, and a multi-classification label with a different classification is generated for labels in a different binary classification label subset {See previous citation to page 2}.
The motivation and rationale to include the additional features of Yi is the same as set forth previously.
Ghazi further teaches: flipped labels {See previous citation to “4 Application of RRWithPrior: Multi-Stage Training” on Page 6.}.
The motivation and rationale to include the additional features of Ghazi is the same as set forth previously.
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zheng and Yi, further in view of Li (US 20180240257).
Claim 9
The combination of Zheng and Yi, while teaching the features above, doesn’t explicitly teach, however, Li, in a similar field of endeavor directed to applying a plurality of neural networks, teaches: wherein the second network is a second parallel network, and generating the label party model further comprises: generating a second parallel network loss function for the second parallel network based on the embedding vectors and corresponding binary classification labels in the binary classification label set {[0056] For example, a particular training image 330 is used to generate the outputs 312-316 at the different resolutions by the generator neural network 310, which are used by the loss neural network 340 to subsequently generate low, medium, and high resolution style features 342, 344, 346. That particular training image 330 is also input to the loss neural network 340, which uses the training image to generate additional low, medium, and high resolution features. The low resolution style features of the low resolution output 312 (e.g., the corresponding low resolution style features 342) are compared to the low resolution style features of the training image to compute a low resolution style loss 352. Similarly, the medium resolution style features of the medium resolution output 314 (e.g., the corresponding medium resolution style features 344) are compared to the medium resolution style features of the training image to compute a medium resolution style loss 354. Likewise, the high resolution style features of the high resolution output 316 (e.g., the corresponding high resolution style features 346) are compared to the high resolution style features of the training image to compute a high resolution style loss 356. The computations of the low, medium, and high style losses 352, 354, 356 can occur in parallel or in serial. Example of a computation of the loss is further illustrated in FIG. 4. The losses 352, 354, 356 are then used to update the parameters of the generator neural network 310. For example, backpropagation can be implemented to iteratively update the parameters such that the losses 352, 354, 356 are minimized. An example of the backpropagation is further described in connection with FIG. 5.}; and training the second parallel network based on the second parallel network loss function {See previous citation to [0056]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Zheng and Yi to include the features of Li. Given that Zheng is directed to multi-party joint model training, one of ordinary skill would have been motivated to look to Li, in order to provide an improved outcome via output synthesis {[0005] of Li}.
Claim 10
Li further teaches: wherein generating the second parallel network loss function for the second parallel network comprises: applying the second parallel network to the embedding vectors to determine a second parallel network classification result; and generating the second parallel network loss function for the second parallel network based on the second parallel network classification result and the corresponding binary classification labels {See previous citation to [0056]}.
The motivation and rationale to include the additional features of Li is the same as set forth previously.
Claim 11
The combination of Zheng and Yi, while teaching the features above, doesn’t explicitly teach, however, Li, in a similar field of endeavor directed to applying a plurality of neural networks, teaches: wherein the second network is a second serial network, and generating the label party model further comprises: generating a second serial network loss function for the second serial network based on the embedding vectors, corresponding binary classification labels in the binary classification label set, and the first network; and training the second serial network based on the second serial network loss function {See previous citation to [0056]}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Zheng and Yi to include the features of Li. Given that Zheng is directed to multi-party joint model training, one of ordinary skill would have been motivated to look to Li, in order to provide an improved outcome via output synthesis {[0005] of Li}.
Claim 12
Li further teaches: wherein generating the second serial network loss function for the second serial network comprises: applying the second serial network to the multi-classification result to determine a second serial network classification result; and generating the second serial network loss function for the second serial network based on the second serial network classification result and the binary classification labels {See previous citation to [0056]}.
The motivation and rationale to include the additional features of Li is the same as set forth previously.
No Prior Art Applied to Claims 6 and 18
There is no prior art applied to claims 6 and 18. Examiner has not been able to find the following features in combination:
[wherein the label party model comprises a first network and a second network;]
wherein generating the label party model comprises:
determining a ratio of a number of first labels to a number of second labels in the binary classification label set;
determining a first weight for a label corresponding to the first labels and a second weight for a label corresponding to the second labels in the multi-classification label set, wherein a ratio of the first weight to the second weight is inversely proportional to the ratio of the number of the first labels to the number of the second labels;
applying the first network to the embedding vector set to obtain a multi-classification result set of the first network; and
generating a first loss function for the first network in the label party model based on the multi-classification result set, the multi-classification label set, the first weight, and the second weight.
In addition to the references above, examiner also identified the following prior art, which, while generally relevant to the field of endeavor, stops short of the specificity required by the claim:
US 20210374605, which teaches: In one embodiment, a method includes accessing a plurality of initial gradients associated with a machine-learning model from a data store associated with a first electronic device, selecting one or more of the plurality of initial gradients for perturbation, generating one or more perturbed gradients for the one or more selected initial gradients based on a gradient-perturbation model, respectively, wherein for each selected initial gradient: an input to the gradient-perturbation model comprises the selected initial gradient having a value x, the gradient-perturbation model changes x into a first continuous value with a first probability or a second continuous value with a second probability, and the first and second probabilities are determined based on x, and sending the one or more perturbed gradients from the first electronic device to a second electronic device.
US 20210406608, which teaches: An example system includes a processor to evaluate a trained first classifier on a test set of labeled data to generate error rates for a number of labels. The processor is to process a set of unlabeled data via the trained first classifier to generate annotated data including labels and associated error rates. The processor is to train a second classifier using the annotated data and the associated error rates.
Accordingly, there is no prior art rejection applied to claims 6 and 18.
Incorporating the entirety of the claim, plus any intervening claim (bracketed above), into the respective parents claims would overcome the prior art rejections above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20210374605, which teaches: In one embodiment, a method includes accessing a plurality of initial gradients associated with a machine-learning model from a data store associated with a first electronic device, selecting one or more of the plurality of initial gradients for perturbation, generating one or more perturbed gradients for the one or more selected initial gradients based on a gradient-perturbation model, respectively, wherein for each selected initial gradient: an input to the gradient-perturbation model comprises the selected initial gradient having a value x, the gradient-perturbation model changes x into a first continuous value with a first probability or a second continuous value with a second probability, and the first and second probabilities are determined based on x, and sending the one or more perturbed gradients from the first electronic device to a second electronic device.
US 20210406608, which teaches: An example system includes a processor to evaluate a trained first classifier on a test set of labeled data to generate error rates for a number of labels. The processor is to process a set of unlabeled data via the trained first classifier to generate annotated data including labels and associated error rates. The processor is to train a second classifier using the annotated data and the associated error rates.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
Prosecution Insights