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 .
The Examiner notes applicant’s assertion that a “computing device” “include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface”, as stated in paragraph 94 of the specification as filed and Figure 2 (paragraph 126).
Response to Amendment
The previous objections on the relevant claims are withdrawn based on the amendments to the claims.
Response to Arguments
Applicant’s arguments have been fully considered but they are not persuasive. The Examiner respectfully disagrees that the claims do not recite an abstract idea and maintains this rejection as pointed out below. In regards to the applicant’s argument that the prior art of Ge does not teach the limitation of labeling each object of a plurality of objects with a probabilistic confidence label comprising a probability classification score for each class of at least two classes (Remarks, p. 10), the Examiner disagrees points out that Ge teaches “the CNN learns to classify objects (e.g., a dog) depicted within novel input data, based on identifying features encoded in the image data. Based on the identified features, and for each classifiable object type, the model may determine a likelihood that an instance of the object type is depicted in the image. That is, for some types of objects, conventional CNNs have been demonstrated to learn the task of object classification via image-level labeled training data” (paragraph 2); and “a classification method may have determined that there is a 0.9 probability that the depicted object is a dog, and a 0.1 probability that the depicted object is a cat” (paragraph 23). That is a confidence label/probability score is assigned to various objects in the image data.
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, 4-9, 22-27, 29-30, 43-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
All claims are directed towards either a method, a system or a computer program product and thus satisfies Step 1 as falling into one of the statutory categories.
Step 2A, Prong One:
Independent Claim 1 recites (the same analysis applies to similar independent Claims 22 and 43):
labeling each object of a plurality of objects with a probabilistic confidence label comprising a probability classification score for each class of at least two classes, resulting in a plurality of probabilistic confidence labels associated with the plurality of objects;
this limitation, under its broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person (such as a labeler) can label objects with a probabilistic confidence label comprising a probability classification score for various classes of objects using observation, evaluation and judgement.
and determining a weighted probability classification score for each object of the plurality of objects based on the plurality of classification scores for the object, wherein the weighted probability classification score is based on weighing scores from each labeler of the plurality of labelers based on a corresponding confidence score of the labeler;
This is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is a person can determine weighted scores for objects based on classification scores using pen, paper and evaluation and can weight scores based on the confidence scores of labelers using pen, paper and evaluation.
Step 2A, Prong Two:
Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 22 and 43):
wherein labeling each object of the plurality of objects comprises: receiving, from a plurality of labelers, a plurality of classification scores for each object of the plurality of objects;
considered as adding insignificant extra-solution activity (receiving data) to the judicial exception - see MPEP 2106.05(g).
and training, with at least one computing device, the machine-learning model based on the plurality of objects and the weighted probability classification score for each object of the plurality of objects.
This limitation is considered as using a machine learning model as a tool to perform an abstract idea, which also include its training - see MPEP 2106.05(f). The further additional element of “computing device” as recited in these independent claims is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements 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 are considered as using a machine learning model as a tool to perform an abstract idea, which also include its training - see MPEP 2106.05(f), and as appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (receiving data), to the judicial exception - see MPEP 2106.05(d); and the further additional element of “computing device” as recited in these independent claims amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are therefore not patent eligible.
The second limitation of dependent Claims 23 and 44 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is a person can determine weighted scores for objects based on classification scores using pen, paper and evaluation. The first limitation is considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) (under Step 2A, Prong Two), and subsequently as appending well-understood, routine, conventional activities (receiving data, under Step 2B) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
Dependent Claim 24 is also considered as falling under the “Mental Processes” groupings of abstract ideas. That is a person can weight scores based on the confidence scores of labelers using pen, paper and evaluation.
Dependent Claims 4 and 25 limitations are considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) (under Step 2A, Prong Two), and subsequently as appending well-understood, routine, conventional activities (under Step 2B, receiving and manipulating/combining data) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
Dependent Claims 5-6 and 26-27 are considered as using a machine learning model as a tool to perform an abstract idea - see MPEP 2106.05(f).
Dependent Claim 7 first limitation is considered as falling under the “Mental Processes” groupings of abstract ideas. That is a person can determine a score for an object using evaluation. The second part of the claim is considered as using a machine learning model as a tool to perform an abstract idea - see MPEP 2106.05(f).
Dependent Claims 8 and 29 are considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) (under Step 2A, Prong Two), and subsequently as appending well-understood, routine, conventional activities (under Step 2B, receiving data) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
The last two limitations of dependent Claims 9 and 30 are also considered as falling under the “Mental Processes” groupings of abstract ideas. That is a person can determine an inner product between two vectors and perform optimization based on the inner product and a loss function using pen, paper and evaluation. The first two limitations are considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) (under Step 2A, Prong Two), and subsequently as appending well-understood, routine, conventional activities (under Step 2B, providing/receiving data) previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-8, 22-27, 29, 43-44 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Ge, US 2021/0027098 A1.
Regarding Claim 1, Ge teaches:
A method for training a machine-learning model, comprising: labeling each object of a plurality of objects with a probabilistic confidence label comprising a probability classification score for each class of at least two classes, resulting in a plurality of probabilistic confidence labels associated with the plurality of objects (paragraph 29: “The initial instance segmentation may include pixel-wise labeled via the initial segmentation. The initial object proposals may employed to supervise the training of the object detection module. The object detection module is trained to refine the initial object proposals and/or the initial segmentation, via generating a class probability map. In various embodiments, a class probability map is derived from a neural network, and provides a probability of likelihood for each pixel in an image, wherein the probability indicates a likelihood (or confidence) that the pixel contributes to a detected object in the image”. The probability of likelihood for each pixel in an image, wherein the probability indicates a likelihood (or confidence) that the pixel contributes to a detected object in the image, is representative of the probability classification score for each class of classes, resulting in a plurality of probabilistic confidence labels associated with the plurality of objects. The objects representative of the classes, see paragraph 26. And, paragraph 2: “the CNN learns to classify objects (e.g., a dog) depicted within novel input data, based on identifying features encoded in the image data. Based on the identified features, and for each classifiable object type, the model may determine a likelihood that an instance of the object type is depicted in the image. That is, for some types of objects, conventional CNNs have been demonstrated to learn the task of object classification via image-level labeled training data”; And paragraph 23: “a classification method may have determined that there is a 0.9 probability that the depicted object is a dog, and a 0.1 probability that the depicted object is a cat”. That is a confidence label/probability score is assigned to various objects in the image data. Examiner’s note: see also Tariq, US 2019/0391578 A1, for example paragraph 23),
wherein labeling each object of the plurality of objects comprises: receiving, from a plurality of labelers, a plurality of classification scores for each object of the plurality of objects; and determining a weighted probability classification score for each object of the plurality of objects based on the plurality of classification scores for the object (paragraph 24: “An object proposal may include an “object bounding box,” or simply a “bounding box,” which is a structure, whose boundaries separates pixels who may contribute to the visualization of an object from pixels that are not believed to contribute to the object. An object proposal may include a weight for each bounding box, where the weight indicates a confidence level in the bounding box”; And, paragraph 32: “when training the object detection module, the object scores may be employed to weight the confidence of the object proposals generated by the multi-labeled classification module”. Note that labeling can be performed by labelers as for example in preparing ground truth data, see for example paragraph 2: “training data often includes images that are labeled via ground truths, which indicate classifications of one or more objects that are visually depicted in the image encoded in the training image data”. And paragraph 29: “the multi-label classification module (or the MLC model) is trained to generate a first (or an initial) object proposal for an image. The first object proposal may include a first (or initial) segmentation of the image (e.g., one or more instance segmentation masks). The first object proposal may additionally include a first set of bounding boxes and a first set of corresponding weights. The initial instance segmentation may include pixel-wise labeled via the initial segmentation. The initial object proposals may employed to supervise the training of the object detection module. The object detection module is trained to refine the initial object proposals and/or the initial segmentation, via generating a class probability map. In various embodiments, a class probability map is derived from a neural network, and provides a probability of likelihood for each pixel in an image, wherein the probability indicates a likelihood (or confidence) that the pixel contributes to a detected object in the image”. That is the MLC model acts as the labeler(s). And, paragraph 32: “object score generated by the multi-label classification module are employed to label the images and supervise the training of the object detection module. The object detection module is trained to detect an object, e.g., generate a bounding box (e.g., an object proposal) for the object. In a non-limiting embodiments, the object detection module may be include a “regions with a CNN” (R-CNN) architecture and/or framework. Thus, the object detection module may be trained via an R-CNN training pipeline (or a variant thereof). The R-CNN pipeline may be a Fast R-CNN pipeline or a Faster R-CNN pipeline. The training pipeline may be a You Only Look Once (YOLO) pipeline. The object proposals generated by the multi-label classification may be of low-confidence and/or inaccurate. Thus, when training the object detection module, the object scores may be employed to weight the confidence of the object proposals generated by the multi-labeled classification module. The object detection module is trained to generate higher confidence object proposals (e.g., object locations) than those generated by the multi-label classification module”),
wherein the weighted probability classification score is based on weighing scores from each labeler of the plurality of labelers based on a corresponding confidence score of the labeler (paragraph 31: “A proposal refinement sub-module of the multi-label classification module is employed to generate locations of objects (e.g., an updated, refined, and/or calibrated object proposal) and assign initial pixel-wise labels to at least a portion of the pixels included in the generated object proposals. As discussed below, the multi-label classification module may generate an object score (e.g., a likelihood and/or confidence score) for each object proposal”; And, paragraph 32: “the object scores may be employed to weight the confidence of the object proposals”. And, labeling can be performed by labelers as for example in preparing ground truth data, see for example paragraph 2, (or the MLC model as previously pointed out): “training data often includes images that are labeled via ground truths, which indicate classifications of one or more objects that are visually depicted in the image encoded in the training image data”);
and training, with at least one computing device, the machine-learning model based on the plurality of objects and the weighted probability classification score for each object of the plurality of objects (paragraph 26: “the training data includes images that are labeled with image-level labels”; And, paragraph 29: “the multi-label classification module (or the MLC model) is trained to generate a first (or an initial) object proposal for an image”; And paragraph 32: “The object locations (object proposals) and object score generated by the multi-label classification module are employed to label the images and supervise the training of the object detection module”. And, paragraph 33: “When training the instance refinement module, the object scores may be employed to weight the confidence of the object proposals generated by the object detection module”. And as pointed out above the object score can be a probabilistic confidence label that includes a probability score/value. Examiner’s note: see also Tariq, US 2019/0391578 A1, for example paragraph 27).
Regarding Claim 4, Ge further teaches:
The method of claim 1, wherein labeling each object of the plurality of objects comprises: receiving, from a plurality of machine-learning models, outputs comprising a plurality of probability classification scores for each object of the plurality of objects; and combining the outputs (paragraph 29: “The object detection module is trained to refine the initial object proposals and/or the initial segmentation, via generating a class probability map. In various embodiments, a class probability map is derived from a neural network, and provides a probability of likelihood for each pixel in an image, wherein the probability indicates a likelihood (or confidence) that the pixel contributes to a detected object in the image”; And, paragraph 30: “The refined segmentation and class attention map are employed to supervise the training of the instance refinement module. In various embodiments, a class attention map aggregates the excitation of a trained neural network, and gives the importance of each pixel that contributes to a corresponding object”. Examiner’s note: see also Tariq, US 2019/0391578 A1, for example paragraphs 27, 42).
Regarding Claim 5, Ge further teaches:
The method of claim 4, wherein the plurality of machine-learning models comprises a plurality of artificial neural networks (ANNs), and wherein the outputs comprise outputs from each last layer of each ANN of the plurality of ANNs (paragraph 4: “Each of the modules may employ a model that is implemented via one or more NNs. The multi-label classification module may implement a multi-label classification (MLC) model. The object detection module may implement an object detection (OD) model. The instance refinement module may implement an instance refine (IR) model. The instance segmentation module may implement an instance segmentation (IS) model. The backbone may implement a backbone model”; And, paragraph 49: “the multi-label classification module 220 includes various NN layers 222. To at least partially implement the OD model, the object detection module 240 includes various NN layers”; and as known in the art, the outputs from the last layer of neural networks are indicative of the result one is after).
Regarding Claim 6, Ge further teaches:
The method of claim 5, further comprising applying a softmax activation layer to each output before or after combining the outputs (paragraph 53: “The regional features are provided as input to two fully-connected layers (e.g., FC and FC), in NN layers 222 to generate classification results… The proposal weights indicate the contribution of each proposal to the C categories in image-level multi-label classification. A softmax function may be applied to normalize the weights”; the softmax function being applied to weights of that layer of the NN making that layer the softmax layer as is known in the art. Examiner’s note: see also Hong, US 2021/0042643 A1, for example paragraph 73).
Regarding Claim 7, Ge further teaches:
The method of claim 4, further comprising: determining a weighted probability classification score for each object of the plurality of objects based on the plurality of probability classification scores for the object, wherein the weighted probability classification score is based on weighing scores from each machine-learning model of the plurality of machine-learning models based on a corresponding accuracy of the machine-learning model (paragraph 32: “The object proposals generated by the multi-label classification may be of low-confidence and/or inaccurate. Thus, when training the object detection module, the object scores may be employed to weight the confidence of the object proposals generated by the multi-labeled classification module”; And, paragraph 33: “The object locations, object scores, and corresponding instance masks generated by the object detection module are employed to train the instance refinement module. The instance refinement module generates a refined object proposal and refined instance masks, as compared to the object proposals and instance masks generated by the object detection module. The instance refinement module may include a Mask R-CNN architecture and/or framework. Thus, the instance refinement module may be trained via a Mask R-CNN pipeline. When training the instance refinement module, the object scores may be employed to weight the confidence of the object proposals generated by the object detection module”).
Regarding Claim 8, Ge further teaches:
The method of claim 1, wherein labeling each object of the plurality of objects comprises: receiving, from a plurality of labelers, a first plurality of probability classification scores for each object of the plurality of objects (paragraph 31: “the multi-label classification module may generate an object score (e.g., a likelihood and/or confidence score) for each object proposal”. Note that labeling can be performed by labelers as for example in preparing ground truth data, see for example paragraph 2: “training data often includes images that are labeled via ground truths, which indicate classifications of one or more objects that are visually depicted in the image encoded in the training image data”);
and receiving, from a plurality of ANNs, outputs from each last layer of each ANN of the plurality of ANNs, the outputs comprising a second plurality of probability classification scores for each object of the plurality of objects (paragraph 32: “The object locations (object proposals) and object score generated by the multi-label classification module are employed to label the images and supervise the training of the object detection module. The object detection module is trained to detect an object, e.g., generate a bounding box (e.g., an object proposal) for the object”; And, paragraph 49: “the multi-label classification module 220 includes various NN layers 222. To at least partially implement the OD model, the object detection module 240 includes various NN layers”; and as known in the art, the outputs from the last layer of neural networks are indicative of the result one is after, in this case, classification scores for objects).
Regarding Claim 23, Ge further teaches:
The system of claim 22, wherein labeling each object of the plurality of objects comprises: receiving, from a plurality of labelers, a plurality of classification scores for each object of the plurality of objects, wherein the weighted probability classification score for each object of the plurality of objects is further based on the plurality of classification scores for the object received from the plurality of labelers (paragraph 24: “An object proposal may include an “object bounding box,” or simply a “bounding box,” which is a structure, whose boundaries separates pixels who may contribute to the visualization of an object from pixels that are not believed to contribute to the object. An object proposal may include a weight for each bounding box, where the weight indicates a confidence level in the bounding box”; And, paragraph 32: “when training the object detection module, the object scores may be employed to weight the confidence of the object proposals generated by the multi-labeled classification module”. Note that labeling can be performed by labelers as for example in preparing ground truth data, see for example paragraph 2: “training data often includes images that are labeled via ground truths, which indicate classifications of one or more objects that are visually depicted in the image encoded in the training image data”. And paragraph 29: “the multi-label classification module (or the MLC model) is trained to generate a first (or an initial) object proposal for an image. The first object proposal may include a first (or initial) segmentation of the image (e.g., one or more instance segmentation masks). The first object proposal may additionally include a first set of bounding boxes and a first set of corresponding weights. The initial instance segmentation may include pixel-wise labeled via the initial segmentation. The initial object proposals may employed to supervise the training of the object detection module. The object detection module is trained to refine the initial object proposals and/or the initial segmentation, via generating a class probability map. In various embodiments, a class probability map is derived from a neural network, and provides a probability of likelihood for each pixel in an image, wherein the probability indicates a likelihood (or confidence) that the pixel contributes to a detected object in the image”. That is the MLC model acts as the labeler(s). And, paragraph 32: “object score generated by the multi-label classification module are employed to label the images and supervise the training of the object detection module. The object detection module is trained to detect an object, e.g., generate a bounding box (e.g., an object proposal) for the object. In a non-limiting embodiments, the object detection module may be include a “regions with a CNN” (R-CNN) architecture and/or framework. Thus, the object detection module may be trained via an R-CNN training pipeline (or a variant thereof). The R-CNN pipeline may be a Fast R-CNN pipeline or a Faster R-CNN pipeline. The training pipeline may be a You Only Look Once (YOLO) pipeline. The object proposals generated by the multi-label classification may be of low-confidence and/or inaccurate. Thus, when training the object detection module, the object scores may be employed to weight the confidence of the object proposals generated by the multi-labeled classification module. The object detection module is trained to generate higher confidence object proposals (e.g., object locations) than those generated by the multi-label classification module”).
Regarding Claim 24, Ge further teaches:
The system of claim 23, wherein the weighted probability classification score is based on weighing scores from each labeler of the plurality of labelers based on a corresponding confidence score of the labeler (paragraph 31: “A proposal refinement sub-module of the multi-label classification module is employed to generate locations of objects (e.g., an updated, refined, and/or calibrated object proposal) and assign initial pixel-wise labels to at least a portion of the pixels included in the generated object proposals. As discussed below, the multi-label classification module may generate an object score (e.g., a likelihood and/or confidence score) for each object proposal”; And, paragraph 32: “the object scores may be employed to weight the confidence of the object proposals”. And, labeling can be performed by labelers as for example in preparing ground truth data, (or the MLC model as previously pointed out), see for example paragraph 2: “training data often includes images that are labeled via ground truths, which indicate classifications of one or more objects that are visually depicted in the image encoded in the training image data”).
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 9, 30 are rejected under 35 U.S.C. 103 as being unpatentable over Ge, US 2021/0027098 A1, in view of Rawal, US 2021/0064676 A1.
Regarding Claim 9, Ge teaches:
The method of claim 1, wherein training the machine-learning model comprises: inputting at least one object of the plurality of objects to the machine-learning model (paragraph 52: “The initial object proposals are employed as input to the multi-label classification module”);
receiving, from the machine-learning model, an output vector (paragraph 53: “An object score vector for the input image… may be generated”);
and optimizing the machine-learning model based on a loss function calculated based on the inner product (paragraph 47: “backpropagation, via a loss or cost function is employed at the corresponding portion of the training to update the parameters or weights of the models in training”. The optimizing of the model being done by the training update via the loss function).
Ge may not have explicitly taught:
determining an inner product of the output vector and a vector based on the plurality of probabilistic confidence labels. (Emphasis added).
However, Rawal shows (paragraph 37: ““Cosine similarity” measures a cosine of an angle between two non-zero vectors of an inner product space (i.e., the vector space) implemented by the machine-learning model”. Cosine similarity is an inner product between two non-zero vectors).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Rawal with that of Ge for determining an inner product between two vectors.
The ordinary artisan would have been motivated to modify Ge in the manner set forth above for the purposes of determining the closeness between two vectors [Rawal: paragraph 37].
Claims 22, 26-27, 29-30 are similar to Claims 1, 5-6, 8-9 and are rejected under the same rationale as stated above for those claims.
Claim 25 limitation is included in Claim 4 and is rejected under the same rationale as stated above in that claim.
Claims 43-44 are similar to Claims 1, 23 and are rejected under the same rationale as stated above for those claims.
Examiner’s Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Zhang, US 2011/0119210 A1, teaches training a plurality of classifiers in an iterative manner, each training iteration associates an adaptive label with each training example, in which during the iterations, the adaptive label of any example is able to be changed by the subsequent reclassification.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
Prosecution Insights