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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on April 6, 2026, in which claims 1-3, 5, 7, 10, 13-15, 17, 19, and 20 are currently amended. Claims 1-20 are currently pending.
Drawings
Applicant's amendments made to the drawings are acknowledged. Examiner’s objection to the drawings are hereby withdrawn, as necessitated by Applicant’s amendments made to the drawings.
Response to Arguments
The rejections to claims 1-20 under 35 U.S.C. § 101 are hereby withdrawn, as necessitated by applicant's amendments and remarks made to the rejections.
Applicant’s arguments with respect to rejection of claims 1-20 under 35 U.S.C. 102/103 based on amendment have been considered and are persuasive. The argument is moot in view of a new ground of rejection set forth below.
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, 2, 6, 13, 14, 18, and 19 are rejected under U.S.C. §103 as being unpatentable over the combination of Wu (“Person Re-Identification by Context-Aware Part Attention and Multi-Head Collaborative Learning”, 2022) and Ye (“Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers”, 2019).
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Regarding claim 1, Wu teaches A computer-implemented method comprising: ([p. 9] "Efficiency Analysis. We adopt the floating-point operations (FLOPs) in number of multiply-adds and the required GPU memory during training to measure the computational cost of CNN model")
receiving, by one or more processors, a composite dataset;([p. 6 §IV] "we thoroughly analyze the effectiveness of our method on four challenging video person re-ID datasets, including PRID2011, iLIDS-VID, MARS and Duke VideoReID, as shown in Fig. 5" [p. 5] "Nid is the total category number of person identities")
training, by the one or more processors, a plurality of teacher models corresponding to the plurality of predictive categories based on the plurality of training datasets, ([p. 2] "each head can transfer its knowledge to each other, which collaboratively improves the accuracy but without extra model architecture design" [p. 5] "we propose to use multiple supervision heads rather than single head to guide the feature learning. In our multi-head collaborative learning framework, each head has the same design and supervision but with different parameters. Each head consists of two fully connected layers named embedding layer and classification layer respectively. The multi-head collaborative learning scheme enables diversity predictions" [p. 5] "the embedding feature fh is fed into the classification layer to obtain classification prediction logit zh, which is supervised by an identity classification loss. The identity loss for head h is denoted by [Eqn. 6]" each head subnetwork (model backbone + respective head) in Wu interpreted as a teacher, each with its own parameters, trained on the datasets to produce identity-category predictions corresponding to the plurality of predictive categories Ni.)
wherein each teacher model is trained by optimizing a triplet loss for the training dataset of the plurality of training datasets; and([p. 5] "the learning objective of multi-head collaborative learning contains two main parts: 1) baseline loss: triplet loss(Ltri) […] the loss function for each head h is formulated as follows [Eqn. 5]" [p. 6] "The total loss L for our multi-head collaborative learning objective framework is defined by L=(∑(h=1,...,H)(Ltrih+...)" See also FIG. 4. Wu explicitly trains each head with its own triplet loss term and includes it in the summed objective across heads. Table VI shows the results of training the teachers for a particular dataset.)
generating, by the one or more processors, a multi-headed composite model based on a respective plurality of trained parameters for each of the plurality of teacher models, ([p. 5] "each head has the same design and supervision but with different parameters" [p. 6] "during inference, we concatenate the fh from all the heads together as video feature descriptors")
wherein the multi-headed composite model comprises a plurality of model heads that correspond to the plurality of teacher models.([p. 5] "FIG. 4. Illustration of multi-head consistency loss. The prediction logits z from all other learning heads are averaged to obtained a soft label" See FIG. 3 and 4.).
However, Wu does not explicitly teach segmenting, by the one or more processors, the composite dataset into a plurality of training datasets,
wherein a training dataset of the plurality of training datasets comprises a portion of the composite dataset that comprises a set of labels associated with a predictive category of a plurality of predictive categories.
Ye, in the same field of endeavor, teaches segmenting, by the one or more processors, the composite dataset into a plurality of training datasets, ([p. 5 §4.2] "There are a total 20 labels in Pascal VOC2007, we divide them randomly into two groups which are learned separately in two teacher networks")
wherein a training dataset of the plurality of training datasets comprises a portion of the composite dataset that comprises a set of labels associated with a predictive category of a plurality of predictive categories ([p. 5] "We use the training set for pre-training the teachers […] The teachers are pre-trained in ResNet-50 with 10 labels […] we conduct two sets of experiments: one is the two visual-irrelated tasks (‘Bus’ from teacher1, ‘Diningtable’ from teacher2), the other one is the two visual-related tasks (‘Dog’ from teacher1, ‘Horse’ from teacher2)." [p. 5] " two teachers each specifying in a 10-label classification task").
Wu as well as Ye are directed towards knowledge distillation. Therefore, Wu as well as Ye are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Wu with the teachings of Ye by using the split label-wise datasets from Ye as input into the multi-head collaborative re-ID framework in Wu. The combination amounts to simple training-data organization and head assignment. Ye provides as additional motivation for combination ([p. 6 §5] “Our experimental results demonstrate that, the derived student model achieves very promising results without any ground truth annotations, even outperforms those of the teachers in their own domain”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 2, the combination of Wu and Ye teaches The computer-implemented method of claim 1, wherein: the plurality of training datasets comprise a respective training dataset for each predictive category of the plurality of predictive categories, (Ye [p. 5 §4.2] "There are a total 20 labels in Pascal VOC2007, we divide them randomly into two groups which are learned separately in two teacher networks" Label grouped training datasets interpreted as predictive categories)
and the plurality of teacher models comprise a respective teacher model for each predictive category of the plurality of predictive categories.(Ye [p. 5 §4.2] "There are a total 20 labels in Pascal VOC2007, we divide them randomly into two groups which are learned separately in two teacher networks" [p. 3] "each teacher An specializes in Tn different tasks, where Tn >= 1 which means the teachers can be either in single or multiple tasks' architecture" Label grouped training datasets interpreted as predictive categories).
Regarding claim 6, the combination of Wu and Ye teaches The computer-implemented method of claim 1, wherein each of the plurality of teacher models is a deep neural network comprising a plurality of attention layers.(Wu [p. 8] "Multi-scale Spatial-Temporal Attention (MSTA) [74]. Our multi-level CPA model performs significantly better than other attention mechanisms. The experimental results demonstrate our multi-level CPA has stronger capability to model spatial-temporal information and learn discriminative features" [p. 2] "Moreover, the informative low-level features (the output of shallow convolutional layers) [22] are ignored in their attention module, but it has been shown that different layers capture different kinds of discriminative features in Fig. 1. Therefore, it motivates us to investigate a solution to simultaneously capture the robust and discriminative multi-level part attention cues in different layers" Wu shows the model path of each teacher subnetwork being a deep neural network comprising multiple layers in FIG. 2 and 3 especially).
Regarding claim 13, claim 13 is directed towards a system for performing the method of claim 1. Therefore the rejection applied to claim 1 also applies to claim 13. Claim 13 also recites additional elements A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Wu [p. 9] "Efficiency Analysis. We adopt the floating-point operations (FLOPs) in number of multiply-adds and the required GPU memory during training to measure the computational cost of CNN model").
Similarly, regarding claim 14, claim 14 is directed towards a system for performing the method of claim 2. Therefore, the rejection applied to claim 2 also applies to claim 14.
Similarly, regarding claim 18, claim 18 is directed towards a system for performing the method of claim 6. Therefore, the rejection applied to claim 6 also applies to claim 18.
Regarding claim 19, claim 19 is directed towards non-transitory computer-readable storage media for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 19. Claim 19 also recites additional elements One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to (Wu [p. 9] "Efficiency Analysis. We adopt the floating-point operations (FLOPs) in number of multiply-adds and the required GPU memory during training to measure the computational cost of CNN model").
Claims 3, 4, 15, and 16 are rejected under U.S.C. §103 as being unpatentable over the combination of Wu and Ye and in further view of He (“BERTMap: a BERT-based ontology alignment system”, 2022).
Regarding claim 3, the combination of Wu and Ye teaches The computer-implemented method of claim 2.
However, the combination of Wu and Ye doesn't explicitly teach wherein a predictive category is indicative of an ontology category for a prediction domain,
and wherein a training dataset for the predictive category comprises a plurality of mapped text sequences for the ontology category.
He, in the same field of endeavor, teaches a predictive category is indicative of an ontology category for a prediction domain, ([p. 2] "The corpora for BERT fine tuning are composed of pairs of such synonymous labels (i.e., “synonyms”) and pairs of such non-synonymous labels (i.e.,“non-synonyms”) […] For each named class c in an input ontology, we derive all its synonyms which are pairs")
and wherein a training dataset for the predictive category comprises a plurality of mapped text sequences for the ontology category. ([p. 2] "The corpora for BERT fine tuning are composed of pairs of such synonymous labels (i.e., “synonyms”) and pairs of such non-synonymous labels (i.e., “non-synonyms”) […] For each named class c in an input ontology, we derive all its synonyms which are pairs").
The combination of Wu and Ye as well as He are directed towards machine learning. Therefore, the combination of Wu and Ye as well as He are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wu and Ye with the teachings of He by using the domain (corpora) specific text sequence corpus and mapping in He as the input data for the architecture in Wu trained as separate models for a plurality of dataset specific domains as suggested by Lambert. He provides as additional motivation for combination ([p. 7] “The mapping extension and repair modules further improve the recall and precision”).
Regarding claim 4, the combination of Wu, Ye, and He teaches The computer-implemented method of claim 3, wherein each of the plurality of mapped text sequences comprises a text sequence and a training label corresponding to the text sequence.(He [p. 2] "we denote a label after preprocessing3 by ω, and denote the set of all the preprocessed labels of a class c […] For each named class c in an input ontology, we derive all its synonyms which are pairs (ω1,ω2)").
Regarding claims 15 and 16, claims 15 and 16 are directed towards a system for performing the methods of claims 3 and 4, respectively. Therefore, the rejections applied to claims 3 and 4 also apply to claims 15 and 16.
Claims 5 and 17 are rejected under U.S.C. §103 as being unpatentable over the combination of Wu and Ye and Lambert (“MSeg: A Composite Dataset for Multi-domain Semantic Segmentation”, 2020).
Regarding claim 5, the combination of Wu and Ye teaches The computer-implemented method of claim 1.
However, the combination of Wu and Ye doesn't explicitly teach, wherein the plurality of training datasets are previously generated based on a semantic similarity between a third-party category and a predictive category..
Lambert, in the same field of endeavor, teaches The computer-implemented method of claim 1, wherein the plurality of training datasets are previously generated based on a semantic similarity between a third-party category and a predictive category.([p. 8] "The ‘Naive merge’ baseline is a model trained on a composite dataset that uses a naively merged taxonomy in which the classes are a union of all training classes, and each test class is only mapped to an universal class if they share the same name. The ‘MSeg (w/o relabeling)’ base line uses the unified MSeg taxonomy").
The combination of Wu and Ye as well as Lambert are directed towards machine learning. Therefore, the combination of Wu and Ye as well as Lambert are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wu and Ye with the teachings of Lambert by using the model in the combination of Wu and Yeas the model architecture for the multiple models trained on different domains/datasets in Lambert. Lambert provides as additional motivation for combination ([p. 3] "A computer vision professional will likely resort to multiple models, each trained on a different dataset."). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 17, claim 17 is directed towards a system for performing the method of claim 5. Therefore, the rejection applied to claim 5 also applies to claim 17.
Claims 7, 8, 9, and 20 are rejected under U.S.C. §103 as being unpatentable over the combination of Wu and Ye and Fisch (“StarSpace: Embed All The Things!”, 2018).
Regarding claim 7, the combination of Wu and Ye teaches The computer-implemented method of claim 1, wherein: the particular training dataset for a teacher model of the plurality of teacher models comprises a plurality of [text] sequences and a plurality of training labels, and(Wu [p. 5] "Illustration of multi-head consistency loss. The prediction logits z from all other learning heads are averaged to obtained a soft label, which presents the prediction consensus of other heads […] the identity loss for head h [...] where yi is the one-hot ground truth label [...] Similar to the identity classification loss, we use the soft label as the supervision by computing the softmax cross entropy between soft label yhi and the identity prediction" [p. 6] "All the identities in dataset are randomly split into 50% for training and 50% for testing" Wu FIG. 3 shows the input is a sequence of frames and explicitly states that the output is soft labels compared against training labels)
the triplet loss is based on (i) a first distance between an anchor text sequence of the plurality of [text] sequences and a positive training label and (ii) a second distance between the anchor [text] sequence and a negative training label.(Wu [p. 5] "typically, the loss function for each head is formulated as follows: [See Eqn. 5] where […] a is the margin between positive and negative pairs […] the feature embeddings of the anchor, positive and negative samples, respectively" Wu eqn. 5 shows an anchor-positive distance term and an anchor-negative distance term (hardest positive minus hardest negative with margin)).
However, the combination of Wu and Ye doesn't explicitly teach The particular training dataset for a teacher model of the plurality of teacher models comprises a plurality of [text] sequences and a plurality of training labels.
Fisch, in the same field of endeavor, teaches The particular training dataset for a teacher model of the plurality of teacher models comprises a plurality of [text] sequences and a plurality of training labels([p. 2] "Multiclass Classification (e.g. Text Classification) The positive pair generator comes directly from a training set of labeled data specifying (a,b) pairs where a are documents (bags-of-words) and b are labels (singleton features). Negative entities b− are sampled from the set of possible labels").
The combination of Wu and Ye as well as Fisch are directed towards machine learning for calculating pairwise similarity. Therefore, the combination of Wu and Ye as well as Fisch are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wu and Ye with the teachings of Fisch by using the text similarity data as the input for the model in Wu. Fisch provides as additional motivation for combination ([p. 3] “The intuition is that semantic similarity between sentences is shared within a document (one can also only select sentences within a certain distance of each other if documents are very long). Further, the embeddings will automatically be optimized for sets of words of sentence length”).
Regarding claim 8, the combination of Wu, Ye, and Fisch teaches The computer-implemented method of claim 7, wherein optimizing the triplet loss comprises minimizing the first distance and maximizing the second distance.(Wu [p. 5] "typically, the loss function for each head is formulated as follows: [See Eqn. 5] where […] a is the margin between positive and negative pairs […] the feature embeddings of the anchor, positive and negative samples, respectively" Wu explicitly maximizes the hardest positive term (the second distance) and minimizes the hardest negative term (the first distance)).
Regarding claim 9, the combination of Wu, Ye, and Fisch teaches The computer-implemented method of claim 7 further comprising: generating, using a machine learning encoder model, a plurality of text embeddings for the plurality of text sequences and the plurality of training labels;(Fisch [p. 1] "We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as in formation retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi relational graphs, and learning word, sentence or document level embeddings" [p. 2] "An entity such as a document or a sentence can be described by a bag of words […] a user entity can be compared with an item entity (recommendation), or a document entity with label entities (text classification)")
generating the first distance based on a first cosine similarity distance between an anchor embedding corresponding to the anchor text sequence and a positive embedding corresponding to the positive training label; and(Fisch [p. 2] "The similarity function sim(·,·). In our system, we have implemented both cosine similarity and inner product […] compares the positive pair (a, b) with the negative pairs (a,b− i)" Starspace explicitly uses the cosine similarity as the similarity metric between embedded entities. The first distance is the cosine similarity for positive embedding similarities.)
generating the second distance based on a second cosine similarity distance between the anchor embedding and a negative embedding corresponding to the negative training label.(Fisch [p. 2] "The similarity function sim(·,·). In our system, we have implemented both cosine similarity and inner product […] compares the positive pair (a, b) with the negative pairs (a,b− i)" The second distance is the cosine similarity for negative embedding similarities.).
Regarding claim 20, claim 20 is directed towards a non-transitory computer readable media for performing the method of claim 7. Therefore, the rejection applied to claim 7 also applies to claim 20.
Claims 10, 11, and 12 are rejected under U.S.C. §103 as being unpatentable over the combination of Wu and Ye and in further view of Wei (“A Flexible Multi-Task Model for BERT Serving”, 2022).
Regarding claim 10, the combination of Wu and Ye teaches The computer-implemented method of claim 1, wherein the multi-headed composite model comprises a model body and the plurality of model heads, and wherein generating the multi-headed composite model comprises:(Wu [p. 4] "Fig. 3. The overview of our approach. It is mainly comprised of two parts: multi-level context-aware part attention feature network and multi-head collaborative learning scheme. The CPA module is seamlessly plugged into different stages of the backbone network to learn multi-level context-aware part attention. SAP represents the spatial average pooling and TAP represents the temporal average pooling. Several supervision heads are applied to the video-level feature simultaneously to provide more robust supervision" FIG. 3 shows model body and plurality of model heads).
However, the combination of Wu and Ye doesn't explicitly teach identifying a teacher model from the plurality of teacher models based on the plurality of training datasets,
wherein the teacher model is identified based on a number of mapped text sequences in a training dataset that corresponds to the teacher model; and
generating the model body based on a plurality of trained parameters for the teacher model..
Wei, in the same field of endeavor, teaches identifying a teacher model from the plurality of teacher models based on the plurality of training datasets, ([Abstract] "For each task, we train independently a single-task (ST) model using partial fine-tuning")
wherein the teacher model is identified based on a number of mapped text sequences in a training dataset that corresponds to the teacher model; and([p. 2] "We propose to experiment for each task with different value of L within range Nmin < L < Nmax, and select the one that gives the best validation performance")
generating the model body based on a plurality of trained parameters for the teacher model.([Abstract] "we compress the task specific layers in each ST model using knowledge distillation. Those compressed ST models are finally merged into one MT model so that the frozen layers of the former are shared across the tasks").
The combination of Wu and Ye as well as Wei are directed towards attention based knowledge distillation. Therefore, the combination of Wu and Ye as well as Wei are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of the combination of Wu and Ye with the teachings of Wei by using the model in the combination of Wu and Ye as the architecture for merging/knowledge distillation in Wei enabling multiple levels of knowledge distillation (where the combination of Wu and Ye enabled intra-model distillation and Wei enables inter-model distillation). Wei provides as additional motivation for combination ([p. 8] “In the box plots of Figure 2 above we report the performance of the student models initialized from pre-trained BERT and from the teacher. It can be clearly seen that the latter initialization scheme generally outperforms the former” [p. 4 §4.3] "The results are summarized in Table 2. From the table it can be seen that the proposed method Ours (mixed) outperforms all KD methods while being more efficient.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 11, the combination of Wu, Ye, and Wei teaches The computer-implemented method of claim 10 further comprising: generating a model head of the plurality of model heads based on the plurality of trained parameters for the teacher model.(Wei [p. 1] "Our method is based on the idea of partial fine tuning, i.e. only fine-tuning some topmost layers of BERT depending on the task and keeping the remaining bottom layers frozen" [p. 3] "Figure 1: Pipeline of the proposed method. (a) For each task we train separately a task-specific model with partial fine-tuning, i.e. only the weights from some topmost layers (blue and red blocks) of the pre-trained model are updated while the rest are kept frozen (gray blocks)" BERT is a multi-head attention model, where each layer has multi-head attention used for particular tasks.).
Regarding claim 12, the combination of Wu, Ye, and Wei teaches The computer-implemented method of claim 11, wherein generating the model head of the multi-headed composite model comprises: generating, using the teacher model, a first output embedding for a mapped text sequence of the training dataset;(Wei [p. 8] "STS-B (The Semantic Textual Similarity Benchmark). A regression task where the goal is to predict whether two sentences are similar in terms of semantic meaning as measured by a score from 1 to 5" See FIG. 1. Each layer of each model outputs an output embedding for a mapped text sequence (the input). Wei explicitly states that the model is used for STS-B which is explicitly a text sequence mapping problem. See also Table 2 and 3 for results on STS-B)
generating, using the multi-headed composite model, a second output embedding for the mapped text sequence; and(Wei [p. 8] "STS-B (The Semantic Textual Similarity Benchmark). A regression task where the goal is to predict whether two sentences are similar in terms of semantic meaning as measured by a score from 1 to 5" See FIG. 1. Each layer of each model outputs an output embedding for a mapped text sequence (the input). Wei explicitly states that the model is used for STS-B which is explicitly a text sequence mapping problem. See also Table 2 and 3 for results on STS-B)
updating one or more parameters of the multi-headed composite model based on a comparison between the first output embedding and the second output embedding.(Wei [p. 4 §4.3] "The results are summarized in Table 2. From the table it can be seen that the proposed method Ours (mixed) outperforms all KD methods while being more efficient. Compared to the single-task fine-tuning baseline, our method reduces up to around two thirds of the total overhead while achieves 99.6% of its performance" See FIG. 1 which explicitly shows merging two models outputting first and second output embeddings).
Conclusion
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 extension fee 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 SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
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, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124
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