DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim(s) 17-28, 30 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 (U.S. PG-PUB NO. 2021/0383199) in view of D2 (U.S. PG-PUB NO. 2022/0058505).
-Regarding claim 17, D1 discloses a method for training a machine learning model that is configured to process measurement data with respect to a given task, the model including a slot building part that is configured to assign features of an inputted record of measurement data to a plurality of slots, and a task part that is configured to process the features of the record of measurement data that are assigned to each slot separately (slot attention module may be configured to generate a plurality of entity-centric representations, referred to herein as slot vectors, based on a plurality of distributed representations, referred to herein as feature vectors, [0021]), the method comprising the following steps: providing training samples for records of measurement data (convolutional neural network models 502 may be used to generate perceptual representation 302 based on input data 500, which may represent image data, time series (e.g., waveform) data, text data, point cloud data, and/or voxel data, among other types of input, [0085]); producing, from each training sample, by at least one feature extraction layer in the slot building part, at least one feature map (feature vectors 304-306 may represent a map of convolutional features of perceptual representation 302, and may thus include the outputs of various convolutional filters, [0052]); initializing a predetermined number K of slots by assigning, to the slots, features from the feature map (Slot vector initializer 318 may be configured to initialize each of slot vectors 322-324 stored by neural network memory unit 320, [0059]; Slot vectors 322-324 may include K vectors each having S dimensions, [0060]); and training the slot building part and the task part towards at least one given objective (Slot attention module 300 and slot decoder model 506 may be trained jointly, thereby resulting in slot vectors 322-324 providing embeddings of entities present in input data 500 that can be used by slot decoder model 506 to reconstruct input data 500 (i.e., generate input data reconstruction 508), [0086]).
D1 is silent to teaching that decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method; and based at least in parts on the N components. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method (In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]); and based at least in parts on the N components (for each prototype pi (of class i), we compute a set of K most confident samples within a certain confidence radius and use the mean of this set as the next prototype (of class i), [0038]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the current state-of-the-art in both transductive and semi-supervised FSL setting.
-Regarding claim 18, the combination further discloses the measurement data includes at least one image, and wherein at least one of the at least one feature extraction layer is a convolutional layer that applies one or more filter kernels to the measurement data in a sliding manner with a predetermined stride (D1, one or more convolutional neural networks may be configured to process the image and generate one or more convolutional feature maps, which may represent the output of various feature filters implemented by the one or more convolutional neural networks, [0019]; feature vectors 304-306 may represent a map of convolutional features of perceptual representation 302, and may thus include the outputs of various convolutional filters, [0052]).
-Regarding claim 19, the combination further discloses the slots represent object types and/or object instances whose presence the measurement data indicates (D1, The first slot vector (associated with values 402A, 402B, and 402C) may be configured to attend to and/or bind to entity 410, thereby representing attributes, properties, and/or characteristics of entity 410, [0079]).
-Regarding claim 20, the combination further discloses both the slot building part and the task part are trained towards one single objective that is measured a result produced by the task part (D1, Slot attention module 300 and slot decoder model 506 may be trained jointly, thereby resulting in slot vectors 322-324 providing embeddings of entities present in input data 500 that can be used by slot decoder model 506 to reconstruct input data 500 (i.e., generate input data reconstruction 508), [0086]).
-Regarding claim 21, the combination further discloses during the initializing of the slots, each feature of the at least one feature map is assigned to at most one slot (D1, Slot vector initializer 318 may be configured to initialize each of slot vectors 322-324 stored by neural network memory unit 320, [0059]; D2, In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]).
-Regarding claim 22, the combination further discloses the predetermined decomposing method includes: clustering the features of the feature map into a plurality of clusters; and in response to a feature belonging to a cluster, assigning the feature to a component corresponding to the cluster (D2, In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]).
-Regarding claim 23, the combination further discloses the clustering is performed by k-means clustering or mean-shift clustering (D2, In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]; in MSP we perform a number of mean-shift like iterations on the prototypes of the classes, [0038]).
-Regarding claim 24, the combination further discloses the predetermined decomposing method includes mapping, by a convolutional layer of the slot building part, each feature to a numeric value; and assigning the features to components based at least in part on the numeric value (D1, feature vectors 304-306 may represent a map of convolutional features of perceptual representation 302, and may thus include the outputs of various convolutional filters, [0052]; D2, a neural network (NN) module, such as may be included in transductive processing block 120 and/or semi-supervised processing block 122 may be attached implementing Principal Component Analysis (PCA) or Independent Component Analysis (ICA) dimensionality reduction computed on all the samples of the few-shot episode (task instance) extended with unlabeled data (optional, if semi-supervised), [0023]).
-Regarding claim 25, the combination further discloses the initializing of the slots includes transforming features that characterize, or belong to, components into features that characterize, or belong to, slots, using at least one trainable transform layer of the slot building part (D1, neural network memory unit 320 may include one or more feed-forward neural network layers configured to further modify the values of slot vectors 322-324 after modification by the GRU and/or LSTM (and prior to being provided to task-specific machine learning model 330), [0069]).
-Regarding claim 26, the combination further discloses at least one trainable transform layer is a fully connected layer with a rectified linear unit (ReLU) activation function (D1, key function 310 may include a multi-layer perceptron that includes one or more hidden layers and that utilizes one or more non-linear activation functions, [0056]).
-Regarding claim 27, the combination further discloses the given task includes: reconstructing original measurement data from the features assigned to the slots (D1, Slot attention module 300 and slot decoder model 506 may be trained jointly, thereby resulting in slot vectors 322-324 providing embeddings of entities present in input data 500 that can be used by slot decoder model 506 to reconstruct input data 500 (i.e., generate input data reconstruction 508), [0086]); and/or classifying a type of object whose presence the measurement data indicates; and/or creating a new record of measurement data that is modified with respect to a particular object whose presence the measurement data indicates; and/or synthetizing multiple records of measurement data into a new record of measurement data.
-Regarding claim 28, the combination further discloses providing records of measurement data captured by at least one sensor to the trained slot building part (D1, Perceptual representation 302 may represent various types of data, [0051]); assigning, by the trained slot building part, features of the records of measurement data to slots (D1, Slot attention module 300 may be configured to generate slot vectors 322-324 based on perceptual representation 302, [0050]); and processing, by the trained task part, the features assigned to each slot, to obtain a task result with respect to the given task (D1, slot decoder model 506 may individually decode each of slot vectors 322-324 using a spatial broadcast decoder, [0087]).
-Regarding claim 30, D1 discloses a non-transitory data carrier on which is stored a computer program including machine-readable instructions for training a machine learning model (data storage 208, [0040]) that is configured to process measurement data with respect to a given task, the model including a slot building part that is configured to assign features of an inputted record of measurement data to a plurality of slots, and a task part that is configured to process the features of the record of measurement data that are assigned to each slot separately (slot attention module may be configured to generate a plurality of entity-centric representations, referred to herein as slot vectors, based on a plurality of distributed representations, referred to herein as feature vectors, [0021]), the instructions, when executed by one or more computers or compute instances, cause the one or more computers and/or compute instances to perform the following steps (processor 206, [0040]): providing training samples for records of measurement data (convolutional neural network models 502 may be used to generate perceptual representation 302 based on input data 500, which may represent image data, time series (e.g., waveform) data, text data, point cloud data, and/or voxel data, among other types of input, [0085]); producing, from each training sample, by at least one feature extraction layer in the slot building part, at least one feature map (feature vectors 304-306 may represent a map of convolutional features of perceptual representation 302, and may thus include the outputs of various convolutional filters, [0052]); initializing a predetermined number K of slots by assigning, to the slots, features from the feature map (Slot vector initializer 318 may be configured to initialize each of slot vectors 322-324 stored by neural network memory unit 320, [0059]; Slot vectors 322-324 may include K vectors each having S dimensions, [0060]); and training the slot building part and the task part towards at least one given objective (Slot attention module 300 and slot decoder model 506 may be trained jointly, thereby resulting in slot vectors 322-324 providing embeddings of entities present in input data 500 that can be used by slot decoder model 506 to reconstruct input data 500 (i.e., generate input data reconstruction 508), [0086]).
D1 is silent to teaching that decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method; and based at least in parts on the N components. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method (In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]); and based at least in parts on the N components (for each prototype pi (of class i), we compute a set of K most confident samples within a certain confidence radius and use the mean of this set as the next prototype (of class i), [0038]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the current state-of-the-art in both transductive and semi-supervised FSL setting.
-Regarding claim 31, D1 discloses one or more computers and/or compute instances including a non-transitory data carrier on which is stored a computer program including machine-readable instructions for training a machine learning model that is configured to process measurement data with respect to a given task (data storage 208, processor 206, [0040]), the model including a slot building part that is configured to assign features of an inputted record of measurement data to a plurality of slots, and a task part that is configured to process the features of the record of measurement data that are assigned to each slot separately (slot attention module may be configured to generate a plurality of entity-centric representations, referred to herein as slot vectors, based on a plurality of distributed representations, referred to herein as feature vectors, [0021]), the instructions, when executed by the one or more computers or compute instances, cause the one or more computers and/or compute instances to perform the following steps: providing training samples for records of measurement data (convolutional neural network models 502 may be used to generate perceptual representation 302 based on input data 500, which may represent image data, time series (e.g., waveform) data, text data, point cloud data, and/or voxel data, among other types of input, [0085]); producing, from each training sample, by at least one feature extraction layer in the slot building part, at least one feature map (feature vectors 304-306 may represent a map of convolutional features of perceptual representation 302, and may thus include the outputs of various convolutional filters, [0052]); initializing a predetermined number K of slots by assigning, to the slots, features from the feature map (Slot vector initializer 318 may be configured to initialize each of slot vectors 322-324 stored by neural network memory unit 320, [0059]; Slot vectors 322-324 may include K vectors each having S dimensions, [0060]); and training the slot building part and the task part towards at least one given objective (Slot attention module 300 and slot decoder model 506 may be trained jointly, thereby resulting in slot vectors 322-324 providing embeddings of entities present in input data 500 that can be used by slot decoder model 506 to reconstruct input data 500 (i.e., generate input data reconstruction 508), [0086]).
D1 is silent to teaching that decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method; and based at least in parts on the N components. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches decomposing each feature map into N components according to a predetermined decomposing criterion and/or a predetermined decomposing method (In BKM, each k-means cluster, obtained for the entire set of (labeled+unlabeled) task ꚍ data, is treated as a Gaussian mixture distribution with a mode for each classv, [0037]); and based at least in parts on the N components (for each prototype pi (of class i), we compute a set of K most confident samples within a certain confidence radius and use the mean of this set as the next prototype (of class i), [0038]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the current state-of-the-art in both transductive and semi-supervised FSL setting.
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 (U.S. PG-PUB NO. 2021/0383199) in view of D2 (U.S. PG-PUB NO. 2022/0058505) and further in view of D3 (U.S. PG-PUB NO. 2021/0326609).
-Regarding claim 29, the combination is silent to teaching that computing, based on the task result, an actuation signal; and actuating, using the actuation signal, a vehicle and/or a driving assistance system and/or a surveillance system and/or a quality inspection system and/or a medical imaging system. However, the claimed limitation is well known in the art as evidenced by D3.
In the same field of endeavor, D3 teaches computing, based on the task result, an actuation signal; and actuating, using the actuation signal, a vehicle and/or a driving assistance system and/or a surveillance system and/or a quality inspection system and/or a medical imaging system (a planning subsystem 136 can use the object classifications 180 to make fully-autonomous or semi-autonomous driving decisions, thereby controlling the vehicle 122 based at least in part on the predicted classifications of the objects of interest, [0041]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of D3 in order to act on the task result applies a known perception to actuation technique to a known object-centric perception model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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, Jennifer Mehmood can be reached at (571) 272-2976. 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.
/PING Y HSIEH/Primary Examiner, Art Unit 2664
Prosecution Insights