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 .
Notice to Applications
This communication is in response to the Application filed on October 09, 2024.
Claims 1-20 are pending.
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.
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 1-6, 11-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable of Dove et al., US 20250285458 A1, (hereinafter “Dove”) in view of Likhomanov et al., US 20240005661 A1, (hereinafter “Likhomanov”).
Regarding claim 1, Dove teaches a method for generating a plurality of machine vision classifiers using synthetic and real-world images, the method comprising:
receiving, via a data interface, a plurality of real-world images corresponding to one or more objects ([0030] “The visual data 104a can be indicative of or include one or more images or video frames of a video or a video stream. The one or more images or video frames of a video or a video stream can be generated by a camera that can be included in or coupled to the computing device 102. Each of the images or video frames of the visual data 104a can be, for instance, a real-world 2D image or a real-world 2D video frame, respectively. For example, any or all of the images or video frames of the visual data 104a can depict a real-world scene having at least one real-world object.”);
providing a plurality of synthetic images configured to mimic real-world images of the one or more objects, wherein a subset of the plurality of synthetic images comprises a simulated image condition configured to modify a respective synthetic image relative to an unmodified synthetic image ([0032] “The visual data 104b can be indicative of or include a scan that can be generated by a scanner. For example, the scan can be a LiDAR scan that can be generated by a LiDAR scanner that can be included in or coupled to the computing device 102. The scan can be, for instance, a synthetic reconstruction of a real-world scene having at least one real-world object. For example, the scan can be a synthetic 3D reconstruction of a real-world scene having at least one real-world object. As described in further detail herein, the visual data 104b can be rendered and annotated by the computing device 102 in a virtual environment.”) ([0031] “In particular, the computing device 102 implement one or more tools or features of a computer graphics application to perform one or more operations described herein with respect to any or all of the visual data 104a, 104b, 104c and the labeled visual datasets 106a, 106b, 106c. Example operations that can be performed by the computing device 102 using the tools and features of a computer graphics application can include at least one of visual data generation, rendering, annotation, and modification, raster graphics editing, digital drawing, 3D modeling, animation, simulation, texturing, UV mapping, UV unwrapping, rigging and skinning, compositing, sculpting, match moving, motion graphics, video editing, or another operation.”);
providing an associated category for each of the synthetic and real-world images ([0020] “The labeling framework described herein can be implemented to label an object of interest one time and then automatically (without human intervention) label the same object once or multiple times in at least one of real-world visual data or synthetic visual data based on the initial labeling of the object.”);
Dove does not specifically disclose extracting features from images and generating the plurality of machine vision classifiers for a machine vision model.
However, Likhomanov teaches extracting features from images ([0042] “FIG. 1 is a diagram depicting an example system 100 effective to provide user-customized computer vision event detection, in accordance with various aspects of the present disclosure. In the example depicted in FIG. 1, a camera device 150 may have a field-of-view that covers an area-of-interest to a particular user. In the example of FIG. 1, camera device 150 generates image data 102a, 102b which depicts a garage (as the camera device 150 captures images of a physical environment including the garage). The garage may be an area-of-interest to the user.” wherein the extracted features are garages) and generating the plurality of machine vision classifiers for a machine vision model ([0044] “As described in further detail below, customized classifier model 108 may be a machine learning model trained based at least in part on images representing each state of the custom event. For example, a pre-trained encoder model 104 (e.g., ViT, a CNN, or any other model that generates embedding data representing an image) may be used to generate embedding data (e.g., a feature map, a vector, etc.) representing an input image. The input image and/or the embedding data representing the input image may be labeled with data identifying the corresponding state represented by the image (e.g., Garage door open or garage door closed). For example, the companion application may prompt the user (through a user interface) for a set of images (e.g., 5 images, 10 images, 2 images, etc.) having the first user-defined state. The user may provide the images that correspond to the first user-defined state (e.g., garage door open). Similarly, the companion application may prompt the user (via a user interface) for images corresponding to the second user-defined state and/or for each other state of the custom event.” wherein a plurality of machine vision classifiers are user defined states for a customized classifier model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the real-world and synthetic image analysis method of Dove with the machine classifier method of Likhomanov to have generate enough training data for the machine classifier of Likhomanov.
Regarding claim 2, Dove in view of Likhomanov teaches the method of claim 1, wherein providing the associated category for each of the synthetic and real-world images comprises assigning each of the synthetic images to a first category and assigning each of the real-world images to a second category (Dove - [0020] “The labeling framework described herein can be implemented to label an object of interest one time and then automatically (without human intervention) label the same object once or multiple times in at least one of real-world visual data or synthetic visual data based on the initial labeling of the object.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 3, Dove in view of Likhomanov teaches the method of claim 1, wherein the synthetic images comprise at least one of a computer-aided design (CAD) image or a three-dimensional (3D) scan of a physical object image (Dove - [0032] “The visual data 104b can be indicative of or include a scan that can be generated by a scanner. For example, the scan can be a LiDAR scan that can be generated by a LiDAR scanner that can be included in or coupled to the computing device 102. The scan can be, for instance, a synthetic reconstruction of a real-world scene having at least one real-world object. For example, the scan can be a synthetic 3D reconstruction of a real-world scene having at least one real-world object. As described in further detail herein, the visual data 104b can be rendered and annotated by the computing device 102 in a virtual environment.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 4, Dove in view of Likhomanov teaches the method of claim 1, further comprising:
receiving the plurality of synthetic images image (Dove - [0032] “The visual data 104b can be indicative of or include a scan that can be generated by a scanner. For example, the scan can be a LiDAR scan that can be generated by a LiDAR scanner that can be included in or coupled to the computing device 102. The scan can be, for instance, a synthetic reconstruction of a real-world scene having at least one real-world object. For example, the scan can be a synthetic 3D reconstruction of a real-world scene having at least one real-world object. As described in further detail herein, the visual data 104b can be rendered and annotated by the computing device 102 in a virtual environment.”); and
modifying each of the one or more of the synthetic images to generate the subset of the plurality of synthetic images (Dove - [0031] “In particular, the computing device 102 implement one or more tools or features of a computer graphics application to perform one or more operations described herein with respect to any or all of the visual data 104a, 104b, 104c and the labeled visual datasets 106a, 106b, 106c. Example operations that can be performed by the computing device 102 using the tools and features of a computer graphics application can include at least one of visual data generation, rendering, annotation, and modification, raster graphics editing, digital drawing, 3D modeling, animation, simulation, texturing, UV mapping, UV unwrapping, rigging and skinning, compositing, sculpting, match moving, motion graphics, video editing, or another operation.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 5, Dove in view of Likhomanov teaches the method of claim 4, wherein modifying each of the one or more of the synthetic images comprises applying the simulated image condition to each of the one or more synthetic images, the simulated image condition comprising at least one of a lighting effect, a prop within the respective image, or an occlusion of a portion of the respective image (Dove - [0031] “In particular, the computing device 102 implement one or more tools or features of a computer graphics application to perform one or more operations described herein with respect to any or all of the visual data 104a, 104b, 104c and the labeled visual datasets 106a, 106b, 106c. Example operations that can be performed by the computing device 102 using the tools and features of a computer graphics application can include at least one of visual data generation, rendering, annotation, and modification, raster graphics editing, digital drawing, 3D modeling, animation, simulation, texturing, UV mapping, UV unwrapping, rigging and skinning, compositing, sculpting, match moving, motion graphics, video editing, or another operation.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 6, Dove in view of Likhomanov teaches the method of claim 1, wherein receiving the plurality of real-world images comprises receiving video imagery of the one or more objects (Dove - [0030] “The visual data 104a can be indicative of or include one or more images or video frames of a video or a video stream. The one or more images or video frames of a video or a video stream can be generated by a camera that can be included in or coupled to the computing device 102. Each of the images or video frames of the visual data 104a can be, for instance, a real-world 2D image or a real-world 2D video frame, respectively. For example, any or all of the images or video frames of the visual data 104a can depict a real-world scene having at least one real-world object.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 11, Dove in view of Likhomanov teaches the method of claim 1, wherein receiving the plurality of real-world images comprises receiving associated metadata for each of the plurality of real-world images, the metadata comprising at least one of the following associated with the corresponding real-world image: an indication of a location, an indication of an imager type, an indication of an imager setting, a time, or a resolution (Dove - [0044] “In another example, the computing device 102 can associate metadata that is indicative of the real-world object with at least one of the raster image, the sub-raster image, the boundary annotation, or the bounding box. For instance, the computing device 102 can encode at least one of the raster image, the sub-raster image, the boundary annotation, or the bounding box with metadata that is indicative of the ground truth label that can be obtained by the computing device 102 via the input data or a model as described above. The ground truth label can be indicative of or include, for example, at least one of a ground truth classification or a ground truth classification label.”) (Dove - [0040] “In some cases, the input data described above can also be indicative of or include a ground truth label that can be provided by the user. The ground truth label can be indicative of and correspond to the real-world object depicted in the real-world 2D images or video frames.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 12, Dove in view of Likhomanov teaches the method of claim 1, wherein extracting the features from each of the synthetic and real-world images comprises determining a state associated with an object type within at least one of the synthetic and real-world images (Likhomanov - [0044] “As described in further detail below, customized classifier model 108 may be a machine learning model trained based at least in part on images representing each state of the custom event. For example, a pre-trained encoder model 104 (e.g., ViT, a CNN, or any other model that generates embedding data representing an image) may be used to generate embedding data (e.g., a feature map, a vector, etc.) representing an input image. The input image and/or the embedding data representing the input image may be labeled with data identifying the corresponding state represented by the image (e.g., Garage door open or garage door closed).”) (Dove - [0030] “The visual data 104a can be indicative of or include one or more images or video frames of a video or a video stream. The one or more images or video frames of a video or a video stream can be generated by a camera that can be included in or coupled to the computing device 102. Each of the images or video frames of the visual data 104a can be, for instance, a real-world 2D image or a real-world 2D video frame, respectively. For example, any or all of the images or video frames of the visual data 104a can depict a real-world scene having at least one real-world object.”) (Dove - [0032] “The visual data 104b can be indicative of or include a scan that can be generated by a scanner. For example, the scan can be a LiDAR scan that can be generated by a LiDAR scanner that can be included in or coupled to the computing device 102. The scan can be, for instance, a synthetic reconstruction of a real-world scene having at least one real-world object. For example, the scan can be a synthetic 3D reconstruction of a real-world scene having at least one real-world object. As described in further detail herein, the visual data 104b can be rendered and annotated by the computing device 102 in a virtual environment.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 13, Dove in view of Likhomanov teaches the method of claim 12, wherein the state comprises an indication of a degree of deployment associated with the at least one of the synthetic and real-world images (Likhomanov - [0044] “As described in further detail below, customized classifier model 108 may be a machine learning model trained based at least in part on images representing each state of the custom event. For example, a pre-trained encoder model 104 (e.g., ViT, a CNN, or any other model that generates embedding data representing an image) may be used to generate embedding data (e.g., a feature map, a vector, etc.) representing an input image. The input image and/or the embedding data representing the input image may be labeled with data identifying the corresponding state represented by the image (e.g., Garage door open or garage door closed).”) (Dove - [0030] “The visual data 104a can be indicative of or include one or more images or video frames of a video or a video stream. The one or more images or video frames of a video or a video stream can be generated by a camera that can be included in or coupled to the computing device 102. Each of the images or video frames of the visual data 104a can be, for instance, a real-world 2D image or a real-world 2D video frame, respectively. For example, any or all of the images or video frames of the visual data 104a can depict a real-world scene having at least one real-world object.”) (Dove - [0032] “The visual data 104b can be indicative of or include a scan that can be generated by a scanner. For example, the scan can be a LiDAR scan that can be generated by a LiDAR scanner that can be included in or coupled to the computing device 102. The scan can be, for instance, a synthetic reconstruction of a real-world scene having at least one real-world object. For example, the scan can be a synthetic 3D reconstruction of a real-world scene having at least one real-world object. As described in further detail herein, the visual data 104b can be rendered and annotated by the computing device 102 in a virtual environment.”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 14, Dove in view of Likhomanov teaches the method of claim 13, wherein the object type comprises a garage door and wherein the degree of deployment corresponds to a degree of openness associated with the garage door (Likhomanov - [0044] “As described in further detail below, customized classifier model 108 may be a machine learning model trained based at least in part on images representing each state of the custom event. For example, a pre-trained encoder model 104 (e.g., ViT, a CNN, or any other model that generates embedding data representing an image) may be used to generate embedding data (e.g., a feature map, a vector, etc.) representing an input image. The input image and/or the embedding data representing the input image may be labeled with data identifying the corresponding state represented by the image (e.g., Garage door open or garage door closed).”).
The motivation for combining Dove and Likhomanov is the same motivation as used for claim 1.
Regarding claim 15, the claim recites similar limitations to claim 1 but in the form of a system comprising a data interface configured to receive a plurality of real-world images corresponding to one or more objects; a non-transitory computer-readable storage storing machine-executable instructions; and a hardware processor in communication with the computer-readable storage, wherein the instructions, when executed by the hardware processor, are configured to cause the system to perform the method of claim 1 (Dove – [0145] “Also, any logic or application described herein, including the truth data labeling service 212, the synthetic projection labeling service 214, the synthetic scan labeling service 216, the localization and mapping labeling service 218, the computer graphics application 220, the model training service 222, and the communications stack 224 can be embodied, at least in part, by software or executable-code components, can be embodied or stored in any tangible or non-transitory computer-readable medium or device for execution by an instruction execution system such as a general-purpose processor.”). Therefore, claim 15 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 16, the claim recites similar limitations to claim 4 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 20, the claim recites similar limitations to claim 1 but in the form of a non-transitory computer-readable medium. Therefore, claim 20 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Claims 7-10 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable of Dove et al., US 20250285458 A1, (hereinafter “Dove”) in view of Likhomanov et al., US 20240005661 A1, (hereinafter “Likhomanov”) in further view of Russell et al., US 20260043915 A1, (hereinafter “Russell”).
Regarding claim 7, Dove in view of Likhomanov teaches the method of claim 1, wherein the data interface comprises a wireless data interface, and wherein receiving the plurality of real-world images comprises receiving at least one of the real-world images ([0030] “The one or more images or video frames of a video or a video stream can be generated by a camera that can be included in or coupled to the computing device 102. Each of the images or video frames of the visual data 104a can be, for instance, a real-world 2D image or a real-world 2D video frame, respectively. For example, any or all of the images or video frames of the visual data 104a can depict a real-world scene having at least one real-world object.”) ([0084] “The networks 112 can include, for instance, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks (e.g., cellular, WiFi®), cable networks, satellite networks, other suitable networks, or any combinations thereof. The computing device 102 and the computing device 110 can communicate data with one another over the networks 112 using any suitable systems interconnect models and/or protocols.”).
Dove in view of Likhomanov does not specifically disclose a remote computing device.
However, Russell teaches a remote computing device ([0139] “A user 122 is shown in the garage with an electronic device 200 configured in accordance with one or more embodiments of the disclosure. The electronic device 200 is operable with several companion electronic devices situated within the house 400. Examples include a smart garage door opener 408, a smart doorbell 407, a smart speaker 409, one or more routers 406,410, a smart stove 401, a smart display 402, a smart refrigerator 403, a sound system 404, and a smart television 405. Other examples of companion electronic devices will be obvious to those of ordinary skill in the art having the benefit of this disclosure.” wherein a remote computing device is a smart garage door opener).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the synthetic and real-world image machine classifier method of Dove in view of Likhomanov to remote smart devices such as garage door openers of Russell because there is a significant lack of sufficient training data for garage door openers.
Regarding claim 8, Dove in view of Likhomanov and Russell teaches the method of claim 7, wherein the remote computing device comprises a smart device (Russell - [0139] “A user 122 is shown in the garage with an electronic device 200 configured in accordance with one or more embodiments of the disclosure. The electronic device 200 is operable with several companion electronic devices situated within the house 400. Examples include a smart garage door opener 408, a smart doorbell 407, a smart speaker 409, one or more routers 406,410, a smart stove 401, a smart display 402, a smart refrigerator 403, a sound system 404, and a smart television 405. Other examples of companion electronic devices will be obvious to those of ordinary skill in the art having the benefit of this disclosure.” wherein a remote computing device is a smart garage door opener).
The motivation for combining Dove, Likhomanov and Russell is the same motivation as used for claim 7.
Regarding claim 9, Dove in view of Likhomanov and Russell teaches the method of claim 8, further comprising:
receiving, via the wireless data interface, a user image (Dove - [0028] “Examples of the computing device 102 can include a computer, a general-purpose computer, a special-purpose computer, a laptop, a tablet, a smartphone, another client computing device, or any combination thereof.”) (Dove - [0083] “The computing device 110 can be communicatively coupled, operatively coupled, or both, to the computing device 102 by way of one or more networks 112 (hereinafter, “the networks 112”).”) (Dove - [0084] “The networks 112 can include, for instance, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks (e.g., cellular, WiFi®), cable networks, satellite networks, other suitable networks, or any combinations thereof. The computing device 102 and the computing device 110 can communicate data with one another over the networks 112 using any suitable systems interconnect models and/or protocols.”) (Likhomanov - [0042] “FIG. 1 is a diagram depicting an example system 100 effective to provide user-customized computer vision event detection, in accordance with various aspects of the present disclosure. In the example depicted in FIG. 1, a camera device 150 may have a field-of-view that covers an area-of-interest to a particular user. In the example of FIG. 1, camera device 150 generates image data 102a, 102b which depicts a garage (as the camera device 150 captures images of a physical environment including the garage). The garage may be an area-of-interest to the user.”); and
determining, using the plurality of machine vision classifiers, a device type associated with a device indicated in the user image (Likhomanov - [0044] “As described in further detail below, customized classifier model 108 may be a machine learning model trained based at least in part on images representing each state of the custom event. For example, a pre-trained encoder model 104 (e.g., ViT, a CNN, or any other model that generates embedding data representing an image) may be used to generate embedding data (e.g., a feature map, a vector, etc.) representing an input image. The input image and/or the embedding data representing the input image may be labeled with data identifying the corresponding state represented by the image (e.g., Garage door open or garage door closed). For example, the companion application may prompt the user (through a user interface) for a set of images (e.g., 5 images, 10 images, 2 images, etc.) having the first user-defined state. The user may provide the images that correspond to the first user-defined state (e.g., garage door open). Similarly, the companion application may prompt the user (via a user interface) for images corresponding to the second user-defined state and/or for each other state of the custom event.” wherein a plurality of machine vision classifiers are user defined states for a customized classifier model) (Russell - [0145] “In one or more embodiments, the user 122 captures one or more images 429 with the electronic device 200 depicting a region of the house 400. In one or more embodiments, the one or more images 429 depict the companion electronic devices situated in that environment. Illustrating by example, the user 122 is capturing one or more images 429 of the smart garage door opener 408 in FIG. 4. One or more processors of the electronic device 200 can identify the companion electronic devices depicted in the one or more images 429 using image analysis.” wherein a device type is the companion electronic device such as the smart garage door opener).
The motivation for combining Dove, Likhomanov and Russell is the same motivation as used for claim 7.
Regarding claim 10, Dove in view of Likhomanov and Russell teaches the method of claim 9, further comprising:
transmitting, via the wireless data interface to the smart device, an indication of the device type (Dove - [0028] “Examples of the computing device 102 can include a computer, a general-purpose computer, a special-purpose computer, a laptop, a tablet, a smartphone, another client computing device, or any combination thereof.”) (Dove - [0083] “The computing device 110 can be communicatively coupled, operatively coupled, or both, to the computing device 102 by way of one or more networks 112 (hereinafter, “the networks 112”).”) (Dove - [0084] “The networks 112 can include, for instance, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks (e.g., cellular, WiFi®), cable networks, satellite networks, other suitable networks, or any combinations thereof. The computing device 102 and the computing device 110 can communicate data with one another over the networks 112 using any suitable systems interconnect models and/or protocols.”) (Russell - [0145] “In one or more embodiments, the user 122 captures one or more images 429 with the electronic device 200 depicting a region of the house 400. In one or more embodiments, the one or more images 429 depict the companion electronic devices situated in that environment. Illustrating by example, the user 122 is capturing one or more images 429 of the smart garage door opener 408 in FIG. 4. One or more processors of the electronic device 200 can identify the companion electronic devices depicted in the one or more images 429 using image analysis.”).
The motivation for combining Dove, Likhomanov and Russell is the same motivation as used for claim 7.
Regarding claim 17, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 17 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 18, the claim recites similar limitations to claim 9 but in the form of a system. Therefore, claim 18 recites similar limitations to claim 9 and is rejected for similar rationale and reasoning (see the analysis for claim 9 above).
Regarding claim 19, the claim recites similar limitations to claim 10 but in the form of a system. Therefore, claim 19 recites similar limitations to claim 10 and is rejected for similar rationale and reasoning (see the analysis for claim 10 above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 9:00 - 5:00.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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