DETAILED ACTION
Claims 1-4, 6-11, 13-17, 19-20 and 24-26 are pending in this application. Claims 5, 12, 18 and 21-23 are canceled, and claims 24-26 have been newly added.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/21/2023 and 10/15/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/02/2026 has been entered.
Response to Arguments
U.S.C. 102(a)(1)
Applicant’s arguments (see Remarks filed 04/02/2026) regarding the rejections made under 35 U.S.C. 102 (a) have been fully considered and are not persuasive. Applicant previously argued that Nadathur does not teach “wherein a motion blob reflects progression of the motion event over a combination of the subset of the plurality of frames into one frame” because the motion blob claimed is distinct from what is taught in Nadathur. The examiner disagrees and reiterates that this limitation as claimed requires that the motion event, for example a person walking by for a few seconds, be captured, and then that group of frames would be subset to include the motion event. Nadathur teaches in [0051] that when motion is detected the camera will capture one or more frames of the whole motion interaction. Further, Nadathur, [0061] notes that when this motion trigger occurs, the frames are analyzed for the triggering object, then in [0064] the system can locate the triggering object across multiple frames and save those as one segment of video depicting the progression of the motion. This would be understood by one of ordinary skill in the art as being analogous to the motion blob definition claimed in amended claims 1, 8 and 15. The examiner further notes that in order to overcome the 102 rejections made in view of Nadathur for this limitation, the applicant is encouraged to amend the claim to further narrow the scope of the “motion blob” to distinguish it from the art.
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Applicant further argues that Nadathur does not teach processing a motion blob to generate one or more attributes and sending the attributes to a cloud processing component. The examiner disagrees, Nadathur teaches in paragraphs [0070] and [0073] that the metadata, which would include the attribute/classification data, is transmitted to a storage device, which is a cloud-based server. For at least the reasons above, the examiner maintains the rejections made under Nadathur. The applicant is encouraged to amend to further narrow the scope of the claimed invention to distinguish it from the art of record.
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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.
Claims 1-4, 6-11, 13-17, 19-23 are rejected under 35 U.S.C. 102(a) as being anticipated by Nadathur (US 20200380266 A1).
Regarding claim 1 Nadathur discloses; A method comprising:
detecting one or more objects of interest in video data captured using one or more cameras (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification);
detecting a motion event in a plurality of frames of the video data (Nadathur, [0051] a camera (302) can detect motion (motion event), [0052] the detection of motion can trigger to capturing of a frame from a video stream for analysis);
generating a motion blob for a subset of the plurality of frames associated with the motion event, wherein a motion blob reflects a progression of the motion event over a combination of the subset of the plurality of frames into one frame (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, [0060]-[0062] the system can take multiple frames of the video streams and locate the motion blob in at least one frame or a collection of frames, further [0064] the system can detect a start and end position of the motion and collect those frames, [0051] when motion is detected the camera will capture one or more frames of the whole motion interaction);
processing the motion blob to generate one or more attributes (Nadathur, [0071] upon detection of motion, the frames associated can be sent to a classifier to classify the frames as a person, animal or vehicle, [0053] the classification is performed using a classifier, [0055]-[0056] the classifier uses RGB values are extracted from the pixels which are used to train the model to classify the object in the frames), wherein each of the one or more attributes are identified once in the motion blob ([0056]-[0058] features of the objects (attributes) are extracted and used by the classifier to identify the object in the frames);
and send the one or more attributes to a cloud processing component (Nadathur, [0073] the computing device and network storage device are coupled such that the frames and metadata (including the attributes/classification) are sent to the storage device [0070] the storage device can be a cloud-based database or server).
Regarding claim 2 Nadathur discloses; The method of claim 1, further comprising:
capturing video data using the one or more cameras at a given frequency (Nadathur, [0072]- [0073] the computing device can receive 15 frames per second of video from the camera);
Regarding claim 3 Nadathur discloses; The method of claim 2, wherein the one or more objects of interest include people and vehicles (Nadathur, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person).
Regarding claim 4 Nadathur discloses; The method of claim 3, wherein the vehicles include one or more of cars, bikes, and flying objects (Nadathur, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, further, [002] of Nadathur discuss that the system is a home system where it may be triggered by passing vehicles or pedestrians, which would indicate the capturing of a street in a neighborhood, so the vehicles would include cars, further [0037] discuses that they system environment may include homes, cars, vehicles, offices and campuses as appreciated by one of ordinary skill in the art).
Regarding claim 6 Nadathur discloses; The method of claim 1, wherein the one or more video cameras are communicatively coupled to an enterprise network (Nadathur, [0028] the accessory devices shown in figures 1 and 2 can include cameras coupled to an internet network, [0053] the cameras stream video data).
Regarding claim 7 Nadathur discloses; The method of claim 1, wherein an attribute search is performed at the cloud processing component for an attribute of interest among the one or more attributes (Nadathur, [0053] the device classifies the frame by comparing one or more aspects of the frame against criteria, [0060] the classifier determines an object by matching the features of the image to similar images (attribute search), [0094] the criteria for comparison is images stored in the database for comparisons of features).
Regarding claim 8 Nadathur discloses; A network device comprising (Nadathur, Figure 1, network environment 100, [0027] discusses the network system components):
one or more memories having computer-readable instructions stored therein (Nadathur, [0074] the system has a memory and a computing device);
and one or more processors configured to execute the computer-readable instructions to (Nadathur, [0015] one or more processors communicate with one or more memories to execute instructions):
detect one or more objects of interest in video data captured using one or more cameras (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification);
detect a motion event in a plurality of frames of the video data (Nadathur, [0051] a camera (302) can detect motion (motion event), [0052] the detection of motion can trigger to capturing of a frame from a video stream for analysis);
generate a motion blob for a subset of the plurality of frames associated with the motion event, wherein a motion blob reflects progression of the motion event over a combination of the subset of the plurality of frames into one frame (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, [0060]-[0062] the system can take multiple frames of the video streams and locate the motion blob in at least one frame or a collection of frames, further [0064] the system can detect a start and end position of the motion and collect those frames, [0051] when motion is detected the camera will capture one or more frames of the whole motion interaction);
process the motion blob to generate one or more attributes (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification);
and send the one or more attributes to a cloud processing component (Nadathur, [0073] the computing device and network storage device are coupled such that the frames and metadata (including the attributes/classification) are sent to the storage device [0070] the storage device can be a cloud-based database or server).
Regarding claim 9, Nadathur discloses; The network device of claim 8, wherein the one or more processors are configured to execute the computer-readable instructions to:
Capture the video data at a given frequency (Nadathur, [0072]- [0073] the computing device can receive 15 frames per second of video from the camera);
Regarding claim 10, Nadathur discloses; The network device of claim 9, wherein the one or more objects of interest include people and vehicles (Nadathur, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person).
Regarding claim 11, Nadathur discloses; The network device of claim 10, wherein the vehicles include one or more of cars, bikes, and flying objects (Nadathur, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, further, [002] of Nadathur discuss that the system is a home system where it may be triggered by passing vehicles or pedestrians, which would indicate the capturing of a street in a neighborhood, so the vehicles would include cars, further [0037] discuses that they system environment may include homes, cars, vehicles, offices and campuses as appreciated by one of ordinary skill in the art).
Regarding claim 13, Nadathur discloses; The network device of claim 8, wherein the one or more video cameras are communicatively coupled to an enterprise network (Nadathur, [0028] the accessory devices shown in figures 1 and 2 can include cameras coupled to an internet network, [0053] the cameras stream video data).
Regarding claim 14, Nadathur discloses; The network device of claim 8, wherein an attribute search is performed at the cloud processing component for an attribute of interest among the one or more attributes (Nadathur, [0053] the device classifies the frame by comparing one or more aspects of the frame against criteria, [0060] the classifier determines an object by matching the features of the image to similar images (attribute search), [0094] the criteria for comparison is images stored in the database for comparisons of features).
Regarding claim 15, Nadathur discloses; One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors of one or more cameras, cause one or more cameras to (Nadathur, [0015] one or more processors communicate with one or more memories to execute instructions):
detect one or more objects of interest in video data captured using the one or more cameras (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification);
detect a motion event in a plurality of frames of the video data captured at the network device (Nadathur, [0051] a camera (302) can detect motion (motion event), [0052] the detection of motion can trigger to capturing of a frame from a video stream for analysis);
generate a motion blob for a subset of the plurality of frames associated with the motion event, wherein a motion blob reflects progression of the motion event over a combination of the subset of the plurality of frames into one frame (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, [0060]-[0062] the system can take multiple frames of the video streams and locate the motion blob in at least one frame or a collection of frames, further [0064] the system can detect a start and end position of the motion and collect those frames, [0051] when motion is detected the camera will capture one or more frames of the whole motion interaction);
process the motion blob to generate one or more attributes (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification);
and send the one or more attributes to a cloud processing component (Nadathur, [0073] the computing device and network storage device are coupled such that the frames and metadata (including the attributes/classification) are sent to the storage device [0070] the storage device can be a cloud-based database or server).
Regarding claim 16 Nadathur discloses; The one or more non-transitory computer-readable media of claim 15, wherein the execution of the computer-readable instructions further causes the one or more cameras to:
capture the video data at a given frequency (Nadathur, [0072]- [0073] the computing device can receive 15 frames per second of video from the camera);
Regarding claim 17, Nadathur discloses; The one or more non-transitory computer-readable media of claim 16, wherein the one or more objects of interest include people and vehicles, and the vehicles include one or more of cars, bikes, and flying objects (Nadathur, [0071] the computing device can detect a trigger in the image frames (object of interest), where the trigger is classified as a vehicle animal or person, further, [002] of Nadathur discuss that the system is a home system where it may be triggered by passing vehicles or pedestrians, which would indicate the capturing of a street in a neighborhood, so the vehicles would include cars, further [0037] discuses that they system environment may include homes, cars, vehicles, offices and campuses as appreciated by one of ordinary skill in the art)
Regarding claim 19, Nadathur discloses; The one or more non-transitory computer-readable media of claim 15 wherein the one or more a video camera are communicatively coupled to an enterprise network (Nadathur, [0028] the accessory devices shown in figures 1 and 2 can include cameras coupled to an internet network, [0053] the cameras stream video data).
Regarding claim 20, Nadathur discloses; The one or more non-transitory computer-readable media of claim 15, wherein an attribute search is performed at the cloud processing component for an attribute of interest among the one or more attributes (Nadathur, [0053] the device classifies the frame by comparing one or more aspects of the frame against criteria, [0060] the classifier determines an object by matching the features of the image to similar images (attribute search), [0094] the criteria for comparison is images stored in the database for comparisons of features).
Regarding claim 24, Nadathur discloses; The method of claim 1, wherein each attribute is generated once in the motion blob regardless of a number of times an object appears in the motion blob (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, further per [0062] this classification is done once over the whole set of frames for a single “triggering item”, i.e. a set of 4 frames containing images of 1 person would be generate a classification output of “person” once. ).
Regarding claim 25, Nadathur discloses; The network device of claim 8, wherein each attribute is generated once in the motion blob regardless of a number of times an object appears in the motion blob (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, further per [0062] this classification is done once over the whole set of frames for a single “triggering item”, i.e. a set of 4 frames containing images of 1 person would be generate a classification output of “person” once).
Regarding claim 26, Nadathur discloses; The one or more non-transitory computer-readable media of claim 15, wherein each attribute is generated once in the motion blob regardless of a number of times an object appears in the motion blob (Nadathur, [0062] the device can analyze the one or more frames for a triggering item which can be an image of a person, this triggering item is detected, the one or more frames would be equivalent to the “motion blob” as described in [0073] of the applicant’s specification, further per [0062] this classification is done once over the whole set of frames for a single “triggering item”, i.e. a set of 4 frames containing images of 1 person would be generate a classification output of “person” once).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous art please see the attached PTO-892 Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.M.E./Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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