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 .
Applicant(s) Response to Official Action
The response filed on 04/08/2026 has been entered and made of record.
Response to Arguments/Amendments
Presented arguments have been fully considered but are held unpersuasive. Examiner’s response to the presented arguments follows below.
Claim Rejections - 35 USC § 103
Summary of Arguments:
Regarding claims 1-9, 11-12, 20, the Applicant argues Saptharishi (US 2016/0205422 A1) in view of Gadnir (US 9,774,823 B1):
The references do not disclose: “the Claimed Cropped-Out Region of Interest […] Saptharishi's 'windowed view' is described in the context of presenting or communicating an event-related view to network nodes-it is not a pixel level spatial extraction operation that produces a sub-image defined by a bounding box corresponding to a detected object. The claimed step requires a specific geometric operation on the video frame that produces a bounded sub-image containing less than all of the video frame, which is a structurally distinct operation from transmitting a notional 'view' of an event. The claimed invention requires that the first video camera specifically "crop[s] out a region of interest (ROI) in the video frame of the video stream that corresponds to the object of interest, wherein the cropped-out region including less than all of the video frame of the video stream." The functional significance of this step is that only the bounded spatial sub-region, not the full frame, is transmitted to the second camera, directly facilitating the bandwidth and processing efficiency gains that are the purpose of the invention. Saptharishi's sensor packet and "windowed view" concepts are not equivalent to, and do not suggest, this spatially-cropped ROI operation nor any bandwidth and processing efficiency gains attributable thereto.” Gadnir does not cure this deficiency. […] transplanting Gadnir's object-extraction technique for still photographs into Saptharishi's sensor packet-based video network would require fundamental redesign of both systems, not a routine modification, and would not yield the claimed camera-to camera ROI offloading architecture.” [Remarks: Page 9]
“There Is No Proper Motivation to Combine Saptharishi and Gadnir” [Remarks: Pages 9-10]
“Claim 5 further requires that the first video camera determines "that the current resource utilization level of the processing resources of the first video camera exceeds a threshold utilization level" as a prerequisite to sending the cropped-out ROI to the second video camera. […] The Examiner maps this limitation to Saptharishi's disclosure that a network camera "reaching a storage capacity limit" sends video data to another camera, and that every device "broadcasts the information about its capabilities/throughput." Applicant respectfully submits that such elements are neither analogous to nor indicative of a threshold utilization level governing the offloading decision. Saptharishi's […] does not disclose a configurable, predefined threshold utilization level at which the camera proactively decides to offload processing in order to preserve performance […] Claim 5 is specifically directed to a threshold governing the utilization of processing resources, i.e., compute capacity. A storage overflow condition is categorically distinct from a processing utilization threshold, and the two are not interchangeable in the context of a load-balancing system whose purpose is to distribute computational workload, not to manage storage.” [Remarks: Pages 10-11]
“Claim 20 recites a specific hierarchical decision logic that is entirely absent from the cited art. The claim requires a first video camera to: (1) identify an object of interest; (2) determine whether to execute a video analytics algorithm on the object of interest at all; (3) only if yes, determine whether the first camera has sufficient idle processing resources; (4) only if not, identify a second camera with sufficient idle resources and send the cropped out ROI; and (5) receive the analytics result returned by the second camera. […] The Examiner maps step (2), namely the determination of whether to execute the video analytics algorithm, to Saptharishi's disclosure that "analytics service converts raw sensor information to symbolic metadata" and that "based on the rules and associated alarm events specified by the user, a series of analytics tasks are performed." This mapping is incorrect. […] It does not disclose a camera making a first-level decision as to whether a video analytics algorithm should be executed on a detected object of interest. […] the system does not make a threshold judgment as to whether analytics should be triggered for a particular detected object before proceeding to resource evaluation. Accordingly, claim 20 is independently patentable over the cited art.” [Remarks: Pages 11-12]
Regarding claim 10, the Applicant argues Saptharishi (US 2016/0205422 A1) in view of Gadnir (US 9,774,823 B1) in further view of Dong (US 2022/0207282 A1):
The references do not disclose: converting “the cropped-out region of interest (ROI) of the video frame to gray scale before sending the cropped-out region of interest (ROI) of the video frame of the video stream to the second video camera.” [Remarks: Page 12]
“The claimed step is specifically directed to converting the already-cropped ROI to grayscale as a data-reduction measure in advance of network transmission to a second camera for distributed processing, which is a purpose and architecture that Dong neither discloses nor suggests.” [Remarks: Page 13]
“Moreover, combining three independently operating references, namely Saptharishi's SIPH-based sensor network, Gadnir's still-camera object extraction, and Dong's single-device grayscale preprocessing pipeline, to arrive at the specific claimed
sequence of distributed ROI cropping followed by grayscale conversion before cross camera transmission requires Applicant's own specification as a guide. This is precisely the hindsight-driven combination that the obviousness doctrine prohibits. Claim 10 is therefore patentable over the cited art.” [Remarks: Page 13]
“The claimed step is directed to a specific functional goal - pre-transmission data reduction for distributed camera-to-camera analytics that is simply not present in Dong's single-device pipeline.” [Remarks: Page 13]
Examiner’s Response:
Regarding claims 1-9, 11-12, 20, the Examiner contends:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a specific geometric operation on the video frame that produces a bounded sub-image containing less than all of the video frame”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Further, Saptharishi's 'windowed view' explicitly “corresponds to the object of interest, wherein the (Saptharishi: Para. [0043] discloses “imaging system 202 may be controlled such that a “windowed view” of the event or object of interest is generated by creating video data in which only the portion [claimed less than all of the video frame of the video stream] of the field of view corresponding to the event or object is displayed.”). One of ordinary skill in the art may even argue that Saptharishi alone teaches this feature. However, to be explicit, Gadnir is relied upon to teach the image processing technique of cropping out an image, i.e., claimed cropped-out region (Gadnir: Col. 1, ll. 43-49 disclose “The processor causes the digital camera to acquire a digital image [full image] of a local participant during a video communication session, extracts [crops] a first image [a portion of the digital image] of a first set of objects [claimed region of interest] … from the digital image”. Further, Gadnir: Col. 11, ll. 60-67 disclose “extract and crop (or digitally zoom) an image of each of the first participant 400, second participant 404, third participant 65 408, whiteboard 412, and meeting room 416 [explicitly describes claimed cropped-out region of interest]).
As explained above, Saptharishi disclosed a cropped region of interest, but lacked the explicit “cropping-out” feature. Therefore, Saptharishi would look to Gadnir to explicitly teach the full image processing technique. Since the image processing technique of Saptharishi is included in its distributed video analytics system, the modification of the distributed video analytics system would result in a configuration that improves optimization and efficiency of the video analytics camera network (i.e., optimize the video “viewing” experience) and as a consequence, further reduces network bandwidth consumption and latency (Gadnir: Col. 1, ll. 15-17-49; Col. 2, ll. 28-45).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a threshold utilization level governing the offloading decision”, “a configurable, predefined threshold utilization level at which the camera proactively decides to offload processing in order to preserve performance”, “a threshold governing the utilization of processing resources, i.e., compute capacity.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a camera making a first-level decision as to whether a video analytics algorithm should be executed on a detected object of interest”, “make a threshold judgment as to whether analytics should be triggered for a particular detected object before proceeding to resource evaluation.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Further, in order to elucidate how the limitation in question (the first video camera determining whether to execute a video analytics algorithm on the object of interest to identify further characteristics of the object of interest) is mapped, please see the following: the first video camera determining whether to execute a video analytics algorithm on the object of interest to identify further characteristics of the object of interest (Saptharishi: Paras. [0016], [0100] disclose analytics service converts raw sensor information to symbolic metadata. In a video sensor [refers to claimed first camera], the analytics services [includes the claimed video analytics algorithm] include object recognition, face recognition [claimed characteristics of the object of interest], license plate recognition [describes claimed execute a video analytics algorithm on the object of interest to identify further characteristics of the object of interest] and uses sensor information packet headers that include analytics tasks [claimed determining whether to execute a video analytics algorithm] to be performed. Based on the rules [additional context, i.e., based on instructions created by a user] and associated alarm events specified by the user, a series of analytics tasks are performed on the sensor data. Furthermore, Saptharishi: Para. [0018] discloses “Rules are a composition of descriptors extracted from raw sensor data using various analytics services. For example, the user might be interested in only red trucks with license plate number ABC 123 (e.g., the specified rule) and the user might want this event to be recorded and a guard notified (e.g., the specified action) while other events are ignored.”. Therefore, determining whether to execute a video analytics algorithm on the object of interest to identify further characteristics of the object of interest is based on the rules provided by the user.).
Regarding claim 10, the Examiner contends:
i.-iv. Saptharishi already describes the concept of a cropped region of interest. Gadnir is relied upon to extend the image processing algorithm of Saptharishi, and Dong provides the notoriously well-known image pre-processing technique to convert RGB images to grayscale. This adaption of Dong is easily integrated as it focuses solely on the algorithm and naturally results in a configuration that improves speed and accuracy of object detection by reducing data dimensionality and computational load required for transmission and subsequent processing (Dong: [0019]).
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.
Claims 1-9 & 11-12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Saptharishi (US 2016/0205422 A1) in view of Gadnir et al., hereinafter referred to as Gadnir (US 9,774,823 B1).
As per claim 1, Saptharishi discloses a method for load balancing video analytic processing among two or more of a plurality of network connected video cameras, each network connected video camera including a video camera for capturing a respective video stream (Saptharishi: Abstract.), the method comprising:
a first video camera (network camera/sensor 102) of the plurality of network connected video cameras (network cameras/sensors 102) identifying an object of interest in a video frame of a video stream captured by the first video camera (Saptharishi: Paras. [0020], [0035], [0039]-[0040] disclose a protocol allows for a network of sensors to discover one another and services (e.g., video analytics) each of the devices offer. Video analytics 304 analyzes the video data produced by imaging system 202 (of network camera sensor 102) to detect whether a predefined event or object of interest is being captured.);
(Saptharishi: Paras. [0017], [0043] disclose sensor packets and/or binary chunks representing valuable pieces of information in raw or processed forms or a “windowed view” of the event or object of interest.);
sending the (Saptharishi: Paras. [0017], [0118] disclose sensor packets and/or binary chunks representing valuable pieces of information in raw or processed forms, for example, all faces detected in a video stream may be transmitted for further processing.);
the second video camera of the plurality of network connected video cameras executing a video analytics algorithm on the (Saptharishi: Paras. [0017], [0053], [0118] disclose sensor packets and/or binary chunks representing valuable pieces of information in raw or processed forms, for example, all faces detected in a video stream may be transmitted for further video analytics processing. Based on the processing results, the task list in the SIPH (sensor information packet header) is amended.); and
the second video camera of the plurality of network connected video cameras sending the video analytics result to the first video camera of the plurality of network connected video cameras (Saptharishi: Para. [0118] discloses “Value back-propagation … value [video analytics result] has to be back-propagated to these devices”.).
However, Saptharishi does not explicitly disclose “cropping out a region of interest”, which creates the “cropped-out region of interest”.
Further, Gadnir is in the same field of endeavor and teaches the cropped-out region of interest (Gadnir: Col. 1, ll. 43-49 disclose the processor causes the digital camera to acquire a digital image and extracts a first image of a first set of objects from the digital image.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Saptharishi and Gadnir before him or her, to modify the distributed video analytics system of Saptharishi to include the cropped-out region of interest feature as described in Gadnir. The motivation for doing so would have been to improve optimization and efficiency of the video analytics camera network by providing a configuration that reduces network bandwidth consumption and latency.
As per claim 20, Saptharishi discloses a method for load balancing video analytic processing among two or more of a plurality of network connected video cameras, each network connected video camera including a video camera for capturing a respective video stream and processing resources (Saptharishi: Abstract.), the method comprising:
a first video camera (network camera/sensor 102) of the plurality of network connected video cameras (network cameras/sensors 102) identifying an object of interest in a video frame of a video stream captured by the first video camera (Saptharishi: Paras. [0020], [0035], [0039]-[0040] disclose a protocol allows for a network of sensors to discover one another and services (e.g., video analytics) each of the devices offer. Video analytics 304 analyzes the video data produced by imaging system 202 (of network camera sensor 102) to detect whether a predefined event or object of interest is being captured.);
the first video camera determining whether to execute a video analytics algorithm on the object of interest to identify further characteristics of the object of interest (Saptharishi: Paras. [0016], [0100] disclose analytics service converts raw sensor information to symbolic metadata. In a video sensor, the analytics services include object recognition, face recognition, license plate recognition and uses sensor information packet headers that include analytics tasks to be performed. Based on the rules and associated alarm events specified by the user, a series of analytics tasks are performed.);
when it is determined to execute the video analytics algorithm on the object of interest, the first video camera determining whether the first video camera has sufficient idle processing resources to perform the video analytics algorithm on the object of interest (Saptharishi: Paras. [0073], [0114], [0118] disclose the optimality of the routes is evaluated based on the capacity of the node or device to execute the service in a timely manner, since it is not always possible to endow sensors with the necessary processing capacity to run all video analytics algorithms; determines if the sensor packet will be locally processed.), and if so, the first video camera executing the video analytics algorithm on the object of interest (Saptharishi: Para. [0118] discloses “Determine if the sensor packet will be locally processed … The analytics processor buffers the necessary packets and performs the processing”.), and if not:
the first video camera identifying a second video camera of the plurality of network connected video cameras that has sufficient idle processing resources to perform the video analytics algorithm on the object of interest (Saptharishi: Paras. [0020], [0114] disclose routes that provide the necessary services (e.g., video analytics) are selected and evaluated based on the capacity of the node/sensor.), and sending a (Saptharishi: Paras. [0017], [0118] disclose sensor packets and/or binary chunks representing valuable pieces of information in raw or processed forms, for example, all faces detected in a video stream may be transmitted for further processing.);
the second video camera:
receiving the (Saptharishi: Paras. [0043], [0046], [0053] disclose a second network camera 102 receiving video data representing the “windowed view” of event or object of interest over network 106. Further, Para. [0118] discloses “Normal Network Execution … Separate sensor packets … The analytics processor buffers the necessary packets”.);
executing the video analytics algorithm on the (Saptharishi: Para. [0118] discloses “performs the processing … Based on the processing results, the task list in the SIPH is amended”.); and
returning the video analytics result to the first video camera (Saptharishi: Para. [0118] discloses “Value back-propagation … value has to be back-propagated to these devices”.).
However, Saptharishi does not explicitly disclose “cropped-out region of interest”.
Further, Gadnir is in the same field of endeavor and teaches the cropped-out region of interest (Gadnir: Col. 1, ll. 43-49 disclose the processor causes the digital camera to acquire a digital image and extracts a first image of a first set of objects from the digital image.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Saptharishi and Gadnir before him or her, to modify the distributed video analytics system of Saptharishi to include the cropped-out region of interest feature as described in Gadnir. The motivation for doing so would have been to improve optimization and efficiency of the video analytics camera network by providing a configuration that reduces network bandwidth consumption and latency.
As per claim 2, Saptharishi-Gadnir the method of claim 1, wherein identifying the object of interest in the video frame of the video stream captured by the first video camera comprises:
identifying a region of pixels in the video frame of the video stream that differ from corresponding pixels in a reference video frame (Saptharishi: Para. [0041] discloses detecting a group of moving pixels as a potential moving object.); and
identifying the object of interest in the video frame as corresponding to the region of pixels in the video frame of the video stream that differ from the corresponding pixels in the reference video frame (Saptharishi: Para. [0041] discloses detecting a group of moving pixels as a potential moving object, may be able to classify the object and be able to recognize the object when it appears in any portion within the field of view of network camera 102.).
As per claim 3, Saptharishi-Gadnir the method of claim 1, comprising:
the first video camera of the plurality of network connected video cameras classifying the object of interest into one of a plurality of classifications (Saptharishi: Para. [0041] discloses video analytics 304 may be able to classify an object as a human being, a vehicle, or another type of object.); and
the first video camera of the plurality of network connected video cameras sending the classification of the object of interest to the second video camera along with the cropped-out region of interest (ROI) of the video frame of the video stream (Saptharishi: Para. [0046] discloses video data representing the event of interest [with associated metadata/classification] may be immediately streamed to another network camera 102. Further, Gadnir: Col. 1, ll. 43-49 disclose the processor causes the digital camera to acquire a digital image and extracts a first image of a first set of objects from the digital image.).
As per claim 4, Saptharishi-Gadnir the method of claim 1, comprising:
the first video camera of the plurality of network connected video cameras sending metadata to the second video camera that identifies the video analytics algorithm (video analytics rule associated with a specified action) from a plurality of predetermined video analytics algorithm (video analytic rules) that is to be executed by the second video camera on the cropped-out region of interest (ROI) of the video frame of the video stream (Saptharishi: Paras. [0017]-[0018], [0046], [0118] disclose video data representing the event of interest [with associated metadata] streamed to another network camera/sensor include rule-action pairs. For example, the user might be interested in only red trucks with license plate number ABC 123 (e.g., the specified rule) and the user might want this event to be recorded and a guard notified (e.g., the specified action) while other events are ignored. Further, Gadnir: Col. 1, ll. 43-49; Col. 11, ll. 60-67 disclose the cropped-out region of interest.).
As per claim 5, Saptharishi-Gadnir the method of claim 1, wherein the first video camera of the plurality of network connected video cameras comprises processing resources that have a current resource utilization level (Saptharishi: Paras. [0103], [0110] discloses every device broadcasts the information about its capabilities/throughput and that of the sensors connected directly to it.), and wherein the first video camera determining that the current resource utilization level of the processing resources of the first video camera exceeds a threshold utilization level before sending the cropped-out region of interest (ROI) of the video frame of the video stream to the second video camera of the plurality of network connected video cameras (Saptharishi: Paras. [0043], [0046], [0053] disclose the concept of a network camera 102 reaching a storage capacity limit and sending video data representing the “windowed view” of event or object of interest over network 106 to another network camera 102. Furthermore, Gadnir: Col. 1, ll. 43-49; Col. 11, ll. 60-67 disclose the cropped-out region of interest.).
As per claim 6, Saptharishi-Gadnir the method of claim 5, wherein the threshold utilization level is dependent on the video analytics algorithm that is to be executed on the cropped-out region of interest (ROI) of the video frame of the video stream (Saptharishi: Para. [0114] discloses evaluating “the capacity of the node or device to execute the service” and Para. [0116] elaborates that these services correspond to specific video analytics algorithms – such as “Vehicle detection” and “License Plate recognition” [region of interest (ROI) of the video frame of the video stream], which are determined by “service dependency”. Furthermore, Gadnir: Col. 1, ll. 43-49; Col. 11, ll. 60-67 disclose the cropped-out region of interest.).
As per claim 7, Saptharishi-Gadnir the method of claim 5, wherein when the first video camera determines that the current resource utilization level of the processing resources of the first video camera does not exceed the threshold utilization level, the first video camera executing the video analytics algorithm on the cropped-out region of interest (ROI) of the video frame of the video stream and not sending the cropped-out region of interest (ROI) of the video frame of the video stream to the second video camera of the plurality of network connected video cameras (Saptharishi: Para. [0012] discloses “video analytics … analyzes the video data produced by the video camera … video data need not be streamed across the network,” therefore allows the camera to execute algorithms locally. Further, Para. [0053] discusses offloading data when capacity is exceeded, but also implies the inverse logic claimed: if the resource utilization (e.g., storage needs) does “not” exceed the threshold (e.g., capacity), the camera processes the data locally and does not send the data to the second camera. This is consistent with the goal of minimizing streaming across the network when local processing/storage is viable.).
As per claim 8, Saptharishi-Gadnir the method of claim 1, wherein each of the plurality of network connected video cameras comprises processing resources that have a respective current resource utilization level, and wherein each of the plurality of network connected video cameras makes their respective current resource utilization level known to all other of the plurality of network connected video cameras (Saptharishi: Paras. [0103], [0110] discloses every device broadcasts the information about its capabilities/throughput and that of the sensors connected directly to it.).
As per claim 9, Saptharishi-Gadnir the method of claim 8, comprising the first video camera of the plurality of network connected video cameras selecting the second video camera from the plurality of network connected video cameras based at least in part on the current resource utilization level of the second video camera (Saptharishi: Paras. [0020], [0114] disclose routes that provide the necessary services (e.g., video analytics) are selected and evaluated based on the capacity of the node/sensor.).
As per claim 11, Saptharishi-Gadnir the method of claim 1, wherein the video analytics result sent by the second video camera to the first video camera includes one or more labels describing one or more characteristics of the object of interest, wherein the first video camera integrating the one or more labels into a live stream of the video stream captured by the first video camera (Saptharishi: Paras. [0017], [0089]-[0090] disclose the packaging process encapsulates a textual script of the content of a sensor's data stream. The analyzed data is summarized and annotated with a tag [labeled] such as “Person Detected”.).
As per claim 12, Saptharishi-Gadnir the method of claim 1, wherein the video analytics result sent by the second video camera includes one or more labels describing one or more characteristics of the object of interest, and wherein the first video camera is operatively coupled to a Network Video Recorder (NVR) that records the video stream captured by the first video camera, the NVR receiving the video analytics result and integrating the one or more labels into the recorded video stream captured by the first video camera (Saptharishi: Paras. [0005], [0033], [0040], [0052], [0089]-[0090] disclose video analytics 304 generates metadata (labels) describing the content, which may be supplied to remote storage unit 116 (NVR), which is connected to network cameras 102. Metadata representing an arbitrary frame “n” can be associated with video data representing frame “n”, thereby integrating the labels into the recorded video sequence. The system summarizes and annotates (tags) the sensor data.).
Claim 10 are rejected under 35 U.S.C. 103 as being unpatentable over Saptharishi in view of Gadnir in further view of Dong et al., hereinafter referred to as Dong (US 2022/0207282 A1).
As per claim 10, Saptharishi-Gadnir the method of claim 1, comprising the first video camera of the plurality of network connected video cameras (Saptharishi: Paras. [0043], [0046], [0053] disclose the network camera 102 sending video data representing the “windowed view” of event or object of interest over network 106 to another network camera 102. Furthermore, Gadnir: Col. 1, ll. 43-49; Col. 11, ll. 60-67 disclose the cropped-out region of interest.).
However, Saptharishi-Gadnir do not explicitly disclose “… converting the cropped-out region of interest (ROI) of the video frame to gray scale before sending …”.
Further, Dong is in the same field of endeavor and teaches converting the cropped-out region of interest (ROI) of the video frame to gray scale before sending (Paras. [0034], [0043], [0053] disclose a preprocessing module 204 performs preprocessing of video frames including converting the RGB video frames to grayscale video frames and objects or regions of interest (e.g., clustered cells) can then be cropped from the video frame and passed to an object detection DNN.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Saptharishi-Gadnir and Dong before him or her, to modify the distributed video analytics system of Saptharishi-Gadnir to include the converting cropped-out region of interest (ROI) to gray scale feature as described in Dong. The motivation for doing so would have been to improve speed and accuracy of object detection or analysis performed by providing a configuration that reduces data dimensionality and computational load required for transmission and subsequent processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be viewed in the list of references.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEET DHILLON whose telephone number is (571)270-5647. The examiner can normally be reached M-F: 5am-1:30pm.
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/PEET DHILLON/Primary Examiner
Art Unit: 2488
Date: 05-31-2026
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