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 .
Status of Claims
Claims 1-18 are pending. Claims 16-18 are new.
Response to Arguments
Applicant’s arguments, see p.7-9, filed 04/16/2026, with respect to the rejections of Claims 1-15 under 35 U.S.C. 101 have been fully considered but are not persuasive. Applicant argues the amended claims are directed to a sophisticated, real-time monitoring system that combines deep convolutional neural networks with optical flow analysis of digital images to proactively identify potentially dangerous motion events in the images wherein the deep neural network AI is trained to identify objects, classify the objects, and recognize patterns within the images that are indicative of dangerous actions towards animals. Additionally, applicant argues that the system combines visual pixel data with optical flow analysis for reduced processing capacity and time requirements for improvement in computation efficiency and accurately determine direction and speed of objects. Applicant states that the claims are directly tied to evaluating frame-to-frame pixels within digital images from the high-resolution digital cameras which are integral to the claims. Lastly, the practical application of Applicant's claims involves using a dashboard for a human operator to review a video clip containing the motion of the object classified as harmful. Examiner respectfully disagrees because the limitations of independent Claim 1 being the capturing of images of an animal and a proximal object, processing those images by calculating object motion using optical flow that evaluates changes in hue and saturation of pixels, and displaying the incident alert on the user interface including a video clip containing the motion of the object classified as harmful are only insignificant extra-solution activities and are only examples of routine and conventional image processing steps and do not amount to significantly more to consider as inventive steps. Therefore, the judicial exception is not integrated into a practical application because the amended claim only recites these additional insignificant extra-solution activities wherein other additional recited elements in certain other claims are just only generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim as a whole, recites an abstract idea. Examiner recommends to amend the independent claims by emphasizing the calculation of the object motion and corresponding determination of the motion being classified as harmful and corrective action being taken in real-time in order to attempt to overcome this rejection.
Applicant’s arguments and amendments, see p. 9, filed 04/16/2026, with respect to the rejections of Claims 6 and 12-15 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Therefore, the rejections of Claims 6 and 12-15 under this section of the Rules has been withdrawn.
Applicant’s arguments, see p.9-13, filed 04/16/2026, with respect to the rejections of Claims 1-15 under 35 U.S.C. 103 have been fully considered but are moot because Applicant’s amendments of the independent claims has altered the scope of the claims, and therefore, necessitated new grounds of rejection which are presented below. Examiner has considered applicants arguments with respect to the new claims 16-18. However, arguments are moot due to new claims being presented and are therefore being analyzed as presented below. Accordingly, THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and the claimed invention is directed to non-statutory subject matter as follows. The claims recite using cameras to capture images of an animal and a proximal object, processing the images using a neural network, the processing including calculating object motion using optical flow that evaluates changes in hue and saturation of pixels, determining if a problem is occurring in the images based on the object motion to classify the motion as harmful, and displaying the video of the harmful motion.
Step 1:
With regard to Step 1, the instant claims are directed to a method, which is among the statutory categories of invention.
Step 2A – Prong 1:
With regard to Step 2A – Prong 1, for example in Claim 1, the limitations of "determining, via the system controller, a problem is occurring within the processed images, the determining including interpreting the calculated object motion to classify a motion of the object as harmful to the animal", as drafted only involves mental processes or mathematical calculations, such as determining if a problem is occurring based on the motion of the object in the image to determine if a motion is harmful. That is, nothing in the above-described claim elements preclude the steps from practically being performed in the mind or on a piece of paper. If a claim limitation, under its broadest reasonably interpretation covers performance of the limitation in the mind or through mathematical calculations, but for the recitation of a generic apparatus components, such as a processor, computer program, or machine-readable media, then it falls within the "mental processes", which include concepts performed in the human mind, including an observation, evaluation, judgement, opinion, or mathematical calculations groupings of the abstract idea. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements in the claims do not apply, rely on, or use the judicial exception.
This judicial exception is not integrated into a practical application because the claim only recites the following additional steps "A method of using artificial intelligence (AI) to monitor situations, the method comprising: capturing, using one or more cameras, images of an animal and an object proximal to the animal"; "processing, via a system controller using an Al neural network, the images of the animal and the object output from the one or more cameras”; "the processing including calculating object motion of the object using optical flow that evaluates changes in hue and saturation of pixels from frame to frame in the images"; "and displaying, via an interactive graphical user interface (UI), an incident alert including a video clip containing the motion of the object classified as harmful", i.e., insignificant extra-solution activity being the capturing of images, processing the images using optical flow and hue and saturation of pixels and displaying the video. The other additional recited elements in certain other claims are just a non-transitory computer-readable storage medium with a processor, which are generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim as a whole, recites an abstract idea.
Step 2B:
Because the claim fails under Step 2A, the claims are further evaluated under Step 2B. The claim herein does not include additional steps that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/steps amount to no more than insignificant extra-solution activities. Mere instructions to apply an exception using generic apparatus component, such as a processor, cannot provide an inventive concept. The claim is not patent eligible. It should be noted that a similar analysis may be performed with respect to independent Claims 10 and 18.
Further, with regard to dependent Claims 2-9, 11-15, and 16-17 viewed individually, these additional steps are under their broadest reasonable interpretation, cover performance of the limitation in the mind and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims’ limitations amount to significantly more than the abstract idea itself. For example, using optical flow to capture motion using key point tracking within the images as recited in Claim 4 or collecting and storing statistics as recited in Claim 6 are only examples of routine and conventional image processing steps and do not amount to significantly more to consider as inventive steps. Accordingly, Claims 1-18 are rejected under 35 U.S.C. 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-6, 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20210271885 A1) in view of Ghyme et al. (US 20210225146 A1).
Regarding Claim 1, Li teaches "A method of using artificial intelligence (AI) to monitor situations, the method comprising: capturing, using one or more cameras, images of an animal and an object proximal to the animal"; (Li, Paras. 4-5, teaches a video analytics system for detecting animal abuse comprising a controller coupled to receive video data from at least one camera wherein the controller processes successive video frames to identify objects of interest including at least humans and analyze movements of such objects relative to an animal, i.e., capturing images of an animal and an object proximal to the animal using at least one camera by analyzing movements of the object relative to the animal);
"processing, via a system controller using an Al neural network, the images of the animal and the object output from the one or more cameras"; (Li, Paras. 7 and 53, teaches the controller using artificial intelligence techniques in the analysis process of the video streams wherein the controller may track the features across multiple images to analyze movements of such objects and determine mathematically and objectively whether or not the analyzed movements meet predetermined criteria for possible abuse in which the video analytics system uses a deep neural network for detection, i.e., processing the images of the animal and the object output from the camera via a controller using an AI neural network);
"";
"determining, via the system controller, a problem is occurring within the processed images, the determining including interpreting the calculated object motion to classify a motion of the object as harmful to the animal"; (Li, Paras. 6-7, teaches determining mathematically and objectively whether or not the analyzed movements meet predetermined criteria for possible abuse of the animal, i.e., determine whether a problem is occurring within the images by interpreting the calculated object motion as classified as a motion to be harmful to the animal as meeting the criteria for abuse of the animal);
"and displaying, via an interactive graphical user interface (UI), an incident alert including a video clip containing the motion of the object classified as harmful"; (Li, Paras. 6-7, teaches outputting information identifying the instance of possible animal abuse via a user interface which allows the user to view video clips showing instances of possible animal abuse based on probability scores, i.e., display an incident alert including a video clip containing the motion of the object classified as harmful or abuse on the user interface).
However, Li does not explicitly teach "the processing including calculating object motion of the object using optical flow that evaluates changes in hue and saturation of pixels from frame to frame in the images".
In an analogous field of endeavor, Ghyme teaches "the processing including calculating object motion of the object using optical flow that evaluates changes in hue and saturation of pixels from frame to frame in the images"; (Ghyme, Para. 74, teaches calculating motion in all of the pixels between consecutive images by applying optical flow technology onto the color space of Hue, Saturation, and Value HSV wherein this motion map sequence enables identifying objects exhibiting distinct motion by removing a stationary background and detecting a change in the internal structure in each of the objects, i.e., calculate object motion using optical flow that evaluates change in hue and saturation of pixels from frame to frame).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li by including the calculating of object motion using optical flow that evaluates hue and saturation of pixels from frame to frame in the images taught by Ghyme. One of ordinary skill in the art would be motivated to combine the references since it improves accuracy of detection (Ghyme, Para. 68, teaches the motivation of combination to be to improve the accuracy of detection of a disaster).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 2, the combination of references of Li in view of Ghyme teaches "The method of claim 1, wherein the calculating the object motion further includes using a hue, saturation, lightness (HSL) representation model or a hue, saturation, value (HSV) representation model to derive a direction and a magnitude of the motion of the object"; (Ghyme, Para. 74, teaches calculating motion in all of the pixels between consecutive images by applying optical flow technology onto the color space of Hue, Saturation, and Value HSV wherein this motion map sequence enables identifying objects exhibiting distinct motion by removing a stationary background and detecting a change in the internal structure in each of the objects, i.e., calculate object motion using HSV representation model to derive direction and magnitude of motion of the object).
The proposed combination as well as the motivation for combining the Li and Ghyme references presented in the rejection of Claim 1, applies to claim 2. Thus, the method recited in claim 2 is met by Li in view of Ghyme.
Regarding Claim 5, the combination of references of Li in view of Ghyme teaches "The method of claim 1, further comprising determining, via the system controller, the object proximal the animal is a human, wherein the motion of the object is classified as animal abuse"; (Li, Paras. 4-5, teaches a video analytics system for detecting animal abuse comprising a controller coupled to receive video data from at least one camera wherein the controller processes successive video frames to identify objects of interest including at least humans and analyze movements of such objects relative to an animal wherein the analyzed movements meet predetermined criteria for possible abuse of the animal, i.e., determining the object proximal to the animal is a human and the motion is animal abuse).
Regarding Claim 6, the combination of references of Li in view of Ghyme teaches "The method of claim 5, further comprising: collecting statistics for animal abuse; and storing the collected statistics of animal abuse in a resident or remote data storage device"; (Li, Para. 41, teaches the video analytics system includes a local surveillance setup, a number of local edge computing systems, and a cloud-based system wherein the edge computing system stores video streams/clips and processes video frames and can upload information to the cloud-based systemand archives videos and video metadata wherein instances of possible abuse are represented by predefined numerical threshold along with critical metadata such as the facility location, time of day, and camera or camera location and wherein the aggregated metrics include one or more of length of video, number of animal-human co-exists, number of animal-human interactions, number of likely abuse instances, historical metrics, and trends, i.e., collect statistics for animal abuse and store the statistics in a remote data storage).
Regarding Claim 8, the combination of references of Li in view of Ghyme teaches "The method of claim 1, wherein the object includes one or more humans"; (Li, Abstract, teaches the video analytics system that processes successive video frames to identify objects of interest such as humans, i.e., monitored objects include humans);
"and wherein the motion of the object classified as harmful includes criminal actions"; (Li, Para. 5, teaches determining mathematically and objectively whether or not the analyzed movements meet predetermined criteria for possible abuse of the animal, i.e., motion of the object classified as harmful or abuse is criminal).
Regarding Claim 9, the combination of references of Li in view of Ghyme teaches "The method of claim 8, wherein the interactive graphical UI includes a dashboard monitored by a human, the dashboard of the interactive graphical UI enabling the human to review the video clip and determine whether the criminal actions are actual criminal actions"; (Li, Para. 34, teaches outputting relevant information via a user interface including a list of possible abuse instances identifying the time and probability of abuse from which the user can select an instance in order to view the corresponding video for human analysis, i.e., dashboard or user interface which is monitored by a human wherein the human would determine if the possible abuse being the criminal action indicated by probability are actually abuse or criminal action through the human analysis).
Claim 10 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Ghyme references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Li and Ghyme references discloses a computer readable storage medium (for example, see Li, Paragraph 5).
Regarding Claim 11, the combination of references of Li in view of Ghyme teaches "The non-transitory computer-readable storage medium of claim 10, wherein execution of the instructions further cause the processor to use the Optical Flow to capture motion of the animal and the object within the images"; (Li, Figs. 2A and 13 and Paras. 63-64, teaches using optical flow to compute motion speed wherein motion speed calculation is only calculated for human animal interaction, i.e., optical flow used to capture motion of the animal and the object within the images).
Claim 12 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 6. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Ghyme references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Li and Ghyme references discloses a computer readable storage medium (for example, see Li, Paragraph 5).
Claim 13 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 8. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Ghyme references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Li and Ghyme references discloses a computer readable storage medium (for example, see Li, Paragraph 5).
Claim 14 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 9. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Ghyme references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Li and Ghyme references discloses a computer readable storage medium (for example, see Li, Paragraph 5).
Claim 15 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 5. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Li and Ghyme references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Li and Ghyme references discloses a computer readable storage medium (for example, see Li, Paragraph 5).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Adeel et al. (US 20240046515 A1).
Regarding Claim 3, the combination of references of Li in view of Ghyme does not explicitly teach "The method of claim 1, wherein the interactive graphical UI includes a dashboard monitored by a human, the dashboard enabling the human to review the video clip, label or relabel the video clip, and select a corrective action for the motion classified as harmful".
In an analogous field of endeavor, Adeel teaches "The method of claim 1, wherein the interactive graphical UI includes a dashboard monitored by a human, the dashboard enabling the human to review the video clip, label or relabel the video clip, and select a corrective action for the motion classified as harmful"; (Adeel, Para. 14, teaches a machine-learning model that is trained to identify a gun in video frames erroneously identified a smartphone wherein the user interface controls may enable a user reviewing the object detection to submit corrective labeling information indicating that an image of the detected object shows a smartphone rather than a gun and the image of the detected object and the corrective labeling information may be used as a part of training data, i.e., UI includes a dashboard monitored by a person to enable the human to review the video frames, properly label the frames, and complete corrective action being the submission of the corrective labeling information for retraining).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme wherein the video contains motion classified as harmful by including the user interface including a dashboard for human review of the video for corrective action and labeling taught by Adeel. One of ordinary skill in the art would be motivated to combine the references since it improves the accuracy of the model (Adeel, Para. 14, teaches the motivation of combination to be to improve accuracy of the model to recognize an object of a specific type).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Bovyrin et al. (US 20180324415 A1).
Regarding Claim 4, the combination of references of Li in view of Ghyme does not explicitly teach "The method of claim 1, wherein using the Optical Flow to capture object motion further includes determining a set of key points within a frame of the images and tracking the set of key points from frame to frame, and wherein the object motion is calculated based on the tracking of the set of key points".
In an analogous field of endeavor, Bovyrin teaches "The method of claim 1, wherein using the Optical Flow to capture object motion further includes determining a set of key points within a frame of the images and tracking the set of key points from frame to frame, and wherein the object motion is calculated based on the tracking of the set of key points"; (Bovyrin, Para. 10 and Claim 1, teaches receiving sequential images from the camera, finding image key points in an area of the image, and tracking the key points using an optical flow calculation of a small subset of relevant points wherein the optical flow methods calculate motion between two image frames, i.e., use optical flow to capture object motion includes determining key points in a frame and tracking the key points from frame to frame in which motion is calculated from that tracking of key points).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme by including the optical flow object motion determining key points to be tracked from frame to frame and determining motion based on the key points taught by Bovyrin. One of ordinary skill in the art would be motivated to combine the references since it reduces calculation time and increases calibration quality (Bovyrin, Para. 14, teaches the motivation of combination to be to reduce calculation time and increase calibration quality).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Zhao et al. (CN 104765959 A).
Regarding Claim 7, the combination of references of Li in view of Ghyme does not explicitly teach "The method of claim 1, wherein using the Optical Flow to capture object motion further includes determining a movement type of the object motion using movement coloration and a color filter".
In an analogous field of endeavor, Zhao teaches "The method of claim 1, wherein using the Optical Flow to capture object motion further includes determining a movement type of the object motion using movement coloration and a color filter"; (Zhao, Claim 1, teaches performing Gaussian smoothing and performing adaptive filtering by selecting an appropriate threshold for the grayscale image after subtracting the green and blue channel of the image and calculating dense optical flow of two adjacent frames of images after Gaussian filtering in which different colors are used to mark the motion areas and different colors are assigned according to the different speeds, i.e., optical flow further includes determining movement types being movement of different speeds using movement coloration and a color filter).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme by including the capturing of object motion including movement type determined by coloration and a color filter taught by Zhao. One of ordinary skill in the art would be motivated to combine the references since it improves evaluation efficiency (Zhao, Pg. 2 Para. 3, teaches the motivation of combination to be to improve the efficiency of the evaluation).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Zhou et al. (CN 114051973 A).
Regarding Claim 16, the combination of references of Li in view of Ghyme does not explicitly teach “The method of claim 1, further comprising: identifying, via a tag reader, an animal type of the animal; generating, via the system controller using the animal type and the images of the animal captured by the one or more cameras, a body condition scoring (BCS) score for the animal; and determining, via the system controller using the BCS score, a real-time condition of the animal”.
In an analogous field of endeavor, Zhou teaches "The method of claim 1, further comprising: identifying, via a tag reader, an animal type of the animal"; (Zhou, Claim 5, teaches an ear tag reading module for obtaining the identification of the animal, i.e., identify animal type via a tag reader);
"generating, via the system controller using the animal type and the images of the animal captured by the one or more cameras, a body condition scoring (BCS) score for the animal"; (Zhou, Abstract, teaches using the image information of the animal to judge the body condition information of the animal according to the animal type and the body size information, i.e., generate a body condition score for the animal using the animal type and images of the animal being the judged body condition information);
"and determining, via the system controller using the BCS score, a real-time condition of the animal"; (Zhou, Abstract, teaches real-time performance after identifying the body size information of the animal and read ear tag information to finish the adjustment of the fat condition with high efficiency, i.e., determine real-time condition of the animal being the final adjusted fat condition for feeding using the body condition).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme by including the identifying of animal type, determining a body condition, and determining a real-time condition of the animal taught by Zhou. One of ordinary skill in the art would be motivated to combine the references since it increases efficiency and provides intelligent management (Zhou, Background, teaches the motivation of combination to be to increase efficiency and intelligent management).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Breitner (US 20170014052 A1).
Regarding Claim 17, the combination of references of Li in view of Ghyme does not explicitly teach "The method of claim 1, wherein the calculating the object motion using the optical flow includes solving a Lucas-Kanade optical flow equation for a plurality of neighboring pixels in a local neighborhood of each of the pixels using a least squared criterion".
In an analogous field of endeavor, Breitner teaches "The method of claim 1, wherein the calculating the object motion using the optical flow includes solving a Lucas-Kanade optical flow equation for a plurality of neighboring pixels in a local neighborhood of each of the pixels using a least squared criterion"; (Breitner, Para. 31, teaches the Lucas-Kanade method of optical flow between images to be essentially constant in a local neighborhood of the feature under consideration, and solves the basic optical flow equations for all pixels in that neighborhood using the least squares criterion, i.e., calculating optical flow includes solving Lucas-Kanade optical flow for a plurality of neighboring pixels in a local neighborhood using a least squared criterion).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme by including the use of a Lucas-Kanade optical flow equation for neighboring pixels using a least squared criterion taught by Breitner. One of ordinary skill in the art would be motivated to combine the references since it calculates displacement quickly (Breitner, Para. 31, teaches the motivation of combination to be to calculate the displacement between tracking images quickly).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ghyme and Kirsch et al. (US 20190353775 A1).
Regarding Claim 18, the combination of references of Li in view of Ghyme teaches "A farm monitoring system for monitoring an animal on a farm, the farm monitoring system comprising: an interactive graphical user interface (UI) configured to display clips to a user and enable the user to selectively modify the clips"; (Li, Paras. 41 and 46, teaches a cloud-based user interface or dashboard which can perform additional video processing and through which a user can view videos or video clips identified by the system as containing possible instances of abuse or other wrongdoing, i.e., interactive UI configured to display clips to a user and modify the image data through additional processing);
"one or more high-resolution digital video cameras configured to mount in or on a barn of the farm and generate real-time digital images of the animal in or near the barn"; (Li, FIGs. 21-22 and Paras. 4-5, teaches video monitoring at animal care facilities wherein video data from at least one camera is received wherein the camera is mounted in or on a barn of the farm and generates real-time digital images of the animal in or near the barn);
"
"and a system controller connected to the VMS and the interactive graphical UI, the system controller programmed to: detect, using object detection artificial intelligence (AI) to analyze the processed real-time digital images, an object proximal the animal in or near the barn"; (Li, Paras. 4-5 and 53, teaches a video analytics system for detecting animal abuse comprising a controller coupled to receive video data from at least one camera wherein the controller processes successive video frames to identify objects of interest including at least humans and analyze movements of such objects relative to an animal wherein the video analytics system uses a custom deep neural network for detection, i.e., using AI for object detection to analyze the images of an object proximal to the animal);
"determine, using object classification AI to analyze the object in the real-time digital images, the object is a human"; (Li, Paras. 4-5 and 53, teaches a video analytics system for detecting animal abuse comprising a controller coupled to receive video data from at least one camera wherein the controller processes successive video frames to identify objects of interest including at least humans and analyze movements of such objects relative to an animal wherein the video analytics system uses a custom deep neural network for detection, i.e., using AI to analyze the object in the image wherein the object is a human);
"calculate, in response to detecting the human proximal the animal, a displacement vector indicative of a direction and magnitude of human motion of the human using optical flow that evaluates changes in hue and saturation of pixels from frame to frame in the processed real-time digital images"; (Ghyme, Para. 74, teaches a motion map sequence indicates a sequence of motion maps which may be an image acquired by mapping a 2D motion vector field which is the result of calculating motion in all of the pixels between consecutive images by applying optical flow technology onto the color space of Hue, Saturation, and Value HSV wherein this motion map sequence enables identifying objects exhibiting distinct motion by removing a stationary background and detecting a change in the internal structure in each of the objects, i.e., calculate displacement vector being the 2D motion vector field indicative of direction and magnitude of motion using optical flow that evaluates change in hue and saturation of pixels from frame to frame).
"classify, using motion classification AI to interpret the calculated human motion, an action of the human as animal abuse"; (Li, Paras. 4-5 and 7, teaches a video analytics system which uses artificial intelligence or machine learning techniques for analysis for detecting animal abuse comprising a controller coupled to receive video data from at least one camera wherein the controller processes successive video frames to identify objects of interest including at least humans and analyze movements of such objects relative to an animal wherein the analyzed movements meet predetermined criteria for possible abuse of the animal, i.e., classify the calculated human motion as animal abuse);
"and display, via the interactive graphical UI, an incident alert including a video clip containing the action of the human classified as animal abuse"; (Li, Paras. 6-7, teaches outputting information identifying the instance of possible animal abuse via a user interface which allows the user to view video clips showing instances of possible animal abuse based on probability scores, i.e., display an incident alert including a video clip containing the motion of the object classified as harmful or abuse on the user interface).
The proposed combination as well as the motivation for combining the Li and Ghyme references presented in the rejection of Claim 1, applies to claim 18.
However, the combination of references of Li in view of Ghyme does not explicitly teach "a video management server (VMS) connected to the one or more cameras and configured to receive therefrom and process the real-time digital images of the animal".
In an analogous field of endeavor, Kirsch teaches "a video management server (VMS) connected to the one or more cameras and configured to receive therefrom and process the real-time digital images of the animal"; (Kirsch, Paras. 68 and 96, teaches a video management system including a central system or server wherein the system can retrieve a video stream of cameras and can receive or end bounding box information indicating the object, human, or animal, i.e.., video management server connected to cameras to receive and process real-time digital images of the animal).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Li and Ghyme by including the video management server connected to cameras to receive and process real-time images of the animal taught by Kirsch. One of ordinary skill in the art would be motivated to combine the references since it improves object tracking (Kirsch, Para. 68, teaches the motivation of combination to be to improve object tracking).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW STEVEN BUDISALICH whose telephone number is (703)756-5568. The examiner can normally be reached Monday - Friday 8:30am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571) 272-3382. 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.
/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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