DETAILED ACTION
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 10-115, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tuia( “Perspectives in machine learning for wildlife conservation.”, 2022).
Regarding claim 1. Tuia teaches an apparatus comprising: at least one computing device comprising at least one processor and at least one non-transitory storage medium( page4, computer); wherein the at least one non-transitory storage medium includes executable instructions stored thereon; wherein executing the instructions by the at least one processor are to result in processing sensor measurements, including images ( page 4, imagery), from at least one optical sensor array( Page 4 camera traps), of at least one object, with a three-dimensional pose estimation model to produce at least one substantially view-invariant morphometric measurement estimate of the at least one object ( page 8, digital reconstruction (three-dimensional, phenotypical) of the environment the animals live in), wherein the at least one optical sensor array comprises at least one sensor.
Regarding claim 2. Tuia teaches the apparatus of claim 1, wherein the at least one processor is to process the sensor measurements by employment of a validated statistical model to estimate and/or predict physical attributes of the at least one object ( page 8, Models performing accurately and robustly on specific classes (e.g., the MegaDetector - see Box 2 - or AIDE; see Table 1) are now being used routinely and integrated within open systems supporting ecologists performing both labeling and detection).
Regarding claim 3. Tuia teaches the apparatus of claim 2, wherein the at least one object comprises multiple living objects from an object population ( Fig. 3).
Regarding claim 4. Tuia teaches the apparatus of claim 3, wherein t the at least one processor is to process the sensor measurements by estimation and/or prediction of physical attributes for multiple types of the multiple living objects of the object population with a validated statistical model( Fig. 3, Posture estimation tools allow researchers to estimate animal postures, which can then be used to infer behaviors using clustering algorithms).
Regarding claim 5. Tuia teaches the apparatus of claim 4, wherein the at least one processor is to estimate and/or predict the physical attributes for the multiple types of the multiple living objects of the object population by processing to report an overall health( page 10, The 3D shape of an individual can be related to its health, age, or reproductive status), demographic and/or growth status of the object population.
Regarding claim 6. Tuia teaches the apparatus of claim 1, wherein the at least one processor is to process the sensor measurements by processing with a trained machine learning process to produce the at least one substantially view-invariant three-dimensional morphometric measurement estimate( Fig. 6, open source AI for wildlife conservation is the Microsoft AI for Earth MegaDetector36 (Fig. 6). This generic, globalscale human, animal, and vehicle detection model works off-the-shelf for most camera trap data … to maintain a stable population of protected wolves).
Regarding claim 7. Tuia teaches the apparatus of claim 6, wherein the trained machine learning process comprises a trained deep convolutional neural network ( Fig. 6, MegaDetector, which uses R-CNN /YOLO) .
Regarding claim 11, Tuia teaches the apparatus of claim 10, wherein the at least one physical support platform comprises a movable platform( Page 6, sensors mounted on moving platforms such as drones, aircraft, or satellites).
Regarding claim 12, Tuia teaches the apparatus of claim 11, wherein the movable platform comprises an aerial vehicle(Page 6, sensors mounted on moving platforms such as drones, aircraft), a water vehicle, an underwater vehicle or any combinations thereof.
Regarding claim 13, Tuia teaches the apparatus of claim 10, wherein the external system comprises at least one server in a cloud ( Fig. 4, Image analysis Server).
Claims 14-15, 19 recite the article for the method in claims 1-7, 11-13. Since Tuia also teaches an article ( page4, computer), claims 14-15, 19 are also rejected.
Claim 20 recites a similar metho to the method in claims 1-7, 11-13, thus is also rejected.
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.
Claim(s) 8-9, 16, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tuia in view of Marshall ( US 20230196839).
Regarding claim 8, Tuia teaches the apparatus of claim 1, wherein the at least one processor is to process the sensor measurements including processing images, from the at least one optical sensor array ( Fig. 6).
Tuia does not expressly teach performing image rectification and image triangulation.
However, Marshall teaches performing image rectification and image triangulation ( [0123], the animal subject’s position in 3D space may be triangulated based on its position within each video frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tuia and Marshall, by following Marshall’s teaching to perform triangulation with multiple cameras in Tuia, with motivation of “ performing long-term kinematic tracking of an animal subject” ( Marshall, Abstract)
Regarding claim 9, Tuia teaches the apparatus of claim 1.
Tuia does not expressly teach wherein the at least one processor is to process the sensor measurements by performing correction and/or calibration of the sensor measurements.
However, Marshall teaches is to process the sensor measurements by performing correction and/or calibration of the sensor measurements ( [00012], camera’s radial and tangential distortion coefficients, respectively. These parameters are fit by a calibration procedure done prior to data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tuia and Marshall, by following Marshall’s teaching to perform camera calibration for the cameras in Tuia, with motivation of “ performing long-term kinematic tracking of an animal subject” ( Marshall, Abstract)
Claims 16, 18 recite the article for the method in claims 8-9. Since Tuia also teaches an article ( page4, computer), claims 16 and 18 are also rejected.
Claim(s) 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tuia in view of Figueiredo ( US 20230237850).
Regarding claim 10, Tuia teaches the apparatus of claim 1, further comprising at least one physical support platform; wherein the at least one optical sensor array is physically integrated with the at least one physical support platform, and wherein the at least one processor is to transport the sensor measurements to an external system to perform the sensor measurement processing( Page 4 camera traps; Page 6, sensors mounted on moving platforms such as drones, aircraft, or satellites).
Tuia does not expressly teach
wherein the at least one optical sensor array includes a stereoscopic camera.
However,
Figueiredo teaches a stereoscopic camera ( Fig. 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Tuia and Figueiredo, by substituting one camera in Tuia with a stereoscopic camera taught by Figueiredo, with motivation of “ enforcement of certain actions by objects or with respect to objects, surveillance of objects or with respect to objects, or other situations involving analyzing behaviors of objects or with respect to objects” ( Figueiredo, Abstract)
Claims 17 recites the article for the method similar in claim 10. Since Tuia also teaches an article ( page4, computer), claims 17 is also rejected.
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
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