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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Regarding dependent claim 18, the claim recites wherein the system of claim 8 is comprised in at least one of a number of systems for various purposes, (i.e., for “conversational AI operations”, “generating synthetic data”, “using cloud computing resources”, etc.). “An [] intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F .3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often … appear in the claim’s preamble,” a statement of intended use or purpose an appear elsewhere in a claim. In re Stencel, 828 F .2d 751, 754 (Fed. Cir. 1987).
Here, the purpose of the various systems are non-limiting. That is, claim 18 fails to provide any limitations such as particular structures that distinguish what the claim systems are as opposed to what they are intended for. Thus claim 18 is directed to the system of claim 8 comprised in at least one of a number of other systems. There being no patentably distinguishing characteristic between the system of claim 8 and the systems in which it may be comprised, claim 18 fails to further limit claim 8.
Accordingly, claim 20 is rejected under 35 U.S.C. § 112(d) as being of improper dependent form for failing to further limit the subject matter of claim 19, from which claim 20 depends on.
Regarding dependent claim 20, the claim recites wherein the system of claim 19 is comprised in at least one of a number of systems for various purposes, (i.e., for “conversational AI operations”, “generating synthetic data”, “using cloud computing resources”, etc.). “An [] intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F .3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often … appear in the claim’s preamble,” a statement of intended use or purpose an appear elsewhere in a claim. In re Stencel, 828 F .2d 751, 754 (Fed. Cir. 1987).
Here, the purpose of the various systems are non-limiting. That is, claim 20 fails to provide any limitations such as particular structures that distinguish what the claim systems are as opposed to what they are intended for. Thus claim 20 is directed to the system of claim 19 comprised in at least one of a number of other systems. There being no patentably distinguishing characteristic between the system of claim 19 and the systems in which it may be comprised, claim 20 fails to further limit claim 19.
Accordingly, claim 20 is rejected under 35 U.S.C. § 112(d) as being of improper dependent form for failing to further limit the subject matter of claim 19, from which claim 20 depends on.
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 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.
Claim(s) 1 – 3, 6 – 12, and 15 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by John; Carter et al. (US 12497077 B2; hereinafter simply referred to as Carter).
Regarding independent claim 1, Carter teaches:
A method comprising determining, based at least on a classification associated with an object, an uncertainty matrix associated with the object (See col 5, Lines 49 – 60, Col 12, Lines 1 – 10, Figure 1, wherein a classification (object classification/unique identifier) indicates that the data is related to the object (vehicle ‘122’ in figure 1), including the uncertainty matrix (uncertainty in the form of a matrix)).
determining, based at least on a current state associated with the object at a first time, a predicted state associated with the object at a second time; (See Col 2 Lines 36 – 59, Col 11 Lines 37 – 59, Col 14 Lines 20 – 37, Col 21 Lines 62 – 66, Fig 3A and 4, wherein a predicted state (‘402’ in figure 4 / ‘314’ In figure 3A) is associated with the object at a second time (next time step) based on a current state (prior predicted state ‘306’ in figure 3))
determining, based at least on sensor data, a measured state associated with the object at the second time; (Col 14 Lines 20 – 37, Col 16 Lines 29 – 62, Col 7 Lines 44 – 62, Fig 3B and 4, 5A, wherein an updated measurement, ‘406’ in figure 4 / ‘324’ in figure 3 (measured state associated with object at second time) is based on sensor data)
determining, based at least on the uncertainty matrix, the predicted state, and the measured state, an estimate of an actual state associated with the object at the second time; and (See Col 15 Lines 24 – 50, Figure 4, wherein the predicted uncertainty, ‘404’ in figure 4 (uncertainty matrix) predicted state, ‘402’ in figure 404, and the updated measurement, ‘406’ in figure 4 (measured state) are used to obtain an estimate of an actual state associated with the object at the second time (estimated state ‘420’ in figure 4)).
performing one or more operations of a machine based at least on the estimate of the actual state at the second time. (See Col 5 Lines 61 – 67, Col 6 Lines 1 – 21, figure 1, wherein one or more operations (operation of autonomous vehicle done by planning component ‘112’ in figure 1) are based on the actual state at the second time (estimated state ‘120’ in figure 1)).
Regarding dependent claim 2, Carter teaches:
Determining, based at least on at least one of the sensor data or second sensor data, one or more classifications associated with one or more points of a sensor representation, the sensor representation representing the object; (See Col 3 Lines 60 – 67, Col 4 Lines 1 – 6, Col 1 lines 60 – 67, Col 5 lines 1 – 60, Figure 1, wherein based on sensor data ‘104’ in figure 1, (such as image or lidar data) a classification (object classification) is associated with one or more points of a sensor representing the object (lidar points, or pixel points of an image representing the object (vehicle ‘122’ in figure 1))).
and determining the classification associated with the object based at least on the one or more classifications (See col 5 Lines 49 – 60 wherein the classification (object classification) is based on one or more previous object detections (each with their own object classifications)).
Regarding dependent claim 3, Carter teaches:
Obtaining information that associates one or more classifications with one or more matrices, (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 - 10 wherein information/data (unique identifier) is used to identify the object and associate the one or more classifications (object classification) with the matrices (uncertainty matrix) associated with the object/vehicle).
wherein the determining the uncertainty matrix associated with the object comprises: determining, based at least on the information, that the classification includes one of the one or more classifications; (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 – 10 wherein the unique identifier (information) is used to identify the object/vehicle and the data associated with it including the one or more classifications)
and determining, based at least on the information, that the classification is associated with the uncertainty matrix from the one or more matrices. (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 – 10 wherein the unique identifier (information) is used to identify the object/vehicle and the data associated with it including the one or more matrices (uncertainty matrix)).
Regarding dependent claim 6, Carter teaches:
The uncertainty matrix is associated with an uncertainty of motion; (See Col 11 Lines 37 – 67 and Col 12 Lines 1 – 11, Figure 3A, wherein the motion model ‘300’, in figure 3A, predicts uncertainty of motion in the form of a matrix (uncertainty matrix))
the method further comprises determining a noise matrix associated with noise for at least one of one or more sensors used to obtain the sensor data, the determining the predicted state, or the determining the measured state (See Col 13 Lines 10 – 42, Figures 3A and 3B, wherein a noise matrix is determined, associated with a bias of sensor data caused by noise, in the determination of the predicted state, ‘314’ in figure 3A, wherein only one of the associations of the noise matrix is required to be taught by the prior art)
and the determining the estimate of the actual state is further based at least on the noise matrix. (See Col 15 lines 24 – 50 wherein the noise matrix is used to determine the actual state (estimated state ’420’ in figure 4) via the use of the noise in the training of the Kalman gain neural network, ’410’ in figure 4 used in the determination of the estimated state).
Regarding dependent claim 7, Carter teaches:
The uncertainty matrix is learned based at least on motion associated with one or more second objects that are also associated with the classification. (See Col 4 Lines 34 – 44, Col 12 Lines 1 – 10, Col 2 Lines 10 – 35, wherein the uncertainty matrix (uncertainty in the form of a matrix) is impacted/based on inaccuracies associated with the environment, (including a plurality of objects detected in the environment such as a second object)).
Regarding independent claim 8, Carter teaches:
A system comprising: one or more processors (See Col 8 Lines 24 – 54 wherein a vehicle computing device ‘204’ in figure 2, comprises a system comprising processors ‘218’ in figure 2).
determine, based at least on a classification associated with an object, an uncertainty of motion associated with the object (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 - 10 wherein the one or more classifications (object classification/unique identifier) are associated with an object/vehicle ‘122’ in figure 1, that is associated with data including the uncertainty of motion (uncertainty matrix) associated with the object/vehicle).
determine, based at least on the uncertainty of motion, a predicted state associated with the object, and a measured state associated with the object, an estimate of an actual state associated with the object (See Col 15 Lines 24 – 50, Figure 4, wherein the predicted uncertainty, ‘404’ in figure 4 (uncertainty of motion) predicted state, ‘402’ in figure 404, and the updated measurement, ‘406’ in figure 4 (measured state) are used to obtain an estimate of an actual state associated with the object (estimated state ‘420’ in figure 4)).
and perform one or more operations of a machine based at least on the estimate of the actual state. (See Col 5 Lines 61 – 67, Col 6 Lines 1 – 21, figure 1, wherein one or more operations (operation of autonomous vehicle done by planning component ‘112’ in figure 1) are based on the actual state (estimated state ‘120’ in figure 1)).
Regarding dependent claim 9, Carter teaches:
Determining, based at least on the classification, a matrix that represents the uncertainty of motion; (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 - 10 wherein the one or more classifications (object classification) are associated with an object, that is associated with data including a matrix (uncertainty in the form of a matrix) representing uncertainty of motion, associated with the object/vehicle).
and the determination of the estimate of the actual state associated with the object is based at least on the matrix, the predicted state, and the measured state. (See Col 15 Lines 24 – 50, Col 11 Lines 60 – 67, Col 12 Lines 1 – 10, Figure 4, wherein the predicted uncertainty, ‘404’ in figure 4 (uncertainty of motion matrix) predicted state, ‘402’ in figure 404, and the updated measurement, ‘406’ in figure 4 (measured state) are used to obtain an estimate of an actual state associated with the object (estimated state ‘420’ in figure 4)).
Regarding dependent claim 10, Carter teaches:
Determine, based at least on a current state associated with the object at a first time the predicted state associated with the object at a second time; (See Col 2 Lines 36 – 59, Col 11 Lines 37 – 59, Col 14 Lines 20 – 37, Col 21 Lines 62 – 66, Fig 3A and 4, wherein a predicted state (‘402’ in figure 4 / ‘314’ In figure 3A) is associated with the object at a second time (next time step) based on a current state (prior predicted state ‘306’ in figure 3))
and determine, based at least on sensor data, the measured state associated with the object at the second time. (Col 14 Lines 20 – 37, Col 16 Lines 29 – 62, Col 7 Lines 44 – 62, Fig 3B and 4, 5A, wherein an updated measurement, ‘406’ in figure 4 / ‘324’ in figure 3 (measured state associated with object at second time) is based on sensor data)
Regarding dependent claim 11, claim 11 is a system claim corresponding to claim 2. Please see the discussion of claim 2 above. Furthermore, Carter teaches of a system comprising one or more processors for object tracking. (See Col 19 Lines 19 – 30 wherein a system is disclosed comprising one or more processors, such as a GPS processor, used in the execution of object tracking).
Regarding dependent claim 12, Carter teaches:
Obtaining information that associates one or more classifications with one or more uncertainties of motion, (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 - 10 wherein information/data (unique identifier) is used to identify the object and associate the one or more classifications (object classification) with the uncertainty of motion (uncertainty of motion in form of matrix/tensor) associated with the object/vehicle).
wherein the uncertainty of motion is further determined based at least on the information (See col 5 Lines 49 – 60, Col 11 Lines 60 – 67, Col 12 Lines 1 - 10 wherein the uncertainty of motion (uncertainty of motion in form of matrix/tensor) is determined based on the information, as the uncertainty is associated with the vehicle/object, ‘122’ in figure 1, which is identified/determined using the information/data (unique identifier)).
Regarding dependent claim 15, Carter teaches:
The uncertainty of motion is associated with a first matrix (See Col 11 Lines 37 – 67 and Col 12 Lines 1 – 11, Figure 3A, wherein the motion model ‘300’, in figure 3A, predicts uncertainty of motion in the form of a matrix (uncertainty matrix)).
determining a second matrix associated with noise for at least one of one or more sensors, the predicted state, or the measured state; (See Col 13 Lines 10 – 42, Figures 3A and 3B, wherein a noise matrix is determined, associated with a bias of sensor data caused by noise, in the determination of the predicted state, ‘314’ in figure 3A, wherein only one of the associations of the noise matrix is required to be taught by the prior art)
and the estimate of the actual state is determined based at least on the first matrix, the second matrix, the predicted state, and the measured state. (See Col 15 lines 24 – 50, figure 4, Col 12, Lines 1 – 10, wherein the noise matrix is used to determine the actual state (estimated state ’420’ in figure 4) via the use of the noise in the training of the Kalman gain neural network, ’410’ in figure 4 used in the determination of the estimated state, alongside the predicted uncertainty, ‘404’ in figure 4 (uncertainty of motion) predicted state, ‘402’ in figure 404, and the updated measurement, ‘406’ in figure 4 (measured state) used to obtain an estimate of an actual state associated with the object (estimated state ‘420’ in figure 4)).
Regarding dependent claim 16, Carter teaches:
The estimate of the actual state is associated with a first time (See Col 2 Lines 36 – 59, Col 11 Lines 37 – 59, wherein the estimate of the actual state (estimated state ‘120’ in figure 1) is associated with a first (current) time )
determine, based at least on the estimate of the actual state, a second predicted state associated with the object at a second time; (See Col 2 Lines 36 – 59 wherein based at least on the estimate of the actual state (uncertainties associated with estimate at current actual state) a second predicted state (predicted next state) at a second time (next time step)).
determine a second measured state associated with the object at the second time; (See Col 2 Lines 36 – 59 wherein a second measured state associated with the object at a second time is determined, being the measurement of the state of the vehicle at the next time step).
and determine, based at least on the uncertainty of motion, the second predicted state, and the second measured state, a second estimate of the actual state associated with the object at the second time. (See Col 15 Lines 24 – 50, Col 2 Lines 36 – 59, Col 12 Lines 1 – 10, Col 11 Lines 37 – 59, Figure 4, 3A wherein the predicted uncertainty, ‘404’ in figure 4 (uncertainty of motion) the second predicted state, ‘402’ in figure 404, and the updated measurement, ‘406’ in figure 4 (second measured state) are used to obtain a second estimate of an actual state associated with the object at the second time (estimated state ‘420’ in figure 4), wherein the process of calculating the estimated state is an iterative process that occurs at multiple time steps repeatedly measuring and using previous, current, and predicted states of the vehicle to determine the estimated state).
Regarding dependent claim 17, Carter teaches:
Determine, based at least on a second classification associated with a second object, (See Col 4 Lines 34 – 44 wherein a classification (classifying of surrounding objects including a second object)), a second uncertainty of motion associated with the second object, (See Col 4 Lines 34 – 44, Col 12 Lines 1 – 10, Col 2 Lines 10 – 35, wherein a second uncertainty of motion, being the uncertainty of motion associated with the surrounding objects, is determined that is needed in the tracking of the surrounding objects) the second uncertainty of motion being different than the uncertainty of motion (See Col 4 Lines 34 – 44, wherein the second uncertainty of motion is associated with the surrounding objects of the environment and is not the same as the uncertainty associated with the vehicle ‘102’ in figure 1).
determine, based at least on the second uncertainty of motion, a second predicted state associated with the second object, and a second measured state associated with the second object, a second estimate of an actual state associated with the second object (See Col 4 Lines 34 – 44, Col 12 Lines 1 – 10, Col 2 Lines 10 – 35, Col 15 Lines 24 – 50, Col 5 Lines 1 – 60, wherein the second predicted uncertainty, ‘404’ in figure 4 (uncertainty of motion) second predicted state, ‘402’ in figure 404, and the second updated measurement, ‘406’ in figure 4 (measured state) are used to obtain a second estimate of an actual state associated with the object at the second time (estimated state ‘420’ in figure 4)) wherein the second predicted state, second uncertainty of motion, second measured state, and second estimated state are part of the tracking of objects done on the plurality of surrounding objects in the environment around the vehicle).
and perform one or more operations of a machine based at least on the estimate of the second actual state. (See Col 5 Lines 60 – 67 and Col 6 Lines 1 – 23, wherein operations (controlling motion of a vehicle) are based on the estimate of the second actual state).
Regarding dependent claim 18, Carter teaches:
The system of claim 8, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system that provides one or more cloud gaming applications; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs);a system for performing operations using one or more vision language models (VLMs); a system for performing operations using one or more multi-modal language models; a system for performing one or more conversational Al operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; systems implementing one or more multi-modal language models; systems using or deploying one or more inference microservices; systems that incorporate or deploy one or more machine learning models in a service or microservice along with an OS-level virtualization package (e.g., a container); a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (See Col 7 Lines 14 – 28, Figure 2, wherein the vehicle ‘202’ in figure 2 implements a cloud computing architecture as a computing device ‘214’ in figure 2).
Regarding independent claim 19, Carter teaches:
An autonomous or semi-autonomous machine comprising: one or more central processing units (CPUs); one or more graphics processing units (GPUs); one or more hardware accelerators; (See Col 3 Lines 38 – 59, Col 8 Lines 28 - 49 wherein an autonomous vehicle comprises a CPU and a GPU, and one or more hardware accelerators (application specific integrated circuits)).
one or more external sensors having fields of view or sensory fields external to the autonomous or semi-autonomous machine; (See Col 7 Lines 39 – 63, wherein external sensors such as cameras are used to capture objects in the surroundings/external of the vehicle).
and one or more internal sensors having fields of view or sensory fields internal to a cabin of the autonomous or semi-autonomous machine, (See Col 7 Lines 39 – 63, wherein internal sensors such as inertial sensors are used that sense information internal to the vehicle such as orientation and acceleration of the vehicle).
wherein the autonomous or semi-autonomous machine performs one or more operations based at least on an actual state of an object perceived based at least on an analysis of sensor data obtained using at least one sensor of the one or more external sensors or the one or more internal sensors, (See Col 5 Lines 59 – 67, Col 6 – 20, Col 14 lines 20 – 37, Figure 1, 3B and 4, wherein based on an estimated state/actual state‘120’ in figure 1, the autonomous vehicle performs one or more operations, such as traversing a route, wherein this estimated state ‘420’ in figure 4, is based on sensor data, updated measurement ‘406’ in figure 4).
the actual state of the object determined based at least on a previously measured state of the object, and a forward-estimated state of the object and one or more uncertainty matrices selected based at least on a classification associated with the object.
(See Col 2 Lines 36 – 59, Col 11 Lines 37 – 59, Col 14 Lines 20 – 37, Col 21 Lines 62 – 66, Col 16 Lines 29 – 62, Col 7 Lines 44 – 62, Col 15 Lines 24 – 50 Fig 1, col 5, Lines 49 – 60, Col 12, Lines 1 – 10 3A, 3B and 4, wherein the actual state of the object is determined based on a previous state (previous object detection and tracking), forwards estimated state of the object, (state of vehicle at next time step) and one or more uncertainty matrices (uncertainty of motion in form of matrix) based on a classification associated with the object (object classification)).
Regarding dependent claim 20, claim 20 is an autonomous machine claim corresponding to claim 18. Please see the discussion of claim 18 above. Furthermore, Carter teaches of a machine comprising one or more processors and a GPU, and a hardware accelerator for an autonomous machine. (See Col 3 Lines 38 – 59, Col 8 Lines 28 - 49 wherein an autonomous vehicle comprises a CPU and a GPU, and one or more hardware accelerators (application specific integrated circuits)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over John; Carter et al. (US 12497077 B2; hereinafter simply referred to as Carter) in view of Furukawa; Hidetoshi et al. (US 20120221273 A1; hereinafter simply referred to as Furukawa).
Regarding dependent claim 4, Carter does not explicitly disclose:
Determining, based at least on the uncertainty matrix, a first weight associated with the predicted state and a second weight associated with the measured state; and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time.
However, Furukawa teaches determining, based at least on the uncertainty matrix, a first weight associated with the predicted state and a second weight associated with the measured state; (See ¶ 119 - 130, wherein a first and second weight are given to the predicted state and measured state (updated state of target based on original state of the target and the state of target based on data from the sensor processing unit respectively) represented by the weighted sum, wherein the variables, being the predicted and measured state, are weighted and added with a weighted covariance matrix (uncertainty matrix) to form a weighted target track output)
and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time. (See ¶ 119 - 130 wherein the estimate of the actual state associated with the object at a second time (target track, used in tracking object over time) output from weighted sum comprising the predicted and measured state (updated state of target) and the covariance matrix, wherein the elements of the weighted sum are weighted).
As taught by Furukawa the estimation of the actual state associate with the object coming from weighted parameters allows for tracking of an object to occur via an object tracking device (See ¶ 122, 130 wherein the estimation of the actual state associated with the object (target track used to acquire combined track) allows for the tracking of an object via an object tracking device). As both the teachings of Carter and Furukawa deal with the technical field of image processing regarding the tracking of objects, 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 teachings of Carter with Furukawa to teach of determining, based at least on the uncertainty matrix, a first weight associated with the predicted state and a second weight associated with the measured state; and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time in order to allow for the tracking of an object.
Regarding dependent claim 13, Carter does not explicitly disclose:
Determining, based at least on the uncertainty of motion, a first weight associated with the predicted state and a second weight associated with the measured state; and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time.
However, Furukawa teaches determining, based at least on the uncertainty of motion, a first weight associated with the predicted state and a second weight associated with the measured state; (See ¶ 119 - 130, wherein a first and second weight are given to the predicted state and measured state (updated state of target based on original state of the target and the state of target based on data from the sensor processing unit respectively) represented by the weighted sum, wherein the variables, being the predicted and measured state, are weighted and added with a weighted covariance matrix (uncertainty of motion) to form a weighted target track output)
and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time. (See ¶ 119 - 130 wherein the estimate of the actual state associated with the object at a second time (target track, used in tracking object over time) output from weighted sum comprising the predicted and measured state (updated state of target) and the covariance matrix, wherein the elements of the weighted sum are weighted).
As taught by Furukawa the estimation of the actual state associate with the object coming from weighted parameters allows for tracking of an object to occur via an object tracking device (See ¶ 122, 130 wherein the estimation of the actual state associated with the object (target track used to acquire combined track) allows for the tracking of an object via an object tracking device). As both the teachings of Carter and Furukawa deal with the technical field of image processing regarding the tracking of objects, 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 teachings of Carter with Furukawa to teach of determining, based at least on the uncertainty matrix, a first weight associated with the predicted state and a second weight associated with the measured state; and determining, based at least on the first weight, the predicted state, the second weight, and the measured state, the estimate of the actual state associated with the object at the second time in order to allow for the tracking of an object.
Allowable Subject Matter
Claims 5 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indications of allowable subject matter:
Regarding claim 5 the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claim 4, further comprising the uncertainty matrix is associated with an uncertainty of motion associated with the object; and one of: the first weight increases and the second weight decreases as the uncertainty of motion decreases; or the first weight decreases and the second weight increases as the uncertainty of motion increases.
Regarding claim 14 the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claim 13, further comprising the first weight increases and the second weight decreases as the uncertainty of motion decreases; or the first weight decreases and the second weight increases as the uncertainty of motion increases.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure and is as follows:
U.S. Patent Application No. US 20160033276 A1 (Bageshwar) discloses the determination of a state of an object based on sensor data, state vectors, and a state covariance matrix, wherein this information is used to track the state of the object over time. (Bageshwar [0042]).
Furthermore please See attached PTO-892.
Conclusion
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/ALEJANDRO HERNANDEZ/ Examiner, Art Unit 2661
/EDWARD PARK/ Primary Examiner, Art Unit 2675
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