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 .
DETAILED ACTION
This office action is in response to the claims filed on 06/03/2022.
Claims 1-12 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) filed 00/00/0000 is in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 5, 9 are objected to because of the following informalities:
Claim 1 recites “and the second state indexed by the first position and the first state.” at line 6 is objected, because this claim limitation is duplicated with the claim limitation “and the second state indexed by the first position and the first state.” at line 5. Therefore, one of the limitations “and the second state indexed by the first position and the first state.” should be removed.
Claim 5 recites “and the second state indexed by the first position and the first state.” at line 7 is objected, because this claim limitation is duplicated with the claim limitation “and the second state indexed by the first position and the first state.” at line 8.Therefore, one of the limitations “and the second state indexed by the first position and the first state.” should be removed.
Claim 9 recites “and the second state indexed by the first position and the first state.” at line 7 is objected, because this claim limitation is duplicated with the claim limitation “and the second state indexed by the first position and the first state.” at line 8. Therefore, one of the limitations “and the second state indexed by the first position and the first state.” should be removed.
Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method (claims 1-4), non-transitory computer readable (claims 5-8) and system (claims 9-12). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” and mathematical concept.
The claim 1 recites:
Step 2A: prong 1 analysis:
-“ determining a first observation indexed by a first state and a first position, based on the first state, a second observation indexed by a second position and a second state indexed by the first position and the first state, wherein the second position is in proximity to the first position” this is a mental process, the human mind can determine the first observation based on the first state, second observation represents the first position and first state, and the second state represents the first position and the first state, for example, the human mind can determine the overview of the current condition/situation of the vehicle based on the first state (at the particular speed) and the first position (current weather condition), (observation/evaluation)
“wherein the second observation was previously determined” this is a mental process, the human mind can determine the second observation /Overview of the current condition, (observation/Evaluation).
“and wherein the second state was previously determined” this is a mental process, the human mind can determine the second state as what is current condition weather or what is the speed of the vehicle, (observation/evaluation).
Step 2A Prong 2 analysis:
-“by dynamic programming” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
- and returning a result indicating the first observation.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data outputting. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data outputting to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
Step 2B analysis:
-“by dynamic programming” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“and returning a result indicating the first observation.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data outputting. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data outputting to a judicial exception do not amount to significantly more than the judicial exception itself.
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 2 recites:
Step 2A: prong 1 analysis:
-“ determining the first observation is based on a multivariate Gaussian distribution comprising a mean vector and a covariance matrix” this is mathematic equation, (mathematical concept)
Step 2A: Prong 2 analysis and Step 2B analysis:
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 3 recites:
Step 2A: Prong 2 analysis:
-“ wherein the mean vector and covariance matrix are learned from training data comprising at least two observations” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B analysis:
-“ wherein the mean vector and covariance matrix are learned from training data comprising at least two observations” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 4 recites:
Step 2A: Prong 2 analysis:
-“ wherein the mean vector and the covariance matrix are learned from training data comprising a first one-hot representation of the first state and a second one-hot representation of the second state.” This/these limitations is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B analysis:
-“ wherein the mean vector and the covariance matrix are learned from training data comprising a first one-hot representation of the first state and a second one-hot representation of the second state.” This/these limitations is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
The claim 5 is rejected for the same reason as the claim 1, since these claims recite the same limitation.
Additionally, the claim 5 recites the additional limitation:
Step 2A: Prong 2 analysis:
“ One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for facilitating forecasting” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
b) Step 2B analysis:
“ One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for facilitating forecasting” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
Regarding claims 6-8 they recited the same limitations as the claims 2-4 and are rejected on the same basis. These claims further recite: the additional limitation:
Step 2A: Prong 2 analysis:
“one or more non-transitory computer-readable storage media” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
b) Step 2B analysis:
“ one or more non-transitory computer-readable storage media” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
Regarding claims 9-12 they recited the same limitations as the claims 1-4 and are rejected on the same basis. These claims further recite: the additional limitation:
Step 2A: Prong 2 analysis:
“one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations for 4 facilitating forecasting” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
b) Step 2B analysis:
“one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations for 4 facilitating forecasting” This/these limitation is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (non-transitory computer readable storage media, computer) to perform the mental process (forecasting) (See MPEP 2106.05(f)).
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, 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.
Claims 1, 5, 9 are rejected under 35 U.S.C. 103 as being unpatentable over McMahon et al. (Pub. No. US 7414542 -hereinafter, McMahon) and further in view of BANIJAMAL et al. (PUB. No. US 20210081843– hereinafter, BANIJAMAL).
Regarding claim 1, McMahon teaches a computer-implemented method for facilitating forecasting comprising: determining a first observation indexed by a first state and a first position, based on the first state (McMahon, [Col.1, lines 40-60], “The processor determines a portion of a route for a vehicle based upon the status of the traffic restrictor... Traffic restrictors may include a traffic light (having a color associated with a particular direction), a weather condition, or a road hazard. Wireless receivers may be located within a vehicle. Positioning systems, such as global positioning system (GPS) locators, may be used to determine positions for traffic restrictors and/or vehicles, and may further be used to determine a vehicle's heading and speed. Methods for determining a route may include steps such as determining that a particular route requires a change in the status of a traffic restrictor and communicating a wireless request for the status change. Methods for signaling traffic flow information may further include receiving a request to change the status of the traffic restrictor and changing the status of the traffic restrictor in response to the request.,” Examiner’s note, determining the portion of route (first observation) based on the position of the vehicle (first position) and the traffic light (first state).)
and returning a result indicating the first observation (McMahon, [Col.3, lines 30-56], “In one example of the operation of the traffic signaling system 100, an emergency response is triggered by a notification that there is an emergency at destination 118. In response to the notification, the ambulance 114 is dispatched from a hospital 114, and the fire truck 116 is dispatched from a fire station 117. The ambulance 114 and the fire truck 116 receive the wireless signals 122 from the central station 102 and from the signaling station 104c that indicate the status of traffic control devices 106. Based on the traffic flow information thus received, the emergency vehicles 114 and 116 may determine availability to respond to an emergency and to select a suitable route to the destination 118. Furthermore, commands may be sent to the traffic flow control devices 106 to control their respective status. Thus, for example, if the fire truck 116 determines that the status of the traffic light 106a will interfere with the fire truck 116 reaching the destination 118 by slowing or stopping the fire truck's progress, then the fire truck 116 may send a command 124 to the signaling station 104a instructing the signaling station 104a to change the status of the traffic light 104a. In other implementations, such requests may be managed and coordinated by the central station 102. Similarly, the ambulance 114 may detect the status change in the traffic light 106a and may take a route that has a traffic flow that is not impeded by the traffic light 106a, so as not to delay the progress of the fire truck 116 to the destination 118..” Examiner’s note, the selected route is considered as the result to indicate the first observation, for example, the different route is selected based on the determining the portion of the route (traffic light on the route).).
However, McMahon does not teach a second observation indexed by a second position and a second state indexed by the first position and the first state, wherein the second position is in proximity to the first position, wherein the second observation was previously determined by dynamic programming, and wherein the second state was previously determined by dynamic programming;
On the other hand, BANIJAMAL teaches a second observation indexed by a second position and a second state indexed by the first position and the first state (BANIJAMAL, Par.0008, “each observation for a given time step comprising a respective view of an environment of the autonomous vehicle and a vehicle state at the given time step; receiving a current action performed by the autonomous vehicle at the current time step” and [Par.0048], “The action-based prediction subsystem 305 receives as input a set of observations about the environment 100 and the vehicle 105 (denoted as O.sub.1:t) including a current observation Ô.sub.t at time step t and previous observations. Also received as input is the current action at that is executed by the vehicle 105 at time step t. From this input, the action-based prediction subsystem 305 generates a predicted future observation Ô.sub.t+1 for a future time step t+1. The current action at and the current observation O.sub.t are received by an embedding module 201, which includes the measurements estimator module 202 and the action embedding module 204. Although specific modules 202, 204 are described and shown in FIG. 3, in some examples there may not be separate modules 202, 204 in the embedding module 201.” Examiner’s note, the future observation is generated based on the previous observation (wherein, the previous observation includes the first view of vehicle and the first state of the vehicle.),
wherein the second position is in proximity to the first position (BANIJAMAL, [Par.0049], “The measurements estimator module 202 receives the current action at of the vehicle 105, and the current state of the vehicle 105 extracted from the current observation O.sub.t. In this example, the current state of the vehicle 105 includes the current position and current velocity of the vehicle 105 (p.sub.t, v.sub.t) among other state parameters. In other examples, other state parameters (e.g., linear or angular acceleration) may additionally or alternatively be included in the current state of the vehicle. The measurements estimator module 202 then computes (e.g., using defined rules based on the known dynamics of the vehicle 105) an estimated change in the state of the vehicle 105 (denoted as (Δp.sub.t+1, Δv.sub.t+1)) at the next time step t+1. For example, if the current action at of the vehicle 105 is a given acceleration of the vehicle 105, the measurements estimator module 202 may compute the estimated change in position and velocity of the vehicle 105 using, as the defined rules, known kinematic formulas.” Examiner’s note, determining the changing state of the vehicle from first position to second potion is corresponding to the second position proximity first position,)
wherein the second observation was previously determined by dynamic programming (BANIJAMA, [Par.0012, 0073], “[0012], In any of the examples, the estimated change in vehicle state caused by the current action may be computed using defined rules based on known dynamics of the autonomous vehicle.” And “0073At 510, the predicted view is re-centered by the re-centering module 206. The re-centering module 206 provides the re-centered predicted view Î.sub.t+1 (e.g., represented as an OGM). The re-centered OGM Î.sub.t+1 is received by the observation constructor 210. The observation constructor 210 also receives the estimated changed to the state of the vehicle 105 (e.g., estimated future position and velocity) ({circumflex over (p)}.sub.t+1, {circumflex over (v)}.sub.t+1), to construct the predicted future observation Ô.sub.t+1 (e.g., by concatenating the re-centered predicted OGM Î.sub.t+1 with the estimated future state ({circumflex over (p)}.sub.1+1, {circumflex over (v)}.sub.t+1)). The predicted future observation Ô.sub.t+1 is fed back to the input of the action-based prediction subsystem 305, as input for multi-step training (e.g., the predicted future observation Ô.sub.t+1 is added to the set of observations for a further training iteration).” Examiner’s note, the predicted observation is feedback to the input is corresponding to the second observation was previously determined by dynamic programming),
and wherein the second state was previously determined by dynamic programming (BANIJAMA, [Par.0012, 0049-0059], “[0012], In any of the examples, the estimated change in vehicle state caused by the current action may be computed using defined rules based on known dynamics of the autonomous vehicle.” And [0049]The measurements estimator module 202 receives the current action at of the vehicle 105, and the current state of the vehicle 105 extracted from the current observation O.sub.t. In this example, the current state of the vehicle 105 includes the current position and current velocity of the vehicle 105 (p.sub.t, v.sub.t) among other state parameters. In other examples, other state parameters (e.g., linear or angular acceleration) may additionally or alternatively be included in the current state of the vehicle. The measurements estimator module 202 then computes (e.g., using defined rules based on the known dynamics of the vehicle 105) an estimated change in the state of the vehicle 105 (denoted as (Δp.sub.t+1, Δv.sub.t+1)) at the next time step t+1. For example, if the current action at of the vehicle 105 is a given acceleration of the vehicle 105, the measurements estimator module 202 may compute the estimated change in position and velocity of the vehicle 105 using, as the defined rules, known kinematic formulas. [0050] ... The action embedding module 204 also receives the estimated state change (Δp.sub.t+1, Δv.sub.t+1) from the measurements estimator module 202. The action embedding module 204 deterministically updates the view of the environment 100 to produce a current-action embedded view, denoted as I.sub.at, that reflects the estimated change in the state of the ego-vehicle 105 (Δp.sub.t+1, Δv.sub.t+1)” Examiner’s note, changed state of the vehicle from one first position to second position is determined by the measurements estimator module 202, that is corresponding to the second state was previously determined by dynamic programming).;
Mchamon and BANIJAMA are analogous in arts because they have the same field of endeavor of determining the observations.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the determining a first observation indexed by a first state and a first position, based on the first state
and returning a result indicating the first observation, as taught by Mchamon to include the second observation indexed by a second position and a second state indexed by the first position and the first state, wherein the second position is in proximity to the first position, wherein the second observation was previously determined by dynamic programming, and wherein the second state was previously determined by dynamic programming, as taught by BANIJAMA. The modification would have been obvious because one of the ordinary skills in art would be motivated to accurately predict the future observation, (BANIJAMA, [Par.0005], “In most conventional action-based prediction models, an action is directly fed to the model in order to generate an observation prediction. When there is insufficient training data for certain rare states, the prediction error is high. Therefore, it would be difficult for a conventional action-based prediction model to accurately predict the effect of some rare actions on future observations.”).
The claim 5 is rejected for the same reason as the claim 1, since these claims recite the same limitation.
The claim 9 is rejected for the same reason as the claim 1, since these claims recite the same limitation.
Claims 2, 3, 6, 7, 10, 11 are rejected under 35 U.S.C. 103 as being unpatentable over McMahon et al. (Pub. No. US 7414542 -hereinafter, McMahon) and further in view of BANIJAMAL et al. (PUB. No. US 20210081843– hereinafter, BANIJAMAL) and further in view of Zhu et al. (PUB. No. US 20080273752– hereinafter, Zhu).
Regarding claim 2, McMahon teaches wherein determining the first observation (McMahon, [Col.1, lines 40-60], “The processor determines a portion of a route for a vehicle based upon the status of the traffic restrictor... Traffic restrictors may include a traffic light (having a color associated with a particular direction), a weather condition, or a road hazard. Wireless receivers may be located within a vehicle. Positioning systems, such as global positioning system (GPS) locators, may be used to determine positions for traffic restrictors and/or vehicles, and may further be used to determine a vehicle's heading and speed. Methods for determining a route may include steps such as determining that a particular route requires a change in the status of a traffic restrictor and communicating a wireless request for the status change. Methods for signaling traffic flow information may further include receiving a request to change the status of the traffic restrictor and changing the status of the traffic restrictor in response to the request.,” Examiner’s note, determining the portion of route (first observation) based on the position of the vehicle (first position) and the traffic light (first state).)
However, McMahon does not teach determining the first observation is based on a multivariate Gaussian distribution comprising a mean vector and a covariance matrix.
On the other hand, Zhu teaches determining the first observation is based on a multivariate Gaussian distribution comprising a mean vector and a covariance matrix (Zhu, [par.0095], “In kernel-based probabilistic shape tracking (KPSTracker), the feature point in the shape template may be denoted as {Y.sub.i=y.sub.i, G.sub.i}, where y.sub.i denotes the image coordinate of the feature point and G.sub.i denotes the normalized gradient vector of the feature point. The feature points in an image frame may be denoted as {Z.sub.i=z.sub.i, g.sub.i}, where z.sub.i and g.sub.i denote the image coordinate and normalized gradient vector of the feature point respectively. A probabilistic model describing the feature point distribution in the image data may be defined by kernel-based representation:
p D ( Z | { Z j } = j k ( Z ; Z j , .LAMBDA. ) = j exp ( - 1 2 .sigma. z 2 z - z j 2 ) exp ( - 1 2 .sigma. g 2 g - g j 2 ) ( 9 ) ##EQU00003##
Where k(Z; Z.sub.j, .LAMBDA.) denotes a Gaussian function with mean Z.sub.j and covariance matrix .LAMBDA. = [ .sigma. z 2 0 0 .sigma. g . 2 ] , ##EQU00004## .sigma..sub.z denotes the bandwidth of a Gaussian kernel for the image location of feature points, and .sigma..sub.g denotes the bandwidth of a Gaussian kernel for the gradient vector of feature points. It may be assumed that the rear side of a vehicle is approximately planar, and the vehicle is moving either along the optical axis or laterally, for example, no significant turning is considered. “Examiner’s note, the feature point distribution in the image data is considered as the first observation. )
. Mchamon and Zhu are analogous in arts because they have the same field of endeavor of determining the observations.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the determining a first observation, as taught by Mchamon to include the determining the first observation is based on a multivariate Gaussian distribution comprising a mean vector and a covariance matrix, as taught by Zhu. The modification would have been obvious because one of the ordinary skills in art would be motivated to optimize the objection function, (ZHu, [Par.0099], “The iterative procedure for optimizing the objective function may achieve sub-pixel accuracy. However it may be computationally expensive and slow. To speed up the matching process, a coarse-to-fine approach may be adopted where two levels of matching are performed.”).
Regarding claim 3, McHamon as modified in view of BANIJAMA teaches the method of claim 2, a training data comprising at least two observations (BANIJAMAL, Par.0008, “The method includes: receiving a set of observations, the set of observations including a current observation for a current time step and one or more previous observations, each observation for a given time step comprising a respective view of an environment of the autonomous vehicle and a vehicle state at the given time step; receiving a current action performed by the autonomous vehicle at the current time step” and [par.0044], “The view of the environment 100, denoted as I.sub.t, may be represented in the form of an image, or an occupancy grid map (OGM), for example. An observation (denoted by O.sub.t) also includes information about the state of the vehicle 105 (i.e. vehicle state), such as a position of the vehicle 105 (denoted by p.sub.t), and a velocity of the vehicle 105 (denoted by v.sub.t). In other words, an observation O.sub.t=(I.sub.t, p.sub.t, v.sub.t), where t denotes a current time step.”).
Mchamon and BANIJAMA are analogous in arts because they have the same field of endeavor of determining the observations.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the determining a first observation, as taught by Mchamon to include the training data comprising at least two observations, as taught by BANIJAMA. The modification would have been obvious because one of the ordinary skills in art would be motivated to accurately predict the future observation, (BANIJAMA, [Par.0005], “In most conventional action-based prediction models, an action is directly fed to the model in order to generate an observation prediction. When there is insufficient training data for certain rare states, the prediction error is high. Therefore, it would be difficult for a conventional action-based prediction model to accurately predict the effect of some rare actions on future observations.”).
However, neither Mchamon nor BANIJAMA teaches wherein the mean vector and covariance matrix are learned from the training data,
On the other hand, Zhu teaches wherein the mean vector and covariance matrix are learned from the training data (Zhu, [par.0042], “. Detection and tracking may be performed by matching acquired video data against a set of known vehicle detectors. A vehicle detector is a set of features indicative of a particular class of vehicles and thus the particular class of vehicle may be identified within the video image by matching an aspect of the video image against the set of known vehicle identifiers. The vehicle identifiers may be trained off-line using training data from video images.” And [par.0095], “In kernel-based probabilistic shape tracking (KPSTracker), the feature point in the shape template may be denoted as {Y.sub.i=y.sub.i, G.sub.i}, where y.sub.i denotes the image coordinate of the feature point and G.sub.i denotes the normalized gradient vector of the feature point. The feature points in an image frame may be denoted as {Z.sub.i=z.sub.i, g.sub.i}, where z.sub.i and g.sub.i denote the image coordinate and normalized gradient vector of the feature point respectively. A probabilistic model describing the feature point distribution in the image data may be defined by kernel-based representation:
p D ( Z | { Z j } = j k ( Z ; Z j , .LAMBDA. ) = j exp ( - 1 2 .sigma. z 2 z - z j 2 ) exp ( - 1 2 .sigma. g 2 g - g j 2 ) ( 9 ) ##EQU00003##
Where k(Z; Z.sub.j, .LAMBDA.) denotes a Gaussian function with mean Z.sub.j and covariance matrix .LAMBDA. = [ .sigma. z 2 0 0 .sigma. g . 2 ] , ##EQU00004## .sigma..sub.z denotes the bandwidth of a Gaussian kernel for the image location of feature points, and .sigma..sub.g denotes the bandwidth of a Gaussian kernel for the gradient vector of feature points. It may be assumed that the rear side of a vehicle is approximately planar, and the vehicle is moving either along the optical axis or laterally, for example, no significant turning is considered.
..” Examiner’s note, the image data is considered as the training data is used to generate the distribute feature),
Mchamon, BANIJAMA and Zhu are analogous in arts because they have the same field of endeavor of determining the observations.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of McHamon and Banijama teaching of a training data comprising at least two observations, as set forth above, to include the mean vector and covariance matrix are learned from 3 training data, as taught by zhu. The modification would have been obvious because one of the ordinary skills in art would be motivated to optimize the objection function, (ZHu, [Par.0099], “The iterative procedure for optimizing the objective function may achieve sub-pixel accuracy. However it may be computationally expensive and slow. To speed up the matching process, a coarse-to-fine approach may be adopted where two levels of matching are performed.”).
The claim 6 is rejected for the same reason as the claim 2, since these claims recite the same limitation.
The claim 7 is rejected for the same reason as the claim 3, since these claims recite the same limitation.
The claim 10 is rejected for the same reason as the claim 2, since these claims recite the same limitation.
The claim 11 is rejected for the same reason as the claim 3, since these claims recite the same limitation.
Claims 4, 8, 12 are rejected under 35 U.S.C. 103 as being unpatentable over McMahon et al. (Pub. No. US 7414542 -hereinafter, McMahon) and further in view of BANIJAMAL et al. (PUB. No. US 20210081843– hereinafter, BANIJAMAL) and further in view of Zhu et al. (PUB. No. US 20080273752– hereinafter, Zhu).
Regarding claim 4, Mchamon, as modified in view of Zhu teach the method of claim 3, 2 wherein the mean vector and the covariance matrix are learned from training data (Zhu, [Par.0042], “. Detection and tracking may be performed by matching acquired video data against a set of known vehicle detectors. A vehicle detector is a set of features indicative of a particular class of vehicles and thus the particular class of vehicle may be identified within the video image by matching an aspect of the video image against the set of known vehicle identifiers. The vehicle identifiers may be trained off-line using training data from video images.” And [par.0095], “In kernel-based probabilistic shape tracking (KPSTracker), the feature point in the shape template may be denoted as {Y.sub.i=y.sub.i, G.sub.i}, where y.sub.i denotes the image coordinate of the feature point and G.sub.i denotes the normalized gradient vector of the feature point. The feature points in an image frame may be denoted as {Z.sub.i=z.sub.i, g.sub.i}, where z.sub.i and g.sub.i denote the image coordinate and normalized gradient vector of the feature point respectively. A probabilistic model describing the feature point distribution in the image data may be defined by kernel-based representation:
p D ( Z | { Z j } = j k ( Z ; Z j , .LAMBDA. ) = j exp ( - 1 2 .sigma. z 2 z - z j 2 ) exp ( - 1 2 .sigma. g 2 g - g j 2 ) ( 9 ) ##EQU00003##
Where k(Z; Z.sub.j, .LAMBDA.) denotes a Gaussian function with mean Z.sub.j and covariance matrix .LAMBDA. = [ .sigma. z 2 0 0 .sigma. g . 2 ] , ##EQU00004## .sigma..sub.z denotes the bandwidth of a Gaussian kernel for the image location of feature points, and .sigma..sub.g denotes the bandwidth of a Gaussian kernel for the gradient vector of feature points. It may be assumed that the rear side of a vehicle is approximately planar, and the vehicle is moving either along the optical axis or laterally, for example, no significant turning is considered. .” Examiner’s note, the image data is considered as the training data is used to generate the distribute feature),
Mchamon, BANIJAMA and Zhu are analogous in arts because they have the same field of endeavor of determining the observations.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the observation determinations, as taught by Mchamon, to include the mean vector and covariance matrix are learned from the training data, as taught by zhu. The modification would have been obvious because one of the ordinary skills in art would be motivated to optimize the objection function, (ZHu, [Par.0099], “The iterative procedure for optimizing the objective function may achieve sub-pixel accuracy. However it may be computationally expensive and slow. To speed up the matching process, a coarse-to-fine approach may be adopted where two levels of matching are performed.”).
However, neither Mchamon nor Zhu teaches training data comprising a first one-hot representation of the first state and a second one-hot representation of the second state
On the other hand, Van teaches training data comprising a first one-hot representation of the first state and a second one-hot representation of the second state (Van, [0304], “The input vector represents an input concept from a training sequence, i.e. consultation, represented using one-hot encoding. The entry in the vector corresponding to the input concept from the sequence is given a value of 1, with the other entries having a value of 0. For a sequence in the training data, a sliding window may be used to extract the target and context concepts. For the case where the first concept in a sequence from the training data is “headache”, the first input vector comprises a 1 in the position corresponding to the concept “headache”, and a 0 in all other positions, as shown in FIG. 7(b).”
Mchamon, Zhu and Van are analogous in arts because they have the same field of endeavor of generating the training data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of Mchamon and Zhu the teaching of wherein the mean vector and the covariance matrix are learned from training data , as set forth above, to include the training data comprising a first one-hot representation of the first state and a second one-hot representation of the second state, as taught by Van. The modification would have been obvious because one of the ordinary skills in art would be motivated to calculated efficiently for each input concept, (Van, [Par.0372], ““The concept embeddings are generated during the training stage and stored in memory, the relevance module 201 may comprise a storage unit in which the concept embeddings are stored. The measure of relevance can therefore be calculated efficiently for each input concept. The concept embeddings are pre-computed, so the relevance module can be scaled horizontally for efficiency and reliability.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure is provide below.
Qiu et al. (Pub. No.:us20220092415-hereinafter, Qiu) teaches the predicting movement of at least one traffic-related object based on observations of the surroundings of the object.
Janjos et al. (Pub. No.:us 20240282190- -hereinafter, Janjos) teaches training the model based on the training data set comprising the plurality observation vector.
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/E.T./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128