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 Status
Claims 1-20 are pending.
Information Disclosure Statement
The IDS filed 06/28/24 and 06/02/25 are considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
"a prediction component" in claim 12 (and by dependency claims 13-16)
“a filtering component” in claim 12 (and by dependency claims 13-16)
“a detection component” in claim 12 (and by dependency claims 13-16)
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-3, 5, 7, 12-13, 15, and 17-19 rejected under 35 U.S.C. 102(a)(1) as being anticipated by LEHMANN (“Recursive Bayesian Filtering for Multitarget Track-Before-Detect in Passive Radars” Hereinafter “LEHMANN”).
Regarding claim 1, LEHMANN teaches a method comprising:
receiving a first set of observations for the region of interest (Page 2461, Fig. 2: Fig 2 shows the region of interest portioned into bins) comprising, for each of a plurality of locations, a probability that a value associated with the location is consistent with a local background of the location (Page 2461, section A: “We associate a target existence variable ei,j k to the delay/frequency bin (i,j). At the discrete instant k, ei,j k =0(resp. ei,j k = 1) corresponds to the absence (resp. presence) of a target in bin (i,j)”. The first set of observations would be observations that define a probability that a value is consistent with the local background (if an object is absent, a value of a space would be consistent with the background, LEHMANN defines this as a probability being value 0 (ei,j k =0) for detected presence in each of the bins, which are different locations in the region), and a second set of observations comprising, for each of the plurality of locations, a probability that the value associated with the location is not consistent with the local background of the location (Page 2464, section A: “Under assumptions 4 and 5, the predicted target existence probability in bin (i,j) can be expanded over the presence of targets in bins (m,n) 2N (i,j)”. The second set of observations would be a probability value when a value is not consistent with the local background (if an object is present, the value would be inconsistent with the local background, this is because the detected object is different than the background. LEHMANN teaches the probability value being a 1 for detected presence in each of the bins, which are different locations in region);
generating a first probability tensor representing probabilities for each of a plurality of states for each of the plurality of locations from the first set of observations and the second set of observations (Page 2461: “Since our objective is to perform Bayesian multitarget detection and tracking, we introduce a suitable state-space representation (see Section III-A). In each bin (to be defined), a discrete-valued random variable models the presence or absence of a target and a continuous-valued random variable models the kinematic state of the target.”. The suitable state-space representation is the first tensor which represents probabilities for each a plurality of states for each a plurality of locations (bins) from the first and second sets of observations), a first state of the plurality of states representing the presence of a target at a location moving at a velocity within a range of velocities (Page 2461, section A: “We associate a target existence variable ei,j k to the delay/frequency bin (i,j). At the discrete instant k, ei,j k =0(resp. ei,j k = 1) corresponds to the absence (resp. presence) of a target in bin (i,j)”. The first state represented in the suitable state-space representation shows when there is a target present (target state details whether or not a target is present in a bin). This target is also moving which is why kinematic state calculations are being used “Collecting all these variables, we obtain the kinematic state vector xk=[aIk,aQk ,bIk,bQk ,tk,vk,ck]T”. The suitable state-space representation contains data for the first state representing the presence of a target, this target is at a location moving at a velocity within a range of velocities (the kinematic state describes the targets movement, the kinematic state vector includes vk which is the velocity vector of the target, and it is a velocity in a range of velocities which can be seen in tables 3 and 4 with the range of differing velocities (Page 2462, first paragraph)), and a second state of the plurality of states representing a state in which no target is present (Page 2461, section A: “We associate a target existence variable ei,j k to the delay/frequency bin (i,j). At the discrete instant k, ei,j k =0(resp. ei,j k = 1) corresponds to the absence (resp. presence) of a target in bin (i,j)”. The second state represented in the suitable state-space representation shows when there is no target present (target state details whether or not a target is present in a bin). The suitable state-space representation acts as a tensor due to representing the bin presence probability and kinematic data probability in a tensor format, which is a matrix of bins containing both the presence or absence of a target and the kinematic state);
updating the first probability tensor according to a second probability tensor representing the probabilities for each of the plurality of states for each of the plurality of locations before receiving the first set of observations and the second set of observations to provide a posterior probability tensor representing probabilities for each of the plurality of states for each of the plurality of locations (Page 2463, section IV: “Since Bayesian filtering is of interest in the present paper, we must calculate the a posteriori target existence probability P(ei,jk =1jy1:k) along with the pdf of an existing target’s kinematic state p(xk jei,j k =1,y1:k), for each bin(i,j)”. In Bayesian filtering, a state estimate (which is a probability tensor representing the probabilities for each of the plurality of states for each of the plurality of locations before receiving the first set of observations and the second set of observations is needed) is used, since they are using Bayesian filtering they have to obtain this second probability tensor (state estimate) and use it to update the first probability tensor to generate a posteriori tensor for the bins “Section IV-A shows how to propagate the a posteriori target existence probability in time for each bin. Similarly, SectionIV-B shows how to propagate the a posteriori pdf of an existing target’s kinematic state in time for each bin” (Page 2463 section IV)); and
determining that a target is present in the region of interest when the posterior probability associated with one of the plurality of states at one of the plurality of locations meets a threshold value (Page 2472, first column, last paragraph: “When
a target crosses the boundary of a frequency bin, occasionally the posterior target existence probability (25) temporarily drops below the 0.5 threshold imposed by the detection rule of Table I. A few (typically one or two) time steps later, the posterior
target existence probability will rise again above the threshold, thanks to the information gathered from future observations”. There is a direct detection threshold defined in table 1 which is 0.5, where if a probability (including posterior) is above it, a target is detected as present and kinematic information is detected).
Regarding claim 2, LEHMANN teaches the method of claim 1, further comprising normalizing the first probability tensor such that, for each of the plurality of locations within the region of interest, the sum of the probabilities across the plurality of states, including the first state and the second state, is equal to one (Page 2461, section A: “We associate a target existence variable ei,j k to the delay/frequency bin (i,j). At the discrete instant k, ei,j k =0(resp. ei,j k = 1) corresponds to the absence (resp. presence) of a target in bin (i,j)”. If a target is absent, its presence probability equals zero, and it says that this is in respect to an object being detected as having a presence probability of 1. This means the probability is normalized, where the combination of absence and presence equals 1, so if the probability of an object being present is 0, the probability of the object not being there is 1).
Regarding claim 3, LEHMANN teaches the method of claim 1, further comprising iteratively repeating the following steps for a plurality of iterations, each of the plurality of iterations occurring at an associated one of a plurality of sequential time steps:
receiving a new set of observations for the region of interest comprising, for each of the plurality of locations, a probability that a value associated with the location is consistent with a local background of the location and a probability that the value associated with the location is not consistent with a local background of the location (Page 2463, section IV: “Our objective is to perform a global surveillance of the entire state-space without omission, at each instant of time”. To perform this at each instance of time, LEHMANN would need to receive new sets of observations for the regions of interest at each time step. This is further supported by the Bayesian filter being recursive “From an implementation point of view, it is desirable to derive this filter in a recursive form” (Page 2463, section IV). The recursive filter works by updating the state of the system by using the previous state estimates and current measurements, which means new measurements are need to be acquired to use alongside the previous state estimates. If a goal is to do this at each instance of time, measurements would be needed for each instance of time and their prior instances);
generating an initial probability tensor representing probabilities for each of the plurality of states for each of the plurality of locations from new set of observations (Page 2463, section IV: “Our objective is to perform a global surveillance of the entire state-space without omission, at each instant of time”. To achieve this, we propose to estimate the
probability that a target is present(which is akin to target detection) along with the corresponding target kinematic state pdf (which is akin to target tracking) in each delay/frequency bin”. Since the goal is to do this at each instance of time, the processing done in claim 1 would be repeated for the subsequent instances of time. So generating new probability tensors with the new observations is necessary. Look to claim 1 for how the process for generating the first probability tensor is mapped);
updating the initial probability tensor according to a posterior probability tensor from a previous time step to provide a posterior probability tensor for a current time step representing probabilities for each of the plurality of states for each of the plurality of locations (Page 2463, section IV: “Our objective is to perform a global surveillance of the entire state-space without omission, at each instant of time”. To achieve this, we propose to estimate the probability that a target is present(which is akin to target detection) along with the corresponding target kinematic state pdf (which is akin to target tracking) in each delay/frequency bin”. Since the goal is to do this at each instance of time, the processing done in claim 1 would be repeated for the subsequent instances of time. So generating new probability tensors with the new observations is necessary. Look to claim 1 for how the process for updating the first probability tensor is mapped); and
determining that a target is present in the region of interest when a posterior probability for the current state and associated with one of the plurality of states at one of the plurality of locations meets the threshold value (Page 2463, section IV: “Our objective is to perform a global surveillance of the entire state-space without omission, at each instant of time”. To achieve this, we propose to estimate the probability that a target is present(which is akin to target detection) along with the corresponding target kinematic state pdf (which is akin to target tracking) in each delay/frequency bin”. Since the goal is to do this at each instance of time, the processing done in claim 1 would be repeated for the subsequent instances of time. So generating new probability tensors with the new observations is necessary. Look to claim 1 for how the process for determining the objects presence is mapped)
Regarding claim 5, LEHMANN teaches the method of claim 1, wherein generating the first probability tensor comprises applying at least one kinematic update matrix, each representing the motion of a target in a state associated with the kinematic update matrix (Page 2461: “Since our objective is to perform Bayesian multitarget detection and tracking, we introduce a suitable state-space representation (see Section III-A). In each bin (to be defined), a discrete-valued random variable models the presence or absence of a target and a continuous-valued random variable models the kinematic state of the target.”. The suitable state-space representation is the first tensor which represents probabilities for each a plurality of states for each a plurality of locations (bins) and a matrix which describes the kinematic state of the object, this updates the understanding of the objects kinematic state), and a transition matrix that defines the likelihood that an object located in a given location of the plurality of locations will end up in a neighboring location of the plurality of locations based on a velocity resolution of the kinematic state, to each of the first set of observations and the second set of observations (Page 2462, Section C: “We begin with the definition of transition probabilities for the discrete-valued target existence variable”. This transition probability is part of the target existence variable, which is part of the suitable state-space representation. The transition probability defines the likelihood that an object located in a given location of the plurality of locations will end up in a neighboring location of the plurality of locations based on a velocity resolution of the kinematic state, to each of the first set of observations and the second set of observations “LetP(i,jjm,n,y1:k¡1) be the probability that a target present in bin (m,n)2N(i,j)n(i,j)at instantk¡1, travels to bin(i,j)at instantk,giveny1:k¡1 and given that it does not die out)” (Page 2462, section C). The whole section C further describes the calculation of the transition probability).
Regarding claim 7, LEHMANN teaches the method of claim 1, wherein
determining that the target is present in the region of interest when the posterior probability associated with the one of the plurality of states at one of the plurality of locations meets a threshold value comprises determining that the target is present when the posterior probability associated with the first state at one of the plurality of locations is not less than a threshold value or the posterior probability at one of the plurality of locations associated with the second state is not greater than a threshold value (Page 2472, first column, last paragraph: “When a target crosses the boundary of a frequency bin, occasionally the posterior target existence probability (25) temporarily drops below the 0.5 threshold imposed by the detection rule of Table I. A few (typically one or two) time steps later, the posterior target existence probability will rise again above the threshold, thanks to the information gathered from future observations”. There is a direct detection threshold defined in table 1 which is 0.5, where if a probability (including posterior) for the first state is above it, a target is detected as present. The same goes for the probability of the second state, if the probability for the first state is above a certain value (presence probability) then the probability of the second state (absence probability) would not be above 0.5. Note the “or” limitation requires only one of the two limitations be met for a case of anticipation).
Regarding claim 12, the content of claim 12 is similar to the content of claim 1, with the additional teachings of a processor, non-transitory computer readable medium, observation interface, recursive filter, prediction component, filtering component, and detection component. LEHMANN also discloses this information (processor (Page 2465: Table 1 describes an algorithm a processor would be needed to perform), non-transitory computer readable medium (Page 2465: Table 1 describes an algorithm a non-transitory computer readable medium would be needed to store), observation interface (Page 2459: “In passive radar systems, the transmit and receive antennas are not collocated, as illustrated by Fig. 1”. The radar system is the observational interface), recursive filter (Page 2463: “From an implementation point of view, it is desirable to derive this filter in a recursive form”), prediction component (Page 2461: A component would be needed to perform the state-space representation (which acts as the tensor), filtering component (Page 2463: A component would be required for performing Bayesian filtering (which is the updating tensor to generate a posteriori tensor), and detection component (Page 2465: C detection component would be needed to perform the detection based off the threshold detailed in table 1). Therefore, claim 12 is rejected for the same reasons of anticipation as claim 1, along with the additional teachings above.
Regarding claim 13, the content of claim 13 is similar to the content of claim 2, therefore it is rejected for the same reasons of anticipation as claim 2.
Regarding claim 15, the content of claim 15 is similar to the content of claim 7, therefore it is rejected for the same reasons of anticipation as claim 7.
Regarding claim 17, the content of claim 17 is similar to the content of claims 1 and 2 combined, therefore it is rejected for the same reasons of anticipation as claims 1 and 2.
Regarding claim 18, the content of claim 18 is similar to the content of claim 6, therefore it is rejected for the same reasons of anticipation as claim 6.
Regarding claim 19, the content of claim 19 is similar to the content of claim 7, therefore it is rejected for the same reasons of anticipation as claim 7.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over LEHMANN (“Recursive Bayesian Filtering for Multitarget Track-Before-Detect in Passive Radars” Hereinafter “LEHMANN”) in view of Cham et al. (US 6226409 B1 Hereinafter “Cham”).
Regarding claim 8, LEHMANN teaches the method of claim 1, wherein receiving the first set of observations (Page 2461, section A: “We associate a target existence variable ei,j k to the delay/frequency bin (i,j). At the discrete instant k, ei,j k =0(resp. ei,j k = 1) corresponds to the absence (resp. presence) of a target in bin (i,j)”. The first set of observations would be observations that define a probability that a value is consistent with the local background (if an object is absent, a value of a space would be consistent with the background, LEHMANN defines this as a probability being value 0 (ei,j k =0) for detected presence in each of the bins, which are different locations in the image) and the second set of observations for the region of interest comprises (Page 2464, section A: “Under assumptions 4 and 5, the predicted target existence probability in bin (i,j) can be expanded over the presence of targets in bins (m,n) 2N (i,j)”. The second set of observations would be a probability value when a value is not consistent with the local background (if an object is present, the value would be inconsistent with the local background, this is because the detected object is different than the background. LEHMANN teaches the probability value being a 1 for detected presence in each of the bins, which are different locations in the image).
LEHMANN does not expressly disclose obtaining these observations from a radar image.
However, Cham teaches use of radar image for state prediction and updating (Col. 2, lines 15-25: “Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data”. The data to perform these operation can come from radar image data “Multidimensional data can be collected by means of many different physical processes, for example: images may be collected by a video camera; by radar systems; by sonar systems; by infrared systems; by astronomical observations of star systems; by medical imaging using x-rays with dynamic image recording, magnetic resonance imaging, ultrasound, satellite imaging of the Earth, or by any other technology capable of generating an image of physical objects. The image data may then be analyzed in order to track targets of interest” (Col. 1, lines 24-35)).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify LEHMANN’s data collection to include Chum’s radar images because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, Chum’s radar images permits use of radar image data to perform state prediction given a circumstance that radar image data is available to use. This known benefit in Chum is applicable to LEHMANN’s data collection as they both share characteristics and capabilities, namely, they are directed to using radar data to perform state prediction of objects. Therefore, it would have been recognized that modifying LEHMANN’s data collection to include Chum’s radar images would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate Chum’s radar images in using radar data to perform state prediction of objects and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Regarding claim 9, the combination of LEHMANN and Chum teaches the method of claim 8, in addition, Chum further teaches wherein the image of the region of interest is a radar image from a radar system (Col. 1, lines 24-35: “Multidimensional data can be collected by means of many different physical processes, for example: images may be collected by a video camera; by radar systems; by sonar systems; by infrared systems; by astronomical observations of star systems; by medical imaging using x-rays with dynamic image recording, magnetic resonance imaging, ultrasound, satellite imaging of the Earth, or by any other technology capable of generating an image of physical objects. The image data may then be analyzed in order to track targets of interest”).
The rationale for this combination is similar to the one mentioned in the claim 8 rejection due to similar methods of combination (a radar image comes from radar system) and similar benefits (use of other types of data in a scenario where that data type is the one being used)
Regarding claim 10, the combination of LEHMANN and Chum teaches the method of claim 8, in addition, Chum further teaches wherein the image of the region of interest is a sonar image from a sonar system (Col. 1, lines 24-35: “Multidimensional data can be collected by means of many different physical processes, for example: images may be collected by a video camera; by radar systems; by sonar systems; by infrared systems; by astronomical observations of star systems; by medical imaging using x-rays with dynamic image recording, magnetic resonance imaging, ultrasound, satellite imaging of the Earth, or by any other technology capable of generating an image of physical objects. The image data may then be analyzed in order to track targets of interest”).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify LEHMANN’s data collection to include Chum’s sonar images and system because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, Chum’s sonar images and system permits use of a sonar system for multidimensional data collection. This known benefit in Chum is applicable to LEHMANN’s data collection as they both share characteristics and capabilities, namely, they are directed to using multidimensional data to perform state prediction of objects. Additionally, some circumstances would arise where sonar data would be more accurate than radar data, making it the ideal data collection system for those circumstances. Therefore, it would have been recognized that modifying LEHMANN’s data collection to include Chum’s sonar images and system would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate Chum’s sonar images and system in using multidimensional data to perform state prediction of objects and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Regarding claim 11, the combination of LEHMANN and Chum teaches the method of claim 8, in addition, Chum further teaches wherein the image of the region of interest is an image from a camera (Col. 1, lines 24-35: “Multidimensional data can be collected by means of many different physical processes, for example: images may be collected by a video camera; by radar systems; by sonar systems; by infrared systems; by astronomical observations of star systems; by medical imaging using x-rays with dynamic image recording, magnetic resonance imaging, ultrasound, satellite imaging of the Earth, or by any other technology capable of generating an image of physical objects. The image data may then be analyzed in order to track targets of interest”).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify LEHMANN’s data collection to include Chum’s camera images and system because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, Chum’s camera images and system permits use of a camera system for multidimensional data collection. This known benefit in Chum is applicable to LEHMANN’s data collection as they both share characteristics and capabilities, namely, they are directed to using multidimensional data to perform state prediction of objects. Additionally, some circumstances would arise where image data would be more accurate than radar data, making it the ideal data collection system for those circumstances. Therefore, it would have been recognized that modifying LEHMANN’s data collection to include Chum’s camera images and system would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate Chum’s camera images and system in using multidimensional data to perform state prediction of objects and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over LEHMANN (“Recursive Bayesian Filtering for Multitarget Track-Before-Detect in Passive Radars” Hereinafter “LEHMANN”) in view of Shimizu et al. (US 20130223686 A1 Hereinafter “Shimizu”).
Regarding claim 16, LEHMANN teaches the system of claim 12, wherein the observation interface comprises:
LEHMANN does not expressly disclose an image interface that receives an image of a region of interest and a probability map associated with the first and second set of observation.
However, Shimizu teaches an image interface that receives an image of the region of interest from an associated imaging system ([0076]: “The camera 18 is configured by a compact CCD camera or a CMOS camera, and is attached for example to an upper portion of a vehicle front windshield so as to image in front of the vehicle. Image data captured by the camera 18 such of the road condition in front is input to the computer 22”); and
a probability map generator that generates each of the first set of observations and the second set of observations from the image of the region of interest ([0082]: “a map generation section 42 that generates a presence probability map in which the presence probability is expressed for each separate type of moving object based on the detected travelling environment conditions”).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify LEHMANN’s data collection to include Shimizu’s image system and probability maps because such a modification is the result of applying a known technique to a known device ready for improvement to yield predictable results. More specifically, Shimizu’s image system and probability maps permits use of a camera system for multidimensional data collection and probability maps for accurate determination of object presence in a multidimensional environment. This known benefit in Shimizu is applicable to LEHMANN’s data collection as they both share characteristics and capabilities, namely, they are directed to using multidimensional data to perform state prediction of objects. Additionally, some circumstances would arise where image data would be more accurate than radar data, making it the ideal data collection system for those circumstances. Also, the probability maps act functionally similar to the bins in LEHMANN, in terms of localizing an objects presence. Therefore, it would have been recognized that modifying LEHMANN’s data collection to include Shimizu’s image system and probability maps would have yielded predictable results because (i) the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate Shimizu’s image system and probability maps in using multidimensional data to perform state prediction of objects and (ii) the benefits of such a combination would have been recognized by those of ordinary skill in the art.
Allowable Subject Matter
Claims 4, 6, 14, and 20 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rodriguez et al. (US 20250304106 A1) teaches tracking individuals with probability arrays
LEE (US 20130287250 A1) teaches tracking an object using the probability for each pixel
Brill et al. (US 6542621 B1) teaches tracking objects using probability templates
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/STEFANO ANTHONY DARDANO/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698