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 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 (and specification support) are:
Claim 29
means for determining a current representation of a current object in a current image (element 402, Fig 4 and ¶ [0072]-[0073], [0094]);
means for computing a joint Gaussian distribution between the current representation of the current object and a previous representation stored in one or more memory buffers (element 404, Fig 4 and ¶ [0070]-0078], [0095]), wherein the previous representation was determined from a previous image ; and
means for updating the one or more memory buffers based on the joint Gaussian distribution (elements 406-416, Fig 4 and ¶ [0079]–[0090], [0095]-[0096]).
Under 35 U.S.C. § 112(f), the broadest reasonable interpretation of the claims each incorporate particular detailed computer processing operations that are considered an improvement upon existing technological processes and therefore are statutory eligible. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-37, 118 USPQ2d 1684, 1689-90 (Fed. Cir. 2016) and MPEP § 2106(II).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, each 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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites an apparatus for multi-object tracking (mathematical concepts as abstract ideas to identify and track objects), the apparatus comprising:
one or more memory buffers configured to store respective representations of one or more objects in an image (generic computer components to implement abstract idea); and one or more processors in communication with the one or more memory buffers (generic computer components to implement abstract idea), the one or more processors configured to:
determine a current representation of a current object in a current image (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a));
compute a joint Gaussian distribution between the current representation of the current object and a previous representation stored in the one or more memory buffers (a joint Gaussian distribution computed between two points (representations) is a mathematical calculation), wherein the previous representation was determined from a previous image (considered data gathering activity; see MPEP § 2106.05(g)); and
update the one or more memory buffers based on the joint Gaussian distribution (considered an insignificant post-solution activity; see MPEP § 2106.05(g)).
Claim 2 recites the apparatus of claim 1 (as described above), wherein the joint Gaussian distribution includes a covariance matrix (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)), and wherein to update the one or more memory buffers based on the joint Gaussian distribution, the one or more processors are configured to: determine whether to remove or replace the previous representation in the one or more memory buffers based on values of the covariance matrix (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 3 recites the apparatus of claim 2 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine to replace the previous representation with the current representation of the current object based on the values of the covariance matrix being less than or equal to a threshold (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 4 recites the apparatus of claim 2 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 5 recites the apparatus of claim 2 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold for a number of frames (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 6 recites the apparatus of claim 2 (as described above), wherein the one or more processors are further configured to: compute respective joint Gaussian distributions between the current representation of the current object and N number of previous representations stored in the one or more memory buffers, wherein the N number of previous representations are from an N nearest previous representations to the current representation (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 7 recites the apparatus of claim 6 (as described above), wherein the one or more processors are further configured to: determine a matching representation of the N number of previous representations, wherein the matching representation is a same object as the current object (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 8 recites the apparatus of claim 7 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine whether to replace the matching representation with the current representation based on the values of the covariance matrix (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 9 recites the apparatus of claim 1 (as described above), wherein the current representation of the current object includes a location of the current object in the current image and a latent representation of one or more features of the current object (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 10 recites the apparatus of claim 1 (as described above), wherein the current representation includes a feature vector (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 11 recites the apparatus of claim 1 (as described above), wherein to determine the current representation of the current object in the current image, the one or more processors are configured to: determine the current representation of the current object in the current image using an encoder-decoder architecture (considered a mathematical relationship as claimed under abstract ideas; see MPEP § 2106.04(a)).
Claim 12 recites the apparatus of claim 1 (as described above), wherein the one or more processors are further configured to: determine an autonomous driving decision based on the respective representations of the one or more objects in the image stored in the updated one or more memory buffers (considered an insignificant post-solution activity; see MPEP § 2106.05(g)).
Claim 13 recites the apparatus of claim 12 (as described above), wherein the apparatus is part of an advanced driver assistance system (ADAS) (considered elements of an insignificant post-solution activity; see MPEP § 2106.05(g)).
Claims 14-26 recite a method of multi-object tracking (mathematical concepts as abstract ideas to identify and track objects), the method comprising: (steps identical to claims 1-13, respectively, as described above).
Claims 27-28 recite a non-transitory computer-readable storage medium storing instructions (generic computer components to implement abstract idea) that, when executed, cause one or more processors to: (steps identical to claims 1-2, respectively, as described above).
The claimed invention is directed to an abstract idea without significantly more. The claims recite mathematical concepts as outlined above and described in the MPEP 2106.04(a)(2)(I) with select limitations directed to extra-solution activity under MPEP 2106.04(g). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation based on mathematical concepts but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, these claims each recite an abstract idea.
This judicial exception is not integrated into a practical application. The computer components are recited at a high-level of generality (i.e., generic computer components, such as a processor, memory and computer instructions) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, the computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the aforementioned claims are directed to abstract ideas.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic placeholder-related computer components, the processor to perform mathematical calculations to determine mathematical relationships amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an invention concept. Therefore, the claims are not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 9, 10, 12, 14, 22, 23, 25, 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reichardt (EP 4 216 191 with priority of application 22152650.2, claimed in US 2023/0234580 and US 2023/0234580 referred to in the rejections below).
Regarding Claim 1, Reichardt teach an apparatus for multi-object tracking (apparatus for multi-object tracking; Fig 2, 4 and ¶ [0066], [0124]), the apparatus comprising:
one or more memory buffers (“memory buffer” is recognized in the art as a temporary storage, such as random access memory (RAM), described in applicant’s specification element 166 of memory 160, described to include RAM, specification ¶ [0051] and Figure 1) configured to store respective representations of one or more objects in an image (memory storage 440 includes RAM to store instructions for data analysis of object tracking; ¶ [0121]-[0124], [0126]-[0127]); and
one or more processors in communication with the one or more memory buffers (processor 410 executes software 450 stored in memory 440; Fig 4 and ¶ [0127]), the one or more processors configured to:
determine a current representation of a current object in a current image (parametric trajectory representations of objects is obtained from camera image data, block 202; Fig 2 and ¶ [0066], [0090]-[0092]);
compute a joint Gaussian distribution (“joint Gaussian distribution” is recognized in the art as a multivariate Gaussian distribution, described as such in applicant’s specification ¶ [0070]) between the current representation of the current object and a previous representation stored in the one or more memory buffers (a multivariate Gaussian distribution over the current state vector of a single object given a sequence of past observations of the object is determined to track the object, block 202; Fig 2 and ¶ [0057]-[0063], [0073]-[0074], [0093]-[0097]),
wherein the previous representation was determined from a previous image (vector data representing the object identified in images is analyzed on a frame by frame basis; ¶ [0057], [0066]); and
update the one or more memory buffers based on the joint Gaussian distribution (the multivariate Gaussian distribution data for the parameter vectors are then used to update the vector state of the observation, block 206; Fig 2 and ¶ [0062]-[0063], [0073]-[0074], [0099]).
Regarding Claim 9, Reichardt et al teach the apparatus of claim 1 (as described above), wherein the current representation of the current object includes a location of the current object in the current image (parametric representations of tracked objects include kinematics state of objects include position; ¶ [0050], [0057]-[0059]) and a latent representation of one or more features of the current object (the observation for a given state vector is returned representing only a position and velocity estimate (parametric representation); ¶ [0046]-[0050], [0059]).
Regarding Claim 10, Reichardt et al teach the apparatus of claim 1 (as described above), wherein the current representation includes a feature vector (parametric representations include state vectors of an object along the trajectory; ¶ [0048]-[0049], [0057]-[0060]).
Regarding Claim 12, Reichardt et al teach the apparatus of claim 1 (as described above), wherein the one or more processors are further configured to: determine an autonomous driving decision based on the respective representations of the one or more objects in the image stored in the updated one or more memory buffers (a control signal for the autonomous vehicle is determined based on the updated tracking data of the object(s), step 208; Fig 2 and ¶ [0119]-[0120]).
Regarding Claim 14, Reichardt teach a method of multi-object tracking (method of using apparatus for multi-object tracking; Fig 2, 4 and ¶ [0066], [0124]), the method comprising: steps identical to claim 1 (as described above).
Regarding Claim 22, Reichardt teach the method of claim 14 (as described above), wherein further steps are claimed identical to claim 9 (as described above).
Regarding Claim 23, Reichardt teach the method of claim 14 (as described above), wherein further steps are claimed identical to claim 10 (as described above).
Regarding Claim 25, Reichardt teach the method of claim 14 (as described above), wherein further steps are claimed identical to claim 12 (as described above).
Regarding Claim 27, Reichardt teach a non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors (processor 410 executes software 450 stored in memory 440; Fig 4 and ¶ [0127]) to: perform steps identical to claim 1 (as described above).
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 2, 3, 6-8, 15-16, 19-21, 28 are rejected under 35 U.S.C. 103 as being unpatentable over Reichardt (US 2023/0234580) in view of Mittal et al (US 2008/0130952).
Regarding Claim 2, Reichardt et al teach the apparatus of claim 1 (as described above), wherein the joint Gaussian distribution includes a covariance matrix (the multivariate Gaussian distribution includes a covariance matrix; ¶ [0058], [0097]), and wherein to update the one or more memory buffers based on the joint Gaussian distribution (the multivariate Gaussian distribution data for the parameter vectors are then used to update the vector state of the observation, block 206; Fig 2 and ¶ [0062]-[0063], [0073]-[0074], [0099]).
Reichardt teaches to replace the previous representation but does not teach the one or more processors are configured to: determine whether to remove or replace the previous representation in the one or more memory buffers based on values of the covariance matrix.
Mittal et al is analogous art pertinent to the technological problem addressed in the current application and teaches the one or more processors are configured to: determine whether to remove or replace the previous representation in the one or more memory buffers based on values of the covariance matrix (the basis vectors may be retained or updated based on eigenvalue representations of the covariance matrix; ¶ [0087], [0106]-[0116]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Reichardt with Mittal et al including the one or more processors are configured to: determine whether to remove or replace the previous representation in the one or more memory buffers based on values of the covariance matrix. By determining to update the basis vectors with the auto-regressive matrix, tracking is performed over time with a continuous update to perform accurate detection, thereby continuously updating the scene analysis with minimizing the impact to the processor to perform the update, as recognition by Mittal et al (¶ [0065]-[0066]).
Regarding Claim 3, Reichardt in view of Mittal et al teach the apparatus of claim 2 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine to replace the previous representation with the current representation of the current object based on the values of the covariance matrix being less than or equal to a threshold (Mittal et al, the covariance matrix (Sigma sub I ) is incrementally updated with the estimates of the basis vectors (updates are I sub m+1, [0113]-[0117]) and can be based on threshold value compared to a mean and variance of the data; ¶ [0106]-[0110], [0127]).
Regarding Claim 6, Reichardt in view of Mittal et al teach the apparatus of claim 2 (as described above), wherein the one or more processors are further configured to: compute respective joint Gaussian distributions between the current representation of the current object and N number of previous representations stored in the one or more memory buffers, wherein the N number of previous representations are from an N nearest previous representations to the current representation (Reichardt, the multivariate Gaussian distribution is analyzed based on the current state vector of a single object given a sequence of all past observations (N previous representations); ¶ [0057]-[0060], [0097]-[0098]).
Regarding Claim 7, Reichardt in view of Mittal et al teach the apparatus of claim 6 (as described above), wherein the one or more processors are further configured to: determine a matching representation of the N number of previous representations, wherein the matching representation is a same object as the current object (Reichardt, the observations analyzed are based on the current state vector of a single object given a sequence of all past observations (N previous representations); ¶ [0057]-[0060], [0097]-[0098]).
Regarding Claim 8, Reichardt in view of Mittal et al teach the apparatus of claim 7 (as described above), wherein to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix, the one or more processors are configured to: determine whether to replace the matching representation with the current representation based on the values of the covariance matrix (Mittal et al, the basis vectors may be retained or updated based on eigenvalue representations of the covariance matrix; ¶ [0087], [0106]-[0116]).
Regarding Claim 15, Reichardt teach the method of claim 14 (as described above), wherein further steps are claimed identical to claim 2 (as described above).
Regarding Claim 16, Reichardt teach the method of claim 15 (as described above), wherein further steps are claimed identical to claim 3 (as described above).
Regarding Claim 19, Reichardt teach the method of claim 15 (as described above), wherein further steps are claimed identical to claim 6 (as described above).
Regarding Claim 20, Reichardt teach the method of claim 19 (as described above), wherein further steps are claimed identical to claim 7 (as described above).
Regarding Claim 21, Reichardt teach the method of claim 20 (as described above), wherein further steps are claimed identical to claim 8 (as described above).
Regarding Claim 28, Reichardt teach the non-transitory computer-readable storage medium of claim 27 (as described above), wherein further steps are claimed identical to claim 2 (as described above).
Claims 4, 5, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Reichardt (US 2023/0234580) in view of Mittal et al (US 2008/0130952) and Porikli et al (US 2008/0240497).
Regarding Claim 4, Reichardt in view of Mittal et al teach the apparatus of claim 2 (as described above), including to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix (Mittal et al, 4444the basis vectors may be retained or updated based on eigenvalue representations of the covariance matrix; ¶ [0087], [0106]-[0116]).
Reichardt in view of Mittal et al do not teach to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold.
Porikli et al is analogous art pertinent to the technological problem addressed in the current application and teaches to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold (a predetermined threshold is used to determine if a tracking error 701 is greater than a predetermined threshold, and used to evaluate the tracking module, including a covariance tracker (understood to include a covariance matrix ¶ [0015]-[0016]), and update the tracker in the memory buffer; Fig 1 and ¶ [0032]-[0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Reichardt in view of Mittal et al with Porikli et al including to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold. By tracking of an object in buffered frames using a predetermined threshold, additional frames may be analyzed or a different tracking module may be utilized, thereby improving the analysis with adaptive scaling, as recognized by Porikli et al (¶ [0019]-[0023]).
Regarding Claim 5, Reichardt in view of Mittal et al teach the apparatus of claim 2 (as described above), including to determine whether to remove or replace the previous representation in the one or more memory buffers based on the values of the covariance matrix (Mittal et al, the basis vectors may be retained or updated based on eigenvalue representations of the covariance matrix; ¶ [0087], [0106]-[0116])
Reichardt in view of Mittal et al do not teach to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold for a number of frames.
Porikli et al is analogous art pertinent to the technological problem addressed in the current application and teaches to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold for a number of frames (a predetermined threshold is used to determine if a tracking error 701 is greater than a predetermined threshold, and used to evaluate the tracking module, including a covariance tracker (understood to include a covariance matrix ¶ [0015]-[0016]), and update the tracker in the memory buffer where the buffer size 809 is determined based on the threshold and the buffer frames is decreased 830 if the tracking error is more than the threshold; Fig 1 8-10 and ¶ [0032]-[0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Reichardt in view of Mittal et al with Porikli et al including to determine to remove the previous representation in the one or more memory buffers based on the values of the covariance matrix being greater than a threshold for a number of frames. By tracking of an object in buffered frames using a predetermined threshold, additional frames may be analyzed or a different tracking module may be utilized, thereby improving the analysis with adaptive scaling, as recognized by Porikli et al (¶ [0019]-[0023]).
Regarding Claim 17, Reichardt teach the method of claim 15 (as described above), wherein further steps are claimed identical to claim 4 (as described above).
Regarding Claim 18, Reichardt teach the method of claim 15 (as described above), wherein further steps are claimed identical to claim 5 (as described above).
Claims 11, 13, 24, 26 are rejected under 35 U.S.C. 103 as being unpatentable over Reichardt (US 2023/0234580) in view of Weng et al (US 2023/0394823).
Regarding Claim 11, Reichardt et al teach the apparatus of claim 1 (as described above), including to determine the current representation of the current object in the current image (parametric trajectory representations of objects is obtained from camera image data, block 202; Fig 2 and ¶ [0066], [0090]-[0092]).
Reichardt et al does not teach to determine the current representation of the current object in the current image using an encoder-decoder architecture.
Weng et al is analogous art pertinent to the technological problem addressed in the current application and teaches to determine the current representation of the current object in the current image using an encoder-decoder architecture (a video encoder and decoder is used to process the image data and determine trajectory predictions of the object; ¶ [0062]-[0063], [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Reichardt with Weng et al including to determine the current representation of the current object in the current image using an encoder-decoder architecture. By using a encoder-decoder system, a series of functions are performed to produce multimodal predictions, thereby improving the analysis of latent variables contained in affinity matrices for detecting the object with improved accuracy, as recognized by Weng et al (¶ [0062]-[0063]).
Regarding Claim 13, Reichardt et al teach the apparatus of claim 12 (as described above).
Reichardt et al does not teach wherein the apparatus is part of an advanced driver assistance system (ADAS).
Weng et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the apparatus is part of an advanced driver assistance system (ADAS) (the image data to perform trajectory predictions of objects is used as part of an advanced driver assistance system; ¶ [0192]-[0195], [0261]-[0262]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Reichardt with Weng et al including wherein the apparatus is part of an advanced driver assistance system (ADAS). By using the image data for ADAS functions, the application of the image data may be applied to an autonomous vehicle in multiple autonomous vehicle functions with an automotive safety integrity level, as recognized by Weng et al (¶ [0190]-[0191]).
Regarding Claim 24, Reichardt teach the method of claim 14 (as described above), wherein further steps are claimed identical to claim 11 (as described above).
Regarding Claim 26, Reichardt teach the method of claim 25 (as described above), wherein further steps are claimed identical to claim 13 (as described above).
Allowable Subject Matter
Claims 29-30 allowed based on the claim construction of claim 29 and interpretation as described above under 35 U.S.C. § 112(f) with claim 30 dependent on claim 29. Should the claims be amended to avoid the interpretation, the claims will be re-evaluated for claim scope using a broadest reasonable interpretation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rangesh et al (US 2021/0056713) teach a surround multi-object tracking and motion prediction framework used by a vehicle based on recognized motion patterns of vehicle traffic and trajectories.
Gautam et al (US 2021/0402991) teach an object tracking and trajectory prediction based on a machine learned model and a temporal data analysis for operating of an autonomous vehicle.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661