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 .
Examiner's Note
The Examiner respectfully notes that the original claims presented on 6/23rd/2021 were not properly amended in the amendments presented on 10/03rd/2024. There were amended claims that were not properly marked (underlining additions and striking through deletions) and amended claims that were improperly presented as “original claims”. Proper presentation of amendments in the future is essential to compact prosecution.
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 07/12th/2022 and 08/11th/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
101 Rejection
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 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Step 1 Analysis for all claims:
Claims 1-12 are directed to a method, which is directed to a process, one of the statutory categories. Claims 13-20 are directed to a non-transitory computer readable medium, which is directed to a product, one of the statutory categories.
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1 Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
determining a parametrization of trajectory data of the object based on the radar data, the trajectory data of the object comprising a position of the object and a direction of the object, the parametrization comprising a plurality of parameters and a polynomial of a pre-determined degree, and the parameters comprising a plurality of coefficients related to elements of a basis of the polynomial space of polynomials of the pre-determined degree Under the broadest reasonable interpretation, this limitation is a process step that covers a mathematical concept. If a claim, under its broadest reasonable interpretation, covers a mathematical concept but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas.
determining a variance of the trajectory data of the object based on the radar data Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper such as an operator projecting different trajectories and pathways of a moving object. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2 Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
acquiring radar data of an object amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
After considering all claim elements, both individually and in combination, it has been determined that the claim does not integrate the abstract idea into a practical application. Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
acquiring radar data of an object the additional elements of collecting data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101.
The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below.
Dependent claim 2 recites:
Step 2A Prong 1: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein determining a variance of the trajectory data of the object comprises: determining a parametrization of the variance of the trajectory data of the object based on the radar data, wherein the parametrization comprises a plurality of further parameters and a further polynomial of a pre-determined further degree, and wherein the further parameters comprise a plurality of further coefficients related to elements of the basis of the polynomial space of polynomials of the pre-determined further degree Under the broadest reasonable interpretation, this limitation is a process step that covers a mathematical concept. If a claim, under its broadest reasonable interpretation, covers a mathematical concept but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas.
Steps 2A Prong 2 and Step 2B:
The clam does not recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 3 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the variance of the trajectory data of the object comprises: a multivariate normal distribution over the parameters is recited at a high-level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the variance of the trajectory data of the object comprises: a multivariate normal distribution over the parameters is recited at a high-level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 4 recites:
Step 2A Prong 1: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein determining the variance of the trajectory data of the object further comprises: determining a positive definite matrix Under the broadest reasonable interpretation, this limitation is a process step that covers a mathematical concept. If a claim, under its broadest reasonable interpretation, covers a mathematical concept but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas.
Steps 2A Prong 2 and Step 2B:
The clam does not recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 5 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; and determining second intermediate data based on the first intermediate data using a feature pyramid, wherein at least one of: the feature pyramid further comprises transposed strided convolutions, the parametrization of the trajectory data of the object is determined based on the second intermediate data, the residual backbone using the recurrent component comprises a residual backbone preceded by a recurrent layer stack, the residual backbone using the recurrent component comprises a recurrent residual backbone comprising a plurality of recurrent layers, the plurality of recurrent layers comprise a convolutional long short-term memory followed by a convolution followed by a normalization, the plurality of recurrent layers comprise a convolution followed by a normalization followed by a rectified linear unit followed by a convolutional long short-term memory followed by a convolution followed by a normalization, the recurrent component comprises a recurrent loop which is carried out once per time frame, or the recurrent component keeps hidden states between time frames is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; and determining second intermediate data based on the first intermediate data using a feature pyramid, wherein at least one of: the feature pyramid further comprises transposed strided convolutions, the parametrization of the trajectory data of the object is determined based on the second intermediate data, the residual backbone using the recurrent component comprises a residual backbone preceded by a recurrent layer stack, the residual backbone using the recurrent component comprises a recurrent residual backbone comprising a plurality of recurrent layers, the plurality of recurrent layers comprise a convolutional long short-term memory followed by a convolution followed by a normalization, the plurality of recurrent layers comprise a convolution followed by a normalization followed by a rectified linear unit followed by a convolutional long short-term memory followed by a convolution followed by a normalization, the recurrent component comprises a recurrent loop which is carried out once per time frame, or the recurrent component keeps hidden states between time frames is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Dependent claim 6 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein acquiring the radar data of the object comprises at least one of: acquiring radar data cubes; or acquiring radar point data amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein acquiring the radar data of the object comprises at least one of: acquiring radar data cubes; or acquiring radar point data the additional elements of collecting data is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Dependent claim 7 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the plurality of coefficients represent a respective mean value is recited at a high-level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the plurality of coefficients represent a respective mean value is recited at a high-level of generality and amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 8 recites:
Step 2A Prong 1: The judicial exception is not integrated into a practical application. In particular, the additional element of:
postprocessing the trajectory data based on the variance of the trajectory data Under the broadest reasonable interpretation, this limitation is a process step that covers a mathematical concept. If a claim, under its broadest reasonable interpretation, covers a mathematical concept but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas.
Steps 2A Prong 2 and Step 2B:
The clam does not recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 9 recites:
Step 2A Prong 1: The judicial exception is not integrated into a practical application. In particular, the additional element of:
postprocessing comprises at least one of: association; aggregation; or scoring Under the broadest reasonable interpretation, this limitation is a process step that covers a mathematical concept. If a claim, under its broadest reasonable interpretation, covers a mathematical concept but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas.
Steps 2A Prong 2 and Step 2B:
The clam does not recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 10 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the method is trained using a training method comprising a first training and a second training, wherein in the first training, parameters for the trajectory data are determined, and wherein in the second training, parameters for the trajectory data and parameters for the variance of the trajectory data are determined is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the method is trained using a training method comprising a first training and a second training, wherein in the first training, parameters for the trajectory data are determined, and wherein in the second training, parameters for the trajectory data and parameters for the variance of the trajectory data are determined is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Dependent claim 11 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
A method for training a machine learning method for predicting trajectory data of an object, the method comprising: a first training determining parameters for the trajectory data; and a second training determining parameters for the trajectory data and determining parameters for a variance of the trajectory data is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
A method for training a machine learning method for predicting trajectory data of an object, the method comprising: a first training determining parameters for the trajectory data; and a second training determining parameters for the trajectory data and determining parameters for a variance of the trajectory data is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Dependent claim 12 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein at least one of: in the first training, a smooth L1 function is used as a loss function; or in the second training, a bivariate normal log-likelihood function is used as a loss function is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein at least one of: in the first training, a smooth L1 function is used as a loss function; or in the second training, a bivariate normal log-likelihood function is used as a loss function is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
1-3, 6, 8-11, 13-15, 17, 19-20
Claim 13: is substantially similar to claim 1 and therefore is rejected on similar grounds as claim 1.
Claim 14: is substantially similar to claim 2 and therefore is rejected on similar grounds as claim 2.
Claim 15: is substantially similar to claim 3 and therefore is rejected on similar grounds as claim 3.
Claim 16: is substantially similar to claim 5 and therefore is rejected on similar grounds as claim 5.
Claim 17: is substantially similar to claim 6 and therefore is rejected on similar grounds as claim 6.
Claim 18: is substantially similar to claim 7 and therefore is rejected on similar grounds as claim 7.
Claim 19: is substantially similar to claim 8 and therefore is rejected on similar grounds as claim 8.
Claim 20: is substantially similar to claim 10 and therefore is rejected on similar grounds as claim 10.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6, 8-11, 13-15, 17, 19-20 are rejected under 35 U.S.C. 102 as being unpatentable over Zhao (US20210192347A1).
Regarding claim 1, Zhao teaches acquiring radar data of an object ([0010] For example, sensor data can be determined at a first point of time, e.g. in form of a radar scan, which captures for example the respective object and other possibly interacting objects.)
determining a parametrization of trajectory data of the object based on the radar data ([0010] In order to determine the desired information on the expected trajectory of the object that is preferably captured by means of the radar scan, it is proposed to determine one or more parameter values for a continuous function, wherein the continuous function and the one or more parameter values together represent the information on the expected trajectory of the object. While the second time is preferably a time period subsequent to the first-time other time relations are also possible.)
the trajectory data of the object comprising a position of the object and a direction of the object ([0045] The sensor data is determined at a predefined repetition rate so that the sensor unit 22 provides data representing the current vicinity, in particular the position and/or motion of the objects 12 to 20.)
the parametrization comprising a plurality of parameters and a polynomial of a pre-determined degree, and the parameters comprising a plurality of coefficients related to elements of a basis of the polynomial space of polynomials of the pre-determined degree ([0048] The first continuous function is a fourth-order polynomial function, namely f(t)=a*t4 +b*t3+c*t2+d*t+e, wherein [a, b, c, d, e] is the first set of parameters and t is a continuous-time variable. It is understood that t can be set to any desired real value within a definition range, hence t is not limited to discrete time instances in the sense that the function f(t) can be evaluated for any real value oft.)
determining a variance of the trajectory data of the object based on the radar data ([0009] The continuous information can generally describe the shape of the expected trajectory but is not limited thereto. For example, the continuous information can represent confidence information of at least one parameter. In one example, at least one parameter value represents a confidence value (for example variance) for the continuous function, in particular for a parameter value of the continuous function.)
Regarding claim 2, Zhao teaches wherein determining a variance of the trajectory data of the object comprises: determining a parametrization of the variance of the trajectory data of the object based on the radar data, wherein the parametrization comprises a plurality of further parameters and a further polynomial of a pre-determined further degree, and wherein the further parameters comprise a plurality of further coefficients related to elements of the basis of the polynomial space of polynomials of the pre-determined further degree ([0048] The first function f( t) describes the trajectory 28 in a first spatial dimension, e.g. the x-dimension of a Cartesian coordinate system. This is, f(t) gives the expected x-coordinate position of the object 18 for a given value oft. Likewise, the second continuous function gives the expected y-coordinate position of the object 18 for a given value oft. In more general terms, the second continuous function describes the trajectory 28 in a second spatial dimension. The second continuous function is g(t)=i*t4 +j*t3 +k*t2+1 *t+r, wherein [i, j, k, 1, r] is the second set of parameters, which are set to the second set of parameter values, e.g. [i=l, j=2, k=4, 1=3, r=0]. It is understood that the position of the object 18 can now be described by a coordinate pair [f(t); g(t)] for a given value of t and with the parameters being set to the assigned parameter values. It is further understood that the functions f(t) and g(t) are equivalent due to the same mathematical structure.)
Regarding claim 3, Zhao teaches wherein the variance of the trajectory data of the object comprises: a multivariate normal distribution over the parameters ([0046] As confidence, multivariate Gaussian variances can be used, and its estimation can be formulated using negative log-likelihood, wherein error propagation can be carried out with respect to the first and/or second continuous function. In this way, continuous information on the expected trajectory 28 is provided, which is indicated by a continuous line in FIG. 1.)
Regarding claim 6, Zhao teaches wherein acquiring the radar data of the object comprises at least one of: acquiring radar data cubes; or acquiring radar point data ([0010] For example, sensor data can be determined at a first point of time, e.g. in form of a radar scan, which captures for example the respective object and other possibly interacting objects. It is assumed that objects will avoid collisions, so their trajectories can be dependent on each other. The radar scan for the second time is not available because the second time is preferably a future time. In order to determine the desired information on the expected trajectory of the object that is preferably captured by means of the radar scan, it is proposed to determine one or more parameter values for a continuous function, wherein the continuous function and the one or more parameter values together represent the information on the expected trajectory of the object. While the second time is preferably a time period subsequent to the first-time other time relations are also possible.
Regarding claim 8, Zhao teaches postprocessing the trajectory data based on the variance of the trajectory data ([0045] It is understood that different data types, for example camera images, radar scans, LiDAR scans and the like can be fused in order to obtain a unified spatial representation of the current vicinity of the object 16.)
Regarding claim 9, Zhao teaches wherein the postprocessing comprises at least one of: association; aggregation; or scoring ([0045] It is understood that different data types, for example camera images, radar scans, LiDAR scans and the like can be fused in order to obtain a unified spatial representation of the current vicinity of the object 16.)
Regarding claim 10, Zhao teaches wherein the method is trained using a training method comprising a first training and a second training, wherein in the first training, parameters for the trajectory data are determined, and wherein in the second training, parameters for the trajectory data and parameters for the variance of the trajectory data are determined ([0033] In another aspect, the present disclosure is directed at a computer-implemented method for training an artificial neural network for determining information on an expected trajectory of an object. The method comprises training the artificial neural network using backpropagation, wherein positions of the object are sampled at random from training data, wherein a loss or error function is evaluated on the basis of the sampled positions, wherein function values of the continuous function are evaluated for sample values of the continuous function. The sample values are associated with the sample positions. Overfitting of the continuous function can thus be avoided. It is understood that the training data and/or the continuous function can be used in different representations, if desired. For example, the continuous function or portions of the continuous function can be transformed into different coordinate systems or otherwise adapted to facilitate comparison with the training data. Training data can comprise known continuous functions, which facilitates training even further.)
Regarding claim 11, Zhao teaches method for training a machine learning method for predicting trajectory data of an object, the method comprising: a first training determining parameters for the trajectory data; and a second training determining parameters for the trajectory data and determining parameters for a variance of the trajectory data ([0033] In another aspect, the present disclosure is directed at a computer-implemented method for training an artificial neural network for determining information on an expected trajectory of an object. The method comprises training the artificial neural network using backpropagation, wherein positions of the object are sampled at random from training data, wherein a loss or error function is evaluated on the basis of the sampled positions, wherein function values of the continuous function are evaluated for sample values of the continuous function. The sample values are associated with the sample positions. Overfitting of the continuous function can thus be avoided. It is understood that the training data and/or the continuous function can be used in different representations, if desired. For example, the continuous function or portions of the continuous function can be transformed into different coordinate systems or otherwise adapted to facilitate comparison with the training data. Training data can comprise known continuous functions, which facilitates training even further.)
Claims 13-15, and 17, 19-20 are rejected based upon the same rationale as claims 1-3, 6, and 10-11 as they are the computer-readable medium claims corresponding to the method claims.
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.
Claim 4 is rejected under 35 U.S.C. 102 as being unpatentable over Zhao (US20210192347A1) in view of Katou (US20200104224A1).
Regarding claim 4, Zhao teaches the method of claim 3. However, Zhao is not relied upon to explicitly teach wherein determining the variance of the trajectory data of the object further comprises: determining a positive definite matrix. On the other hand, Katou teaches wherein determining the variance of the trajectory data of the object further comprises: determining a positive definite matrix ([0050] Note that in the equation (1) below, x denotes a vector including sensor values of m dimensions, μ denotes a mean vector including a mean value of respective sensor values of the time series data X of m dimensions X, and Σ denotes an m-order variance-covariance matrix corresponding to a positive definite symmetric matrix including variance and covariance in the time series data X of m dimensions. The examiner notes that Katou teaches utilizing positive definite matrices in determining the variance. The examiner further notes that Zhao and Katou are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Zhao’s trajectory variance determination to incorporate wherein determining the variance of the trajectory data of the object further comprises: determining a positive definite matrix as taught by Katou [0050] in order to enable determination of the presence or absence of anomaly with high accuracy from actually obtained data even if characteristics of noise included in the data vary time-sequentially [0015].)
Claim 5 and 16 is rejected under 35 U.S.C. 102 as being unpatentable over Zhao (US20210192347A1) in view of Kunio (US20220346885A1).
Regarding claim 5, Zhao teaches the method of claim 1. However, Zhao is not relied upon to explicitly teach determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; and determining second intermediate data based on the first intermediate data using a feature pyramid, wherein at least one of: the feature pyramid further comprises transposed strided convolutions, the parametrization of the trajectory data of the object is determined based on the second intermediate data, the residual backbone using the recurrent component comprises a residual backbone preceded by a recurrent layer stack, the residual backbone using the recurrent component comprises a recurrent residual backbone comprising a plurality of recurrent layers, the plurality of recurrent layers comprise a convolutional long short-term memory followed by a convolution followed by a normalization, the plurality of recurrent layers comprise a convolution followed by a normalization followed by a rectified linear unit followed by a convolutional long short-term memory followed by a convolution followed by a normalization, the recurrent component comprises a recurrent loop which is carried out once per time frame, or the recurrent component keeps hidden states between time frames. on the other hand, Kunio teaches determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; and determining second intermediate data based on the first intermediate data using a feature pyramid, wherein at least one of: the feature pyramid further comprises transposed strided convolutions, the parametrization of the trajectory data of the object is determined based on the second intermediate data, the residual backbone using the recurrent component comprises a residual backbone preceded by a recurrent layer stack, the residual backbone using the recurrent component comprises a recurrent residual backbone comprising a plurality of recurrent layers, the plurality of recurrent layers comprise a convolutional long short-term memory followed by a convolution followed by a normalization, the plurality of recurrent layers comprise a convolution followed by a normalization followed by a rectified linear unit followed by a convolutional long short-term memory followed by a convolution followed by a normalization, the recurrent component comprises a recurrent loop which is carried out once per time frame, or the recurrent component keeps hidden states between time frames ([0027] the model is one or a combination of the following: a segmentation (classification) model, a segmentation model with pre-processing, a segmentation model with post-processing, an object detection (regression) model, an object detection model with pre-processing, an object detection model with post-processing, a combination of a segmentation (classification) model and an object detection (regression) model, a deep convolutional neural network model, a recurrent neural network model with long short-term memory that can take temporal relationships across images or frames into account, a model using feature pyramid(s) that can take different image resolutions into account, and/or a model using residual learning technique(s); (iv) the ground truth includes one or more of the following: locations of two endpoints of a major axis of a target marker in each angiography frame, locations of two endpoints of a major axis of a target marker in each angiography frame captured during Optical Coherence Tomography (OCT) pullback, a mask including a line that connects the two endpoint locations with a certain width as a positive area for the segmentation model, all of the markers included in an the acquired or received angiography image data, a centroid of two edge locations, a centroid of two edge locations for the regression or object detection model, and two marker locations in each frame of the acquired or received angiography image data graphically annoted by a user or an expert of the apparatus; (v) the one or more processors further operate to use one or more neural networks or convolutional neural networks to one or more of: train the model, estimate the generalization error, determine whether the performance of the trained model is sufficient or not, and/or to detect the marker(s) or radiopaque marker(s); (vi) the method further comprises estimating a generalization error of the trained model with data in the test set or group; and (vii) the method further comprises estimating a generalization error of multiple trained models with data in the test set or group, and selects one model based on its performance on the validation set or group. The examiner notes that Kunio teaches determining trajectory and trajectory variance utilizing residual techniques, recurrent networks, and feature pyramids. The examiner further notes that Zhao and Kunio are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Zhao’s trajectory variance determination to incorporate determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; and determining second intermediate data based on the first intermediate data using a feature pyramid, wherein at least one of: the feature pyramid further comprises transposed strided convolutions, the parametrization of the trajectory data of the object is determined based on the second intermediate data, the residual backbone using the recurrent component comprises a residual backbone preceded by a recurrent layer stack, the residual backbone using the recurrent component comprises a recurrent residual backbone comprising a plurality of recurrent layers, the plurality of recurrent layers comprise a convolutional long short-term memory followed by a convolution followed by a normalization, the plurality of recurrent layers comprise a convolution followed by a normalization followed by a rectified linear unit followed by a convolutional long short-term memory followed by a convolution followed by a normalization, the recurrent component comprises a recurrent loop which is carried out once per time frame, or the recurrent component keeps hidden states between time frames as taught by Kunio [0027] to enable the selection of one or more models with the highest performance defined by one or more predefined evaluation metrics [0026].)
Claim 16 is rejected based upon the same rationale as claim 5 as it is the computer-readable medium claim corresponding to the method claim.
Claim 7 and 18 is rejected under 35 U.S.C. 102 as being unpatentable over Zhao (US20210192347A1) in view of YANG (US20210303995A1).
Regarding claim 7, Zhao teaches the method of claim 1. However, Zhao is not relied upon to explicitly teach wherein the plurality of coefficients represent a respective mean value. on the other hand, Yang teaches wherein the plurality of coefficients represent a respective mean value ([0031] where, k(⋅,⋅) represents the polynomial kernel function; au represents the slope of the u-th polynomial kernel function; U represents the number of the implanted polynomial kernel functions; βu represents the weighting coefficient of the maximum mean discrepancy implanted by the uth polynomial kernel, and βu∈β*, where β* represents the optimal weighting coefficient. The examiner further notes that Zhao and Yang are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Zhao’s trajectory variance determination to incorporate wherein the plurality of coefficients represent a respective mean value as taught by Yang [0031] to enable obtaining the optimal weighting coefficient [0031].)
Claim 18 is rejected based upon the same rationale as claim 7 as it is the computer-readable medium claim corresponding to the method claim.
Claim 12 is rejected under 35 U.S.C. 102 as being unpatentable over Zhao (US20210192347A1) in view of LV (US20210342621A1).
Regarding claim 12, Zhao teaches the method of claim 11. However, Zhao is not relied upon to explicitly teach wherein at least one of: in the first training, a smooth L1 function is used as a loss function; or in the second training, a bivariate normal log-likelihood function is used as a loss function. on the other hand, LV teaches wherein at least one of: in the first training, a smooth L1 function is used as a loss function; or in the second training, a bivariate normal log-likelihood function is used as a loss function ([0048] Certainly, for training the neural network model for character recognition, a classification loss function may be adopted for the purpose of optimization. That is, the labelled character categories and the labelled character position codes are compared with the character categories and the character position codes of the sample image input to the neural network model, to calculate a loss value. When the loss value is greater than a preset threshold, a model coefficient of the neural network model is adjusted until the loss value is less than the preset threshold. Theoretically, regression loss functions, such as L2 loss, L1 loss Smooth L1 loss may be used as a loss function. The examiner further notes that Zhao and LV are both directed to machine learning and both are reasonably analogous to each other. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Zhao’s training to incorporate wherein at least one of: in the first training, a smooth L1 function is used as a loss function; or in the second training, a bivariate normal log-likelihood function is used as a loss function as taught by LV [0048] to optimize training the neural network model for character recognition [0048].)
Conclusion
The following references have been determined to be related to the application, but were not applied in any specific rejection. They are nonetheless listed below for reference.
Spata (US20220026556A1)
“Spata teaches a method for predicting a trajectory of an object”
Manivasagam (US20200301799A1)
“Manivasagam teaches a method for combining physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system”
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128