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 .
Status of the Claims
Claims 1-10 filed on 16 JAN 2024 are currently pending and have been examined.
Priority
The pending application 18/414,079, filed on 16 JAN 2024, claims priority from European patent application EP23305059.0, filed on 17 JAN 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 19 FEB 2024 has been considered by the examiner.
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 (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5, and 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chefson (US 2019/0195983 A1, cited by applicant in IDS dated 19 FEB 2024) in view of Mottier et al. (“Deinterleaving and Clustering unknown RADAR pulses,” cited by applicant in IDS dated 19 FEB 2024).
Regarding claim 1 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
A method for deinterleaving radar pulses (Chefson “The purpose of de-interleaving algorithms is to separate and gather the pulses from each of the radars from the mixture provided by the sensor.” - ¶ [0006]), each radar pulse of said radar pulses being associated with a frequency, a duration and a time of arrival (Chefson “Beneficially, the primary characteristics of a pulse can be chosen from at least the group formed by the following characteristics: pulse frequency, pulse duration, pulse level, pulse direction of arrival and pulse internal modulation.” - ¶ [0022]; “Beneficially, the secondary characteristics of a pulse include the pulse time instant.” - ¶ [0043]), wherein the method is computer-implemented and wherein the method comprises:
implementing a first clustering algorithm to assign said each radar pulse to a corresponding first class, based on the frequency, the duration and the time of arrival associated therewith (Chefson “gathering the pulses as a function of their primary characteristics to produce primary groups” - ¶ [0014]; “The primary characteristics 1031 of a pulse 1030 can for example be the pulse frequency, pulse duration, pulse level, pulse direction of arrival or even the pulse internal modulation.” - ¶ [0092]);
for each first class that is assigned to said each radar pulse, estimating, based on the frequency and the duration of said each radar pulse, a respective average frequency and a respective average pulse duration (Chefson “Beneficially, the primary characteristics of primary groups of a primary group are calculated by averaging the values of the primary characteristics of the pulses gathered in the primary group.” - ¶ [0032]; “Thus, the primary characteristics of primary groups 1060 are for example the mean frequency defined as the mean of the pulse frequencies of the pulses 1030 gathered in the primary group 1050…” - ¶ [0102]);
implementing a second clustering algorithm to group all of the each first class of said each radar pulse into second classes based on the respective average frequency and the respective average pulse duration associated therewith (Chefson “gathering the primary groups as a function of the primary characteristics of primary groups to produce secondary groups” - ¶ [0017]; “Thus, a first merging step is made by a first similarity measurement based on the primary characteristics of the primary groups.” - ¶ [0042]; where the secondary groups are formed by the first merging step);
for each second class of said second classes, determining a distribution of the time of arrival of the each radar pulse associated therewith (Chefson “Beneficially, the secondary characteristics of a pulse include the pulse time instant. Thus, it is possible to make a histogram of the pulse time instants differences which contain useful information to determine time values characterising a radar.” - ¶ [0043]-[0044]; “making a histogram of the pulse time instant differences of the pulses of the secondary group, which histogram of the pulse time instant differences represents the occurrence of the pulse time instant differences on a plurality of time intervals called bins” - ¶ 0047]);
implementing a third clustering algorithm to group the second classes into third classes (Chefson “gathering the secondary groups as a function of secondary characteristics of the pulses gathered in each secondary group to produce tertiary groups” - ¶ [0018]), (Chefson “detecting at least one radar, each tertiary group being considered as a radar or a radar mode.” - ¶ [0019]).
Mottier et al. discloses:
implementing a third clustering algorithm to group the second classes into third classes, based on optimal transport distances between the distribution of the time of arrival of the each radar pulse that is determined (Mottier et al. “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” – p. 4, left column), wherein each third class of said third classes is associated with a respective radar transmitter (Mottier et al. “It is able to identify the number of transmitters present in the signal by deinterleaving the pulses.” – p. 6, Section V, left column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 1 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson discloses the limitations of claim 1 outlined above. However, Chefson fails to explicitly disclose the third clustering algorithm uses the optimal transport distances between the distribution of time of arrival of each radar pulse. This feature is disclosed by Mottier et al. where “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” (Mottier et al. p. 4, left column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 2 (previously presented), Chefson discloses:
The method according to claim 1, wherein the third clustering algorithm is implemented only for the radar pulses associated with a second class of said second classes having a size greater than a predetermined minimum size (Chefson “After the step 107 of producing the tertiary groups 1080, the tertiary groups 1080 containing a number of pulses 1030 below a limit number are removed. The limit number is for example 1% of the number of pulses 1030 in the pulse block 1040. The pulses 1030 contained in the tertiary groups 1080 removed are added to the residues 1081.” - ¶ [0119]).
Regarding claim 3 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 2, further comprising, after said implementing the third clustering algorithm,
for said each second class having a size less than the predetermined minimum size, comprising a non-significant class (Chefson “After the step 107 of producing the tertiary groups 1080, the tertiary groups 1080 containing a number of pulses 1030 below a limit number are removed. The limit number is for example 1% of the number of pulses 1030 in the pulse block 1040. The pulses 1030 contained in the tertiary groups 1080 removed are added to the residues 1081.” - ¶ [0119]),
calculating a likelihood of said probability distribution, for the time of arrival of the each radar pulse corresponding therewith, from the probability density function that is estimated (Chefson “After the step 108a of calculating the primary histograms 1084 and the secondary histograms 1085, for each residue 1081, a matching score 1086 is calculated between the residue 1081 and each tertiary group 1080. Each matching score 1086 depends on the primary histograms 1084 and secondary histograms 1085 of the tertiary group the matching score of which is calculated.” - ¶ [0123]);
associating the non-significant class with a third class of said third classes corresponding to the probability distribution for which the likelihood that is calculated is highest (Chefson “If the maximum matching score 1087 is above the first matching threshold 1088, the residue 1081 is associated with the tertiary group 1080 corresponding to the maximum matching score 1087.” - ¶ [0126]).
Mottier et al. discloses:
for said each third class, estimating a probability density function of a probability distribution of the time of arrival of the each radar pulse corresponding therewith (Mottier et al. “This step assumes that clusters from a RADAR will be active at the same time periods, i.e. the time intervals where the RADAR is pointed towards the receiver. This is formalized using optimal transport distances, defined between probability measures.” – p. 3, right column)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 3 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson as modified above discloses the limitations of claim 1. However, Chefson fails to explicitly disclose estimating a probability density function of a probability distribution of the time of arrival of the each radar pulse corresponding therewith. This feature is disclosed by Mottier et al. where “This step assumes that clusters from a RADAR will be active at the same time periods, i.e. the time intervals where the RADAR is pointed towards the receiver. This is formalized using optimal transport distances, defined between probability measures.” (Mottier et al. p. 3, right column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 5 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 1
Mottier et al. discloses:
wherein the first clustering algorithm is a density-based unsupervised clustering algorithm, comprising a hierarchical density-based spatial clustering of applications with noise (Mottier et al. “To handle this problem , HDBSCAN has been recently introduced. This algorithm relies on a hierarchical approach allowing to omit the crucial ϵ parameter, by providing the dendrograms for all DBSCAN clustering solutions.” – p. 3, right column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 5 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson as modified above discloses the limitations of claim 1. However, Chefson fails to explicitly disclose estimating a probability density function of a probability distribution of the time of arrival of the each radar pulse corresponding therewith. This feature is disclosed by Mottier et al. where “To handle this problem , HDBSCAN has been recently introduced. This algorithm relies on a hierarchical approach allowing to omit the crucial ϵ parameter, by providing the dendrograms for all DBSCAN clustering solutions.”(Mottier et al. p. 3, right column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 7 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 1, wherein the third clustering algorithm implements a test, based on (Chefson “After the step 106 of producing the secondary groups 1070, the secondary groups 1070 are gathered to form tertiary groups 1080. The tertiary groups 1080 are groups formed by a secondary group 1070 or by the union of a plurality of secondary groups 1070. The step 107 of producing the tertiary groups 1080 uses the secondary characteristics 1032 of the pulses 1030 gathered in the tertiary groups 1080 and in particular the pulse time instants.” - ¶ [0110]).
Mottier et al. discloses:
wherein the third clustering algorithm implements a test, based on optimal transport distances between said distribution of the time of arrival of all of the radar pulses of the second classes (Mottier et al. “This step assumes that clusters from a RADAR will be active at the same time periods, i.e. the time intervals where the RADAR is pointed towards the receiver. This is formalized using optimal transport distances, defined between probability measures.” – p. 3, right column), to determine whether grouping second distinct classes into a same third class is to be performed or not (Mottier et al. “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” – p. 4, left column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 7 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson as modified above discloses the limitations of claim 1. However, Chefson fails to explicitly disclose uses the optimal transport distances between the distribution of time of arrival of each radar pulse. This feature is disclosed by Mottier et al. where “To handle this problem , HDBSCAN has been recently introduced. This algorithm relies on a hierarchical approach allowing to omit the crucial ϵ parameter, by providing the dendrograms for all DBSCAN clustering solutions.”(Mottier et al. p. 3, right column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 8 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 1, wherein one or more of the second clustering algorithm and the third clustering algorithm is a hierarchical agglomerative clustering algorithm (Chefson “ ).
Mottier et al. discloses:
wherein one or more of the second clustering algorithm and the third clustering algorithm is a hierarchical agglomerative clustering algorithm (Mottier et al. “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance.” – p. 4, left column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 8 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson as modified above discloses the limitations of claim 1. However, Chefson fails to explicitly disclose a hierarchical agglomerative clustering algorithm. This feature is disclosed by Mottier et al. where “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance.” (Mottier et al. p. 4, left column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 9 (currently amended),
A non-transitory computer program comprising executable instructions which, when executed by a computer, implement a method (Chefson “A fourth aspect of the invention relates to a non-transitory computer readable recording medium comprising instructions which, when executed by a computer, cause the same to implement the method according to a first aspect of the invention.” - ¶ [0074]) for deinterleaving radar pulses (Chefson “The purpose of de-interleaving algorithms is to separate and gather the pulses from each of the radars from the mixture provided by the sensor.” - ¶ [0006]), each radar pulse of said radar pulses being associated with a frequency, a duration and a time of arrival (Chefson “Beneficially, the primary characteristics of a pulse can be chosen from at least the group formed by the following characteristics: pulse frequency, pulse duration, pulse level, pulse direction of arrival and pulse internal modulation.” - ¶ [0022]; “Beneficially, the secondary characteristics of a pulse include the pulse time instant.” - ¶ [0043]), wherein the method comprises:
implementing a first clustering algorithm to assign said each radar pulse to a corresponding first class, based on the frequency, the duration and the time of arrival associated therewith (Chefson “gathering the pulses as a function of their primary characteristics to produce primary groups” - ¶ [0014]; “The primary characteristics 1031 of a pulse 1030 can for example be the pulse frequency, pulse duration, pulse level, pulse direction of arrival or even the pulse internal modulation.” - ¶ [0092]);
for each first class that is assigned to said each radar pulse, estimating, based on the frequency and the duration of said each radar pulse, a respective average frequency and a respective average pulse duration (Chefson “Beneficially, the primary characteristics of primary groups of a primary group are calculated by averaging the values of the primary characteristics of the pulses gathered in the primary group.” - ¶ [0032]; “Thus, the primary characteristics of primary groups 1060 are for example the mean frequency defined as the mean of the pulse frequencies of the pulses 1030 gathered in the primary group 1050…” - ¶ [0102]);
implementing a second clustering algorithm to group all of the each first class of said each radar pulse into second classes based on the respective average frequency and the respective average pulse duration associated therewith (Chefson “gathering the primary groups as a function of the primary characteristics of primary groups to produce secondary groups” - ¶ [0017]; “Thus, a first merging step is made by a first similarity measurement based on the primary characteristics of the primary groups.” - ¶ [0042]; where the secondary groups are formed by the first merging step);
for each second class of said second classes, determining a distribution of the time of arrival of the each radar pulse associated therewith (Chefson “Beneficially, the secondary characteristics of a pulse include the pulse time instant. Thus, it is possible to make a histogram of the pulse time instants differences which contain useful information to determine time values characterising a radar.” - ¶ [0043]-[0044]; “making a histogram of the pulse time instant differences of the pulses of the secondary group, which histogram of the pulse time instant differences represents the occurrence of the pulse time instant differences on a plurality of time intervals called bins” - ¶ 0047]);
implementing a third clustering algorithm to group the second classes into third classes (Chefson “gathering the secondary groups as a function of secondary characteristics of the pulses gathered in each secondary group to produce tertiary groups” - ¶ [0018]), (Chefson “detecting at least one radar, each tertiary group being considered as a radar or a radar mode.” - ¶ [0019]).
Mottier et al. discloses:
implementing a third clustering algorithm to group the second classes into third classes, based on optimal transport distances between the distribution of the time of arrival of the each radar pulse that is determined (Mottier et al. “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” – p. 4, left column), wherein each third class of said third classes is associated with a respective radar transmitter (Mottier et al. “It is able to identify the number of transmitters present in the signal by deinterleaving the pulses.” – p. 6, Section V, left column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 9 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson discloses the limitations of claim 9 outlined above. However, Chefson fails to explicitly disclose the third clustering algorithm uses the optimal transport distances between the distribution of time of arrival of each radar pulse. This feature is disclosed by Mottier et al. where “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” (Mottier et al. p. 4, left column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Regarding claim 10 (previously presented),
A device (Chefson “A first aspect of the invention relates to a method for detecting at least one radar in an environment, the method being implemented by a device…” - ¶ [0009]) that deinterleaves radar pulses (Chefson “The purpose of de-interleaving algorithms is to separate and gather the pulses from each of the radars from the mixture provided by the sensor.” - ¶ [0006]), each radar pulse of said radar pulses being associated with a frequency, a duration and a time of arrival (Chefson “Beneficially, the primary characteristics of a pulse can be chosen from at least the group formed by the following characteristics: pulse frequency, pulse duration, pulse level, pulse direction of arrival and pulse internal modulation.” - ¶ [0022]; “Beneficially, the secondary characteristics of a pulse include the pulse time instant.” - ¶ [0043]), the device comprising:
a processor (Chefson “One or more devices, processors or processing devices may be configured to carry out the function(s) of each of the elements and modules of the structural arrangement described herein.” - ¶ [0131]), wherein said processor is configured to
implement a first clustering algorithm to assign said each radar pulse to a corresponding first class, based on the frequency, the duration and the time of arrival of said each radar pulse (Chefson “gathering the pulses as a function of their primary characteristics to produce primary groups” - ¶ [0014]; “The primary characteristics 1031 of a pulse 1030 can for example be the pulse frequency, pulse duration, pulse level, pulse direction of arrival or even the pulse internal modulation.” - ¶ [0092]);
for each first class of said radar pulses, estimate, based on the frequency and the duration of said each radar pulse, a respective average frequency and a respective average pulse duration (Chefson “Beneficially, the primary characteristics of primary groups of a primary group are calculated by averaging the values of the primary characteristics of the pulses gathered in the primary group.” - ¶ [0032]);
implement a second clustering algorithm to group all of the each first class of said radar pulses into second classes based on the respective average frequency and the respective average pulse duration (Chefson “gathering the primary groups as a function of the primary characteristics of primary groups to produce secondary groups” - ¶ [0017]; “Thus, a first merging step is made by a first similarity measurement based on the primary characteristics of the primary groups.” - ¶ [0042]; where the secondary groups are formed by the first merging step);
for each second class of said second classes, determine a distribution of the time of arrival of the radar pulses associated therewith (Chefson “Beneficially, the secondary characteristics of a pulse include the pulse time instant. Thus, it is possible to make a histogram of the pulse time instants differences which contain useful information to determine time values characterising a radar.” - ¶ [0043]-[0044]; “making a histogram of the pulse time instant differences of the pulses of the secondary group, which histogram of the pulse time instant differences represents the occurrence of the pulse time instant differences on a plurality of time intervals called bins” - ¶ 0047]);
implement a third clustering algorithm to group the second classes into third classes (Chefson “gathering the secondary groups as a function of secondary characteristics of the pulses gathered in each secondary group to produce tertiary groups” - ¶ [0018]), (Chefson “detecting at least one radar, each tertiary group being considered as a radar or a radar mode.” - ¶ [0019]).
Mottier et al. discloses:
implement a third clustering algorithm to group the second classes into third classes, based on optimal transport distances between the distribution of the time of arrival of the each radar pulse that is determined (Mottier et al. “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” – p. 4, left column), each third class of said third classes is associated with a respective radar transmitter (Mottier et al. “It is able to identify the number of transmitters present in the signal by deinterleaving the pulses.” – p. 6, Section V, left column).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Mottier et al. into the invention of Chefson to yield the invention of claim 10 above. Both Chefson and Mottier et al. are considered analogous arts to the claimed invention as they both disclose methods for deinterleaving radar pulses. Chefson discloses the limitations of claim 10 outlined above. However, Chefson fails to explicitly disclose the third clustering algorithm uses the optimal transport distances between the distribution of time of arrival of each radar pulse. This feature is disclosed by Mottier et al. where “Clusters are fused in a hierarchical way, by iteratively agglomerate clusters with the smallest optimal transport distance. After fusion the distance between the fused clusters and the other clusters is updated. The process is halted when all the clusters are merged and there I only one cluster left.” (Mottier et al. p. 4, left column). The combination of Chefson and Mottier et al. would be obvious with a reasonable expectation of success to “improve classification accuracy and have more robustness” (Mottier et al. p. 1, right column) without “making assumptions about the regularity of the pulse repetition intervals” (Mottier et al. p4, left column).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chefson (US 2019/0195983 A1, cited by applicant in IDS dated 19 FEB 2024) in view of Mottier et al. (“Deinterleaving and Clustering unknown RADAR pulses,” cited by applicant in IDS dated 19 FEB 2024) as applied to claim 3 above, and further in view of Kolouri et al. (“Slied Wasserstein Kernels for Probability Distributions”).
Regarding claim 4 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 3
Kolouri et al. discloses:
wherein, for said each third class, estimating the probability density function comprises implementing a parametric method, comprising a kernel method (Kolouri et al. Section 4. “The Sliced Wasserstein Kernel-based algorithms” – p. 5).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Kolouri et al. into the invention of Chefson as modified above to yield the invention of claim 4 above. Chefson, Mottier et al. and Kolouri et al. are considered analogous arts to the claimed invention as they disclose processing for separating signals. Chefson as modified above discloses the method of claim 3. However, Chefson fails to explicitly disclose for said each third class, estimating the probability density function comprises implementing a parametric method, comprising a kernel method. This feature is disclosed by Kolouri et al. where sliced Wasserstein kernel-based algorithms are used to quantify the geometric discrepancy between two distributions (Kolouri et al. p. 1, right column; Section 4. “The Sliced Wasserstein Kernel-based algorithms”). The combination of Chefson, Mottier et al. and Kolouri et al. would be obvious with a reasonable expectation of success to “render signal/image classes more linearly separable, thus facilitating a variety of pattern recognition tasks” (Kolouri et al. introduction, p. 1, right column) and (Kolouri et al. “capturing more of the variations of the dataset with fewer parameters.” – section 6, p. 8, right column).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chefson (US 2019/0195983 A1, cited by applicant in IDS dated 19 FEB 2024) in view of Mottier et al. (“Deinterleaving and Clustering unknown RADAR pulses,” cited by applicant in IDS dated 19 FEB 2024) as applied to claim 1 above, and further in view of Malas et al. (US 8,350,749 B1).
Regarding claim 6 (previously presented), Chefson discloses:
[Note: what is not explicitly taught by Chefson has been struck-through]
The method according to claim 1, wherein the second clustering algorithm implements a (Chefson “The primary groups 1050 having a rank higher than the current rank 1059 and having a first similarity measurement 1051 higher than a first similarity threshold 1052 with the current primary group 1076 are merged with the current primary group 1076, that is all the pulses 1030 gathered within the primary groups 1050 merged are gathered with the pulses 1030 of the current primary group 1076.” - ¶ [0106]).
Malas et al. discloses:
implements a Kolmogorov-Smirnov test (Malas et al. “The K-S statistic indicates the level of agreement between two continuous distributions that are fully specified (location, scale and shape).” – Col. 7, lines 19-21).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Malas et al. into the invention of Chefson as modified above to yield the invention of claim 6 above. Chefson, Mottier et al. and Malas et al. are considered analogous arts to the claimed invention as they disclose processing for separating signals. Chefson as modified above discloses the method of claim 3. However, Chefson fails to explicitly disclose for said each third class, estimating the probability density function comprises implementing a parametric method, comprising a kernel method. This feature is disclosed by Malas et al. where “The K-S statistic indicates the level of agreement between two continuous distributions that are fully specified (location, scale and shape).” (Malas et al. Col. 7, lines 19-21). The combination of Chefson, Mottier et al. and Malas et al. would be obvious with a reasonable expectation of success to reduce false positives, false negatives and inconclusive results (Malas et al. Col. 4, lines 2-6).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAOMI M WOLFORD whose telephone number is (571)272-3929. The examiner can normally be reached Monday - Friday, 8:30 am - 4:30 pm EST.
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./ Examiner, Art Unit 3648
23 DEC 2025
/VLADIMIR MAGLOIRE/ Supervisory Patent Examiner, Art Unit 3648