DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending.
Response to Arguments
101 Rejection: Applicant’s arguments, filed 03/02/2026, with respect to claims 1-5, 7-10, 12-17, 19-20 have been fully considered and are persuasive. The claims recite “identifying one or more contextual attributes based on a second subset of the plurality of sensor-based feature values that correspond to the one or more defined contextual attributes” which integrates the judicial exception into the technological improvement disclosed in the specification (Para 0004, The sensing device includes a local filtering mechanism that locally processes and filters sensor data based on a relevance of the sensor data to a predictive task). The 101 of rejection of claims 1-5, 7-10, 12-17, 19-20 has been withdrawn.
103 Rejection: Applicant’s arguments with respect to claims 1-20 have been fully considered and are
persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of
rejection is made in view of newly cited reference Nguyen.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 (and similar claims 13 and 19) recites the following new matter: “an anonymous excursion message”. The closest the original disclosure comes to teaching this limitation is “an excursion message” (Specification Para 0023). Claims 2-12, 14-18, and 20 are rejected due to dependence on independent claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 (and similar claims 13 and 19) is provisionally rejected on the ground of anticipation-type nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 19/007315. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim under analysis is anticipated by the reference claim. Dependent claims 2-12, 14-19, and 20 are distinct from the copending application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim under analysis
Copending Application
1. (Currently Amended) A computer-implemented method comprising: receiving, by one or more processors and originating from an ambient sensing device, an anonymous excursion message reflective of an excursion event and that comprises a plurality of sensor-based feature values respectively corresponding to a plurality of excursion feature parameters associated with (a) an entity signature definition and (b) one or more defined contextual attributes; identifying, by the one or more processors, (a) a candidate entity signature for the excursion event based on a first subset of the plurality of sensor-based feature values that correspond to the entity signature definition, and (b) one or more contextual attributes based on a second subset of the plurality of sensor-based feature values that correspond to the one or more defined contextual attributes; generating, by the one or more processors and using a point clustering model, a plurality of candidate entity clusters based on a comparison between the candidate entity signature and a plurality of candidate entity signatures respectively corresponding to a plurality of anonymous excursion messages received within a calibration time period; identifying, by the one or more processors, one or more tracking targets based on the plurality of candidate entity clusters and the one or more contextual attributes; and storing, by the one or more processors, one or more tracking target signatures, wherein a tracking target signature comprises at least one of a centroid mean or a centroid standard deviation respectively corresponding to the one or more tracking targets for a target log file corresponding to the ambient sensing device.
1. A computer-implemented method comprising: receiving, by one or more processors and originating from an ambient sensing device, an excursion message that comprises a plurality of sensor-based feature values respectively corresponding to a plurality of excursion feature parameters associated with (a) an entity signature definition and (b) one or more defined contextual attributes; identifying, by the one or more processors, (a) an entity signature for the excursion message based on a first subset of the plurality of sensor-based feature values that correspond to the entity signature definition, and (b) one or more contextual attributes based on a second subset of the plurality of sensor-based feature values that correspond to the one or more defined contextual attributes; identifying, by the one or more processors, a target log file corresponding to the excursion message based on a comparison between the entity signature and a tracking target signature corresponding to the target log file; storing, by the one or more processors, the one or more contextual attributes as one or more of a plurality of historical contextual attributes of the target log file; in response to a temporal assessment trigger event, (i) generating, by the one or more processors, a plurality of predictive features for a tracking target based on the plurality of historical contextual attributes and one or more evaluation time intervals, and (ii) generating, by the one or more processors and using a predictive model, a predictive output for the tracking target based on the plurality of predictive features; and initiating, by the one or more processors, a prediction-based action based on the predictive output.
5. The computer-implemented method of claim 4, wherein the tracking target signature comprises (a) a centroid mean that defines a median aggregated feature value for each of the first subset of the plurality of sensor-based feature values and (b) a centroid standard deviation.
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 1-5, 7-10, 12-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al (US 20070023662 A1) hereafter Brady in view of Lau et al (US 20200363501 A1) hereafter Lau in view of Nguyen et al (US 20240310504 A1) hereafter Nguyen
Regarding claim 1, Brady teaches a computer-implemented method comprising: receiving, by one or more processors and originating from an ambient sensing device, an excursion message reflective of an excursion event and that comprises a plurality of sensor-based feature values respectively corresponding to a plurality of excursion feature parameters associated with (a) an entity signature definition and (b) one or more defined contextual attributes (Para 0008, The sensor detects radiation from the object as the object moves over time. The processor is coupled to the sensor. The processor converts the detected radiation to a spectral radiation signature); identifying, by the one or more processors, (a) a candidate entity signature for the excursion event based on a first subset of the plurality of sensor-based feature values that correspond to the entity signature definition, and (b) one or more contextual attributes based on a second subset of the plurality of sensor-based feature values that correspond to the one or more defined contextual attributes (Para 0009, The processor applies principal components analysis to the second spectral radiation signature to produce underlying factors and scores for the second spectral radiation signature); generating, by the one or more processors and using a point clustering model, a plurality of candidate entity clusters based on a comparison between the candidate entity signature and a plurality of candidate entity signatures respectively corresponding to a plurality of excursion messages (Para 0037, By using a principal component regression (PCR) method, those spectral features are clustered around a set of points, along a unit circle in a 2-D label plane. From the training process, a regression vector locating a cluster is obtained, as well as the mean and covariance of a number of clusters); identifying, by the one or more processors, one or more tracking targets based on the plurality of candidate entity clusters and the one or more contextual attributes (Para 0037, Then new data, of persons walking at random speeds, are used for testing the recognition capability); and storing, by the one or more processors, one or more tracking target signatures respectively corresponding to the one or more tracking targets for a target log file corresponding to the ambient sensing device (Para 0009, The processor obtains temporal radiation data from a second object moving along a second path using the sensor).
Brady does not appear to explicitly teach a plurality of excursion messages received within a calibration time period.
In analogous art, Lau teaches a plurality of excursion messages received within a calibration time period (Para 0042, One or more radar sensors coupled to a vehicle receive readings during a calibration time period). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brady to include the teaching of Lau. One of ordinary skill in the art would be motivated to implement this modification in order to calibrate sensors, as taught by Lau (Para 0043, The described vehicle sensor calibration technologies ultimately transform vehicle sensors from an uncalibrated state to a calibrated state).
Brady in view of Lau does not appear to explicitly teach wherein a tracking target signature comprises at least one of a centroid mean or a centroid standard deviation.
In analogous art, Nguyen teaches wherein a tracking target signature comprises at least one of a centroid mean or a centroid standard deviation (Para 0005, The method includes obtaining a set of centroids based on UWB radar measurements. The method includes, for each respective centroid among the set of centroids). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brady in view of Lau to include the teaching of Nguyen. One of ordinary skill in the art would be motivated to implement this modification in order to determine the relevance of senor data, as taught by Nguyen (Para 0005, classifying the respective centroid as one among human movement and non-human movement, based on a set of features for the respective centroid satisfying a human-movement condition).
Regarding claim 2, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 1, wherein a candidate entity cluster of the plurality of candidate entity clusters identifies a subset of the plurality of excursion messages and identifying the one or more tracking targets comprises: generating a plurality of candidate target parameter values for the plurality of candidate entity clusters, wherein a candidate target parameter value of the plurality of candidate target parameter values is generated for the candidate entity cluster by aggregating one or more candidate contextual attributes from the subset of the plurality of excursion messages (Brady, Para 0084, During training, 120 data sets are clustered from each sensor-lens pair into 6 clusters, two persons, and three speeds. Since the label of each data set is known, the clustering process is viewed as supervised training).
Regarding claim 3, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 2, wherein a contextual attribute of the one or more contextual attributes is a velocity feature and the candidate target parameter value identifies a median velocity of a plurality of velocity features respectively associated with the subset of the plurality of excursion messages (Brady, Para 0085, The six circles 1110 represent the locations of clustered results for two different people walking at three different speeds.).
Regarding claim 4, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 2, wherein identifying the one or more tracking targets comprises: identifying a primary tracking target based on a first minimum candidate target parameter value of the plurality of candidate target parameter values; and identifying a secondary tracking target based on a second minimum candidate target parameter value of the plurality of candidate target parameter values (Brady, Para 0087, These contours are used to specify a threshold that determines whether or not an object has been identified).
Regarding claim 5, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 1, wherein the plurality of excursion messages is temporarily stored during the calibration time period and the computer-implemented method further comprises: identifying a termination of the calibration time period; and responsive to the termination of the calibration time period,(i) identifying the one or more tracking target signatures, and(ii) discarding the plurality of excursion messages (Lau, Para 0042, One or more radar sensors coupled to a vehicle receive readings during a calibration time period). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brady to include the teaching of Lau. One of ordinary skill in the art would be motivated to implement this modification in order to calibrate sensors, as taught by Lau (Para 0043, The described vehicle sensor calibration technologies ultimately transform vehicle sensors from an uncalibrated state to a calibrated state).
Regarding claim 7, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 6, wherein the movement data comprises multi-dimensional point cloud data and the plurality of sensor-based feature values is generated from the multi-dimensional point cloud data based on the plurality of excursion feature parameters (Brady, Para 0037, By using a principal component regression (PCR) method, those spectral features are clustered around a set of points, along a unit circle in a 2-D label plane).
Regarding claim 8, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 7, wherein the multi-dimensional point cloud data comprises one or more three-dimensional point clouds and the plurality of excursion feature parameters identify a point cloud height, a point cloud width, a point cloud girth, a point cloud's centroid location, a point cloud velocity, a point cloud acceleration, a point cloud confidence level, a point cloud gating function gain, a point cloud tracking error variance, and a point cloud group variance (Brady, Para 0041, Parameters that impact the identification performance of processor 220 include but are not limited to characteristics of a coded aperture, height 270, and distance 260 ).
Regarding claim 9, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 8, wherein the first subset of the plurality of sensor-based feature values correspond to an aggregated height parameter, aggregated width, an aggregated girth parameter, an aggregated velocity parameter, an aggregated acceleration parameter, an aggregated confidence parameter, an aggregated gain parameter, an aggregated tracking error variance parameter, and an aggregated group variance parameter (Brady, Para 0062, Multiple linear regression is used to reduce multiple lists (e.g., a matrix) of spectral components obtained from multiple samples of training data to a single list (e.g., a vector) of spectral components that uniquely identify the movement of the object).
Regarding claim 10, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 8, wherein the second subset of the plurality of sensor-based feature values correspond to an aggregated velocity parameter, a distance feature parameter, and a duration feature parameter (Brady, Para 0041, Parameters that impact the identification performance of processor 220 include but are not limited to characteristics of a coded aperture, height 270, and distance 260 ).
Regarding claim 12, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 1, wherein storing the one or more tracking target signatures comprises: identifying a candidate entity cluster corresponding to a tracking target signature of the one or more tracking target signatures; generating a centroid mean and a centroid standard deviation for the candidate entity cluster; and storing the centroid mean and the centroid standard deviation in the target log file corresponding to the ambient sensing device (Para 0171, The DBSCAN algorithm may be run with a pre-determined epsilon value (e.g., 0.3 m), a pre-determined minimum number of samples (e.g., 5 samples), and may be run to locate clusters or centroids of targets 200 with highest density and correct separation of the pre-determined value).
Claim 13 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 14 is the system claim corresponding to the method claim 2, and is analyzed and rejected accordingly.
Claim 15 is the system claim corresponding to the method claim 3, and is analyzed and rejected accordingly.
Claim 16 is the system claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 17 is the system claim corresponding to the method claim 5, and is analyzed and rejected accordingly.
Claim 19 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 20 is the system claim corresponding to the method claim 12, and is analyzed and rejected accordingly.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Brady in view of Lau in view of Nguyen further in view of Nagy (US 20200132803 A1) hereafter Nagy
Regarding claim 6, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 1, as shown above. Brady in view of Lau does not appear to explicitly teach wherein the ambient sensing device (i) comprises a radar sensor configured to generate movement data and (ii) is configured to (a) provide the excursion message responsive to the movement data and one or more excursion event criteria and (b) provide a heartbeat message with a same size as the excursion message at a random interval.
In analogous art, Nagy teaches wherein the ambient sensing device (i) comprises a radar sensor configured to generate movement data and (ii) is configured to (a) provide the excursion message responsive to the movement data and one or more excursion event criteria and (b) provide a heartbeat message with a same size as the excursion message at a random interval (Para 0042, heartbeat and health checks may be performed for the radar module 1002. If the radar module 1002 is available, it can send a heartbeat signal ($READY) every five seconds). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brady in view of Lau to include the teaching of Nagy. One of ordinary skill in the art would be motivated to implement this modification in order to implement a radar detection system, as taught by Nagy (Abs, A radar module is provided for use in association with monitoring and predicting traffic conditions in the vicinity of a system vehicle having a stop indicator system).
Claim 18 is the system claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Brady in view of Lau in view of Nguyen further in view of Lipson et al (US 7167583 B1) hereafter Lipson
Regarding claim 11, Brady in view of Lau in view of Nguyen teaches the computer-implemented method of claim 1, as shown above. Brady in view of Lau does not appear to explicitly teach wherein the point clustering model comprises a multi-dimensional k-means clustering algorithm
In analogous art, Lipson teaches wherein the point clustering model comprises a multi-dimensional k-means clustering algorithm (Para 154, There are several clustering algorithms, such as K-means, well known to those of ordinary skill in the art that are suitable for this purpose). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Brady in view of Lau to include the teaching of Lipson. One of ordinary skill in the art would be motivated to implement this modification in order to classify objects, as taught by Lipson (Para 120, classifying patterns for detecting objects from synthetic aperture radar).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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