DETAILED ACTION
Response to Arguments
Applicant’s arguments, filled 03/11/2026 with respect to the existing 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely solely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 1 is objected to because of the following informalities: In claim 1, lines 17-18, “updating an identification of the object to a second object type of object different from the first object type”
should be read “updating an identification of the object to a second object type, different from the first object type” Appropriate correction is required.
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.
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.
Claims 1, 3-8, 10-12, and 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy(US6094164A) in view of Parikh(US 20220207308 A1) and further in view of SHIMIZU(US20210325524A1).
Regarding claim 1, Murphy discloses
A computer-implemented method comprising, by one or more hardware processors executing program instructions: determining, by using data collected from a direction finder or a first set of antennas : that first signals are being emitted from an object (“each of the respective transmitter devices has a unique identification signal associated therewith” [0071]), and a velocity associated with the object (“measuring the Doppler shift of signals transmitted from transmitter device 16 to tracking units 10a-10c, the velocity of object 12 with respect to tracking units 10a-10c is also readily determined”[Col.11, ll.57-60]); collecting, by using the first set of antennas, the first signals associated with the object (“ Once tracking unit 10 detects the signal from transmitter device 16, tracking unit 10 measures the strength of the signal transmitted from transmitter device 16. “ [Col.4, ll.42-45]); […] collecting, using a second set of antennas, the first RF signals being emitted from the object (FIG. 1B, Part 22c) […]
Murphy does not explicitly disclose nor limit wherein the first data set is input into a first and second machine learning model for object detection. Parikh discloses the method comprising, inputting, into a first machine learning (ML)model, the first signals collected by the first set of antennas (“ inputting first input data into a first ML model”[0113]); based on an output from the first ML model, identifying a first object type associated with the object;( receiving a first classification associated with an object from the first ML model.”[0026] & ” an object type classification (e.g., vehicle, pedestrian, bicycle, etc.)” [0079]) inputting, into a second ML model separate from the first ML model (“inputting second input data into a second ML model” [0133] & “the second ML model may correspond to any one of the processing pipelines 240, 250, or 260,” [0138]), the first RF signals received by the second set of antennas (“second input data representing a second area of the environment”[0117]); based on an output from the second ML model, updating (“determining whether a first probability associated with the first classification or a second probability associated with the second classification satisfy a threshold probability” [0141]) an identification of the object to a second object type of object (“receiving second output data from the second ML model, the second output data comprising a second classification associated with the object;” [0163]) different from the first object type (“an object type classification (e.g., vehicle, pedestrian, bicycle, etc.)” [0030]) based at least in part on the second type of object, generating and causing transmission (“The vehicle 402 may connect to computing device(s) 442 via network 440 “ [0114])
Parikh teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy with the teachings of Parikh to incorporate the features of inputting a first data into a first and second machine learning models to receive a first and second object classification so as to gain the advantage of improving processing times [0080, Parikh]. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Murphy as modified by Parikh disclose generating second RF signals different from the first RF signals, but do not explicitly disclose nor limit transmitting the second signal towards the object. SHIMIZU discloses, generating and causing transmission, toward the object using the second set of antennas, of the second RF signals (“and radiates the second beam 16-2, which is an RF signal, from the second transmission antenna 15-2 toward the target.” [0127]).
SHIMIZU teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy as modified by Parikh with the teachings of SHIMIZU to incorporate the features of transmitting the second signal towards the object so as to gain the advantage of improving measurement capabilities. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Regarding claim 3, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy fails to set forth the machine learning model of claim 3. Parikh discloses the method wherein, the first set of RF signal data is sampled prior to inputting the first set of RF signal data into the machine learning model (“the operation 102 can include extracting a portion of the sensor data for processing” [0034]).
Parikh teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy with the teachings of Parikh to incorporate the features of sampling the first data set prior to inputting into the machine learning model so as to gain the advantage of reducing latency [0031, Parikh]. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Regarding claim 4, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method further comprising, by the one or more hardware processors executing program instructions (“position processor 24” [Col.5, ll.2-3]): determine additional features associated with the first set of RF signal data or the type of object (“ calculates the location of object 12.” [Col.5, ll.3-4]).
Regarding claim 5, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 4. Murphy discloses the method wherein, the additional features associated with the first set of RF signal data signals include one or more of: bandwidth (“That is, base station 20 can, optionally, cause tracking units 10a-10c to scan a selected frequency range for a transceiver emitted signal” [Col.9, ll.62-64]), channel (“tracking unit 10 transmits an activation signal via communication link 18 “ [Col.4, ll.28-30]), and signal rate (“More specifically, the location of object 12 is determined by the present invention at various time intervals” [Col.5, ll53-55]).
Regarding claim 6, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method wherein, the first set of antennas are the same as the second set of antennas (FIG.1A, Part 10).
Regarding claim 7, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method wherein, the first set of antennas are different from the second set of antennas (FIG.2, Part.36).
Regarding claim 8, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method wherein, the transmission of the second set of RF signals includes transmission of the second set of RF signals in a direction associated with the object (“a transmitter device activation signal to be transmitted from multi-directional antenna 26” [Col.7, ll.7-8]).
Regarding claim 10, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method further comprising by the one or more hardware processors executing program instructions: while causing transmission of the second set of RF signals, automatically tracking a location of an object associated with the first set of RF signals (“ the location of object 12 is determined by the present invention at various time intervals” [Col.5, ll.54-55]).
Regarding claim 11, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 10. Murphy discloses the method wherein, the tracking of the location of the object is performed by a radio direction finder (“Tracking unit 10 also includes a direction determining system 700 “ [Col.3, ll.66-67]).
Regarding claim 12, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 10. Murphy discloses the method wherein, a non-transitory computer readable storage medium having program instructions embodied therewith FIG.2, Part 40); and one or more processors configured to execute the program instructions to cause the system to perform the computer-implemented method of any one of claims 1 and claims 3 to 11 (“The received radio-navigation signals are then processed by processor 40”[Col.7, ll.18-20]).
Regarding claim 24, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the system comprising: a computer readable storage medium having program instructions embodied therewith (FIG.2, Part 40); and one or more processors configured to execute the program instructions to cause the system to perform the computer-implemented method of any of a computer readable storage medium having program instructions embodied therewith (“The received radio-navigation signals are then processed by processor 40”[Col.7, ll.18-20]); and one or more processors configured to execute the program instructions to cause the system to perform the computer-implemented method of any one of claims 1 and claims 3 to 11 (FIG.2, Part.40).
Regarding claim 25, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy does not explicitly disclose nor limit a first ML model. Parikh discloses wherein, the first ML model is trained to identify an object type (“object type classification (e.g., vehicle, pedestrian, bicycle, etc.)” [0057] and “receiving a first classification associated with an object from the first ML model” [0133]).
Parikh teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy with the teachings of Parikh to incorporate the features of a first ML model trained to identify an object type so as to gain the advantage of improving classification. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Regarding claim 26, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy does not explicitly disclose nor limit a first ML model. Parikh discloses wherein, the first ML model is trained to determine an object classification ( “receiving a first classification associated with an object from the first ML model” [0133]) and a respective probability (“the output data may represent a plurality of logits (e.g., a function that represents probability values from 0, or negative infinity, to 1, or infinity) “ [0022] & “The first portion of the ML model may process the multi-channel image data and determine intermediate output data” [0127]).
Parikh teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy with the teachings of Parikh to incorporate the features of a first ML model trained to determine an object classification and a respective probability so as to gain the advantage of improving classification. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Regarding claim 27, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 26. Murphy does not explicitly disclose nor limit object classification. Parikh discloses the method further comprising, by the one or more hardware processors executing program instructions (“the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations” [0122]): identifying the first object type based on the object classification (“object type classification (e.g., vehicle, pedestrian, bicycle, etc.)” [0057] and “receiving a first classification associated with an object from the first ML model” [0133]) and the respective probability (FIG.6, Step.616).
Parikh teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy with the teachings of Parikh to incorporate the features of identifying the first object type based on the object classification and the respective probability so as to gain the advantage of improving classification. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy(US6094164A) as modified by Parikh(US 20220207308 A1) and SHIMIZU(US20210325524A1) as applied to claim 1 above, and further in view of SINGHAL(WO2021108361A1).
Regarding claim 9, Murphy as modified by Parikh and further modified by SHIMIZU discloses all of the limitations of claim 1. Murphy discloses the method further comprising, by the one or more hardware processors executing program instructions: accessing a preconfigured list of RF frequencies (“That is, base station 20 can, optionally, cause tracking units 10a-10c to scan a selected frequency range for a transceiver emitted signal” [Col.9, ll62-64]);
Murphy as modified by Parikh and SHIMIZU does not explicitly disclose nor limit wherein frequencies are filtered out of the second RF signal. Singhal discloses the method comprising, prior to transmission of the second set of RF signals, filtering the second set of RF signals to remove one or more RF frequencies based at least in part on the preconfigured list (“the wireless signals 804 […] are received by the same set of receiver antennas, but decoupled by a transceiver circuit or a digital signal processing (DSP) module to separate the first subcarrier frequencies and the second subcarrier frequencies.” [0067]).
Singhal teaches in the same field of radar object detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Murphy as modified by Parikh and SHIMIZU with the teachings of Singhal to incorporate the features of filtering out frequencies from the second RF signal so as to gain the advantage of improving channel information [0071, Singhal]. Also, since it has been held that if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond his or her skill (MPEP 2143).
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.
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/C.P.R./Examiner, Art Unit 3646
/JACK W KEITH/Supervisory Patent Examiner, Art Unit 3646