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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/09/2026 has been entered.
Response to Amendment
The amendment filed on 01/09/2026 has been entered. Claims 1-2, 4, and 6-22 remain pending in this application. Claims 1, 11, and 21 have been amended. No claims are cancelled or are new.
Response to Arguments
Applicant’s arguments filed 01/09/2026 regarding prior art rejections have been fully considered and are persuasive. All prior art rejections are overcome in consideration of amendments, however additional prior art rejections are presented below.
Beginning on page 8 of Applicant’s remarks, the Applicant argues that the instant application is directed towards identification of certain mobile objects in comparison to Arditi which is directed to static objects. However, any distinctions between of static and mobile objects presented by the applicant are merely represented in the specification and do not form any clear distinction in the claims from the prior art. The Examiner similarly argues that distinctions in locational data are not made within the claims and notes that claims are not limited in interpretation to suggestions of the instant specification.
Further, beginning on page 12 of Applicant’s remarks, the Applicant argues that “radar as taught by Arditi actively emits a radio beam and receives its reflection from the surrounding scene”. The Applicant continues the argument with “As a universal rule, technical precautions are made to ensure that the radio waves received and processed by a radar do not comprise radio waves emitted by other sources”. The Examiner respectfully disagrees, and reminds the applicant of both passive radar/lidar systems and bistatic/multistatic radar/lidar systems which both may operate to passively receive radar/lidar signals emitted by active sources. However, the Examiner does note that Arditi does not explicitly discloses a passive, bistatic, or multistatic system.
Additionally, beginning on page 13 of Applicant’s remarks, the Applicant argues that “the present application teaches using transmission metadata included in wireless transmissions emitted by a target object within a physical scene”. However, this interpretation of the claims is far too narrow. The claims do not recite that the transmission is emitted by a target object, rather claim 1 recites “receive a dataset comprising data representing a plurality of radio frequency (RF) wireless transmissions associated with a plurality of objects”. The mere associated of a transmission to an object does not limit interpretation to an emission from an object by the broadest reasonable interpretation of the claim.
The same or similar arguments as above are applied to all independent and dependent claims.
Regarding claim 2, the Applicant further argues that “Arditi’s teachings are directed towards excluding from a dataset objects of classes that are likely move on their own. In contrast, Claim 2 of the present invention is directed towards explicitly including in a dataset solely such object classes”. However, the Applicant’s intent with the classification is not present in claim 2, and claim 1 clearly recites that the objects be classified, which Arditi does and further includes a step of filtering out. Claim 2 as presented does not present any additional functions after object classification, as suggested in arguments. Therefore, Arditi’s classification and filtering system is applicable to the limitations of claim 2. The same or similar arguments as above are applied to similar claims 12, 13, and 22.
Regarding claim 4, the Applicant further argues that the claimed term “associated” has a BRI limited to “co-located”, however, as the Examiner noted in the previous rejection, the BRI of the claim includes interpretation that the wireless transmissions as claimed are not required to originate from a transmitter collocated with an object. While the Examiner notes that a possible interpretation of this claim is presented in figures 7A-7C of the instant application, the language of the claims is not limited to that displayed embodiment. The mere association of a device is independent of a location. Therefore, the Examiner maintains that the teachings of Ariditi are applicable to the limitations of claim 4. The same or similar arguments as above are applied to similar claim 14.
Regarding arguments directed to claim 6 and similarly claims 15 and 16, the prior art rejections are overcome in consideration of amendments, however additional prior art rejections are presented below.
Regarding arguments directed to claims 7-8 and similarly claims 17 and 18, the prior art rejections are overcome in consideration of amendments, however additional prior art rejections are presented below.
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 of this title, 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 1-2, 4, 6-7, 9-14, 16-17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dzierwa (US 20190208491 A1), hereinafter Dzierwa, in view of Arditi (US 20190147331 A1), hereinafter Arditi.
Regarding claim 1, Dzierwa, as shown below, discloses a system comprising the following limitations:
at least one hardware processor (See at least Fig. 2A, Item 214, [0096] “In an embodiment, the RF receiver 210 may be connected to a signal processor 214”); and
a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to (See at least [0264] “The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a non-transitory computer-readable”):
receive a dataset comprising data representing a plurality of radio frequency (RF) wireless transmissions associated with a plurality of objects within a plurality of physical scenes, wherein said dataset comprises, with respect to each of said objects, (See at least Fig. 1, [0095] “The wireless environment 100 may include various sources 104, 106, 108, 110, 112, and 114 generating various radio frequency (RF) signals 116, 118, 120, 122, 124, 126 […] A spectrum management device 102 in the wireless environment 100 may measure the RF energy in the wireless environment 100 across a wide spectrum and identify the different RF signals 116, 118, 120, 122, 124, 126” Dzierwa discloses receiving a plurality of RF signals transmitted from various sources in different locations.) at least two of:
(i) signal parameters of said wireless transmissions, (ii) transmission metadata included in said wireless transmissions, and (iii) locational parameters with respect to said object (See at least [0099] “The measured signal data 234 may include the raw RF energy measurements, time stamps, location information, one or more signal parameters for any identified signals” Dzierwa discloses obtaining at least (i) and (iii)),
(See at least [0096] “The spectrum management device 202 may include an antenna structure 204 configured to receive RF energy expressed in a wireless environment.”)
Dzierwa does not explicitly disclose at a training stage, train a machine learning model on a training set comprising said dataset and labels indicating a type of each of said objects, and at an inference stage, apply said trained machine learning model to a target dataset comprising signal parameters, transmission metadata, and locational parameters
at a training stage, train a machine learning model on a training set comprising said dataset and labels indicating a type of each of said objects (See at least [0065] “The machine-learning models may be trained using any suitable training algorithm, including supervised learning based on labeled training data”), and
at an inference stage, apply said trained machine learning model to a target dataset comprising signal parameters, transmission metadata, and locational parameters (See at least [0025] “The radar's metadata 422 may include any combination of, e.g.: the mounting location of the radar in three-dimensional space relative to a reference point; the radar's orientation; radar type; radio wave transceiver configurations; […] and any other pertinent data at the time the radar data 421 was captured” The Examiner notes that the radar system of Arditi corresponds to the RF receiving system of Dzierwa, which operates as a passive radar system), to classify at least one of: (i) a type of said target object, (ii) movement behavior of said target object, and (iii) usage parameters of said target object (See at least [0044] “HD map to be updated may depend on the classification of the new object (e.g., as previously described, the machine-learning model may output map data with labeled objects). For example, the HD map may not be updated if the object is classified as being a person, an animal, a car, or any other object that is likely to move on its own.” Arditi discloses each of items (i), (ii), and (iii) )
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi. One would have been motivated to do so in order to advantageously increase system accuracy (See at least [0017] “Since data from every data-gathering vehicle may be mapped to this common space, discrepancies between the data would be reduced or minimized, which in turn may yield a more accurate HD map”).
Regarding claim 2, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claim 1. Dzierwa does not disclose said plurality of objects are selected from a group consisting of: a pedestrian, a bicycle rider, a scooter rider, a vehicle operator, a vehicle occupant, a vehicle passenger, and a public transportation passenger; wherein said plurality of scenes are selected from the group consisting of: roadways, highways, public roads, public transportation systems, public venues, work sites, manufacturing facilities, and warehousing facilities. However, Arditi further discloses
said plurality of objects are selected from a group consisting of: a pedestrian, a bicycle rider, a scooter rider, a vehicle operator, a vehicle occupant, a vehicle passenger, and a public transportation passenger (See at least [0044] “HD map to be updated may depend on the classification of the new object (e.g., as previously described, the machine-learning model may output map data with labeled objects). For example, the HD map may not be updated if the object is classified as being a person, an animal, a car, or any other object that is likely to move on its own.”);
wherein said plurality of scenes are selected from the group consisting of: roadways, highways, public roads, public transportation systems, public venues, work sites, manufacturing facilities, and warehousing facilities (See at least [0042] “HD map may be considered as complete when it has map data for every drivable street within a geographic region.”)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi. One would have been motivated to do so in order to advantageously increase system accuracy (See at least [0017] “Since data from every data-gathering vehicle may be mapped to this common space, discrepancies between the data would be reduced or minimized, which in turn may yield a more accurate HD map”).
Regarding claim 4, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claim 1. Dzierwa further discloses
said wireless transmissions are transmitted from at least one wireless device associated with each of said objects (See at least Fig. 1, Items 104, 114, [0095] “The wireless environment 100 may include various sources 104, 106, 108, 110, 112, and 114 generating various radio frequency (RF) signals 116, 118, 120, 122, 124, 126”)
Regarding claim 6, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claims 1 and 4. Dzierwa further discloses
said wireless device is selected from a group consisting of: a mobile device, a smartphone, a smart watch, wireless headphones, a tablet, a laptop, a micro-mobility mounted telematics unit, vehicle-mounted telematics unit, vehicle infotainment system, vehicle handsfree system, vehicle tire pressure monitoring system, a drone, a camera, a dashcam, a printer, an access point, and a kitchen appliance (See at least Fig. 1, Items 104, 106, [0095] “The wireless environment 100 may include various sources 104, 106, 108, 110, 112, and 114 generating various radio frequency (RF) signals 116, 118, 120, 122, 124, 126”)
Regarding claim 7, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claim 1. Dzierwa further discloses
said signal parameters of said wireless transmissions are selected from the group consisting of: signal frequency, signal bandwidth, signal strength, signal phase, signal coherence, and signal timing (See at least [0099] “The measured signal data 234 may include the raw RF energy measurements, time stamps, location information, one or more signal parameters for any identified signals, such as center frequency, bandwidth”)
Regarding claim 9, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claim 1. Dzierwa does not disclose said dataset is labelled with said labels. However, Arditi further discloses
said dataset is labelled with said labels (See at least [0044] “HD map to be updated may depend on the classification of the new object (e.g., as previously described, the machine-learning model may output map data with labeled objects)).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi. One would have been motivated to do so in order to advantageously increase system accuracy (See at least [0017] “Since data from every data-gathering vehicle may be mapped to this common space, discrepancies between the data would be reduced or minimized, which in turn may yield a more accurate HD map”).
Regarding claim 10, the combination of Dzierwa and Arditi, as shown in the rejection above, discloses all of the limitations of claims 1 and 9. Dzierwa does not disclose said labelling comprises:(i) automatically determining a label for at least one of: object type, object movement behavior or object's data usage based on at least one data instance within said dataset associated with one of said objects; and (ii) applying said label as a label to all of said data instances associated with said one of said objects. However, Arditi further discloses
said labelling comprises:(i) automatically determining a label for at least one of: object type, object movement behavior or object's data usage based on at least one data instance within said dataset associated with one of said objects; and (ii) applying said label as a label to all of said data instances associated with said one of said objects (See at least [0044] “HD map to be updated may depend on the classification of the new object (e.g., as previously described, the machine-learning model may output map data with labeled objects), [0046] “he object classifier may further label the detected objects by classification type (e.g., the box 620 and pothole 630 may be specifically labeled as such, or generally labeled as debris)”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi. One would have been motivated to do so in order to advantageously increase system accuracy (See at least [0017] “Since data from every data-gathering vehicle may be mapped to this common space, discrepancies between the data would be reduced or minimized, which in turn may yield a more accurate HD map”).
Regarding claim 11, applicant recites limitations of the same or substantially the same scope as claim 1. Accordingly, claim 11 is rejected in the same or substantially the same manner as claim 1, shown above.
Regarding claim 12, applicant recites limitations of the same or substantially the same scope as claim 2. Accordingly, claim 12 is rejected in the same or substantially the same manner as claim 2, shown above.
Regarding claim 13, applicant recites limitations of the same or substantially the same scope as claim 2. Accordingly, claim 13 is rejected in the same or substantially the same manner as claim 2, shown above.
Regarding claim 14, applicant recites limitations of the same or substantially the same scope as claim 4. Accordingly, claim 14 is rejected in the same or substantially the same manner as claim 4, shown above.
Regarding claim 16, applicant recites limitations of the same or substantially the same scope as claim 6. Accordingly, claim 16 is rejected in the same or substantially the same manner as claim 6, shown above.
Regarding claim 17, applicant recites limitations of the same or substantially the same scope as claim 7. Accordingly, claim 17 is rejected in the same or substantially the same manner as claim 7, shown above.
Regarding claim 19, applicant recites limitations of the same or substantially the same scope as claim 9. Accordingly, claim 19 is rejected in the same or substantially the same manner as claim 9, shown above.
Regarding claim 20, applicant recites limitations of the same or substantially the same scope as claim 10. Accordingly, claim 20 is rejected in the same or substantially the same manner as claim 10, shown above.
Regarding claim 21, applicant recites limitations of the same or substantially the same scope as claim 1. Accordingly, claim 21 is rejected in the same or substantially the same manner as claim 1, shown above.
Regarding claim 22, applicant recites limitations of the same or substantially the same scope as claim 2. Accordingly, claim 22 is rejected in the same or substantially the same manner as claim 2, shown above.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dzierwa and Arditi, in view of Ewert (US 20180165965 A1), hereinafter Ewert.
Regarding claim 8, The combination of Dzierwa and Arditi, as shown above, discloses all the limitations of claim 1. The combination of Dzierwa and Arditi does not explicitly disclose aid transmission metadata included in said wireless transmissions are selected from the group consisting of: data packet parameters, unique device identifier, MAC address, Service Set Identifier (SSID), Basic Service Set Identifier (BSSID), Extended Basic Service Set (ESS), international mobile subscriber identity (IMSI), and temporary IMSI. However, Ewert, in the same or in a similar field of endeavor, discloses
aid transmission metadata included in said wireless transmissions are selected from the group consisting of: data packet parameters, unique device identifier, MAC address, Service Set Identifier (SSID), Basic Service Set Identifier (BSSID), Extended Basic Service Set (ESS), international mobile subscriber identity (IMSI), and temporary IMSI (See at least [0037] “According to one alternative exemplary embodiment, pedestrian 110 is, or further pedestrians are, additionally or alternatively located with the aid of radio position finding, such as via NFC (near field communication) chips” NFC is a unique device identifier).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi with the signal parameter system disclosed by Ewert. One would have been motivated to do so in order to advantageously quickly and easily obtain precise positions (See at least [0006] “As a result of the externally situated unit reading in the pedestrian signal, highly precise pedestrian positions, which, for example, were read in or provided by the external unit itself, such as a cell phone, may advantageously be utilized quickly and easily.”).
Regarding claim 18, applicant recites limitations of the same or substantially the same scope as claim 8. Accordingly, claim 18 is rejected in the same or substantially the same manner as claim 8, shown above.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Dzierwa and Arditi, in view of Wang (US 20160366548 A1), hereinafter Wang.
Regarding claim 15, The combination of Dzierwa and Arditi, as shown above, discloses all the limitations of claims 14 and 11. The combination of Dzierwa and Arditi does not explicitly disclose at least some of said wireless devices comprise more than one transmitter. However, Wang, in the same or in a similar field of endeavor, discloses
at least some of said wireless devices comprise more than one transmitter (See at least [0008] “direction finding (DF) positioning in a wireless location area network (WLAN) is proposed. A multiple antenna IEEE 802.11 transmitting device can transmit signal preamble containing multiple Long Training Field (LTF) symbols in a radio frame from multiple antennas”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the radiofrequency system disclosed by Dzierwa with the machine learning system disclosed by Arditi with the transmitter system disclosed by Wang. One would have been motivated to do so in order to advantageously improve accuracy by decreasing potential error (See at least [0008] “As a result, angle of departure (AoD) of the transmitting device can be resolved by using the resolved signals from each antenna for DF positioning purpose. Furthermore, when the radial resolution error of AoD or AoA positioning increases, DF positioning and fine-timing measurement (FTM) ranging can be jointly applied to reduce the radial resolution error and extends the AoD/AoA service area with positing accuracy”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khairmode (US 20220057486 A1) - Techniques of machine learning of a radar are disclosed, where the radar has a plurality of antennas that are arranged on an antenna array. In an example, a method of machine learning includes obtaining a real training sample from a first real target in field of view of the radar, where the real training sample includes a plurality of first real data signals, where each of the first real data signals are obtained from a corresponding antenna from amongst the plurality of antennas. The method further includes deriving a synthetic training sample by manipulating the plurality of first real data signals to simulate a rotation of the first real target about a pre-determined axis of the antenna array.
Nickel (US 20210294331 A1) - An autonomous road vehicle includes means for receiving wireless identification signals during vehicle navigation and using the wireless identification signals to determine position and identification of nearby road objects. The autonomous road vehicle further includes an autonomous vehicle control system responsive to the positions and the identifications.
Duksta (US 10725139 B1) - Navigation beacons may be trained to receive signals of opportunity from one or more vehicles, to recognize their own position based on such signals, and to transmit information regarding their own position to one or more other vehicles accordingly. The navigation beacons may be of small size and feature a basic construction including one or more transceivers, power sources and the like, and may communicate via a Bluetooth® Low Energy, Ultra Wideband or long-range low-power wireless standard, or any other standard. The navigation beacons may be installed in any location, preferably being mounted to one or more existing fixed structures or facilities (e.g., transportation structures or facilities), and may operate in active and/or passive modes when learning their positions or servicing position information to one or more remote devices.
Mathews (US 20160131751 A1) - Doppler Aided Inertial Navigation (DAIN) facilitates the determination of position, velocity and direction of mobile devices operating in highly obstructed GPS/GNSS environments. Delivering high precision, high resolution positioning information using signals of opportunity, the present invention measures the Doppler shift of a moving device using a variety of signals combined with inertial accelerometers and environmental sensors to deliver an autonomous positioning and navigation capability that does not require external infrastructure or a priori knowledge of signal sources.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH W GOOD whose telephone number is (571)272-4186. The examiner can normally be reached Mon - Thu 7:30 am - 5:00 pm.
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/KENNETH W GOOD/Examiner, Art Unit 3648