Patent Application 17616749 - WIRELESS COMMUNICATION-BASED CLASSIFICATION OF - Rejection
Appearance
Patent Application 17616749 - WIRELESS COMMUNICATION-BASED CLASSIFICATION OF
Title: WIRELESS COMMUNICATION-BASED CLASSIFICATION OF OBJECTS
Application Information
- Invention Title: WIRELESS COMMUNICATION-BASED CLASSIFICATION OF OBJECTS
- Application Number: 17616749
- Submission Date: 2025-04-07T00:00:00.000Z
- Effective Filing Date: 2021-12-06T00:00:00.000Z
- Filing Date: 2021-12-06T00:00:00.000Z
- National Class: 342
- National Sub-Class: 450000
- Examiner Employee Number: 96878
- Art Unit: 3648
- Tech Center: 3600
Rejection Summary
- 102 Rejections: 0
- 103 Rejections: 2
Cited Patents
No patents were cited in this rejection.
Office Action Text
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/23/2024 has been entered. Response to Amendment The amendment filed on 12/23/2024 has been entered. Claims 1-2, 4, and 6-22 remain pending in this application. Claims 1, 6, 11, 16, and 21 have been amended. Applicant's amendments to the claims have overcome each and every objection set forth in the Non-Final Office Action dated 09/23/2024. Response to Arguments Applicant’s arguments filed 12/23/2024 regarding prior art rejections have been fully considered and are persuasive. All prior art rejections are overcome in consideration of amendments. 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, 6-14, and 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over Ewert (US 20180165965 A1), hereinafter Ewert, in view of Jia (US 20180173971 A1), hereinafter Jia. Regarding claim 1, Ewert, as shown below, discloses an RF system comprising the following limitations: at least one hardware processor (See at least Fig. 2, [0044] “device 105 includes a read-in unit 200, a processing unit 205”); 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 [0017] “having program code which may be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory, and which is used to carry out, implement and/or activate the steps of the method according to one of the specific embodiments described above, in particular if the program product or program is executed on a computer or a device.”): 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 (See at least [0050] “Possible interfaces for the exchange of radio signals and/or position data between pedestrians and vehicles are: The data exchange between pedestrians and the vehicle takes place via radio connections, such as NFC, WLAN or mobile communication”), wherein said dataset comprises, with respect to each of said objects, at least two of: (i) signal parameters of said associated wireless transmissions (See at least [0051] “data read in with the aid of pedestrian signal 125, which are exchanged within the scope of the communication between the pedestrian and vehicles are: GPS and/or GLONASS and/or BeiDou and/or Galileo coordinates of the pedestrian and/or radio signals including a time stamp and/or a signal strength of the radio signals and/or parameters for a communication quality and/or pedestrian identification number”), (ii) data included in said associated wireless transmissions (See at least [0051] “data read in with the aid of pedestrian signal 125, which are exchanged within the scope of the communication between the pedestrian and vehicles are: GPS and/or GLONASS and/or BeiDou and/or Galileo coordinates of the pedestrian and/or radio signals including a time stamp and/or a signal strength of the radio signals and/or parameters for a communication quality and/or pedestrian identification number” Ewert discloses data including coordinates), and (iii) locational parameters with respect to said object (See at least [0051] “data read in with the aid of pedestrian signal 125, which are exchanged within the scope of the communication between the pedestrian and vehicles are: GPS and/or GLONASS and/or BeiDou and/or Galileo coordinates of the pedestrian and/or radio signals including a time stamp and/or a signal strength of the radio signals and/or parameters for a communication quality and/or pedestrian identification number” Ewert discloses data including coordinates), Ewert 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, data, and locational parameters obtained from wireless transmissions associated with a target object within a physical scene, 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. However, Jia, in the same or in a similar field of endeavor, discloses: 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 [0054] “The on-board neural network subsystem 134 can also use the input sensor data 155 to generate training data 123.” [0059] “The neural network subsystem 114 can receive training examples 123 as input. The training examples 123 can include auto-labeled training data”), and at an inference stage, apply said trained machine learning model to a target dataset comprising signal parameters, data, and locational parameters obtained from wireless transmissions associated with a target object within a physical scene, to classify at least one of (See at least [0015] In some implementations, an autonomous or semi-autonomous vehicle is capable of using a high precision object detection neural network to automatically identify and classify objects of interest in an environment around the vehicle.): (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 [0016] “An autonomous or semi-autonomous vehicle system can use a fully-trained neural network subsystem to classify objects corresponding to pedestrians or cyclists.”) 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 RF system disclosed by Ewert with the machine learning system disclosed by Jia. One would have been motivated to do so in order to advantageously improve prediction accuracy (See at least [0016] “An autonomous or semi-autonomous vehicle system can use a fully-trained neural network subsystem to classify objects corresponding to pedestrians or cyclists. The system can use techniques to improve the accuracy of the predictions.”). Regarding claim 2, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claim 1. Ewert further discloses wherein 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 [0027] “pedestrian 110 situated in a pedestrian position. Device 105 is furthermore designed to process pedestrian signal 125 to provide a signal for detecting pedestrian 110.”); 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 [0026] “According to this exemplary embodiment, vehicle 100 is driving on a road 112 and situated in front of an intersection 114 of road 112 with a cross street 115.”). Regarding claim 4, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claim 1. Ewert further discloses said wireless transmissions are transmitted from at least one wireless device associated with each of said objects (See at least Fig. 1 [0028] “According to this exemplary embodiment, device 105 reads in pedestrian signal 125 with the aid of radio transmission from a cell phone 130 of pedestrian 110.” ). Regarding claim 6, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claims 1 and 4. Ewert 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 [0028] “According to this exemplary embodiment, device 105 reads in pedestrian signal 125 with the aid of radio transmission from a cell phone 130 of pedestrian 110.” ). Regarding claim 7, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claim 1. Ewert 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 [0051] “Possible data, i.e., data read in with the aid of pedestrian signal 125, which are exchanged within the scope of the communication between the pedestrian and vehicles are: […] signal strength of the radio signals and/or parameters for a communication quality and/or pedestrian identification numbers.”). Regarding claim 8, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claim 1. Ewert further discloses said data 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 (MSI), 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). Regarding claim 9, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claim 1. Ewert does not disclose said dataset is labelled with said labels. However, Jia further discloses said dataset is labelled with said labels (See at least [0060] “A training engine 116 analyzes the object detection predictions 135 and compares the object detection predictions to the labels in the training examples 123.”). 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 RF system disclosed by Ewert with the machine learning system disclosed by Jia. One would have been motivated to do so in order to advantageously improve prediction accuracy (See at least [0016] “An autonomous or semi-autonomous vehicle system can use a fully-trained neural network subsystem to classify objects corresponding to pedestrians or cyclists. The system can use techniques to improve the accuracy of the predictions.”). Regarding claim 10, the combination of Ewert and Jia, as shown in the rejection above, discloses all of the limitations of claims 1 and 9. Ewert 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, Jia 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 (See at least [0060] “A training engine 116 analyzes the object detection predictions 135 and compares the object detection predictions to the labels in the training examples 123.” , [0059] “categories, e.g., pedestrians, cyclists”); and (ii) applying said label as a label to all of said data instances associated with said one of said objects (See at least [0104] “In this regard, the high-recall object detection neural network 305A may compare the values of the respective confidence scores to classify a detected object from among multiple object categories.”). 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 RF system disclosed by Ewert with the machine learning system disclosed by Jia. One would have been motivated to do so in order to advantageously improve prediction accuracy (See at least [0016] “An autonomous or semi-autonomous vehicle system can use a fully-trained neural network subsystem to classify objects corresponding to pedestrians or cyclists. The system can use techniques to improve the accuracy of the predictions.”). Regarding claims 11 and 21, applicant recites limitations of the same or substantially the same scope as claim 1. Accordingly, claims 11 and 21 are rejected in the same or substantially the same manner as claim 1, shown above. Regarding claims 12, 13, and 22, applicant recites limitations of the same or substantially the same scope as claim 2. Accordingly, claims 12, 13 and 22 are 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 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. 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. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ewert, in view of Jia, in further view of Casas (US 20190382007 A1), hereinafter Casas. Regarding claim 15, Ewert and Jia, as shown above, discloses all the limitations of claims 11 and 14. The combination of Ewert and Jia does not explicitly disclose at least some of said wireless devices comprise more than one transmitter. However, Casas, 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 [0072] “the communications system 136 can include a plurality of components (e.g., antennas, transmitters, and/or receivers) that allow it to implement and utilize multiple-input, multiple-output (MIMO) technology and communication techniques.”). 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 RF system disclosed by Ewert with the machine learning system disclosed by Jia with the data system disclosed by Casas. One would have been motivated to do so in order to advantageously enhance communication technique options (See at least [0072] “the communications system 136 can include a plurality of components (e.g., antennas, transmitters, and/or receivers) that allow it to implement and utilize multiple-input, multiple-output (MIMO) technology and communication techniques.”). Conclusion 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William J. Kelleher can be reached on (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KENNETH W GOOD/Examiner, Art Unit 3648 /William Kelleher/Supervisory Patent Examiner, Art Unit 3648