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 .
Examiner’s Note
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Response to Election/Restrictions
Applicant's election without traverse of Group I (claim(s) 1-17) in the reply filed on 06 April, 2026 is acknowledged. Group II (claim(s) 18-20) are withdrawn from consideration.
Claim Objections
Claim(s) 6 and 15 is/are objected to because of the following informalities:
Claim 6 recites “configured to identify one or more features in the object” which is suggested to be amended to “configured to identify the one or more features in the object”.
Claim 15 recites “configured to identify one or more features in the object” which is suggested to be amended to “configured to identify the one or more features in the object”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 6-8, 10, 12, and 15-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Guo et al. (US 2022/0317280 A1 “GUO”).
Regarding claim 1, GUO discloses a system comprising: at least one radar component (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015]); and at least one controller (the radar detection and identification device 1 is electrically connected to a server equipment 2 which includes at least one processor 21 and at least one computer readable recording media 22 which stores one or more media files and radar wave data [0018]) that is configured to: cause the radar component to emit one or more radar signals; and cause the radar component to analyze one or more return signals, wherein the analysis includes identifying one or more features of an object toward which the radar signals were directed (the present invention effectively applies the features of millimeter wave radar: the delay between transmission and reflection signals, calculating the distance between the system and the object; meanwhile, confirming the speed of the object by measuring the corresponding phase difference [0016]).
Regarding claim 3, GUO discloses the system of claim 1, wherein the radar component is configured to measure at least one of amplitude, phase, or frequency of the return signals (calculating the distance between the system and the object; meanwhile, confirming the speed of the object by measuring the corresponding phase difference [0016], cited and incorporated in the rejection of claim 1).
Regarding claim 6, GUO discloses the system of claim 1, wherein the radar component is configured to identify one or more features in the object using feature extraction (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015], cited and incorporated in the rejection of claim 1).
Regarding claim 7, GUO discloses the system of claim 6, wherein the object comprises a user's face or body (the human body physiological signal information [0015], cited and incorporated in the rejection of claim 1).
Regarding claim 8, GUO discloses the system of claim 1, wherein the radar component is configured to sense a user's heartbeat by analyzing pulse or body movements (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015], cited and incorporated in the rejection of claim 1).
Regarding claim 10, GUO discloses a computer-implemented method comprising: causing a radar component to emit one or more radar signals; and causing the radar component to analyze one or more return signals, wherein the analysis includes identifying one or more features of an object toward which the radar signals were directed (the present invention effectively applies the features of millimeter wave radar: the delay between transmission and reflection signals, calculating the distance between the system and the object; meanwhile, confirming the speed of the object by measuring the corresponding phase difference [0016]).
Regarding claim 12, GUO discloses the computer-implemented method of claim 10, further comprising measuring at least one of amplitude, phase, or frequency of the return signals (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015]).
Regarding claim 15, GUO discloses the computer-implemented method of claim 10, wherein the radar component is configured to identify one or more features in the object using feature extraction (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015]).
Regarding claim 16, GUO discloses the computer-implemented method of claim 15, wherein the object comprises a user's face or body (the human body physiological signal information [0015]).
Regarding claim 17, GUO discloses the computer-implemented method of claim 10, further comprising sensing a user's heartbeat by analyzing pulse or body movements (the radar detector 13 can detect the radar wave data and convert into the human body physiological signal information (e.g., the information about the number of breaths and heartbeats per minute) [0015]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over GUO.
Regarding claim 2, GUO discloses the system of claim 1, wherein the radar component includes a transmitter, an antenna, and a receiver (the present invention effectively applies the features of millimeter wave radar [0016]). The Examiner further noted that GUO discloses the use of millimeter wave radar, but does not explicitly disclose the millimeter wave radar having a transmitter, an antenna, and a receiver. However, a transmitter, antenna, and receiver are considered to be inherently included in a millimeter wave radar. Alternatively, these components are ubiquitous in the radar arts and therefore considered to be obvious to one of ordinary skill in the art to include in a radar system. Without these components, a radar would not be able to function because it could not transmit and receive radio waves.
Regarding claim 11, GUO discloses the computer-implemented method of claim 10, wherein the radar component includes a transmitter, an antenna, and a receiver (the present invention effectively applies the features of millimeter wave radar [0016]). The Examiner further noted that GUO discloses the use of millimeter wave radar, but does not explicitly disclose the millimeter wave radar having a transmitter, an antenna, and a receiver. However, a transmitter, antenna, and receiver are considered to be inherently included in a millimeter wave radar. Alternatively, these components are ubiquitous in the radar arts and therefore considered to be obvious to one of ordinary skill in the art to include in a radar system. Without these components, a radar would not be able to function because it could not transmit and receive radio waves.
Claim(s) 4-5 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over GUO, in view of Harrison (US 2021/0103027 A1 “HARRISON”).
Regarding claim 4, GUO discloses (Examiner’s note: What GUO does not disclose is ) the system of claim 1,
In a same or similar field of endeavor, HARRISON teaches that the training of a radar perception engine is bootstrapped by the training of camera and lidar perception engines in the multi-sensor fusion platform [0017]. The trained networks enable the detection and identification of objects in both camera and FMCW lidar data. By combining information from previous measurements, expected measurement uncertainties, and some physical knowledge, the Kalman filter 622 can generate robust, accurate estimates of moving objects. The result are object detection labels 624 and super-resolution labels 626 that are used to train the radar network 620 [0056].
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 system of GUO to include the teachings of HARRISON, because doing so would improve the performance/accuracy of the object detection and identification over time, as recognized by HARRISON. In addition, both of the prior art references, GUO and HARRISON, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, object and image detection in a radar system.
Regarding claim 5, GUO/ HARRISON discloses the system of claim 4, wherein the trained machine learning model is trained using radar return signals and camera data (the training of a radar perception engine is bootstrapped by the training of camera and lidar perception engines in the multi-sensor fusion platform [HARRISON 0017]. The trained networks enable the detection and identification of objects in both camera and FMCW lidar data. By combining information from previous measurements, expected measurement uncertainties, and some physical knowledge, the Kalman filter 622 can generate robust, accurate estimates of moving objects. The result are object detection labels 624 and super-resolution labels 626 that are used to train the radar network 620 [HARRISON 0056], cited and incorporated in the rejection of claim 4).
Regarding claim 13, GUO discloses the computer-implemented method of claim 10,
In a same or similar field of endeavor, HARRISON teaches that the training of a radar perception engine is bootstrapped by the training of camera and lidar perception engines in the multi-sensor fusion platform [0017]. The trained networks enable the detection and identification of objects in both camera and FMCW lidar data. By combining information from previous measurements, expected measurement uncertainties, and some physical knowledge, the Kalman filter 622 can generate robust, accurate estimates of moving objects. The result are object detection labels 624 and super-resolution labels 626 that are used to train the radar network 620 [0056].
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 system of GUO to include the teachings of HARRISON, because doing so would improve the performance/accuracy of the object detection and identification over time, as recognized by HARRISON.
Regarding claim 14, GUO/ HARRISON discloses the computer-implemented method of claim 13, wherein the trained machine learning model is trained using radar return signals and camera data (the training of a radar perception engine is bootstrapped by the training of camera and lidar perception engines in the multi-sensor fusion platform [HARRISON 0017]. The trained networks enable the detection and identification of objects in both camera and FMCW lidar data. By combining information from previous measurements, expected measurement uncertainties, and some physical knowledge, the Kalman filter 622 can generate robust, accurate estimates of moving objects. The result are object detection labels 624 and super-resolution labels 626 that are used to train the radar network 620 [HARRISON 0056], cited and incorporated in the rejection of claim 13).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over GUO, in view of Adachi (US 4,210,357 A “ADACHI”).
Regarding claim 9, GUO discloses the system of claim 1,
In a same or similar field of endeavor, ADACHI teaches that the light-transmissible antenna reflector 26 can be made by coating one side (concave side) of a suitably curved plate 28 of transparent glass with a thin, i.e. a few microns thick, film 30 of a metal or a transparent and conductive metal oxide such as tin oxide [col. 3, lines 25-30]. Since each of the antenna reflectors 26 in FIG. 5C is located in a front portion of the car and protrudes sidewise from the outline of the car body, the radar beam from each antenna 26 can be directed nearly parallel to the center axis of the car and, hence, leaves little blind zone alongside the car or in the side-rear of the car. In other words, the radar with the antenna reflectors 26 thus arranged can accomplish side-rear lookout to the extent necessary for the car to change its course or lane safely [col. 3, lines 62-70].
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 system of GUO to include the teachings of ADACHI, because doing so would further enhance the side lookout ability of the radar, by utilizing an antenna reflector, as recognized by ADACHI. In addition, both of the prior art references, GUO and ADACHI, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, radar system for object detection and tracking.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zeng et al. (US 2014/0035775 A1) is considered pertinent art for the disclosure of a vehicle obstacle detection system including an imaging system for capturing objects in a field of view and a radar device for sensing objects in a substantially same field of view. The substantially same field of view is partitioned into an occupancy grid having a plurality of observation cells. A fusion module receives radar data from the radar device and imaging data from the imaging system. The fusion module extracts features from each corresponding cell using sensor data from the radar device and imaging data from the imaging system. A primary classifier determines whether an extracted feature extracted from a respective observation cell is an obstacle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WILLIAM J KELLEHER can be reached at (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Hailey R Le/Examiner, Art Unit 3648 April 29, 2026
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