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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10 March, 2026 has been entered.
Response to Amendment
Applicant’s amendment filed 10 March, 2026 is acknowledged and has been entered.
Response to Arguments
Applicant’s remarks filed 10 March, 2026 have been fully considered but are moot in view of a new ground of rejection.
Claim Objections
Claim(s) 19 is/are objected to because of the following informalities:
Claim 19 recites “the teacher data relates a plurality of concatenated images” which is suggested to be amended to “the teacher data relates to a plurality of concatenated images”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 3 and 5 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, the limitation “m combinations” renders the claim indefinite, because it is unclear what “m” refers to.
Claim 5 is rejected by virtue of its dependence on claim 3.
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) 1, 3, 6-7, 10, 13, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deangelus et al. (US 2022/0334243 A1 “DEANGELUS”), and further in view of Gautam et al. (US 2020/0334494 A1 “GAUTAM”).
Regarding claim 1, DEANGELUS discloses (Examiner’s note: What DEANGELUS does not explicitly disclose is ) a processing system comprising:
a hardware processor configured to execute processes (computer(s) 105 can include one or more processor(s) 115, one or more memory(s) 125, and storage 135 [0054]) comprising:
a concatenating process comprising forming a first concatenated image by concatenating a plurality of images based on received signals acquired by two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual. In this orientation, the first RF image and the second RF image can be fused together [0197])
and a determination process comprising determining, for the first concatenated image, whether or not a predetermined object is present, based on an output of a machine learning model trained using a plurality of in this orientation, the first RF image and the second RF image can be fused together to detect objects that can be located on either side of the individual, and/or behind or in front of the individual. Because the RF images correspond to profile views of the individual, the RF images can be analyzed by the one or more processors of the imaging sensor system to detect objects that are in the bag and/or concealed and secured to the body of the individual [0197]); (to facilitate training of the neural network techniques, RF imagery data can be systematically collected in a controlled setting. RF imagery data can be collected by one or more operators of the imaging sensor system, and the one or more operators can identify one or more target items, of interest, in a scene of the RF imagery data and input instructions to the imaging sensor system to detect the one or more target items using the neural network techniques [0120])
wherein: each of the first concatenated image FIG. 20B illustrates a RF imaging sensor of an embodiment of the imaging sensor system with two RF imaging sensors opposing spaced from each other to illuminate the left and right hand side of an individual, at a 90 degree angle with respect to the individual's direction of movement. For example, FIG. 20B illustrates a hallway 2015 with a panel array orientation in which a first panel array images a right side of the individual and a second panel array images a left side of the individual as the individual walks between the two panel arrays [0195])
In a same or similar field of endeavor, GAUTAM teaches that multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images to determine whether the classified object meet or violate security criteria [0058]. Data storage 104 may be configured to store program instructions that are executable to cause processor 104 and/or GPU 106 to train and execute an object detector that may comprise a multi-perspective object detection model (i.e. a multi-perspective object detector) that employs one or more fusion layers [0066]. Specifically, GAUTAM teaches that at a high level, fusion layer 300 inputs a feature map from a first perspective as input, pass the feature map through a series of transformational layers, such as convolutional, residual, and/or pooling layers, to form a set of values (referred to as a set of “summary values”) that summarizes a feature map along a given dimension, such as a height dimension. After forming the set of summary values, first fusion layer 300 combines the set of summary values generated by that fusion layer with a representation of a feature map from a different perspective, thereby resulting in a combined set of values that represent a combination of a representation of feature map 302 from the first perspective and a representation of feature map 322 from the second perspective [0085].
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 DEANGELUS to include the teachings of GAUTAM, because doing so would improve security screenings, and would be able to analyze images from multiple perspectives by cross-referencing the images from different perspectives in a set of multi-perspective images of a scene, in order to better detect and identify objects as compared to analyzing only a set of single perspective of that scene, as recognized by GAUTAM. In addition, both of the prior art references, DEANGELUS and GAUTAM, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, object detection using neural network.
Regarding claim 3, DEANGELUS/ GAUTAM discloses the processing system according to claim 1, wherein each of the part of the images corresponds to m combinations of a transmitting circuit of the first radar and reception circuits among reception circuits included in the two or more radars, and each of the another part of the images corresponds to a combinations of a transmitting circuit of the second radar and reception circuits among reception circuits included in the two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual [DEANGELUS 0197 & FIG. 20B], cited and incorporated in the rejection of claim 1); (multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images [GAUTAM 0058], cited and incorporated in the rejection of claim 1).
Regarding claim 6, DEANGELUS/ GAUTAM discloses the processing system according to claim 1, wherein: the concatenating process generates the first concatenated image by concatenating m1 images based on received signals acquired by the two or more radars at a first timing and m2 images based on received signals acquired by the two or more radars at a second timing different from the first timing, and m1 and m2 are integers equal to or more than 2 (an individual can walk through a corridor. The walls, ceiling, and/or floor of the corridor can be equipped with one or more RF imaging sensors. The one or more RF imaging sensors can illuminate a portion of the area of the corridor through which the individual can walk through. The RF imaging sensors can detect one or more objects on the person of the individual from one or more of the first, second, third, and/or fourth point-of-views of the individual captured by the RF imaging sensors as the individual walks through the area illuminated by the RF imaging sensors [DEANGELUS 0082 & FIGS. 20A-20E]).
Regarding claim 7, DEANGELUS/ GAUTAM discloses the processing system according to claim 6, wherein: the m1 images are based on received signals corresponding to m1 combinations of a transmitting circuit among transmitting circuits included in the two or more radars and a reception circuit among reception circuits included in the two or more radars, the m2 images are based on received signals corresponding to m2 combinations of a transmitting circuit among the transmitting circuits included in the two or more radars and a reception unit among the reception circuits included in the two or more radars, and m1 and m2 are different from one another (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual [DEANGELUS 0197], cited and incorporated in the rejection of claim 1); (an individual can walk through a corridor. The walls, ceiling, and/or floor of the corridor can be equipped with one or more RF imaging sensors. The one or more RF imaging sensors can illuminate a portion of the area of the corridor through which the individual can walk through. The RF imaging sensors can detect one or more objects on the person of the individual from one or more of the first, second, third, and/or fourth point-of-views of the individual captured by the RF imaging sensors as the individual walks through the area illuminated by the RF imaging sensors [DEANGELUS 0082 & FIGS. 20A-20E]).
Regarding claim 10, DEANGELUS/ GAUTAM discloses the processing system according to claim 1, wherein: the concatenating process generates the first concatenated image by concatenating the plurality of images based on received signals acquired by the two or more radars at p timings among images based on received signals acquired by the two or more radars at o timings, p is an integer equal to or more than 2, and o is an integer equal to or more than p (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual [DEANGELUS 0197], cited and incorporated in the rejection of claim 1); (an individual can walk through a corridor. The walls, ceiling, and/or floor of the corridor can be equipped with one or more RF imaging sensors. The one or more RF imaging sensors can illuminate a portion of the area of the corridor through which the individual can walk through. The RF imaging sensors can detect one or more objects on the person of the individual from one or more of the first, second, third, and/or fourth point-of-views of the individual captured by the RF imaging sensors as the individual walks through the area illuminated by the RF imaging sensors [DEANGELUS 0082 & FIGS. 20A-20E]).
Regarding claim 13, DEANGELUS/ GAUTAM discloses the processing system according to claim 1, further comprising the two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual [DEANGELUS 0197 & FIG. 20B], cited and incorporated in the rejection of claim 1).
Regarding claim 15, DEANGELUS/ GAUTAM discloses the processing system according to claim 1, wherein the predetermined object comprises metal or powder (the one or more processors can compare the portion of the one or more 3-D images at which the metal canister is located to a plurality of known RF images that include objects that are also metal to determine that the object is indeed metal [DEANGELUS 0075]).
Regarding claim 16, DEANGELUS discloses a processing method comprising:
forming a first concatenated image by concatenating a plurality of images based on received signals acquired by two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual. In this orientation, the first RF image and the second RF image can be fused together [0197])
and determining, for the first concatenated image, whether or not a predetermined object is present, based on an output of a machine learning model trained using a plurality of in this orientation, the first RF image and the second RF image can be fused together to detect objects that can be located on either side of the individual, and/or behind or in front of the individual. Because the RF images correspond to profile views of the individual, the RF images can be analyzed by the one or more processors of the imaging sensor system to detect objects that are in the bag and/or concealed and secured to the body of the individual [0197]); (to facilitate training of the neural network techniques, RF imagery data can be systematically collected in a controlled setting. RF imagery data can be collected by one or more operators of the imaging sensor system, and the one or more operators can identify one or more target items, of interest, in a scene of the RF imagery data and input instructions to the imaging sensor system to detect the one or more target items using the neural network techniques [0120])
wherein: each of the first concatenated image FIG. 20B illustrates a RF imaging sensor of an embodiment of the imaging sensor system with two RF imaging sensors opposing spaced from each other to illuminate the left and right hand side of an individual, at a 90 degree angle with respect to the individual's direction of movement. For example, FIG. 20B illustrates a hallway 2015 with a panel array orientation in which a first panel array images a right side of the individual and a second panel array images a left side of the individual as the individual walks between the two panel arrays [0195])
In a same or similar field of endeavor, GAUTAM teaches that multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images to determine whether the classified object meet or violate security criteria [0058]. Data storage 104 may be configured to store program instructions that are executable to cause processor 104 and/or GPU 106 to train and execute an object detector that may comprise a multi-perspective object detection model (i.e. a multi-perspective object detector) that employs one or more fusion layers [0066]. Specifically, GAUTAM teaches that at a high level, fusion layer 300 inputs a feature map from a first perspective as input, pass the feature map through a series of transformational layers, such as convolutional, residual, and/or pooling layers, to form a set of values (referred to as a set of “summary values”) that summarizes a feature map along a given dimension, such as a height dimension. After forming the set of summary values, first fusion layer 300 combines the set of summary values generated by that fusion layer with a representation of a feature map from a different perspective, thereby resulting in a combined set of values that represent a combination of a representation of feature map 302 from the first perspective and a representation of feature map 322 from the second perspective [0085].
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 DEANGELUS to include the teachings of GAUTAM, because doing so would improve security screenings, and would be able to analyze images from multiple perspectives by cross-referencing the images from different perspectives in a set of multi-perspective images of a scene, in order to better detect and identify objects as compared to analyzing only a set of single perspective of that scene, as recognized by GAUTAM.
Regarding claim 17, DEANGELUS discloses a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to execute processes (computer(s) 105 can include one or more processor(s) 115, one or more memory(s) 125, and storage 135 [0054]) comprising:
forming a first concatenated image by concatenating a plurality of images based on received signals acquired by two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual. In this orientation, the first RF image and the second RF image can be fused together [0197])
and determining, for the first concatenated image, whether or not a predetermined object is present, based on an output of a machine learning model trained using a plurality of in this orientation, the first RF image and the second RF image can be fused together to detect objects that can be located on either side of the individual, and/or behind or in front of the individual. Because the RF images correspond to profile views of the individual, the RF images can be analyzed by the one or more processors of the imaging sensor system to detect objects that are in the bag and/or concealed and secured to the body of the individual [0197]); (to facilitate training of the neural network techniques, RF imagery data can be systematically collected in a controlled setting. RF imagery data can be collected by one or more operators of the imaging sensor system, and the one or more operators can identify one or more target items, of interest, in a scene of the RF imagery data and input instructions to the imaging sensor system to detect the one or more target items using the neural network techniques [0120])
wherein: each of the first concatenated image FIG. 20B illustrates a RF imaging sensor of an embodiment of the imaging sensor system with two RF imaging sensors opposing spaced from each other to illuminate the left and right hand side of an individual, at a 90 degree angle with respect to the individual's direction of movement. For example, FIG. 20B illustrates a hallway 2015 with a panel array orientation in which a first panel array images a right side of the individual and a second panel array images a left side of the individual as the individual walks between the two panel arrays [0195])
In a same or similar field of endeavor, GAUTAM teaches that multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images to determine whether the classified object meet or violate security criteria [0058]. Data storage 104 may be configured to store program instructions that are executable to cause processor 104 and/or GPU 106 to train and execute an object detector that may comprise a multi-perspective object detection model (i.e. a multi-perspective object detector) that employs one or more fusion layers [0066]. Specifically, GAUTAM teaches that at a high level, fusion layer 300 inputs a feature map from a first perspective as input, pass the feature map through a series of transformational layers, such as convolutional, residual, and/or pooling layers, to form a set of values (referred to as a set of “summary values”) that summarizes a feature map along a given dimension, such as a height dimension. After forming the set of summary values, first fusion layer 300 combines the set of summary values generated by that fusion layer with a representation of a feature map from a different perspective, thereby resulting in a combined set of values that represent a combination of a representation of feature map 302 from the first perspective and a representation of feature map 322 from the second perspective [0085].
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 DEANGELUS to include the teachings of GAUTAM, because doing so would improve security screenings, and would be able to analyze images from multiple perspectives by cross-referencing the images from different perspectives in a set of multi-perspective images of a scene, in order to better detect and identify objects as compared to analyzing only a set of single perspective of that scene, as recognized by GAUTAM.
Regarding claim 18, DEANGELUS discloses a processing system comprising:
hardware processor (computer(s) 105 can include one or more processor(s) 115, one or more memory(s) 125, and storage 135 [0054]) configured to execute processes comprising:
a concatenating process comprising forming a first concatenated image by concatenating m images based on received signals acquired by two or more radars (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual. In this orientation, the first RF image and the second RF image can be fused together [0197])
and a determination process comprising determining, for the first concatenated image, whether or not a predetermined object is present, based on an output of a machine learning model trained using a plurality of in this orientation, the first RF image and the second RF image can be fused together to detect objects that can be located on either side of the individual, and/or behind or in front of the individual. Because the RF images correspond to profile views of the individual, the RF images can be analyzed by the one or more processors of the imaging sensor system to detect objects that are in the bag and/or concealed and secured to the body of the individual [0197]); (to facilitate training of the neural network techniques, RF imagery data can be systematically collected in a controlled setting. RF imagery data can be collected by one or more operators of the imaging sensor system, and the one or more operators can identify one or more target items, of interest, in a scene of the RF imagery data and input instructions to the imaging sensor system to detect the one or more target items using the neural network techniques [0120])
wherein: the concatenating process comprises forming the first concatenated image by concatenating m1 images based on received signals acquired by the two or more radars at a first timing, and m2 images based on received signals acquired by the two or more radars at a second timing different from the first timing, where m1 is an integer equal to or more than 2, m2 is an integer equal to or more than 2, and m is equal to a sum of m1 and m2, the m1 images and the m2 images are among images based on received signals acquired by the two or more radars at p timings, and p is an integer equal to or more than 2 (FIG. 20B illustrates a RF imaging sensor of an embodiment of the imaging sensor system with two RF imaging sensors opposing spaced from each other to illuminate the left and right hand side of an individual, at a 90 degree angle with respect to the individual's direction of movement. For example, FIG. 20B illustrates a hallway 2015 with a panel array orientation in which a first panel array images a right side of the individual and a second panel array images a left side of the individual as the individual walks between the two panel arrays [0195])
In a same or similar field of endeavor, GAUTAM teaches that multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images to determine whether the classified object meet or violate security criteria [0058]. Data storage 104 may be configured to store program instructions that are executable to cause processor 104 and/or GPU 106 to train and execute an object detector that may comprise a multi-perspective object detection model (i.e. a multi-perspective object detector) that employs one or more fusion layers [0066]. Specifically, GAUTAM teaches that at a high level, fusion layer 300 inputs a feature map from a first perspective as input, pass the feature map through a series of transformational layers, such as convolutional, residual, and/or pooling layers, to form a set of values (referred to as a set of “summary values”) that summarizes a feature map along a given dimension, such as a height dimension. After forming the set of summary values, first fusion layer 300 combines the set of summary values generated by that fusion layer with a representation of a feature map from a different perspective, thereby resulting in a combined set of values that represent a combination of a representation of feature map 302 from the first perspective and a representation of feature map 322 from the second perspective [0085].
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 DEANGELUS to include the teachings of GAUTAM, because doing so would improve security screenings, and would be able to analyze images from multiple perspectives by cross-referencing the images from different perspectives in a set of multi-perspective images of a scene, in order to better detect and identify objects as compared to analyzing only a set of single perspective of that scene, as recognized by GAUTAM.
Regarding claim 19, DEANGELUS/ GAUTAM discloses the processing system according to claim 18, wherein: the teacher data relates a plurality of concatenated images at first positions of the predetermined object in an inspection area, and the m1 images and the m2 images are images at positions corresponding to the first positions (the panel array 2005 generates a first RF image corresponding to the right side of the individual, and the panel array 2007 generates a second RF image corresponding to the left side of the individual [DEANGELUS 0197 & FIG. 20B], cited and incorporated in the rejection of claim 18); (multi-perspective object detectors may be used to analyze multi-perspective data to detect and classify objects from multi-perspective images [GAUTAM 0058], cited and incorporated in the rejection of claim 18); (an individual can walk through a corridor. The RF imaging sensors can detect one or more objects on the person of the individual from one or more of the first, second, third, and/or fourth point-of-views of the individual captured by the RF imaging sensors as the individual walks through the area illuminated by the RF imaging sensors [DEANGELUS 0082 & FIGS. 20A-20E]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over DEANGELUS, in view of GAUTAM, and further in view of Igarashi et al. (US 2004/0267452 A1 “IGARASHI”).
Regarding claim 5, DEANGELUS/ GAUTAM discloses the processing system according to claim 3,
In a same or similar field of endeavor, IGARASHI teaches that the radar device 56 sets a threshold of strength of reflected radio waves for detecting an object at a higher level [0034].
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 DEANGELUS to include the teachings of IGARASHI, because doing so would allow for dynamic sensitivity for obstruction detection, as recognized by IGARASHI. In addition, both of the prior art references, DEANGELUS and IGARASHI, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, object detection.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over DEANGELUS, in view of GAUTAM, and further in view of Ahmed et al. (US 2016/0216371 A1 “AHMED”).
Regarding claim 14, DEANGELUS/ GAUTAM discloses the processing system according to claim 13,
In a same or similar field of endeavor, AHMED teaches that for the acquisition of inter-panel scans, an inter-panel synchronisation of the phase position must be possible, that is, for example, a central or common phase position must be specifiable. For this purpose, for example, the central computer 402 or one of the panels 104, 106 or 108 can be used as a central source. With one configuration, for example, a master panel can therefore function as a clock-generator or respectively phase-generator for two slave panels [0106].
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 DEANGELUS to include the teachings of AHMED, because doing so would improve target object detection accuracy, as recognized by AHMED. In addition, both of the prior art references, DEANGELUS and AHMED, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, object detection using radar arrays.
Allowable Subject Matter
Claim(s) 8-9, and 11-12 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 8, ANGELUS, as modified by GAUTAM, discloses the processing system of claim 1; however, Applicant’s claim 9 also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Specifically, there is nothing in the prior art that would suggest modifying DEANGELUS to have the missing elements without the improper use of hindsight. Specifically, nothing in the prior art would suggest that “wherein: each of the m1 images is based on each of received signals corresponding to m1 combinations of a transmitting circuit among the transmitting circuits included in the two or more radars and a reception circuit among the reception circuits included in the two or more radars, among n1 combinations of a transmitting circuit among the transmitting circuits included in the two or more radars and a reception circuit among the reception circuits included in the two or more radars, each of the m2 images is based on each of received signals corresponding to m2 combinations of a transmitting circuit among the transmitting circuits included in the two or more radars and a reception circuit among the reception circuits included in the two or more radars, among n2 combinations of a transmitting circuit among the transmitting circuits included in the two or more radars and a reception circuit among the reception circuits included in the two or more radars, n1 is an integer equal to or more than m1, and n2 is an integer equal to or more than m2”.
Within the context of Applicant’s claimed invention as a whole, the prior arts made of record individually or in any combination, failed to teach, render obvious, or fairly suggest to one of ordinary skill in the art at the time of filing the combination of the claimed feature(s) of claim(s) 8.
Claim 9 would be allowed by virtue of its dependence on claim 8.
Regarding claim 11, Similarly, ANGELUS, as modified by GAUTAM, discloses the processing system of claim 10; however, Applicant’s claim 9 also encompasses an invention that the prior art does not disclose, teach, or otherwise render obvious. Specifically, there is nothing in the prior art that would suggest modifying DEANGELUS to have the missing elements without the improper use of hindsight. Specifically, nothing in the prior art would suggest that “wherein: received signals acquired at o timings are received signals corresponding to combinations each formed by a transmitting circuit among transmitting circuits included in the two or more radars and a reception circuit among reception circuits included in the two or more radars, received signals acquired at a first timing among p timings are received signals corresponding to m1 combinations each formed by a transmitting circuit among transmitting circuits included in the two or more radars and a reception circuit among reception circuits included in the two or more radars, received signals acquired at a second timing different from the first timing among p timings are received signals corresponding to m2 combinations each formed by a transmitting circuit among transmitting circuits included in the two or more radars and a reception circuit among reception circuits included in the two or more radars, and m1 and m2 are integers equal to or more than 2”.
Claim 12 would be allowed by virtue of its dependence on claim 11.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Graham et al. (US 2022/0214447 A1) is cited as pertinent art for the disclosure overall, and particularly the disclosure of applying multiple scans can be a Hall Monitor (HM) with four radar imaging sensors aimed 90 degrees apart so that they could be used at an intersection of two intersecting (in this case, perpendicular) hallways, allowing for full front and back scans of targets moving in any direction down the two halls. Likewise, multiple HMs could be placed in a hallway at fixed intervals, or in different hallways, to provide the same capability.
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 May 2, 2026
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