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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/26/2024 and 11/03/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 2 is objected to for the following informalities:
In Claim 2, “AIS” should be defined the first time it is used, e.g., “Automatic Identification System (AIS).”
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.
Claims 1, 7, and 8 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 1, the claim recites the limitation “acquired one radar image” in line 5. It is unclear if the “acquired one radar image” is related to the “time-series radar images” in line 4 or a new radar image.
Regarding Claim 1, the claim recites the limitation “acquired time-series radar images” in line 8. It is unclear if these are the same “time-series radar images” as in line 4.
Regarding Claim 7, the claim recites the limitation “acquired one radar image” in line 15. It is unclear if the “acquired one radar image” is related to the “time-series radar images” in line 14 or a new radar image.
Regarding Claim 7, the claim recites the limitation “acquired time-series radar images” in line 18. It is unclear if these are the same “time-series radar images” as in line 14.
Regarding Claim 8, the claim recites the limitation “acquired one radar image” in line 2. It is unclear if the “acquired one radar image” is related to the “time-series radar images” in line 1 or a new radar image.
Regarding Claim 8, the claim recites the limitation “acquired time-series radar images” in line 5. It is unclear if these are the same “time-series radar” images as in line 1.
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 1 and 3-8 are rejected under 35 U.S.C. 103 as being unpatentable over Goto (US 2018/0149731) in view of Baird (Baird et al., “A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data,” 2020).
Regarding Claim 1, Goto teaches:
An information processing method, comprising:
acquiring image data that comprises time-series radar images ([0045]: “repeatedly transmit and receive the radio wave at every given timing”; [0048]: “radar image”);
inputting acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input, to acquire the position data of the first false image candidate ([0080-0089]: “unnecessary object”; [0081]: “sea clutter”; Fig. 16);
inputting acquired … radar images to a second training model that outputs position data of a second false image candidate in response to … radar images being input, to acquire the position data of the second false image candidate ([0109-0111]: “false image caused by multiple reflections”; Fig. 19); and
detecting a false image of a radar echo based on the acquired position data of the first false image candidate and the second false image candidate ([0048]: “Echoes displayed in a center section of the display screen may be reflection waves from a sea surface (a sea clutter) near the ship position S.”; [0111]: “the echo image TG2 may be surrounded by a marker MK2 indicated by a dashed line as illustrated in FIG. 19”; Examiner note: Fig. 19 shows the position of the first false image candidate (sea clutter) and second false image candidate (multiple reflections).).
Goto does not explicitly teach inputting time-series radar images into a second training model. Both the sea clutter detection and the multiple reflection detection of Goto appear to use a single radar image.
However, Baird teaches:
inputting acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input (Baird [p. 179283]: “a CNN which is tasked with extracting spatial information within the data”; [p. 179286]: “the CNN has no time dependency”),
inputting acquired time-series radar images to a second training model that outputs position data of a second false image candidate in response to time-series radar images being input (Baird [p. 179283]: “an LSTM, for analyzing temporal changes in the spatial information of the data”; [p. 179287]: “LSTM operates on sequences of segments”), and
detecting a false image based on the outputs of both the first and second training models (Baird [p. 179283]: “Finally, fully connected layers are used to make the classification.”; [p. 179286]: “clutter-only class”; [p. 179289]: “CNN-LSTM combined approach performed better than the baseline.”).
It would have been obvious to one of ordinary skill in the art to modify Goto and input time-series radar images into a second training model and detect a false image based the acquired position data of both the first false image candidate from a single radar image and the second false image candidate from time-series radar images, as taught by Baird. Doing so is beneficial for improving false image detection (Baird [p. 179289]: “the CNN-LSTM combined approach performed better than the baseline.”).
Regarding Claim 3, Goto teaches: wherein the first false image candidate comprises a false image caused by at least one of a side lobe, a backwash, and a cloud ([0048]: “Echoes displayed in a center section of the display screen may be reflection waves from a sea surface (a sea clutter) near the ship position S.”).
Regarding Claim 4, Goto teaches: wherein the second false image candidate comprises a false image caused by at least one of multiple reflections of a detection wave between an own ship and another ship, reflection between another ship and another ship, and reflection between another ship and land ([0109-0111]: “false image caused by multiple reflections”; Fig. 19).
Regarding Claim 5, Goto teaches: the method further comprising differentiating a display mode according to whether the radar echo is a false image or not ([0111]: “in order to facilitate the understanding that the echo image TG2 estimated to be the false image is caused by multiple reflections, for example, the echo image TG2 may be surrounded by a marker MK2 indicated by a dashed line as illustrated in FIG. 19”).
Regarding Claim 6, Goto teaches: the method further comprising differentiating a display mode of the false image based on a false image probability in response to the radar echo being a false image ([0062-0063]: “calculate similarity”; [0111]: “dashed line”).
Regarding Claim 7, Goto teaches:
A radar apparatus, comprising:
processing circuitry ([0043]: “a radar image generating module 3, a tracking processing module 4, the echo identification processor 10”) configured to:
acquire image data that comprises time-series radar images ([0045]: “repeatedly transmit and receive the radio wave at every given timing”; [0048]: “radar image”);
input acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input, to acquire the position data of the first false image candidate ([0080-0089]: “unnecessary object”; [0081]: “sea clutter”; Fig. 16);
input acquired … radar images to a second training model that outputs position data of a second false image candidate in response to … radar images being input, to acquire the position data of the second false image candidate ([0109-0111]: “false image caused by multiple reflections”; Fig. 19); and
detect a false image of a radar echo based on the acquired position data of the first false image candidate and the second false image candidate ([0048]: “Echoes displayed in a center section of the display screen may be reflection waves from a sea surface (a sea clutter) near the ship position S.”; [0111]: “the echo image TG2 may be surrounded by a marker MK2 indicated by a dashed line as illustrated in FIG. 19”; Examiner note: Fig. 19 shows the position of the first false image candidate (sea clutter) and second false image candidate (multiple reflections).).
Goto does not explicitly teach inputting time-series radar images into a second training model. Both the sea clutter detection and the multiple reflection detection of Goto appear to use a single radar image.
However, Baird teaches:
inputting acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input (Baird [p. 179283]: “a CNN which is tasked with extracting spatial information within the data”; [p. 179286]: “the CNN has no time dependency”),
inputting acquired time-series radar images to a second training model that outputs position data of a second false image candidate in response to time-series radar images being input (Baird [p. 179283]: “an LSTM, for analyzing temporal changes in the spatial information of the data”; [p. 179287]: “LSTM operates on sequences of segments”), and
detecting a false image based on the outputs of both the first and second training models (Baird [p. 179283]: “Finally, fully connected layers are used to make the classification.”; [p. 179286]: “clutter-only class”; [p. 179289]: “CNN-LSTM combined approach performed better than the baseline.”).
The rationale to modify Goto with the teachings of Baird persists from Claim 1.
Regarding Claim 8, Goto teaches:
A non-transient computer-readable recording medium ([0043]; [0050]: “memory”), recording a computer program configured to enable a computer to perform processes of:
acquiring image data that comprises time-series radar images ([0045]: “repeatedly transmit and receive the radio wave at every given timing”; [0048]: “radar image”);
inputting acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input, to acquire the position data of the first false image candidate ([0080-0089]: “unnecessary object”; [0081]: “sea clutter”; Fig. 16);
inputting acquired … radar images to a second training model that outputs position data of a second false image candidate in response to … radar images being input, to acquire the position data of the second false image candidate ([0109-0111]: “false image caused by multiple reflections”; Fig. 19); and
detecting a false image of a radar echo based on the acquired position data of the first false image candidate and the second false image candidate ([0048]: “Echoes displayed in a center section of the display screen may be reflection waves from a sea surface (a sea clutter) near the ship position S.”; [0111]: “the echo image TG2 may be surrounded by a marker MK2 indicated by a dashed line as illustrated in FIG. 19”; Examiner note: Fig. 19 shows the position of the first false image candidate (sea clutter) and second false image candidate (multiple reflections).).
Goto does not explicitly teach inputting time-series radar images into a second training model. Both the sea clutter detection and the multiple reflection detection of Goto appear to use a single radar image.
However, Baird teaches:
inputting acquired one radar image to a first training model that outputs position data of a first false image candidate in response to one radar image being input (Baird [p. 179283]: “a CNN which is tasked with extracting spatial information within the data”; [p. 179286]: “the CNN has no time dependency”),
inputting acquired time-series radar images to a second training model that outputs position data of a second false image candidate in response to time-series radar images being input (Baird [p. 179283]: “an LSTM, for analyzing temporal changes in the spatial information of the data”; [p. 179287]: “LSTM operates on sequences of segments”), and
detecting a false image based on the outputs of both the first and second training models (Baird [p. 179283]: “Finally, fully connected layers are used to make the classification.”; [p. 179286]: “clutter-only class”; [p. 179289]: “CNN-LSTM combined approach performed better than the baseline.”).
The rationale to modify Goto with the teachings of Baird persists from Claim 1.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Goto (US 2018/0149731) in view of Baird (Baird et al., “A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data,” 2020), as applied to Claim 1 above, and further in view of Shibata (US 2023/0110788).
Regarding Claim 2, Goto teaches: the method further comprising:
acquiring a detection result at a sensor unit … ([0041]: “detects a target object from an echo signal”); and
detecting the false image of the radar echo based on the acquired detection result, a probability regarding the position data of the first false image candidate output by the first training model, and a probability regarding the position data of the second false image candidate output by the second training model ([0041]; [0062-0063]; Examiner note: Goto detects a target object and then uses similarity scores to determine if the detection is a false image or a real object.).
Goto does not explicitly teach – but Shibata teaches: a sensor unit that comprises at least one of an AIS and an image sensor (Shibata [0085]: “camera”; “AIS”).
It would have been obvious to one of ordinary skill in the art to modify Goto and use an AIS or an image sensor to acquire a detection result, as taught by Shibata. AIS and image sensors are considered ordinary and well-known in the art, and using an AIS or image sensor in addition to a radar sensor would be beneficial for improving detection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH Y. ZHU whose telephone number is (571)270-0170. The examiner can normally be reached Monday-Friday, 8AM-4PM.
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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.
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/NOAH YI MIN ZHU/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648