Prosecution Insights
Last updated: May 29, 2026
Application No. 18/427,561

WATERBORNE AUTONOMOUS RESCUE DEVICE AND SYSTEM

Non-Final OA §103
Filed
Jan 30, 2024
Priority
Oct 23, 2023 — TW 112140453
Examiner
N'DURE, AMIE MERCEDES
Art Unit
3645
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Feng Chia University
OA Round
1 (Non-Final)
78%
Grant Probability
Favorable
1-2
OA Rounds
10m
Est. Remaining
93%
With Interview

Examiner Intelligence

Grants 78% — above average
78%
Career Allowance Rate
419 granted / 538 resolved
+25.9% vs TC avg
Strong +15% interview lift
Without
With
+15.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
15 currently pending
Career history
554
Total Applications
across all art units

Statute-Specific Performance

§101
1.6%
-38.4% vs TC avg
§103
79.9%
+39.9% vs TC avg
§102
10.4%
-29.6% vs TC avg
§112
3.1%
-36.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 538 resolved cases

Office Action

§103
DETAILED ACTION Non-Final Rejection 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 . Benefit of an Earlier Filing Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in Foreign Application No. (TW) 112140453 filed on 23rd October, 2023. Information Disclosure Statement The information disclosure statement (IDS) submitted on 10/31/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Specification The lengthy specification (more than 20 pages) has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification. Claim Rejections - 35 USC § 103 This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 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. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: Determining the scope and contents of the prior art. Ascertaining the differences between the prior art and the claims at issue. Resolving the level of ordinary skill in the pertinent art. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim(s) 1-3, 5-12, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over TARIYAN (WO 2022/139750 A1) in view of SALLOUM (US 2017/0139031 A1). Referring to Claim 1, TARIYAN teaches a waterborne autonomous rescue device (Abstract; Pg. 1, Ln. 5-8: unmanned life-saving surface vehicle […] prevent people from losing their lives as a result of drowning in areas such as sea, river, lake), including: a buoyant object (Pg. 1, Ln. 5-8; Pg. 2, Ln. 13-16: unmanned life-saving surface vehicle); an audio acquisition unit, disposed on the buoyant object configured to sense an ambient audio in a body of water (pg. 5, Ln 1-12: sonar scanning sensor group; pg. 5, Ln 28-33: the sound system); a driving module, disposed on the buoyant object (Pg. 2, Ln. 27-29; Pg. 4, Ln. 30-Pg. 5, Ln. 12: steering system (11) that provides directing the vehicle for movement and has a BLOC (brushless DC) electric motor, a servo motor and a water jet propulsion system); a central processing module (Pg. 4, Ln. 30-32: main computer unit (1) that processes signals receiving from sensors), disposed on the buoyant object (Pg. 2, Ln. 30-31) to control the driving module to drive the buoyant object to move in the direction of the emission source (Pg. 2, Ln. 30-31: go to the desired coordinate by itself), and electrically connected to the audio acquisition unit and the driving module (Pg. 4, Ln. 30-34: main computer unit (1) that processes signals receiving from sensors) TARIYAN doesn’t explicitly teach the central processing module receiving the ambient audio and executing program data of a voice recognition model to recognize a cry-out-for-help voice in the ambient audio, and the central processing module calculating a direction of an emission source based on the cry-out-for-help voice. SALLOUM teaches an audio acquisition unit ([0031-[0034]: cluster of acoustic sensors), configured to sense an ambient audio in a body of water ([0028]: […] process the received acoustic signals […]); and the central processing module receiving the ambient audio and executing program data of a voice recognition model to recognize a cry-out-for-help voice in the ambient audio ([0035]; Fig. 2: classification of the acoustic source to its target), and the central processing module calculating a direction of an emission source based on the cry-out-for-help voice ([0032]: determining a direction of arrival of an acoustic wave onto a cluster of acoustic sensors in three dimensions using time difference of arrival (TDOA) […] used with simplified method of calculation of direction) It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the waterborne autonomous rescue device disclosed in TARIYAN with the audio acquisition unit and central processing module taught in SALLOUM with a reasonable expectation of success because it would have provide target location, thereby enable location and navigation towards a target (person of destress) based on the detected signals as taught by SALLOUM ([0035]). Referring to Claim 2, SALLOUM teaches the waterborne autonomous rescue device as claimed in claim 1, wherein the central processing module includes an audio separation unit and a feature acquisition unit ([0031]-[0033]: extraction and tracking of the tonal components in the spectrum of a signal acquired by acoustic, hydro-acoustic or seismic sensors that can detect the presence of targets whose acoustic emissions have tonal components); the audio separation unit receives the ambient audio and uses the independent components analysis (ICA) principle and blind source separation (BSS) technology to separate a plurality of independent audios from the ambient audio ([0031]-[0033]: extraction and tracking of the tonal components in the spectrum of a signal acquired by acoustic, hydro-acoustic or seismic sensors that can detect the presence of targets whose acoustic emissions have tonal components); the feature acquisition unit receives the plurality of independent audios and uses the Mel-frequency cepstral coefficients (MFCC) algorithm to perform feature extraction on the plurality of independent audios to output a plurality of feature information ([0031]-[0033]: extraction and tracking of the tonal components in the spectrum of a signal acquired by acoustic, hydro-acoustic or seismic sensors that can detect the presence of targets whose acoustic emissions have tonal components). Referring to Claim 3, SALLOUM teaches the waterborne autonomous rescue device as claimed in claim 2, wherein the central processing module includes a voice recognition unit, the voice recognition unit receives the plurality of feature information, and executes the program data of the voice recognition model to classify the plurality of feature information to confirm whether the plurality of feature information contains the feature information of the cry-out-for-help voice ([0035]: communicate the result of target detection, direction finding relative to multiple separate acoustic sources, and feature vectors extracted from data to be used for classification of the targets). Referring to Claim 5, SALLOUM teaches the waterborne autonomous rescue device as claimed in claim 1, wherein the audio acquisition unit includes four audio receiver units, and the central processing module calculates a time difference of the cry-out-for-help voice sent to any two of the four audio receiver units through the Time difference of arrival (TDOA) method, and then calculates an angle between the two audio receiver units and the emission source through the time difference, where the direction of the emission source is an average value of six non-overlapping included angles obtained by the central processing module after six calculations ([0031]). Referring to Claim 6, TARIYAN teaches the waterborne autonomous rescue device as claimed in claim 1, wherein the central processing module is further electrically connected to a positioning module which is disposed on the buoyant object and transmits a positioning signal to the central processing module according to the position of the buoyant object, and the positioning module includes an electronic compass, such that the positioning signal includes a coordinate information and an azimuth information (Pg. 2, Ln. 29-30). Referring to Claim 7, TARIYAN teaches the waterborne autonomous rescue device as claimed in claim 6, wherein the driving module includes two motors arranged at a bottom of the buoyant object, and rotation speeds of the two motors can be separately controlled (Pg. 5, Ln. 1-12); SALLOUM teaches when the central processing module calculates the direction of the emission source, the central processing module combines the emission source direction and the azimuth information to control the driving module to drive the buoyant object to move forward in the emission source direction ([0032]). Referring to Claim 8, TARIYAN teaches the waterborne autonomous rescue device as claimed in claim 1, wherein the central processing module stores coordinate information of a safe location, and the central processing module is electrically connected to an input module which is disposed on the buoyant object and able to generate an input signal to the central processing module according to operation of a user, and when the central processing module receives the input signal, the central processing module controls the driving module to drive the buoyant object to move forward in the direction of the safe location, where the location of the user is the location of the emission source (Pg. 2, Ln 19-Pg. 3, Ln 8). Referring to Claim 9, TARIYAN teaches the waterborne autonomous rescue device as claimed in claim 1, wherein the central processing module is further electrically connected to a camera module which is disposed on the buoyant object and used to capture underwater images of the body of water to output real time images to be output to the central processing module, and the central processing module identifies a target in the real time images that sends out an ask-for-help signal, and controls the driving module to drive the buoyant object to move forward to the target (Pg. 5, Ln. 1-12 and 28-33). Claim 10 is essentially the same as Claim 1 and refers to a waterborne autonomous rescue system including: the waterborne autonomous rescue device includes of Claim 1; and further including: at least one waterborne autonomous rescue device sending out an operation status information; a server, communicating with the at least one waterborne autonomous rescue device, and receiving and storing the operation status information of the at least one waterborne autonomous rescue device; a monitoring device communicating with the server to read and display the operation status information of the at least one waterborne autonomous rescue device (TARIYAN’ Pg. 3, Ln. 23-Pg. 5, Ln. 33). Therefore Claim 10 is rejected for the same reasons as applied to Claim 1 above. Claim 11 is essentially the same as Claim 2 and is rejected for the same reasons as applied to Claim 2 above. Claim 12 is essentially the same as Claim 3 and is rejected for the same reasons as applied to Claim 3 above. Claim 14 is essentially the same as Claim 5 and is rejected for the same reasons as applied to Claim 5 above. Claim 15 is essentially the same as Claim 6 and is rejected for the same reasons as applied to Claim 6 above. Claim 16 is essentially the same as Claim 7 and is rejected for the same reasons as applied to Claim 7 above. Claim 17 is essentially the same as Claim 8 and is rejected for the same reasons as applied to Claim 8 above. Claim 18 is essentially the same as Claim 9 and is rejected for the same reasons as applied to Claim 9 above. Claim(s) 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over TARIYAN in view of SALLOUM as applied to Claim(s) 1 and 8 above, and further in view of YAN (US 2020/0234673 A1). Referring to Claim 4, TARIYAN as modified with SALLOUM teaches the waterborne autonomous rescue device as claimed in claim 1, furthermore, SALLOUM teaches the central processing module calculates the direction of the emission source that emits the cry-out-for-help voice through a Time difference of arrival (TDOA) method ([0035]); However, SALLOUM doesn’t explicitly teach the voice recognition model is trained through a Convolutional neural network (CNN) algorithm. YAN teaches the voice recognition model is trained through a Convolutional neural network (CNN) algorithm ([0045]-[0048]). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the waterborne autonomous rescue device disclosed in TARIYAN with the Convolutional neural network taught in YAN with a reasonable expectation of success because it would have improved the accuracy and reliability of detecting and locating a target. Claim 13 is essentially the same as Claim 4 and is rejected for the same reasons as applied to Claim 4 above. Examiner’s Note Examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the Applicant. However, any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. 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)). Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIE M N'DURE whose telephone number is (571)272-6031. The examiner can normally be reached on 8AM-5:30PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Isam Alsomiri can be reached on 571-272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AMIE M NDURE/Examiner, Art Unit 3645 /ABDALLAH ABULABAN/Primary Examiner, Art Unit 3645
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Prosecution Timeline

Jan 30, 2024
Application Filed
Apr 08, 2026
Non-Final Rejection mailed — §103 (current)

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Prosecution Projections

1-2
Expected OA Rounds
78%
Grant Probability
93%
With Interview (+15.2%)
3y 2m (~10m remaining)
Median Time to Grant
Low
PTA Risk
Based on 538 resolved cases by this examiner. Grant probability derived from career allowance rate.

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