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 05/21/2026 has been considered by the examiner.
Response to Amendment
The amendment filed on 08/10/2026 is being entered. Claims 1-20 are pending. Claims 1-20 are pending. The amendment overcomes the 35 U.S.C. 112(d) rejection and previous 35 U.S.C. 103 rejection. Further, some of the objections to the claims and some the rejections under 35 U.S.C. 112(b) are overcome. However, after further consideration and search, the claims and drawings are objected to and the claims are also rejected under 35 U.S.C. 112(b) and 35 U.S.C. 103. Therefore, in response to this amendment, this rejection has been made final, as necessitated by the amendment.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the reference numerals as described in the specification. The new drawings submitted 08/10/2026 point to different areas of the submarine and not any specific component that indicates it is different than the other component (see for example 640, 650, 660). Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “760” has been used to designate both communication packages and navigation systems. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 260, 220, 201, 240, 620, 630, 650, 660, 710-750 , 770-780, 801, 830, 850, 860, and 870. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 3 are objected to because of the following informalities:
Claim 1 is objected to because of the following informalities: the claim recites “wherein depending on maritime conditions the swarm of AUVs”. Examiner suggests amending to “wherein depending on maritime conditions, the swarm of AUVs”. Appropriate correction is required.
Claim 1 is further objected to because of the following informalities: the claim recites “RF” in the second to last line. Examiner suggests amending to “radio frequency (RF)”. Appropriate correction is required.
Claim 3 is objected to because of the following informalities: the claim recites “solar cells (i.e. photovoltaic cells)”. Examiner suggests amending this to “solar cells” or “photovoltaic cells”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 1-20 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.
Claim 1 recites the limitation “the navigation trajectory network" in line 15. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-20 depend from claim 1 and are also rejected under 35 U.S.C. 112(b).
Claim 5 further recites “the AUVs can configure a trajectory model using the generative learning system”. This make at it unclear as whether the AUVs is able to configure a trajectory model using the generative learning system”. Examiner is interpreting that the AUVs configure a trajectory model using the generative learning system.
Claim 16 recites “the underwater vehicle” in line 5. There is insufficient antecedent basis for this limitation in the claim. Examiner suggest amending to UAV.
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, 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 4-20 are rejected under 35 U.S.C. 103 as being unpatentable over Larson et al. (U.S. Publication No. 2019/0127034 A1) hereinafter Larson in view of Husain (U.S. Publication No. 2022/0075061 A1) hereinafter Husain further in view of Rempe et al. (U.S. Publication No. 2024/0160888 A1) hereinafter Rempe.
Regarding claim 1, Larson discloses a swarm of two or more autonomous underwater vehicles (AUVs), each AUV comprising:
a. a plurality of sensors enclosed within a housing of each AUV [see Paragraph 0007- "embodiments of autonomous underwater vehicles described herein serve as platforms for a variety of sensing devices, including but not limited to: video cameras, LiDAR, side scan SONAR, acoustic modems, spectrophotometer, fluorimeter, thermometer, bathometer, and pH meter. These sensors can be used to perform a variety of missions including: reconnaissance, bathymetry, mapping, search and recovery, intruder detection, chemical detection, and tracking"];
wherein a first set of data associated with each of the one or more sensors indicates a navigation model, at least one processor configured to at least:
i. automatically process the first set of data using the deployed learning network model to generate a navigation trajectory [see Paragraph 0083 - "planned ocean mapping route of the AUV is defined by a starting location (where the AUV is submerged in the ocean) a sequence of waypoints, and a destination (rally point). Each of these are defined by their latitude and longitude coordinates. This set of coordinates (starting point, waypoints, rally point) comprise the AUV's navigation path through the ocean, or "mission". The navigation may be performed based on the locating mechanism described above including an inertial navigation unit and a sensor array configured to provide dead reckoning navigation once submerged"];
However, Larson fails to disclose:
a generative learning system comprising at least one processor and at least one memory configured to implement a deployed learning network model, the deployed learning network model generated from a training network, wherein the training network is tuned using features extracted from a first set of data received from the one or more sensors in the swarm of AUVs,
compute the first navigation trajectory metric associated with a second set of data using the deployed learning network model by leveraging the features and associated target value for the navigation trajectory network to determine the associated navigation trajectory for the swarm of AUVs; and
wherein the AUVs further comprise a linked communication system wherein depending on maritime conditions the swarm of AUVs communicates with one another and with a command-and-control system (C2) using a combination of acoustic, RF, and optical communication systems.
Husain discloses:
a generative learning system comprising at least one processor and at least one memory configured to implement a deployed learning network model, the deployed learning network model generated from a training network, wherein the training network is tuned using features extracted from a first set of data received from the one or more sensors in the swarm of AUVs [see Paragraphs 0020 and 0033 - "AMB engine may also train and test one or more models in order to generate a machine-learning model based on a (first) set of input data and specified goals"; "machine -learning model can be used to coordinate movement and data gathering efforts of the AUDs 120 of the swarm 106, as described further with reference to FIG. 3D. To illustrate, a ML model onboard one of the A UDs 120 may obtain information from one or more other devices of the swarm 106 to generate input data for a tactical ML model. The information provided to the tactical ML model can include sonar image data, position data ( e.g., locations of one of more devices of the swarm 106), etc. The tactical ML model may be trained to predict adversarial actions during an engagement between an adversarial agent (e.g., the suspected enemy device 150) and one or more friendly agents (e.g., one or more other AUDs 120), to select responses to detected adversarial actions, or both"], and
compute the first navigation trajectory metric associated with a second set of data using the deployed learning network model by leveraging the features and associated target value for the navigation trajectory network to determine the associated navigation trajectory for the swarm of AUVs [see Paragraphs 0042-0044 - "processor(s) 202 are coupled to the communication system 220, the position sensors 234, or both, and are configured to obtain position data indicating locations of one or more underwater devices of a swarm of devices in an aquatic environment, determine navigation data (first navigation metric) for at least one device of the swarm of devices based on output of a tactical machine learning model (second set of data) that is trained to predict adversarial actions during an engagement between an adversarial agent and one or more friendly agents, to select responses to detected adversarial actions, or both, and send the navigation data to a navigation control system onboard the at least one device to cause the at least one device to, in cooperation with one or more other devices 228 of the swarm 106, gather synthetic aperture sonar data in a particular area. In such implementations, the model output data 270 includes the swarm operation data"; "movements of the AUD 120 are controlled based on the SAS settings 218 (e.g., to time pings with movement of the SAS system 170), sensor data 250 (e.g., to avoid obstacles), position data 252 (e.g., to limit surge, sway, heave, and yaw), the navigation data 254 (e.g., to move along a specified course), the swarm operation data 256 (e.g., to coordinate movement with the other devices 228), or a combination thereof"]; and
wherein the AUVs further comprise a linked communication system wherein depending on maritime conditions the swarm of AUVs communicates with one another and with a command-and-control system (C2) using a combination of acoustic, RF, and optical communication systems [see Paragraphs 0034 and 0045-0046 - "one or more of the surface vehicles 130 is configured to facilitate navigation, communications, or both, among the plurality of AUDs 120 of the swarm 106. For example, the surface vehicles 130 can communicate with other devices (e.g., command and control devices, the satellite, naval vessels, etc.) to obtain information, commands, or both, via relatively long range radiofrequency communications, and can provide information, commands, or both, via relatively short-range communications [e.g., short wavelength light beams, sound waves, tethers 160, etc.) to one or more AUDs"; "communications system 220 is coupled to the one or more of the other devices 228 via a wire or fiber. Alternatively, or additionally, the communications system 220 sends and/or receives the communication signal(s) 226 wirelessly. For example, the senor(s) 222 may be configured to detect electromagnetic waveforms encoding the communication signal(s) 226 propagating in the aquatic environment. As another example, the emitter(s) 224 may be configured to emit electromagnetic waveforms encoding the communication signal(s) 226 to propagate in the aquatic environment"].
Husian suggest that using machine learning models and communications in AUDs, facilitates cooperative operation of the AUDs of a swarm [see Paragraphs 0030 and 0034].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify the AUV as taught by Larson to utilize a generative learning system comprising at least one processor and at least one memory configured to implement a deployed learning network model, the deployed learning network model generated from a training network, wherein the training network is tuned using features extracted from a first set of data received from the one or more sensors in the swarm of AUVs, compute the first navigation trajectory metric associated with a second set of data using the deployed learning network model by leveraging the features and associated target value for the navigation trajectory network to determine the associated navigation trajectory for the swarm of AUVs, and the AUVs further comprise a linked communication system wherein depending on maritime conditions the swarm of AUVs communicates with one another and with a command-and-control system (C2) using a combination of acoustic, RF, and optical communication systems as taught by Husain in order to provide a method, apparatus, and system for sea floor mapping, as taught by Larson, to facilitate cooperative operation of the AUDs of a swarm [Husain, see Paragraphs 0030 and 0034].
However, the combination of Larson and Husain fails to disclose:
wherein the generative learning system comprises a neural network comprising at least one diffusion model, wherein the diffusion model comprises a continuous time diffusion model, and the diffusion model comprises a neural network comprising a denoising network, wherein, the diffusion model comprises a network that is trained, updated, and/or configured using training data comprising data elements to which noise is applied, and configuring the network to modify noisy data elements to recover the (un-noisy) data elements.
Rempe discloses wherein a generative learning system comprises a neural network comprising at least one diffusion model, wherein the diffusion model comprises a continuous time diffusion model, and the diffusion model comprises a neural network comprising a denoising network, wherein, the diffusion model comprises a network that is trained, updated, and/or configured using training data comprising data elements to which noise is applied, and configuring the network to modify noisy data elements to recover the (un-noisy) data elements [see Paragraphs 0006 and 0033 – “In some implementations, the trajectory model 104 includes at least one generative model or neural network, such as at least one diffusion model. The diffusion model can be a continuous time diffusion model. The diffusion model can include a neural network, such as a denoising network (e.g., denoising network 106 as described further herein). For example, in brief overview, the diffusion model can include a network that is trained, updated, and/or configured using training data that includes data elements to which noise is applied, and configuring the network to modify the noisy data elements to recover the (un-noisy) data elements.”].
Rempe suggests that diffusion networks allow for more realistic, controllable trajectory generation compared to convention neural networks [see Paragraphs 0002-0003]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify the generative learning system as taught by Husain to comprises a neural network comprising at least one diffusion model, wherein the diffusion model comprises a continuous time diffusion model, and the diffusion model comprises a neural network comprising a denoising network, wherein, the diffusion model comprises a network that is trained, updated, and/or configured using training data comprising data elements to which noise is applied, and configuring the network to modify noisy data elements to recover the (un-noisy) data elements as taught by Rempe in order to allow for more realistic, controllable trajectory generation compared to convention neural networks [Rempe, see Paragraphs 0002-0003].
Regarding claim 2, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Husain further discloses wherein the optical communication system includes a plurality of lasers acquired and characterized by an acquisition module; a tracking module; a beacon feedback and beam dispersion mechanism [see Paragraph 0029, 0035-0037, and 0066 "[0029] signals transmitted between the AUDs 120 can indicate or be used to determine relative positions or position changes among the AUDs 120 to improve beamforming calculations ... For example, a first AUD can transmit fore and aft ranging signals (e.g., light pulses) to a second AUD"; "the swarm 106 also includes at least one stationary AUD 120F configured to facilitate navigation, communications, or both, among the plurality of autonomous underwater devices of the swarm"; "AUD 120 of FIG. 2 also includes a SAS system 170, a communications system 220, the propulsion system 230, the navigation control system 232, one or more position sensors 234, a notification system 236, and other equipment 238 (such as effectors, weapons systems, etc.)"; "pre-processing 306 is performed to generate the input data 260 based on the sonar image data 310 and possibly other data 304 (such as indicators of types of target objects that are to be labeled). In some implementations, the pre-processing 306 includes extracting features from the sonar image data 310, the other data 304, or both, to generate one or more vectors of features corresponding to the input data", “[0035] In the example illustrated in FIG. 1, the swarm 106 also includes at least one stationary AUD 120F configured to facilitate navigation, communications, or both, among the plurality of autonomous underwater devices of the swarm. For example, the stationary AUD 120F can be deployed at a fixed position and can emit electromagnetic waveforms, sound energy, or both, that the other AUDs 120 can detect to determine their position, ping characteristics, or other information. As another example, the stationary AUD 120F can relay information between two or more of the AUDs 120 to increase an effective communication range among devices of the swarm 106.”].
Regarding claim 4, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Husain further discloses wherein the AUV further comprises a processor that controls a GPS, a depth sensor, and an inertial navigation system to calculate the posture and the position of the AUV [see Paragraphs 0023, 0029, and 0043 - "surface vehicle 130B may receive position information, such as global position system data, from the satellite 140 and may provide information or commands based on the position information to the AUD"; "AUDs 120 can select navigation courses, speeds, or depths of one or more of the AUDs 120 based on information shared among devices of the swarm 106. As yet another example, the AUDs 120 can use signals transmitted between the AUDs 120 to determine the position of a particular AUD 120 to facilitate processing of sonar returns by the particular AUD"; "movements of the AUD 120 are controlled based on the SAS settings 218 (e.g., to time pings with movement of the SAS system 170), sensor data 250 ( e.g., to avoid obstacles), position data 252 ( e.g., to limit surge, sway, heave, and yaw), the navigation data 254 (e.g., to move along a specified course), the swarm operation data 256 (e.g., to coordinate movement with the other devices 228), or a combination thereof"). TAMPA further teaches a DVL (Doppler Velocity Log) (para [0039], [0051] - "SONAR using sound pulses may be more susceptible to error when used at relatively high speeds due to factors such as the Doppler effect when the speed of travel of a submersible is not negligible relative to the speed of sound in the water"; "in conjunction with the LiDAR system to determine the speed of the AUV relative to the sea floor to correct/verify the speed of the INS system"].
Regarding claim 5, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Rempe further discloses wherein AUVs can configure a trajectory model using the generative learning system to determine for a AUV at a given time a state trajectory indicating at least one future state of the AUV [see Paragraphs 0033-0036 – discusses configuring the trajectory model to determine, for a subject, at a given time step t, a state trajectory indicating at least one future state of the subject. The future state of the subject can be a state of the subject at a time step subsequent to t. The trajectory model 104 can determine a plurality of future states of the subject τ.sub.S=[S.sub.t+1, S.sub.t+2, . . . S.sub.t+Tf], where T.sub.f represents a number of time steps, and the state s can be defined as a vector or matrix of position and/or motion data of the subject, such as [x, y, θ, v].sup.T, where x and y represent position of the subject in two dimensions (e.g., horizontal/vertical directions in a 2D plan view of the environment), θ represents a heading angle (e.g., angle of direction) of the subject, and v represents a speed of the subject. The states s can include, among various parameters, the heading as a two-dimensional heading vector (e.g., in the dimensions of x and y), a bounding box of the subject (e.g., length and width dimensions of the bounding box), an indication of whether the person is visible or occluded, or various combinations thereof.”, and see Paragraph 0024 – discusses ].
Rempe suggests that diffusion networks allow for more realistic, controllable trajectory generation compared to convention neural networks [see Paragraphs 0002-0003]
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify the generative learning system as taught by Husain to configure a trajectory model using the generative learning system to determine for each of the AUVs at a given time a state trajectory indicating at least one future state of each of the AUVs as taught by Rempe in order to allow for more realistic, controllable trajectory generation compared to convention neural networks [Rempe, see Paragraphs 0002-0003].
Regarding claim 6, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses further comprising a low-power laser-based LiDAR (Light Distancing and Ranging) system, a video camera, a GPU (Graphic Processing Unit), a magnetometer, and multibeam echo sounder [see Paragraph 0026 - "sensors, such as: a low-power laser-based LiDAR (Light Distancing and Ranging) system, a video camera, a GPS (Global Positioning System) antenna, a GPU (Graphic Processing Unit) ... sensor package may optionally contain other mission specific sensors, such as: a fluorometer, a thermometer, pH meter, single or multibeam sonar, etc."]. Husain further discloses side-scan-sonar [see Paragraph 0002 - "side scanning sonar"].
Regarding claim 7, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses a fluorometer, a magnetometer, a thermometer, and pH meter [see Paragraph 0026].
Regarding claim 8, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs are powered by one or more lithium- ion batteries [see Paragraph 0030 - "AUVs may be powered by four 10,000 mah (milliamp hour) Lithium Ion batteries"].
Regarding claim 9, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs incorporate an internal counterweight system and dive plane mechanism for operating autonomously underwater [see Paragraph 0031 - "AUV as described herein may incorporate an internal counterweight system and dive plane mechanism"].
Regarding claim 10, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs incorporate a unique pitch and yaw control [see Paragraph 0031 - "Embodiments of the AUV disclosed herein incorporate a unique pitch and yaw control"].
Regarding claim 11, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs comprise a conventional torpedo shaped designs having a single aft-located motor [see Paragraph 0031 - "conventional torpedo shaped designs having a single aft-located motor"].
Regarding claim 12, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein each AUV comprise an internal counterweight system to turn and drive the AUV [see Paragraph 0031 - "an internal counterweight system to turn and drive the AUV"].
Regarding claim 13, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs work in swarms of 10 or more AUVs [see Paragraph 0052 - "Each AUV swarm is composed of a number of AUVs, each independent of the other members of the swarm with a preprogrammed search area to explore. The size and number of swarms is determined by the size and shape of the target area to be mapped. A typical swarm may consist of approximately 10-12 AUVs"].
Regarding claim 14, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs work in swarms of 100 or more AUVs [see Paragraphs 0052-0053 ["The size of the swarm will be determined by the size of the area to be covered"].
Regarding claim 15, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein each of the AUVs comprises one or more systems selected from the swarm consisting of a cruising system, exploration condition setting systems, exploration mission executing systems, recording systems, cruising speed setting section, cruising-control section, imaging systems, and geological layer researching systems [see Paragraphs 0022 and 0052-0054 - "AUV of example embodiments may generate a bathymetric map of the sea floor and generate high-definition images of specified features such as archeological, biological, or geological features"; "Each AUV swarm is composed of a number of A UV s, each independent of the other members of the swarm with a preprogrammed search area to explore"].
Regarding claim 16, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs are configured for autonomous exploration, reconnaissance, or military/tactical conditions by remotely inputting, to the one or more AUVs, information which is necessary for the mission, comprising an exploration region and a cruising path of the underwater vehicle using the exploration condition setting system before the AUV is introduced into the exploration water area [see Paragraphs 0005, 0008, 0022, 0052-0054 - "underwater vehicles described herein can be operated remotely"; "A swarm of these AUVs could easily map a harbor within a 24-hour period and return with detailed data regarding depth, water temperature, obstacles, hazards, and potential targets. The AUVs could also detect specific chemicals and trace them to their source. This data would be invaluable for harbor protection, anti-smuggling efforts, environmental protection, as well as military applications"; "Each member of the swarm may be assigned a prescribed search area; however, if an AUV is unable to complete its search function, the AUV may communicate with the deployment vessel, such as by acoustic modem"; "Swarms can be deployed from a single or multiple locations, autonomously or manually. A deployment vessel may be used to deploy a swarm, where each AUV may be deployed from a single location, or deployed as the deployment vessel traverses the surface of the water above the target area to be mapped"].
Regarding claim 17, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the exploration missions, the exploration regions, and the cruising paths are optionally differently set for the each of the AUVs to explore the exploration regions more efficiently [see Paragraphs 0005, 0008, 0022, 0052-0054].
Regarding claim 18, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs each have an endurance of 24 hours or more [see Paragraph 0008 and 0030 - "A swarm of these AUVs could easily map a harbor within a 24-hour period and return with detailed data regarding depth, water temperature, obstacles, hazards, and potential targets"].
Regarding claim 19, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Larson further discloses wherein the AUVs each have a weight of 50 pounds or less [see Paragraphs 0023 - "AUV of example embodiments may be sized according to the specific use case and sensor package of the AUV; however, a preferred embodiment may be approximately 3.3 feet (1 meter) long, 2 feet (0.6 meters) wide, and weigh around 20 pounds"].
Regarding claim 20, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Husain further discloses wherein the AUVs provide real-time data to a command-and-control center using an interface [see Paragraphs 0034 and 0133 - "the final model can be output for use with respect to other data (e.g., real-time data)", “communicate with other devices (e.g., command and control devices, the satellite, naval vessels, etc.) to obtain information, commands, or both, via relatively long range radiofrequency communications, and can provide information, commands” – command and control devices use interfaces].
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Larson in view of Husain in view of Rempe further in view of Kong et al. (U.S. Publication No. 2023/0163638 A1) hereinafter Kong.
Regarding claim 3, Larson, Husain, and Rempe disclose the invention with respect to claim 1. Husain further discloses the autonomous underwater vehicles of claim 1.
However, the combination of Larson, Husain, and Rempe fails to disclose wherein the AUV comprises one or more solar cells (i.e., photovoltaic cells) embedded on the body of the A UV, wherein the housing or body of one or more AUVs includes a surface provided with a support and with a transparent covering layer, wherein a thin-film solar cell is applied to the support, and the support together with the thin-film solar cell is covered by a transparent covering layer, and the thin -film solar cell is copper indium diselenide (CIS-), copper indium gallium selenide (CIGS-), copper indium gallium sulfide selenide (CIGSS-), cadmium tellurium (CdTe-) or an silicon-based (Si-based) wherein the silicon based is silicon/silicon germanium (Si/SiGe) thin-film solar cell.
Kong discloses wherein the AUV comprises one or more solar cells (i.e., photovoltaic cells) embedded on the body of the AUV, wherein the housing or body of one or more AUVs includes a surface provided with a support and with a transparent covering layer, wherein a thin-film solar cell is applied to the support, and the support together with the thin-film solar cell is covered by a transparent covering layer, [0070 - "as the field trial underwater was primarily designed to test communication performance, the monocrystalline Si solar panel used for energy harvesting was not connected to system 600. However, the system 600 can be deployed on the surface of water or in shallow water, where sunlight can reach it, to implement the simultaneous energy harvesting and VLC. It can also be installed on autonomous underwater vehicles, which can hover over the surface of water or in shallow water to recharge after completing missions in deep water"] and the thin-film solar cell is a CIS-, CIGS-, CIGSS-, CdTe- or an Si-based (particularly Si/SiGe) thin-film solar cell [see Paragraph 0060].
Kong suggests recharging after completing missions in deep water [see Paragraph 0070].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify each AUV as taught Husain to include one or more solar cells (i.e., photovoltaic cells) embedded on the body of the AUV, wherein the housing or body of one or more AUVs includes a surface provided with a support and with a transparent covering layer, wherein a thin-film solar cell is applied to the support, and the support together with the thin-film solar cell is covered by a transparent covering layer, and the thin -film solar cell is copper indium diselenide (CIS-), copper indium gallium selenide (CIGS-), copper indium gallium sulfide selenide (CIGSS-), cadmium tellurium (CdTe-) or an silicon-based (Si-based) wherein the silicon based is silicon/silicon germanium (Si/SiGe) thin-film solar cellas taught by Kong in order to recharge AUVs after completing missions in deep water [Kong, see Paragraph 0070].
Response to Arguments
Applicants’ arguments appear to be directed solely to the amended subject matter, and are not persuasive, as noted supra in the rejections of that claimed subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SHAYNE M. GILBERTSON/Examiner, Art Unit 3665
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