DETAILED ACTION
This communication is a Non-Final Rejection Office Action in response to the 6/27/2024 filling of Application 18/757,111. Claims 1-29 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the instant case, Claims 1-22 are directed toward systems for generating at least one maintenance insight about the at least one vessel. Claims 23-29 are directed toward a method for generating at least one maintenance insight about the at least one vessel.
As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 12 is directed to the abstract idea of
analyzing data to generate at least one maintenance insight about the at least one vessel which falls into the abstract idea categories of certain methods of organizing human activity and mental processes.
The elements of Claim 12 that represent the Abstract idea include:
A system for maritime asset management comprising:
aggregate the data from the at least one vessel,
analyze the data from the at least one vessel and from said at least one user,
generate at least one maintenance insight about the at least one vessel and at least one other insight
MPEP 2106.04(a)(2) I. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the limitations of aggregating, analyzing, an generating are directed to fundamental business practices of analyzing data to provide recommendations for maintaining or operating a vessel.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
In the instant case, the aggregating, analyzing, an generating cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” nothing in the claim element precludes the steps from being performed in the human mind.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
A system for maritime asset management comprising:
at least one data acquisition device, said at least one data acquisition device configured to collect data from at least one vessel,
at least one server aboard the at least one vessel, said at least one server disposed in digital communication with said at least one data acquisition device and configured to receive the data from the at least one vessel,
said at least one server further configured to upload the data from said at least one server to a cloud infrastructure,
a software application configured to be accessed by at least one user from a client device,
said software application configured to receive data from said at least one user,
said software application further configured to upload the data from said at least one user to said cloud infrastructure, said cloud infrastructure further configured to perform the abstract idea
said cloud infrastructure comprising at least one machine learning algorithm,
said at least one maintenance insight and said at least one other insight stored in at least one virtual database.
However, the computer elements (at least one server aboard the at least one vessel and the cloud infrastructure) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer.
Further, the collecting data, the receiving data, and the uploading data are recited broadly. Under the broadest reasonable interpretation, the limitations amounts to data gathering which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g). Further, the storage of data in a virtual database is also recited broadly and amounts to insignificant post solution activity.
Further, the software application configured to be accessed by at least one user from a client device amounts to a general link to a particular technological environment. For example, the abstract idea of analyzing maritime vessel data is generally linked to the platform comprising a software application configured to be accessed by at least one user from a client device, said software application configured to receive data from said at least one user. Generally linking the abstract idea to a particular technological environment does bot integrate the abstract idea into a practical application.
Further, claim 12 recites the additional elements of machine learning. However, the use of a machine learning model is indicative of adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f) states:
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem")).
In the instant case, the additional elements of the broadly recited machine learning attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. As such, the broadly recited ML algorithm does not integrate a judicial exception into a practical application or provide significantly more.
Viewing the generic computer elements in combination with the broadly recited data gathering, data storage, machine learning and general link to a particular technological environment does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea.
In step 2B, the examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
As discussed with respect to Step 2A Prong Two, the additional elements of the at least one server aboard the at least one vessel and the cloud infrastructure amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, similar to the analysis with respect to step 2A prong 2 recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished (i.e. the machine learning) and limitations that generally link the abstract idea to a particular technological environment cannot provide an inventive concept under step 2B of the eligibility analysis.
Viewing the generic computer elements in combination with the broadly recited data gathering, data storage, machine learning and general link to a cloud infrastructure environment does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. As such, the additional elements do not amount to an inventive concept.
Further, Claims 13-22 further limit the abstract idea of mental processes or certain methods of human activity that were already rejected in claim 12, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 12-22 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 12 otherwise styled as a method or computer program product, for example, would be subject to the same analysis.
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.
Claim(s) 1-6, 8-17, 19-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Molloy US 2023/0406464 A1 in view of Naslavsky US 2024/0144133 A1 in view of Grant US 2021/0073060 A1.
As per claim 1 Malloy teaches a system for maritime asset management comprising:
at least one data acquisition device, said at least one data acquisition device configured to collect data from at least one vessel, (Molloy para. 25 teaches the processor being configured to: (a) receive operational data and/or sensor data from at least one operational system of the vessel and/or the at least one sensor, respectively; and (b) store the operational data and/or sensor data on the local data storage device and/or transmit the operational data and/or sensor data via the communications module to the remote device.)
at least one storage aboard the at least one vessel, said at least one storage disposed in digital communication with said at least one data acquisition device and configured to receive the data from the at least one vessel, (Molloy para. 101 teaches the data acquisition unit 126 includes a data storage device 140 and a communications module 142 (e.g., including a cellular, satellite, or Internet transceiver). While a single storage device 140 and a single communications module 142 are illustrated, in some example embodiments the data acquisition unit 126 includes a plurality of storage devices 140 and/or a plurality of communications modules 142. The storage device 140 is provided to store vessel data in a permanent or temporary fashion, e.g., to store data until the data acquisition unit 120 can be communicatively coupled to the remote server 112. Para.104 teaches Each peripheral device coupling 132 is operable to communicatively couple the data storage device 140 to a peripheral device to receive information from the peripheral device. The vessel data of the subject vessel 112 includes the information received from the plurality of peripheral devices. In the illustrated example, a first coupling 146 of the peripheral device couplings 132 is operable to be coupled to the illustrated operational system 136 of the vessel 110 to transfer operational data from the operational system 136 to the local data storage device 140 and a second coupling 148 of the peripheral device couplings 132 is operable to be coupled to the sensor 138 to transfer sensor data from the sensor 138 to the local data storage device 140. The communications module 142 is communicatively coupled to the local data storage device 140 and operable to transmit the vessel data to the remote data storage device 114 (e.g., via the processor 116) to be stored in a database or other data storage structure(s) at the remote data storage device 114.)
said at least one storage further configured to upload the data from said at least one storage to a cloud infrastructure, said cloud infrastructure configured to aggregate the data from the at least one vessel,
Molloy para. 184 teaches with the system and/or method described herein, the data collected during the trip can be continuously pushed to the cloud.
said infrastructure comprising at least one machine learning algorithm, Molloy para. 174 teaches
at step 664 the application 656 requests information from a third-party weather data source 668 (e.g., WindFinder™). At step 670 the third-party weather data source sends back weather data (e.g., meteorological data) to the application 656. At step 672 all the data gathered by the application 656 is pushed to the remote server 674 (e.g., server 112). The server 674 may match the data to a class based on the vessel specifications and/or weather data related to the vessel data gathered at step 662 as described earlier for FIG. 16. The server 674 analyses the data (e.g., via machine learning model(s) trained on class-specific data) at step 676 and send the analysis back to the application 656. At step 678 the application 656 will generate at least one data visualization and provide that to the user 652. At step 680 the user 652 may view the data visualization. Para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
said at least one machine learning algorithm configured to analyze the data from the at least one vessel, Molloy para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the
estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
said infrastructure further configured to generate at least one maintenance insight about the at least one vessel, said at least one maintenance insight stored in at least one virtual database.
Molloy para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the
estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
Malloy does not teach a server aboard the at least one vessel However, Naslavsky teaches para. 23 teaches the processes 150 that run the dashboard and other data-handling operations in the system and method can be performed in whole or in part with the onboard server 130, and/or using a remote computing (server) platform 140 that is part of a land-based, or other generally fixed, location with sufficient computing/bandwidth resources (a base location 142). Both Molloy and Grant are directed to predictive maintenance. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Molloy to include said cloud infrastructure comprising at least one machine learning algorithm as taught by Grant to allow greater flexibility and more efficient processing.
Malloy does not explicitly disclose said cloud infrastructure comprising at least one machine learning algorithm However, Grant teaches according to such embodiment, some or all aspects of the machine learning knowledge model may run within server system 135. Additionally or alternatively, some or all aspects of machine learning knowledge model may run externally to server system 135, e.g., via a cloud-based implementation, in which case server system 135 communicates with or accesses such aspects of the machine learning knowledge model via machine learning knowledge model representation 151. Both Molloy and Grant are directed to predictive maintenance. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Molloy to include said cloud infrastructure comprising at least one machine learning algorithm as taught by Grant to allow greater flexibility and more efficient processing.
As per claim 2 Malloy teaches the system of claim 1, wherein said at least one data acquisition device is configured to measure at least one of strain, ultrasonic signals, acceleration, vibrations, thermal properties, and viscosity. Malloy para. 110 teaches the data acquisition unit 226 includes a plurality of peripheral device couplings 232 extending from the housing 228. As illustrated particularly in FIG. 3, the peripheral device couplings 232 include a coupling 270 for a GPS device, a coupling 272 for a torque sensor, a coupling 274 for an external inertial measurement unit, a coupling 276 for an anemometer (i.e., to gather wind speed and/or direction), a coupling 278 for a power source, a coupling 280 for a user terminal or interface for use in controlling the data acquisition system, a first coupling 282 for a fuel flow sensor, and a second coupling 284 for a second fuel flow sensor. As illustrated in FIG. 4, the data acquisition unit 226 includes and/or is coupled to a GPS system 286, a flow meter 288, a torque sensor 290 (including a tachometer 292 and a strain gauge 294). Para. 127 teaches Referring again to FIGS. 9-11, the sensor array 364 is added to the vessel 310 to provide ancillary data, such as vessel data and/or weather data in the vicinity of the vessel, that will be provided to the web application 380. The ancillary data include one or more of: GPS data, vessel location, vessel heading, vessel speed, time, IMU data, 9-axis accelerometer/gyroscope/magnetometer, anemometer data, wind velocity, fuel flow, dual fuel flow meter data, drive shaft torque measurements and/or power measurements. The ancillary data is accessed by the data acquisition unit 326.
As per claim 3 Malloy teaches the system of claim 1, wherein said at least one data acquisition device is configured to measure currents and voltages of at least one electrical machine. Malloy para 127 teaches Referring again to FIGS. 9-11, the sensor array 364 is added to the vessel 310 to provide ancillary data, such as vessel data and/or weather data in the vicinity of the vessel, that will be provided to the web application 380. The ancillary data include one or more of: GPS data, vessel location, vessel heading, vessel speed, time, IMU data, 9-axis accelerometer/gyroscope/magnetometer, anemometer data, wind velocity, fuel flow, dual fuel flow meter data, drive shaft torque measurements and/or power measurements. The ancillary data is accessed by the data acquisition unit 326.
As per claim 4 Malloy teaches the system of claim 1, wherein said at least one server is further configured to receive the data from the at least one vessel via said at least one data acquisition device. Molloy para. 25 teaches the processor being configured to: (a) receive operational data and/or sensor data from at least one operational system of the vessel and/or the at least one sensor, respectively; and (b) store the operational data and/or sensor data on the local data storage device and/or transmit the operational data and/or sensor data via the communications module to the remote device.
As per claim 5 Malloy teaches the system of claim 1, wherein said at least one server is further configured to separately process data about at least one electrical machine. Molloy para. 182-183 teach the following are example application scenarios. Vessel A is a shrimp boat out of New Bedford that uses a system and associated method(s) described herein to get reliable data on how even a small change in bearing can improve fuel costs for the next leg of a given trip. For example, the vessel's captain may use a system and associated method(s) described herein to monitor the vessel's position relative to “navigational boundaries” that are both physical and operational, established by insurers or regulators. When planning the next trip, the captain can use a system and associated method(s) described herein to recommend optimal cost route options informed by predicted weather conditions and previous driving habits collected from multiple shrimp trap locations. A system and associated method(s) described herein may be used to continuously monitor the vessel's operating conditions and the local environmental conditions for generating alerts for system components that may need attention such as, but not limited to, one or more of engine performance, propeller fatigue, drive shaft slip/drag, battery levels, emission levels and more.
As per claim 6 Malloy teaches the system of claim 1, wherein said at least one server is further configured to compress the data from the at least one vessel. (Molloy para. 123 teaches the communications manager 342 may be a combined device (e.g., transceiver) and software component that manages data communications. The communications manager 342 may handle related functions such as data compression, data cleaning, asynchronous communication, data buffering, and data forwarding.)
As per claim 8 Molloy teaches the system of claim 1, wherein said cloud infrastructure is further configured to generate at least one other insight. (Molloy para. 168 teaches in a fifth example, step 602 includes obtaining data which can be used to monitor for long term degradation of ship performance such as one or more of propeller damage, fouling, hull damage, etc. At step 632 potential remediation or maintenance suggestions are generated.)
As per claim 9 Molloy teaches the system of claim 8, wherein said at least one other insight comprises fouling and performance information about the at least one vessel. (Molloy para. 168 teaches in a fifth example, step 602 includes obtaining data which can be used to monitor for long term degradation of ship performance such as one or more of propeller damage, fouling, hull damage, etc. At step 632 potential remediation or maintenance suggestions are generated.)
As per claim 10 Molloy teaches the system of claim 1, wherein said at least one maintenance insight comprises information about at least one electrical machine aboard the at least one vessel. Molloy para. 182-183 teach the following are example application scenarios. Vessel A is a shrimp boat out of New Bedford that uses a system and associated method(s) described herein to get reliable data on how even a small change in bearing can improve fuel costs for the next leg of a given trip. For example, the vessel's captain may use a system and associated method(s) described herein to monitor the vessel's position relative to “navigational boundaries” that are both physical and operational, established by insurers or regulators. When planning the next trip, the captain can use a system and associated method(s) described herein to recommend optimal cost route options informed by predicted weather conditions and previous driving habits collected from multiple shrimp trap locations. A system and associated method(s) described herein may be used to continuously monitor the vessel's operating conditions and the local environmental conditions for generating alerts for system components that may need attention such as, but not limited to, one or more of engine performance, propeller fatigue, drive shaft slip/drag, battery levels, emission levels and more.
As per claim 11 Molloy teaches the system of claim 1, wherein said at least one maintenance insight comprises at least one qualitative insight and at least one quantitative insight. Molloy para. 166 teach In a third example, step 602 includes gathering data from fuel flow sensors and sensors providing vessel speed information. Step 602 also includes gathering data from weather sensors and/or third-party weather applications. At step 610 increases in fuel flow are identified while speed does not change. Possible causes (such as tidal changes, wind velocity, wave height, and/or local conditions) are identified. Potential remediation options may be identified. Since weather data that could explain the fuel use is available, the weather data is identified and may be presented to the user. There may be remediation options to reduce negative impacts of the weather conditions on vessel operations (e.g., reducing speed for more efficient fuel use or recommending an alternative route).
As per claim 12 Molloy teaches a system for maritime asset management comprising:
at least one data acquisition device, said at least one data acquisition device configured to collect data from at least one vessel, Molloy para. 25 teaches the processor being configured to: (a) receive operational data and/or sensor data from at least one operational system of the vessel and/or the at least one sensor, respectively; and (b) store the operational data and/or sensor data on the local data storage device and/or transmit the operational data and/or sensor data via the communications module to the remote device.
at least one storage aboard the at least one vessel, said at least one storage disposed in digital communication with said at least one data acquisition device and configured to receive the data from the at least one vessel, (Molloy para. 101 teaches the data acquisition unit 126 includes a data storage device 140 and a communications module 142 (e.g., including a cellular, satellite, or Internet transceiver). While a single storage device 140 and a single communications module 142 are illustrated, in some example embodiments the data acquisition unit 126 includes a plurality of storage devices 140 and/or a plurality of communications modules 142. The storage device 140 is provided to store vessel data in a permanent or temporary fashion, e.g., to store data until the data acquisition unit 120 can be communicatively coupled to the remote server 112. Para.104 teaches Each peripheral device coupling 132 is operable to communicatively couple the data storage device 140 to a peripheral device to receive information from the peripheral device. The vessel data of the subject vessel 112 includes the information received from the plurality of peripheral devices. In the illustrated example, a first coupling 146 of the peripheral device couplings 132 is operable to be coupled to the illustrated operational system 136 of the vessel 110 to transfer operational data from the operational system 136 to the local data storage device 140 and a second coupling 148 of the peripheral device couplings 132 is operable to be coupled to the sensor 138 to transfer sensor data from the sensor 138 to the local data storage device 140. The communications module 142 is communicatively coupled to the local data storage device 140 and operable to transmit the vessel data to the remote data storage device 114 (e.g., via the processor 116) to be stored in a database or other data storage structure(s) at the remote data storage device 114.)
said at least one storage further configured to upload the data from said at least one storage to a cloud infrastructure, said cloud infrastructure configured to aggregate the data from the at least one vessel,
Molloy para. 184 teaches with the system and/or method described herein, the data collected during the trip can be continuously pushed to the cloud.
a software application configured to be accessed by at least one user from a client device, said software application configured to receive data from said at least one user,
(Molloy para. 118 teaches in some example embodiments, the user will initiate the web application 380 and will input boat specifications and fuel price data. In some example embodiments, inputting boat specifications and/or fuel price data is automated. Boat specifications and/or fuel price data may be used as to provide values for input parameters of machine learning models.)
said software application further configured to upload the data from said at least one user to said cloud infrastructure, Molloy para. 184 teaches with the system and/or method described herein, the data collected during the trip can be continuously pushed to the cloud.
said cloud infrastructure further configured to aggregate the data from said at least one user, Molloy para. 184 teaches with the system and/or method described herein, the data collected during the trip can be continuously pushed to the cloud.
said infrastructure comprising at least one machine learning algorithm, Molloy para. 174 teaches
at step 664 the application 656 requests information from a third-party weather data source 668 (e.g., WindFinder™). At step 670 the third-party weather data source sends back weather data (e.g., meteorological data) to the application 656. At step 672 all the data gathered by the application 656 is pushed to the remote server 674 (e.g., server 112). The server 674 may match the data to a class based on the vessel specifications and/or weather data related to the vessel data gathered at step 662 as described earlier for FIG. 16. The server 674 analyses the data (e.g., via machine learning model(s) trained on class-specific data) at step 676 and send the analysis back to the application 656. At step 678 the application 656 will generate at least one data visualization and provide that to the user 652. At step 680 the user 652 may view the data visualization. Para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
said at least one machine learning algorithm configured to analyze the data from the at least one vessel and from said at least one user, Molloy para. 121 teaches in some example embodiments, the user inputs the vessel specifications (e.g., length, engine size, fuel cost per unit, engine weight, number of people aboard, general hull shape, number of engines, size of propeller, type of propeller, loaded versus empty weight, width, gross tonnage, instrumentation on board, draft, appendages hull condition, and/or hull coating) for a given vessel. The web application 380 then accesses a database at the server 312 to access a stored vehicle data set that best matches the vessel specifications (e.g., a relevant bin of data). The stored vehicle data set may be used to give the vessel operator a preliminary power curve and fuel consumption curve. The curves may be built from data collected in particular meteorological conditions. In some embodiments, an average of data sets over time will be used to see if there are trends, e.g. more power required, which may be used to identify issues, e.g., hull founding slowing the boat. Sudden power curve changes (e.g., more than 5% or more than 10% between data gathering sessions) may indicate damage, e.g., to the propeller. Molloy para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
said infrastructure further configured to generate at least one maintenance insight about the at least one vessel and at least one other insight, said at least one maintenance insight and said at least one other insight stored in at least one virtual database. Molloy para. 155 teaches in some example embodiments, the alert includes a remediation suggestion or a maintenance warning. The remediation suggestion may be a suggestion to check a component that is associated with a data item for which an estimated value is flagged based on the difference between an estimated value and an expected value is larger than a corresponding threshold or the estimated value is outside of an acceptable range of values. The component may be determined from estimated values that are flagged as described previously for estimated values that are generated by the vehicle-class based machine learning model that is trained using supervised or unsupervised learning based on training data from vessels of the same vehicle class as the subject vessel. For example in some cases, the remediation suggestion may include inspecting a propeller or inspecting a filter. In another example, the alert is a maintenance warning that may suggest at least one operational system component for review. The operating system component suggested for checking may be determined by the machine learning model based on supervised or unsupervised learning based on the database of data from the vessels of the same class as the subject vessel. As another example, the comparison of the estimated value to a threshold or acceptable operating range may indicate high hydrocarbons which indicate overfilling with oil or leaks in the engine.
Malloy does not teach a server aboard the at least one vessel However, Naslavsky teaches para. 23 teaches the processes 150 that run the dashboard and other data-handling operations in the system and method can be performed in whole or in part with the onboard server 130, and/or using a remote computing (server) platform 140 that is part of a land-based, or other generally fixed, location with sufficient computing/bandwidth resources (a base location 142). Both Molloy and Grant are directed to predictive maintenance. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Molloy to include said cloud infrastructure comprising at least one machine learning algorithm as taught by Grant to allow greater flexibili