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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mathematical or mental activity algorithm for creating and using a health model for a hydraulic system for assessing the status/health of the hydraulic system.
This judicial exception is not integrated into a practical application because no improvement to the hydraulic system or its components is realized through the performance of the algorithm.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited “obtaining” of data needed to implement the algorithm amounts to the recitation of necessary and routine data gathering. The recited processor, memory or storage medium, and regression or machine learning amounts to the recitation of general-purpose computer elements for implementing the abstract idea through use of a general-purpose computer and do not serve to amount to the recitation of significantly more than the abstract idea itself (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)). The recited “performance of maintenance” is non-specific and would not serve to amount to any specific improvement to the underlying hydraulic equipment and amounts to the recitation of “use it” with regards to the abstract idea. The recited “elastomer component” amounts to a mere field-of-use limitation with regards to the abstract idea and does not amount to the recitation of either a particular practical application of significantly more than the recitation of the abstract idea itself.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6 and 11-16 is/are rejected under 35 U.S.C. 102(A)(a) as being anticipated by Gomez et al. (US 20220197243 A1)[hereinafter “Gomez”].
Regarding Claims 1 and 11, Gomez discloses a system (and corresponding method) for monitoring health of hydraulic equipment components [See Fig. 1A and Paragraph [0029] – “the system 100 can enable assessments of hydraulic apparatus health”], the system comprising:
one or more physical processors configured by machine-readable instructions [Fig. 1A – Processing Subsystem 140Paragraph [0027] – “a processing subsystem 140 operatively coupled to the monitor 130 and including non-transitory media storing instructions that, when executed by the processing subsystem 140, perform operations for identifying, from outputs of the monitor 130/sensor subsystem no, a set of unique signatures corresponding to states and events of the hydraulic apparatus 10.”] to:
obtain sensor information [Fig. 1A – Sensor Subsystem 110. See Figs. 5-7.] for a piece of hydraulic equipment [Fig. 1A – Hydraulic Apparatus 10], the sensor information characterizing one or more operating characteristics of the piece of hydraulic equipment [Paragraph [0027] – “a sensor subsystem 110 (e.g., sensor cluster) including one or more of: a pressure sensor 112, a temperature sensor 114, a flow sensor 116, and a pump demand sensor 118”] over a period of time [See Figs. 5-7.Paragraph [0031] – “In specific examples, the system 100 provides innovation in advanced sensor system design and machine learning approaches, in fields using hydraulic systems, to provide a plug-and-play, real-time monitoring and predictive maintenance solution in a cost-effective manner.”];
obtain status information for the piece of hydraulic equipment, the status information characterizing status of the piece of hydraulic equipment over the period of time; generate an equipment health model for the piece of hydraulic equipment based on the sensor information and the status information [See Fig. 4, steps S320-S340 are performed through machine learning architecture per Paragraph [0106].Paragraph [0107] – “To refine the model(s), the method 300 can include generating one or more training sets of data, from the sensor subsystem and/or other sensors, in order to train the AI/NN model(s) in or more stages of training, to identify unique signatures from various inputs. In variations, generating training sets of data can include generating sensor data (e.g., from the main pump output), from scenarios for each subcomponent type of the hydraulic apparatus and/or other hydraulic apparatuses, tagged with associated system events, statuses (e.g., health statuses), and performance.” The sensor data, tagged with corresponding status information, is used to train the AI model to identify fig data signatures.], wherein the equipment health model receives as input the one or more operating characteristics of the piece of hydraulic equipment [Fig. 4, step S310] and provides as output the status of the piece of hydraulic equipment [Paragraph [0129] – “Block S340 functions to process any returned signatures from prior steps, to generate analyses pertaining to health of the hydraulic apparatus, efficacy of operations, efficiency of operations, and/or other actionable insights.”]; and
store the equipment health model for the piece of hydraulic equipment in a storage medium [The trained AI/NN model(s) is inherently stored in memory for use by the processing subsystem.].
Regarding Claims 2 and 12, Gomez discloses that the one or more physical processors are further configured by the machine-readable instructions to: obtain usage information for the piece of hydraulic equipment, the usage information characterizing the one or more operating characteristics of the piece of hydraulic equipment for a duration of time [See Figs. 5-7.];
input the usage information into the equipment health model for the piece of hydraulic equipment, wherein the equipment health model outputs the status of the piece of hydraulic equipment [Paragraph [0103] – “Block S330 recites: identifying a set of unique signatures corresponding to states and events of the hydraulic apparatus and subcomponents of the hydraulic apparatus, from the set of transformation operations, which functions to process input data streams and/or derivative data, in order to properly classify signatures associated with events and statuses of the hydraulic apparatus.”]; and
facilitate health monitoring for the piece of hydraulic equipment based on the status of the piece of hydraulic equipment output by the equipment health model [Paragraph [0129] – “Block S340 functions to process any returned signatures from prior steps, to generate analyses pertaining to health of the hydraulic apparatus, efficacy of operations, efficiency of operations, and/or other actionable insights.”].
Regarding Claims 3 and 13, Gomez discloses that facilitation of the health monitoring for the piece of hydraulic equipment based on the status of the piece of hydraulic equipment output by the equipment health model includes performance of one or more maintenance operations for the piece of hydraulic equipment based on the status of the piece of hydraulic equipment output by the equipment health model [Fig. 4 – step S350. See Paragraph [0137]].
Regarding Claims 4 and 14, Gomez discloses that the equipment health model includes a regression model and/or a machine-learning model [Paragraph [0106] – “In relation to identification of unique signatures corresponding to events and/or statuses of the hydraulic apparatus, the processing subsystem described above can implement architecture for classification and regression, with training of models by processing suitable training datasets. In particular, the unique characteristics of each event and status of the hydraulic apparatus are not practically detectable in the mind, and are instead learned by the machine learning architecture in relation to Blocks S320-S340 of the method 300.”].
Regarding Claims 5 and 15, Gomez discloses that the one or more operating characteristics of the piece of hydraulic equipment include pressure, fluid flow rate, fluid volume, open/closure count, actuation duration, and temperature [Paragraph [0027] – “a sensor subsystem 110 (e.g., sensor cluster) including one or more of: a pressure sensor 112, a temperature sensor 114, a flow sensor 116, and a pump demand sensor 118”].
Regarding Claims 6 and 16, Gomez discloses that the one or more operating characteristics of the piece of hydraulic equipment further include sound [Paragraph [0044] – “In variations, the sensor subsystem 110 can include sensor types not described above. For instance, the sensor subsystem 110 can include one or more of: … ultrasonic/vibration sensors coupled to one or more motors and/or cylinders associated with the hydraulic apparatus”].
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.
Claim(s) 7-10 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gomez et al. (US 20220197243 A1)[hereinafter “Gomez”] and Zhu et al. (US 20130233081 A1)[hereinafter “Zhu”].
Regarding Claims 7 and 17, Gomez fails to disclose that the piece of hydraulic equipment includes an elastomer component; and the status of the piece of hydraulic equipment including status of the elastomer component.
However, Zhu discloses the use of sensors for monitoring the health of hydraulic hoses made of rubber [See Paragraphs [0002]-[0009]].
It would have been obvious to monitor the status/health of rubber hydraulic hoses on a hydraulic system having such hoses in order to better ascertain the health of such a system and its components through use of machine learning.
Regarding Claims 8 and 18, Gomez, as applied to the context of Zhu, would disclose that the status of the elastomer component includes remaining life of the elastomer component [Paragraph [0019] – “Additionally, the inventions described implement algorithms and/or models for analyzing individual subcomponents of hydraulic equipment with a single set of sensors coupled to the hydraulic equipment at a single position, thereby enabling users to obtain operating life, health, remaining life, and/or other statuses of individual subcomponents in a manner that is significantly more efficient and lower in cost.”].
Regarding Claims 9 and 19, Gomez, as applied to the context of Zhu, would disclose that the status of the elastomer component includes one or more defects in the elastomer component [Paragraph [0123] – “In relation to returned outputs of the model(s) corresponding to unique signatures of subcomponents of the hydraulic apparatus, outputs and signatures can be associated with one or more of: … fluid conduits (e.g., heat aging, abrasion, hardening, cracking, blockage, etc.)”].
Regarding Claims 10 and 20, Gomez, as applied to the context of Zhu, would disclose that the status of the elastomer component includes prediction on future usage of the elastomer component [Paragraph [0024] – “Extensions of the invention(s) can also be used to create models that simulate hydraulic equipment behavior and performance under various use scenarios, in relation to anticipated events (e.g., failure modes) corresponding to use scenarios.”Paragraph [0029] – “The system 100 functions to provide improved tools for monitoring, forecasting, and troubleshooting events (e.g., failure modes, lifespans, etc.) of hydraulic apparatus components at global and subcomponent levels.”Paragraph [0132] – “models associated with Block S330 can process mean time between failure (MTBF) for various subcomponents in order to predict the likelihood of failure within a given time period.”].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20170184138 A1 – SYSTEM AND METHOD FOR HEALTH MONITORING OF HYDRAULIC SYSTEMS
US 20150145533 A1 – METHOD AND SYSTEM FOR HEALTH MONITORING OF COMPOSITE ELASTOMERIC FLEXIBLE ELEMENTS
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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