Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Change in Examiner
2. Julie Shanker is no longer continuing prosecution on application number
17/605,833. It has been transferred to Examiner Jessica Lemieux.
DETAILED ACTION
3. This Final Office action is in response to the application filed on October 22nd, 2021 and in response to Applicant’s Arguments/Remarks filed on May 5th, 2026.
Claims 7-9 and 11are pending.
Priority
4. Application 17/605,833 was filed on October 22nd, 2021 and is a 371 of PCT/TR2020/050343 filed on April 22nd, 2020 which has foreign priority to application TR2019/06067 filed on April 24th, 2019.
Examiner Request
5. The Applicant is requested to indicate where in the specification there is support
for amendments to claims should Applicant amend. The purpose of this is to reduce
potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims
are amended without support in the specification. The Examiner thanks the Applicant in
advance.
Response to Arguments
6. Examiner Applicant’s arguments, with respect to objection of claims 7 and 9 regarding the abbreviations have been fully considered and are persuasive in view of the amended claim language. The objection of claims 7 and 9 regarding the abbreviations has been withdrawn.
7. Examiner Applicant’s arguments, with respect to 35 U.S.C. 112 (a) and (b) rejections of claims 7-9 and 11 has been rendered moot as the offending language has been cancelled or amended.
8. Applicant argues that the present invention is directed toward statutory subject matter as the independent claims 7 and 9 recite the structural limitation of a “plurality of items of heavy equipment” and “sensor” and the remaining elements are recited so as to effectively predict malfunctions in the heavy equipment by analyzing historical records and real-time sensor data. Examiner notes that Applicant's arguments have been fully considered but they are not persuasive. Examiner notes that eligibility does not turn merely on whether the claim nominally recites physical components or falls within a statutory category at Step 1. Rather, the claim must be considered under Step 2A and Step 2B. Here the claimed collection of equipment-status data, conversion of the data for modeling, estimation of potential failures, and analysis of the estimated failures recites an abstract idea in the form of mental processes and mathematical concepts. The additional elements, including the sensors, ERP system, data warehouse, servers, and mobile device, are recited at a high level of generality and there is no improvement to the operation of the sensors, heavy equipment or other recited additional elements. The claims do not recite a technological improvement to the operation of the sensors, ERP system, data warehouse, servers, mobile device or other computer technology. Rather, the claims recite the abstract estimation/analysis process using the generic additional elements that are recited at such a high level of generality that they represents no more than mere instructions to apply the judicial exception. The claims generally link the abstract estimation/analysis process to the technological environment monitoring and predictive maintenance. The use of sensors to gather equipment data and computer components to store, analyze and transmit the results constitutes data gathering, generic computer implementation, and insignificant extra-solution activity. Accordingly, the additional elements, individually and in combination, do not integrate the abstract idea into a practical application and do not amount to significantly more. Therefore, the rejection under 35 USC 101 is maintained.
9. Applicant’s arguments regarding the rejection under 35 USC 103 have been fully considered but are not persuasive.
Applicant argues that Morris does not disclose ERP integration, data warehouses, customer interaction layers, fleet-level scalability, IoT/GPS/GSM/satellite technologies, and various other features discussed in Applicant’s specification. Examiner notes that the rejection is based upon the combination of Morris and Kohli rather than Morris alone. Further, many of the cited features are not recited in the claims. Non-claimed features appearing in Applicant’s disclosure cannot distinguish the claimed invention from the prior art. The rejection addresses the claiming ERP system and data warehouse limitations through Kohli and relies on Morris for the predictive maintenance and machine-learning functionality. Accordingly, the argument is not persuasive.
Applicant argues that Morris is directed generally to machines and hardware devices rather than heavy equipment. Examiner is not persuaded. Morris expressly teaches monitoring machines and equipment using sensors and predicting operational failures thereof. Applicant has not identified any claim language requiring a specialized form of heavy equipment beyond the broad recitation present in the claims. Accordingly, Morris reasonably teaches or suggests the claimed plurality of items of heavy equipment under the broadest reasonable interpretation.
Applicant argues that Morris utilizes statistical models whereas Applicant’s disclosure describes machine-learning techniques such as gradient boosting and separate learning and estimating components. Examiner notes that the claims do not recite gradient boosting or any specific machine-learning technique. Further, Morris teaches generating predictive models, estimating operational outcomes, and continuously improving predictive functionality using machine learning techniques as cited. The rejection relies on those teachings rather than the specific embodiments emphasized by Applicant. Accordingly, the argument is not persuasive.
Applicant argues that Morris relies upon sensor data and operational logs rather than historical service records and customer input. Examiner notes that claims 7 and 9 do not require historical service records or customer input. The rejection addresses the limitations actually recited in the claims. Arguments directed to unclaimed features are not commensurate in scope with the claims and therefore are not persuasive.
Applicant argues that Morris does not expressly disclose an ERP system and data warehouse. Examiner agrees that Morris does not expressly disclose those particular limitations. For that reason, the rejection relies on Kohli, which teaches ERP systems utilizing data warehouse functionality. The rejection further explains why one of ordinary skill in the art would have incorporated Kohli’s ERP/data warehouse architecture into Morris’s predictive maintenance system to improve storage, processing, and exchange of operational data. Applicant does not specifically address the teachings of Kohli. Accordingly, the argument is not persuasive.
Applicant argues that Morris emphasizes dynamic model updates and removal of outdated models whereas Applicant’s disclosure describes real-time ERP integration and predictive maintenance workflows. Examiner is not persuaded. Claim 8 merely requires that the estimation component be integrated with the ERP system in real time. As set forth in the rejection, the combination of Morris and Kohli teach the ERP system architecture relied upon for the ERP limitations, and Morris teaches analyzing monitored information to evaluate real-time or substantially real-time conditions and predict future failures. Accordingly, the combination reasonably suggests an estimation component integrated with the ERP system in real time. Accordingly, the arguments is not persuasive.
Claim Objections
10. Claim 9 recites “sending the estimated potential failure to be cloud server by the data warehouse from the ERP system.” The examiner believes Applicant intended the claim to read “sending…to the cloud server.” Appropriate correction is requested.
The claim identifier for claim 9 recites that it is previously presented however it should read that it is currently amended. For sake of compact prosecution Examiner will read it as though Applicant intended for the claim identifier to read as 9. (currently amended).
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.
11. Claims 7-9 and 11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Step 1
Claims 7-8 is directed to a malfunction estimation system and claims 9 and 11 are directed to a malfunction estimation method. Thus, each of the claims falls within one of the four statutory categories as required by Step 1.
Step 2A, Prong One
For step 2A, the claim(s) recite(s) an abstract idea. Using claim 9 as a representative example that is applicable to claim 7, the abstract idea is defined by the elements of the claim in bold:
A method of malfunction estimation for use with a plurality of items of heavy equipment, the method comprising:
applying at least one sensor to each of the plurality of items of heavy equipment;
measuring a status of each of the plurality of items of heavy equipment with the at least one sensor;
sending data pertaining to the measured status of each of the plurality of items of heavy equipment as measured by the at least one sensor via a Global System for Mobile Communications (GSM) and satellite through data transfer device;
recording data relative to each of the plurality of items of heavy equipment in an ERP system through an internet platform, the internet platform running on a mobile application on a m bile device;
storing the recorded data in the ERP system via data warehouse;
converting the stored data in the ERP system into the desired format for modeling;
integrating the ERP system with a server;
sending the converted stored data in the desired format for modeling to the server through a data warehouse;
estimating potential failures by using machine learning to the sent converted stored data in the desired format;
sending the estimated potential failure to be cloud server by the data warehouse from the ERP system;
performing an analysis of the sent estimated potential failures on the cloud server;
sending the analysis of the sent estimated potential failures to a mobile device via the data warehouse; and
sharing the sent analysis with others through the application on the mobile device on the internet platform.
Under the broadest reasonable interpretation of the claims, the bolded steps recited above recite steps that can performed in the human mind including observation, evaluation, judgment, and opinion. For example, the claimed measuring, developing, estimating and performing an analysis encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Additionally, the steps recited above relates to mathematic concepts in estimating failure. Thus, the claims recite an abstract idea.
Step 2A, Prong Two
Next, the examiner considers whether claims 7-9 and 11 recites any additional elements that integrate the abstract idea into a practical application. The claims recite: at least one sensor, at least one data transfer device cooperative with said sensors and in communication with GSM and satellite infrastructure, an ERP system cooperative with the at least one data transfer device that allows the storage and processing of various data, a data processing device communicative with the at least one data transfer device and having an Internet platform, a data warehouse that allows data exchange between the ERP system, a server having an estimation algorithm incorporation machine learning, the server having a learning and estimation component, a test platform, a cloud server and a mobile device having a mobile application. The additional elements, do not, either individually or in combination, integrate the abstract idea into a practical application. Specifically, these elements are generic computer components, recited at such a high level of generality that they represents no more than mere instructions to apply the judicial exception. The additional limitation of developing a machine learning algorithm additionally represents no more than mere instructions to apply the judicial exception on a computer. In particular, there are no details about a particular machine learning algorithm or how it operates to derive the estimated potential failures other than that it is being used to determine potential failures. The machine learning algorithm is used to generally apply the abstract idea. These additional elements can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer system. Moreover, the sensors, transfer devices and mobile devices having mobile applications are claimed at a high level of generality and could describe receiving and sending data with any type of sensor and/or transfer/mobile device. The “measuring” “sending”, and “sharing” limitations does not impose any other meaningful limits on the claim. Therefore, the additional limitations are insignificant extra-solution activity. See MPEP 2106.05(g). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application. Therefore, the claim as a whole is not considered to integrate the recited judicial exception into a practical application of the exception.
Step 2B
If the claims are not integrated into a judicial exception, the Examiner must consider whether there is “significantly more” recited in the claim in step 2B. As noted above, the additional elements were found to represent no more than mere instructions to apply the judicial exception on a computer using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Here, the step of receiving, measuring and sending sensor data, sending the analysis, and sharing the sent analysis is considered to be mere data gathering and data output that is recited at a high level of generality, and are well-understood (e.g., the prior art portion of the Specification explains that sensors have been used to gather and send data regarding machines). Moreover, MPEP 2106.5(d) and MPEP 2106.5(g) notes that receiving or transmitting data over a network and storing and retrieving information in memory are well-understood. Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. The claim is not eligible.
The dependent claims are merely reciting further embellishment of the abstract idea and do not amount to anything that is significantly more than the abstract idea itself. In other words, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limitations that, in an ordered combination provide “significantly more;” rather, the dependent claims are merely further reciting features that are just as abstract as independent claims 7 and 9. Therefore, Claims 7-9 and 11 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
12. Claim(s) 7-9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morris, II et al., (US 20160350671) (hereinafter referred to as Morris) in view of Kohli, Manu, “Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures” (International Journal of Engineering & Technology) (Pages 312-319) (2017) (hereinafter referred to as Morris).
Claims 7: Morris describes malfunction estimation system comprising:
A plurality of items heavy equipment [0044] (describes equipment monitored);
a sensor affixed to each of said plurality of items of heavy equipment measuring a status of each of the plurality of items of heavy equipment [0044] (describes sensors used to gather equipment data); [0045] (describes “the one or more sensors 120 can each, independently, be any suitable sensor including, for example, thermometers, hygrometers, pressure sensors, flow meters, vibration sensors, odometers, accelerometers, ammeters, voltmeters, power meters, digital multi-meters, digital imagers, microscopes, distance measurement devices, tackiness detectors, rigidity detectors, stiffness detectors, alignment detectors, pH detectors, GPS locators, combinations thereof, or the like.”);[0046](describes “Source data generated by these one or more sensors 120 will be associated with the specific past or current operation of the equipment 112 that operates within the operating system 110 or processes associated therewith. For example, if the one or plurality of sensor(s) comprises a temperature sensor, data will be generated related to temperature readings or behavior of equipment 112 (e.g., a machine or hardware device) with which that one or more sensors 120 is associated will be generated during operation thereof. Moreover, each of the one or more sensors 120 can have multiple functionalities so as to generate multiple sets of source data, or one or more sensors 120 can generate a single set of source data that provides information about multiple operating parameters of the equipment 112 with which the one or more sensors 120 is associated. In this regard, the one or more sensors 120 can be configured to provide both temperature and vibration information, for example, where the temperature and vibration information can be streamed as separate source data outputs (i.e., temperature data and vibration data), or the information can be combined to provide a unique source data parameter that incorporates both temperature and vibration data into a single source data component for analysis according to the methodology herein.”);
at least one data transfer device cooperative with said sensor so as to transfer data corresponding to the status of said plurality of items of heavy equipment to be passed via GSM and satellite [0048] (describes sensors being coupled to one or more, user devices, a server or master controller);[0055] (describes multiple ways of data transfer between components of the system);
an Enterprise Resource Planning (ERP) system cooperative with said at least one data transfer device so as to store and process the transferred data [0050;0055] (describes recording the data and storing the data utilizing computer infrastructure ). Note, Morris, does not expressly describe an ERP system (addressed below);
at least one data processing device adapted to allow entry of information pertaining to the plurality of items of heavy equipment, said at least one data processing device communicative with said at least one data transfer device so as to monitor the status of the item of said plurality of items of heavy equipment[0048] (describes sensors being coupled to one or more, user devices, a server or master controller);[0055] (describes multiple ways of data transfer between components of the system);, said at least one data processing device having an internet platform [0049-50] (describes processing devices having an internet platform);
data warehouse cooperative with said ERP system so as to allow data exchange between the stored and processed data of said ERP system [Fig. 1] (describes flow of data exchange between system components); [0049-0050] (describes components allowing data exchange). Note, Morris, does not expressly describe an ERP system having a data warehouse (addressed below);
at least one server having an estimation algorithm, the estimation algorithm incorporating a machine learning method, said at least one server having a learning component and an estimating component adapted to continuously improve the estimation algorithm; at least one test platform cooperative with said at least one server so as to allow the estimation algorithm to be measured by said at least one test platform [ 0057; 0073-77]; [0094] (describe generating and creating predictive models components using machine learning and continuously improving the algorithms utilizing components of the predictive system); [0075-077](describes estimating outcomes of interest using machine learning and sending the estimated outcomes to the system)[ 0089-91] (describes estimating potential failures using predictive modeling to the formatted source data).
at least one cloud server that analyses the estimated data from the measured result of the estimation algorithm [0077] (describes performing an analysis of the sent estimated failure to determine actual occurrence). Note, Morris, does not expressly describe an ERP system (addressed below);
a mobile device having a mobile application that allows entry of the information pertaining to the plurality of items of heavy equipment [0124] (describes sending and sharing analysis info to users devices);[0049-0050] (describes components allowing data exchange)
As noted above, while Morris describes a system and method for predicting operational failures of interest in machines and Morris further describes a number of system configurations , Morris does not expressly describe sending data through a data warehouse and utilizing an ERP system.
Kohli teaches an ERP system having a data warehouse that allows the storage and processing of various data [page 313] (describes use of ERP systems utilizing data ware houses).
It would have been obvious to one skilled in the art before the effective filing date to modify Morris to implement the system on an ERP system utilizing data ware housing functionality (i.e., SAP HANA) as taught by Kohli because they are well known parts of computer networking architecture allowing the exchange of data in a controlled manner with predictable results. The combination would result in a more efficient system for the expected benefit of processing greater amounts of data in a fast and more accurate manner. Furthermore, such combination would have been obvious because “a system that can successfully predict machine breakdowns early can ensure improved productivity, lower emission risk and improved safety in workplaces.” Kohli [Page 318].
Claim 9: Morris describes a method of malfunction estimation for use with a plurality of items of heavy equipment, the method comprising:
applying at least one sensor to each of the plurality of items of heavy equipment [0044] (describes sensors used to gather equipment data); [0045] (describes “the one or more sensors 120 can each, independently, be any suitable sensor including, for example, thermometers, hygrometers, pressure sensors, flow meters, vibration sensors, odometers, accelerometers, ammeters, voltmeters, power meters, digital multi-meters, digital imagers, microscopes, distance measurement devices, tackiness detectors, rigidity detectors, stiffness detectors, alignment detectors, pH detectors, GPS locators, combinations thereof, or the like.”);
measuring a status of each of the plurality of items of heavy equipment with the at least one sensor [0046](describes “Source data generated by these one or more sensors 120 will be associated with the specific past or current operation of the equipment 112 that operates within the operating system 110 or processes associated therewith. For example, if the one or plurality of sensor(s) comprises a temperature sensor, data will be generated related to temperature readings or behavior of equipment 112 (e.g., a machine or hardware device) with which that one or more sensors 120 is associated will be generated during operation thereof. Moreover, each of the one or more sensors 120 can have multiple functionalities so as to generate multiple sets of source data, or one or more sensors 120 can generate a single set of source data that provides information about multiple operating parameters of the equipment 112 with which the one or more sensors 120 is associated. In this regard, the one or more sensors 120 can be configured to provide both temperature and vibration information, for example, where the temperature and vibration information can be streamed as separate source data outputs (i.e., temperature data and vibration data), or the information can be combined to provide a unique source data parameter that incorporates both temperature and vibration data into a single source data component for analysis according to the methodology herein.”);
sending data pertaining to the measured status of each of the plurality of items of heavy equipment as measured by the at least one sensor via a Global System for Mobile Communications (GSM) and satellite through data transfer device [0048] (describes sensors being coupled to one or more, user devices, a server or master controller);[0055] (describes data transfer between components of the system);
recording data relative to each of the plurality of items of heavy equipment in an ERP system through an internet platform, the internet platform running on a mobile application on a mobile device and storing the recorded data in the ERP system via data warehouse [0050;0055] (describes recording the data and storing the data utilizing Internet networks ). Note, Morris, does not expressly describe an ERP system (addressed below);
converting the stored data in the system into the desired format for modeling [0059] (describes converting stored source data into formatted data). Note, Morris, does not expressly describe an ERP system (addressed below);
integrating the ERP system with a server[ 0057] (describe generating and creating predictive models);
sending the converted stored data in the desired format for modeling to the server through a data warehouse[0059] (describes sending the formatted source data) Note, Morris, does not expressly describe an ERP system having a cloud server(addressed below);
estimating potential failures by using machine learning to the sent converted stored data in the desired format and sending the estimated potential failure to be server by the data warehouse from the system [0075-077](describes estimating outcomes of interest using machine learning and sending the estimated outcomes to the system)[ 0089-91] (describes estimating potential failures using predictive modeling to the formatted source data). Note, Morris, does not expressly describe an ERP system having a cloud server (addressed below);
performing an analysis of the sent estimated potential failures on the server [0077] (describes performing an analysis of the sent estimated failure to determine actual occurrence). Note, Morris, does not expressly describe an ERP system having a cloud server (addressed below);
sending the analysis of the sent estimated potential failures to a mobile device via the data warehouse; and sharing the sent analysis with others through the application on the mobile device on the internet platform [0124] (describes sending and sharing analysis info to users devices).
As noted above, while Morris describes a system and method for predicting operational failures of interest in machines and Morris further describes a number of system configurations , Morris does not expressly describe sending data through a data warehouse and utilizing an ERP system.
Kohli teaches an ERP system having a data warehouse that allows the storage and processing of various data [page 313] (describes use of ERP systems utilizing data ware houses).
It would have been obvious to one skilled in the art before the effective filing date to modify Morris to implement the system on an ERP system utilizing data ware housing functionality (i.e., SAP HANA) as taught by Kohli because they are well known parts of computer networking architecture allowing the exchange of data in a controlled manner with predictable results. The combination would result in a more efficient system for the expected benefit of processing greater amounts of data in a fast and more accurate manner. Furthermore, such combination would have been obvious because “a system that can successfully predict machine breakdowns early can ensure improved productivity, lower emission risk and improved safety in workplaces.” Kohli [Page 318].
Claim 8: Morris and Kohli describes the system of claim 7. Morris further describes real-time estimations. [0004] (describes “[0004] The monitored information can also be analyzed to evaluate the real time or substantially real time conditions of the operating system and to predict the likelihood of whether future operational conditions that may be of interest, such as, for example, faults or failures of the process, device or individual equipment/hardware might occur.”).
Claim 11. Morris and Kohli describes the method of claim 9. Morris further describes adding GPS data to the sent data. [0045] (describes GPS locators).
Conclusion
13. THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LEMIEUX whose telephone number is (571)270-3445. The examiner can normally be reached Monday-Friday 7AM-3PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626
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