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 .
Priority
Application 18/025,122 filed on 03/07/2023 is a 371 of PCT/BR2021/050098 filed on 03/09/2021and claims foreign priority to BRAZIL BR 10 20200182919 filed on 09/08/2020.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/01/2025 has been entered.
Response to Amendment
This office action is in response to amendments submitted on 07/31/2025 wherein claims 1-6 are pending and ready for examination. Claim 6 has been newly added.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Independent claim 1: Claim 1 recites the limitation “the drive device” (line 30-31). There is insufficient antecedent basis for these limitations in the claim. Examiner believes this should read driving device.
Claim 1 will be examined based on the merits as best understood.
Regarding claims 2-6: Claims 2-6 are also rejected under 35 U.S.C. 112(b) as they depend from parent claim 1.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi, U.S. Pub. No. 2021/0039139 A1 in view of Strudwicke et al., U.S. Pub. No. 2022/0252485 A1, in view of Cella et al., U.S. Pub. No. 2020/0166922 A1 in view of Mahi et al., U.S. Pat. No. 10,567,244 B1.
Regarding Independent claim 1 Mdlazi teaches:
“A system for monitoring, predictive maintenance, and operational optimization of vibrating screening equipment, the system comprising: the vibrating screening equipment including: a body; a suspension that supports and suspends the body; a screening device that screens an object; a driving device that vibrates the body” (Mdlazi, fig 1, fig 2, ¶ 0045, ¶ 0066-¶ 0067: Mdlazi teaches “calculating how material from a vibrating screen feeder should be re-directed to reduce any planar deviations and restore standard performance of the vibrating screen” (¶ 0045) disclosing “monitoring, predictive maintenance, and operational optimization of vibrating screeners” where figs. 1 and 2 depicts “vibrating screening equipment” including an external chassis 14 (a body), dampers 16 mounted on suspension brackets 20 secured to the sidewalls 18 (suspension that supports and suspends the body), a mesh surface 22 (screening device), and drive mechanism 42 (driving device) (¶ 0067).)
“a hardware module, the hardware module including: structure sensors and bearing sensors provided on the body of the vibrating screen equipment to measure a degree of vibration of the body” (Mdlazi fig 1, fig 2, ¶ 0075-¶ 0078: Mdlazi teaches a gyroscope sensor 60 and an accelerometer 62 mounted on the bridge 40 which is mounted on the external chassis 14 (structure sensors), “one temperature sensor 64 in each gearbox 48 to measure the temperature of the oil (or other lubricant/coolant) in that gearbox 48” (¶ 0078) disclosing “bearing sensors.”)
While Mdlazi teaches a bearing sensor (¶ 0078) Mdlazi does not teach a bearing sensor that collects vibration data.
Strudwicke teaches equipment such as a vibrating screen (¶ 0053) with a “a plurality of sensors 16a,b, each sensor 16 a,b being coupled to a respective mounting point 14 a,b” (¶ 0054) where “data harvesters 18 use the sensors 16a,b to monitor the pump bearing health, which includes measuring the temperature and vibrations of the bearings” (¶ 0122, see also fig. 1). Therefore the combination of Mdlazi as modified by Strudwicke teaches the limitation “a hardware module the hardware module including: structure sensors and bearing sensors provided on the body of the vibrating screen equipment to measure a degree of vibration of the body.”
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by including collecting vibration data from bearing sensors as taught by Strudwicke as bearing sensor vibration data is able to diagnose failure at an earlier stage as compared with temperature data thus providing a system which collects data indicating how the equipment is working and “to process such data to improve performance or reliability of such equipment” (Strudwick, ¶ 0003).
Mdlazi teaches:
“a gateway, which collects data from the structural sensors and the bearing sensors; and a router that receives the data from the gateway to send the received data to an internet gateway” (Mdlazi, fig. 1, fig. 2, ¶ 0080-¶ 0081: Mdlazi teaches a data management unit 70 which “receives transmitted signals from each of the sensors” (¶ 0080) disclosing “a gateway, which collects data from the structural sensors and the bearing sensors.” Moreover, the data management unit 70 that transmits data to a cloud-based analytics system 72 (¶ 0081). A person of ordinary skill in the art would understand that transmitting data to a cloud-based analytics system requires a “router” and an internet service provider which discloses an “internet gateway.” Mdlazi teaches transmitting data to a cloud-based analytics system therefore Mdlazi discloses a “router” and an “internet gateway.”)
“a processor connected to the hardware module via a signal line or a network and configured to function as: an intelligence generation module that transmits information between the hardware module and computer services for both calculating and manipulating data necessary for proper functioning of the system,” (Mdlazi, ¶ 0016: Mdlazi teaches “a monitoring computer (disclosing computer services which would include a processor) in communication with the sensing mechanism (hardware module) and operable to pre-process received signals (calculating and manipulating data) from the sensing mechanism and to provide an indication of how efficiently the vibrating screen is performing” (¶ 0016) where “communication” discloses “transmission” and “an indication of how efficiently the vibrating screen is performing” leads to disclosing “the proper functioning of the system.”)
While Mdlazi teaches “By combining the outputs from different types of sensors, the operation of the vibrating screen 10 can be diagnosed and optimized” (¶ 0091), Mdlazi does not explicitly teach the diagnosing and optimization is done with a machine learning algorithm or machine learning (MAQ) services.
Cella teaches a predictive maintenance system that applies machine learning to machine health monitoring data where a predictive maintenance facility produces service recommendation in response to health monitoring data (“back-end services”) receiving and processing information regarding the services performed on the machines in response to orders and requests (front-end services) (¶ 0010) in addition to storing signals (information in the system) (¶ 0012). Therefore the combination of Mdlazi and Cella teaches the limitation “the computer services including machine learning algorithms or machine learning (MAQ) services.”
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by including machine learning applied to machine health data as disclosed by Cella to provide predictive maintenance as machine learning helps to analyze existing data by identifying patterns and making more accurate predictions concerning machine health.
Mdlazi teaches:
“a traffic management module that manages the transmission of the information from the network in different locations of a container registry, front-end services, and back-end services;” (Mdlazi, fig 1, fig 2, ¶ 0084-¶ 0089: Mdlazi teaches a video camera system with includes a processor programmed to detect the profile of the aggregate using an automated machine vision algorithm (¶ 0084) disclosing the “container registry” where “the video camera system 80 transmits a loading parameter to the cloud-based analytics system 72 (either directly or via the data management unit 70) based on the detected or anticipated loading” (¶ 0084) the cloud-based analytics system 72 uses a “dashboard view on a mobile application presented on a mobile device carried by the registered operator” (¶ 0089) disclosing “front-end services,” and “the analytics system 72 sends a signal to the controller 108, which can then decrease the speed of the motor 46 or stop the motor 46”(¶ 0088) disclosing “back-end services.” The “cloud-based analytics system” discloses “traffic management module” and the “container registry,” “front-end services,” and “back-end services” would be in different locations as they are different algorithms or programs.)
“adjusts operational parameters for the drive device and the screening device; repositories or databases that stores the information, wherein the driving device and the screening device are operated with the operational parameters adjusted based on the decision-making processes.” (Mdlazi, fig. 1, ¶ 0081, ¶ 0088-¶ 0091: Mdlazi teaches the “data management unit 70 pre-processes the data to make it easier to analyse, and then transmits the pre-processed data to a cloud-based analytics system for analysis” (¶ 0081) where the “cloud-based analytics system” discloses “repositories or databases that stores the information,” as a person of ordinary skill in the art would understand a “cloud-based analytics system” would include storage. Moreover, “a vibrating screen 10 can be monitored and changes to the operation can be made automatically to ensure that the vibrating screen 10 remains operational or operates more effectively. By combining the outputs from different types of sensors, the operation of the vibrating screen 10 can be diagnosed and optimized” (¶ 0091). The displacement is detected by the gyroscope sensor and this displacement signal transmitted to the cloud-based analytics system via the data management unit. If the cloud-based analytics system determines the displacement is beyond a predefined criterion, the controller can decrease the speed of or stop the motor (¶ 0088) thereby adjusting the operational parameters of and operating “the driving module and the screening module” “based on the decision-making processes” and “adjusted operational parameters.”)
While Mdlazi teaches a bearing sensor (¶ 0078) and adjusting operational parameters (see above) Mdlazi does not teach “the operational parameters” include “a vibration parameter for a set of bearing components of the driving device and a temperature parameter for the set of bearing components.”
Strudwicke teaches equipment such as a vibrating screen (¶ 0053) with a “a plurality of sensors 16a,b, each sensor 16 a,b being coupled to a respective mounting point 14 a,b” (¶ 0054) where “data harvesters 18 use the sensors 16a,b to monitor the pump bearing health, which includes measuring the temperature and vibrations of the bearings” (¶ 0122, see also fig. 1) disclosing vibration and temperature parameters for bearing components. Therefore the combination of Mdlazi as modified by Strudwicke teaches the limitation “adjusts operational parameters for the drive device and the screening device based on the operational parameters including a vibration parameter for a set of bearing components of the driving device and a temperature parameter for the set of bearing components.”
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by including collecting vibration data from bearing sensors as taught by Strudwicke as bearing sensor vibration data is able to diagnose failure at an earlier stage as compared with temperature data thus providing a system which collects data indicating how the equipment is working and “to process such data to improve performance or reliability of such equipment” (Strudwick, ¶ 0003).
Mdlazi does not teach:
“APIs (Application Programming Interfaces)” that enable the transmission of data.
“a data persistent layer module that communicates with the intelligence generation module”
“an event management module that receives the information from the intelligence generation module, to then assist in a decision-making process through scheduling of maintenance events for the vibrating screening equipment monitored in the geographic locations where it is distributed”
Cella teaches transmitting data using application programming interfaces (APIs)
(¶ 2155).
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by using application programming interfaces (APIs) for transmitting data as disclosed by Cella as APIs allow for faster data exchange with increased efficiency.”
“an event management module that receives the information from the intelligence generation module, to then assist in a decision-making process through scheduling of maintenance events for the vibrating screening equipment monitored in the geographic locations where it is distributed” (Cella, ¶ 0010, ¶ 0675-¶ 0678: Cella teaches a system that includes “a computerized maintenance management system (CMMS) that produces at least one of orders and request for service and parts responsive to receiving the industrial machine service recommendations” (¶ 0010) where the “industrial machine service recommendations” are produced by the “predictive maintenance facility” (¶ 0010). Moreover, “an industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines receives via a data collection network” (¶ 0010) where the industrial machines include a vibrating conveyor that vibrates in order to screen material (¶ 0675.) Additionally, sensor data may be collected from “one or more pieces of equipment/components, disparate but interconnected areas of an installation” or from buildings in separate different geographic locations (¶ 1522) in addition to managing “moment-to-moment traffic on the network” (¶ 1647).)
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by including machine learning applied to machine health data as disclosed by Cella to provide predictive maintenance as machine learning helps to analyze existing data by identifying patterns and making more accurate predictions concerning machine health.
Mahi teaches:
“a data persistent layer module that communicates with the intelligence generation module” (Mahi, fig 12, fig 14, col 20 line 12-53: Mahi teaches the “data processing layer transmits the persistent trend data to trend processing layer 518 which stores the trend data in tag trend database 520”(col 20 line 21-24) disclosing persistent data from which a custom tag API (the API is part of the “intelligence generation module” (see above)) retrieves data from the “tag trend database” disclosing a “data persistent layer module.”)
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi by including a well known data persistent layer module as disclosed by Mahi as modified as the data persistent layer provides for secure, efficient, and flexible applications and ignoring the data persistent layer in software can cause data problems including slowing down the performance of applications.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi as modified by Strudwicke , Cella and Mahi as applied to claim 1 above, and further in view of Shanbhogue et al., hereinafter Shanbhogue, U. S. Pub. No. 2013/0329356 A1.
Regarding claim 2 Mdlazi as modified teaches:
“the structure sensors and the bearing sensors are specified as being wireless (Mdlazi, ¶ 0047. Mdlazi teaches the sensors may be wired or wireless.)
While Mdlazi teaches protecting the gyroscope sensor from an ingress of dust or water (¶ 0075), Mdlazi does not teach an “IP69K degree of protection.”
Shanbhogue teaches an enclosure that houses sensors and has an IP69K rating for ingress protection therefore the sensors also have the IP69K protection.
Mdlazi and Shanbhogue both teach collecting data from sensors in environmentally challenging situations therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi as modified by including the well known IP69K rating for ingress protection for sensors as taught by Shanbhogue as the IP69K rating signifies the sensor has the ability to withstand incredibly harsh conditions, including high-pressure, high-temperature water jets, and steam cleaning indicating the highest level of protection for dust and water resistance in the Ingress Protection system.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi as modified by Strudwicke, Cella and Mahi as applied to claim 1 above, and further in view of Kumar KN et al., hereinafter Kumar, U.S. Pub. No. 2018/0107609 A1 and Schreib et al., U.S. Pub. No. 2019/0219703 A1.
Regarding claim 3 Mdlazi as modified teaches:
“the gateway and the router are specified as being wireless” (Mdlazi, ¶ 0081: Mdlazi teaches the data management unit 70 that transmits data to a cloud-based analytics system 72 (¶ 0081). A person of ordinary skill in the art would understand that transmitting data to a cloud-based analytics system requires “wireless” communication which would include a “router” and an internet service provider which discloses an “gateway.” Therefore Mdlazi discloses the “router” and “gateway” are “wireless.”)
Mdlazi does not teach the gateway and router are rated “with IP65 and IP67 protection, respectively, in addition to being equipped with long-range antennas.”
Kumar teaches:
the gateway is rated with “IP65 protection” (Kumar, fig. 1, ¶ 0022: Kumar teaches “Each FIM 110 can also be configured to operate as a gateway” and “Each FIM 110 is enclosed in a mechanical enclosure that may be compliant with an Ingress Protection standard (such as IP65) or other standard” (¶ 0022) disclosing the gateway is rated with “IP65 protection.”).
Mdlazi and Kumar teach transmitting data with devices in environmentally challenging situations therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen which separates and removes water from aggregate as taught by Mdlazi as modified by including the well known IP65 protection rating for the gateway as taught by Kumar as the IP65 rating signifies the gateway is dust tight and has the ability to withstand water from low-pressure water jets from any angle thereby increasing the lifetime of the gateway.
Schreib teaches:
the router is rated “with IP67 protection, respectively, in addition to being equipped with long-range antennas.” (Schreib, fig 6, fig 7, ¶ 0080-¶ 0081, ¶ 0132-¶ 0133: Schreib teaches a GRIDSAT tag enclosure with “thick-walled polycarbonate plastic with elastomer and pressurized screws to provide IP67 sealing” (¶ 0132) where the GRIDSAT tab enclosure includes “an MCU 32 to act as a border router host” (¶ 0081) and an “RF module 34 (that) functions as the border router node” disclosing the routed is rated with “IP67 protection.” Moreover, the GRIDSAT enclosure “contains the satellite antenna 46’ as well as the 2.4 GHz antenna for the mesh radio” (¶ 0133) disclosing “long-range antennas.”)
Mdlazi and Schreib teach transmitting data with devices in environmentally challenging situations therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen which separates and removes water from aggregate as taught by Mdlazi as modified by including the well known IP67 protection rating for the router as taught by Schreib as the IP67 rating signifies the router is dust tight and has the ability to withstand immersion in water thereby increasing the lifetime of the router.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi as modified by Strudwicke, Cella, Mahi, Kumar, and Schreib as applied to claim 3 above, and further in view of Yarvis et al., hereinafter Yarvis, U.S. Pub. No. 2019/0363905 A1.
Regarding claim 4 Mdlazi as modified does not teach:
“the gateway has an output via physical cable specified for communication with local supervisory software through a Profibus protocol.”
Yarvis teaches:
“the gateway has an output via physical cable specified for communication with local supervisory software through a Profibus protocol” (Yarvis, ¶ 0032, ¶ 0132: Yarvis teaches using wired gateways to couple each device to other devices (¶ 0054) disclosing a “gateway” that “has an output via physical cable.” Moreover, the wired communication of the network controller “may be based on other types of networks, such as (among others) PROFIBUS” disclosing “communication with local supervisory software through a Profibus protocol.”)
Mdlazi and Yarvis teach transmitting data to the cloud therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi as modified by including wired communication using the well known PROFIBUS protocol as taught by Yarvis as the PROFIBUS protocol is cost savings as it uses reduced wiring with improved reliability.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi as modified by Strudwicke, Cella and Mahi as applied to claim 1 above, and further in view of Maimon et al., hereinafter Maimon, “Introduction to Knowledge Discovery in Databased” downloaded from DOI 10.1007/0-387-25465-X_1.
Regarding claim 5 Mdlazi teaches:
“the structure sensors and the bearing sensors strategically installed on the vibrating screening equipment” Mdlazi teaches a gyroscope sensor 60 and an accelerometer 62 mounted on the bridge 40 which is mounted on the external chassis 14 (structure sensors), “one temperature sensor 64 in each gearbox 48 to measure the temperature of the oil (or other lubricant/coolant) in that gearbox 48” (¶ 0078) disclosing “bearing sensors.”)
While Mdlazi teaches a bearing sensor (¶ 0078) Mdlazi does not teach a bearing sensor that collects vibration data.
Strudwicke teaches equipment such as a vibrating screen (¶ 0053) with a “a plurality of sensors 16a,b, each sensor 16 a,b being coupled to a respective mounting point 14 a,b” (¶ 0054) where “data harvesters 18 use the sensors 16a,b to monitor the pump bearing health, which includes measuring the temperature and vibrations of the bearings” (¶ 0122, see also fig. 1).
It would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi as modified by including collecting vibration data from bearing sensors as taught by Strudwicke as bearing sensor vibration data is able to diagnose failure at an earlier stage as compared with temperature data thus providing a system which collects data indicating how the equipment is working and “to process such data to improve performance or reliability of such equipment” (Strudwick, ¶ 0003).
Mdlazi teaches:
send information to the gateway, transmits the information to the database (Bd) in the network, which treats and analyzes the information, resulting in the delivery of the information on the vibrating screening equipment (Eq), and, based on the collection of historical performance data for the vibrating screening equipment” (Mdlazi, fig. 1, fig. 2, ¶ 0080-¶ 0081: Mdlazi teaches a data management unit 70 which “receives transmitted signals from each of the sensors” (¶ 0080) disclosing “send information to the gateway.” Moreover, the data management unit 70 that transmits data to a cloud-based analytics system 72 (¶ 0081). A person of ordinary skill in the art would understand that transmitting data to a cloud-based analytics system requires a “router” and an internet service provider which discloses an “internet gateway.” Mdlazi teaches transmitting data to a cloud-based analytics system therefore Mdlazi discloses a “router” and an “internet gateway.” Additionally, a monitoring computer” provides “an indication of how efficiently the vibrating screen is performing by comparing the pre-processed signals with stored signals” (¶ 0016) where the “stored signals” “comprise historic baseline signals” (¶ 0017) disclosing the results are “based on the collection of historical performance data for vibrating screening equipment.”)
“Stage 6. Adjusting the operational parameters for the driving module and the screening modules based on the decision-making processes and operating the driving module and the screening module with the adjusted operational parameters” (Mdlazi, fig. 1, ¶ 0081, ¶ 0088-¶ 0091: Mdlazi teaches “a vibrating screen 10 can be monitored and changes to the operation can be made automatically to ensure that the vibrating screen 10 remains operational or operates more effectively. By combining the outputs from different types of sensors, the operation of the vibrating screen 10 can be diagnosed and optimized” (¶ 0091). The displacement is detected by the gyroscope sensor and this displacement signal transmitted to the cloud-based analytics system via the data management unit. If the cloud-based analytics system determines the displacement is beyond a predefined criterion, the controller can decrease the speed of or stop the motor (¶ 0088) thereby adjusting the operational parameters of and operating “the driving module and the screening module” “based on the decision-making processes” and “adjusted operational parameters.”)
Mdlazi does not teach:
“fed the database (Bd) used in KDD - Knowledge Discovery in Databases, which operate by stages, where: Stage 1. Data understanding: Substage 1.1 Evaluation of the databases in the intelligence generation module (GI); Substage 1.2 Evaluation of the information contained in fields of the database; Substage 1.3 Visual evaluation of basic relationships between attributes presented; Stage 2. Data preparation: Stage 3. Selection and application of processes by the Machine Learning to extract knowledge from data; Stage 4. Selection and application of forecasting algorithms to predict a value of important variables; wherein Stage 2 Data preparation includes: Substage 2.1 Detection of outliers, errors, duplicate data, irrelevant fields, and estimation of missing data; Substage 2.2. Creation of new attributes / data transformation, if needed; Stage 5. Detailed design, which consists in converting operational needs of a customer owning the vibrating screening equipment into a description of operational parameters of the vibrating screening equipment through functional analysis, synthesis, modeling, simulation, optimization, design, testing, and evaluation, integrating the performance parameters with other requirements in a modeling process.”
Maimon teaches:
“fed the database (Bd) used in KDD - Knowledge Discovery in Databases, which operate by stages, where: Stage 1. Data understanding: Substage 1.1 Evaluation of the databases in the intelligence generation module (GI); Substage 1.2 Evaluation of the information contained in fields of the database; Substage 1.3 Visual evaluation of basic relationships between attributes presented; Stage 2. Data preparation: Stage 3. Selection and application of processes by the Machine Learning to extract knowledge from data; Stage 4. Selection and application of forecasting algorithms to predict a value of important variables; wherein Stage 2 Data preparation includes: Substage 2.1 Detection of outliers, errors, duplicate data, irrelevant fields, and estimation of missing data; Substage 2.2. Creation of new attributes / data transformation, if needed; Stage 5. Detailed design, which consists in converting operational needs of a customer owning the vibrating screening equipment into a description of operational parameters of the vibrating screening equipment through functional analysis, synthesis, modeling, simulation, optimization, design, testing, and evaluation, integrating the performance parameters with other requirements in a modeling process.” (Maimon teaches the well known “Knowledge Discovery in Databases (KDD)” summarized in figure 1.1 (page 3). Starting with
developing an understanding of the KDD goals followed by “Selecting and creating a data set on which discovery will be performed” by determining “what data is available, obtaining additional necessary data, and then integrating all the data for the knowledge discovery into one data set, including the attributes that will be considered for the process” (substage 1.1, 1.2) (#2 page 4). “Preprocessing and cleansing” includes data clearing, such as handline missing values and removal of noise or outliers” where “if one suspects that a certain attribute is of insufficient reliability or has many missing data” “a prediction model for this attribute will be developed , and then missing data can be predicted” (substage 2.1) (#3, page 4). “Data transformation” includes “the generation of better data” by “dimension reduction” and “attribute transformation” (substage 2.2) (#4, page 4-5). Next “Choosing the appropriate Data Mining task”(stage 3) follows where “Prediction is often referred to as supervised Data Mining, while descriptive Data Mining includes the unsupervised and visualization aspects of Data Mining” where “supervised” and “unsupervised” disclose machine learning (page 7 2nd paragraph). The next step “Employing the Data Mining algorithm” (Stage 4) where there may be a “need to employ the algorithm several times until a satisfied result (value) is obtained” (#7, page 5). The next step “Evaluation” evaluates and interprets the mined patterns with respect to the goals defined previously (#8, page 5-6) and finally, “Using the discovered knowledge” incorporates the “knowledge into another system for further action” as “Data structures may change (certain attributes become unavailable), and data domain may be modified (such as, an attribute may have a value that was not assumed before)” (#9, page 6) where “data” discloses the “operational parameters” and the steps of “Evaluation” and “Using the discovered knowledge” disclose “stage 5.”)
Mdlazi uses algorithms to analyze datasets Maimon uses Knowledge Discovery in Databases (KDD) which is a methodology for extracting useful knowledge from datasets therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi as modified by including KDD as disclosed by Maimon as KDD is data-driven, uncovers hidden patterns and trends and improves efficiency and productivity by making better predictions and identifying risks.
Mdlazi teaches “vibrating screening equipment” (see above.)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mdlazi as modified by Strudwicke , Cella and Mahi as applied to claim 1 above, and further in view of Birchfield et al., hereinafter Birchfield, U.S. Pub. No. 2021/0016297 A1.
Regarding claim 6 Mdlazi teaches:
“wherein the operational parameters further including an acceleration parameter of the screening device” (Mdlazi, ¶ 0042: Mdlazi teaches “using an accelerometer to detect vibrational information relating to the chassis” (¶ 0042) where the chassis is part of the vibrating screen device (fig. 1 element 10, 14).
Mdlazi does not teach:
“an amplitude parameter for the screening device, a rotation parameter for the screening device”
Birchfield teaches:
“an amplitude parameter for the screening device, a rotation parameter for the screening device” (Birchfield, ¶ 0010: Birchfield teaches “operational parameters include a screen angle (rotation parameter) and “an amplitude of vibratory motion” (amplitude parameter) (¶ 0010).)
Mdlazi and Birchfield both teach managing a vibratory shaker therefore it would have been obvious for a person of ordinary skill in the art to have modified the system and method of monitoring and optimizing a vibrating screen as taught by Mdlazi as modified by including amplitude and rotation parameters as taught by Birchfield as increasing the type of data collected leads to detection betters patterns leading to more reliable predictions in order to provide “an improved system that reduces maintenance costs” (Birchfield, ¶ 0118).
Response to Arguments
Applicant’s arguments (remarks) filed on 12/01/2025 have been fully considered.
Regarding Rejections under 35 USC 112 page 5 of Applicant’s remarks, based on Applicant’s arguments and changes made to the claims, the 35 USC 112(b) rejections have been withdrawn. New 35 USC 112(b) rejections have been entered.
Regarding Rejections under 35 USC 103 page 5-6 of Applicant’s remarks, Applicant argues “The applied references, alone or in combination, fail to teach or suggest each and every feature of the claimed invention. That is, the applied references fail to teach or suggest "the operational parameters including a vibration parameter for a set of bearing components of the driving device and a temperature parameter for the set of bearing components... wherein the driving device and the screening device are operated with the operational parameters adjusted based on the decision-making processes" as recited in exemplary independent claim 1” (remarks, page 6).
Examiner respectfully disagrees. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Applicant argues “The Examiner alleges that Mdlazi teaches operating a driving device and a screening device with the adjusted operational parameters.
Mdlazi, however, merely teaches decreasing the speed of or stopping a motor based on a detected displacement. Applicants respectfully disagree.
That is, the Examiner's rejection appears to equate the reduction of speed of the motor to
(1) adjusting the parameters, (2) operating the driving device and (3) operating the screening device, which is an incorrect interpretation of the claimed invention and the teachings of Mdlazi.
Notwithstanding the above, Mdlazi would still not teach or suggest the above feature of the claimed invention. That is, as best understood from the rejection as represented, the Examiner is analogizing the "displacement" detected by the gyroscope as the claimed parameter. Mdlazi, however, does not teach or suggest that the operational parameters include a vibration parameter for a set of bearing components of the driving device and a temperature parameter for the set of bearing components” (remarks page 6).
Examiner respectfully disagrees. Mdlazi teaches a drive mechanism (driving device) (fig. 1 element 42) which “imparts motion to a deck (or multiple decks) of the screen” (¶ 0013) where a temperature sensor measures the temperature of a drive mechanism “(or each drive component within the drive mechanism)” (¶ 0010). The drive mechanism contains exciters (fig. 2 element 44) where the exciters each include a gearbox where the gearbox is “a drive component within the drive mechanism” and where, a person or ordinary skill in the art would understand, the gearbox contains bearings and “one temperature sensor 64 in each gearbox 48 to measure the temperature of the oil (or other lubricant/coolant) in that gearbox 48” (¶ 0078) disclosing “a temperature parameter for the set of bearing components.” Mdlazi teaches “using an accelerometer to detect vibrational information relating to the chassis” (¶ 0042) however, Mdlazi does not explicitly teach “a vibration parameter for a set of bearing components.” Strudwicke teaches “measuring the temperature and vibrations of the bearings” (¶ 0122) therefore a combination of Mdlazi and Strudwicke teach the limitations "the operational parameters including a vibration parameter for a set of bearing components of the driving device and a temperature parameter for the set of bearing components” (see rejection above). Moreover, Mdlazi teaches a data management unit (fig. 1 element 70) which “receives transmitted signals from each of the sensors 60 to 66” (¶ 0080, fig. 2) where the data management unit “pre-processes the data to make it easier to analyse, and then transmits the pre-processed data to a cloud-based analytics system 72 for analysis” (¶ 0081, fig. 1) and where “the analytics system 72 sends a signal to the controller 108, which can then decrease the speed of the motor 46 or stop the motor 46 (¶ 0086, fig. 4). The motor 46 is a component of the drive mechanism therefore Mdlazi as modified by Strudwicke teaches “the driving device and the screening device are operated with the operational parameters adjusted based on the decision-making processes.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jones et al., U.S. Pub. No. 2015/0283581 A1, teaches a method of controlling the vibration of a vibratory separator.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857