DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication received on 12/18/2024. The claims 1-3 are pending, of which the claim(s) 1, 2, & 3 is/are in independent form.
Claim Objections
Claims 1- 2 objected to because of the following informalities:
In claim 1, step (e), the limitation of “a reconfigurable engine running the server” should be changed to “a reconfigurable engine running in the server” to improve clarity and to be consistent with the disclosure of specification. See spec, para 022, 053.
In claim 2, the limitation (d) should start with a verb word rather than with a noun (the limitation “a program running in the server for forming automated rules” shown be written as “configuring/causing a program running in the server for forming automated rules”).
In claim 2, limitation (f), the limitation of “a reconfigurable engine running the server” should be changed to “a reconfigurable engine running in the server”.
Appropriate correction/clarification is required.
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.
Claim 1 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 claim 1, in line 1, the claim’s preamble recites “Apparatus for quality assurance of at least one industrial process using rule formation from sensor data comprising:”. However, the apparatus comprising several other structural elements such as “a plurality of sensors”, “a server”, “a controller”, “a program”, “a reconfigurable engine”. Examiner notes applicant’s specification and associated drawing show that the server 106, machines and sensors 102s as part of a system rather than as part of the same apparatus/device. See Fig. 1, para. 038. Accordingly, the scope of the claim is indefinite in light of applicant’s specification. That is, it is not clear how a single apparatus can include all of these claimed structural elements (namely, a controller, a s server, sensors) thereby rendering the scope of the claim indefinite.
For the examination purpose, “apparatus” of line 1 of claim 1 is interpreted as “A system” to be consistent with applicant’s disclosure.
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- 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asenjo et al. (US 20140337429 A1, hereinafter Asenjo) in view of Sharpe (US 20060020423 A1).
Regarding claim 1, Asenjo teaches/suggests apparatus for quality assurance of at least one industrial process1 [process performed in the industrial facility 104s, fig. 1] using rule formation from sensor data comprising: (Fig. 1);
a) a plurality of sensors [sensors used by the automation system and field devices under control of the PLC. For example, “data from multiple sensors measuring related aspects of an automation system”, “industrial devices 108 and 110 can include such devices as industrial controllers (e.g., programmable logic controllers or other types of programmable automation controllers); field devices such as sensors and meters”] attached to a machine [one or more of the industrial devices (e.g., device 108 fig. 1 or device 6062 of fig. 6) out of “multiple industrial devices” that use cloud proxy device/controller/cloud-aware smart device] for measuring information [information/data that are collected/captured and transmitted to the local cloud-aware controller/PLC and the “cloud platform” for processing/analyzing] about the industrial process and operation of the machine [“industrial device 6062”] (Figs. 1, 6, [003, 045, 066, 0124, 0135]);
b) a server [one or more of the hardware/software computers (“Computers and servers include one or more processors” like “computer 2412” of fig. 24) used by the cloud platform] connected to the sensors over a wireless [“public cloud accessible via the Internet by devices having Internet connectivity”] communication network, for processing the information related to the industrial process received from the sensors ([20048, 0088, 0109]);
c) a controller [cloud-aware device of the facility, “one or more industrial controllers that facilitate monitoring and control of their respective processes”, e.g., “Industrial controller 302” shown in fig. 3 and “industrial device 6061” of fig. 6, “cloud-aware device, industrial device 1002”, “cloud-aware smart device 1702” of fig. 17] in communication with the server, the controller being capable of
controlling the industrial process based on data received from the server ([046, 057, 0075, 0085]),
d) using automated (not requiring a human user to identify possible problem) rules [software means of the cloud platform that perform “predicting impending device failures or operational inefficiencies” rather than via a person/user] for predictive maintenance and process quality assurance [“processes (e.g., quality audit”] from data received from the sensors ([0126, 0111, 0134]); and
e) a reconfigurable engine [storage/database (which can be configured/reconfigured when new data is stored or deleted) used to store customer data in the cloud platform, e.g., “customer data store 802” shown in figs. 8, 10 and “BDFM Data Storage 1102” of fig. 11. The stored data are used for various purposes including classifying/grouping and analysis via an analysis component 1106] running the server receiving output from the controller for classifying predictive maintenance data [generated results (“generation of a system assessment report 1502”) at the cloud platform after processing the facility’s data (e.g., device data 806)] and controller data [data of a facility provided via the cloud-aware device, e.g., cloud data 604 of fig. 6] and performing analytical processing on information extracted from sensor data for [“to generate customized predictions and recommendations relevant to the customer's particular industrial assets”] predictive maintenance and process quality assurance of the machine ([0077-0079, 0090, 0101-0105]).
In summary, Asenjo teaches a system comprising a cloud platform [e.g., “cloud platform 102”] coupled with one or more industrial facilities (104s) each having pluralities of the industrial devices/machines 108s/110s, wherein the facilities also include a local controller [cloud aware smart device, 106/202/302] to facilitate transmitting of the sensor data to the cloud platform (fig. 1, 3 & associated texts). The cloud platform provides various cloud services 112 such as risk assessment services, predictive maintenance services to the facilities having pluralities machines coupled with sensors ([0049]).
Accordingly, while Asenjo teaches a computer/server of the cloud platform using automated rules for providing predictive maintenance and process quality assurance services by analyzing/monitoring of the collected sensor data [any data of the facility, e.g., device data, process data, asset data, system data etc., of fig. 8], it still does not teach how the cloud platform/server forms/develops/creates these automated rules used to determine predictive maintenance and process quality. Asenjo does not teach how its used automated rules are created/formed so that they are used for its predictive maintenance and process quality assurance by monitoring the received data of the facilities at the cloud. Simply put, Asenjo does not teach the apparatus/system to comprise
a program for forming automated rules for predictive maintenance and process quality assurance from data received from the sensors as claimed and shown above with the
Sharpe teaches a cloud/server system [computer system 30+ maintenance computer 18+ “workstation 14”, analogous to “cloud platform” of the Asenjo] for quality assurance of at least on industrial process [“process plant 10”] comprising a controller [process control systems 12A-12C] and the pluralities of the machines and sensors/field devices by monitoring data captured by the pluralities of the sensors attached to at least a machine of the process plant 10 (Abstract, figs. 1-2, [0047, 0050, 0053, 0092]). Specifically, Sharpe teaches a system comprises a program [“rules development application or routine 462 may enable a user to develop one or more expert system rules”/“Similar or different rules development applications”/“the rules engine development and execution application 42”] running on the server for forming automated rules [the created rules themselves/automatically can predict existence of an abnormal/maintenance/quality assurance values to take preemptive corrective actions] for predictive maintenance [“create rules for detecting abnormal situations and/or, if desired, for generating alarms, alerts”] and process quality assurance [“used to monitor variables, such as quality variables, associated with a process”] ([0012-0013, 0051, 0069, 0090, 0095]).
It would have been obvious to one ordinary skill in the art at the time of the filing of this application to have combined the teachings of Sharpe and Asenjo because they both related to a server/cloud collecting for monitoring and analyzing of the data of an industrial process and their machines, and modified the system of Asenjo to use a program running on the server of Sharpe to form/generate its automated rules used for determining predictive maintenance and process quality assurance. Sharpe teaches missing details for Asenjo about how (e.g., using “rules development application”) the cloud platform/server of Asenjo can generate automated rules that can be used to determine predictive maintenance and process quality assurance from the collected data (Sharpe, [0095]). Furthermore, by using a program running on the server, as in Sharpe to dynamically form automated rules about abnormal situations in the facility 104s (Asenjo, fig. 1) with machines (devices 108s, maintenance/quality based conditions can be detected in early states which allow corrective/mitigating actions to be taken before more serious actions need to be taken (Sharpe, [0051]).
Regarding claim 2, rejection of the claim 1 is incorporated. Therefore, only in summary, Asenjo further teaches a method of maintaining interoperability in an industrial process having rule management, predictive maintenance and quality assurance aspects, comprising:
162040.01701 /Application 100922773.vl22a) configuring [“data from multiple sensors measuring related aspects of an automation system” means sensors are configured] a plurality of sensors attached to a machine [one or more industrial devices of the facility 104s] operating as a part of the process for measuring information about the process and operation of the machine ([0066, 0124], fig. 1);
b) configuring [“data is collected from multiple industrial enterprises in a cloud platform” means cloud platform is already configured] a server [processor/computer of the cloud platform] connected to the plurality of sensors over a wireless communication network for processing [“At 2006, collective analysis is performed”] the plurality of information related to the process received from the plurality of sensors ([0126]);
c) configuring [the cloud aware smart device is configured/connected with the cloud platform] a controller [cloud aware smart device, e.g., device 202/302/6061] in communication with the server, the controller being capable of controlling the process based data received from the server ([0057-0058, 0072, 0074]);
d) receiving [from data stores to the analysis system 814/1402 for processing and analyzing] on a rule engine interface of the server a selection of data [“customer-specific data 1504” or “device data 806” for a particular customer] associated with the machine measuring information about the process machine operation (figs. 8, 14, [0102-0103]);
e) computer of the cloud platform] using automated rules from the data received from the plurality of sensors, the automated rules being applied for predictive maintenance and process quality assurance ([0126, 0134]);
f) mapping [sending data (e.g., “profiled data” of fig. 3, cloud data 604) from the cloud aware smart device 202/302/606 to the cloud platform’s data storage] output from the controller for the process into a reconfigurable engine [storage/database used by the cloud platform which gets reconfigured based on updating on the data] running the server for classifying [“group”] predictive maintenance data and controller data to perform analytical processing (figs. 3, 6, [0062, 0100- 0102]);
g) extracting [filtering of the customer/facility data] useful information from sensor data and performing [“analysis allows the cloud-based analysis system to generate asset configuration recommendations”] an analytical processing for predictive maintenance and process quality assurance on the server ([0060, 0092-0096]).
Asenjo does not teach using a program running in the server for forming automated rules from the data received from the plurality of sensors (from step e) used for forming/generating/developing/creating rules being applied for predictive maintenance and process quality assurance.
Sharpe teaches a method comprising configuring a program running in the server for forming [“rules development application or routine 462”] automated rules [e.g., “one or more expert system rules”. Here, the rules themselves are automated] from the data received from the plurality of sensors, the automated rules being applied for predictive maintenance and process quality assurance for the received data of the sensors of the one or more machines of the industrial processes ([0012-0013, 0051, 0069 0095]).
It would have been obvious to one ordinary skill in the art at the time of the filing of this application to have combined the teachings of Sharpe and Asenjo because they both related to a server/cloud collecting/monitoring and analyzing of the data of an industrial process and modified the system of Asenjo to use a program running on the server of Sharpe to form/generate its automated rules used for determining predictive maintenance and process quality assurance. Sharpe teaches missing details for Asenjo about how (e.g., using “rules development application”) the cloud platform/server of Asenjo can generate automated rules that can be used to determine predictive maintenance and process quality assurance from the collected data (Sharpe, [0095]). Furthermore, by using a program running on the server, as in Sharpe to form automated rules abnormal situations in the facility 104s (Asenjo, fig. 1) with machines (devices 108s) maintenance/quality based conditions can be detected in early states which allows corrective/mitigating actions to be taken before more serious actions need to be taken (Sharpe, [0051]).
Regarding claim 3, rejections of the claims 1- 2 are incorporated. Therefore, only in summary, Asenjo further teaches a system [system of fig. 1] for obtaining rule management, predictive maintenance and quality assurance information for an industrial process [process performed in one of the automation system/facility 104s] from sensor data [measured/captured data/information about the facility], comprising:
a) a plurality of sensors [“sensors and metering devices”] capable of being attached to a machine [one of the industrial device 108s/606s of the facility] for measuring information associated with the industrial process [one of the facility monitored by the cloud platform providing cloud services like 112] and the machine operating as a part of the process ([0003, 0066, 070], fig. 1);
b) a server [computer(s) of the cloud platform] connected to the plurality of sensors over a wireless communication network, for processing the information related to the industrial process received from the plurality of sensors (Figs. 1, 6, 10);
c) a controller [cloud aware industrial controller/PLC of the facility, e.g., 202/302/606] associated with the server, for controlling the industrial process based on data received from the server (figs. 2- 6),
d) using automated rules from the data received from the plurality of sensors, the automated rules being for predictive maintenance and process quality assurance ([0126, 0134]);
e) wherein output [sending data (e.g., device model 1006) from the cloud-aware smart device to the cloud platform] from the controller associated with the industrial process is mapped into a reconfigurable engine [database/storage/data store 802 used by the cloud platform] running in the server classifying [grouping/filtering] predictive maintenance data and controller data to perform analytical processing on extracted information from the sensor data (Fig. 8, 10, [0095]);
f) wherein the analytical processing is for [“risk assessment services, predictive maintenance services”] predictive maintenance and process quality assurance [“prediction of potential quality issues”] ([0045, 0049, 0117]); and
g) wherein the server includes multi-tier architecture [one or more services 112 of the cloud platform] for:
162040.01701 /Application
base-lining3 [normalizing of the raw data which includes sensor data as can be clear to PHOSITA based on the disclosure of Asenjo] the sensor data ([0011, 0062, 0091]); and
Asenjo does not teach:
(d) a program running in the server and forming automated rules from the data received from the plurality of sensors,
g) the multi-tier architecture for:
(i) calibrating the plurality of sensors based on an auto calibration signal;
iii) calibrating a gauge associated with the predictive maintenance information but they are cured by Sharpe.
Sharpe teaches a system and a method comprising:
a) a plurality of sensors [sensors of the “field devices 15, 16,”] ([0002, 0053], fig. 1);
b) a server [a computer of the “a computer system 30”] (fig. 1, [0050]);
d) a program [“rules development application or routine 462”/ “the rules engine development and execution application 42 may enable an operator or other user to create additional rules”] running in the server and forming [creating/developing rules] automated rules [e.g., “one or more expert system rules”. Here, the rules themselves are automated] from the data received from the plurality of sensors, the automated rules [“rules engine development and execution application 42 may use one or more rules stored”] being applied for predictive maintenance and process quality assurance ([0012-0013, 0051, 0069 0095]);
(g) wherein the server includes multi-tier architecture [“abnormal situation prevention system 35” of the computer system 30 and “optimization applications” of the maintenance workstations like 18 fig. 1] for:
i) calibrating [“calibrate field devices or other equipment,” wherein sensors can be seen as field devices (para. 0002) by the applications] the plurality of sensors based on an auto calibration signal [information from the “these and other diagnostic applications” that is 4another system compared to the field devices] ([0009, 0047]);
(ii) base-lining [identifying “expected values”/behavior which can be used to compare with recently measured values] the sensor data [“the signature previously generated”] ([0082, 0104, 0112]); and
iii) calibrating [“create additional rules to be implemented” based on user experience] a gauge (measuring/determining software module) [rules used to determine abnormalities can be called a gauge associated with predictive maintenance information because they detect whether a maintenance is required or not] associated with the predictive maintenance information ([0051, 0095]).
It would have been obvious to one ordinary skill in the art at the time of the filing of this application to have (1) combined the teachings of Sharpe and Asenjo because they both related to a server/cloud collecting/monitoring and analyzing of the data of an industrial process and (2) modified the system of Asenjo to use a program running on the server of Sharpe to form/generate its automated rules used for determining predictive maintenance and process quality assurance and have the cloud applications (multi-tier architecture) to additionally perform calibration of the sensors (of the facility 104) and a gauge/measure used to detect predictive maintenance. Doing so additional problem detection and correction activities can be achieved for the devices/sensors of the facilities of Asenjo (Sharpe, [0095). Furthermore, Sharpe teaches missing details for Asenjo about how [e.g., using “rules development application”] the cloud platform/server of Asenjo can generate automated rules that can be used to determine predictive maintenance and process quality assurance from the collected data (Sharpe, [0095]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Drevik ( US 20120046880 A1) teaches server for forming [“easily creating validation rules for automated measurements.”] automated rules from the data received from the plurality of sensors ([008, 024]).
2) Lamba (US 20140149409 A1) teaches server for forming automated rules from the data received from the plurality of sensors ([044 062]).
3) Baird (US 20050222794 A1) teaches calibrating the plurality of sensors based on an auto calibration signal ([030]).
4) Rubin (US 20140289011 A1) teaches generating prediction markets are described to gauge business uncertainties surrounding a project with an uncertain timeline and/or an uncertain result (Abstract).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 [0046], “automation systems can include one or more industrial controllers that facilitate monitoring and control of their respective processes” (emphasis added).
2 “An exemplary private cloud platform can comprise a set of servers hosting the cloud services 112” (emphasis added).
3 “data normalization can be performed on the cloud side by the analytics service after migration of the data to the cloud platform” & “in some embodiments, the data may be normalized by the cloud-based
application itself, rather than by the normalization component 402 on the industrial device.” & “a normalization component 1202 can receive the collected multi-enterprise data… normalize the data according to the required format before moving the data to BDFM data storage 1102”
4 Examiner notes the phrase “auto calibration signal” is described as “signal received from another system” and used for calibration (Spec, [0060).
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