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 .
DETAILED ACTION
This Office action is in response to the Request for Continued Examination (RCE) filed on March 2, 2026.
Claims 1-17 and 19-20 are pending and examined below.
Claim 18 was previously cancelled.
The double patenting rejections of claims 1-3, 5-15, 17, and 19-20 are maintained and further explained below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 2, 2026 has been entered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5-15, and 17-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-6, and 8 of U.S. Patent No. 12,056,485 (hereafter ‘485’). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-3, 5-15, and 17-20 of the instant application define an obvious variation of the invention claimed in ‘485.
The following side-by-side comparison between the representative claim 1 of ‘485 and the representative claim 1 of the instant application with the differences boldfaced for the Applicant’s convenience.
Claim 1 of ‘485
Claim 1 of instant application
1. A method comprising:
receiving a first sensor data stream from a first physical sensor of a local network at an edge computing platform coupled between the first physical sensor and a remote network, wherein the first sensor data stream is on a first topic and comprises data about a condition [e.g., a condition of a physical device] being monitored; producing on the edge computing platform a first stream data corresponding to the first sensor data stream;
maintaining a plurality of sets of analytics expressions of an expression language on the edge computing platform;
creating a virtual sensor connected to the physical sensor using the expression language;
producing on the edge computing platform a second stream data based on the virtual sensor; based on the topic of the first sensor data stream, selecting one of the plurality of sets of analytics expressions;
processing the first stream data and the second stream data in real time at an analytics engine of the edge computing platform without first transferring the first stream data and the second stream data to the remote network, wherein the processing comprises
executing the selected set of analytic expressions on the first stream data and the second stream data,
identifying the presence of a pattern [a topic associated with the condition] in the first stream data and the second stream data comprising an indication of the condition [the condition of the physical device], and
generating intelligence information about the condition; and
executing an application on the edge computing platform, wherein
the application determines based on the intelligence information whether to transmit at least a portion of the intelligence information to the remote network for additional processing [responsive to transmitting] and whether to take selected action [an indication of an adjustment] in the local network affecting the condition monitored by the first physical sensor without awaiting the additional processing.
1. A method, comprising:
receiving, by one or more processing circuits from a physical device, a first stream data associated with a condition of the physical device;
producing, by the one or more processing circuits, a second stream data based on a virtual device, wherein the virtual device is associated with the physical device, and wherein the virtual device is created using an expression language;
executing, by the one or more processing circuits, a set of analytic expressions on the first stream data and the second stream data based on a topic associated with the condition of the physical device;
identifying, by the one or more processing circuits, responsive to execution of the set of analytic expressions, one or more changes to a pattern of data values in the first stream data and the second stream data, the pattern of the data values indicating the condition of the physical device;
determining, by the one or more processing circuits, based at least on the one or more changes to the pattern of the data values,
to transmit at least a portion of the first stream data or at least a portion of the second stream data to a remote device for additional processing;
receiving, by the one or more processing circuits, responsive to transmitting at least the portion of the first stream data or at least the portion of the second stream data, an indication of one or more adjustments to operational control of a building from the remote device, wherein the one or more adjustments are determined by the remote device to address the one or more changes to the pattern of the data values; and
implementing, by the one or more processing circuits, the one or more adjustments to the operational control of the building.
Claim 1 of ‘485 does not explicitly disclose one or more processing circuits; the virtual device is associated with the physical device; executing, by the one or more processing circuits, a set of analytic expressions on the first stream data and the second stream data based on a topic associated with the condition of the physical device; identifying, by the one or more processing circuits, responsive to execution of the set of analytic expressions, one or more changes to a pattern of data values in the first stream data and the second stream data, the pattern of the data values indicating that indicates the condition of the physical device; implementing, by the one or more processing circuits, the one or more adjustments to the operational control of the building. However, in an analogous art to the claimed invention in the field of data analysis, Yanovich teaches one or more processing circuits (Yanovich, ¶ 31, The statistical process monitoring apparatus 105 may be a single server that analyzes incoming process data from the manufacturing machines 110, sensors 112 and process controllers 150. Alternatively the statistical process monitoring apparatus 105 may include multiple servers and/or computers); the virtual device is associated with the physical device (Yanovich, ¶ 35, The virtual sensor manager 190 receives the selected data from at least one of an external physical sensor associated with a machine tool [the physical device]; ¶ 38, virtual sensors perform calculations on data from real sensors, configuration data (e.g., chamber configuration), process set-points, external physical sensors); executing, by the one or more processing circuits, a set of analytic expressions on the first stream data and the second stream data based on a topic associated with the condition of the physical device (Yanovich, ¶ 27, ¶ 27, Each manufacturing process that is performed on a manufacturing machine 110 is characterized by various physical conditions and properties measured by the sensors 112, and by various operating parameters, collectively referred to as process data; ¶ 36, The statistical process control module 184 analyzes the output of the calculated data tag in real-time and generates an error notification in real-time based on analyzing the output of the calculated data tag. The statistical process control module 184 can stop a manufacturing machine in real-time in response to analyzing the output of the calculated data tag and determining a fault condition; ¶ 42, identifying data indicating desired functionality (e.g., fault detection, error detection, process monitoring) …. acquiring the identified data from one or more of manufacturing machines, sensors, physical sensors …. The identified data may include, for example, chamber temperature, pressure, gas flow rates, etc.); identifying, by the one or more processing circuits, responsive to execution of the set of analytic expressions, one or more changes to a pattern of data values in the first stream data and the second stream data, the pattern of the data values indicating that indicates the condition of the physical device (Yanovich, Fig. 1, ¶ 29, Each recipe 120 may define operating parameters of a manufacturing machine 110 [the physical device] at each step of a process; Fig. 2, ¶ 53, stopping a machine tool in real-time in response to the data analysis module analyzing the output of the first virtual sensor in real-time and determining a fault condition [of the physical device]; ¶ 42, The identified data used by a first virtual sensor can be formed from a combination of data sources including at least one of the following: a database, a machine tool, a real sensor attached to the machine tool, an external physical sensor associated with the machine tool … The identified data may be indicative of a fault if, for example, the temperature is too high or too low, the gas flow rates are erratic, the pressure is different than is required for a current process); implementing, by the one or more processing circuits, the one or more adjustments to the operational control of the building (Yanovich, ¶ 40, monitor the real-time data and analysis in order to quickly correct a fault or error condition resulting in improved product yield for manufacturing machines; ¶ 24, The statistical process monitoring system 100 may include all manufacturing machines 110 in a factory … such as all of the manufacturing machines 110 that run one or more specific processes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of ‘485 with teaching of Yanovich. The modification would be obvious because one of ordinary skill in the art would be motivated to collect data using various sensors located on the manufacturing equipment and analyze the data to determine an action based on the data analysis. For example, faults or errors may indicate a malfunctioning equipment or a need to modify a process parameter immediately on the fly during real-time (Yanovich, ¶ ¶ 5-6).
Claim 1 of ‘485 does not explicitly disclose receiving an indication of one or more adjustments to operational control of a building from the remote device, wherein the one or more adjustments are determined by the remote device to address the one or more changes; and implementing, by the one or more processing circuits, the one or more adjustments to the operational control of the building.
However, in an analogous art to the claimed invention in the field of data analysis, Slupik discloses an indication of one or more adjustments to operational control of a building from the remote device (Splupik, Fig. 1 and 7-8, ¶ 170, system controller 202 transmits one or more messages, or signals that are based on the one or more messages, to one or more actor devices associated with building 400, based on one or more of the results generated at operation 503; ¶ ¶ 131-138, system controller 202 generates one or more decisions based on the representation of a state of building 400. … A generated decision can be used by one or more of actor devices 203-1 through 203-N … turn one or more lights on or off; increase or decrease the temperature in a particular room or set of rooms; activate or deactivate a security alarm; open or close one or more doors, windows, and/or shades … turn one or more household systems on or off; turn one or more appliances on or off), wherein the one or more adjustments are determined by the remote device to address the one or more changes to the pattern of the data values (Slupik, Fig. 1 and 7-8, ¶¶ 131-138, system controller 202 generates one or more decisions based on the representation of a state of building 400 … A generated decision can be used by one or more of actor devices 203-1 through 203-N … turn one or more lights on or off; increase or decrease the temperature in a particular room or set of rooms; activate or deactivate a security alarm; open or close one or more doors, windows, and/or shades … turn one or more household systems on or off; vii. turn one or more appliances on or off).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of ‘485 modified by Yanovich with the teaching of Slupik. The modification would be obvious because one of ordinary skill in the art would be motivated to implement a system controller that has access to the signals transmitted by each sensor device that is relevant to the environment being controlled. Each sensor device monitors a particular physical condition, senses changes in the condition being monitored and the system controller is able to generate and continually update a representation of the state of the controlled environment. Having such context awareness enables the design and implementation of sophisticated reasoning logic and conditional logic (Slupik, Abstract).
Dependent claims 2-3 and 5-9 are obvious variations of claims 1, 4-6 and 8 in ‘485. Therefore, they are rejected for the same reason set forth in the rejection of claim 1.
Independent claims 10 and 17 are corresponding to the method claim 1. Therefore, they are rejected for the same reason set forth in the rejection of claim 1.
Dependent claims 11-15 and 18-20 are obvious variations of claims 1, 4-6 and 8 in ‘485. Therefore, they are rejected for the same reason set forth in the rejection of claim 1.
This is a non-provisional obviousness-type double patenting rejection because the conflicting claims have been patented.
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.
Claims 1-2, 4-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2009/0055126 (hereinafter “Yanovich”) in view of US 2015/0192914 (hereinafter “Slupik”).
In the following claim analysis, Applicant’s claim language is in bold text and Examiner’s explanations are enclosed in square brackets.
As to claim 1, Yanovich discloses A method (Yanovich, Abstract, a method to provide a virtual sensor in real-time includes identifying data indicating desired functionality. The method further includes executing the virtual sensor in real-time based on the identified data to generate an output of the virtual sensor with the output being used by at least one data analysis module in real-time for statistical process monitoring), comprising:
receiving, by one or more processing circuits from a physical device, a first stream data associated with a condition of a building the physical device (Yanovich, Fig. 1, ¶ 24, The statistical process monitoring system 100 [including one or more processing circuits] includes a statistical process monitoring apparatus 1055 coupled with one or more manufacturing machines 110 and one or more process controllers 150 by data communication links 160 and 162. The statistical process monitoring system 100 may include all manufacturing machines 110 in a factory; Fig. 2, ¶ 42, acquiring the identified data from one or more of manufacturing machines [the physical device], sensors, physical sensors. The identified data may include, for example, chamber temperature, pressure, gas flow rates, etc.; ¶ 35, a real sensor attached to the machine tool [the physical device]; ¶ 26, Each of the manufacturing machines 110 [a physical device] may include multiple sensors 112 for monitoring processes run on the manufacturing machines 110);
producing, by the one or more processing circuits, a second stream data (Yanovich, ¶ 16, executing the virtual sensor in real-time based on the identified data to generate an output [a second stream data] of the virtual sensor; ¶ 38, virtual sensors perform calculations on data from real sensors; ¶ 42, The identified data used by a first virtual sensor can be formed from a combination of data sources including … a machine tool, a real sensor attached to the machine tool, a external physical sensor associated with the machine tool … The identified data may be indicative of a fault if, for example, the temperature is too high or too low, the gas flow rates are erratic, the pressure is different than is required for a current process) based on a virtual device, wherein the virtual device is associated with the physical device (Yanovich, ¶ 35, The virtual sensor manager 190 receives the selected data from at least one of an external physical sensor associated with a machine tool, a second calculated data tag, the database, the machine tool, and a real sensor attached to the machine tool; ¶ 38, virtual sensors perform calculations on data from real sensors, and wherein the virtual device is created using an expression language (Yanovich, ¶ 43, The first virtual sensor (e.g., in response to user input) may be coded using a software programming language (e.g., C Sharp (C#), Visual Basic) and stored in a xml file);
executing, by the one or more processing circuits, a set of analytic expressions on the first stream data and the second stream data based on a topic associated with the condition of the physical device (Yanovich, ¶ 36, The statistical process control module 184 analyzes the output of the calculated data tag in real-time and generates an error notification in real-time based on analyzing the output of the calculated data tag. The statistical process control module 184 can stop a manufacturing machine in real-time in response to analyzing the output of the calculated data tag and determining a fault condition; ¶ 42, identifying data indicating desired functionality (e.g., fault detection, error detection, process monitoring) …. acquiring the identified data from one or more of manufacturing machines, sensors, physical sensors …. The identified data may include, for example, chamber temperature, pressure, gas flow rates, etc. The identified data used by a first virtual sensor can be formed from a combination of data sources including at least one of the following: a database, a machine tool, a real sensor attached to the machine tool, a external physical sensor associated with the machine tool, a second virtual sensor, and a third virtual sensor. The identified data may be indicative of a fault if, for example, the temperature is too high or too low, the gas flow rates are erratic, the pressure is different than is required for a current process; ¶ 27, Each manufacturing process that is performed on a manufacturing machine 110 is characterized by various physical conditions and properties measured by the sensors 112, and by various operating parameters, collectively referred to as process data);
identifying, by the one or more processing circuits, responsive to execution of the set of analytic expressions, one or more changes to a pattern of data values in the first stream data and the second stream data, the pattern of the data values indicating the condition of the physical device (Yanovich, Fig. 2, ¶ 43, executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The statistical process monitoring may include detecting fault conditions, error conditions, and/or monitoring various parameters and/or derived parameters (e.g., temperature set-point, temperature reading, minimum/maximum/standard deviation during recipe steps, moving average of pressure reading during a recipe step, average value of a plurality of temperature sensors during a recipe step, and width of temperature peak for predetermined temperature deviation from maximum temperature during a recipe; Fig. 1, ¶ 29, Each recipe 120 may define operating parameters of a manufacturing machine 110 [the physical device] at each step of a process; Fig. 2, ¶ 53, stopping a machine tool in real-time in response to the data analysis module analyzing the output of the first virtual sensor in real-time and determining a fault condition [one or more changes to a pattern of data values] at block 214; ¶ 42, The identified data used by a first virtual sensor can be formed from a combination of data sources including … a machine tool, a real sensor attached to the machine tool, a external physical sensor associated with the machine tool … The identified data may be indicative of a fault if, for example, the temperature is too high or too low, the gas flow rates are erratic, the pressure is different than is required for a current process); implementing, by the one or more processing circuits, the one or more adjustments to the operational control of the building (Yanovich, ¶ 40, monitor the real-time data and analysis in order to quickly correct a fault or error condition resulting in improved product yield for manufacturing machines; ¶ 24, The statistical process monitoring system 100 may include all manufacturing machines 110 in a factory … such as all of the manufacturing machines 110 that run one or more specific processes).
Yanovich discloses identifying at least on the one or more changes to the pattern of the data values, the pattern of the data values indicating that indicates the condition of the building physical device, but does not appear to explicitly disclose determining, by the one or more processing circuits, based at least on the one or more changes to the pattern of the data values, to transmit at least a portion of the first stream data or at least a portion of the second stream data to a remote device for additional processing; receiving, by the one or more processing circuits, responsive to transmitting at least the portion of the first stream data or at least the portion of the second stream data, an indication of one or more adjustments to operational control of [[the]] a building from the remote device, wherein the one or more adjustments are determined by the remote device to address the one or more changes to the pattern of the data values; However, Slupik teaches determining, by the one or more processing circuits, based at least on the one or more changes to the pattern of the data values, to transmit at least a portion of the first stream data or at least a portion of the second stream data to a remote device for additional processing (Slupik, Fig. 1 and 7-8, ¶ 170, system controller 202 transmits one or more messages, or signals that are based on the one or more messages, to one or more actor devices [remoted devices] associated with building 400, based on one or more of the results [one or more changes to the pattern] generated at operation 503; ¶ ¶ 131-138, system controller 202 generates one or more decisions based on the representation of a state of building 400. … A generated decision can be used by one or more of actor devices 203-1 through 203-N … turn one or more lights on or off; increase or decrease the temperature in a particular room or set of rooms; activate or deactivate a security alarm; open or close one or more doors, windows, and/or shades … turn one or more household systems on or off; vii. turn one or more appliances on or off);
receiving, by the one or more processing circuits, responsive to transmitting at least the portion of the first stream data or at least the portion of the second stream data, an indication of one or more adjustments to operational control of [[the]] a building from the remote device (Slupik, ¶ ¶ 131-138, system controller 202 generates one or more decisions based on the representation of a state of building 400. … A generated decision can be used by one or more of actor devices 203-1 through 203-N), wherein the one or more adjustments are determined by the remote device to address the one or more changes to the pattern of the data values (Slupik, ¶ ¶ 131-138, system controller 202 generates one or more decisions based on the representation of a state of building 400. … A generated decision can be used by one or more of actor devices 203-1 through 203-N … turn one or more lights on or off; increase or decrease the temperature in a particular room or set of rooms; activate or deactivate a security alarm; open or close one or more doors, windows, and/or shades … turn one or more household systems on or off; turn one or more appliances on or off), wherein the one or more adjustments are determined by the remote device to address the one or more changes to the pattern of the data values (Slupik, ¶ 166, system controller 202 selects at least one of a group of actor devices that includes … an actor device configured [determined] to perform a second function (e.g., regulating air temperature, something unrelated to security, etc.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Yanovich with the teaching of Slupik. The modification would be obvious because one of ordinary skill in the art would be motivated to implement a system controller that has access to the signals transmitted by each sensor device that is relevant to the environment being controlled. Each sensor device monitors a particular physical condition, senses changes in the condition being monitored and the system controller is able to generate and continually update a representation of the state of the controlled environment. Having such context awareness enables the design and implementation of sophisticated reasoning logic and conditional logic (Slupik, Abstract).
As to claim 2, the rejection of claim 1 is incorporated. Yanovich as modified further discloses The method of claim 1, comprising: executing, by the one or more processing circuits, the set of analytic expressions without first transferring the first stream data or the second stream data to the remote device (Yanovich, , Fig. 2, ¶ 43, executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The statistical process monitoring may include detecting fault conditions, error conditions, and/or monitoring various parameters and/or derived parameters [without first transferring the first stream data or the second stream data to the remote device]).
As to claim 4, the rejection of claim 1 is incorporated. Yanovich as modified further discloses The method of claim 1, comprising: generating, by the one or more processing circuits, information that corresponds to the condition of the building (Yanovich, , Fig. 2, ¶ 43, executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The statistical process monitoring may include detecting fault conditions, error conditions, and/or monitoring various parameters and/or derived parameters (e.g., temperature set-point, temperature reading, minimum/maximum/standard deviation during recipe steps, moving average of pressure reading during a recipe step, average value of a plurality of temperature sensors during a recipe step, and width of temperature peak for predetermined temperature deviation from maximum temperature during a recipe)); and causing, by the one or more processing circuits, one or more control systems of the building to perform actions based on the information that corresponds to the condition of the building (Yanovich, Fig. 2, ¶ 44, generating an error notification in real-time based on the data analysis module analyzing the output of the first virtual sensor in real-time at block 212. The method further includes stopping a machine tool in real-time in response to the data analysis module analyzing the output of the first virtual sensor in real-time and determining [an action] a fault condition at block 214); wherein the one or more control systems perform the actions prior to the one or more processing circuits receiving a response from the remote device (Yanovich, Fig. 2, ¶ 44, generating an error notification in real-time based on the data analysis module analyzing the output [prior to receiving a response] of the first virtual sensor in real-time at block 212).
As to claim 5, the rejection of claim 1 is incorporated. Yanovich as modified further discloses The method of claim 1, wherein identifying the one or more changes to the pattern in the first stream of data and the second stream of data includes: processing, by the one or more processing circuits, the first stream data and the second stream data in real time (Yanovich, Fig. 3, ¶ 33, A virtual sensor 192 is a script executed in real-time during data collection e.g., collecting the first stream data] to perform arbitrary complex logic functions; ¶ 43, the method further includes executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208).
As to claim 6, the rejection of claim 1 is incorporated. Yanovich as modified further discloses The method of claim 1, comprising: providing, by the one or more processing circuits, the first stream data to a data ingestion agent (Yanovich, Fig. 2, ¶ 42, acquiring the identified data from one or more of manufacturing machines, sensors, physical sensors); and receiving, by the one or more processing circuits from the data ingestion agent, responsive to providing the first stream data to the data ingestion agent, an ingested stream data from the first stream data (Yanovich, Fig. 2, ¶ 42, The method further includes executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring).
As to claim 7, the rejection of claim 6 is incorporated. Yanovich as modified further discloses The method of claim 6, comprising: providing, by the one or more processing circuits, the ingested stream data to a data enrichment component (Yanovich, Fig. 2, ¶ 42, The method further includes executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The statistical process monitoring may include detecting fault conditions, error conditions, and/or monitoring various parameters and/or derived parameters (e.g., temperature set-point, temperature reading, minimum/maximum/standard deviation during recipe steps, moving average of pressure reading during a recipe step, average value of a plurality of temperature sensors during a recipe step, and width of temperature peak for predetermined temperature deviation from maximum temperature during a recipe)); and receiving, by the one or more processing circuits from the data enrichment component, an enrichment of the ingested stream data (Yanovich, Fig. 2, ¶ 45, A dynamically created virtual sensor generates temperature error data in real-time based on the temperature set-point and temperature reading data. The temperature error data is sent to a data analysis module in real-time for analysis and corrective action is taken if necessary).
As to claim 8, the rejection of claim 1 is incorporated. Yanovich as modified further discloses The method of claim 1, comprising: generating, by the one or more processing circuits, information that corresponds to the condition of the building (Yanovich, Fig. 2, ¶ 42, The method further includes executing the first virtual sensor in real-time based on the identified data to generate an output of the first virtual sensor at block 208. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The method further includes analyzing the output of the first virtual sensor in real-time at block 210 with the data analysis module for statistical process monitoring. The statistical process monitoring may include detecting fault conditions, error conditions, and/or monitoring various parameters and/or derived parameters (e.g., temperature set-point, temperature reading, minimum/maximum/standard deviation during recipe steps, moving average of pressure reading during a recipe step, average value of a plurality of temperature sensors during a recipe step, and width of temperature peak for predetermined temperature deviation from maximum temperature during a recipe); and performing, by the one or more processing circuits, based at least on the information that corresponds to the condition of the building, one or more actions (Yanovich, Fig. 2, ¶ 45, a tool records temperature set-points and temperature readings on a continuous basis during process conditions. … A dynamically created virtual sensor generates temperature error data in real-time based on the temperature set-point and temperature reading data. The temperature error data is sent to a data analysis module in real-time for analysis and corrective action is taken if necessary).
As to claim 9, the rejection of claim 8 is incorporated. Yanovich as modified further discloses The method of claim 8, wherein the one or more actions include at least one of: generating, by the one or more processing circuits, an alert (Yanovich, Fig. 2, ¶ 44, The method further includes generating an error notification in real-time based on the data analysis module analyzing the output of the first virtual sensor in real-time); or altering, by the one or more processing circuits, an operation in a network that includes the physical device (Yanovich, Fig. 2, ¶ 44, The method further includes stopping a machine tool [an operation] in real-time in response to the data analysis module analyzing the output of the first virtual sensor [relating to the physical device] the in real-time ).
Claims 3 and 10-16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20090055126 (hereinafter “Yanovich”) in view of US 2015/0192914 (hereinafter “Slupik”) and in view of US 2015/0312125 (hereinafter “Subramanian”).
As to claim 3, the rejection of claim 1 is incorporated. Yanovich as modified does not appear to explicitly disclose The method of claim 1, wherein the one or more processing circuits are integrated into an edge device. However, Subramanian teaches Subramanian teaches The method of claim 1, wherein the one or more processing circuits are integrated into an edge device (Subramanian, Fig. 1, ¶ 14, Within this architecture, data or traffic streams 160a-d are received from the sensors and/or data sources 110a-d at the edge network device 120 …. edge network device 120 to provide a preliminary analysis of the received traffic streams before some or all of the data contained in the streams is forwarded to data backend 140).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the techniques of Yanovich integrated with a network environment 100 taught by Subramanian. The modification for doing so would have been to address IoT and/or IoE challenges. For example, from a particular user's perspective, not all data in one or more of traffic streams 160a-d may be of interest. Accordingly, the ability to perform preliminary processing and/or analysis of the data in traffic streams 160a-d at the edge network device 120 provides benefits for both users 150 and data backend 140. Furthermore, the preliminary processing and/or analysis performed at edge network device 120 may be used to trigger subsequent context-aware actions, thereby providing further benefits (Subramanian, ¶ 15).
As to claim 10, the claim is an edge computing platform corresponding to method claim 1. Therefore, it is rejected under the same rational set forth in the rejection of claim 1. Further, Yanovich as modified teaches An edge computing platform for a building (The combination of Yanovich, Abstract and Subramanian, Fig. 1 and its associated paragraphs), the edge computing platform comprising: one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: … (Yanovich, claim 9, A machine-accessible medium including data that, when accessed by a machine, cause the machine to perform a method comprising …). The motivation to combine the references is the same as set forth in the rejection of claim 1.
As to claims 11-15, the rejection of claim 10 is incorporated and the claims are corresponding to method claims 2, 5, 6, 7, and 9. Therefore, they are rejected under the same rational set forth in the rejections of the method claims.
As to claim 16, the rejection of claim 10 is incorporated. Yanovich as modified further discloses The edge computing platform of claim 10, wherein the instructions cause the one or more processors to: generate information that corresponds to the condition of the building (Yanovich, Fig. 2, ¶ ¶ 43-45); and transmit, responsive to execution of an application stored on the edge computing platform (Subramanian, ¶ 25, a determination is made, as a result of the preliminary data analysis, that further analysis of the traffic stream should be performed. The determination may be in response to a result of a query executed against data which has been indexed or to which a schema has been applied. The determination of the rule-triggering condition is not limited to the execution of queries. The determination may also be in response to a statistical value, such as an average, a sum or a standard deviation, surpassing a predetermined threshold; ¶ 26, traffic stream data [e.g., at least a portion of the first stream data or at least a portion of the second stream data] is sent to another network device for further analysis. The sending of the traffic stream data may comprise sending some or all of the traffic stream to a non-edge network device, such as user 150), at least a portion of the information that corresponds to the condition of the building to a remote device for processing (Subramanian, ¶ 26, traffic stream data [e.g., at least a portion of the first stream data or at least a portion of the second stream data] is sent to another network device for further analysis. The sending of the traffic stream data may comprise sending some or all of the traffic stream to a non-edge network device, such as user 150). The motivation to combine the references is the same as set forth in the rejection of claim 1.
As to claim 17, the claim is a system claim corresponding to method claim 1. Therefore, it is rejected under the same rational set forth in the rejection of claim 1. Further Yanovich as modified discloses A system, comprising: an edge device in communication with a physical device of a building (The combination of Yanovich, Abstract and Subramanian, Fig. 1 and its associated paragraphs), the edge device including one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: … (Subramanian, claim 19, One or more computer readable storage media encoded with software comprising computer executable instructions and when the software is executed operable to: receive, at an edge network device, information describing a rule to be applied to a traffic stream …). The motivation to combine the references is the same as set forth in the rejection of claim 1.
As to claims 19-20, the rejection of claim 17 is incorporated and the claims are corresponding to method claims 2 and 5. Therefore, they are rejected under the same rational set forth in the rejections of the method claims.
Response to Arguments
Applicant's arguments have been considered but are moot in view of new ground(s) of rejection. In these arguments applicant relies on the amended claims and not the original ones. See above rejections under 35 USC § 103 for response to arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/DAXIN WU/ Primary Examiner, Art Unit 2191
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