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 .
Status of Claims
This Office Action is in response to the application filed on January 22nd, 2026. Claims 1-20 are presently pending and are presented for examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 22nd, 2026 has been entered.
Response to Amendment
In response to Applicant’s amendment filed January 22nd, 2026, Examiner; withdraws the previous claim interpretation; and maintains the previous 35 U.S.C. 102 and 103 prior art rejections.
Response to Arguments
Applicant’s arguments filed January 22nd, 2026 have been fully considered but they are not persuasive.
Regarding the arguments provided for the rejections of claims 1, 2, 4, 6, 8, 10, and 11 as put forth on pages 7 and 8 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 1 “the cited portions of Cella fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 1…consequently, the cited portions of Cella fail to disclose each and every element of claim 1. Hence, claim 1 is allowable. Claims 2, 4, 6, 8, 10, and 11 are allowable, at least by virtue of depending from an allowable claim” (from remarks pgs. 7-8).
As to point (a), examiner respectfully disagrees. As elaborated upon in the 35 U.S.C. 102 rejection below, Cella discloses this limitation in paragraphs 1072-1075 of their specification. While the data collector of Cella is operating on its predetermined data collection route, the data collector is able to receive an alarm, which triggers the data collector to modify its route and begin to further inspect the device which triggered the alarm. The alarm reviewed by the data collector corresponds to Applicant’s other sensor data as it regards a device different from the one which the data collector may currently be observing on its route. Therefore, Examiner asserts that this limitation is taught by Cella and maintains the corresponding 35 U.S.C. 102 rejection.
Regarding the arguments provided for the rejections of claims 18 and 19 as put forth on page 8 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 18 “the cited portions of Cella fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 19…consequently, the cited portions of Cella fail to disclose each and every element of claim 18. Hence, claim 18 is allowable. Claim 19 is allowable, at least by virtue of depending from an allowable claim” (from remarks pg. 8).
As to point (b), see point (a).
Regarding the arguments provided for the rejections of claim 20 as put forth on pages 8-9 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 20 “the cited portions of Cella fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 20” (from remarks pg. 8).
As to point (c), see point (a).
Regarding the arguments provided for the rejections of claim 3 as put forth on pages 9-10 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 1 “the cited portions of Cella and Green, individually or in combination, fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 1” (from remarks pg. 9).
As to point (d), see point (a).
Regarding the arguments provided for the rejections of claim 5 as put forth on pages 10-11 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 1 “the cited portions of Cella and Guenther, individually or in combination, fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 1” (from remarks pg. 10).
As to point (e), see point (a).
Regarding the arguments provided for the rejections of claims 7 and 9 as put forth on pages 11-12 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 1 “the cited portions of Cella and Zhou, individually or in combination, fail to disclose that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 1” (from remarks pg. 11).
As to point (f), see point (a).
Regarding the arguments provided for the rejections of claims 12-17 as put forth on pages 12-13 of applicant’s remarks, the Applicant’s arguments have been fully considered but they are not persuasive. Applicant argues claim 1 “the cited portions of Cella and Karakama, individually or in combination, fail to disclose…that a "mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly" as in claim 1” (from remarks pg. 12).
As to point (g), see point (a).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 6, 8, 10, 11, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as anticipated by US-20200103894 (hereinafter, “Cella”).
Regarding claim 1 Cella discloses a method (see at least [0003]; “The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments”) comprising:
receiving, from a first sensor of a mobile sensor platform configured to actively monitor a first monitored device (see at least [0056]; “a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine,” the mobile data collector corresponds to Applicant’s mobile sensor platform and the portion of an industrial machine corresponds to the first monitored device), first sensor data indicative of operation of the first monitored device, wherein the first monitored device is distinct from the mobile sensor platform (see at least [0421]; “In embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102…the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and/or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines,” the data collector corresponds to Applicant’s mobile sensor platform and the machine corresponds to the monitored device), and wherein the mobile sensor platform is configured to receive other sensor data related to at least one other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly (see at least [1072]; “The data collector may modify the sensor collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like” [1075]; “Rapid route creation and modification in an industrial environment may employ smart route changes based on incoming data or alarms, such as changes enabling dynamic selection of data collection for analysis or correlation. Smart route changes may enable the system to alter current routing of sensor data based on incoming data or alarms. For instance, a user may set up a routing configuration that establishes a schedule of sensor collection for analysis, but when the analysis (or an alarm) indicates a special need, the system may change the sensor routing to address that need. For example, in the case where a change in a motor vibration profile (as one example among any of the machines described throughout this disclosure), such as rapidly increasing the peak amplitude of shaking on at least one axis of a vibration sensor set, that indicates a potential early failure of the motor, the system may change the routing to collect more focused data collection for analysis, such as initiating collection on more axes of the motor, initiating collection on additional bearings of the motor, and/or initiating collection using other sensors (such as temperature or heat flux sensors), that may confirm an initial hypothesis that the failure mode is occurring or otherwise assist in analysis of the state or operational condition of the machine,” while the data collector is on its route it may receive an alarm from a machine which it’s not currently monitoring which will trigger the data collector to further perform sensor collection and analysis on the device which triggered an alarm, the alarm corresponds to sensor data);
providing, as input to a trained behavior model associated with the monitored device, input data based at least in part on the first sensor data to generate behavior model output data (see at least [0401]; “For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof…The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like),” indication of the presence of faults corresponds to behavior model output data);
generating, based on the behavior model output data, a first control command (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”) and a second control command (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust an operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis”); and
sending the first control command to the mobile sensor platform, wherein the first control command instructs the mobile sensor platform to obtain second sensor data indicative of operation of a second monitored device (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”); and
sending the second control command to the first monitored device (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust am operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis,” and [0648]; “ultrasonic monitoring in an industrial environment may be performed by a system for data collection as described herein on rotating elements (e.g., motor shafts and the like), bearings, fittings, couplings, housings, load bearing elements, and the like,” the element corresponds to Applicant’s monitored device).
Regarding claim 2 Cella discloses all of the limitations of claim 1. Additionally Cella discloses further comprising selecting the trained behavior model from among a plurality of trained behavior models, wherein each of the plurality of trained behavior models is associated with one or more monitored devices (see at least [0401]; “data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof.”).
Regarding claim 4 Cella discloses all of the limitations of claim 2. Additionally, Cella discloses wherein selecting the trained behavior model comprises selecting the trained behavior model based on a model selection criterion, the model selection criterion associated with a device type of the first monitored device (see at least [0401]; “data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof,” the operating characteristic corresponds to the device type of the monitored device)
Regarding claim 6 Cella discloses all of the limitations of claim 1. Additionally, Cella discloses wherein the input data is provided as input to the trained behavior model by a computing device, wherein the computing device is distinct from the mobile sensor platform and the first monitored device (see at least [0706]; “In embodiments, as shown in FIG. 57, the communication circuit 8146 may communicate data directly to the remote server 8148. In embodiments, as shown in FIG. 58, the communication circuit 8146 may communicate data to the intermediate computer 8152 which may include the processor 8154 running the operating system 8156 and the data storage circuit 8158. There may be an individual intermediate computer 8152 associated with each monitoring device 8140 or an individual intermediate computer 8152 may be associated with a plurality of monitoring devices 8144 where the intermediate computer 8152 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 8148. Communication to the remote server 8148 may be streaming, batch (e.g., when a connection is available), or opportunistic,” and [0710]; “In embodiments, the monitoring application 8150 may include a remote learning circuit structured to analyze sensor status data (e.g., sensor overload or sensor failure) together with data from other sensors, failure data on components being monitored, equipment being monitored, output being produced, and the like. The remote learning system may identify correlations between sensor overload and data from other sensors,” the remote learning circuit corresponds to the computing device and analyzes all of the sensor data inputted).
Regarding claim 8 Cella discloses all of the limitations of claim 1. Additionally, Cella discloses wherein the behavior model output data is generated by a computing device, wherein the computing device is distinct from the mobile sensor platform and the first monitored device (see at least [0706]; “In embodiments, as shown in FIG. 57, the communication circuit 8146 may communicate data directly to the remote server 8148. In embodiments, as shown in FIG. 58, the communication circuit 8146 may communicate data to the intermediate computer 8152 which may include the processor 8154 running the operating system 8156 and the data storage circuit 8158. There may be an individual intermediate computer 8152 associated with each monitoring device 8140 or an individual intermediate computer 8152 may be associated with a plurality of monitoring devices 8144 where the intermediate computer 8152 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 8148. Communication to the remote server 8148 may be streaming, batch (e.g., when a connection is available), or opportunistic,” and [0710]; “In embodiments, the monitoring application 8150 may include a remote learning circuit structured to analyze sensor status data (e.g., sensor overload or sensor failure) together with data from other sensors, failure data on components being monitored, equipment being monitored, output being produced, and the like. The remote learning system may identify correlations between sensor overload and data from other sensors,” the remote learning circuit corresponds to the computing device and analyzes all of the sensor data inputted).
Regarding claim 10 Cella discloses all of the limitations of claim 1. Additionally, Cella discloses wherein the mobile sensor platform comprises an autonomous or semi-autonomous vehicle comprising a propulsion system and a navigation system (see at least [2453]; “Systems and methods for using mobile robots and/or mobile vehicles for mobile data collection within an environment for industrial IoT data collection are next described with respect to FIGS. 290 to 292… the term "mobile robot" may refer to, but is not limited to, a robotic arm, android robot, small or large autonomous robot, remote-controlled robot, programmable configured robot, or other robotic mechanism).
Regarding claim 11 Cella discloses all of the limitations of claim 10. Cella does not disclose wherein the mobile sensor platform comprises an unmanned aerial vehicle (see at least [2453]; “the term “mobile vehicle” may refer to, but is not limited to…unmanned vehicles (e.g., drones or other autonomous aircraft”).
Regarding claim 18 Cella discloses a system for behavior monitoring (see at least [0003]; “The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments”), the system comprising:
one or more processors (see at least [0637]; “In embodiments, a system for data collection in an industrial environment may include a plurality of sensors for sensing conditions of a machine in the environment, a hierarchical multiplexer, a plurality of analog-to-digital converters (ADCs), a processor, local storage, and an external interface.”) configured to:
receive, from a first sensor of a mobile sensor platform configured to actively monitor a first monitored device (see at least [0056]; “a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine”), first sensor data indicative of operation of the first monitored device, wherein the first monitored device is distinct from the mobile sensor platform (see at least [0421]; “In embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102…the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and/or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines,” the data collector corresponds to Applicant’s mobile sensor platform and the machine corresponds to the monitored device), and wherein the mobile sensor platform is configured to receive other sensor data related to one or more other monitored device while the mobile sensor platform is actively monitoring the first monitored device and select the other monitored device for active monitoring in response to the other data indicating an anomaly (see at least [1072]; “The data collector may modify the sensor collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like” [1075]; “Rapid route creation and modification in an industrial environment may employ smart route changes based on incoming data or alarms, such as changes enabling dynamic selection of data collection for analysis or correlation. Smart route changes may enable the system to alter current routing of sensor data based on incoming data or alarms. For instance, a user may set up a routing configuration that establishes a schedule of sensor collection for analysis, but when the analysis (or an alarm) indicates a special need, the system may change the sensor routing to address that need. For example, in the case where a change in a motor vibration profile (as one example among any of the machines described throughout this disclosure), such as rapidly increasing the peak amplitude of shaking on at least one axis of a vibration sensor set, that indicates a potential early failure of the motor, the system may change the routing to collect more focused data collection for analysis, such as initiating collection on more axes of the motor, initiating collection on additional bearings of the motor, and/or initiating collection using other sensors (such as temperature or heat flux sensors), that may confirm an initial hypothesis that the failure mode is occurring or otherwise assist in analysis of the state or operational condition of the machine,” while the data collector is on its route it may receive an alarm from a machine which it’s not currently monitoring which will trigger the data collector to further perform sensor collection and analysis on the device which triggered an alarm, the alarm corresponds to sensor data);
provide, as input to a trained behavior model associated with the first monitored device, input data based at least in part on the first sensor data to generate behavior model output data (see at least [0401]; “For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof…The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like),” indication of the presence of faults corresponds to behavior model output data);
generate, based on the behavior model output data, a first control command (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”)and a second control command (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust an operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis”); and
send the first control command to the mobile sensor platform, wherein the first control command instructs the mobile sensor platform to obtain second sensor data indicative of operation of a second monitored device (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”); and
sending the second control command to the first monitored device (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust am operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis,” and [0648]; “ultrasonic monitoring in an industrial environment may be performed by a system for data collection as described herein on rotating elements (e.g., motor shafts and the like), bearings, fittings, couplings, housings, load bearing elements, and the like,” the element corresponds to Applicant’s monitored device).
Regarding claim 19 Green discloses all of the limitations of claim 18. Green does not disclose wherein the first control command instructs the mobile sensor platform to physically move towards the second monitored device (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like,” to collect data from an additional set of sensors would include the data collecting device moving to that location) and wherein the second control command instructs a first component of the first monitored device modify operation of a second component of the first monitored device (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust am operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis,” a controller corresponds to a first component and the second component is the component being analyzed by the sensor).
Regarding claim 20 Cella discloses a computer-readable storage device storing instructions that, when executed by one or more processors (see at least [1943]; “The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines”), cause the one or more processors to:
receive, from a first sensor of a mobile sensor platform configured to actively monitor a first monitored device (see at least [0056]; “a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine”), first sensor data indicative of operation of the first monitored device, wherein the first monitored device is distinct from the mobile sensor platform (see at least [0421]; “In embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102…the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and/or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines,” the data collector corresponds to Applicant’s mobile sensor platform and the machine corresponds to the monitored device), and wherein the mobile sensor platform is configured to receive other sensor data associated with one or more monitored device while the mobile sensor platform is actively monitoring the first monitored device, the mobile sensor platform configured to select the other monitored device for active monitoring in response to the other sensor data indicating an anomaly at the other monitored device (see at least [1072]; “The data collector may modify the sensor collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like” [1075]; “Rapid route creation and modification in an industrial environment may employ smart route changes based on incoming data or alarms, such as changes enabling dynamic selection of data collection for analysis or correlation. Smart route changes may enable the system to alter current routing of sensor data based on incoming data or alarms. For instance, a user may set up a routing configuration that establishes a schedule of sensor collection for analysis, but when the analysis (or an alarm) indicates a special need, the system may change the sensor routing to address that need. For example, in the case where a change in a motor vibration profile (as one example among any of the machines described throughout this disclosure), such as rapidly increasing the peak amplitude of shaking on at least one axis of a vibration sensor set, that indicates a potential early failure of the motor, the system may change the routing to collect more focused data collection for analysis, such as initiating collection on more axes of the motor, initiating collection on additional bearings of the motor, and/or initiating collection using other sensors (such as temperature or heat flux sensors), that may confirm an initial hypothesis that the failure mode is occurring or otherwise assist in analysis of the state or operational condition of the machine,” while the data collector is on its route it may receive an alarm from a machine which it’s not currently monitoring which will trigger the data collector to further perform sensor collection and analysis on the device which triggered an alarm, the alarm corresponds to sensor data);
provide, as input to a trained behavior model associated with the first monitored device, input data based at least in part on the first sensor data to generate behavior model output data (see at least [0401]; “For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof…The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like),” indication of the presence of faults corresponds to behavior model output data);
generate, based on the behavior model output data, a first control command (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”) and a second control command (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust an operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis”); and
send the first control command to the mobile sensor platform, wherein the first control command instructs the mobile sensor platform to obtain second sensor data indicative of operation of a second monitored device (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like”); and
sending the second control command to the first monitored device (see at least [0650]; “a system for data collection in an industrial environment may perform continuous ultrasonic monitoring…processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust am operating condition such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis,” and [0648]; “ultrasonic monitoring in an industrial environment may be performed by a system for data collection as described herein on rotating elements (e.g., motor shafts and the like), bearings, fittings, couplings, housings, load bearing elements, and the like,” the element corresponds to Applicant’s monitored device).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cella as applied to claim 2 above, in view of US-20210070286 (hereinafter, “Green”).
Regarding claim 3 Cella discloses all of the limitations of claim 2. Additionally, Cella discloses wherein selecting the trained behavior model comprises selecting the trained behavior model based on model selection criteria, the model selection criterion criteria including…a second model selection criterion associated with a device type of the first monitored device (see at least [0776]; “the monitoring application 8776 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for plurality of component types, operational history, historical detection values, component life models, and the like for use in analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 8776 may feed a neural net with the selected subset to learn to recognize various operating state, health states (e.g., lifetime predictions) and fault states utilizing deep learning techniques. In embodiments, a hybrid of the two techniques (model-based learning and deep learning) may be used.”) a third model selection criterion associated with a maintenance history of the first monitored device (see at least [0776]; “the monitoring application 8776 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for plurality of component types, operational history, historical detection values, component life models, and the like for use in analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 8776 may feed a neural net with the selected subset to learn to recognize various operating state, health states (e.g., lifetime predictions) and fault states utilizing deep learning techniques. In embodiments, a hybrid of the two techniques (model-based learning and deep learning) may be used.”).
Cella does not teach a first model selection criterion associated with a location of the mobile sensor platform.
Green, in the same field of endeavor, teaches a first model selection criterion associated with a location of the mobile sensor platform (see at least [0020]; “In particular embodiments, the vehicle data and contextual data may be collected by a fleet of vehicles and aggregated into a database in a remote server computer. The vehicle data may be aggregated into the corresponding vehicle models (e.g., individualized vehicle models, vehicle type models, vehicle region models, vehicle models associated with certain driving behaviors) as weight values or parameter values of corresponding vehicle models and stored in a database of a remote server computer. A vehicle model may include historical driving behavior data associated with one or more anonymous features (e.g., an anonymous vehicle identifier for an individual vehicle, a vehicle type, a vehicle region, a driving behavior).”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the location-based model selection of Green. One of ordinary skill in the art would have been motivated to make this modification for the benefit of leveraging driving behavior associated with specific regions to determine the best prediction for vehicle state since vehicles from different states may drive differently (see at least Green; [0040]).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Cella as applied to claim 2 above, in view of US-20090265118 (hereinafter, “Guenther”).
Regarding claim 5 Cella discloses all of the limitations of claim 2. Cella does not disclose wherein selecting the trained behavior model comprises selecting the trained behavior model based on a model selection criterion, the model selection criterion associated with a maintenance history of the first monitored device.
Guenther, in the same field of endeavor, teaches wherein selecting the trained behavior model comprises selecting the trained behavior model based on a model selection criterion, the model selection criterion associated with a maintenance history of the first monitored device (see at least [0008]; “In one aspect, a method for predicting a probability of failure for a component of a platform at a specified time is provided. The method includes collecting historical maintenance data relating to the component, selecting, utilizing the collected historical maintenance data, a lifetime distribution model that best fits the historical maintenance data, estimating upcoming component failures using the selected lifetime distribution model, and applying maintenance schedule dates for the platform to the upcoming component failures to determine a likelihood of failure of the component on one of the scheduled maintenance dates.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the lifetime prediction of Guenther. One of ordinary skill in the art would have been motivated to make this modification for the benefit of predicting when vehicle parts may fail ahead of time and avoiding long lead times for maintenance parts (see at least Guenther; [0007]).
Claim(s) 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Cella, as applied to claim 6 and 8 above, in view of US-9952594 (hereinafter, “Zhou”).
Regarding claim 7 Cella discloses all of the limitations of claim 6. Cella does not disclose further comprising: prior to providing the input data, preprocessing the first sensor data at the mobile sensor platform to generate preprocessed first sensor data; and
communicating the preprocessed first sensor data to the computing device.
Zhou, in the same field of endeavor, teaches further comprising: prior to providing the input data, preprocessing the first sensor data at the mobile sensor platform to generate preprocessed first sensor data (see at least [col. 7, lines 23-56]; “A UAV configured with a camera, is controlled to record traffic activity from an elevated position at a monitored location or track and record activity of a specific target vehicle, the UAV being configured to hover or cruise while capturing the video data (processing block 805). The captured video data is calibrated and processed to a higher quality or fidelity level (processing block 807)”); and
communicating the preprocessed first sensor data to the computing device (see at least Fig. 10 and [Col. 7, lines 66-67]; “transfer the captured video data to a processing system (processing block 1030).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the maintenance system of Cella with the data preprocessing of Zhou. One of ordinary skill in the art would have been motivated to make this modification for the benefit of ensuring a higher quality of the data collected (see at least Zhou [Col. 7, lines 23-56]).
Regarding claim 9 Cella discloses all of the limitations of claim 8. Cella does not disclose further comprising: prior to providing the input data, preprocessing the first sensor data at the mobile sensor platform to generate preprocessed first sensor data; and
communicating the preprocessed first sensor data to the computing device
Additionally, Zhou discloses further comprising: prior to providing the input data, preprocessing the first sensor data at the mobile sensor platform to generate preprocessed first sensor data (see at least [col. 7, lines 23-56]; “A UAV configured with a camera, is controlled to record traffic activity from an elevated position at a monitored location or track and record activity of a specific target vehicle, the UAV being configured to hover or cruise while capturing the video data (processing block 805). The captured video data is calibrated and processed to a higher quality or fidelity level (processing block 807)”); and
communicating the preprocessed first sensor data to the computing device (see at least Fig. 10 and [Col. 7, lines 66-67]; “transfer the captured video data to a processing system (processing block 1030).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the maintenance system of Cella with the data preprocessing of Zhou. One of ordinary skill in the art would have been motivated to make this modification for the benefit of ensuring a higher quality of the data collected (see at least Zhou [Col. 7, lines 23-56]).
Claim(s) 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Cella, as applied to claim 10 above, in view of US-20230315061 (hereinafter, “Karakama”).
Regarding claim 12 Cella discloses all of the limitations of claim 10. Cella does not disclose wherein the mobile sensor platform is configured to automatically select the first monitored device from among a plurality of monitored devices based on a device monitoring criterion.
Karakama, in the same field of endeavor, teaches wherein the device is configured to automatically select the first monitored device from among a plurality of monitored devices based on a device monitoring criterion (see at least [0013]; “In a factory where many machines exist, a series of operations of predetermining a machine being a target of maintenance or inspection and subsequently locating the installation location of the machine on site is a heavy burden. In recent years in particular, robotization of manual operations in a factory, downsizing and diversification of machines, and the like have advanced, and the number and the installation density of machines installed in a factory have increasing tendencies. Further, aging of operators performing operations on machines in a factory has also advanced. Based on such a situation, the operational burden is expected to further increase in the future. Accordingly, development of a technology for lightening an operational burden with respect to a plurality of machines installed in a factory has been desired. According to an aspect of the present disclosure, an assistance device configured to assist an operation on a plurality of machines,” the assistance device selects a device from a plurality of devices that is to be inspected).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the multitude of monitored equipment selection of Karakama. One of ordinary skill in the art would have been motivated to make this modification for the benefit of lightening an operational burden (see at least Karakama; [0014]).
Regarding claim 13 Cella in view of Karakama renders obvious all of the limitations of claim 12. Additionally, Karakama, in the same field of endeavor, teaches wherein the device monitoring criterion comprises a temporal criterion (see at least [0051]; “A search condition specifies identification information of a machine 2 to be acquired by an operator by using the assistance device 1. For example, when an operator prefers to search for a machine 2 for which battery replacement is completed by a certain date, the operator sets information about "a date of battery replacement" or the like as a search condition. A "date" hereinafter may include concepts of "year, month, and day" and "time."”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the multitude of monitored equipment selection of Karakama. One of ordinary skill in the art would have been motivated to make this modification for the benefit of lightening an operational burden (see at least Karakama; [0014]).
Regarding claim 14 Cella in view of Karakama renders obvious all of the limitations of claim 13. Additionally, Karakama, in the same field of endeavor, teaches wherein the temporal criterion is associated with a particular time of day (see at least [0051]; “A search condition specifies identification information of a machine 2 to be acquired by an operator by using the assistance device 1. For example, when an operator prefers to search for a machine 2 for which battery replacement is completed by a certain date, the operator sets information about "a date of battery replacement" or the like as a search condition. A "date" hereinafter may include concepts of "year, month, and day" and "time."”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the multitude of monitored equipment selection of Karakama. One of ordinary skill in the art would have been motivated to make this modification for the benefit of lightening an operational burden (see at least Karakama; [0014]).
Regarding claim 15 Cella discloses all of the limitations of claim 10. Cella does not disclose wherein the mobile sensor platform is configured to automatically select the first monitored device from among a plurality of monitored devices based on device monitoring criteria that comprises temporal criteria, wherein the temporal criterion criteria comprises a first temporal criterion associated with a particular day, a second temporal criterion associated with a particular period of time associated with an operational schedule or a maintenance schedule for the first monitored device, and a third temporal criterion identifying a particular sensing time period.
Karakama, in the same field of endeavor, teaches wherein the device is configured to automatically select the first monitored device from among a plurality of monitored devices based on a device monitoring criterion (see at least [0013]; “In a factory where many machines exist, a series of operations of predetermining a machine being a target of maintenance or inspection and subsequently locating the installation location of the machine on site is a heavy burden. In recent years in particular, robotization of manual operations in a factory, downsizing and diversification of machines, and the like have advanced, and the number and the installation density of machines installed in a factory have increasing tendencies. Further, aging of operators performing operations on machines in a factory has also advanced. Based on such a situation, the operational burden is expected to further increase in the future. Accordingly, development of a technology for lightening an operational burden with respect to a plurality of machines installed in a factory has been desired. According to an aspect of the present disclosure, an assistance device configured to assist an operation on a plurality of machines,” the assistance device selects a device from a plurality of devices that is to be inspected);
wherein…device monitoring criteria…comprises temporal criteria (see at least [0051]; “A search condition specifies identification information of a machine 2 to be acquired by an operator by using the assistance device 1. For example, when an operator prefers to search for a machine 2 for which battery replacement is completed by a certain date, the operator sets information about "a date of battery replacement" or the like as a search condition. A "date" hereinafter may include concepts of "year, month, and day" and "time."”), wherein the temporal criterion criteria comprises a first temporal criterion associated with a particular day (see at least [0051]; “A search condition specifies identification information of a machine 2 to be acquired by an operator by using the assistance device 1. For example, when an operator prefers to search for a machine 2 for which battery replacement is completed by a certain date, the operator sets information about "a date of battery replacement" or the like as a search condition. A "date" hereinafter may include concepts of "year, month, and day" and "time."”), a second temporal criterion associated with a particular period of time associated with an operational schedule or a maintenance schedule for the first monitored device (see at least [0087]; “Operation plan information for each machine 2 is stored in the information storage unit 11 as machine information, and the “machine being out of operation” is prepared to be set as a search condition”), and a third temporal criterion identifying a particular sensing time period (see at least [0051]; “Further, for example, when an operator prefers to search for a machine 2 considered to reach the end of life within a certain period in the future, the operator sets information about "a predicted lifespan of a machine 2" or the like as a search condition. Specific examples of a search condition will be described later,” and Fig. 5B the third condition of Last battery replacement time corresponds to Applicant’s sensing time period).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the multitude of monitored equipment selection of Karakama. One of ordinary skill in the art would have been motivated to make this modification for the benefit of lightening an operational burden (see at least Karakama; [0014]).
Regarding claim 16 Cella in view of Karakama renders obvious all of the limitations of claim 14. Additionally, Karakama, in the same field of endeavor, teaches wherein the temporal criterion is associated with a particular period of time associated with an operational schedule or a maintenance schedule for the first monitored device (see at least [0087]; “Operation plan information for each machine 2 is stored in the information storage unit 11 as machine information, and the “machine being out of operation” is prepared to be set as a search condition”)
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the facility monitoring method of Cella with the multitude of monitored equipment selection of Karakama. One of ordinary skill in the art would have been motivated to make this modification for the benefit of lightening an operational burden (see at least Karakama; [0014]).
Regarding claim 17 Cella in view of Karakama renders obvious all of the limitations of claim 14. Additionally, Cella, in the same field of endeavor, teaches wherein the first monitored device is a first component of a first system, and wherein the second monitored device is a second component of the first system (see at least [0600]; “a system for data collection in an industrial environment…based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventative maintenance, triggering data collection from additional sets of sensors, and the like,” the first set of sensors correspond to the first component and the second set of sensors correspond to the second component).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20220148445 teaches a method and system for implementing an unmanned aerial vehicle to perform inspections of a designated area.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEIGH NICOLE TURNBAUGH whose telephone number is (703)756-1982. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/ASHLEIGH NICOLE TURNBAUGH/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
2/10/26