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
2. This action is in response to the application filed November 11, 2024.
3. Claims 1-23 have been examined and are pending with this action.
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.
4. Claims 1-4, 6-14, and 16-23 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Bures et al. (US 2020/0322703 A1).
INDEPENDENT:
As per claim 1, Bures teaches a computer-implemented method for a monitoring device for determining one or more analytics for aggregated assets, comprising:
capturing via a communications network, data from a plurality of assets associated with at least one entity, wherein the captured data have a plurality of different data file formats (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190.”; and [0132]: “Alternatively, if the listening windows are unknown and/or are dynamically changed by multi-sensor units in response to conserving additional power due to the power constraint data, the gateway device 130 and/or other computing devices can transmit frequent and/or periodic transmissions of the information and/or can transmit frequent and/or periodic queries as to whether or not the listening window is currently open.”);
storing the captured data in a storage device (see Bures, [0218]: “Some or all of this data can be otherwise stored and/or processed by the monitoring data analysis system 140, allowing the monitoring data analysis system 140 to utilize additional types and/or sources of information to better monitor and/or analyze the conditions and/or operations of the facility.”; [0240]: “The monitoring data analysis system 140 can perform functions on the measurement values stored in the measurement database system by utilizing a function database 543.”; and [0260]: “As these statistical measurements do not correspond to time-series data, a separate database of statistical measurements can store entries populated by these measurements, and/or the contextual database can be utilized to store entries populated by statistical measurements, for example, for different types of sensor devices and/or for different locations.”);
normalizing the captured data stored in the storage device to have one or more standardized data formats (see Bures, [0210]: “The measurement value received by the monitoring data analysis system 140 that was collected by a certain device and/or generated as output to a certain function performed can be normalized in accordance with the expected and/or absolute bounds of the measurement collection of the device and/or output of the certain function.”; [0261]: “The input to a normalizing function can correspond to a plurality of measurement entries, and the output can include a corresponding plurality of measurement entries with normalized measurement values. The normalized measurement values of the can be utilized to replace the un-normalized measurement values of the corresponding measurement entries in the measurement database 542. Alternatively, the output measurement entries can be added to measurement database 542 to supplement the un-normalized measurement entries, where the output measurement entries further include an identifier indicating the normalizing function and/or indicating that the measurement value corresponds to a normalized value.”; and [0291]: “The detection function can include normalizing each measurement, for example, by converting each measurement to be within the same range, based on its respective distance from known thresholds and/or based on a known range of the measurement.”);
analyzing the normalized captured data using one or more Artificial Intelligence (AI) techniques to aggregate the captured asset data into one or more asset data sets wherein each aggregated asset data set is associated with a common asset functionality (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190. This analysis can be utilized, for example,… train machine learning models; utilize the machine learning models to generate inference data predicting current conditions and/or future conditions; automatically generate heat map data illustrating current and/or predicted conditions across different locations within the facility;… ”; [0061]: “These thousand time-series measurements collected by the sensor device can be processed to generate aggregate and/or summary data for the one second time window.”; [0218]: “Some or all of this data can be otherwise stored and/or processed by the monitoring data analysis system 140, allowing the monitoring data analysis system 140 to utilize additional types and/or sources of information to better monitor and/or analyze the conditions and/or operations of the facility.”; [0257]: “The time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database, to determine statistical trends for selected sets of measurement entries in the measurement database, and/or to otherwise determine summary measures for environmental and/or electrical conditions captured across the facility as a whole, captured in particular locations, captured at particular times, and/or captured over time”; and [0322]: “determined by the monitoring data analysis system 140 to be correlated to the type of measurement utilized to detect the condition of interest as a result of performing statistical measurement functions, inference functions, and/or other analysis on previously collected data as discussed herein.”); and
analyzing, each aggregated asset data set to determine one or more functionality trends associated with assets in each aggregated asset data set based at least in part on previously collected historical data and anomalies associated with a respective asset in each aggregated asset data set (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190... utilize the machine learning models to generate inference data predicting current conditions and/or future conditions; automatically generate heat map data illustrating current and/or predicted conditions across different locations within the facility; automatically determine optimal environmental and/or electrical conditions for different parts of the facility; automatically determine specialized optimal environmental and/or electrical conditions for different features in the facility; automatically detect anomalies, detect non-optimal conditions, and/or detect other conditions of interest by utilizing user-defined functions and/or inference data generated by inference functions; automatically generate alert data to alert users, personnel and/or customers of detected anomalies and/or detected non-optimal conditions via a notification to a client devices 160 associated with the users personnel and/or customers; automatically generate updated control data for transmission, via the gateway, to one or more multi-sensor units to cause the one or more multi-sensor units 120 to change the rate of collection and/or richness of collection of one or more of their sensors in response to a detected anomalies and/or non-optimal conditions; and/or automatically generate control data for transmission to equipment in the facility to adjust current electrical and/or environmental conditions to detected anomalies and/or detected non-optimal conditions.”; [0257]: “Measurements in the measurement database can be further summarized by utilizing one or more statistical measurements. The time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database, to determine statistical trends for selected sets of measurement entries in the measurement database, and/or to otherwise determine summary measures for environmental and/or electrical conditions captured across the facility as a whole, captured in particular locations, captured at particular times, and/or captured over time.”; [0259]: “The statistical measurement functions on the other hand can be utilized to determine trends of the measurement values over longer periods time for the sensor device, generating data describing how a particular type of environmental and/or electrical condition in a particular location changes with time and/or otherwise describing trends for the particular type of condition in a particular location.”; and [0267]: “normal operating behavior for the feature, ideal environmental conditions for operating and/or maintaining the feature, location of the feature within the facility, historical measurement data and/or statistical trends of historical data collected for the feature, and/or other information describing the feature.”).
As per claim 10, Bures teaches a non-transitory computer readable medium having computer executable instructions configured to cause a computer to perform a method for determining one or more analytics for aggregated assets (see Bures, [0424]: “a non-transitory computer readable storage medium includes at least one memory section that stores operational instructions that, when executed by a processing module that includes a processor and a memory, cause the processing module to:”), the method comprising:
capturing, via a communications network, data from a plurality of assets associated with at least one entity, wherein the captured data have a plurality of different data file formats (see Claims 1 rejection above);
storing the captured data, in a storage device (see Claims 1 rejection above);
normalizing the captured data stored in the storage device, to have one or more standardized data formats (see Claims 1 rejection above);
analyzing, the normalized captured data using one or more Artificial Intelligence (AI) techniques to aggregate the captured asset data into one or more assets data sets wherein each aggregated asset data set is associated with a common asset functionality (see Claims 1 rejection above); and
analyzing, by the computer processor, using one or more AI techniques, each aggregated asset data set to determine one or more functionality trends associated with assets in each aggregated asset data set based at least in part on previously collected historical data and anomalies associated with a respective asset in each aggregated asset data set (see Claims 1 rejection above).
As per claim 19, Bures teaches a monitoring system, comprising:
at least one monitoring device for determining one or more analytics for aggregated assets, having a data receiving module and a data analytics module, wherein the data receiving module is configured (see Bures, FIG. 1 & FIG. 5) to:
capture via a communications network data from a plurality of assets associated with at least one entity, wherein the captured data have a plurality of different data file formats (see Claims 1 rejection above);
store the captured data in a storage device (see Claims 1 rejection above); and
normalize the captured data stored in the storage device to have one or more standardized data formats (see Claims 1 rejection above);
wherein the data analytics module is configured to:
analyze the normalized captured data using one or more Artificial Intelligence (AI) techniques to aggregate the captured asset data into one or more assets data sets wherein each aggregated asset data set is associated with a common asset functionality (see Claims 1 rejection above); and
analyze each aggregated asset data set using one or more AI techniques to determine one or more functionality trends associated with assets in each aggregated asset data set based at least in part on previously collected historical data and anomalies associated with a respective asset in each aggregated asset data set (see Claims 1 rejection above).
As per claim 21, Bures teaches a record management system for managing a security system having a plurality of assets, comprising:
a plurality of management modules operated on a single engine, the plurality of management modules (see Bures, FIG. 1 & FIG. 5), comprising:
an asset monitoring module configured to:
continuously capture in real time, via a communications network, data from a plurality of assets associated with an entity (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190.”; and [0132]: “Alternatively, if the listening windows are unknown and/or are dynamically changed by multi-sensor units in response to conserving additional power due to the power constraint data, the gateway device 130 and/or other computing devices can transmit frequent and/or periodic transmissions of the information and/or can transmit frequent and/or periodic queries as to whether or not the listening window is currently open.”);
store the captured data in a storage device (see Bures, [0218]: “Some or all of this data can be otherwise stored and/or processed by the monitoring data analysis system 140, allowing the monitoring data analysis system 140 to utilize additional types and/or sources of information to better monitor and/or analyze the conditions and/or operations of the facility.”; [0240]: “The monitoring data analysis system 140 can perform functions on the measurement values stored in the measurement database system by utilizing a function database 543.”; and [0260]: “As these statistical measurements do not correspond to time-series data, a separate database of statistical measurements can store entries populated by these measurements, and/or the contextual database can be utilized to store entries populated by statistical measurements, for example, for different types of sensor devices and/or for different locations.”); and
normalize the captured data stored in the storage device to have one or more standardized data formats (see Bures, [0210]: “The measurement value received by the monitoring data analysis system 140 that was collected by a certain device and/or generated as output to a certain function performed can be normalized in accordance with the expected and/or absolute bounds of the measurement collection of the device and/or output of the certain function.”; [0261]: “The input to a normalizing function can correspond to a plurality of measurement entries, and the output can include a corresponding plurality of measurement entries with normalized measurement values. The normalized measurement values of the can be utilized to replace the un-normalized measurement values of the corresponding measurement entries in the measurement database 542. Alternatively, the output measurement entries can be added to measurement database 542 to supplement the un-normalized measurement entries, where the output measurement entries further include an identifier indicating the normalizing function and/or indicating that the measurement value corresponds to a normalized value.”; and [0291]: “The detection function can include normalizing each measurement, for example, by converting each measurement to be within the same range, based on its respective distance from known thresholds and/or based on a known range of the measurement.”);
analyze the normalized captured data to aggregate the captured asset data into one or more assets data sets, wherein each aggregated asset data set is associated with a common asset functionality (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190.”; [0061]: “These thousand time-series measurements collected by the sensor device can be processed to generate aggregate and/or summary data for the one second time window.”; [0218]: “Some or all of this data can be otherwise stored and/or processed by the monitoring data analysis system 140, allowing the monitoring data analysis system 140 to utilize additional types and/or sources of information to better monitor and/or analyze the conditions and/or operations of the facility.”; [0257]: “The time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database, to determine statistical trends for selected sets of measurement entries in the measurement database, and/or to otherwise determine summary measures for environmental and/or electrical conditions captured across the facility as a whole, captured in particular locations, captured at particular times, and/or captured over time”; and [0322]: “determined by the monitoring data analysis system 140 to be correlated to the type of measurement utilized to detect the condition of interest as a result of performing statistical measurement functions, inference functions, and/or other analysis on previously collected data as discussed herein.”); and
analyze each aggregated asset data set to determine one or more functionality trends associated with assets in each aggregated asset data set based at least in part on previously captured and stored historical data and recorded anomalies associated with a respective asset (see Bures, [0022]: “The monitoring data analysis system 140 can analyze data collected in the measurement database automatically and/or in response to queries and/or function calls received from or more client devices 160 via graphical user interface 190... utilize the machine learning models to generate inference data predicting current conditions and/or future conditions; automatically generate heat map data illustrating current and/or predicted conditions across different locations within the facility; automatically determine optimal environmental and/or electrical conditions for different parts of the facility; automatically determine specialized optimal environmental and/or electrical conditions for different features in the facility; automatically detect anomalies, detect non-optimal conditions, and/or detect other conditions of interest by utilizing user-defined functions and/or inference data generated by inference functions; automatically generate alert data to alert users, personnel and/or customers of detected anomalies and/or detected non-optimal conditions via a notification to a client devices 160 associated with the users personnel and/or customers; automatically generate updated control data for transmission, via the gateway, to one or more multi-sensor units to cause the one or more multi-sensor units 120 to change the rate of collection and/or richness of collection of one or more of their sensors in response to a detected anomalies and/or non-optimal conditions; and/or automatically generate control data for transmission to equipment in the facility to adjust current electrical and/or environmental conditions to detected anomalies and/or detected non-optimal conditions.”; [0257]: “Measurements in the measurement database can be further summarized by utilizing one or more statistical measurements. The time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database, to determine statistical trends for selected sets of measurement entries in the measurement database, and/or to otherwise determine summary measures for environmental and/or electrical conditions captured across the facility as a whole, captured in particular locations, captured at particular times, and/or captured over time.”; [0259]: “The statistical measurement functions on the other hand can be utilized to determine trends of the measurement values over longer periods time for the sensor device, generating data describing how a particular type of environmental and/or electrical condition in a particular location changes with time and/or otherwise describing trends for the particular type of condition in a particular location.”; and [0267]: “normal operating behavior for the feature, ideal environmental conditions for operating and/or maintaining the feature, location of the feature within the facility, historical measurement data and/or statistical trends of historical data collected for the feature, and/or other information describing the feature.”) and
at least one or more of:
an asset management module configured to track deployed and stored asset inventory, wherein at least a portion of input data to the asset management module is derived from the aggregated data sets stored in the asset monitoring module (see Bures, [0054]: “Alternatively or in addition, the measurement processing module 242 can dynamically and/or automatically dictate which functions will be utilized based on the packet constraint data.”; [0150]: “At least one multi-sensor unit 120 that includes one or more accelerometers, gyroscopes, and/or magnetometers can be installed on a moving feature within the facility, for example, to capture changes in position, orientation, velocity, and/or acceleration of the feature to characterize the motion of the object. This can be utilized to track and/or characterize changes in motion type and/or location of the moving feature across the facility. For example, at least one multi-sensor unit 120 can be installed upon features that move throughout the facility, equipment that move throughout the facility, vehicles that move throughout of the facility, animals that move throughout the facility, customers that move throughout the facility, and/or personnel that move throughout the facility can be monitored, where their motion patterns across multiple locations are tracked and/or to monitor the cadence of movement, velocity of movement, acceleration changes, orientation changes, and/or characteristics and/or changes in movement of the moving features.”; [0170]: “In some embodiments, if a multi-sensor unit is installed in a wrong location and/or, configuration and/or moves from its original location and/or configuration, measurements captured by its accelerometer, gyroscope and/or magnetometer sensors can be utilized to automatically send an alert, for transmission to the monitoring data analysis system via the gateway device, that the sensor is in an unideal configuration and/or has moved from its intended installation configuration.”; and [0243]: “utilize the machine learning models to generate inference data predicting current conditions and/or future conditions; automatically generate heat map data illustrating current and/or predicted conditions across different locations within the facility; automatically determine optimal environmental and/or electrical conditions for different parts of the facility; automatically determine specialized optimal environmental and/or electrical conditions for different features in the facility; automatically detect anomalies, detect non-optimal conditions, and/or detect other conditions of interest by utilizing user-defined functions and/or inference data generated by inference functions; automatically generate alert data to alert users, personnel and/or customers of detected anomalies and/or detected non-optimal conditions via a notification to a client devices 160 associated with the users personnel and/or customers; automatically generate updated control data for transmission, via the gateway, to one or more multi-sensor units to cause the one or more multi-sensor units 120 to change the rate of collection and/or richness of collection of one or more of their sensors in response to a detected anomalies and/or non-optimal conditions; and/or automatically generate control data for transmission to equipment in the facility to adjust current electrical and/or environmental conditions to detected anomalies and/or detected non-optimal conditions.”);
a project management module configured to track and manage progress of installation of the assets at entity sites, wherein at least a portion of input data to the project management module is derived from the aggregated data sets stored in the asset monitoring module (see Bures, [0054]: “Alternatively or in addition, the measurement processing module 242 can dynamically and/or automatically dictate which functions will be utilized based on the packet constraint data.”; and [0257]: “Measurements in the measurement database can be further summarized by utilizing one or more statistical measurements. The time-series data for various raw and/or synthetic measurements of the measurement database can be utilized to aggregate and/or summarize selected sets of measurement entries in the measurement database, to determine statistical trends for selected sets of measurement entries in the measurement database, and/or to otherwise determine summary measures for environmental and/or electrical conditions captured across the facility as a whole, captured in particular locations, captured at particular times, and/or captured over time.”); and/or
an operations management module configured to track and respond to incidents noted by the entity, wherein at least a portion of input data to the asset management module is derived from the aggregated data sets stored in the asset monitoring module and wherein at least a portion of input data to the operations management module is derived from communications received from the entity (see Bures, [0022]: “automatically generate alert data to alert users, personnel and/or customers of detected anomalies and/or detected non-optimal conditions via a notification to a client devices 160 associated with the users personnel and/or customers; automatically generate updated control data for transmission, via the gateway, to one or more multi-sensor units to cause the one or more multi-sensor units 120 to change the rate of collection and/or richness of collection of one or more of their sensors in response to a detected anomalies and/or non-optimal conditions; and/or automatically generate control data for transmission to equipment in the facility to adjust current electrical and/or environmental conditions to detected anomalies and/or detected non-optimal conditions.”; [0046]: “Some or all of this sensor control data dictated by current mode of operation can be instead be determined and/or dynamically adjusted by the processing module 240. For example, the sensor control module 241 can generate some or all of its own sensor control data dynamically, despite a fixed current mode of operation dictated by control data received via transceiver 220 and/or received via at least one other communication interface. In particular, the sensor control module 241 can dynamically generate some or all of its own sensor control data within bounds dictated by the current mode of operation.”; and [0170]: “This can be utilized to send an alert to a user for display via graphical user interface 190 of a client device 160, notifying the user of the particular multi-sensor unit, the location of the multi-sensor unit, and instructions for correction of orientation and/or position of the re-installment of the multi-sensor unit.”).
DEPENDENT:
As per claims 2 and 11, which respectively depend on claims 1 and 10, Bures teaches further comprising, applying one or more AI techniques to the normalized captured asset data for classifying the normalized captured asset data so as to be aggregated into the one or more assets data sets (see Bures, [0152]: “The motion data can be calibrated and/or normalized over time by the processing module 240 and/or the monitoring data analysis system 140 via performance of one or more processing functions.”; [0203]: “The standardization of entries to measurement database 542 for measurements generated by all of these measurement sources, time-aligning the timestamps of entries of measurement database 542, and/or normalizing the measurement values of entries of measurement database 542 can ease the consumability of some of all of this various measurement data collected over time.”; [0210]: “Measurement values can be normalized for its corresponding measurement source. A normalization function to be performed on the measurement values can be a function of the measurement source. The measurement value received by the monitoring data analysis system 140 that was collected by a certain device and/or generated as output to a certain function performed can be normalized in accordance with the expected and/or absolute bounds of the measurement collection of the device and/or output of the certain function. For example, every normalization function can output values within the same range, given different ranges of input values.”; and [0261]: “normalizing function”).
As per claims 3 and 12, which respectively depend on claims 2 and 11, Bures further teaches wherein the one or more AI techniques apply one or more AI models to the one or more asset data sets for determining the one or more functionality trends associated with assets in each aggregated asset data set and/or for determining the historical data and anomalies associated with the respective asset (see Claim 1 rejection above).
As per claims 4 and 13, which respectively depend on claims 1 and 10, Bures further teaches wherein the functionality trends includes performance metrics according to prescribed criteria (see Bures, [0222]: “As another example, if the facility services customers by providing a service within the facility, information about occupancy, attendance of various customers, length of time various customers stay at the facility, customer satisfaction metrics for their time at the facility, customer performance metrics for their time at the facility, locations within the facility that various customers frequent and/or traffic patterns of the various customers across different locations within the facility over time can be utilized in analyzing and/or automatically adjusting electrical, environmental, and/or operational conditions of the facility monitored by the monitoring system 100.”).
As per claims 5 and 14, which respectively depend on claims 1 and 10, Bures further teaches wherein the communication network includes use a LoRaWAN network (see Bures, [0018]: “The gateway device 130 can communicate bidirectionally with the monitoring data analysis system 140, for example via wired and/or wireless network 150. Each of the plurality of additional data sources 170 can communicate bidirectionally with the monitoring data analysis system 140, for example, via the same or different network 150. Each of the plurality of client devices 160 can communicate bidirectionally with the monitoring data analysis system 140, for example, via the same or different network 150. Network 150 can include the Internet, cellular communications, an Ethernet connection, a Local Area Network, a fiber optic connection, short range radio transmissions, long range radio transmissions, satellite communications, a low-power wide area network, and/or other wired and/or wireless communication.”).
As per claims 7, 16, and 20, which respectively depend on claims 1, 16, and 19, Bures further teaches wherein the plurality of assets includes one or more of security devices, security cameras, security door locks, security key card access panels, security biometric panels, security alarms, seismic detection systems, fire detection systems, fire control devices, key or key card control devices, video access devices, building access panel devices, and/or environment control systems (see Bures, [0019]: “Monitoring system 100 can be implemented to monitor environmental, electrical, and/or operational activity at an indoor and/or outdoor facility.”; and [0139]: “The set of sensor devices 1-W can include one or more temperature sensors. At least one of the temperature sensors can be operable to air temperature or ambient temperature. One or more temperature sensors can be implemented by utilizing a negative temperature coefficient thermistor, a resistance temperature detector, a thermocouple, a semi-conductor based temperature sensor, a thermometer, and/or another thermal sensor device operable to collect temperature measurements.”).
As per claims 8 and 17, which respectively depend on claims 7 and 16, Bures further teaches wherein the captured data includes operational data associated with a respective asset (see Bures, [0019]: “Monitoring system 100 can be implemented to monitor environmental, electrical, and/or operational activity at an indoor and/or outdoor facility.”; and [0037]: “For example, the operational data 212 can indicate a current mode of operation. The current mode of operation can correspond to current functionality and/or currently utilized executable instructions. The processing module can execute the operational data to cause the multi-sensor unit 120 to operate in accordance with the current mode of operation.”).
As per claims 9 and 18, which respectively depend on claims 8 and 17, Bures further teaches wherein the captured operational data is captured in real-time (see Bures, [0087]: “This can be unideal, and it may be more favorable for most recent packets to be transmitted by the multi-sensor unit to keep the monitoring system 100 as close to real-time as possible, even if there are gaps in certain time intervals corresponding times that network congestion caused the maximum data transmission rate of a multi-sensor unit to lower to a point that data packets generated in those time intervals needed to be skipped.”; and [0284]: “The detection functions can be performed in response to determining all necessary input measurement data for a most recent timestamp and/or time window, for example, to facilitate detection of conditions of interest as close to real-time as possible.”).
As per claim 22, which depends on claim 21, Bures further teaches wherein the asset monitoring module is configured to continuously capture in real time, via a communications network, data from a plurality of assets associated with a plurality of entities and wherein the previously captured and stored historical data and recorded anomalies associated with a respective asset is specific to a respective entity (see Bures, [0087]: “This can be unideal, and it may be more favorable for most recent packets to be transmitted by the multi-sensor unit to keep the monitoring system 100 as close to real-time as possible, even if there are gaps in certain time intervals corresponding times that network congestion caused the maximum data transmission rate of a multi-sensor unit to lower to a point that data packets generated in those time intervals needed to be skipped.”; [0204]: “The measurement value can be one of a plurality of possible discrete and/or continuous values measured by the sensor device. Alternatively, the measurement value can include or indicate a set of different discrete and/or continuous values measured by one or more correlated sensor devices. The measurement value can include and/or be generated based on processing signal data such as frequency domain data and/or captured time-series data within a time window centered by or otherwise indicated by the timestamp.”; and [0284]: “The detection functions can be performed in response to determining all necessary input measurement data for a most recent timestamp and/or time window, for example, to facilitate detection of conditions of interest as close to real-time as possible.”).
As per claim 23, which depends on claim 21, Bures further teaches wherein the asset monitoring module is configured to continuously capture in real time, via a communications network, data from a plurality of assets associated with a plurality of entities and wherein the previously captured and stored historical data and recorded anomalies associated with a respective asset is aggregated for all entities (see Claim 21 and 22 rejections above).
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.
5. Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bures et al. (US 2020/0322703 A1) in view of Yang et al. (US 2005/0165788 A1)
As per claims 5 and 15, which respectively depend on claims 1 and 10, Bures does not explicitly teach wherein the data format files include binary or text-based files.
Yang teaches wherein the data format files include binary or text-based files (see Yang, [0073]: “In general, the two de facto data transmission formats are text based and binary based.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system of Bures in view of Yang so that the data format files include binary or text-based files. One would be motivated to do so because such formats of data are well-known, routine, and conventional.
Conclusion
6. For the reasons above, claims 1-20 have been rejected and remain pending.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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/Michael Won/Primary Examiner, Art Unit 2443