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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iyengar (U.S. 2023/0075067).
With regard to claim 1, Iyengar teaches a computer-implemented method for a simplified user interface for monitoring devices in a distributed industrial facility system ([abstract] distributed surveillance of an area to monitor a process and visual effects of the process; [0056] Users at individual stations or at locations within the facility or along the process may have barcode scanners), comprising:
generating an interactive user interface ([0037] user interface for a process section) ([0035] to receive inputs for the available resources, including, for example, the number of shift workers, the skill sets of the shift workers, the availability of machinery, etc.), wherein the plurality of interactive input fields are generated based at least in part on accessible sensor data obtained from one or more devices located within one or more facilities ([0035] After a process improvement plan is generated for each workstation, bottleneck, and/or process section, the process improvement plans (playbooks), may be used sequentially and/or in parallel based on time varying nature of the operator availability, operator characteristics, machine availability, resource availability, etc. across shifts and within shifts…The system may thereafter provide an optimized execution plan to be executed for the process for that duration having the inputted resources. In an exemplary embodiment, the process may be optimized between shifts, when personnel changes, when machinery and/or work stations are added or removed, when resources become available for are not available, etc.);
obtaining user input data indicative of population of one or more interactive input fields ([0086] the episodes may be consolidated into blocks and a total number of episodes occurring within a given duration that is a subunit of the displayed timeline duration is provided to the user… The user may see the total number of events for the period);
generating, based on the user input data, one or more generated rules ([0042] The system may be configured to run simulations running the suggestions and predict improvements in the process based on the selected changes. Operators may therefore make informed decisions on investments into additional available resources and the return achieved in the process improvements based on those investments; [0086] Zooming in on the timeline may permit the timeline to expand such that a total illustrated time duration is reduced. Such expansion of the time line may permit more detail into the episodes of the timeline. For example, when a timeline is zoomed in, the root causes may be identified within a given episode);
periodically obtaining sensor data from one or more sensors across one or more devices in the distributed industrial facility system (Figs. 18A-18B; [0081] the user interface is exemplary only and may incorporate any data stream captured by the system, such as sensor information, audio, visual, or other data. Exemplary embodiments may also include combinations of the timeline and lists of epochs as described and shown by FIGS. 18A and 18B);
comparing the obtained sensor data to the one or more generated rules ([0083] FIG. 18C illustrates an exemplary embodiment in which the visual display can be used to compare different lines at different locations. Such comparison can be used to determine relative efficiencies between plants, compare causes of events, etc.);
determining a violation of one of the one or more generated rules ([0175] For safety compliance, the system and methods may provide personal protective equipment verification, proximity monitoring or compliance monitoring and violation notification);
providing a notification of the violation of the one or generated more rules ([0175] violation notification); and
storing the sensor data and record of violation of the one or more generated rules (Fig. 1, data source 120; Figs. 2-3, database; [0059] The system may output information from the analytics into one or more data sources, such as a database, record, another software program, or management system; [0066] Process Metrics are also fed to the data base block for storage; [0128] segments of sensor feeds, such as videos, may be identified by a user and stored in a database for quality assurance, line process, personnel review, or other administrative needs; [0132] the system may then store the entered information in the timeline associated with the process and/or other sensor feeds corresponding to the same areas of the process).
With regard to claim 2, the limitations are addressed above and Iyengar teaches wherein generating the interactive user interface (Fig. 18B) is based on obtaining sensor data from a plurality of devices within a plurality of physical facilities within the distributed industrial facility system ([0080] The system and/or user may associated tags with critical events, and/or associated with periods of time of the sensor feeds; [0081] As illustrated, two cameras are selected that correspond to images that contributed to a given “episode”. The system may automatically select one or more camera feeds that may identify or assist the viewer in identifying or understanding the cause of one or more episode…Although illustrated as camera feeds, the user interface is exemplary only and may incorporate any data stream captured by the system, such as sensor information, audio, visual, or other data).
With regard to claim 3, the limitations are addressed above and Iyengar teaches comprising:
automatically sorting the devices based on the location of the devices ([0081] The system may automatically select one or more camera feeds that may identify or assist the viewer in identifying or understanding the cause of one or more episode. The user may also select one or more cameras to display and/or add or remove one or more cameras from the display for the selected time); and
generating each sensor of each device as a selectable input from a drop-down menu for setting the rule in the generated interactive user interface ([0163] The search feature may be configured to receive an input from a user…the user may, for example, select from a drop down menu of a list of available options; [0164] using the observation and analyzed data from the one or more cameras to distribute additional sensors. If the analysis identifies locations of inefficiency within the process, the distribution of sensors may be about the locations of inefficiencies).
With regard to claim 4, the limitations are addressed above and Iyengar teaches comprising:
updating, based on the obtained sensor data ([0164] using the observation and analyzed data from the one or more cameras to distribute additional sensors), the user interface to include selectable options that are tied to one or more physical devices associated with the distributed industrial facility system ([0164] the system may be distributed including one or more cameras to observe a segment of the process. The method may further including using the observation and analyzed data from the one or more cameras to distribute additional sensors…The input may correspond to a desired process improvement, such as to change the use of resource(s), change the processing time, etc.).
With regard to claim 5, the limitations are addressed above and Iyengar teaches wherein the input fields comprise: (i) device type ([0022] we may need one type of sensor to monitor pressure changes and another type of sensor to monitor temperature changes), (ii) a condition ([0022] Cameras are not limited to the visual spectrum continuous data capture devices for replay on a display, but may include any large field sensor detector that may take continuous data or sequential periodic data feeds), and (iii) an alert type ([0069] if an operator is away from a station that causes a delay, the operator may receive a notification of the time away from their station, videos showing the absence, and/or videos showing where they are, and/or explanations of the inefficiency event, associated delays, root causes, possible improvement suggestions, etc. The same and/or similar information may be provided through an alert and/or user interface of the system to the process line administrator).
With regard to claim 6, the limitations are addressed above and Iyengar teaches comprising:
automatically generating one or more suggested rules based on historical sensor/user data ([0068] a use to redistribute resources and/or add and/or remove resources and run simulations based on history or real time data...The system may use historic information about machine and/or personnel throughput for a given activity in order to estimate the effects on the process).
With regard to claim 7, the limitations are addressed above and Iyengar teaches wherein providing a notification of the violation of the one or more generated rules comprising providing one or more alerts for display ([0069] The same and/or similar information may be provided through an alert and/or user interface of the system to the process line administrator), wherein the alerts for display include at least one of a real-time alert ([0069] if an operator is away from a station that causes a delay, the operator may receive a notification of the time away from their station, videos showing the absence, and/or videos showing where they are, and/or explanations of the inefficiency event, associated delays, root causes, possible improvement suggestions, etc. The same and/or similar information may be provided through an alert and/or user interface of the system to the process line administrator) or a historical alert.
With regard to claim 8, the limitations are addressed above and Iyengar teaches comprising:
organizing the sensor data by:
receiving a plurality of datasets ([0047] FIG. 1A illustrates an exemplary process floor 100 with a plurality of workers 102 running a plurality of machines 104, 106, 108; [0059] The system may output information from the analytics into one or more data sources, such as a database, record, another software program, or management system) comprising signals obtained from a plurality of signals on a plurality of devices within the distributed industrial facility system ([0021] The multiple sensors, such as cameras, may be connected through processing algorithms such that an output from one sensor may inform an input to another sensor, and/or may provide control signals to another sensor; [0022]-[0024] Cameras are not limited to the visual spectrum continuous data capture devices for replay on a display, but may include any large field sensor detector that may take continuous data or sequential periodic data feeds…include multiple cameras (or sensors) that can combine the scope and precision with and without adaptive pan, tilt, and zoom);
aggregating the plurality of datasets in a database of a server computing system ([0149] wherein the first image from the first data stream and the second image from the second data stream correspond to a simultaneous time. As another example, the received data may be aggregated to generate a first single processing frame including at least two images from the one or more cameras and a second single processing frame includes at least two other images from the one or more cameras);
organizing the plurality of datasets based on at least one of sensor location ([0064] location of the sensor), sensor type ([0022] in the case of Internet of Things (IoT) sensors, we may need one type of sensor to monitor pressure changes and another type of sensor to monitor temperature changes), device type ([0082] include camera feeds and/or other information/data types), device location ([0083] The user may also select one or more camera feeds (or other received data stream) in order to review desired locations or information within the one or more locations, facilities, and/or lines); and
comparing the plurality of datasets to the one or more rules ([0083] FIG. 18C illustrates an exemplary embodiment in which the visual display can be used to compare different lines at different locations. Such comparison can be used to determine relative efficiencies between plants, compare causes of events, etc.).
With regard to claim 9, Iyengar teaches a computing system for a simplified user interface for monitoring devices in a distributed industrial facility system ([abstract] distributed surveillance of an area to monitor a process and visual effects of the process; [0056] Users at individual stations or at locations within the facility or along the process may have barcode scanners), comprising:
one or more processors ([0058] The system may include one or more processor(s)); and
one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations ([0058] where the memor(y/ies) include non-transitory machine readable medium that when executed by the one or more processor(s) perform the functions described herein.), the operations comprising:
generating an interactive user interface ([0037] user interface for a process section) ([0035] to receive inputs for the available resources, including, for example, the number of shift workers, the skill sets of the shift workers, the availability of machinery, etc.) comprising a plurality of interactive input fields, wherein the plurality of interactive input fields are generated based at least in part on accessible sensor data obtained from one or more devices located within one or more facilities ([0035] After a process improvement plan is generated for each workstation, bottleneck, and/or process section, the process improvement plans (playbooks), may be used sequentially and/or in parallel based on time varying nature of the operator availability, operator characteristics, machine availability, resource availability, etc. across shifts and within shifts…The system may thereafter provide an optimized execution plan to be executed for the process for that duration having the inputted resources. In an exemplary embodiment, the process may be optimized between shifts, when personnel changes, when machinery and/or work stations are added or removed, when resources become available for are not available, etc.);
obtaining user input data indicative of population of one or more interactive input fields ([0086] the episodes may be consolidated into blocks and a total number of episodes occurring within a given duration that is a subunit of the displayed timeline duration is provided to the user… The user may see the total number of events for the period);
generating, based on the user input data, one or more generated rules ([0042] The system may be configured to run simulations running the suggestions and predict improvements in the process based on the selected changes. Operators may therefore make informed decisions on investments into additional available resources and the return achieved in the process improvements based on those investments; [0086] Zooming in on the timeline may permit the timeline to expand such that a total illustrated time duration is reduced. Such expansion of the time line may permit more detail into the episodes of the timeline. For example, when a timeline is zoomed in, the root causes may be identified within a given episode);
periodically obtaining sensor data from one or more sensors across one or more devices in the distributed industrial facility system (Figs. 18A-18B; [0081] the user interface is exemplary only and may incorporate any data stream captured by the system, such as sensor information, audio, visual, or other data. Exemplary embodiments may also include combinations of the timeline and lists of epochs as described and shown by FIGS. 18A and 18B);
comparing the obtained sensor data to the one or more generated rules ([0083] FIG. 18C illustrates an exemplary embodiment in which the visual display can be used to compare different lines at different locations. Such comparison can be used to determine relative efficiencies between plants, compare causes of events, etc.);
determining a violation of one of the one or more generated rules ([0175] For safety compliance, the system and methods may provide personal protective equipment verification, proximity monitoring or compliance monitoring and violation notification);
providing a notification of the violation of the one or generated more rules ([0175] violation notification); and
storing the sensor data and record of violation of the one or more generated rules (Fig. 1, data source 120; Figs. 2-3, database; [0059] The system may output information from the analytics into one or more data sources, such as a database, record, another software program, or management system; [0066] Process Metrics are also fed to the data base block for storage; [0128] segments of sensor feeds, such as videos, may be identified by a user and stored in a database for quality assurance, line process, personnel review, or other administrative needs; [0132] the system may then store the entered information in the timeline associated with the process and/or other sensor feeds corresponding to the same areas of the process).
With regard to claim 10, the system claim corresponds to the method claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 11, the system claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 12, the system claim corresponds to the method claim 4, respectively, and therefore is rejected with the same rationale.
With regard to claim 13, the system claim corresponds to the method claim 5, respectively, and therefore is rejected with the same rationale.
With regard to claim 14, the system claim corresponds to the method claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 15, the system claim corresponds to the method claim 7, respectively, and therefore is rejected with the same rationale.
With regard to claim 16, the system claim corresponds to the method claim 8, respectively, and therefore is rejected with the same rationale.
With regard to claim 17, the media claim corresponds to the method claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 18, the media claim corresponds to the method claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 19, the media claim corresponds to the method claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 20, the media claim corresponds to the method claim 4, respectively, and therefore is rejected with the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tsai et al. (US 2020/0342230) teaches an event notification system which detects events based on sensor data collected at one more sensor devices and presenting notifications.
Jacob et al. (US 2022/0122183) teaches an intelligent user interface monitoring and alert system which identifies faults associated with a user interface.
Abdelsalam et al. (US 2022/0278900) teaches linking related events for various devices and services in computer log files on a centralized server.
Lingle et al. (US Patent No. 11,284,544) teaches a system for sensing, recording, analyzing and reporting environmental conditions in data centers and similar facilities.
Chandrasekar et al. (US 2015/0227862) teaches a system for supervising industrial vehicles via encoded vehicular objects shown on a mobile device.
Frei et al. (US 2014/0006506) teaches a method for monitoring and processing sensor data from an electrical outlet.
Kraytem et al. (US 2019/0286646) teaches a system provides various dynamic filters to detect and identify data items with missing data or metadata.
Singh et al. (US 2018/0114437) teaches a system of correlated video and lighting for parking management and control.
Cook et al. (US Patent No. 6,668,203) teaches a state machine analysis of sensor data from dynamic processes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached at 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREA C LEGGETT/Primary Examiner, Art Unit 2171
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