DETAILED ACTION
Notice of 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 8 is cancelled. Claim(s) 1-7 and 9-13 are pending and are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 02/06/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
This Office Action is responsive to the amendment filed on 12/11/2025.
Claims 1-2, 7, and 9 are amended and claim 13 is new. Accordingly, the amended claims and the new claim are being fully considered by the examiner.
Applicant’s amendments to the abstract and the specification filled on12/11/2025 are acknowledged and are acceptable for the examination purpose.
Applicant’s amendments to claim 2 has overcome all the claim objections as set forth in the previous office action.
Applicant’s amendments to claims 1, 7 and 9 has overcome all the 35 USC § 112 rejections of claims 1-12 as set forth in the previous office action.
In response to applicant’s amendments to claim 1, all the 35 USC §101 rejections as set forth in the previous office action has been withdrawn.
This action is MADE FINAL. Please see response to arguments section for further details.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 10 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardenberg et al. (US20230068328A1) [hereinafter Sardenberg] and Markham et al. (US20060149407A1) [hereinafter Markham], and further in view of LULU’527 et al. (US20220058527A1) [hereinafter LULU’527]
Regarding claim 1 (amended):
Sardenberg disclose(s), A method for tracking downtime of a production machine, the method comprising: [(¶5) “method for minimizing customer and jobsite downtime due to an unexpected machine repair”… (¶32) “machine learning model is configured to output an estimated time that the machine will be in repair.”];
…receiving, by a control unit, sensor data from the production machine and production target data, [ (¶5) “receiving,” “from a machine at a customer” “sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure;” “retrieving,” “informational data” “the received sensor data and the retrieved informational data to a machine learning model for computing an estimated time to when the failed machine will be repaired” … (¶27) “Such data may include but are not limited to: hour trend for asset;” “various measurements including usage trends over a large period of time”
Examiner notes that, in broadest reasonable interpretation, production target data can be any production target related data etc. As such, Sardenberg teaches, continuously receiving sensor data 212, and hour trend for asset 214 etc. fig. 2];
combining the sensor data and the production target data over a certain period providing combined data and calculating characteristic data of the combined data by the control unit, [(Sardenberg figure 2.) shows sensor data 212 and hour trend data that is usage trend over a period of time 214 are combined in 218 into “all inputs,” and calculating machine characteristics such as assessment targeting 90% accuracy on machine being down];
determining if the combined data is from a downtime period of the production machine based on the characteristic data, and [(Sardenberg figure 2) combined data in 218 into “all inputs,” as described above, and it is determined that these data are of a machine/asset that is with issue/failure/down such that data is taken from a moment when the machine is in issue/failure/down; calculating machine characteristics such as assessment targeting 90% accuracy on machine being down…(¶5) “sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure.”… (¶27) “With regard to hour trend for asset,” “machine operating hours are provided to the machine learning model so that such data allows the model to calculate various measurements including usage trends over a large period of time; the model then determines, based on historical data, if an occurrence of low to no usage constitutes normal (for example: Weekends or Holidays) or abnormal and therefore needs to be flagged or refined to determine if equipment is down.”
Examiner notes that, in broadest reasonable interpretation, data is “from a downtime period” means that data is from an instance/moment where the machine experience issue or downtime that is data is from any moment when the machine experienced fault/issue/downtime];
characterizing the downtime period using a machine learning module implemented in the control unit, wherein the machine learning module provides one of a set of predefined reasons based on input values,…the machine learning module receiving the characteristic data of the downtime period as an input value and providing a reason for the downtime period as an output value, [(¶28) “the machine learning model in step 218 receives input from steps 210, 212, and 214 as described above.” “The machine learning model processes such input and when the machine learning model computes that the machine is down, the machine learning model further computes output as described above.” “After individually classifying each data input, the system then calculates the possible combinations of such events to provide a final score and calculation of equipment down status.”… (¶24) “At step 218, the machine learning model determines in the machine at the jobsite is down or has stopped. The machine learning model uses inputs” “to make an assessment targeting 90% accuracy on the machine being down with a failure that prevents operation.” “When the machine learning model determines that the machine is down, the model outputs the following data: machine information (e.g., the unique machine identifier such as the serial number (S/N)), hour meter or hours machine has operated since put into service, location, major failure information (component, likely cause), etc.”… (¶27) “machine operating hours are provided to the machine learning model so that such data allows the model to calculate various measurements including usage trends over a large period of time; the model then determines, based on historical data, if an occurrence of low to no usage constitutes normal (for example: Weekends or Holidays) or abnormal and therefore needs to be flagged or refined to determine if equipment is down.”
Examiner notes that, in broadest reasonable interpretation, the limitation “characterizing the downtime period” means evaluating a time period in which machine’s expected run time period is such that machine is in failure/down condition. As such Sardenber teaches, occurrence of low period uses of the machine/equipment can be flagged as abnormal such that machine/equipment being down.
Examiner notes that, as described above, Sardenberg figure 2. Shows the machine learning receives the combined inputs at 218 (212 +214) and then outputs reasons for the downtime period (i.e.; the moment machine is down) such as machine being down with a failure that prevents operation, major failure information (component, likely cause), etc. ];
wherein, in case the downtime period is caused by a specific component of the production machine, the machine learning module additionally provides a respective identifier of the component of the production machine causing the downtime of the production machine and suggests a reaction to the downtime.. [(¶24) “At step 218,” “When the machine learning model determines that the machine is down, the model outputs the following data: machine information (e.g., the unique machine identifier such as the serial number (S/N))… (¶29) “at step 218, the machine learning model is configured to monitor individual machine sensor information and is further configured to automatically provide a fix for a repair….machine learning model is further configured to determine the outstanding repairs that require fixing based on open service records of the machine. As an example, based on a specific open service record, the machine learning model determines that a specific part needs to be replaced. The machine learning model can be configured to send out a message with the corresponding information about the machine, the part to be replaced, and the replacement part, to an appropriate processor,”], but doesn’t explicitly disclose, and
Markham discloses, continuously receiving, by a control unit, sensor data from the production machine and production target data [(¶41) “machine data from sensors and other control means are continually monitored for events related to productivity and/or product quality, such as product waste, machine down time, machine slow downs, product maintenance, machine failure, etc.”
Examiner notes that, in broadest reasonable interpretation, production target data can be any production target related data etc. As such, Markham teaches, continuously receiving target data such as events related to productivity and/or product quality and sensor data];
wherein the predefined reasons are taught to the machine learning module through training, [(¶30) “the agent may use a neural network to learn patterns in the data. Deviations from learned patterns may be flagged as anomalies. The neural network may be trained with historical data and may be re-trained after a given time period to be updated with the most current process information.”];
wherein the control unit stores the downtime period, [(¶30) “a method collects, stores, and reports” “delay information on an event basis in a manufacturing system.”… (¶46) “The data from the machine are monitored and logged by a PIPE Event Logger, which may include an event logger and a machine logger.” “The machine logger provides an interface for operators to provide explanations about delay states”… (¶43) “Examples of events may include” “a component failure in a machine,” “a loss of power, a fire, machine shutdown to change a grade (“changeover”) or perform routine maintenance,”… (¶41) “PIPE collects, stores, and reports production information such as converting” “delay information on an event basis.” “Customized rules may be established to specify how events are classified and what types of events are to be logged (normally, all sources of delay may be logged and coupled with additional data).” “These events may be spaced apart in time by time steps” “may be characterized in that the standard deviation of the time step between successive events is large relative to the mean, such that the ratio of the standard deviation to the mean time step during a week of production is about 0.2 or greater, specifically about 0.5 or greater, and most specifically about 1.0 or greater.”
Examiner notes that, based on the 35 U.S.C. 112(b) rejections as set forth in the current office action, in broadest reasonable interpretation, the meaning of the limitation “the downtime period characterization time-resolved” is construed as any information related to downtime resolution. Markham teaches logging period of downtime related events and downtime resolution related information such as identifying cause of downtime such as machine/component failure, shutdown etc.].
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the technique of continuous monitoring of data; training of machine learning model to identify cause of downtime/failure; and storing/logging of various downtime period and resolution related data to automatically indicating one or more possible remedial actions that may be taken to reduce the production problem and to enable better or more rapid decision-making taught by Markham with the method taught by Sardenberg in order to have a reasonable expectation of success such as to automatically indicating one or more possible remedial actions that may be taken to reduce the production problem and to enable better or more rapid decision-making by monitoring the downtime and taking appropriate fast action to reduce impact of the downtime [Markham: (¶67) “automatically indicating one or more possible remedial actions that may be taken to reduce the production problem.” “to enable better or more rapid decision-making”], but Sardenberg and Markham do not explicitly disclose, and
LULU’527 disclose(s), wherein the control unit provides an expected duration of the downtime of the production machine, [(¶67) “FIG. 4A is an example graph 400A illustrating representation of a training process of a machine failure detection process according to an embodiment. The graph shown in FIG. 4A includes a graph 400A in which a curve 410A is shown and represents sensor data of a certain parameter of a machine, such as the revolutions per minute (RPM) of the machine engine. The curve 420A represents a labeled machine failure provided to the machine failure detection process which is utilized to train the machine failure detection process to detect machine failures. A point at which the machine failure begins is indicated by 430A and a point at which the machine failure ends is indicated by 440A.”];
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the technique of providing an expected duration of the downtime of the production machine to automatically update and improve failure/downtime detection taught by LULU’527 with the method taught by Sardenberg and Markham in order to have a reasonable expectation of success such as to automatically update and improve failure/downtime detection [LULU’527: (¶19) “automatically update the supervised machine failure prediction process with the new tagged machine failure indicators, such that the supervised machine failure prediction process is continuously updated and improved.”].
Regarding claim 2 (amended):
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and Markham further disclose(s), wherein the control unit only considers these periods as downtime periods in which the production of the production machine has stopped for at least one minute, in particular for at least three minutes. [(¶41) “FIG. 4” “plot 122 of machine speed versus time to illustrate an exemplary definition of delay during a series of events relating to machine productivity over time.” “The trigger may be due to a machine-detected web break,” “a machine error, or other cause. The trigger initiates a machine shut down. The machine decelerates to zero speed. In one definition, the delay time only begins when the machine is at substantially zero speed, and ends when the machine begins moving again. This is the definition used to mark delay in FIG. 4. In an alternative definition (not shown), the delay time may be defined to span the time from the trigger to end the run state (or from the time when the machine has decelerated to a predetermined speed after the trigger) until the machine begins moving again.”… (¶68) “The time delay between the occurrence of an event” “The time delay between events and reports according to the present invention may less than a day, less than eight hours, less than an hour, less than ten minutes, less than three minutes, or less than a minute.”
Examiner notes that, claim limitation “these periods” is construed as “periods” according to the claim objections as set forth in the current office action.
Examiner notes that, in broadest reasonable interpretation, the limitation “machine has stopped for at least one minute, in particular for at least three minutes” means that the minimum threshold considered is one minute such that if machine stopped at least more than one minute, it can be considered as down. Markham discloses, the downtime period can be less than an hour or ten minutes, that can be any time such as can be for example minimum of one minute or can be 3 minutes.].
Regarding claim 3:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), wherein the control unit additionally receives event data from the production machine and provides the event data additionally to the machine learning module as an input value. [(¶28) “the machine learning model in step 218 receives input from steps 210, 212, and 214 as described above.”… (¶16) “From the telematics sensor data,” “obtains data reflecting a specific critical failure that required the machine to stop and provides such data as input to the machine learning model”… (¶27) “machine operating hours are provided to the machine learning model so that such data allows the model to calculate various measurements including usage trends over a large period of time;”… (¶26) “At step 212, data from the machine sensors are provided as input to step 218 and, more specifically, to the machine learning model. As described above, examples of machine sensor data may include but are not limited to GPS data, engine-related data, driver behavior data, and activity-related data by the machine.”].
Regarding claim 4:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), the reason being one of an idle time, an out of production schedule, an equipment defect, a shop floor process defect, a maintenance or cleaning of the production machine, a setup of the production machine, or an unknown reason. [(¶24) “At step 218, the machine learning model determines in the machine at the jobsite is down or has stopped.” “When the machine learning model determines that the machine is down, the model outputs the following data:” “major failure information (component, likely cause), etc.”… (¶27) “if an occurrence of low to no usage constitutes normal (for example: Weekends or Holidays) or abnormal and therefore needs to be flagged or refined to determine if equipment is down.”
Examiner notes that, claim requires, the reason being only one of an idle time, an out of production schedule, an equipment defect, a shop floor process defect, a maintenance or cleaning of the production machine, a setup of the production machine, or an unknown reason. Sardenberg teaches the reason being at least one of an equipment defect or down/idle time].
Regarding claim 5:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), wherein the characteristic data comprises at least one of the following values: a time since the last production of the production machine, a time since the last downtime period of the production machine, and/or a productivity of the production machine in the respective period. [(¶27) “At step 214, data from the entity's internal data store are provided as input to step 218 and, more specifically, to the machine learning model. Such data may include but are not limited to: hour trend for asset;” “machine operating hours are provided to the machine learning model so that such data allows the model to calculate various measurements including usage trends over a large period of time;” “if an occurrence of low to no usage constitutes normal (for example: Weekends or Holidays) or abnormal and therefore needs to be flagged or refined to determine if equipment is down.”
Examiner notes that, claim requires, characteristic data comprises only one of the following values: a time since the last production of the production machine, a time since the last downtime period of the production machine, and/or a productivity of the production machine in the respective period. Sardenberg teaches productivity of the production machine in the respective period such as low or no use over a period of time or usage trend over a period of time].
Regarding claim 6:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), wherein the combined data of subsequent periods comprises a temporal overlap. [(Sardenberg figure 2) combined data in 218 into “all inputs,” as described above, and it is determined that these data are of a machine/asset that is with issue/failure/down such that data is taken from a moment when the machine is in issue/failure/down;… (¶5) “sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure.”… (¶27) “With regard to hour trend for asset,” “machine operating hours are provided to the machine learning model so that such data allows the model to calculate various measurements including usage trends over a large period of time; the model then determines, based on historical data, if an occurrence of low to no usage constitutes normal (for example: Weekends or Holidays) or abnormal and therefore needs to be flagged or refined to determine if equipment is down.”
Examiner notes that, in broadest reasonable interpretation, the plain meaning of the limitation “temporal overlap” is considered, such that it means the combined data are from a timeframe/period when machine experience failure. Sardenberg teaches combined data such as sensor data and event data are taken from the time period when the machine/component experiences fault/downtime.]
Regarding claim 7 (amended):
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), wherein a display is assigned to the control unit and wherein the control unit shows a visual evaluation of the downtime period on the display. [(¶22) “At step 206, the machine learning model processes the input data described in step 204 and outputs recommended actions, e.g., wait until the technician comes and replaces a part, that are provided to the dealer, e.g., via a screen display of an associated application.” “the output is a series of recommendations, ranked from most effective to least effective.”… (¶34) “The CPU 310 can communicate with a hardware controller for devices, such as for a display 330. Display 330 can be used to display text and graphics. In some examples, display 330 provides graphical and textual visual feedback to a user.”], but doesn’t explicitly disclose, and
Markham further disclose(s), wherein the control unit stores the downtime period, [(¶30) “a method collects, stores, and reports” “delay information on an event basis in a manufacturing system.”… (¶46) “The data from the machine are monitored and logged by a PIPE Event Logger, which may include an event logger and a machine logger.” “The machine logger provides an interface for operators to provide explanations about delay states”… (¶43) “Examples of events may include” “a component failure in a machine,” “a loss of power, a fire, machine shutdown to change a grade (“changeover”) or perform routine maintenance,”… (¶41) “PIPE collects, stores, and reports production information such as converting” “delay information on an event basis.” “Customized rules may be established to specify how events are classified and what types of events are to be logged (normally, all sources of delay may be logged and coupled with additional data).” “These events may be spaced apart in time by time steps” “may be characterized in that the standard deviation of the time step between successive events is large relative to the mean, such that the ratio of the standard deviation to the mean time step during a week of production is about 0.2 or greater, specifically about 0.5 or greater, and most specifically about 1.0 or greater.”
Examiner notes that, according to the 35 U.S.C. 112(b) rejections as set forth in the current office action, “the downtime periods” in the limitation “stores the downtime periods” is construed as downtime periods.
Examiner notes that, based on the 35 U.S.C. 112(b) rejections as set forth in the current office action, in broadest reasonable interpretation, the meaning of the limitation “the downtime period characterization time-resolved” is construed as any information related to downtime resolution. Markham teaches logging period of downtime related events and downtime resolution related information such as identifying cause of downtime such as machine/component failure, shutdown etc.];
Regarding claim 10:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), A system for tracking the downtime of a production machine, the system comprising: [(¶6) “a system for minimizing customer and jobsite downtime due to an unexpected machine repair can include” “receive, from a machine at a customer jobsite of a customer, sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure;”… (¶32) “machine learning model is configured to output an estimated time that the machine will be in repair.”];
the production machine having at least one sensor providing sensor data, [(¶20) “one of the sensors on the machine measures and detects that the temperature of the oil is higher than a pre-determined threshold, causing the machine to stop operation.” (¶5) “receiving,” “from a machine at a customer” “sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure;”]
a control unit receiving…the sensor data from the production machine, [(¶5) “receiving,” “from a machine at a customer” “sensor data from one or more machine sensors, wherein the sensor data indicates a machine failure;”];
As described above in claim 1, combination of Sardenberg and Markham teaches, the control unit being adapted to perform the method according to claim 1. [Examiner notes, see the method taught by combination of Sardenberg and Markham as described above in claim 1 is performed by the system as described above in claim 10],
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined system for tracking the downtime of a production machine with the production machine having at least one sensor providing sensor data and with a control unit receiving…the sensor data from the production machine to calculate an optimal solution in real-time taught by Sardenberg with the method taught by Sardenberg, Markham and LULU’527 in order to have a reasonable expectation of success such as to calculate an optimal solution in real-time to address machine fault/downtime [Sardenberg: (¶13) “in response to a machine failure, calculating an optimal solution in real-time”], but Sardenberg doesn’t explicitly disclose, and
Markham further disclose(s), a production machine processing a product according to production target data, [(¶41) “PIPE collects, stores, and reports production information such as converting machine productivity,” “on an event basis. In this system, machine data from sensors and other control means are continually monitored for events related to productivity and/or product quality, such as product waste”… (¶42) “An “event,” as used herein, refers to any incident that may affect the productivity of a process or machine in use to produce a product, or that may adversely affect the quality of the product being produced.”
Examiner notes that, Markham teaches, production machine produce a product and target product quality is being monitored such that product is produced to meet target product quality];
a control unit receiving continuously the sensor data from the production machine, [(¶41) “In this system, machine data from sensors and other control means are continually monitored”].
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the above described teachings of Markham with the method taught by Sardenberg, Markham and LULU’527 in order to have a reasonable expectation of success such as to automatically indicating one or more possible remedial actions that may be taken to reduce the production problem and to enable better or more rapid decision-making by monitoring the downtime and taking appropriate fast action to reduce impact of the downtime [Markham: (¶67) “automatically indicating one or more possible remedial actions that may be taken to reduce the production problem.” “to enable better or more rapid decision-making”].
Regarding claim 12:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claims 1 and 10, but Sardenberg does not explicitly disclose, and
Markham further disclose(s), the system comprising at least one additional production machine processing a product according to production target data, [(¶42) “The PIPE system may be used to track” “events from multiple machines and processes wherein intermediate products from early processes or machines are used as raw materials in later processes or machines,” “the event data for the intermediate products are used by operators or process control equipment to properly execute the subsequent processes based on the events associated with the intermediate product or, in general, with the quality and property attributes of the intermediate product”… (¶84) “The present invention may be adapted for” “a series of unit operations or machines, a group of related or unrelated machines at a single production facility (plant or mill),” “or for corporate-wide operations for all products or a subset of products and processes.”
Examiner notes that Markham teaches more than one production machines produce products according to target data such as according to the quality and property attributes of the products];
the at least one additional production machine having at least one sensor providing sensor data, [(¶221) “Process sensors 46 associated with a process (not shown) provide data that allow the system to monitor events 86 related to productivity.”… (¶234) “Multiple sensors 46 (boxes labeled with “S”) detect process conditions and other variables pertaining to the machine 48 and the process 36 of converting raw materials 36 to the product 42.”];
wherein the control unit continuously receives the sensor data from the at least one additional production machine and [(¶41) “In this system, machine data from sensors and other control means are continually monitored”… (¶221) “Process sensors 46 associated with a process (not shown) provide data that allow the system to monitor events 86 related to productivity.”… (¶234) “Multiple sensors 46 (boxes labeled with “S”) detect process conditions and other variables pertaining to the machine 48 and the process 36 of converting raw materials 36 to the product 42.”];
characterizes the downtime periods of all production machines. [(¶43) “events may include” “a component failure in a machine,” “a loss of power,” “machine shutdown to change a grade (“changeover”) or perform routine maintenance,” “an experimental run”… (¶58) “PIPE systems may provide information about production modes. Production modes may describe the status of a machine at any given moment, such as whether a machine is” “down for scheduled maintenance, being used for a research run, and so forth. The production mode information from the PIPE system allows down time or delays in production to be counted appropriately”].
Regarding claim 13 (new):
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), wherein the reaction includes a replacement of the component. [ (¶29) “at step 218, the machine learning model is configured to monitor individual machine sensor information and is further configured to automatically provide a fix for a repair….machine learning model is further configured to determine the outstanding repairs that require fixing based on open service records of the machine. As an example, based on a specific open service record, the machine learning model determines that a specific part needs to be replaced. The machine learning model can be configured to send out a message with the corresponding information about the machine, the part to be replaced, and the replacement part, to an appropriate processor,”].
Claim(s) 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardenberg, Markham and LULU’527, and further in view of Weaver et al. (US20210109690A1) [hereinafter Weaver].
Regarding claim 9 (amended):
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claim 1, but they do not explicitly disclose, and
Weaver disclose(s), wherein the control unit additionally performs the following operations to determine a setup of the production machine: [(¶85) “reporting productivity of a production line, according to an embodiment. Such reports can be performed per production line, as in step 343, or for multiple production lines or multiple facilities, as in step 251.”… (¶93) “FIG. 7A through FIG. 7B are block diagrams that illustrate example screens for reporting productivity of a production line,” “an area 713 that presents text that indicates current status (e.g., operating at target efficiency for 3 hours, or running at marginal efficiency for 15 minutes, or down for 25 minutes, etc.)”
Examiner notes that, in broadest reasonable interpretation, determination of a setup of the production machine means determination of information related to any status/setting of the production machine];
tracking the current job identifier of the production performed at the production machine, [(¶93) “This graphic provides for several production lines, production line status, the SKU they are currently running and all relevant progress towards target data.” “an area 712 that presents text that indicates an identifier for the production line;”
Examiner notes that, according to the 35 U.S.C. 112(b) rejections as set forth in the current office action, “the current job identifier” is construed as current job identifier.
Examiner notes that, in broadest reasonable interpretation, current job identifier means any current production job/operation identified];
detecting a change in the current job identifier, [(¶93) “an area 713 that presents text that indicates current status (e.g., operating at target efficiency for 3 hours, or running at marginal efficiency for 15 minutes, or down for 25 minutes, etc.);” “a bar running parallel to the time axis with different colors representing different status intervals (e.g., color 725 running at or near target efficiency, color 726 running at marginal efficiency or below, and color 727 for down time);” “an area 717 presenting a graphic that indicates trend relative to target with an arrow pointing downward, level or upward;”];
characterizing the downtime period as a setup period in case the length of the period is within a certain time range. [(¶96) “view downtime by amount per time period, or cumulatively by code. FIG. 9A through FIG. 9C are block diagrams that illustrate example screens for reporting downtime for a production line”… (¶97) “FIG. 9A is an example user interface that is used to define different codes for different events or combination of events.” “code 1 indicates filler down; code 2 indicates printer down as a result of any of the smart printer events that lead to printer down; code 3 indicates conveyer down, as inferred by no product coming before the representative printer or a motion detector directed to the convey means; code 4 indicates awaiting raw material, such a liquid that fills a can; code 5 indicates down for planned maintenance as inferred from data in fields 228 and 229; and, code 6 indicates a quality assurance hold as determined by an event recorded in the field 253.”
Examiner notes that, according to the 35 U.S.C. 112(b) rejections as set forth in the current office action, “the length” is construed as a length.
Examiner notes that, Weaver teaches, figure 9B and 9C, downtime periods are characterized as setup periods such as indicated by different codes showing different downtimes in different lengths of time].
Therefore, it would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to have combined the technique of determining a setup of production machine; tracking identified current job of production machine; detecting change; and characterizing downtime period as setup time period to gain transparency into production line operation that enables increases in production throughput and reduces operating costs taught by Weaver with the method taught by Sardenberg, Markham and LULU’527 in order to have a reasonable expectation of success such as to gain transparency into production line operation that enables increases in production throughput and reduces operating costs [Weaver: (¶3) “to gain transparency into production line operation that enables increases in production throughput and reduces operating costs”].
Regarding claim 11:
Sardenberg, Markham and LULU’527 disclose(s) all the elements of claims 1 and 10, but they do not explicitly disclose, and
Weaver further disclose(s), the production machine being one of a printing machine, a die-cutting machine, a hot foil stamping machine, a folding-gluing machine, or a litho-laminating machine. [(¶4) “a method includes obtaining initialization data that indicates a representative industrial printer used on a production line at a facility and a product to be output by the production line.”… (¶11) “system includes a production line at a facility” “The system operates the representative industrial printer to report a count of print operations for the product at a plurality of time intervals.”
Examiner notes that, claim requires, only one of a printing machine, a die-cutting machine, a hot foil stamping machine, a folding-gluing machine, or a litho-laminating machine. Weaver teaches a printing machine.].
Response to Arguments
Applicant's arguments filed 12/11/2025 has been fully considered but they are not persuasive.
Applicant responds
(a) Rejections under 35 U.S.C. § 103
The Office Action concedes that Sardenberg and Markham do not disclose this element, and instead relies on Lulu's Figure 4 and paragraph [0067] as allegedly disclosing a control unit that provides an expected duration of the downtime. Nothing in that disclosure of Lulu provides an expected duration of downtime.
…In the following paragraph [0068], Lulu states that this training is applied to new sensor data to detect a new machine failure.
Lulu, however, includes no disclosure of predicting an expected duration of the downtime. Therefore, although Lulu detects downtimes in a machine and not to provide an expected duration of the downtime.
Lulu does not remedy the deficiencies of Sardenberg and Markham, and claim 1 is patentable over the cited references.
(Pages: 10-12)
With respect to (a) above, Examiner appreciates the interpretative description given by Applicant in response.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., predicting an expected duration of the downtime) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The limitation “wherein the control unit provides an expected duration of the downtime of the production machine,” is broad. In broadest reasonable interpretation, expected can mean expected or desired duration of the downtime, it doesn’t mean that this information (duration of the downtime) is predicted.
As described in the current office action, LULU’527 discloses, expected machine failure/downtime is provided/determined such that labeled machine failure are provided in the curve 420A.
Applicant’s arguments are fully considered, but for the above described reasons, they are not persuasive; therefore, claim 1 is rejected under 35 USC § 103 in view of the references as set forth in the current office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed in the PTO-892 Notice of Reference Cited document mailed on 09/05/2025..
LULU’125 et al. (US20180293125A1) - System and method for unsupervised prediction of machine failures:
(¶12): monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.
Kane (US20220014635A1) - Devices, systems, and methods for forecasting device failures:
(¶6): obtaining sensor data that were generated by a plurality of sensors; obtaining event data that include occurrences of an event; calculating, for each of one or more sensors of the plurality of sensors, respective characteristics of the sensor data that were generated by the sensor within a temporal range of a respective occurrence of the occurrences of the event; calculating respective correlations between at least some of the sensor data; normalizing at least some of the sensor data based on the characteristics, thereby generating normalized sensor data; and training a machine-learning model based on the normalized training data, wherein the machine-learning model receives sensor data as inputs, and the machine-learning model outputs either a normal indicator or, alternatively, an abnormal indicator.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED SHAFAYET whose telephone number is (571)272-8239. The examiner can normally be reached M-F 8:30 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/ Supervisory Patent Examiner, Art Unit 2116