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 was previously cancelled. Claims 6, 11 and 13 are currently cancelled. Claims 14-16 are new.
Claim(s) 1-5, 7, 9-10, 12, and 14-16 are pending.
Claim(s) 1-5, 7, 9-10, 12, and 15 are rejected.
Claims 14 are allowed.
Claim 16 is allowable; however is objected to.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/20/2026 has been entered.
Response to Amendment
This Office Action is responsive to the RCE filed on 07/20/2026.
Claims 1, 4, and 10 are amended and claims 14-16 are new. Accordingly, the amended claims and the new claim are being fully considered by the examiner.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 recites a list of “the reasons” 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, an unknown reason, and then the amended claim 4 now lists “the reaction being a replacement of the component” being one of the reasons (listed after “and/or”).
There may be a typographical error that erroneously lists “the reaction being a replacement of the component” as one of the reasons.
Parent claim 1 recites, “suggests a reaction to the downtime period” and also recites, “providing a reason for the downtime period as an output value” such that the downtime period is caused because one of one of those listed “the reason” recited by claim 4 and then the reaction is suggested to resolve the downtime period. Therefore, it isn’t clear how the “suggested” reaction to resolve the downtime period can be a cause of the downtime period.
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
At the end of claim 16, after the period, there is typographical error such that “respe” is written after the period. This seems to be typographical error, or claim is incomplete.
Claim 16 recites, “the expected duration;” however parent claim 1 recites, “an expected duration of the downtime period.” To maintain consistency and clarity with parent claim 1, claim 16 should recite, the expected duration of the downtime period.
Appropriate correction is required.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 has been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant responds
(a) Section 103 Rejections
The cited references fail to disclose or suggest, and the rejection fails to otherwise consider, each and every element of the rejected claims. For example, as discussed in the interview, the cited references fail to disclose or suggest, inter alia, "wherein the control unit provides an expected duration of the downtime of the production machine," as recited in independent claim 1.
Lulu nowhere teaches using FIG 4A for providing an expected duration of a downtime. Lulu therefore does not disclose, inter alia, "wherein the control unit provides an expected duration of the downtime of the production machine," as recited in independent claim 1 (emphasis added).
For at least the above reasons, claim 1 cannot be rendered obvious in view of the references . Accordingly, Applicant requests that the obviousness rejection of independent claim 1 be withdrawn.
(Page(s): 9-10)
With respect to (a) above, Examiner appreciates the interpretative description given by Applicant in response.
In response to applicant’s amendments to claim 1, a new grounds of rejections in view of Wang has been introduced.
Combination of Sardenberg, Markham and Wang teach all the limitations of claim 1 as described in the current office action.
Applicant’s arguments are fully considered, but for the above described reasons, the arguments are moot; therefore, claims 1-5, 7, 9-10, 12, and 15 are rejected under 35 U.S.C. 103 in view of the references as presented in the current office action.
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) 1-5 and 7 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 Wang et al. (US20200201950A1) [hereinafter Wang].
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 period of the production machine and suggests a reaction to the downtime period. [(¶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.”].
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
WANG disclose(s), wherein the control unit provides an expected duration of the downtime period of the production machine, [(¶81) “FIG. 6… a predicted for time period”…
(¶82) “The predicted time window is a time period where failure is expected to happen.”…
(¶83) “FIG. 6,…the predicted time window is 0 to 2 days after the lookahead time window.”];
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 capability of providing an expected duration of the downtime period of the production machine in order to increase the accuracy of determining expected downtime/failure duration to enable proactive actions to prevent failure taught by WANG with the method taught by Sardenberg and Markham in order to have a reasonable expectation of success such as to increase the accuracy of determining expected downtime/failure duration to enable proactive actions to prevent failure [WANG: (¶84) “One of the goals…is to achieve an acceptable level of accuracy of a model with a sufficient lead time before the predicted time window to enable proactive actions to prevent failure, to scale the system to enable detection of a number of component failures, and to improve the accuracy of the system (e.g., to avoid false positives).”].
Regarding claim 2:
Sardenberg, Markham and WANG 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 WANG 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 (amended):
Sardenberg, Markham and WANG 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, an unknown reason [Examiner notes that, claim requires, only one of the elements separated by “or” and only one of them is given the patentable weight.
Sardenberg teaches, as described below, the reason being at least one of an equipment defect or down/idle time
(¶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 the claim objections set forth in the current office action, where it isn’t clear how “the reaction being a replacement of the component” is one of the reasons for downtime period];
and/or the reaction being 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,”
Examiner notes the claim objections set forth in the current office action, where it isn’t clear how “the reaction being a replacement of the component” is one of the reasons for downtime period].
Regarding claim 5:
Sardenberg, Markham and WANG 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. [Examiner notes that, claim requires, only one of the elements separated by “or” and only one of them is given the patentable weight.
Sardenberg teaches, as described below, characteristic data comprises at least one of 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
(¶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.”].
Regarding claim 7:
Sardenberg, Markham and WANG 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.”];
Claim(s) 9-10 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardenberg, Markham and WANG, and further in view of Weaver et al. (US20210109690A1) [hereinafter Weaver].
Regarding claim 9:
Sardenberg, Markham and WANG 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, 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, 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 WANG 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 10 (amended):
Sardenberg, Markham and WANG disclose(s) all the elements of claim 1, and
Sardenberg further disclose(s), A system for tracking 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], but 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”], but doesn’t 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 and [Examiner notes that, claim requires, only one of the elements separated by “or” and only one of them is given the patentable weight.
Weaver teaches, as described, below, a printing 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.”].
Regarding claim 12:
Sardenberg, Markham and WANG 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”].
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sardenberg, Markham and WANG, and further in view Hoste (US5327349A) [hereinafter Hoste].
Regarding claim 15 (new):
Sardenberg, Markham and WANG disclose(s) all the elements of claim 1, but they do not explicitly disclose, and
Hoste disclose(s), wherein, for each previous downtime period, a downtime duration and an associated reason for the downtime period are stored in a database. [(col. 4, lines17-19, 21-26): “Alarm data register 50 are used for storing information concerning each alarm… Among the data registers 44 are registers for recording elapsed time 52, total downtime 53,”
(col. 5, lines 3-11): “a received alarm register 70 stores alarms as they are received and the alarm processor 62 will select the alarm from that register which was the most probable cause of the downtime. The selected alarm number will be stored in”
(col. 5, lines 12-14): “During a downtime incident, the control processor 42 will add the elapsed time 52 to the total downtime register 53”
(col. 5, lines 45-48): “If an alarm that caused a downtime is detected, the event relay 55 is energized at step S2. As long as this relay is energized, the elapsed timer 52 at step S3 is also enabled”];
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 capability of for storing in the database, a downtime duration and an associated reason for the downtime period for each previous downtime period in order to provide best solution for recovering from downtime by accurately monitoring and recording downtime data including elapsed time of downtime and accurate cause of the downtime taught by Hoste with the method taught by Sardenberg, Markham and WANG in order to have a reasonable expectation of success such as to provide best solution for recovering from downtime by accurately monitoring and recording downtime data including elapsed time of downtime and accurate cause of the downtime [Hoste: (col. 3, lines 24-28:) “The result will be an accurate represenatation of the defects of the manufacturing process and provide a means for addressing those defects which, when corrected, will provide the greatest return.”].
Allowable Subject Matter
Claims 14 includes allowable subject matter.
Claim 16 would be allowable if rewritten or amended to overcome the claim objections set forth in this Office action.
Reasons for allowance of claim 14 and Indicating Allowable Subject Matter of claim 16
Claim 14 is allowed. The following is an examiner’s statement of reasons for allowance of claim 14:
Claim 14:
Regarding Claim 14:
Sardenberg et al. (US20230068328A1), Markham et al. (US20060149407A1), and Wang et al. (US20200201950A1) disclose all the elements of claim 1 as described in the current office action.
Hoste (US5327349A) discloses, determining and logging elapsed downtime period and cause of the downtime period [(col. 4, lines17-19, 21-26): “Alarm data register 50 are used for storing information concerning each alarm… Among the data registers 44 are registers for recording elapsed time 52, total downtime 53,”
(col. 5, lines 3-11): “a received alarm register 70 stores alarms as they are received and the alarm processor 62 will select the alarm from that register which was the most probable cause of the downtime. The selected alarm number will be stored in”
(col. 5, lines 12-14): “During a downtime incident, the control processor 42 will add the elapsed time 52 to the total downtime register 53”
(col. 5, lines 45-48): “If an alarm that caused a downtime is detected, the event relay 55 is energized at step S2. As long as this relay is energized, the elapsed timer 52 at step S3 is also enabled”];, but doesn’t explicitly teach, calculation of expected duration of the downtime period in combination with other elements of the claim.
However, none of the above described prior arts, or any of the other prior arts listed in this correspondence and in all the previous correspondence taken either alone or in obvious combination disclose, A method, specifically including:
the expected duration of the downtime period is calculated based on the reason for the downtime period and the respective identifier of the component of the production machine causing the downtime as provided by the machine learning module.
(in combination with other elements of the claim)
having all the claimed features of applicant’s instant invention, including:
The method according to claim 1,
wherein the expected duration of the downtime period is calculated based on the reason for the downtime period and the respective identifier of the component of the production machine causing the downtime as provided by the machine learning module.
Claim 16 is indicated to have allowable subject matter. The following is an examiner’s statement of reasons for indicating allowable subject matter of claim 16:
Claim 16:
Regarding Claim 16:
Sardenberg et al. (US20230068328A1), Markham et al. (US20060149407A1), Wang et al. (US20200201950A1), and Hoste (US5327349A) disclose all the elements of claim 15 as described in the current office action.
Kudo et al. (US20220035356A1) discloses, for each previous downtime period, the respective identifier of the component of the production machine causing the downtime is further stored in the database [¶72: The failure history data table 320 is a table in which the information of the failure sensor data table 300 and the information of the maintenance history data table 310 are merged, and has fields of notification date and time, model, equipment number, sensor A, . . . , sensor X, and failure cause part as shown in FIG. 6.] but doesn’t explicitly teach, wherein the expected duration is calculated by searching the database for entries with a same reason and a same identifier as the reason for the downtime period and the respective identifier of the component, and providing a mean value of previous downtime durations for the found entries in combination with other elements of the claim.
However, regarding the amended claim, none of the above described prior arts, or any of the other prior arts listed in this correspondence and in all the previous correspondence taken either alone or in obvious combination disclose, A method, specifically including:
wherein the expected duration is calculated by searching the database for entries with a same reason and a same identifier as the reason for the downtime period and the respective identifier of the component, and providing a mean value of previous downtime durations for the found entries.
(in combination with other elements of the claim)
having all the claimed features of applicant’s instant invention, including:
The method according to claim 15,
wherein, for each previous downtime period, the respective identifier of the component of the production machine causing the downtime is further stored in the database, and wherein the expected duration is calculated by searching the database for entries with a same reason and a same identifier as the reason for the downtime period and the respective identifier of the component, and providing a mean value of previous downtime durations for the found entries.
It is for these reasons that applicant's invention defines over the prior art of the record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
LULU’220 et al. (CN112534371A listed in the IDS filled 06/10/2026, US20210158220A1 version is used for convenience for English translation) - Optimizing accuracy of machine learning algorithms for monitoring industrial machine operation:
¶63: At S510, a first ambiguous segment of a first machine behavioral model that indicates a suspected downtime is identified. The suspected downtime may be identified based on ambiguous parameters of sensory inputs received from one or more sensors of the machine. Ambiguous parameters may be represented by unusual parameters that their meaning, i.e., their influence on the machine operation, has not been determined.
WEI (CN114140069A listed in the IDS filled 06/10/2026) - Milk powder production equipment halt statistical method and device:
Page 4, ¶9: the information input association module is used for inputting a production line identifier and an equipment identifier which are shut down, automatically associating the shut down type coding information of the corresponding production line equipment, and displaying the shutdown type coding information on a front-end display interface.
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.
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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.
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/M.S./
Patent Examiner,
Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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