DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12090718. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding claim 1, 12090718 claims:
1. A system to manufacture a tire, the system comprising: See 12090718 claim 1, “1. A system to manufacture a tire, the system comprising:”
one or more processors and one or more memories, the one or more memories configured to store instructions that are executable by the one or more processors and cause the one or more processors to: See 12090718 claim 1, “one or more processors and a memory, the memory configured to store instructions that are executable by the one or more processors and cause the one or more processors to:”
receive, from one or more sensors of one or more pieces of tire manufacturing equipment, one or more values corresponding to manufacturing of a tire; See 12090718 claim 1, “receive, from one or more sensors of one or more pieces of tire manufacturing equipment in a tire manufacturing plant, one or more values corresponding to manufacture of a tire by the one or more pieces of tire manufacturing equipment;”
generate a matrix based on the one or more values; See 12090718 claim 1, “generate a matrix based on the one or more values”
predict, via input of the matrix into a machine learning model, a value for a splice tolerance metric for the tire; See 12090718 claim 1, “predict, via input of the matrix into a machine learning model constructed to output tire manufacturing performance data, a value for a splice tolerance metric for the tire, …”
determine, based on the value for the splice tolerance metric, a first parameter of a first piece of the one or more pieces of tire manufacturing equipment to adjust; and See 12090718 claim 1, “determine, based on the value for the splice tolerance metric, a parameter of at least one piece of equipment in the tire manufacturing plant to adjust; and”
provide a command to adjust the first parameter of the first piece of equipment responsive to determining the value of the splice tolerance metric. See 12090718 claim 1, “provide a command to adjust a first parameter used by the at least one piece of equipment in the tire manufacturing plant responsive to the value of the splice tolerance metric.”
Regarding claim 2, 12090718 claims:
2. The system of claim 1, wherein the one or more processors are further caused to determine one or more metrics based on the one or more values, wherein generating the matrix is further based on the one or more metrics. See 12090718 claim 1, “determine one or more metrics based on the one or more values; generate a matrix based on the one or more values and the one or more metrics; …”
Regarding claim 3, 12090718 claims:
3. The system of claim 1, wherein the one or more processors are further caused to construct a plurality of machine learning models for each of the one or more pieces of tire manufacturing equipment paired with each of a plurality of splices of the tire, wherein the machine learning model is one of the plurality of machine learning models. See 12090718 claim 2: “2. The system of claim 1, further comprising: a model generation component having one or more processors to construct a plurality of machine learning models for each of the one or more pieces of tire manufacturing equipment paired with each of a plurality of splices of the tire, wherein the machine learning model corresponds to one of the plurality of machine learning models.”
Regarding claim 4, 12090718 claims:
4. The system of claim 1, wherein the machine learning model is a first machine learning model of a plurality of machine learning models, the one or more processors further caused to: See 12090718 claim 3: “3. The system of claim 1, wherein the machine learning model is a first machine learning model of a plurality of machine learning models, the one or more processors further caused to: ”
construct the first machine learning model of the plurality of machine learning models for a first pressure roller component of the one or more pieces of tire manufacturing equipment and a first splice of the tire; See 12090718 claim 3: “construct the first machine learning model of the plurality of machine learning models for a first pressure roller component of the one or more pieces of tire manufacturing equipment and the first splice of the tire;”
construct a second machine learning model of the plurality of machine learning models for the first pressure roller component and a second splice of the tire; construct a third machine learning model of the plurality of machine learning models for the first pressure roller component and a third splice of the tire; construct a fourth machine learning model of the plurality of machine learning models for a second pressure roller component and the first splice of the tire; construct a fifth machine learning model of the plurality of machine learning models for the second pressure roller component and the second splice of the tire; construct a sixth machine learning model of the plurality of machine learning models for the second pressure roller component and the third splice of the tire; construct a seventh machine learning model of the plurality of machine learning models for a third pressure roller component and the first splice of the tire; construct an eighth machine learning model of the plurality of machine learning models for the third pressure roller component and the second splice of the tire; and construct a ninth machine learning model of the plurality of machine learning models for the third pressure roller component and the third splice of the tire. See 12090718 claim 3: “construct a second machine learning model of the plurality of machine learning models for the first pressure roller component and a second splice of the tire; construct a third machine learning model of the plurality of machine learning models for the first pressure roller component and a third splice of the tire; construct a fourth machine learning model of the plurality of machine learning models for a second pressure roller component and the first splice of the tire; construct a fifth machine learning model of the plurality of machine learning models for the second pressure roller component and the second splice of the tire; construct a sixth machine learning model of the plurality of machine learning models for the second pressure roller component and the third splice of the tire; construct a seventh machine learning model of the plurality of machine learning models for a third pressure roller component and the first splice of the tire; construct an eighth machine learning model of the plurality of machine learning models for the third pressure roller component and the second splice of the tire; and construct a ninth machine learning model of the plurality of machine learning models for the third pressure roller component and the third splice of the tire.”
Regarding claim 5, 12090718 claims:
5. The system of claim 1, wherein the first parameter is one of a pressure, an orientation, an alignment, or a position value. See 12090718 claim 1, “wherein the first parameter includes an amount of pressure.”
Regarding claim 6, 12090718 claims:
6. The system of claim 1, wherein the one or more processors are further caused to train the machine learning model based on a degree to which a previous splice deviated from a predetermined splice point, a moving average of a splice point for a predetermined number of periods, or a slope of a predetermined number of moving averages of the splice point. See 12090718 claim 4, “4. The system of claim 1, wherein the one or more processors is further caused to train the machine learning model based on a degree to which splices for tires manufactured prior to the tire deviated from a predetermined splice point.” Also see claim 5, “5. The system of claim 1, wherein the one or more processors is further caused to train the machine learning model based on a result of a previous splice, a moving average of a splice point for a predetermined number of periods, or a slope of a predetermined number of moving averages of the splice point.”
Regarding claim 7, 12090718 claims:
7. The system of claim 1, wherein the one or more processors are further caused to: determine, based on a plurality of machine learning models, that the value for the splice tolerance metric predicts that a first splice of a plurality of splices of the tire exceeds a tolerance window; select, based on a policy, the first piece of equipment corresponding to the first splice of the plurality of splices of the tire; and command the first piece of equipment selected based on the policy to adjust a characteristic of the first splice to bring the first splice within the tolerance window. See 12090718 claim 6, “6. The system of claim 1, wherein the one or more processors is further caused to: determine, based on a plurality of machine learning models, that the value for the splice tolerance metric predicts that the first splice of the plurality of splices of the tire exceeds the tolerance window; select, based on a policy, the at least one piece of equipment corresponding to the first splice of the plurality of splices of the tire; and command the at least one piece of equipment selected based on the policy to adjust a characteristic of the first splice to bring the first splice within the tolerance window.”
Regarding claim 8, 12090718 claims:
8. The system of claim 1, wherein the one or more processors are further caused to: receive, from the one or more pieces of tire manufacturing equipment, a time series of the one or more values; determine, based on the time series of the one or more values, a moving average over a plurality of predetermined periods, wherein the plurality of predetermined periods comprise at least two different predetermined periods; determine a slope of the moving average over the plurality of predetermined periods; and generate the matrix based on the slope of the moving average over the plurality of predetermined periods. See 12090718 claim 7, “7. The system of claim 1, wherein the one or more processors is further caused to: receive, from the one or more pieces of tire manufacturing equipment, a time series of the one or more values; determine, based on the time series of the one or more values, a moving average over a plurality of predetermined periods, wherein the plurality of predetermined periods comprise at least two different predetermined periods; determine a slope of the moving average over the plurality of predetermined periods; and generate the matrix based on the slope of the moving average over the plurality of predetermined periods.”
Regarding claim 9, 12090718 claims:
9. The system of claim 1, wherein the one or more processors are further caused to: identify a confidence score associated with the value for the splice tolerance metric predicted via the machine learning model; compare the confidence score with a threshold; and determine to adjust the first parameter of the first piece of equipment responsive to the confidence score satisfying the threshold. See 12090718 claim 8 , “8. The system of claim 1, wherein the one or more processors is further caused to: identify a confidence score associated with the value for the splice tolerance metric predicted via the machine learning model; compare the confidence score with a threshold; and determine to adjust the parameter of the at least one piece of equipment responsive to the confidence score satisfying the threshold.”
Regarding claim 10, 12090718 claims:
10. The system of claim 1, wherein the one or more processors are further caused to:
predict, based on an output of the machine learning model, that a splice for the tire does not satisfy a tolerance window, wherein the first parameter is adjusted based on the prediction; See 12090718 claim 1, “predicts that a first splice of a plurality of splices of the tire exceeds a tolerance window; … adjust a first parameter used by the at least one piece of equipment in the tire manufacturing plant responsive to the value of the splice tolerance metric,”
receive, subsequent to the adjustment of the first parameter, one or more updated values corresponding to the manufacture of the tire by the one or more pieces of tire manufacturing equipment; See 12090718 claim 1, “receive, subsequent to the adjustment of the first parameter, one or more values corresponding to the manufacture of the tire by the one or more pieces of tire manufacturing equipment;”
generate an updated matrix based on the one or more updated values received subsequent to the adjustment of the first parameter; See 12090718 claim 1, “generate an updated matrix based on the one or more values received subsequent to the adjustment of the first parameter;”
predict, via input of the updated matrix into the machine learning model, whether the splice for the tire satisfies the tolerance window; and See 12090718 claim 1, “predict, via input of the updated matrix into the machine learning model, that the first splice for the tire does not satisfy the tolerance window;”
when the prediction indicates that the splice for the tire does not satisfy the tolerance window subsequent to the adjustment, adjust at least one of a second parameter of the first piece of equipment or a parameter of a second piece of equipment. See 12090718 claim 1, “adjust, responsive to the prediction that the first splice for the tire does not satisfy the tolerance window subsequent to the adjustment, at least one of a second parameter of the at least one piece of equipment or a parameter of a second piece of equipment of the tire manufacturing plant.”
Regarding claim 11, 12090718 claims:
11. A method of manufacturing a tire, the method comprising: See 12090718 claim 9, “9. A method of manufacturing a tire, the method comprising:”
12090718 claim 9 is similar to 12090718 claim 1. Further limitations of claim 11 are found in 12090718 claim 9 in a similar manner as claim 1 as addressed above.
Regarding claims 12-20:, 12090718 claims:
12090718 claims 10-16 are similar to 12090718 claims 2-8. Further limitations of claims 12-20 are found in 12090718 claims 10-16 in a similar manner as claims 2-10 as addressed above.
Claim Rejections - 35 USC § 103
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.
Claims 1-3, 5-7, 10-13, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2007/0023952 by Bull et al. (“Bull”) in view of U.S. Patent Application Publication 2020/0141653 by Kucera et al. (“Kucera”).
Regarding claim 1, Bull discloses:
1. A system to manufacture a tire, the system comprising: one or more processors and one or more memories, the one or more memories configured to store instructions that are executable by the one or more processors and cause the one or more processors to: See Bull, ¶ 0021, e.g. “A tire belt making system according to the concepts of the present invention is generally indicated by the numeral 10 in the drawings. The belt making system employs a central control unit 14 that electronically controls various systems.” Also see ¶ 0024, e.g. “The central control unit 14 may be any electronic device such as a computer or a programmable logic controller, which is capable of receiving and executing computer-readable media tangibly embodying a program of instructions.”
receive, from one or more sensors of one or more pieces of tire manufacturing equipment, one or more values corresponding to manufacturing of a tire; See Bull, ¶ 0023, e.g. “The width measurement device 18 of the present invention may employ one or more sensors mounted in any fashion that places the sensor in sensing relation to the fiber reinforced strip 20.”
… a value for a splice tolerance metric for the tire; Bull, ¶ 0026, “… the software in the central control unit 14 may include a range within which the strip width W may vary …”
Bull does not expressly disclose the following limitations. However, they are taught by Kucera:
generate a matrix based on the one or more values; See Kucera, ¶ 0031, e.g. “observation vectors are combined into a matrix.”
predict, via input of the matrix into a machine learning model, a value for a control process … See Kucera, ¶ 0031, e.g. “Machine learning (ML) may be a tool for automated monitoring of industrial assets. … The result of this computation may be used similarly to a look-up table in order to predict the value of an observation based on the other measurement.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Bull’s tire metrics with Kucera’s industrial machine learning tools in order to predict process faults and improve product quality as suggested by Kucera (see ¶ 0018 and 0031).
determine, based on the value for the splice tolerance metric, a first parameter of a first piece of the one or more pieces of tire manufacturing equipment to adjust; and provide a command to adjust the first parameter of the first piece of equipment responsive to determining the value of the splice tolerance metric. See Bull, ¶ 0024, “The central control unit 14 then relays a command to the cutting station 22, commanding the conveyor 16a to index this new distance for subsequent splices.” Also ¶ 0026, e.g. “If the strip width W is larger than the previous cycle, the conveyor index I is increased by a calculated amount that maintains the same splice overlap amount 108. … Depending upon system precision, the software in the central control unit 14 may include a range within which the strip width W may vary without making adjustments to the index distance I.”
Regarding claim 2, Bull also discloses:
2. The system of claim 1, wherein the one or more processors are further caused to determine one or more metrics based on the one or more values, wherein generating the matrix is further based on the one or more metrics. See Bull, ¶ 0023, e.g. “The sensor may be located in a position that allows the sensor to measure the strip 20 as it exits the cooling drums 24.”
Regarding claim 3, Bull and Kucera also teach:
3. The system of claim 1, wherein the one or more processors are further caused to construct a plurality of machine learning models for each of the one or more pieces of tire manufacturing equipment paired with each of a plurality of splices of the tire, wherein the machine learning model is one of the plurality of machine learning models. See Kucera, Fig. 3, depicting the use of multiple models for various equipment.
Regarding claim 5, Bull also discloses:
5. The system of claim 1, wherein the first parameter is one of a pressure, an orientation, an alignment, or a position value. Bull, ¶ 0031, “pressure.” Also ¶ 0036, “conveyor index distances I.”
Regarding claim 6, Bull and Kucera also teach:
6. The system of claim 1, wherein the one or more processors are further caused to train the machine learning model based on a degree to which a previous splice deviated from a predetermined splice point, a moving average of a splice point for a predetermined number of periods, or a slope of a predetermined number of moving averages of the splice point. See Bull, ¶ 0026 as cited above, e.g. ““If the strip width W is larger than the previous cycle.” Also see Kucera, ¶ 0027, e.g. “Historical data 36, saved on DAQ 33, may be used to train the machine learning algorithm 35. … Once such learning algorithm is advanced pattern recognition (APR), which is used to detect when runtime data statistically deviates from the data that the model was trained on.” Also Kucera, ¶ 0031.
Regarding claim 7, Bull and Kucera also teach:
7. The system of claim 1, wherein the one or more processors are further caused to: determine, based on a plurality of machine learning models, that the value for the splice tolerance metric predicts that a first splice of a plurality of splices of the tire exceeds a tolerance window; See Bull, ¶ 0022, “The central control unit 14 evaluates the data and determines if the strip width W is within a predetermined range.” Also see Kucera, ¶ 0027, e.g. “If predictions 38 differ from the measured variables by more than a certain threshold, an alarm may be issued.”
select, based on a policy, the first piece of equipment corresponding to the first splice of the plurality of splices of the tire; and command the first piece of equipment selected based on the policy to adjust a characteristic of the first splice to bring the first splice within the tolerance window. See Bull, ¶ 0026, e.g. “If the strip width W is larger than the previous cycle, the conveyor index I is increased by a calculated amount that maintains the same splice overlap amount 108. … Depending upon system precision, the software in the central control unit 14 may include a range within which the strip width W may vary without making adjustments to the index distance I.”
Regarding claim 10, Bull and Kucera also teach:
10. The system of claim 1, wherein the one or more processors are further caused to: predict, based on an output of the machine learning model, that a splice for the tire does not satisfy a tolerance window, wherein the first parameter is adjusted based on the prediction; See Bull, ¶ 0026 and Kucera, ¶ 0031.
receive, subsequent to the adjustment of the first parameter, one or more updated values corresponding to the manufacture of the tire by the one or more pieces of tire manufacturing equipment; See Bull, Fig. 3, depicting cyclical process monitoring.
generate an updated matrix based on the one or more updated values received subsequent to the adjustment of the first parameter; See Kucera, ¶ 0031, e.g. “APR may use historical time-series observations taken from a number of conventional point sensors (such as, for flow measurements, temperatures, static pressures, and so on, as well as advanced sensors such as acoustic and video) and combine them into a multidimensional vector of observations in time. These observation vectors are combined into a matrix, and a nonlinear pattern recognition operator is be applied to this matrix in a process analogous to matrix inversion. The result of this computation may be used similarly to a look-up table in order to predict the value of an observation based on the other measurements.”
predict, via input of the updated matrix into the machine learning model, whether the splice for the tire satisfies the tolerance window; and See Kucera, ¶ 0031, e.g. “Machine learning (ML) may be a tool for automated monitoring of industrial assets. … The result of this computation may be used similarly to a look-up table in order to predict the value of an observation based on the other measurement.”
when the prediction indicates that the splice for the tire does not satisfy the tolerance window subsequent to the adjustment, adjust at least one of a second parameter of the first piece of equipment or a parameter of a second piece of equipment. See Bull, ¶ 0026 and Kucera, ¶ 0031 as cited above. Also see Bull, ¶ 0027, e.g. “the central control unit 14 monitors excess strip length and, if needed, communicates with the cooling drums 24, to adjust the cooling drum speed.”
Regarding claim 11, Bull discloses:
11. A method of manufacturing a tire, the method comprising: See Bull, Fig. 3, broadly depicting a method.
All further limitations of claim 11 have been addressed in the above rejection of claim 1.
Regarding claims 12-13, 15-17 and 20, parent claim 11 is addressed above.
All further limitations of claims 12-13, 15-17 and 20 have been addressed in the above rejections of claims 2-3, 5-7 and 10, respectively.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bull in view of Kucera as applied above, and further in view of U.S. Patent Application Publication 2006/0137804 by Downing (“Downing”).
Regarding claim 4, Bull and Kucera also teach:
4. The system of claim 1, wherein the machine learning model is a first machine learning model of a plurality of machine learning models, the one or more processors further caused to: construct the first machine learning model of the plurality of machine learning models for a … component of the one or more pieces of tire manufacturing equipment and a first splice of the tire; construct a second machine learning model of the plurality of machine learning models for the … component and a second splice of the tire; construct a third machine learning model of the plurality of machine learning models for the … component and a third splice of the tire; See Bull, ¶ 0022, e.g. “modifying the index distance I that the belt conveyor 16a advances after each splice.” Also ¶ 0024, e.g. “subsequent splices.” Also see Fig. 3, depicting a cyclical flowchart for analyzing splices. Note that the cyclical nature of the flowchart allows for multiple, e.g. three, splices. Additionally, Bull teaches the use of multiple sensors for monitoring components. See Fig. 2, elements 18, 27, and 46. Also, Kucera teaches utilization of multiple models. See Kucera, Fig. 3, depicting utilization of multiple models along with ¶ 0028, e.g. “Creating a submodel for each combustion process may allow faults to be better isolated.”
Bull and Kucera do not expressly teach: first pressure roller component. However, this is taught by Downing. See Downing, Fig. 5, element 62, depicting a first pressure roller component. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Bull’s components with Downing’s rollers in order to form elastomer material into a strip as suggested by Downing (see ¶ 0047)
construct a fourth machine learning model of the plurality of machine learning models for a … component and the first splice of the tire; construct a fifth machine learning model of the plurality of machine learning models for the … component and the second splice of the tire; construct a sixth machine learning model of the plurality of machine learning models for the … component and the third splice of the tire; See Bull, Figs. 2 and 3 and ¶ 0022 and 0024 along with Kucera, Fig. 3 and ¶ 0028 as noted above.
Bull and Kucera do not expressly teach: second pressure roller component. However, this is taught by Downing. See Downing, Fig. 5, element 64, depicting a second pressure roller component.
construct a seventh machine learning model of the plurality of machine learning models for a … component and the first splice of the tire; construct an eighth machine learning model of the plurality of machine learning models for the … component and the second splice of the tire; and construct a ninth machine learning model of the plurality of machine learning models for the … component and the third splice of the tire. See Bull, Figs. 2 and 3 and ¶ 0022 and 0024 along with Kucera, Fig. 3 and ¶ 0028 as noted above.
Bull and Kucera do not expressly teach: third pressure roller component. However, this is taught by Downing. See Downing, Fig. 5, element 68, depicting a third pressure roller component.
Regarding claim 14, parent claim 11 is addressed above.
All further limitations of claim 14 have been addressed in the above rejection of claim 4.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bull in view of Kucera as applied above, and further in view of U.S. Patent 10,725,463 to Dixit (“Dixit”).
Regarding claim 8, Bull and Kucera also teach:
8. The system of claim 1, wherein the one or more processors are further caused to:
receive, from the one or more pieces of tire manufacturing equipment, a time series of the one or more values; See Kucera, ¶ 0031, e.g. “historical time-series observations.”
Bull and Kucera do not expressly teach:
determine, based on the time series of the one or more values, a moving average over a plurality of predetermined periods, wherein the plurality of predetermined periods comprise at least two different predetermined periods; determine a slope of the moving average over the plurality of predetermined periods; and generate the matrix based on the slope of the moving average over the plurality of predetermined periods. However, this is taught by Dixit. See Dixit, col. 14, lines 19-22, e.g. “The data fusion module 1170 (see FIG. 13 for a more detailed description) maps the incoming sensor data streams within a moving time window into groups of sensor data that are correlated.” Also see col. 20, lines 27-33, e.g. “The fact that the baseline slope is slowly increasing during large samplings (large number of sequential moving average calculations) of the parameter values indicates an off-nominal behavior, i.e., a persistent-shift in the moving averages (note in all curves). This is registered (tagged) as an anomaly as well as a degradation event in the corresponding parameter data.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the time series data matrix of Bull and Kucera with Dixit’s moving average slope data in order to identify off-nominal behavior as suggested by Dixit.
Regarding claim 18, parent claim 11 is addressed above.
All further limitations of claim 18 have been addressed in the above rejection of claim 8.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bull in view of Kucera as applied above, and further in view of U.S. Patent Application Publication 2018/0037521 by Myers et al. (“Myers”).
Regarding claim 9, Bull and Kucera do not expressly teach:
9. The system of claim 1, wherein the one or more processors are further caused to: identify a confidence score associated with the value for the splice tolerance metric predicted via the machine learning model; compare the confidence score with a threshold; and determine to adjust the first parameter of the first piece of equipment responsive to the confidence score satisfying the threshold. However, this is taught by Myers. See Myers, ¶ 0029, e.g. “If the confidence score is greater than the predetermined threshold value, the vehicle computing system 206 may reduce a speed of the vehicle 202 and adjust other driving parameters as needed to facilitate safe navigation of the vehicle 202 over the bridge 300 and prevent slippage.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the splice tolerance model of Bull and Kucera with Myer’s confidence score in order to evaluate performance as essentially suggested by Myers.
Regarding claim 19, parent claim 11 is addressed above.
All further limitations of claim 19 have been addressed in the above rejection of claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 20210245465 by Van Der Made et al. See ¶ 0086, “Alternatively, the measurements can be used as dataset for a machine-learning and/or artificial intelligence program to automatically adjust and/or control the building parameters and/or the production mode PM.”
U.S. Patent Application Publication 20190155262 by Sundar et al. See ¶ 0031, “The decision tree algorithm may generate predictions for potential failure of the shop floor manufacturing machines 203, and the shop floor manufacturing machines 203 are controlled and/or fixed to prevent failure.” ¶ 0039, “Also, operational behavior of the machine may be controlled to allow the sensor data to fall within the preferred range.”
U.S. Patent Application Publication 20120267031 by Mawby et al. See Mawby ¶ 0033, “The challenge is to estimate the gain vectors, the product vectors, the tooling vectors and the intercept vector. This is accomplished by vector rotation and regression analysis.” Also see ¶ 0040-0041, “Once these data have been acquired for a suitable sample set of tires, the harmonic data are extracted from the RRO waveforms. … When the predictive coefficients vectors (a,b), (c,d), (e,f), (g,h), (i,j), (k,l), (m,n), (o,p), (q,r), and (Ilx,Ily) are known, the matrix equation provides a modeling equation by which the FRH1 vector for an individual tire may be estimated.” Also ¶ 0043, “The final step is to apply the model to optimize the RRO of individual tires as they are manufactured according to the illustration shown in FIG. 4.” Also ¶ 0045, “Because the green RRO is continuously measured, the model may be updated at periodic intervals with these new production data so as to adjust the predictive equations for changes in the process.” ¶ 0045, “Because the green RRO is continuously measured, the model may be updated at periodic intervals with these new production data so as to adjust the predictive equations for changes in the process.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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/James D. Rutten/Primary Examiner, Art Unit 2121
