DETAILED ACTION
1. Claims 1-4, and 6-20 have been presented for examination.
Claim 5 has been cancelled.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made that this application is a 371 of PCT/JP2021/006534 filed 02/22/2021.
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to JAPAN 2020-033208 filed 02/28/2020.
Response to Arguments
4. Applicant's arguments filed 11/25/25 have been fully considered but they are not persuasive.
i) Following Applicants arguments and amendments the previously presented 101 rejection is WITHDRAWN.
ii) Following Applicants arguments and amendments the prior art rejection has been modified, see below. With respect to Applicants arguments that Singh does not teach “the second air temperature data that is after the prediction time being predicted temperatures based on historical air temperatures.” The Examiner notes that Figure 8 shows historical values of each seasons wear. These values include weather as can be seen in at least [0070] The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Singh clearly denotes the aspect of ambient temperature/weather as a predictor for tire wear and uses said value in the calculation. This is particularly relevant as the rejection has been made under 103 in view of Applicants amendments. Therefore a prior art rejection is MAINTAINED.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 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.
5. Claims 1-4, 6-7, and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 20180272813 hereafter, Singh, in view of Zakrajsek, Andrew J., et al. "Improved aircraft tire life through laboratory tire wear testing and computational modeling." 56th AIA A/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2015, hereafter Zakrajsek.
Regarding Claim 1: The reference discloses A wear state prediction method for predicting a wear state of an aircraft tire, comprising:
generating a model for predicting the wear state as an objective variable by using a predetermined algorithm in which air temperature data relating to an aircraft acquired in advance is set as an explanatory variable;
inputting the air temperature data to the generated model and predicting the wear state. (Singh. “[0070] Turning to FIG. 14, the second embodiment of the wear estimation system 100 may be implemented using a cloud-based server 110. More particularly, sensors on the tire 12 and/or the vehicle 10 are a first source 114 that measure real-time predictors 102, which are wirelessly transmitted by means known in the art 112 to the server 110. The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Still other selected predictors 52 for estimation of the wear rate 60, such as tread compound data 82 and tread structure data 84, may be sent from a third source 118 to the server 110. On the server 110, the predictors 52 are input into the model 86 for estimation of the wear rate 60, which is integrated with the real-time predictors 102 to yield the predicted wear state 104. The predicted wear state 104 is wirelessly transmitted by means known in the art 112 to a device 120 for display to a user or a technician, such as a smartphone.”) and
wherein the air temperature data relating to the aircraft is defined as first air temperature data; and (Singh. “[0070] Turning to FIG. 14, the second embodiment of the wear estimation system 100 may be implemented using a cloud-based server 110. More particularly, sensors on the tire 12 and/or the vehicle 10 are a first source 114 that measure real-time predictors 102, which are wirelessly transmitted by means known in the art 112 to the server 110. The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Still other selected predictors 52 for estimation of the wear rate 60, such as tread compound data 82 and tread structure data 84, may be sent from a third source 118 to the server 110. On the server 110, the predictors 52 are input into the model 86 for estimation of the wear rate 60, which is integrated with the real-time predictors 102 to yield the predicted wear state 104. The predicted wear state 104 is wirelessly transmitted by means known in the art 112 to a device 120 for display to a user or a technician, such as a smartphone.”)
when predicting the wear state, data including second air temperature data that is after a prediction time and the first air temperature data are inputted to the model and the wear state is predicted; and (Singh. “[0070] Turning to FIG. 14, the second embodiment of the wear estimation system 100 may be implemented using a cloud-based server 110. More particularly, sensors on the tire 12 and/or the vehicle 10 are a first source 114 that measure real-time predictors 102, which are wirelessly transmitted by means known in the art 112 to the server 110. The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Still other selected predictors 52 for estimation of the wear rate 60, such as tread compound data 82 and tread structure data 84, may be sent from a third source 118 to the server 110. On the server 110, the predictors 52 are input into the model 86 for estimation of the wear rate 60, which is integrated with the real-time predictors 102 to yield the predicted wear state 104. The predicted wear state 104 is wirelessly transmitted by means known in the art 112 to a device 120 for display to a user or a technician, such as a smartphone.”)
the second air temperature data that is after the prediction time being predicted temperatures based on historical air temperatures. (Figure 8. See also [0070] The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110.)
Singh does not explicitly recite dismounting the aircraft tire in the aircraft based on the predicted wear state.
However Zakrajsek discloses dismounting the aircraft tire in the aircraft based on the predicted wear state. (Zakrajsek, page 11, bottom, “Maximum Wear Level (MWL), denoted as 𝑇𝑊𝐸𝑇𝑜𝑡𝑎𝑙, divided by weight loss (tread material worn off) vs. the 168i cycles to reach the MWL for a variety of aircraft platforms and tires. The tire MWL is the ultimate cord level the tire is allowed to wear to before it needs to be replaced, normally marked with a MWL=1 (1st structural ply).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the replacement of a tire based on wear state prediction of Zakrajsek with the tire modeling of Singh since, as per Singh, “[0002] Tire wear plays an important role in vehicle factors such as safety, reliability, and performance. Tread wear, which refers to the loss of material from the tread of the tire, directly affects such vehicle factors. As a result, it is desirable to monitor and/or measure the amount of tread wear experienced by a tire.”
Regarding Claim 2: Singh does not explicitly recite The wear state prediction method according to claim 1, wherein the wear state is wear rate defined as a parameter of wear amount of the aircraft tire divided by the cumulative value for flights.
However Zakrajsek discloses The wear state prediction method according to claim 1, wherein the wear state is wear rate defined as a parameter of wear amount of the aircraft tire divided by the cumulative value for flights. (Page 16-17, “Using these simplified predictive models, in Fig. 11-14, a tire wear testing profile can be developed to estimate fielded landings. Taking the information from Fig. 12 and Fig. 13, an estimated 𝑇𝑊𝐸𝑇𝑜𝑡𝑎𝑙 can be approximated. The approximated 𝑇𝑊𝐸𝑇𝑜𝑡𝑎𝑙 can still be split into DWE and SWE based on the discussion of the SAE AIR 5797. Utilizing this, an understanding of the approximate energy to wear the tire to the MWL can be selected for the new tire. Following this, a prediction for the amount of landings the tire will see in the field would be made using Fig. 14. Again, using the known aircraft, brake, and tire system level parameters, an estimation of the fielded landings can be completed.” See also equation 17)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the wear state prediction of Zakrajsek with the tire modeling of Singh since it “can then be used to provide realistic tire life predictions to the field.” (Zakrajsek, top of page 17)
Regarding Claim 3: Singh does not explicitly recite The wear state prediction method according to claim 2, wherein the wear amount is predicted by multiplying the cumulative value for the flights to the wear rate predicted.
However Zakrajsek discloses The wear state prediction method according to claim 2, wherein the wear amount is predicted by multiplying the cumulative value for the flights to the wear rate predicted. (Zakrajsek. Page 11, bottom, “Figure 11 shows the normalized TWE to reach the Maximum Wear Level (MWL), denoted as 𝑇𝑊𝐸𝑇𝑜𝑡𝑎𝑙, divided by weight loss (tread material worn off) vs. the 168i cycles to reach the MWL for a variety of aircraft platforms and tires. The tire MWL is the ultimate cord level the tire is allowed to wear to before it needs to be replaced, normally marked with a MWL=1 (1st structural ply).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the wear amount of Zakrajsek with the tire modeling of Singh since it “can then be used to provide realistic tire life predictions to the field.” (Zakrajsek, top of page 17)
Regarding Claim 4: The reference discloses The wear state prediction method according to claim 2, wherein a usable number of times of the aircraft tire is predicted by using a groove depth of the aircraft tire acquired in advance when the aircraft tire is new and the wear rate predicted. (Singh, “[0053] A first one of the predictors 52 for the tire wear estimation system 50 includes vehicle effects 54. More particularly, one vehicle effect 54 is a wheel position 56 on the vehicle 10. The vehicle 10 includes four different wheel positions 56: driver side or left side front, passenger side or right side front, driver side or left side rear, and passenger side or right side rear. The tire 12 at each wheel position 56 experiences a different wear pattern, which leads to different tread wear. For example, as shown in FIG. 2, each wheel position 56 of left front (LF), right front (RF), left rear (LR) and right rear (RR) undergoes different tread wear, as indicated by the tread depth, as the vehicle 10 is driven. Therefore, the wheel position 56 is one of the predictors 52 to be input into the tire wear estimation system 50.”)
Regarding Claim 6: The reference discloses The wear state prediction method according to claim 1, wherein the first air temperature data relating to the aircraft is acquired by a sensor installed on the aircraft. (Singh. “[0070] Turning to FIG. 14, the second embodiment of the wear estimation system 100 may be implemented using a cloud-based server 110. More particularly, sensors on the tire 12 and/or the vehicle 10 are a first source 114 that measure real-time predictors 102, which are wirelessly transmitted by means known in the art 112 to the server 110. The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Still other selected predictors 52 for estimation of the wear rate 60, such as tread compound data 82 and tread structure data 84, may be sent from a third source 118 to the server 110. On the server 110, the predictors 52 are input into the model 86 for estimation of the wear rate 60, which is integrated with the real-time predictors 102 to yield the predicted wear state 104. The predicted wear state 104 is wirelessly transmitted by means known in the art 112 to a device 120 for display to a user or a technician, such as a smartphone.”)
Regarding Claim 7: The reference discloses The wear state prediction method according to claim 1, wherein the model is generated by using the predetermined algorithm using the air temperature data relating to the aircraft and acceleration data of the aircraft acquired in advance as the explanatory variables. (Singh. “[0057] The driver severity 66 takes into account the driving style of the driver of the vehicle 10. More aggressive driving, such as aggressive starts and stops, generates more frictional energy, which increases tire force and increases tread wear. As shown in FIG. 6, the driver severity 66 may be expressed as the force severity on the tire 10. Calculation of the force severity on the tire 10 may be done through a variety of techniques. One exemplary technique is described in U.S. patent application Ser. No. 14/918,928, which is owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and is incorporated herein by reference. FIG. 6 is a graphical representation showing the relationship between tread wear and tire force severity, which indicates that a higher driver severity 66 creates more tire wear.” “[0070] Turning to FIG. 14, the second embodiment of the wear estimation system 100 may be implemented using a cloud-based server 110. More particularly, sensors on the tire 12 and/or the vehicle 10 are a first source 114 that measure real-time predictors 102, which are wirelessly transmitted by means known in the art 112 to the server 110. The tire sensor 24 may also transmit certain selected predictors 52, such as the ambient temperature 78 and tire identification data, to the server 110. Other selected predictors 52 for estimation of the wear rate 60, such as location, weather, and road condition data, may be transmitted from a second source 116 to the server 110. Still other selected predictors 52 for estimation of the wear rate 60, such as tread compound data 82 and tread structure data 84, may be sent from a third source 118 to the server 110. On the server 110, the predictors 52 are input into the model 86 for estimation of the wear rate 60, which is integrated with the real-time predictors 102 to yield the predicted wear state 104. The predicted wear state 104 is wirelessly transmitted by means known in the art 112 to a device 120 for display to a user or a technician, such as a smartphone.”)
Regarding Claim 10: The reference discloses A wear state prediction device for predicting a wear state of an aircraft tire, comprising: a model generation unit that generates a model for predicting the wear state as an objective variable by using a predetermined algorithm in which air temperature data relating to the aircraft acquired in advance is set as an explanatory variable; and a prediction unit that inputs the air temperature data to the model generated by the model generation unit and predicts the wear state. (See rejection for claim 1)
Regarding Claim 11: The reference discloses A wear state prediction program for predicting a wear state of an aircraft tire, the wear state prediction program causes a computer of the terminal device to execute the steps of: generating a model for predicting the wear state as an objective variable by using a predetermined algorithm in which air temperature data relating to the aircraft acquired in advance is set as an explanatory variable; and inputting the air temperature data to the model and predicting the wear state. (See rejection for claim 1)
Regarding Claim 12: The reference discloses The wear state prediction method according to claim 3, wherein a usable number of times of the aircraft tire is predicted by using a groove depth of the aircraft tire acquired in advance when the aircraft tire is new and the wear rate predicted. (See rejection for claim 4)
Regarding Claim 13: The reference discloses The wear state prediction method according to claim 2, wherein the air temperature data relating to the aircraft is defined as first air temperature data; and when predicting the wear state, data including second air temperature data that is after a prediction time and the first air temperature data are inputted to the model and the wear state is predicted. (See rejection for claim 5)
Regarding Claim 14: The reference discloses The wear state prediction method according to claim 3, wherein the air temperature data relating to the aircraft is defined as first air temperature data; and when predicting the wear state, data including second air temperature data that is after a prediction time and the first air temperature data are inputted to the model and the wear state is predicted. (See rejection for claim 5)
Regarding Claim 15: The reference discloses The wear state prediction method according to claim 4, wherein the air temperature data relating to the aircraft is defined as first air temperature data; and when predicting the wear state, data including second air temperature data that is after a prediction time and the first air temperature data are inputted to the model and the wear state is predicted. (See rejection for claim 5)
Regarding Claim 16: The reference discloses The wear state prediction method according to claim 2, wherein the air temperature data relating to the aircraft is acquired by a sensor installed on the aircraft. (See rejection for claim 6)
Regarding Claim 17: The reference discloses The wear state prediction method according to claim 3, wherein the air temperature data relating to the aircraft is acquired by a sensor installed on the aircraft. (See rejection for claim 6)
Regarding Claim 18: The reference discloses The wear state prediction method according to claim 4, wherein the air temperature data relating to the aircraft is acquired by a sensor installed on the aircraft. (See rejection for claim 6)
Regarding Claim 19: The reference discloses The wear state prediction method according to claim 1, wherein the air temperature data relating to the aircraft is acquired by a sensor installed on the aircraft. (See rejection for claim 6)
Regarding Claim 20: The reference discloses The wear state prediction method according to claim 2, wherein the model is generated by using the predetermined algorithm using the air temperature data relating to the aircraft and acceleration data of the aircraft acquired in advance as the explanatory variables. (See rejection for claim 7)
6. Claim(s) 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Singh in view of Zakrajsek further in view of Thomas et al. U.S. Patent Publication No. 20070279203, hereafter Thomas.
Regarding Claim 8: Singh and Zakrajsek do not explicitly recite The wear state prediction method according to claim 6, wherein the air temperature data related to the aircraft is an average of a part of air temperature data for each flight.
However Thomas discloses The wear state prediction method according to claim 6, wherein the air temperature data related to the aircraft is an average of a part of air temperature data for each flight. (Thomas “[0054] In another embodiment, a or ultrasonic sensor can be employed in the inside tire sensor module 504 to determine if the tires are under-inflated, over-inflated or properly inflated. The sensor can be made to point directly at the center of the tire 502 that makes contact with the road. If the distance from the TSM 504 to the tire center increases (e.g., calibrated for the correct pressure) the tire can be over inflated, and if the distance is less that the calibrated distance the tire can be under inflated. It is to be appreciated that the tire 502 is a dynamic system and can change based on various conditions. The algorithm that is used to make the various calculations can compensate for those conditions, for example, vehicle loading, vehicle speed, ambient conditions (e.g., temperature, pressure, humidity, etc.), road conditions, bumps, uneven terrain, etc., by taking average or comparative readings.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize an average temperature value as per Thomas for the wear state prediction in Singh and Zakrajsek in order to support a “dynamic system” that “can change based on various conditions and can compensate for those conditions…” (Thomas [0054])
Regarding Claim 9: Singh and Zakrajsek do not explicitly recite The wear state prediction method according to claim 1, wherein the period in which the air temperature data relating to the aircraft is stored is classified into two or more different periods, and the model is generated by using a plurality of classified periods.
However Thomas discloses The wear state prediction method according to claim 1, wherein the period in which the air temperature data relating to the aircraft is stored is classified into two or more different periods, and the model is generated by using a plurality of classified periods. (Thomas “[0054] In another embodiment, a or ultrasonic sensor can be employed in the inside tire sensor module 504 to determine if the tires are under-inflated, over-inflated or properly inflated. The sensor can be made to point directly at the center of the tire 502 that makes contact with the road. If the distance from the TSM 504 to the tire center increases (e.g., calibrated for the correct pressure) the tire can be over inflated, and if the distance is less that the calibrated distance the tire can be under inflated. It is to be appreciated that the tire 502 is a dynamic system and can change based on various conditions. The algorithm that is used to make the various calculations can compensate for those conditions, for example, vehicle loading, vehicle speed, ambient conditions (e.g., temperature, pressure, humidity, etc.), road conditions, bumps, uneven terrain, etc., by taking average or comparative readings.” [0029] “In one example, a contactless tire parameter sensor only acquires tire data during the respective predetermined time interval, however, in alternative embodiments the sensor may periodically activate, acquire data and save such data locally, either at the TSM 102 in the storage memory 104 or in the central control unit 106. Subsequently, during the predetermined time interval, multiple segments of tire data can be transmitted. It is to be appreciated, that although this disclosure refers to contactless sensors, the invention can make use of contact type sensors as well, for example, load cells, thermocouples, and the like.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize different time periods as per Thomas for the wear state prediction in Singh and Zakrajsek in order to support a “dynamic system” that “can change based on various conditions and can compensate for those conditions…” (Thomas [0054])
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
8. All Claims are rejected.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent Publication No. 20050150283
ii) U.S. Patent Publication No. 20020104717
iii) U.S. Patent Publication No. 20170323403
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2188