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 .
Status of Claims
Claims 1, 3-11, 13-17, and 19-20 were previously pending. Claims 1, 7, 11, 15, 17, and 20 have been amended. No claims have been cancelled. Claim 21 has been newly added. Accordingly, claims 1, 3-11, 13-17, and 19-21 are currently pending and have been examined in this application.
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, 3-11, 13-14, 16-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Patnaik (US 2021/0181737 A1) [disclosed by applicant on IDS filed 04/08/2025] in view of Sogabe (JP 2001-180239 A, a machine translation is attached and is being relied upon) and Raje (US 2022/0326703 A1).
Regarding claim 1, Patnaik discloses:
A vehicle comprising: (Patnaik - vehicle -> Fig. 1A-1C, Par. 23-28)
a camera configured to capture a plurality of images of a tire; (Patnaik - camera capturing plurality of images of a tire -> Par. 27, Par. 46, Par. 51, Par. 68, Par. 74)
a memory storing instructions; (Patnaik - memory -> Par. 30-34)
one or more processors configured to access the memory and execute the instructions to, during a trip of the vehicle: (Patnaik - processors -> Par. 30-34)
receive a first tire image of the plurality of images; (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 68)
receive a second tire image of the plurality of images, wherein the second tire image is captured subsequent to the first tire image; (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 68)
compare the first and second tire images; (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 68)
detect a change in a shape of the tire from the comparison of the first and second tire images; (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 68)
detect a change in compression of one or more other tires; (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B (tire with abnormal compression 502), Par. 8-9, Par. 56-59, Par. 68)
input the detected change in the shape of the tire and the detected change in compression of the one or more other tires into a trained machine learning model; (Patnaik – trained machine learning model -> Par. 7-8, Par. 56, Par. 72-74)
and control operation of the vehicle based on the type of tired-related irregularity. (Patnaik - control operation -> Par. 25, Par. 33, Par. 35-36)
Patnaik does not appear to explicitly disclose the change in the shape of the tire including a change in a contour of a profile of a portion of the tire, the portion of the tire being in contact with and within a threshold distance of a road on which the vehicle travels; receive, as output from the trained machine learning model, a classification of a type of tire-related irregularity, from a plurality of types of tire-related irregularities, being experienced by the tire.
Sogabe teaches the change in the shape of the tire including a change in a contour of a profile of a portion of the tire, the portion of the tire being in contact with and within a threshold distance of a road on which the vehicle travels (Sogabe - The tire pressure check procedure uses a tire image obtained when the vehicle is stopped, as shown in Figure 5(a), where the tire direction and the optical axis of camera 2a (or 2c) are in the same plane. The tire images obtained from cameras 2a to 2d are processed by the air pressure suitability determination device 20, and the suitability of the air pressure is determined by measuring the external shape. -> Figs. 2a, 2b, 4a, 4b, 5a, 5b, [0027, 0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Sogabe into the invention of Patnaik with a reasonable expectation of success in order to determine when the tire pressure is unsuitable using the cameras and warn the driver, thereby preventing traffic accidents (Sogabe – [0039-0040]). Patnaik already teaches a camera that monitors the tires during driving and for detecting changes in shape of the tire. Therefore, utilizing Patnaik’s cameras that are already used for monitoring the tires to implement Sogabe’s method of detecting changes in the contour of the profile of the tire on a portion of the tire being in contact with the ground would improve the ability to identify when the tires do not have sufficient pressure and need to be replaced and doing so would yield predictable results.
Raje teaches input the detected change in the shape of the tire and the detected change in compression of the one or more other tires into a trained machine learning model; (Raje – input various information from sensors or from a database storing inspection data related to a tire -> Par. 58)
receive, as output from the trained machine learning model, a classification of a type of tire-related irregularity, from a plurality of types of tire-related irregularities, being experienced by the tire. (Raje – the tire maintenance category can include at least one of worn out category, impact damage category, or durability category… the output prediction can correspond to a maintenance category -> Par. 8, Par. 43, Par. 60)
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Raje into the invention of Patnaik with a reasonable expectation of success in order to improve inventory management of the tires, the longevity of the tires, operation efficiency, budgetary decisions influenced by tire maintenance, reducing operating costs associated with damage to the tire due to overutilization, and improve the operability of the vehicles by reducing downtime to replace a damaged tire (Raje – Par. 4).
Regarding claim 3, Patnaik discloses:
The vehicle of claim 2, wherein the one or more processors are further configured to train the machine learning model with recorded images of the plurality of types of tire-related irregularities that includes the type of tire-related irregularity. (Patnaik - images used to train model -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claim 4, Patnaik discloses:
The vehicle of claim 1, wherein the one or more processors are further configured to determine the type of tire-related irregularity based on additional sensor input. (Patnaik - additional sensor input used for irregularity determination -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claim 5, Patnaik discloses:
The vehicle of claim 1, wherein the one or more processors are further configured to detect the change in compression of the one or more other tires based on sensor input from a tire-pressure monitoring system (TPMS). (Patnaik - observed tire shape over time compared to baseline in order to determine type of irregularity -> Fig. 5A-5B (tire with abnormal compression 502), Par. 8-9, Par. 56-59, Par. 68; TPMS data used -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claim 6, Patnaik discloses:
The vehicle of claim 1, wherein the vehicle is at least one of an autonomous or semiautonomous vehicle. (Patnaik - autonomous vehicle -> Fig. 1A-1C, Par. 23-29)
Regarding claim 7, Patnaik discloses:
The vehicle of claim 1, wherein the one or more processors are further configured to determine the type of tire-related irregularity is one of a plurality of designated types of tire-related failures to communicate to a mission control center remote from the vehicle and in network communication with the vehicle (Patnaik - system (i.e. mission control center) may be onboard or remote -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74; type of tire-related irregularity from a plurality -> Par. 24, Par. 63, claim 10).
Regarding claim 8, Patnaik discloses:
The vehicle of claim 7, wherein the one or more processors are further configured to upload the first and second tire images to the mission control center. (Patnaik - system (i.e. mission control center) may be onboard or remote -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claim 9, Patnaik discloses:
The vehicle of claim 1, wherein the one or more processors are further configured to upload the first and second tire images to a mission control center. (Patnaik - system (i.e. mission control center) may be onboard or remote -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claim 10, Patnaik discloses:
The vehicle of claim 1, wherein the one or more processors are further configured to determine a rate of the change in the shape of the tire. (Patnaik - change in tire parameter may be calculated within designated timeframe (i.e. rate of change) -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74)
Regarding claims 11, 13-14, 16-17, and 19-20, all the limitations have been analyzed in view of claims 1 and 3-9, and it has been determined that claims 11, 13-14, 16-17, and 19-20 do not teach or define any new limitations beyond those previously recited in claims 1 and 3-9; therefore, claims 11, 13-14, 16-17, and 19-20 are also rejected over the same rationale as claims 1 and 3-9.
Regarding claim 21, Patnaik discloses:
The at least one computer-readable storage medium of claim 17.
Patnaik does not appear to explicitly disclose wherein the at least one processor identifies a state of inflation of the tire, based on the contour of the profile of the portion of the tire.
Sogabe teaches wherein the at least one processor identifies a state of inflation of the tire, based on the contour of the profile of the portion of the tire (Sogabe - The tire pressure check procedure uses a tire image obtained when the vehicle is stopped, as shown in Figure 5(a), where the tire direction and the optical axis of camera 2a (or 2c) are in the same plane. The tire images obtained from cameras 2a to 2d are processed by the air pressure suitability determination device 20, and the suitability of the air pressure is determined by measuring the external shape. -> Figs. 2a, 2b, 4a, 4b, 5a, 5b, [0027, 0039]).
The motivation to combine Patnaik and Sogabe is the same as in the rejection of claim 1 above.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Patnaik (US 2021/0181737 A1) [disclosed by applicant on IDS filed 04/08/2025] in view of Sogabe (JP 2001-180239 A, a machine translation is attached and is being relied upon), Raje (US 2022/0326703 A1), and Puranik (US 2020/0130420 A1).
Regarding claim 15, Patnaik discloses:
The method of claim 11, further comprising; determining whether the type of tire-irregularity is one of a plurality of designated types of tire-related irregularities failures to communicate to a mission control center remote from the vehicle and in network communication with the vehicle (Patnaik – system (i.e. mission control center) may be onboard or remote -> Fig. 5A-5B, Par. 8-9, Par. 56-59, Par. 62-68, Par. 74; type of tire-related irregularity from a plurality -> Par. 24, Par. 63, claim 10).;
Patnaik does not appear to explicitly disclose uploading the first and second tire images to the mission control center.
Purinik teaches uploading the first and second tire images to the mission control center (Purinik - The reference image data is based on a filtered subset of image data captured by a network of vehicles similar to the vehicle 10 that are each capturing image data of their own tires and uploading the data to a remote server 30. -> [0060]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Purinik into the invention of Patnaik with a reasonable expectation of success in order to provide a library of image data that can be sorted and filtered and used for determining a reference image in order to more accurately diagnose when a vehicle needs to replace or repair tires (Purinik – [0045, 0051, 0060]).
Response to Arguments
Applicant’s arguments, see page 7 filed 8/14/2026, with respect to the prior art rejections have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sogabe.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVID MEHDIZADEH whose telephone number is (571)272-7691. The examiner can normally be reached on 8:30 AM-5:00 PM.
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/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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