CTNF 18/651,027 CTNF 98550 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The Examiner has considered the 04/30/2024 IDS except for the video “Automated Car Tire Tread Depth Monitoring” by Continental Automotive Global. The Examiner was unable consider this NPL reference because the video does not appear available online. Specification Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art. Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. This application’s abstract is not in concise, narrative form. Instead, it is merely a copy of claim 1. To promote patent quality, the abstract should be corrected to follow the guidelines for abstracts laid out in the MPEP. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 3-4, 6, 9, 10-11, 13-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180268532 A1 to Wang, Jinsong et al. (“Wang”) , further in view of US 20250238942 A1 to Iwata, Mariko (“Iwata”) . Regarding claim 1, Wang teaches a system comprising a computer including a processor and memory (Wang [0031]]) , the memory storing instructions executable by the processor to: receive surface data of a tread area of a vehicle tire on a vehicle (Wang [0014]: “In various embodiments, the cameras 102 provide images of the tires 114 and/or tracks made by the tires 114, and the control system 108 analyzes the tires 114 based on the images from the cameras 102 during operation of the vehicle 100”) ; run a machine learning model on the surface data to classify tread characteristics of the tread area (Wang Claim 7: “ . . . the determining of the first tread pattern comprises determining the first tread pattern using a machine learning method . . . .”) ; Wang does not appear to expressly teach the instructions control the processor to identify uneven wear on the vehicle tire based on the classifications of tread characteristics of the tread area; identify a cause of the uneven wear on the vehicle tire based on the classification of tread characteristics of the tread area. Iwata [0162] teaches that uneven tire wear can result from over- or under-inflation or a need to rotate tires. This paragraph also teaches generating a recommended countermeasure against the uneven wear from this information. In light of Iwata, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention that Wang teaches the instructions control the processor to identify uneven wear on the vehicle tire based on the classifications of tread characteristics of the tread area (Wang [0071]: “In various embodiments, values of wear and tear (and/or other tire health values, as noted above) can be ascertained via a comparison between the first tread pattern of 420 with a known, new tire tread pattern (or a tire in good condition) from the historical database . . . .”) ; identify a cause of the uneven wear on the vehicle tire based on the classification of tread characteristics of the tread area (Wang [0072]: “In addition, in certain embodiments, other diagnosis is performed at 426 based on the tire information of 424. For example, in certain embodiments, the processor 142 diagnoses possible issues pertaining to the tires, such as possible wear, inflation, misalignment, punctures, other imperfections, and so on. Also in certain embodiments, the processor 142 diagnoses possible issues with respect to other vehicle systems that could cause and/or be affected by any tire issues, such as the suspension system or wheel 101 misalignment of the vehicle 100, and so on.”) . generate an alert indicating the cause of the uneven wear (Wang [0073]: “A determination is made at 428 as to whether a warning, notification, or other action is warranted.”) . Claim 11 is rejected over similar reasons to claim 1, applied to a method. Regarding claim 3, the above combination of Wang and Iwata teaches the system as set forth in claim 1. This combination does not appear to expressly teach wherein the instructions to identify the cause of uneven wear of the tire include instructions to compare the classifications of tread characteristics of the tread area of the tire with classifications of tread characteristics of a tread area of another tire of the vehicle (Wang [0051]: “In addition, in certain embodiments, such values (e.g., of wear and tear, and/or other health values) may also be compared to the logged historical values of the same tire (or other tires in front/rear axle of the vehicle) when it was rather new.”) . Claim 13 is rejected over similar reasons to claim 3, applied to a method. Regarding claim 4, the above combination of Wang and Iwata teaches the system as set forth in claim 1. This combination does not appear to expressly teach wherein the instructions include instructions to receive an identification of a style of the tire, and wherein the instructions to run the machine learning model includes instructions to classify the tread characteristics based on the style of tire. However, Iwata further teaches wherein the instructions include instructions to receive an identification of a style of the tire (Iwata [0118]: “The selection boxes B5 to B7 are displayed for three types of tires, for example, summer tires, winter tires, and all-season tires. The user visually confirms the tire T or determines whether the tire T is a summer tire, a winter tire, or an all-season tire by hearing from the driver, and taps any of the selection boxes B5 to B7 corresponding to the tire T.”) , and wherein the instructions to run the machine learning model includes instructions to classify the tread characteristics based on the style of tire (Iwata [0127]: “In subsequent step S12, the derivation unit 10B selects an appropriate model from the first machine learning models 130A to C and the second machine learning models 131A to C for each tire T on the basis of the received type of the tire T.”) . It would have been obvious to one of ordinary skill in the art before the effective filing date to have further combined the tire wear detection system that uses machine learning models of the above combination of Wang and Iwata with the multiple machine learning models trained specially for wear detection on different tire types of Iwata. Doing so would have improved the estimation of tire wear by allowing the system to detect wear using data optimized for particular tire types. Claim 14 is rejected over similar reasons to claim 4, applied to a method. Regarding claim 6, the above combination of Wang and Iwata teaches the system as set forth in claim 1, wherein the instructions include instructions to identify a vehicle service action based on the cause of uneven wear on the vehicle tire (Wang [0073]: “In certain embodiments, the processor 142 determines, based on the information of 424 and 426, whether . . . any remedial action may be appropriate (e.g., such as inflating the tires, rotating the tires, changing the tires, and so on).” In light of Iwata, one of ordinary skill in the art would have recognized that uneven wear can be used to recognize problems with the tire like under- or over-inflation, etc.. It follows one of ordinary skill in the art would have understood that the instructions to service the tire issued by Wang would have been based on an identified cause of uneven wear (i.e., tire inflation level or other detected issues). ) . Claim 16 is rejected over similar reasons to claim 6, applied to a method. Regarding claim 9, the above combination of Wang and Iwata teaches the computer as set forth in claim 1, wherein the surface data is an image detected by an image sensor (Wang [0026]: “The sensor array 122 includes one or more sensors for obtaining information for use by the control system 108, for example for analyzing the tires 114. Specifically, in various embodiments, the senor array 122 includes the cameras 102 as well as one or more additional detection sensors 131 (e.g., radar, lidar, sonar) . . . .”) . Regarding claim 10, the above combination of Wang and Iwata teaches the computer as set forth in claim 1, wherein the surface data is three-dimensional data detected by a lidar sensor (Wang [0026]: “The sensor array 122 includes one or more sensors for obtaining information for use by the control system 108, for example for analyzing the tires 114. Specifically, in various embodiments, the senor array 122 includes the cameras 102 as well as one or more additional detection sensors 131 (e.g., radar, lidar, sonar) . . . .” One of ordinary skill in the art at the time of filing would have recognized that LIDAR data comprises three-dimensional point clouds. ) . 07-21-aia AIA Claim s 2, 5, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180268532 A1 to Wang, Jinsong et al. (“Wang”) in view of US 20250238942 A1 to Iwata, Mariko (“Iwata”) , further in view of US 20170190223 A1 to Fish, James et al. (“Fish”) . Regarding claim 2, the above combination of Wang and Iwata teaches the system as set forth in claim 1. While teaching identification of uneven wear based on tread depth, this combination does not appear to expressly teach wherein the tread characteristics include at least one of sipe presence, inboard tread block height, outboard tread block height, and wear bar exposure. However, Fish teaches wherein the tread characteristics include at least one of sipe presence, inboard tread block height (Fish FIG. 7A-B: Inboard and outboard tread depth characteristics are broadly interpreted as tread block height. Without a reference point against which block height is measured, block height can be interpreted as tread depth. Fish depicts measurement of all inboard and outboard tread depths. ) , outboard tread block height, and wear bar exposure (Fish FIG. 7A-B: Inboard and outboard tread depth characteristics classified by a machine learning model are broadly interpreted as tread block height. Without a reference point against which block height is measured, block height can be interpreted as tread depth. ) . It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system that estimates tread wear based on sensor data used to derive tread depth of the above combination of Wang and Iwata with the system that measures inboard and outboard tread depths as part of wear determinations taught by Fish. Doing so would have improved the accuracy of the tire evaluation by providing fine-grained data about the wear pattern of the tire. This combination does not appear to expressly teach that the tread depth, as tread characteristic data, is measured by a machine learning model as required by claim 1. However, Iwata [0109] teaches a machine learning model that measures tire tread depth. It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates uneven tire wear using characteristics classified by a machine learning model taught by the above combination of Wang, Iwata, and Fish with the machine learning model of Iwata that classifies tread depth. Doing so would have improved the accuracy of the tread characteristic estimation by allowing it to consider tread depth data. Claim 12 is rejected over similar reasons to claim 2, applied to a method. Regarding claim 5, the above combination of Wang and Iwata teaches the system as set forth in claim 1. This combination does not appear to expressly teach wherein the instructions include instructions to train the machine learning model with the classification of the tread characteristics, the identification of uneven wear, and/or the identification of the cause of uneven wear. However, Fish teaches wherein the instructions include instructions to train the machine learning model with the classification of the tread characteristics (Fish [0047]: “When using the supervised machine learning algorithm, a collection of data points, such as images of tires having a variety of tire tread statuses may be provided to the remote computer 110. The remote computer 110 may then use this collection of data points to understand which tire tread depths are in good condition, which may need to be replaced, and which need to be replaced. The remote computer 110 may be constantly trained to reach more accurate determinations of tire tread status by, for example, providing additional tire tread depth images.”) , the identification of uneven wear ( The term “and/or” requires consideration of only one of the listed options. ) , and/or the identification of the cause of uneven wear ( The term “and/or” requires consideration of only one of the listed options. ) . It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the machine learning model that classifies tire tread characteristics using tire tread patterns like depth taught by the above combination of Wang and Iwata with the real-time retraining technique for machine learning models that estimate tread depth taught by Fish. Doing so would have improved tread characteristic classifications by constantly improving the model by which the characteristics are classified. Claim 15 is rejected over similar reasons to claim 1, applied to a method . 07-21-aia AIA Claim s 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180268532 A1 to Wang, Jinsong et al. (“Wang”) in view of US 20250238942 A1 to Iwata, Mariko (“Iwata”) , further in view of US 20210197625 B1 to Laperle, Ghislain et al. (“Laperle”) . Regarding claim 7, the above combination of Wang and Iwata teaches the system as set forth in claim 6. This combination does not appear to expressly teach wherein the instructions include instructions to receive service technician input verifying the vehicle service action. However, Laperle teaches wherein the instructions include instructions to receive service technician input verifying the vehicle service action (Laperle [0129]-[0130]: “If the system 100 has determined that a critical error has taken place or is imminent, it can prompt the user to establish an audiovisual and/or textual connection with a technician at 1703. . . . In the case of a video call, the technician may be able to instruct the user to point the camera of the electronic device at a specific component of the vehicle 10 in order to provide the technician with more information about the vehicle status.”) . It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the tire wear detection system that prompts user action when tire wear requires it of the above combination of Wang and Iwata with the tire wear detection system that prompts the user to contact a technician so they can verify the vehicle status of Laperle. Doing so would have improved the accuracy of the diagnosis by allowing a human technician to check the work of the tire wear monitoring system. Claim 17 is rejected over similar reasons to claim 7, applied to a method . 07-21-aia AIA Claim s 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180268532 A1 to Wang, Jinsong et al. (“Wang”) in view of US 20250238942 A1 to Iwata, Mariko (“Iwata”) , further in view of US 9805697 B1 to Dorrance, Daniel et al. (“Dorrance”) . Regarding claim 8, the above combination of Wang and Iwata teaches the system as set forth in claim 6. While teaching identifying the service action based on tread characteristics like depth, this combination does not appear to expressly teach wherein instructions to identify the vehicle service action are based on driving style of a driver of the vehicle. However, Dorrance teaches the following about tire tread depth and wear at [3:6-10]: “Tire tread depth, as an indication of tire wear, is but one indicator of the overall state of a vehicle, and can be influenced by a number of factors, including tire pressure, operator driving style, wheel alignment settings, and suspension components.” In light of Dorrance, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention that the above combination of Wang and Iwata teaches instructions to identify the vehicle service action are based on driving style of a driver of the vehicle ( The Examiner notes Dorrance is merely brought in to teach that driving style is one of the factors that influences tire tread depth over time. Thus, the tread depth measurement and wear estimation of the above combination of Wang and Iwata would have been at least based on driving style of a driver of the vehicle. ) . Claim 18 is rejected over similar reasons to claim 8, applied to a method . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hosu, Ionel-Alexandru et al.. US 20220339969 A1. SYSTEM AND METHOD FOR AUTOMATIC TREADWEAR CLASSIFICATION. Shklyar, Roman et al.. US 12296622 B2. Systems And Methods Of Determining Tread Depth. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY RICHARD HINTON whose telephone number is (703)756-1051. The examiner can normally be reached Monday-Friday 7:30-4:30. 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, Hunter Lonsberry can be reached at (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HENRY R HINTON/Examiner, Art Unit 3665 /HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665 Application/Control Number: 18/651,027 Page 2 Art Unit: 3665 Application/Control Number: 18/651,027 Page 3 Art Unit: 3665 Application/Control Number: 18/651,027 Page 4 Art Unit: 3665 Application/Control Number: 18/651,027 Page 5 Art Unit: 3665 Application/Control Number: 18/651,027 Page 6 Art Unit: 3665 Application/Control Number: 18/651,027 Page 7 Art Unit: 3665 Application/Control Number: 18/651,027 Page 8 Art Unit: 3665 Application/Control Number: 18/651,027 Page 9 Art Unit: 3665 Application/Control Number: 18/651,027 Page 10 Art Unit: 3665 Application/Control Number: 18/651,027 Page 11 Art Unit: 3665 Application/Control Number: 18/651,027 Page 12 Art Unit: 3665
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