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 .
2. Claims 1-7, 9-21 are pending in this amended application.
Claim Rejections - 35 USC § 103
3. 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.
4. Claims 1-7, 9-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bogomolny et al. [hereafter Bogomolny], US Pub 2022/0051391 in view of McIntosh et al. [hereafter McIntosh], US Pub 2012/0191617 and Hu et al. [hereafter Hu], CN Pub 115358987.
As to claim 1 [independent], Bogomolny teaches an apparatus, comprising [fig. 1, element 100; 0039]:
a processor [fig. 1, element 102; 0044]; and
a memory [fig. 1, element 122; 0047] that stores code executable by the processor to [fig. 1, element 102; 0044, 0106]:
receive an image of a tire [fig. 2; 0040, 0057-0060 Bogomonly teaches that the processor 102 captures or acquires the images of the tire];
determine a location of damage on the tire [fig. 2; 0040, 0049, 0057-0061, 0071-0072, 0075-0078, 0091-0093 Bogomonly teaches that the processor 102 captures or acquires the images of the tire, determines the location of the tire (e.g., 0075-0076) and determines the type of damage or degradation or deterioration occurred in the tire (e.g., 0049 or 0090-0093)];
classify the damage as a damage type [fig. 2; 0049, 0057-0061, 0071-0072, 0075-0078, 0091-0093 Bogomonly teaches that the processor 102 captures or acquires the images of the tire, determines the location of the tire (e.g., 0075-0076) and determines the type of damage or degradation or deterioration in the tire (e.g., 0049 or 0090-0093)];
Bogomonly doesn’t teach predict whether the tire is repairable based at least in part on the location and the damage type;
McIntosh teaches predict whether the tire is repairable based at least in part on the location and the damage type [figs. 4-5; abstract, 0007-0008, 0050-0053, 0059-0062 McIntosh teaches that the processor predicts whether the tire is repairable based at least in part on the location and the damage type];
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
Bogomonly and McIntosh don’t teach determine a confidence score for the prediction based at least in part on at least one of: a quality of the image, the damage type, and the location; and
determine whether the confidence score is greater than or equal to a threshold confidence score.
Hu teaches determine a confidence score for the prediction based at least in part on at least one of: a quality of the image, the damage type, and the location [abstract Hu teaches that a processor that is configured to perform determination to first calculate the normal/abnormal score based on at least quality of tire image and further judge whether the calculated score is greater than or equal to a preset threshold]; and
determine whether the confidence score is greater than or equal to a threshold confidence score [abstract Hu teaches that a processor that is configured to perform determination to first calculate the normal/abnormal score based on at least quality of tire image and further judge whether the calculated score is greater than or equal to a preset threshold].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hu teaching to determine whether the confidence score is greater than or equal to a threshold confidence score to modify Bogomonly and McIntosh’s teaching to perform X-ray imaging of the tire to determine defect and classification in the tire in order to calculate loss function of predicted image of the tire and the image of the tire, and further judge whether the loss function is greater than or equal to threshold based on score. The suggestion/motivation for doing so would have been benefitted to the user to provide a tire X ray detection system based on improved resistance generating network, the detection system can improve the identification rate of the X-ray image of the tire.
As to claim 2 [dependent from claim 1], Bogomolny teaches wherein the type of the damage comprises at least one of a bubble, a puncture, a blowout, a flat, a bulge, a crack, a cut, irregular wear, regular wear, or any combination thereof [0049].
As to claim 3 [dependent from claim 1], Bogomolny teaches wherein the code is executable by the processor to input the image into an artificial intelligence (AI) model [0062-0063, 0067, 0082-0083] and classify the type of damage based at least in part on an output of the AI model [0049, 0062-0063, 0067, 0082-0083].
As to claim 4 [dependent from claim 1], Bogomolny teaches wherein the code is executable by the processor to input the image into an artificial intelligence model [0062-0063, 0067, 0082-0083] and determine the location of the damage based at least in part on an output of the artificial intelligence model [fig. 2; 0049, 0057-0061, 0062-0063, 0067, 0071-0072, 0075-0078, 0082-0083, 0091-0093 Bogomonly teaches that the processor 102 captures or acquires the images of the tire, determines the location of the tire (e.g., 0075-0076) and determines the type of damage or degradation or deterioration occurred in the tire (e.g., 0049 or 0090-0093)].
As to claim 5 [dependent from claim 1], McIntosh teaches wherein the code is executable by the processor to determine whether the location of the damage is within a repairable zone of the tire [figs. 4-5; abstract, 0007-0008, 0050-0053, 0059-0062 McIntosh teaches that the processor predicts whether the tire is repairable based at least in part on the location and the damage type].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 6 [dependent from claim 5], Bogomonly teaches wherein the repairable zone comprises a zone on a surface area of the tire that is between a first side of the tire and a second side of the tire opposite of the first side [fig. 3; 0049, 0057-0061, 0071-0072, 0075-0078, 0091-0093 Bogomonly teaches that the processor 102 determines the location of the tire (e.g., 0075-0076) and determines whether the damage or degradation or deterioration occurred in the tire between the first side and the second side is repairable (e.g., 0049 or 0090-0093)].
As to claim 7 [dependent from claim 5], Bogomonly teaches wherein the code is further executable by the processor to predict that the tire is not repairable in response to determining whether the location of the damage is within a repairable zone of the tire [fig. 3; 0049, 0057-0061, 0071-0072, 0075-0078, 0091-0093 Bogomonly teaches that the processor 102 determines the location of the tire (e.g., 0075-0076) and determines the damage or degradation or deterioration occurred in the tire between the first side and the second side MAY NOT be repairable (e.g., 0049 or 0090-0093)].
As to claim 9 [dependent from claim 1], Bogomonly teaches wherein the code is executable by the processor to:
receive the image from a user via a mobile application fig. 2; 0040-0041, 0057-0060 Bogomonly teaches that the processor 102 captures or acquires the images of the tire from the mobile device 101]; and
McIntosh teaches in response to determining that the confidence score is greater than or equal to a threshold confidence score, automatically transmit a notification comprising an appointment request from the user to a technician or salesperson [figs. 3-5; 0026-0027, 0029-0030, 0060-0061 McIntosh teaches that the processor determines the threshold confidence score is less than the threshold value or doesn’t meet the threshold value, the message display for the user for further action].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 10 [dependent from claim 9], McIntosh teaches wherein the appointment request comprises an appointment type and the code is executable by the processor to determine an appointment type based at least in part on determining whether the tire is repairable [figs. 3-5; 0026-0027, 0029-0030, 0060-0061 McIntosh teaches that the processor determines the threshold confidence score is less than the threshold value or doesn’t meet the threshold value, the message display for the user for further action].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 11 [dependent from claim 1], McIntosh teaches a graphical user interface (GUI), wherein the code is executable by the processor to output at least one of the following to the GUI: an indication that the tire is repairable based at least in part on predicting that the tire is repairable [figs. 3, 4-5; abstract, 0007-0008, 0050-0053, 0059-0062 McIntosh teaches that the processor predicts whether the tire is repairable based at least in part on the location and the damage type (e.g., fig. 3, steps 114, 116)], an indication that the tire is not repairable based at least in part on predicting that the tire is not repairable, the confidence score, or any combination thereof [figs. 4-5; abstract, 0007-0008, 0029, 0050-0053, 0059-0062 McIntosh teaches that the processor predicts whether the tire is not repairable based on passing of the inspection (e.g., fig. 3, steps 114, 122)].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 12 [dependent from claim 11], McIntosh teaches wherein the GUI is configured to receive input from the user to transmit an appointment request to a technician or a salesperson [figs. 3-5; 0026-0027, 0029-0030, 0060-0061 McIntosh teaches that the processor determines the threshold confidence score is less than the threshold value or doesn’t meet the threshold value, the message display for the user for further action].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 13 [dependent from claim 1], McIntosh teaches wherein the code is further executable by the processor to determine that the damage type is a non-repairable damage type and to predict that the tire is not repairable in response to the location of damage [figs. 4-5; abstract, 0007-0008, 0029, 0050-0053, 0059-0062 McIntosh teaches that the processor predicts whether the tire is not repairable based on passing of the inspection (e.g., fig. 3, steps 114, 122)].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate McIntosh teaching to predict whether the tire is repairable based at least in part on the location and the damage type to modify Bogomonly’s teaching to represent different sequential step in and shows tire cross section image with regions, responds by touching one of regions through interface that allows user to enter tire injury information associated with region of selected tire which responds by touching one of regions through interface that allows user to enter tire repair information associated with region of selected tire. The suggestion/motivation for doing so would have been benefitted to the user to provide message on a screen to enter tire repair information for technician so the computer can automatically check entered information against customer retread specifications and alerts the technician if the tire does not fit the customer's requirements for repairing or retreading the tire.
As to claim 14 [independent], However, the independent claim 14 essentially claimed same subject matter as claimed in the independent claim 1 for/and/with other claim limitations, and are therefore the independent claim 14 would be rejected based on same rationale as applied to the independent claim 1.
As to claim 15 [dependent from claim 14], However, the dependent claim 15 essentially claimed same subject matter as claimed in the dependent claim 3 for/and/with other claim limitations, and are therefore the dependent claim 15 would be rejected based on same rationale as applied to the dependent claim 3.
As to claim 16 [dependent from claim 14], However, the dependent claim 16 essentially claimed same subject matter as claimed in the dependent claim 4 for/and/with other claim limitations, and are therefore the dependent claim 16 would be rejected based on same rationale as applied to the dependent claim 4.
As to claim 17 [dependent from claim 14], Bogomonly teaches wherein classifying the damage further comprises detecting an object puncturing the tire and determining a type of the object [fig. 2; 0049, 0057-0061, 0071-0072, 0075-0078, 0091-0093 Bogomonly teaches that the processor 102 captures or acquires the images of the tire, determines the location of the tire (e.g., 0075-0076) and determines the type of damage or degradation or deterioration in the tire (e.g., 0049 or 0090-0093)].
As to claim 18 [independent], However, the independent claim 18 essentially claimed same subject matter as claimed in the independent claim 1 for/and/with other claim limitations, and are therefore the independent claim 18 would be rejected based on same rationale as applied to the independent claim 1.
5. Claims 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Bogomolny et al. [hereafter Bogomolny], US Pub 2022/0051391 in view of McIntosh et al. [hereafter McIntosh], US Pub 2012/0191617 and Hu et al. [hereafter Hu], CN Pub 115358987, further in view of Feng et al. [hereafter Feng], CN Pub 114889654.
As to claim 19 [dependent from claim 1], Bogomonly, McIntosh and Hu don’t teach wherein the code is further executable by the processor to:
input the image into an artificial intelligence (AI) model that is trained on a corpus of images of damaged tires, wherein each image of the corpus of images is classified as either repairable or not repairable; and
predict whether the tire is repairable based at least in part on an output of the AI model.
Feng teaches wherein the code is further executable by the processor to:
input the image into an artificial intelligence (AI) model that is trained on a corpus of images of damaged tires, wherein each image of the corpus of images is classified as either repairable or not repairable [pages 3-4 Feng teaches that the processor determines that whether the images of tire related to the damage is repairable or not repairable using artificial intelligence model]; and
predict whether the tire is repairable based at least in part on an output of the AI model [pages 3-4 Feng teaches that the processor determines that whether the images of tire related to the damage is repairable or not repairable using artificial intelligence].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Feng teaching to predict whether the tire is repairable based at least in part on an output of the AI model to modify Bogomonly, McIntosh and Hu’s teaching to collect the tyre pressure value, working temperature in real-time, taking it into the obtained algorithm, obtaining the level of each tyre operation abnormal and the lifting movement abnormal level, calculating the level of each tyre operation abnormality and the elevator carriage movement abnormal level input into the control system of the ropeway, the ropeway system judges whether it is necessary to decelerate or stop, whether the tyre needs to be replaced, whether need to overhaul the crane carriage, replacing and other related operations based on adopted AI model. The suggestion/motivation for doing so would have been benefitted to the user to provide a technique that can predict service life of the tire by collecting the tire pressure of the hanging ropeway tire, temperature and acceleration data, and providing data support for the control system for predicting tire changing time.
As to claim 20 [dependent from claim 1], Bogomonly, McIntosh and Hu don’t wherein the confidence score comprises a reliability measure for the prediction of whether the tire is repairable.
Feng teaches wherein the confidence score comprises a reliability measure for the prediction of whether the tire is repairable [pages 3-4 Feng teaches that the processor determines that whether the images of tire related to the damage is repairable or not repairable using artificial intelligence].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Feng teaching to predict whether the tire is repairable based at least in part on an output of the AI model to modify Bogomonly, McIntosh and Hu’s teaching to collect the tyre pressure value, working temperature in real-time, taking it into the obtained algorithm, obtaining the level of each tyre operation abnormal and the lifting movement abnormal level, calculating the level of each tyre operation abnormality and the elevator carriage movement abnormal level input into the control system of the ropeway, the ropeway system judges whether it is necessary to decelerate or stop, whether the tyre needs to be replaced, whether need to overhaul the crane carriage, replacing and other related operations based on adopted AI model. The suggestion/motivation for doing so would have been benefitted to the user to provide a technique that can predict service life of the tire by collecting the tire pressure of the hanging ropeway tire, temperature and acceleration data, and providing data support for the control system for predicting tire changing time.
As to claim 21 [dependent from claim 1], Bogomonly, McIntosh and Hu don’t wherein the code is executable by the processor to:
input the image into an artificial intelligence (AI) model trained on a corpus of images of tires, wherein each image of the corpus of images is of a tire having damage classified in at least one of a plurality of categories, the plurality of categories comprising at least one of: a bubble, a puncture, a blowout, a flat, a bulge, a crack, a cut, irregular wear, or regular wear; and
classify the type of damage into at least one of the plurality of categories based at least in part on an output of the AI model.
Feng teaches wherein the code is executable by the processor to:
input the image into an artificial intelligence (AI) model trained on a corpus of images of tires, wherein each image of the corpus of images is of a tire having damage classified in at least one of a plurality of categories, the plurality of categories comprising at least one of: a bubble, a puncture, a blowout, a flat, a bulge, a crack, a cut, irregular wear, or regular wear [pages 3-4 Feng teaches that the processor determines that whether the images of tire related to the damage is repairable or not repairable using artificial intelligence]; and
classify the type of damage into at least one of the plurality of categories based at least in part on an output of the AI model [pages 3-4 Feng teaches that the processor determines that whether the images of tire related to the damage is repairable or not repairable using artificial intelligence].
Thus, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate Feng teaching to predict whether the tire is repairable based at least in part on an output of the AI model to modify Bogomonly, McIntosh and Hu’s teaching to collect the tyre pressure value, working temperature in real-time, taking it into the obtained algorithm, obtaining the level of each tyre operation abnormal and the lifting movement abnormal level, calculating the level of each tyre operation abnormality and the elevator carriage movement abnormal level input into the control system of the ropeway, the ropeway system judges whether it is necessary to decelerate or stop, whether the tyre needs to be replaced, whether need to overhaul the crane carriage, replacing and other related operations based on adopted AI model. The suggestion/motivation for doing so would have been benefitted to the user to provide a technique that can predict service life of the tire by collecting the tire pressure of the hanging ropeway tire, temperature and acceleration data, and providing data support for the control system for predicting tire changing time.
Response to Arguments
6. Applicant’s arguments with respect to claims 1-7, 9-21 have been considered but are moot because the new prior reference(s) is/are being employed in this current rejection and the arguments do not apply to any of the references being used in the current rejection.
Conclusion
7. THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARIS SABAH whose telephone number is (571)270-3917. The examiner can normally be reached on Monday/Friday from 9:00AM to 5:30PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Benny Tieu, can be reached on (571)272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. The Examiner’s personal fax number is (571)270-4917.
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/HARIS SABAH/Examiner, Art Unit 2682