DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 11 is drawn to a “computer-implemented data structure”. A data structure is software. Software does not fall within the definition of a process, machine, manufacture, or composition of matter (In re Nuijten), and is therefore non-statutory. Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5 and 6 contain the term, “the prediction interval”. There is lack of antecedent basis for the term. It is unclear if the prediction interval is any prediction interval or referring to “the conformal box coordinate prediction interval” as claimed in Claim 1. Appropriate corrections are required. The dependent claims do not alleviate the issues in Claims 5 and 6 and are also rejected under 35 U.S.C. 112(b).
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 of this title, 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 1-2 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. US2024/0403728 hereinafter referred to as Gong in view of Grancey et al. “Object Detection With Probabilistic Guarantees: a Conformal Prediction Approach” hereinafter referred to as Grancey as provided in the applicant’s information disclosure statement.
As per Claim 1, Gong teaches a method for determining a prediction interval for a coordinate of a bounding box, for checking whether an object detector operates safely or not, the method comprising the following steps:
determining, using the object detector, (Gong, Paragraph [0032], “The object detection model 110 may be trained to detect that an object is depicted in image data 108 (e.g., in an image frame, in an image sequence). In a first variation, the system may include object detection model 110 for each of a predetermined set of object types”)
selecting, depending on the conformal label quantiles, a conformal box coordinate quantile for the box coordinate from the conformal box coordinate quantiles for the respective box coordinates; and (Gong, Paragraph[0071], “In an example, if the samples are distinct, there is no ambiguity about their order, and the new sample will either fall below or above the quantile, leading to an exact probability. Conformal prediction algorithms use empirical quantiles to construct confidence intervals”)
determining the conformal box coordinate prediction interval
Gong does not teach providing calibration data and a test sample, wherein the calibration data includes digital images that are associated with a respective ground truth bounding box coordinate and class label, and wherein the test sample includes a digital image;
determining, using the object detector, a predicted box coordinate for the box coordinate depending on the digital image of the test sample; determining, depending on the calibration data, conformal label quantiles for respective classes and conformal box coordinate quantiles for respective box coordinates;
Grancey teaches providing calibration data and a test sample, wherein the calibration data includes digital images that are associated with a respective ground truth bounding box coordinate and class label, and wherein the test sample includes a digital image; (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”, “Confomalization step. Assume that we have assigned predicted boxes to true boxes as in Section 4.1. In order to compare the i-th predicted box with the i-th true box, we compare each of the four predicted coordinates ^xi min; ^xi max; ^yi min; ^yi max with the four true coordinates xi min; xi max; yi min; yi max, by counting errors positively when the truth lies outside the prediction (e.g. ^xi min > xi min or ^xi max < ximax), and negatively otherwise)
determining, using the object detector, a predicted box coordinate for the box coordinate depending on the digital image of the test sample; determining, depending on the calibration data, conformal label quantiles for respective classes and conformal box coordinate quantiles for respective box coordinates; (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”, “Confomalization step…Note that nc is given by the total number of predicted objects assigned to atrue object, which is larger than the number of images in the calibration set. Then, following Step 3 of Section 2, we compute a quantile q for each of the four errors above, defined as the d(1 )(nc + 1)e-th largest value among the observed errors Ri. These four quantiles will serve as error margins for each coordinate”)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Grancey into Gong because by utilizing the method ground truth boxes and predicted coordinates with the conformal prediction of Gong will result in increasing utility of Gong’s conformal prediction.
Therefore it would have been obvious to one of ordinary skill to combine the two references to obtain the invention in Claim 1.
As per Claim 2, Gong in view of Grancey teaches the method according to claim 1, wherein the selecting of the conformal box coordinate quantile for the box coordinate includes determining a conformal label set depending on the conformal label quantiles, wherein the conformal label set includes a subset of class labels of the calibration data, and selecting a largest of the conformal box coordinate quantiles over the classes in the conformal label set as the box coordinate quantile for the box coordinate. (Gong, Paragraph [0032], [0071], [0072] and (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 9, Claim 9 claims a device comprising at least one processor and at least one memory (Gong, Paragraph [0045]) performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
As per Claim 10, Claim 10 claims a non-transitory computer-readable medium on which is stored a computer program including computer readable instructions(Gong, Paragraph [0045]) performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
As per Claim 11, Claim 11 claims a computer implemented data structure (Gong, Paragraph [0045]) performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
As per Claim 12, Gong in view of Grancey teaches the data structure according to claim 11, further comprising: at least one data field for a class associated with a respective digital image of the calibration data; at least one data field for a class associated with the digital image of the test sample; and at least one data field for a predicted class for the predicted box coordinate, for a conformal box coordinate quantile that is associated with a respective class of the set of classes, and for a conformal label set including a subset of the classes. (Gong, Paragraph [0032], [0071], [0072] and (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”)
The rationale applied to the rejection of claim 11 has been incorporated herein.
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. US2024/0403728 hereinafter referred to as Gong in view of Grancey et al. “Object Detection With Probabilistic Guarantees: a Conformal Prediction Approach” hereinafter referred to as Grancey as applied to Claim 1 and further in view of Goldman et al. US2024/0036571 hereinafter referred to as Goldman.
As per Claim 5, Gong in view of Grancey teaches the method according to claim 1,
Gong in view of Grancey does not explicitly teach the method further comprising determining that the object detector operates unsafely when the predicted coordinate is outside of the prediction interval.
Goldman teaches the method further comprising determining that the object detector operates unsafely(Goldman, Paragraph [0013], “In some examples, the autonomous vehicle may be unable to plan a path to traverse an obstacle and/or may determine that a confidence level associated with one or more maneuvers (e.g., a planned trajectory or path of the autonomous vehicle) and/or events (e.g., detection or classification of a particular object, prediction of a behavior of an object, etc.) is insufficient (e.g., is below a threshold confidence level) to proceed autonomously. In such cases, the autonomous vehicle may send a request to the remote operation system to obtain guidance”)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Goldman into Gong in view of Grancey will provide a utilization of the conformal prediction intervals of Gong will allow for safer operation of the autonomous vehicle.
Therefore it would have been obvious to one of ordinary skill to combine the three references to obtain the invention in Claim 5.
As per Claim 6, Gong in view of Grancey teaches the method according to claim 1, further comprising: determining, using the object detector, the predicted coordinates of the bounding box; determining the prediction interval for the respective predicted coordinates; and (Gong, Paragraph [0071], [0072] and (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”)
Gong in view of Grancey does not explicitly teach (i) determining that the object detector operates safely when the predicted coordinates are within the prediction interval, or (ii) determining that the object detector operates unsafely when the predicted coordinates are outside of the prediction interval.
Goldman teaches (i) determining that the object detector operates safely when the predicted coordinates are within the prediction interval, or (ii) determining that the object detector operates unsafely when the predicted coordinates are outside of the prediction interval. (Goldman, Paragraph [0013], “In some examples, the autonomous vehicle may be unable to plan a path to traverse an obstacle and/or may determine that a confidence level associated with one or more maneuvers (e.g., a planned trajectory or path of the autonomous vehicle) and/or events (e.g., detection or classification of a particular object, prediction of a behavior of an object, etc.) is insufficient (e.g., is below a threshold confidence level) to proceed autonomously. In such cases, the autonomous vehicle may send a request to the remote operation system to obtain guidance”)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Goldman into Gong in view of Grancey will provide a utilization of the conformal prediction intervals of Gong will allow for safer operation of the autonomous vehicle.
Therefore it would have been obvious to one of ordinary skill to combine the three references to obtain the invention in Claim 6.
As per Claim 7, Gong in view of Grancey and Goldman teaches the method according to claim 6, further comprising: operating an autonomous vehicle depending on the bounding box coordinate, (Gong, Paragraph [0071], [0072] and (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”) when it is detected that the object detector operates safely, and otherwise not operating the autonomous vehicle depending on the bounding box coordinate. (Goldman, Paragraph [0013])
The rationale applied to the rejection of claim 6 has been incorporated herein.
As per Claim 8, Gong in view of Grancey and Goldman teaches the method according to claim 6, further comprising: operating an autonomous vehicle depending on the bounding box coordinate or depending on the predicted class, (Gong, Paragraph [0071], [0072] and (Grancey, p321, Section 4.2, “Coordinate-Wise Conformalization”) when it is detected that the object detector operates safely, and otherwise not operating the autonomous vehicle depending on the bounding box coordinate or depending on the predicted class. (Goldman, Paragraph [0013])
The rationale applied to the rejection of claim 6 has been incorporated herein.
Allowable Subject Matter
Claims 3-4 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MING HON whose telephone number is (571)270-5245. The examiner can normally be reached M-F 9am - 5pm.
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, Emily Terrell can be reached on 571-270-3717. 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.
/MING Y HON/Primary Examiner, Art Unit 2666
Prosecution Insights