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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 6-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Uncertainty-aware Mean Teacher for Source-Free Unsupervised Domain Adaptive 3D Object Detection” by Hegde et al. (hereinafter ‘Hegde’).
In regards to claim 1, Hegde teaches a method for measuring an ability of a trained machine learning model for processing of measurement data to generalize, with respect to a given task, to a target domain and/or distribution to which one or more input records of measurement data belong, the method comprising the following steps: (See Hegde Section 3.2, Hegde teaches domain adaption from Waymo to KITTI data.)
determining, from the input records of measurement data, a target style that characterizes the target domain and/or distribution; (See Hegde page 9881, Hedge teaches introducing target style such as weather conditions into KITTI domain data.)
obtaining, based at least in part on the target style, validation examples in the target domain and/or distribution, and respective ground truth labels; (See Hegde page 9880, Hegde teaches generating pseudo labels ground truth labels.)
processing, by the trained machine learning model, the validation examples into outputs; and (See Hedge page 7880, Hegde teaches determining outputs based on pseudo labels.)
determining, based on a comparison between the outputs and the respective ground truth labels, an accuracy of the trained machine learning model as the ability of the trained machine learning model to generalize to the target domain and/or distribution. (see Hegde section 3.2 on page 9879 as mentioned above: "Naively training the object detector on pseudo-labels generated by cps and filtered by a threshold could reinforce errors due to the fact that the source trained model may produce incorrect of higher confidence as well as correct predictions of lower confidence. We demonstrate the prevalence of label noise in Figure 4, in which we plot the density of correct and incorrect pseudo-labels with respect to their confidence scored at each step for the Waymo- KITTI domain scenario.” Thus Hegde teaches the assessment of the confidence score as high or low as being related with the corresponding accuracy of the model in the target domain).
In regards to claim 2, Hegde teaches wherein the determining of the target style includes: processing, by a trained feature extractor network, the input records of measurement data into target feature maps; and determining, from the target feature maps, features of the measurement data that characterize the target domain. (See Hegde Figure 2, Hegde teaches feature encoder.)
In regards to claim 6, Hegde teaches wherein the obtaining of the validation examples includes retrieving, based on the target style, validation examples from a library. (See Hegde page 9881, Hedge teaches acquiring datasets.)
In regards to claim 7, Hedge teaches wherein the input records of measurement data include: (i) images, and/or (ii) point clouds that assign measurement values of at least one measured quantity to locations in a plane and/or in space. (See Hegde page 9881).
In regards to claim 8, Hegde teaches wherein the trained machine learning model is a classifier that maps records of measurement data to classification scores with respect to one or more classes of a given classification. (See Hedge Figure 2 and Page 7880).
In regards to claim 9, Hegde teaches wherein the input records of measurement data include input records of measurement data that have been captured by at least one sensor carried on board a vehicle or robot. (See Hegde page 9881).
In regards to claim 10, Hegde teaches wherein: the validation examples are obtained from an external server that is outside the vehicle or robot; and the processing of the validation examples, and the determining of the ability to generalize, are performed on board the vehicle or robot. (See Hedge page 9881).
In regards to claim 11, Hegde teaches further comprising: actuating, in response to determining that the determined ability of the trained machine learning model fulfils a predetermined criterion, a downstream technical system that uses outputs of the machine learning model to move the technical system into an operational state where it can better tolerate noisy or incorrect outputs. (See Hegde Section 4.2).
Claims 12-14 recite limitations that are similar to that of claim 1. Therefore, claims 12-14 are rejected similarly as claim 1.
Allowable Subject Matter
Claims 3-5 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.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 3, the applied art does not teach or suggest “wherein the obtaining of the validation examples includes: providing respective source examples in a source domain and/or distribution and corresponding ground truth labels (5*); determining, from each of the source examples, a source content that characterizes a content of the source examples within the source domain and/or distribution; and combining each source content and the target style into a validation example in the target domain and/or distribution, so that the corresponding ground truth label of the respective source example remains valid for the validation example.”
Claims 4-5 are indicated allowable for being dependent on claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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, Vu Le can be reached at (571) 272-7332. 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.
/UTPAL D SHAH/
Primary Examiner, Art Unit 2668