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 Objections
Claim 3 is objected to because of the following informalities: the claim states “generating by the machine vision component an output including data relating to the at least one object and the video file analyzing, by a learning system, the output;”. However, the Examiner believes, like in independent claim 1, that there should be a semicolon in between the two limitations. For example “generating by the machine vision component an output including data relating to the at least one object and the video file; analyzing, by a learning system, the output;”. Appropriate correction is 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 1-7 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. Independent claims 1 and 3 (and dependent claims 2 and 4-7) state “a method for training a learning system…”, however, none of the claims include limitations that would show how “training” occurs. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al., “Toward Interactive Self-Annotation For Video Object Bounding Box: Recurrent Self-Learning And Hierarchical Annotation Based Framework” (Le).
Regarding claim 1, Le teaches a method for training a learning system to identify components of time-based data streams (training the detector to detect objects within a video) (p. 3220; Abstract), the method comprising:
processing, by a machine vision component (a detector) (p. 3222; Section 3.1.1, 1st paragraph) in communication with a learning system (through the self-supervised learning) (p. 3222; Section 3.1.1, 1st paragraph), a video file (in videos) (p. 3222; Section 3.1.1, 1st paragraph) to detect at least one object in the video file (generating bounding boxes for all objects in the videos) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs);
generating by the machine vision component (a detector) (p. 3222; Section 3.1.1, 1st paragraph) an output including data relating to the at least one object (outputting bounding boxes around one or more objects) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs) and the video file (learning simple properties such as day/night, weather, landscape, etc. as well as spatial and temporal information) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs);
analyzing, by a learning system, the output (wherein each output/iteration the detector is trained to generate bounding boxes) (p. 3222; Section 3.1.1, 2nd paragraph); and
identifying, by the learning system (through the self-supervised learning) (p. 3222; Section 3.1.1, 1st paragraph), an unidentified object in the processed video file (wherein, when objects are eliminated mistakenly during noise elimination, the system can recover accidentally deleted objects as well as add miss-detected object by the detector) (p. 3223; right column, 1st paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, that the “learning system” is obviously the detector system in Le (even though it doesn’t explicitly state “learning system”) since the detector is trained and learns for annotating videos (Le; p. 3222, Section 3.1.1, 1st paragraph).
Regarding claim 2, Le teaches further comprising modifying, by the learning system, the processed video file to include an identification of the unidentified object (adding new bounding boxes for the miss-detected objects by the detector) (p. 3222; Figure 2 and Section 3.1.1, 2nd paragraph and p. 3223; Figure 4 and right column, 1st paragraph).
Regarding claim 3, Le teaches a method for training a learning system to identify components of time-based data streams (training the detector to detect objects within a video) (p. 3220; Abstract), the method comprising:
processing, by a machine vision component (a detector) (p. 3222; Section 3.1.1, 1st paragraph) and in communication with a learning system (through the self-supervised learning) (p. 3222; Section 3.1.1, 1st paragraph), a video file (in videos) (p. 3222; Section 3.1.1, 1st paragraph) to detect at least one object in the video file (generating bounding boxes for all objects in the videos) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs);
generating by the machine vision component (a detector) (p. 3222; Section 3.1.1, 1st paragraph) an output including data relating to the at least one object (outputting bounding boxes around one or more objects) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs) and the video file
analyzing, by a learning system, the output (wherein each output/iteration the detector is trained to generate bounding boxes) (p. 3222; Section 3.1.1, 2nd paragraph); and
modifying, by the learning system (through the self-supervised learning) (p. 3222; Section 3.1.1, 1st paragraph), an identification of the at least one object in the processed video file (wherein, when objects are eliminated mistakenly during noise elimination, the system can recover accidentally deleted objects as well as add miss-detected object by the detector) (p. 3223; right column, 1st paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, that the “learning system” is obviously the detector system in Le (even though it doesn’t explicitly state “learning system”) since the detector is trained and learns for annotating videos (Le; p. 3222, Section 3.1.1, 1st paragraph).
Regarding claim 4, Le teaches wherein analyzing the output further comprises identifying an error in an identification associated with the detected at least one object (wherein, when objects are eliminated mistakenly during noise elimination, the system can recover accidentally deleted objects as well as add miss-detected object by the detector) (p. 3223; right column, 1st paragraph).
Regarding claim 5, Le teaches wherein modifying further comprises modifying the identification to correct the identified error (wherein, when objects are eliminated mistakenly during noise elimination, the system can recover accidentally deleted objects as well as add miss-detected object by the detector) (p. 3223; right column, 1st paragraph).
Regarding claim 6, Le teaches wherein modifying further comprises adding an identifier to an object (adding a bounding box to an object) that the machine vision component (the detector) detected but did not identify (that the detector miss-detected) (p. 3222; Figure 2 and Section 3.1.1, 2nd paragraph and p. 3223; Figure 4 and right column, 1st paragraph).
Regarding claim 7, Le teaches further comprises identifying, by the learning system, a second object in the video file (generating bounding boxes for all objects in the videos) (p. 3222; Section 3.1.1, 1st and 2nd paragraphs).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Acharya et al., US 2021/0192972 A1: teaches the machine learning system updates the domain model by correlating objects recognized in the video data to references to the objects within the audio data and/or the textual data as well as measurements in the sensor data so as to identify portions of the video data, portions of the audio data, portions of the sensor data, and portions of the textual data that describe a same step in a plurality of steps for performing the task ([0006]). Nussbaum et al., US 11,210,851 B1: teaches indicating an unidentified object's label prior to utilizing object recognition to identify the object may actually increase the accuracy of the object recognition; for example, if an object recognition engine has input indicating that an object within a bounding frame is a tree, the engine may use that data to look for groups of pixels that have certain characteristics pertaining to a tree (e.g., green leaves, brown trunk, etc.), and thus may more accurately identify the bounds of the tree (col. 27, lines 20-28).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J VANCHY JR whose telephone number is (571)270-1193. The examiner can normally be reached Monday - Friday 9am - 5pm.
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/MICHAEL J VANCHY JR/Primary Examiner, Art Unit 2666 Michael.Vanchy@uspto.gov
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