DETAILED ACTION
Information Disclosure Statement
The information disclosure statements submitted on 03/31/2024 have been considered by the Examiner and made of record in the application file.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-4, 9-12 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsova (US 2017/0109582 A1) in view of Simard (US 2015/0019460 A1).
Regarding claims 1, 9 and 17, Kuznetsova discloses an Artificial Intelligence (AI) based self-labelling method/system/non-transitory CRM comprising:
[claim 9: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: (paragraph 26)]
[claim 17: a non-transitory computer-readable medium storing computer-executable instructions for Artificial Intelligence (AI) based self-labelling, the stored instructions, when executed by a processor, cause the processor to perform operations comprises (paragraphs 49-52)]
creating, in real-time, image vectors from multimedia content captured via a camera; (paragraphs 28, 33 and 43: Kuznetsova teaches image patches and object proposals as feature vectors and projects them into a low-dimensional embedding vector.)
identifying, by a trained AI model, a set of image vectors associated with at least one predefined category of interest from the image vectors; (paragraphs 6, 22 and 28: Kuznetsova teaches predefined object classes and associates instances with those classes via labels and prototype matching.)
assigning at least one dimension to each of the set of image vectors (paragraphs 21-22: low-dimensional embedding space)
Kuznetsova fails to specifically disclose determining, by the trained AI model, for a subset of image vectors within the set of image vectors, the availability of at least one relevant label from a plurality of pre-created labels, based on the at least one assigned dimension and associated attributes; receiving, by the trained AI model, a user input for assigning a new label to the subset of image vectors, in response to determining non-availability of a relevant label from the plurality of pre-created labels; and performing, by the trained AI model, incremental learning based on the new label received from the user.
In related art, Simard discloses determining, by the trained AI model, for a subset of image vectors within the set of image vectors, the availability of at least one relevant label from a plurality of pre-created labels, based on the at least one assigned dimension and associated attributes; (paragraph 38: Simard discloses that the system first scores data items with a classifier and then selects a subset of items for labeling based on those scores, which is consistent with the system using existing class definition (i.e., the current label set or class of data items) to decide what label applies to those items. In Simard, this availability determination is expressed as classification into the existing class framework (positive or negative) based on classifier scoring. Simard’s process determines, for a selected subset, whether an existing label state (positive or negative for the class) applies based on computed probabilities or scores.)
receiving, by the trained AI model, a user input for assigning a new label to the subset of image vectors, in response to determining non-availability of a relevant label from the plurality of pre-created labels; and (paragraphs 38, 45 and 70: Simard teaches receiving user-provided labels via a UI for the presented subset of items. That is the core receive user input to label the subset operation.)
performing, by the trained AI model, incremental learning based on the new label received from the user. (paragraphs 38, 42 and 70: Simard teaches retraining the classifier based on user-provided labels, and repeating the present, label and retrain loop.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Simard into the teachings of Kuznetsova to effectively improve the precision and recall of the classifier.
Regarding claims 2 and 10, Kuznetsova, as modified by Simard, discloses the claimed invention wherein the predefined category of interest comprises at least one of a threat, debris, reconnaissance, surveillance, intrusion detection, intrusion elimination, unknown object detection, suspicious object detection swarm detection, payload analysis, accident investigation, anti-drone measures, environment monitoring, traffic monitoring, wildfire monitoring, flood monitoring, oil spill monitoring, urban planning, weapon detection, violence detection, agricultural monitoring, vessel classification, border monitoring, illegal activity detection, or danger. (Kuznetsova: paragraph 27: identifies and tracks object instances in frames of the videos)
Regarding claims 3, 11 and 18, Kuznetsova, as modified by Simard, discloses the claimed invention wherein determining availability of the at least one relevant label comprises: comparing the at least one assigned dimension and associated attributes for the subset of image vectors with dimensions and attributes of each of the plurality of pre-created labels; and identifying the at least one relevant label from the plurality of pre-created labels matching the at least one assigned dimension and associated attributes for the subset of image vectors. (Kuznetsova: paragraph 29: comparing similarity of a sample to class prototypes and determining label and class based on that comparison)
Regarding claims 4, 12 and 19, Kuznetsova, as modified by Simard, discloses the claimed invention wherein each of the plurality of pre-created labels comprises a multi-tiered hierarchy of child labels. (Simard: paragraphs 440, 443: concept hierarchy with rood node and children)
Allowable Subject Matter
Claims 5-8, 13-16 and 20-21 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 BOBBAK SAFAIPOUR whose telephone number is (571)270-1092. The examiner can normally be reached Monday - Friday, 8:00am - 5:00pm.
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/BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665