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 .
Priority
Acknowledgement is made of the application’s status as a continuation of KR 10-2024-0077451
Claim Status
Claim(s) 1, 10, and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yun (US 20200034666 A1).
Claim 2-9 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.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1, 10, and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yun (US 20200034666 A1).
Regarding claim 1, Yun discloses An object detection method comprising: (¶6 “ Some example embodiments provide object recognition devices capable of processing artificial intelligent data more efficiently.”)
acquiring, by an object detection system, image information including correct answer information on objects, (¶138 “The processor 510 may determine a common object included in a plurality of images”) and additional information on objects which is extracted through a generalization intelligence model; (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
training, by the object detection system, an object detection engine based on the acquired information; and (¶69 “the object recognition model 105 may train the recognition model 105 through supervised learning. The supervised learning refers to a method of inputting learning data and an output data corresponding to the learning data into the artificial neural network engine 100 and updating the weights so that the learning data and the output data corresponding to the learning data may be output.”)
performing, by the object detection system, object detection by using the trained object detection engine, (¶159 “FIG. 17 is a flow chart illustrating a method of recognizing an object in an object recognition device according to an example embodiment”)
wherein performing the object detection comprises selectively determining whether to reflect the additional information in the process of performing the object detection according to whether the additional information on the objects is acquired. (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
Regarding claim 10, Yun discloses An object detection system comprising: (¶6 “ Some example embodiments provide object recognition devices capable of processing artificial intelligent data more efficiently.”)
an input unit configured to acquire image information including correct answer information on objects, (¶138 “The processor 510 may determine a common object included in a plurality of images”) and additional information on objects which is extracted through a generalization intelligence model; and (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
a processor (¶117 “an electronic device 400 may include a processor”) configured to train an object detection engine based on the acquired information, (¶69 “the object recognition model 105 may train the recognition model 105 through supervised learning. The supervised learning refers to a method of inputting learning data and an output data corresponding to the learning data into the artificial neural network engine 100 and updating the weights so that the learning data and the output data corresponding to the learning data may be output.”) and to perform object detection by using the trained object detection engine, (¶159 “FIG. 17 is a flow chart illustrating a method of recognizing an object in an object recognition device according to an example embodiment”)
wherein the processor (¶117 “an electronic device 400 may include a processor”) is configured to selectively determine whether to reflect the additional information in the process of performing the object detection according to whether the additional information on the objects is acquired. (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
Regarding claim 11, Yun discloses An object detection method comprising: (¶6 “ Some example embodiments provide object recognition devices capable of processing artificial intelligent data more efficiently.”)
training, (¶69 discloses training the recognition model) by an object detection system, an object detection engine based on image information including correct answer information on objects (¶138 “The processor 510 may determine a common object included in a plurality of images”) and additional information on objects which is extracted through a generalization intelligence model; and (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
performing, by the object detection system, object detection by using the trained object detection engine, (¶159 “FIG. 17 is a flow chart illustrating a method of recognizing an object in an object recognition device according to an example embodiment”)
wherein performing the object detection comprises selectively determining whether to reflect the additional information in the process of performing the object detection according to whether the additional information on the objects is acquired. (¶120 “The voice recognizer 430 may apply the feature vector FV to the above-described learned object recognition model to generate a sentence data SDT corresponding to the voice signal VS, and may provide the sentence data SDT to the natural language analyzer 460”)
Allowable Subject Matter
Claim 2-9 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pham (US 11055566 B1) discloses an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.
Zadeh (US 20250104422 A1) discloses A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL KIM MAIDEN whose telephone number is (703)756-1264. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Koziol can be reached at 4089187630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL KIM MAIDEN/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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