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 .
Applicant’s preliminary amendment filed on July 31, 2023 has ben entered and made of record.
Claim Interpretation
Claims 7-12 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are all method claims.
Claims 1-6 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “vehicle” and “MEC controller” provide sufficient structure to perform all claimed limitations.
Claim 13 is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it is an article of manufacture claim.
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 1-9 and 13 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.
Regarding claim 1 as a representative claim, claim limitation “transceiver” recited in line 13 lacks clarity as to whether it is referred the one or more transceivers included in the MEC center.
Claims 2-6 depend on claim 1 and thus are rejected for the same reasons as well.
Claim 7 is also rejected for the same reasons as set forth in claim 1 above because it also recites the same claim limitation “transceiver” in line 12.
Claims 8-9 and 13 depend on claim 7 and thus are rejected for the same reasons as well.
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Regarding claim 13, it recites “a computer-readable recording medium” which typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter); and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). The examiner suggests an amendment to the claim to recite “a non-transitory computer-readable recording medium” to limit the scope to only the statutory media in order to meet 35 U.S.C. 101 requirements. Any amendment to claim and/or specification should be commensurate with its corresponding disclosure.
Furthermore, claim also fails to recite “when executed by a computer or processor” to perform claim functions. Claim limitation “hardware” is not specifically referred to a computer and/or process or similar fashion and thus could be interpreted as a CD-ROM, for example. In this case, there is no functional relationship between program and a computer or processor. This could lead to a failure to satisfy the 101 requirement as being one of the four categories of patent eligible subject matter.
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.
Claim(s) 1-9 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over PARK et al. “Self-Controllable Super-Resolution Deep Learning Framework for Surveillance Drones in Security Applications”, EAI Endorsed Transactions on Security and Safety, Vol. 7, Issue 23, e5, pp. 1-7, 30 June 2020, Art of record IDS filed on 7/31/2023, referred as Park hereinafter) in view of Hall (U.S. Pat. App. Pub. No. 2017/0171319 A1, referred as Hall hereinafter).
Regarding claim 1 as a representative claim, Park discloses a deep learning-based super-resolution image processing system comprises:
an unmanned aerial vehicle system configured to receive an image captured by an unmanned aerial vehicle and allocate the received image data to a transmission queue for transmission (see figure 1: platform includes a drone, camera, and queue; subsection 2.1 (Reference System Model)); and
a Mobile Edge Computing (MEC) center (see section 1 (Introduction): ground surveillance monitoring centers included in the platforms; figure 1: controller) including one or more transceivers (see section 1 (Introduction): wireless channels which inherently include transceivers; figure 1: transceiver) each having a reception queue and configured to receive the image data from the unmanned aerial vehicle system through the one or more transceivers (see figure 1: queue) and generate a super resolution image corresponding to the image data through a deep learning computation based on a super-resolution model (see figure 1: super resolution framework which includes a deep learning model as described in section 3 (Proposed Algorithm)).
Park does not specifically disclose claim limitations “wherein the unmanned aerial vehicle system is further configured to determine which transceiver to transmit the image data based on a data amount difference between the transmission queue and the reception queue”.
However, such claim limitations are well known in the art at evidenced by Hall.
Hall, in the field of endeavor that of image transmission, discloses, see figure 1, for example, an UAV 102, base station 104, transceivers 106 and 110, wherein the transceiver comprising queue 216 and queue monitors 222 (see figure 2). Hall further discloses determining a transceiver to transmit image data (see para. [0045] and figure 4, items 404 (initiate communication sessions), 406 (transmit first set of data chunks to first input queue), 412 (all data chunks transmitted?), and 414 (distribute next set of data chunks to first input queue); the queue in this case servers as both transmission queue and receiving queue).
The motivation for doing so is to maintain the queue stability so that to prevent data overflow.
Therefore, before the effective filing of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations as taught by Hall in combination with Park for that reasons.
Regarding claim 2, the combination of Park and Hall further discloses an input unit configured to receive the image from a photographing means (see Park, figure 1, camera for inputting image captured; Hall, para. [0056] (input devices 822 such as camera are connected to interface 820 for receiving image from camera); and a transmission unit including the transmission queue and configured to determine which transceiver to transmit the image data (see analysis applied to claim 1 above) by means of a scheduling using a difference between the amount of data in a backlog of the transmission queue and the amount of data in a backlog of the reception queue as a weight (see Hall, para. [0030] (timers 318 and queue monitor 222) and figure 4; monitor 22 puts a timer (schedule) on the queue; when queue is emptied, the next data chunk is transmitted).
Regarding claim 3, the combination of Park and Hall further discloses wherein the unmanned aerial vehicle system further includes a layering unit configured to generate the image data to be allocated to the transmission queue through layering for the image received by the input unit (see Hall, para. [0025] (transmission may use layer to transmit the image).
Regarding claim 4, the combination of Park and Hall further teaches determining, by the mobile edge computing center, which super resolution model to be applied to the image data received through the reception queue based on a free space of each of the one or more reception queues (see Park, page 2 left column, 1st paragraph (selecting an optimal super resolution algorithm among a set of candidate super resolution algorithms depending on queue backlog size)) and generate the super resolution image using the determined super resolution model (see figure 1: super resolution framework which includes a deep learning model as described in section 3 (Proposed Algorithm); section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle)).
Regarding claim 5, the combination of Park and Hall further teaches wherein the super-resolution module includes:
a storage unit configured to store a plurality of super resolution models that differ in at least one of processing speed and processing quality (see Park, page 2 left column, 1st paragraph (selecting an optimal super resolution algorithm among a set of candidate super resolution algorithms depending on queue backlog size); thus, storage is inherently included in order to store these algorithms for performing enhancing video data stream stored in the queue); figure 1: super resolution framework which includes a deep learning model as described in section 3 (Proposed Algorithm); section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle)),; and
a control unit configured to determine which super-resolution model to be applied to the image data from among the plurality of super-resolution models so as to maximize time-averaged super resolution performance for the image data (see Park, section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle; thus it is to maximize time average super resolution performance; controller in figure 1; page 1 section 1 (Introduction): mobile computing, computer).
Regarding claim 6, the advanced statements as applied to claim 5 above are incorporated hereinafter. The combination of Park and Hall further discloses the use of Lyapunov optimization (see Park, page 4 left column (“Based on the Lyapunov optimization, which for the time-average performance…”) and right column, paragraph before section 5).
Regarding claim 7, it is noted that claim recites similar claim limitations called for in the counterpart claim 1 and thus is rejected for the same reasons as above.
Regarding claim 8, it is noted that claim recites similar claim limitations called for in the counterpart claim 2 and thus is rejected for the same reasons as above.
Regarding claim 9, it is noted that claim recites similar claim limitations called for in the counterpart claim 3 and thus is rejected for the same reasons as above.
Regarding claim 13, the advanced statements as applied to claim 7 above are incorporated hereinafter. The combination of Park and Hall further disclose a computer program, hardware and medium (see Park, page 1 left column (computer), page 3 left column (software); Hall, para. [0043] (processor, medium, software, instructions)).
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.
Claim(s) 10-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Park.
Regarding claim 10, Park discloses a deep learning-based super-resolution image processing method comprising the steps of:
receiving, by a mobile edge computing center including one or more transceivers each having a reception queue, image data from an unmanned aerial vehicle system through the one or more transceivers (see figure 1: platform includes a drone, camera, transceiver, and queue; subsection 2.1 (Reference System Model));
determining, by the mobile edge computing center, which super resolution model to be applied to the image data received through the reception queue based on a free space of each of the one or more reception queues (see page 2 left column, 1st paragraph (selecting an optimal super resolution algorithm among a set of candidate super resolution algorithms depending on queue backlog size)); and
generating, by the mobile edge computing center, a super resolution image corresponding to the image data through a deep learning computation based on the determined super-resolution model (see figure 1: super resolution framework which includes a deep learning model as described in section 3 (Proposed Algorithm); section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle)).
Regarding claim 11, Park further teaches further comprising the step of storing a plurality of super-resolution models different in at least one of processing speed and processing quality in the mobile edge computing center (see page 2 left column, 1st paragraph (selecting an optimal super resolution algorithm among a set of candidate super resolution algorithms depending on queue backlog size; with regard to “storing”, these algorithms are inherently stored in the storage in order to enhance video data stream stored in the queue); figure 1: super resolution framework which includes a deep learning model as described in section 3 (Proposed Algorithm); section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle)), wherein the step of determining which super-resolution model to be applied to the image data includes determining by the mobile edge computing center a super-resolution model to be applied to the image data from among the plurality of super-resolution models so as to maximize a time-averaged super-resolution performance for the image data (see section 5 (Concluding Remarks and Future Work): super resolution computation is applied to each stream in the queue in order to enhance the video stream quality; deep super resolution algorithm is used for better performance when queue is idle; thus it is to maximize time average super resolution performance).
Regarding claim 12, the advanced statements as applied to claim 11 above are incorporated hereinafter. Park further discloses the use of Lyapunov optimization (see page 4 left column (“Based on the Lyapunov optimization, which for the time-average performance…”) and right column, paragraph before section 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (“Quality-Aware Streaming and Scheduling for Device-to-Device Video Delivery”, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 24, NO. 4, AUGUST 2016, pages 2319-2331) teaches video stream and scheduling, wherein the scheduling determines which D2D pairs are allowed to transmit at given time based on queue backlog size (page 2313, right column, first full paragraph; figure 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY M DANG whose telephone number is (571)272-7389. The examiner can normally be reached Monday to Friday from 7:00AM to 3:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at 571-272-3382. 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.
DMD
1/2026
/DUY M DANG/Primary Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662