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 .
Double Patenting
1. A rejection based on double patenting of the "same invention" type finds its support in the language of 35 U.S.C. 101 which states that "whoever invents or discovers any new and useful process ... may obtain a patent therefor ..." (Emphasis added). Thus, the term "same invention," in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957); and In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970).
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
3. Claims 21-22, 25-28, 32-35, 39-40 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 12,131,536. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 21-22, 25-28, 32-35, 39-40 of the application is merely broader in scope than patented claims 1-21 with added limitations of the type of event being a package, presence of an individual and variations of supervised learning with user input and a display which are notoriously well-known in the in art as detailed below in the rejections, thereby being an obvious variant.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the patented claims to have incorporated supervised learning for confirmation of different types of events for the mere benefit of allowing for a more accurate learning model.
Claims 21-22, 25-28, 32-35, 39-40 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-26 of U.S. Patent No. 11,430,312. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 21-22, 25-28, 32-35, 39-40 of the application is merely broader in scope than patented claims 1-26 with added limitations of the type of event being a package, presence of an individual and variations of supervised learning with user input and a display which are notoriously well-known in the in art as detailed below in the rejections, thereby being an obvious variant.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the patented claims to have incorporated supervised learning for confirmation of different types of events for the mere benefit of allowing for a more accurate learning model.
Claim Rejections - 35 USC § 103
4. 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 of this title, 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.
5. Claims 21-22 and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Mathew et al., US 2015/0347908.
Regarding claim 21, Nagarajan teaches of an apparatus (See Fig.1, 120; Fig.2, 270) comprising:
interface circuitry (See col.22-23);
computer readable instructions (See col.22-23); and
at least one processor circuit to be programmed based on the instructions (See col.22-23) to:
cause a first machine learning model to be deployed to a first device, the first machine learning model trained with first training data to detect at least one event depicted in first video segments (See Figs.1-3; col.1-4; col.6-8; col.11-13; and col.17-19);
second training data (See Figs.1-3; col.1-4; col.6-8; col.11-13; and col.17-19), retrain the first machine learning model based on the second training data to obtain a second machine learning model, the second training data based on output data from the first device, the output data associated with execution of the first machine learning model by the first device (See Figs.1-3; col.1-4; col.6-8; col.11-13; and col.17-19); and
cause the second machine learning model to be deployed to a second device different from the first device (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19).
Nagarajan is silent with respect to where retraining occurs after availability of at least a threshold amount of data.
However, in the same field of endeavor, Mathew teaches of where retraining occurs after availability of at least a threshold amount of data (See [0024]).
It would have been obvious to one of ordinary skill in the art before the time effective filing date of the claimed invention to have modified the teachings of Nagarajan to have incorporated the teachings of Mathew for the mere benefit of more efficient use of bandwidth and resources.
Regarding claim 22, the combination teaches the apparatus of claim 21, wherein the output data is from a plurality of devices that respectively executed the first machine learning model, the plurality of devices including the first device (See Nagarajan, Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 output data is from the robots/devices that executed the grasp model).
Regarding claim 25, the combination teaches the apparatus of claim 21, wherein one or more of the at least one processor circuit is to train the first machine learning model to output values representative of respective likelihoods that corresponding events are depicted in an input video segment (See Nagarajan, Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19).
Regarding claim 26, the combination teaches the apparatus of claim 21.
The combination silent with respect to wherein the least one event corresponds to arrival of a package.
OFFICIAL NOTICE is taken to note that recognition of events such as package arrivals is notoriously well-known the art and would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated into the combined teachings of Nagarajan and Mathew for the mere benefit being able to recognize and classify different types of objects/events for different types of applications.
Regarding claim 27, the combination teaches the apparatus of claim 21.
The combination silent with respect to wherein the least one event corresponds to a presence of an individual.
OFFICIAL NOTICE is taken to note that recognition of events such presence of an individual is notoriously well-known the art and would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated into the combined teachings of Nagarajan and Mathew for the mere benefit being able to recognize and classify different types of objects/events for different types of applications.
6. Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Mathew et al., US 2015/0347908, and in further view of Cao, US 2019/0348152.
Regarding claim 23, the combination of Nagarajan and Mathew teaches the apparatus of claim 21, wherein one or more of the at least one processor circuit is to retrain the first machine learning model (See Nagarajan, analysis of claim 21; Mathew, [0024]) after (i) the availability of at least the threshold amount of second training data (See Mathew, [0024]).
The combination is silent with respect to updating after an (ii) expiration of a periodic interval.
However, in the same field of endeavor, Cao teaches of updating after an (ii) expiration of a periodic interval (See [0090]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nagarajan and Mathew to have incorporated the teachings of Cao for the mere benefit of having a more efficient way of updating the model with respect to the training data.
7. Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Mathew et al., US 2015/0347908, in further view of Cao, US 2019/0348152, and in further view of Kasaragod et al., US 2019/0036716.
Regarding claim 24, the combination of Nagarajan, Mathew, and Cao teaches the apparatus of claim 21, wherein one or more of the at least one processor circuit is to retrain the first machine learning model after (i) the availability of at least the threshold amount of second training data, (ii) expiration of a periodic interval (See analysis of claim 23.
The combination is silent with respect to the updating after (iii) receipt of a user input.
However, in the same field of endeavor, Kasaragod teaches of updating after (iii) receipt of a user input (See [0126]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nagarajan, Mathew, and Cao to have incorporated the teachings of Kasaragod for the mere benefit of having a more efficient way of updating the model with respect to the training data.
8. Claims 30 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Desmulliez et al., US 2019/0257771.
Regarding claim 30, Nagarajan teaches the apparatus of claim 29.
Nagarajan is silent with respect to wherein the characteristic includes a sweep rate of the image sensor.
However, in the same field of endeavor, Desmulliez teaches of wherein the characteristic includes a sweep rate of the image sensor (See [0109]-[0116]).
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to have modified the teachings of Nagarajan to have incorporated the teachings of Desmulliez for the mere benefit of providing a more indepth training model.
Regarding claim 37, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 30.
9. Claims 31 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Amato et al., US 2019/0065901.
Regarding claim 31, Nagarajan teaches the apparatus of claim 29.
Nagarajan is silent with respect to wherein the characteristic includes a capture rate of the image sensor.
However, in the same field of endeavor, Amato teaches of wherein the characteristic includes a capture rate of the image sensor (See [0055]).
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to have modified the teachings of Nagarajan to have incorporated the teachings of Amato for the mere benefit of providing a more in depth training model.
Regarding claim 38, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 31.
10. Claims 34 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Nagarajan et al., US 10,981,272 in view of Fridental et al., US 2018/0293442.
Regarding claim 34, Nagarajan teaches the apparatus of claim 28, wherein the first output data includes an indication of whether the machine learning model inferred that the first input video segment depicts the at least one event (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-20 grasp attempt data being data indicative of being a success of failure), and one or more of the at least one processor circuit is to determine the second training data based on (i) the indication of whether the machine learning model inferred that the first input video segment depicts the at least one event and (ii) the label specifying whether the first input video segment depicts the at least one event (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-20 labeling and annotating of the event by the user for verification based on second training data of the labeling).
Nagarajan is silent with respect to data being based on a comparison of the inferred event and the label.
However, in the same field of endeavor, Fridental teaches of data being based on a comparison of the inferred event and the label (See [0076]-[0077], and [0092]-[0098]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nagarajan to have incorporated the teachings of Fridental for the mere benefit of being able to provide corrected data from supervised training for a more accurate model.
Regarding claim 40, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 34.
Claim Rejections - 35 USC § 102
11. 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.
12. Claims 28-29, 32-33, 35-36, and 39 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nagarajan et al., US 10,981,272.
Regarding claim 28, Nagarajan teaches of an apparatus (See Figs.1-3, robot) comprising:
interface circuitry to download a machine learning model (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 downloading of the model);
computer readable instructions (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19); and
at least one processor circuit to be programmed based on the instructions (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19) to:
execute the machine learning model to produce first output data corresponding to a first input video segment, the machine learning model trained based on first training data to detect at least one event depicted in training video segments (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 robot executes the model according to the image sensor and object detection event in a location for grasping based on trained segments and data);
cause the interface circuitry to report second training data, the second training data based on the first input video segment, the first output data and a label specifying whether the first input video segment depicts the at least one event (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 robots report and send updated data/second training data which is based on the first output and of labeling/annotated/verifying the event); and
execute a retrained instance of the machine learning model to produce second output data corresponding to a second input video segment, the retrained instance of the machine learning model based on the second training data (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 the robot executes the updated/retrained model which produces the second output data/results of a second video and the updated model is based on the updated reported data through updated the model through an iterative process).
Regarding claim 29, Nagarajan teaches the apparatus of claim 28, wherein one or more of the at least one processor circuit is to segment a video feed based on a characteristic of an image sensor to obtain the first input video segment and the second input video segment, the image sensor to capture the video feed (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 wherein the image sensor has a characteristic of at least a placement on the robot which obtains the first and second video segments and segments the video with bounding boxes for the object).
Regarding claim 32, Nagarajan teaches the apparatus of claim 28, wherein the label specifying whether the first input video segment depicts the at least one event is based on a user input (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 annotating and labeling whether the event as a successful grasp by a user).
Regarding claim 33, Nagarajan teaches the apparatus of claim 32, wherein one or more of the at least one processor circuit is to: cause the first input video segment to be presented on a display; and generate the label based on the user input (See Figs. 1-3; col.1-4; col.6-8; col.11-13; and col.17-19 viewer/human views the recording, thereby having being displayed and presented and annotating labeling by the user).
Regarding claim 35, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 28.
Regarding claim 36, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 29.
Regarding claim 39, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 33.
Contact
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ricky Chin whose telephone number is 571-270-3753. The examiner can normally be reached on M-F 8:30-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached on 571-272-3982. The fax phone number for the organization where this application or proceeding is assigned is 703-872-9306.
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/Ricky Chin/
Primary Examiner
AU 2424
(571) 270-3753
Ricky.Chin@uspto.gov
Prosecution Insights