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 .
DETAILED ACTION
Response to Amendment
In response to applicant’s amendment received on 12/8/2025, all requested changes to the claims have been entered.
Response to Argument
Applicant’s arguments filed on 12/8/2025 have been considered but they are moot in view of the new ground(s) of rejection.
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.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim 1-3, 5-8, 10-13 and 15 are rejected under 35 USC 103 as being unpatentable over BULZACKI et al. (WO 2016/191856) in view of Jiang et al. (“Object Detection and Counting with Low Quality Videos”, Stanford University, 2016 ).
With respect to claim 1, BULZACKI et al. teach one or more cameras configured to capture a plurality of betting areas arranged in a two- dimensional orientation on a gaming table from diagonally above ([00222] discloses that the camera is elevated angle; Fig. 5 and 6) and generate an image including a plurality of gaming chips wagered in a betting area of the plurality of betting areas (Fig. 5 and 6, [00133], [00144]-[0146] discloses that several chip stacks are imaged at various 2D coordinates on the table);
a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning ([00133], [00138] discloses the use of machine learning and depth and coordinates are considered by the data at [00137]-[0146]);
a device configured to acquire teacher data for generating or learning the learning model by capturing the images (The camera at [00144) is suitable for and configured to acquire image data, which is what the machine learning at [00138] would be trained on); and
a learning device configured to train the learning model using the teacher data, wherein the teacher data includes a plurality of images in which illumination conditions and chip hiding states are different from each other ([00132]-[00134], [00142)-[0144] disclose that images with
different illumination conditions and obstructions are used in the calibration process, which can use machine learning).
BULZACKI et al. do not teach expressly that a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal;
Jiang et al. teach a learning model configured to recognize a number of object by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal; (Abstract, The models are integrated with low resolution webcam videos and evaluated with an object counting agent; 1. Introduction, The application we are looking into is enabling robot to
count objects in a comparatively fixed scene, such as counting number of people in certain store or number of cars in certain parking lot; 3.2 Faster R-CNN, 3.2. Faster RCNN R-CNN is a neural network architecture for object detection, with region proposal network (RPN) which extracts object candidates from raw image and have them processed by convolutional networks).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to count number of object in raw image in the method of BULZACKI et al.
The suggestion/motivation for doing so would have been that so that conveniently input raw image without pre-process..
Therefore, it would have been obvious to combine Jiang et al. with BULZACKI et al.to obtain the invention as specified in claim 1.
With respect to claim 2, BULZACKI et al. teach that the teacher data includes a training image showing a single unstacked gaming chip (See [00249], The system may also be trained to differentiate between new versions of chips from obsolete versions of chips, in order to do that system should be able to identify each chip).
With respect to claim 3, BULZACKI et al. teach that the teacher data includes a training image showing a second plurality of gaming chips stacked on top of each other (See [00231]).
With respect to claim 5, BULZACKI et al. teach that the learning model is configured to recognize the position, the type, and the number of the gaming chips wagered on both the first near place and the second place (para [00060], Machine-learning techniques (e.g., random forests) may be utilized and refined such that visual features representative of different chip values are readily identified, despite variations between different facilities, lighting conditions and chip types.; para [00133] and [00137]-[00138]).
With respect to claim 6, BULZACKI et al. teach one or more cameras configured to capture a plurality of betting areas arranged in a two- dimensional orientation on a gaming table from diagonally above ([00222] discloses that the camera is elevated angle; Fig. 5 and 6) and generate an image including a plurality of gaming chips wagered in a betting area of the plurality of betting areas (Fig. 5 and 6, [00133], [00144]-[0146] discloses that several chip stacks are imaged at various 2D coordinates on the table);
a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning ([00133], [00138] discloses the use of machine learning and depth and coordinates are considered by the data at [00137]-[0146]);
a device configured to acquire teacher data for generating or learning the learning model by capturing the images (The camera at [00144) is suitable for and configured to acquire image data, which is what the machine learning at [00138] would be trained on); and
a learning device configured to train the learning model using the teacher data, wherein the teacher data includes an image showing a stack of a plurality of gaming chips having a specific color that differs from each other on a side (para [00226]-[00227] and [00245])]).
BULZACKI et al. do not teach expressly that a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal;
Jiang et al. teach a learning model configured to recognize a number of object by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal; (Abstract, The models are integrated with low resolution webcam videos and evaluated with an object counting agent; 1. Introduction, The application we are looking into is enabling robot to
count objects in a comparatively fixed scene, such as counting number of people in certain store or number of cars in certain parking lot; 3.2 Faster R-CNN, 3.2. Faster RCNN R-CNN is a neural network architecture for object detection, with region proposal network (RPN) which extracts object candidates from raw image and have them processed by convolutional networks).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to count number of object in raw image in the method of BULZACKI et al.
The suggestion/motivation for doing so would have been that so that conveniently input raw image without pre-process..
Therefore, it would have been obvious to combine Jiang et al. with BULZACKI et al.to obtain the invention as specified in claim 6.
With respect to claim 7, BULZACKI et al. teach that the teacher data includes a training image showing a single unstacked gaming chip (See [00249], The system may also be trained to differentiate between new versions of chips from obsolete versions of chips, in order to do that system should be able to identify each chip).
With respect to claim 8, BULZACKI et al. teach that the teacher data includes a training image showing a second plurality of gaming chips stacked on top of each other (See [00231]).
With respect to claim 10, BULZACKI et al. teach that the learning model is configured to recognize the position, the type, and the number of the gaming chips wagered on both the first place and the second place (para [00060], Machine-learning techniques (e.g., random forests) may be utilized and refined such that visual features representative of different chip values are readily identified, despite variations between different facilities, lighting conditions and chip types.; para [00133] and [00137]-[00138]).
With respect to claim 11, BULZACKI et al. teach one or more cameras configured to capture a plurality of betting areas arranged in a two- dimensional orientation on a gaming table from diagonally above ([00222] discloses that the camera is elevated angle; Fig. 5 and 6) and generate an image including a plurality of gaming chips wagered in a betting area of the plurality of betting areas (Fig. 5 and 6, [00133], [00144]-[0146] discloses that several chip stacks are imaged at various 2D coordinates on the table);
a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning ([00133], [00138] discloses the use of machine learning and depth and coordinates are considered by the data at [00137]-[0146]);
a device configured to acquire teacher data for generating or learning the learning model by capturing the images (The camera at [00144) is suitable for and configured to acquire image data, which is what the machine learning at [00138] would be trained on); and
a learning device configured to train the learning model using the teacher data, wherein the teacher data includes an image of a plurality of gaming chips stacked out of alignment with each other (para [0061], and [00231]).
BULZACKI et al. do not teach expressly that a learning model configured to recognize a position, a type, or a number of gaming chips wagered on a first place or on a second place on the gaming table by a player by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal;
Jiang et al. teach a learning model configured to recognize a number of object by analyzing one image using trained artificial intelligence or deep learning without performing image processing including color adjustment and/or noise removal; (Abstract, The models are integrated with low resolution webcam videos and evaluated with an object counting agent; 1. Introduction, The application we are looking into is enabling robot to
count objects in a comparatively fixed scene, such as counting number of people in certain store or number of cars in certain parking lot; 3.2 Faster R-CNN, 3.2. Faster RCNN R-CNN is a neural network architecture for object detection, with region proposal network (RPN) which extracts object candidates from raw image and have them processed by convolutional networks).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to count number of object in raw image in the method of BULZACKI et al.
The suggestion/motivation for doing so would have been that so that conveniently input raw image without pre-process..
Therefore, it would have been obvious to combine Jiang et al. with BULZACKI et al.to obtain the invention as specified in claim 11.
With respect to claim 12, BULZACKI et al. teach that the teacher data includes a training image showing a single unstacked gaming chip (See [00249], The system may also be trained to differentiate between new versions of chips from obsolete versions of chips, in order to do that system should be able to identify each chip).
With respect to claim 13, BULZACKI et al. teach that the teacher data includes a training image showing a second plurality of gaming chips stacked on top of each other (See [00231]).
With respect to claim 15, BULZACKI et al. teach that the learning model is configured to recognize the position, the type, and the number of the gaming chips wagered on both the first place and the second place (para [00060], Machine-learning techniques (e.g., random forests) may be utilized and refined such that visual features representative of different chip values are readily identified, despite variations between different facilities, lighting conditions and chip types.; para [00133] and [00137]-[00138]).
Claim 4, 9 and 14 are rejected under 35 USC 103 as being unpatentable over BULZACKI et al. (WO 2016/191856) in view of Jiang et al. (“Object Detection and Counting with Low Quality Videos”, Stanford University, 2016 ) and in further view of ZHONG (CN 110678908).
BULZACKI et al. and Jiang et al. teach all the limitations of claim 1 as applied above from which claim 4 respectively depend.
BULZACKI et al. and Jiang et al.do not teach expressly that the teacher data includes a training image showing a gaming chip that is partially hidden due to a blind spot of the one or more cameras.
ZHONG teaches the teacher data includes a training image showing a gaming chip that is partially hidden due to a blind spot of the one or more cameras (page 8 last paragraph).
At the time of effective filing, it would have been obvious to a person of ordinary skill in the art to train data with part of data is hidden in the method of BULZACKI et al. and Jiang et al.
The suggestion/motivation for doing so would have been that so that accurately identify chip later.
Therefore, it would have been obvious to combine ZHONG with BULZACKI et al. and Jiang et al. to obtain the invention as specified in claim 4.
With respect to claim 9, claim 9 is rejected same reason as claim 4 above.
With respect to claim 14, claim 9 is rejected same reason as claim 4 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Randolph Chu whose telephone number is 571-270-1145. The examiner can normally be reached on Monday to Thursday from 7:30 am - 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached on (571) 272-7778.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANDOLPH I CHU/
Primary Examiner, Art Unit 2667
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