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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are “a display module” and “a main control module” in claim 14. See para. 20 “In a fifth aspect, an embodiment of the present disclosure further provides a computer device, including a processor, a memory and a bus, where the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other through the bus, when the computer device is running, and the machine-readable instructions, when executed by a processor, cause the processor to perform steps of the control method of a broadcast monitoring system according to any one in the first aspect, and/or steps of the control method of a broadcast monitoring system according to any one in the second aspect.” See para. 142 “Here, the display module may be, for example, a display screen, and the main control module may be, for example, a System On Chip (SOC). The main control module and the detection apparatus may communicate with each other through a protocols such as UART.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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.
Claims 1, 13, 15-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Liu et al. (US20210382542A1), hereinafter Liu.
Regarding claim 1, Liu teaches a control method of a broadcast monitoring system, comprising: acquiring a detected image (Abstract see "This application provides a screen wakeup method and apparatus."; Para. 6 see "According to a first aspect, a screen wakeup method is provided, including: obtaining M image frames, where each image frame includes a first face image, and M is an integer greater than or equal to 1; determining, based on a preconfigured neural network, whether each first face image matches a preset face image and belongs to a user who is gazing at a screen of a device; and when each first face image matches the preset face image and belongs to the user, switching the screen from a screen-off state to a screen-on state."; Para. 97 see "Step 210: Obtain M image frames, where each image frame includes a first face image, and M is an integer greater than or equal to 1. In a possible implementation, before the M image frames are obtained, status information of a screen may be detected."; Para. 129 see "Step 302: Obtain a latest image frame in an image storage module as an input image for subsequent gaze identification or owner identification.") performing gaze recognition on the detected image through a target neural network model which is trained in advance, to obtain a recognition result of the detected image (Para. 74 see "For example, a preconfigured neural network may be a gaze identification network, that is, whether a user corresponding to an obtained face image is gazing at a screen can be determined based on the face image and the pre-trained neural network. The preconfigured neural network may be applied to the screen wakeup method provided in the embodiments of this application, so that input data (for example, a first face image in this application) is input into the trained gaze identification network, to obtain output data (for example, a first probability in this application)."; Para. 141 see "Step 309: Execute the gaze identification network by using the face image as an input, to output a gaze probability."; Para. 142 see "In this application, the gaze identification network may be a pre-trained deep neural network, and the obtained face image is input to the pre-trained deep neural network, to output a gaze or non-gaze probability.") and sending the recognition result to a terminal, so that the terminal determines a display state based on at least the recognition result (Para. 119 see "Step 230: When each first face image matches the preset face image and belongs to the user, switch the screen from the screen-off state to the screen-on state."; Para. 151 see "Step 315: Wake up the screen, that is, adjust the screen state to the screen-on state."; Para. 158 see "The wakeup processing module may be configured to determine, based on gaze information and owner information that are obtained by the foregoing modules, whether the owner is currently gazing at the screen in M consecutive image frames. If the owner is currently gazing at the screen in M consecutive image frames, a report is sent to a related wakeup unit to wake up the screen. If the owner is currently not gazing at the screen in M consecutive image frames, no reporting is performed.").
Regarding claim 16, Liu teaches the detection apparatus according to claim 15.
In addition, Liu teaches a control apparatus of a broadcast monitoring system, comprising detection apparatus according to claim 15 and a terminal; wherein the terminal is configured to determine a display state based on the recognition result (Para. 25 see "a screen wakeup apparatus is provided, where the apparatus includes: an obtaining unit, configured to obtain M image frames, where each image frame includes a first face image, and M is an integer greater than or equal to 1; a processing unit, configured to determine, based on a preconfigured neural network, whether each first face image matches a preset face image and belongs to a user who is gazing at a screen of a device; and a wakeup unit, configured to: when each first face image matches the preset face image and belongs to the user, switch the screen from a screen-off state to a screen-on state."; Para. 56 see "When the first face image meets both the gaze identification and the owner identification, that is, the current image frame can meet a wakeup condition, a screen state of a device may be switched from a screen-off state to a screen-on state when M consecutive image frames meet the wakeup condition."; Para. 57 see "the device may be a terminal device having a screen, for example, may be user equipment, a mobile device, a user terminal, a terminal, a wireless communications device, or a user apparatus.").
Regarding claim 17, Liu teaches the control method of a broadcast monitoring system according to claim 1.
In addition, Liu teaches a computer device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other through the bus, when the computer device is running, and the machine-readable instructions, when executed by the processor, cause the processor to perform steps of the control method of a broadcast monitoring system according to claim 1 (Para. 37 see "According to a third aspect, a screen wakeup apparatus is provided, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to invoke the computer program from the memory and run the computer program, to enable the screen wakeup apparatus to perform the screen wakeup method"; Para. 184 see "A bus interface unit (BIU) 710 is configured to implement interaction between the host CPU, the DMAC, and an instruction fetch buffer 709 by using a bus. The instruction fetch buffer 709 connected to the controller 704 is configured to store an instruction to be used by the controller 704. The controller 704 is configured to invoke the instruction buffered in the instruction fetch buffer 709, to control a working process of the operation accelerator. An operation of each layer in the convolutional neural network shown in FIG. 1 may be performed by the operation circuit 703 or the vector computing unit 707.").
Regarding claim 18, Liu teaches the control method of a broadcast monitoring system according to claim 1.
In addition, Liu teaches a non-transitory computer readable storage medium storing thereon a computer program which, when being executed by a processor, performs steps of the control method of a broadcast monitoring system according to claim 1 (Para. 37 see "According to a third aspect, a screen wakeup apparatus is provided, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to invoke the computer program from the memory and run the computer program, to enable the screen wakeup apparatus to perform the screen wakeup method"; Para. 184 see "A bus interface unit (BIU) 710 is configured to implement interaction between the host CPU, the DMAC, and an instruction fetch buffer 709 by using a bus. The instruction fetch buffer 709 connected to the controller 704 is configured to store an instruction to be used by the controller 704. The controller 704 is configured to invoke the instruction buffered in the instruction fetch buffer 709, to control a working process of the operation accelerator. An operation of each layer in the convolutional neural network shown in FIG. 1 may be performed by the operation circuit 703 or the vector computing unit 707.").
Regarding claim 19, Liu teaches the control method of a broadcast monitoring system according to claim 13.
In addition, Liu teaches a computer device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other through the bus, when the computer device is running, and the machine-readable instructions, when executed by the processor, cause the processor to perform steps of the control method of a broadcast monitoring system according to claim 13 (Para. 37 see "According to a third aspect, a screen wakeup apparatus is provided, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to invoke the computer program from the memory and run the computer program, to enable the screen wakeup apparatus to perform the screen wakeup method"; Para. 184 see "A bus interface unit (BIU) 710 is configured to implement interaction between the host CPU, the DMAC, and an instruction fetch buffer 709 by using a bus. The instruction fetch buffer 709 connected to the controller 704 is configured to store an instruction to be used by the controller 704. The controller 704 is configured to invoke the instruction buffered in the instruction fetch buffer 709, to control a working process of the operation accelerator. An operation of each layer in the convolutional neural network shown in FIG. 1 may be performed by the operation circuit 703 or the vector computing unit 707.").
Regarding claim 20, Liu teaches the control method of a broadcast monitoring system according to claim 13.
In addition, Liu teaches a non-transitory computer readable storage medium storing thereon a computer program which, when being executed by a processor, performs steps of the control method of a broadcast monitoring system according to claim 13 (Para. 37 see "According to a third aspect, a screen wakeup apparatus is provided, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to invoke the computer program from the memory and run the computer program, to enable the screen wakeup apparatus to perform the screen wakeup method"; Para. 184 see "A bus interface unit (BIU) 710 is configured to implement interaction between the host CPU, the DMAC, and an instruction fetch buffer 709 by using a bus. The instruction fetch buffer 709 connected to the controller 704 is configured to store an instruction to be used by the controller 704. The controller 704 is configured to invoke the instruction buffered in the instruction fetch buffer 709, to control a working process of the operation accelerator. An operation of each layer in the convolutional neural network shown in FIG. 1 may be performed by the operation circuit 703 or the vector computing unit 707.").
Claim 13 is rejected under the same analysis as claim 1 above.
Claim 15 is rejected under the same analysis as claim 1 above.
Claim Rejections - 35 USC § 103
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.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US20210382542A1), hereinafter Liu, in view of Hinton et al.: "Distilling the Knowledge in a Neural Network", Arxiv, Published 9 Mar 2015 [Retrieved on 10/07/2026]. Retrieved from google scholar <https://arxiv.org/abs/1503.02531>, hereinafter Hinton.
Regarding claim 2, Liu teaches the control method of a broadcast monitoring system according to claim 1.
While Liu teaches training a neural network on acquired gaze and non-gaze images of a user to obtain the pre-trained network used to decide whether the user is gazing at the screen, Liu does not teach wherein the target neural network model is trained by the following steps: training a to-be-trained teacher network learning model according to an acquired training data set, to obtain a trained teacher network learning model and training a to-be-trained student network learning model by a knowledge distillation training method, according to the training data set and the trained teacher network learning model, to obtain a trained student network learning model as the target neural network model.
However, Hinton teaches wherein the target neural network model is trained by the following steps: training a to-be-trained teacher network learning model according to an acquired training data set, to obtain a trained teacher network learning model (Pg. 1, Para. 2 see "The cumbersome model could be an ensemble of separately trained models or a single very large model trained with a very strong regularizer such as dropout [9]. Once the cumbersome model has been trained, we can then use a different kind of training, which we call “distillation” to transfer the knowledge from the cumbersome model to a small model that is more suitable for deployment."; Pg. 3, Para. 6 see "To see how well distillation works, we trained a single large neural net with two hidden layers of 1200 rectified linear hidden units on all 60,000 training cases."; Examiner Note: The teacher model is the cumbersome model.) and training a to-be-trained student network learning model by a knowledge distillation training method, according to the training data set and the trained teacher network learning model, to obtain a trained student network learning model as the target neural network model (Pg. 2, Para. 5 see "The transfer set that is used to train the small model could consist entirely of unlabeled data [1] or we could use the original training set. We have found that using the original training set works well, especially if we add a small term to the objective function that encourages the small model to predict the true targets as well as matching the soft targets provided by the cumbersome model."; Pg. 3, Para. 2 see "In the simplest form of distillation, knowledge is transferred to the distilled model by training it on a transfer set and using a soft target distribution for each case in the transfer set that is produced by using the cumbersome model with a high temperature in its softmax."; Examiner Note: The student model is the distilled model.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Liu to incorporate the teachings of Hinton to train the gaze network by first training a larger teacher model on the training images, then training a smaller student model on those same images together with the trained teacher, and use the trained student as the gaze network. Doing so would predictably reduce the computing power and time needed to run gaze recognition by copying what the larger model has learned into a smaller model that runs faster and cheaper.
Regarding claim 3, Liu in view of Hinton teaches the control method of a broadcast monitoring system according to claim 2.
In addition, Liu teaches wherein the training data set comprises a plurality of training images labeled with sample labels (Para. 77 see "In the embodiments of this application, training data may include a feature image of a user, that is, an image that reflect a current feature of the user, which, for example, may be a gaze image, a non-gaze image, head pose information, and gaze direction information of the user."; Para. 79 see "For example, the facial feature image of the at least one user may be a non-gaze face image, a gaze face image, head pose information, and gaze direction information of the at least one user.") adjusting parameters of the to-be-trained network learning model until the loss function is converged, to obtain the trained network learning model as the target neural network model (Para. 70 see "a predicted value of a current network and a target value that is actually expected may be compared, and then, a weight vector of each layer of neural network is updated based on a difference between the two ... The weight vector is continuously adjusted until the deep neural network can predict the target value that is actually expected or a value that is quite close to the target value that is actually expected. ... training of the deep neural network becomes a process of reducing the loss as much as possible.").
While Liu teaches training one neural network on gaze and non-gaze images by comparing the network's predicted value with the expected target value and updating each layer's weights from that difference until the prediction is close to the target, Liu does not teach the training a to-be-trained student network learning model by a knowledge distillation training method, according to the training data set and the trained teacher network learning model, to obtain a trained student network learning model as the target neural network model, comprises: inputting a training image of the plurality of training images into the trained teacher network learning model, to obtain a first output result of the trained teacher network learning model; inputting the training image into the to-be-trained student network learning model, to obtain a second output result of the to-be-trained student network learning model determining a first loss function, according to the first output result and the second output result determining a second loss function, according to the second output result and a sample label of the training image obtaining a weighted loss function, according to the first loss function and the second loss function training the student model according to the weighted loss function, to obtain the trained student network learning model as the target neural network model.
However, Hinton teaches the training a to-be-trained student network learning model by a knowledge distillation training method, according to the training data set and the trained teacher network learning model, to obtain a trained student network learning model as the target neural network model, comprises: inputting a training image of the plurality of training images into the trained teacher network learning model, to obtain a first output result of the trained teacher network learning model; inputting the training image into the to-be-trained student network learning model, to obtain a second output result of the to-be-trained student network learning model (Pg. 1, Para. 2 see "The cumbersome model could be an ensemble of separately trained models or a single very large model trained with a very strong regularizer such as dropout [9]. Once the cumbersome model has been trained, we can then use a different kind of training, which we call “distillation” to transfer the knowledge from the cumbersome model to a small model that is more suitable for deployment."; Pg. 2, Para. 1 see "An image of a BMW, for example, may only have a very small chance of being mistaken for a garbage truck, but that mistake is still many times more probable than mistaking it for a carrot."; Pg. 2, Para. 3 see "An obvious way to transfer the generalization ability of the cumbersome model to a small model is to use the class probabilities produced by the cumbersome model as “soft targets” for training the small model."; Pg. 2, Para. 4 see "Caruana and his collaborators circumvent this problem by using the logits (the inputs to the final softmax) rather than the probabilities produced by the soft max as the targets for learning the small model and they minimize the squared difference between the logits produced by the cumbersome model and the logits produced by the small model."; Pg. 2, Para. 5 see "The transfer set that is used to train the small model could consist entirely of unlabeled data [1] or we could use the original training set. We have found that using the original training set works well, especially if we add a small term to the objective function that encourages the small model to predict the true targets as well as matching the soft targets provided by the cumbersome model."; Pg. 3, Para. 2 see "In the simplest form of distillation, knowledge is transferred to the distilled model by training it on a transfer set and using a soft target distribution for each case in the transfer set that is produced by using the cumbersome model with a high temperature in its softmax."; Pg. 3, Para. 4 see "Each case in the transfer set contributes a cross-entropy gradient, dC/dzi, with respect to each logit, zi of the distilled model. If the cumbersome model has logits vi which produce soft target probabilities pi"; Pg. 3, Para. 6 see "To see how well distillation works, we trained a single large neural net with two hidden layers of 1200 rectified linear hidden units on all 60,000 training cases."; Pg. 5, Para. 6 see "JFT is an internal Google dataset that has 100 million labeled images with 15,000 labels."; Examiner Note: An example use case of the distillation training method is provided in the domain of images. The logits of each model are the outputs. The distilled model is the student model and the cumbersome model is the teacher model.) determining a first loss function, according to the first output result and the second output result (Pg. 3, Para. 3 see "The first objective function is the cross entropy with the soft targets and this cross entropy is computed using the same high temperature in the softmax of the distilled model as was used for generating the soft targets from the cumbersome model."; Pg. 3, Para. 4 see "Each case in the transfer set contributes a cross-entropy gradient, dC/dzi, with respect to each logit, zi of the distilled model. If the cumbersome model has logits vi which produce soft target probabilities pi and the transfer training is done at a temperature of T, this gradient is given by [equation 2]"; Examiner Note: The first objective (loss) function is the cross entropy of the teacher output and the student output.) determining a second loss function, according to the second output result and a sample label of the training image (Pg. 2, Para. 1 see "An image of a BMW, for example, may only have a very small chance of being mistaken for a garbage truck, but that mistake is still many times more probable than mistaking it for a carrot."; Pg. 3, Para. 3 see "The second objective function is the cross entropy with the correct labels. This is computed using exactly the same logits in softmax of the distilled model but at a temperature of 1."; Pg. 5, Para. 6 see "JFT is an internal Google dataset that has 100 million labeled images with 15,000 labels."; Examiner Note: The second objective function is the cross entropy of the student output and the correct labels. An example use case of the distillation training method is provided in the domain of images.) obtaining a weighted loss function, according to the first loss function and the second loss function (Pg. 3, Para. 3 see "When the correct labels are known for all or some of the transfer set, this method can be significantly improved by also training the distilled model to produce the correct labels. One way to do this is to use the correct labels to modify the soft targets, but we found that a better way is to simply use a weighted average of two different objective functions. The first objective function is the cross entropy with the soft targets and this cross entropy is computed using the same high temperature in the softmax of the distilled model as was used for generating the soft targets from the cumbersome model. The second objective function is the cross entropy with the correct labels. This is computed using exactly the same logits in softmax of the distilled model but at a temperature of 1."; Pg. 5, Para. 1 see "For the distillation we tried temperatures of [1,2,5,10] and used a relative weight of 0.5 on the cross-entropy for the hard targets") training the student model according to the weighted loss function, to obtain the trained student network learning model as the target neural network model (Pg. 3, Para. 3 see "When the correct labels are known for all or some of the transfer set, this method can be significantly improved by also training the distilled model to produce the correct labels. One way to do this is to use the correct labels to modify the soft targets, but we found that a better way is to simply use a weighted average of two different objective functions. The first objective function is the cross entropy with the soft targets and this cross entropy is computed using the same high temperature in the softmax of the distilled model as was used for generating the soft targets from the cumbersome model. The second objective function is the cross entropy with the correct labels. This is computed using exactly the same logits in softmax of the distilled model but at a temperature of 1."; Pg. 5, Para. 1 see "For the distillation we tried temperatures of [1,2,5,10] and used a relative weight of 0.5 on the cross-entropy for the hard targets").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Liu in view of Hinton to incorporate the teachings of Hinton to pass each labeled training image through a larger teacher model and a smaller student model, compute one loss from the two outputs and a second loss from the student output and the image label, combine those two losses by a weight, and train the student with that weighted loss so the trained student model is the gaze network. Doing so would predictably reduce the computing power and time needed to run gaze recognition by copying what the larger model has learned into a smaller model that runs faster and cheaper.
Allowable Subject Matter
Claims 4-12, 14 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.
Regarding claim 4, none of the references cited above teach building the training set for the gaze network by checking each original image for a face, drawing a box around the face, and using the face's key points to find its pitch, yaw, and roll and moving the box by the pose and its size, then labeling the region inside it as gaze or as one of two non-gaze classes.
Regarding claim 5, none of the references cited above teach the control method of a broadcast monitoring system according to claim 4.
Regarding claim 6, none of the references cited above teach the control method of a broadcast monitoring system according to claim 5.
Regarding claim 7, none of the references cited above teach the control method of a broadcast monitoring system according to claim 4.
Regarding claim 8, none of the references cited above teach the control method of a broadcast monitoring system according to claim 7.
Regarding claim 9, none of the references cited above teach the control method of a broadcast monitoring system according to claim 4.
Regarding claim 10, none of the references cited above teach the control method of a broadcast monitoring system according to claim 9.
Regarding claim 11, none of the references cited above teach the control method of a broadcast monitoring system according to claim 4.
Regarding claim 12, none of the references cited above teach the control method of a broadcast monitoring system according to claim 4.
Regarding claim 14, none of the references cited above teach sending a wake request from the terminal's control module to its display when the recognition result says an object is gazing, and sending a sleep request when the result says no object is gazing and a preset time has passed since the display last woke.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bui et al. (US20220147142A1) discloses an apparatus to control a display of an electronic device including a user presence detector to determine a presence of a user relative to the device based on image data generated by an image sensor of the device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER VAUGHN whose telephone number is (571) 272-5253. The examiner can normally be reached M-F 11am-7pm (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER MEHMOOD can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER VAUGHN/
Examiner, Art Unit 2675
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights