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 .
Specification
The disclosure is objected to because of the following informalities:
The specification (paragraphs 21,34,38,51) uses the term “convolutional neutral networks”. The Examiner believes this should be “neural”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “wherein the neural network generates the probability score based on comparing characteristics of a qualifying behavior in the video feed to a distraction profile comprising distraction characteristics;” and “ generating, by the neural network, a probability score for the background region based on comparing characteristics of a qualifying behavior in the background region to the distraction profile;” Based on the claim as written, the neural network is generating two different probability scores. It is not readily apparent where Applicant is deriving support for these limitations.
The word “neural” only appears in paragraph 21(“The distraction detection model 115 may include any combination of machine learning based techniques and rules and/or computer vision techniques. In some cases, the machine learning based techniques may include techniques such as artificial neural networks, convolutional neutral networks, Bayesian classifiers, and/or genetically derived algorithms and/or functions.”), 34(“The gesture detection model 140 may include any combination of machine learning based techniques and rules. In some cases, the machine learning based techniques may include techniques such as artificial neural networks, convolutional neutral networks, Bayesian classifiers, and/or genetically derived algorithms and/or functions.”), 38 (“The distraction detection model 215 may include any combination of machine learning based techniques and rules and/or computer vision techniques. In some cases, the machine learning based techniques may include techniques such as artificial neural networks, convolutional neutral networks, Bayesian classifiers, and/or genetically derived algorithms and/or functions.”) and 51 (“The gesture detection model 240 may include any combination of machine learning based techniques and rules. In some cases, the machine learning based techniques may include techniques such as artificial neural networks, convolutional neutral networks, Bayesian classifiers, and/or genetically derived algorithms and/or functions.”)
Thus details on how the neural network works in conjunction with the method steps in the disclosure are not explicitly disclosed. In other words, the Specification indicates that the distraction detection model can be a neural network, but fails to disclose that the classification component is a neural network.
Additionally, the specification discloses using deep learning models (or traditional vision algorithms) by inputting background data (No disclosure of characteristics of qualifying behavior) and using atleast one distraction profile include distraction characteristics that indicate the qualifying behavior belongs to a distracting category of data. Followed by or possibly part of the model also includes comparing the atleast one qualifying behavior to the distraction profile1. In other words, the Specification fails to disclose how one generates a probability score, other than it uses background data and a distraction profile. Claiming any details other than that would lead to a new matter rejection.
Additionally, the Specification indicates there is a threshold for each type of qualifying behavior (see paragraph 47), not a single threshold for all types for “qualifying behaviors”.
Finally, there is clearly no disclosure of two different probability scores.
Pararaph 15 ->” the probability score may be obtained by supplying the
background data to a classification component. The classification component may include
one or more classification models that include at least one of traditional computer visioning
techniques and deep learning models. In this regard, the classification component may
include at least a distraction profile. The distraction profile may include distraction
characteristics that indicate the qualifying behavior belongs to a distracting category of data.
The background data including the at least one qualifying behavior may be compared to the
distraction profile.”
Paragraph 29 -> “Obtaining a probability score may include
supplying the background data to the classification component 120 (e.g., supplying the
background data to one or more classification models). When the background data is
supplied to the classification component 120, the classification component 120 may compare
the background data including at least one qualifying behavior to a distraction profile. The
distraction profile may include distraction characteristics that indicate the qualifying behavior
belongs to the distracting category of data. For example, the distraction profile may indicate
that XYZ characteristics of a qualifying behavior are distracting and belong to the distracting
category of data. In this regard, when the detected qualifying behavior is compared to the
distraction profile, a match percentage between the characteristics of the detected qualifying
behavior and the distraction characteristics included in the distraction profile may be
determined.”
Paragraph 46 -> “Obtaining a probability score may include
supplying the background data to the classification component 220 (e.g., supplying the
background data to one or more classification models). When the background data is
supplied to the classification component 220, the classification component 220 may compare
the background data including at least one qualifying behavior to a distraction profile. The
distraction profile may include distraction characteristics that indicate the qualifying behavior
belongs to the distracting category of data. For example, the distraction profile may indicate
that XYZ characteristics of a qualifying behavior are distracting and belong to the distracting
category of data. In this regard, when the detected qualifying behavior is compared to the
distraction profile, a match percentage between the characteristics of the detected qualifying
behavior and the distraction characteristics included in the distraction profile may be
determined.”
Paragraph 66 -> “Obtaining a probability score may
include supplying the background data to a classification component (e.g., supplying the
background data to one or more classification models). When the background data is
supplied to the classification component, the classification component may compare the
background data including at least one qualifying behavior to a distraction profile. The
distraction profile may include distraction characteristics that indicate the qualifying behavior
belongs to the distracting category of data. For example, the distraction profile may indicate
that XYZ characteristics of a qualifying behavior are distracting and belong to the distracting
category of data. In this regard, when the detected qualifying behavior is compared to the
distraction profile, a match percentage between the characteristics of the detected qualifying
behavior and the distraction characteristics included in the distraction profile may be
determined.”
Claims 9 and 18 are rejected under similar grounds as claim 1.
Claims 2-8,10-17,19-20 are rejected as dependent upon a rejected claim.
No Prior Art reads on claims 1-20. A neural network does not work as claimed in the independent claims. A neural network takes labeled dataset as input and trains, by adjusting internal weights, to minimize the difference between the predicted and ground truth labels. During testing, a test input is applied to the neural network and it outputs one or more probability scores. There are no “distraction profiles” with “distraction characteristics” which are compared.
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 GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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/GANDHI THIRUGNANAM/Primary Examiner, Art Unit 2672
1 It isn’t readily apparent how a neural network would accomplish this. It is simply just not the way a neural network works. It more likely relates to traditional computer vision techniques.