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 Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Please note: Paragraphs from the disclosure are provided at the end of the rationales in order to directly support and highlight the issues with the disclosure.
Claim 1 recites the limitation “determining, by at least one neural network based on the gaze direction information and the object tracking information, that the object has at least a minimum probability of being outside a field of view of the operator”. Claim 8 recites the limitation “determine, by at least one neural network based on the gaze direction information and the first set of object tracking information, that the object has at least a minimum probability of being outside a field of view of the operator” Claim 16 recites the limitation “determine, via at least one neural network based on one or more gaze direction information associated with an operator of a machine and one or more object tracking information associated with an object outside of the machine, that the object has at least a minimum probability of being outside a field of view of the operator”.
Per MPEP 2164.01(a) the following Wands factors are to be considered in determining undue experimentation. These factors include, but are not limited to:
(A) The breadth of the claims;
(B) The nature of the invention;
(C) The state of the prior art;
(D) The level of one of ordinary skill;
(E) The level of predictability in the art;
(F) The amount of direction provided by the inventor;
(G) The existence of working examples; and
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
In each of claims 1, 8 and 16, there are two elements that fail to meet the enablement requirement.
First, that the ‘determination’ is done by ‘at least one neural network’ is not enabled. The disclosure at Par 103 states that a “risk assessment module whether cross-traffic is out of the driver's field of view and provides appropriate warnings. FIG. 13 illustrates one scenario in which the risk assessment module uses information from a DNN for gaze detection (5004) and information from Controller to alert driver.” Paragraphs 81 and 83 discuss in greater detail the risk assessment module. Nowhere in the description is the risk assessment module described as ‘at least one neural networks’. There is the mention of a software stack (IX) performing risk assessment, but this lacks any type of specificity or disclosure to the use of a neural network to perform the specified function. Broadening out and looking at the entire disclosure, there is no disclosure of inputting gaze results and object information results into a neural network at all. In contrast, the disclosure discusses using neural networks to process the gaze information and the object information but never teaches using those results in an additional neural network to determine if the object is within a field of view of the operator, see paragraphs 103 and 105. Turning now to the Wands factors as provided above, the disclosure has stated the use of two other neural network sources to provide outputs that drive the determination processing. These outputs take in quantities of available information and provide the FOV and objection positioning. The job then of the ‘determining’ or of the risk assessment, is to look at the FOV and object positioning and determine if the object is within the FOV of the operator. More specifically, the heavy lifting of data processing has been done to provide these two outputs. The disclosure itself points to a processor laden system on a chip design. There is nothing in the disclosure that would lead of skill in the art to take these two processed outputs and presume the use of an additional neural network to compare the two points and determine if the location is in or out of a FOV. Looking at specific Wands factors (G) there are none, (F) there is none, (A)-(E) in contrast could be potentially interpreted as supporting endeavors not using additional neural networks and rather relying on processor/evaluation level processing. Thus is (H) the level of experimentation in not quantifiable because it is unclear how one of skill in the art would be directed to landing at this operational design. Therefore, the lack of Wands factors leading to this design creates an interpretation that undue experimentation would be required by one of skill in the art. Therefore, the disclosure is interpreted as failing meet the enablement requirement for the ‘at least one of more neural networks’ performing the ‘determining’.
The second limitation of issue is “that the object has at least a minimum probability of being outside a field of view of the operator”. Nowhere in the disclosure is any discussion of a minimum probability as it relates to positioning within the FOV as determined. The disclosure, noted below, only discloses the ability to determine if the item is in or out of the field of view. Therefore, the disclosure has supported a binary yes or no determination, and has not provided any support for the use of a probability from the binary designation of in or out of a FOV. Again turning to the Wand factors, (G) there are none, (F) there is none, (A)-(E) in contrast the disclosure states and supports a binary determination, which is more precise that a probability, and the disclosure has provided no rationale as to why a probability would be used instead of the binary yes or no determination. Thus is (H) the level of experimentation in not quantifiable because it is unclear how one of skill in the art would be directed to landing at this operational design. Therefore, the lack of Wands factors leading to this design creates an interpretation that undue experimentation would be required by one of skill in the art. Therefore, the disclosure is interpreted as failing meet the enablement requirement for the “that the object has at least a minimum probability of being outside a field of view of the operator”.
Therefore, claims 1, 8 and 16 are rejected for failing to sufficiently meet the enablement requirements around each of the limitations noted in detail above. Their dependent claims fail to rectify either issue.
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 the limitation “determining, by at least one neural network based on the gaze direction information and the object tracking information, that the object has at least a minimum probability of being outside a field of view of the operator”. Claim 8 recites the limitation “determine, by at least one neural network based on the gaze direction information and the first set of object tracking information, that the object has at least a minimum probability of being outside a field of view of the operator” Claim 16 recites the limitation “determine, via at least one neural network based on one or more gaze direction information associated with an operator of a machine and one or more object tracking information associated with an object outside of the machine, that the object has at least a minimum probability of being outside a field of view of the operator”.
In each of claims 1, 8 and 16, there are two elements that fail to meet the written description requirement.
First, that the ‘determination’ is done by ‘at least one neural network’ is not enabled. As can be seen below in the relevant paragraphs from the disclosure, nowhere in the description is the ‘determination’ described as done by ‘at least one neural networks’. There is the mention of a software stack (IX) performing risk assessment, but this lacks any type of specificity or disclosure to the use of a neural network to perform the specified function. There is no description provided of inputting gaze results and object information results into a neural network at all. In contrast, the disclosure discusses using neural networks to process the gaze information and the object information but never teaches using those results in an additional neural network to determine if the object is within a field of view of the operator, see paragraphs 103 and 105. There is no support provided for the use of a neural network in this claimed functionality. Therefore, the limitation is interpreted as lacking sufficient written description.
The second limitation of issue is “that the object has at least a minimum probability of being outside a field of view of the operator”. Nowhere in the disclosure is any discussion of a minimum probability as it relates to positioning within the FOV as determined. The disclosure, noted below, only discloses the ability to determine if the item is in or out of the field of view. Therefore, the disclosure has supported a binary yes or no determination, and has not provided any support for the use of a probability from the binary designation of in or out of a FOV. The practice of determining a probability using the stated input data is not described in the disclosure, only the practice of a yes or no determination of the location being within a gaze or FOV, see citations below. There is also no considerations of minimum probability. The concept of a minimum is not even related to the determination processing by the provided disclosure.
Therefore, claims 1, 8 and 16 are rejected for failing to sufficiently meet the written description requirements around each of the limitations noted in detail above. Their dependent claims fail to rectify either issue.
Supporting citations from specification:
[0081] FIG. 6 illustrates a high-level system architecture according to one embodiment of the invention. The system 600 preferably includes a plurality of controllers 602(1)-602(N), including a controller and system for autonomous or semi-autonomous driving. One or more of the controllers 602 may include an Advanced SoC or platform used to execute an intelligent assistant software stack (IX) that conducts risk assessments and provides the notifications, warnings, and autonomously control the vehicle, in whole or in part, executing the risk assessment and advanced driver assistance functions described herein. Two or more of the controllers are used to provide for autonomous driving functionality, executing an autonomous vehicle (AV) software stack to perform autonomous or semi-autonomous driving functionality.
[0083] FIG. 7 illustrates a system architecture according to one embodiment. This system includes a controller and system for autonomous or semi-autonomous driving. Controller (100) receives input from one or more cameras (72, 73, 74, 75) deployed around the vehicle. Controller (100) detects objects and provides information regarding the object's presence and trajectory to the risk assessment module (6000). System includes a plurality of cameras (77) located inside the vehicle. Cameras (77) may be arranged as illustrated in FIG. 8, or in any other manner to provide coverage of the driver and other occupants. Cameras (77) provide input to a plurality of deep neural networks (5000) for monitoring the driver, other occupants, and/or conditions in the vehicle. Alternatively, multi-sensor camera modules (500), (600(1)-(N)), and/or (700) may be used to view either the inside of the vehicle or the outside environment.
[0103] In one embodiment, risk assessment module determines whether cross-traffic is out of the driver's field of view and provides appropriate warnings. FIG. 13 illustrates one scenario in which the risk assessment module uses information from a DNN for gaze detection (5004) and information from Controller to alert driver.
[0105] While gaze detection DNN classifies the region of the driver's gaze, controller (100(2)) uses DNNs executing on an Advanced SoC to detect cross-traffic outside the driver's field of view.
[0128] Once a training manager has determined that training of a model is complete, such as by using at least one end criterion discussed herein, trained model 1508 can be provided for use by a classifier 1514 in classifying (or otherwise generating inferences for) validation data 1512. As illustrated, this involves a logical transition between a training mode for a model and an inference mode for a model. In at least one embodiment, however, trained model 1508 will first be passed to an evaluator 1510, which may include an application, process, or service executing on at least one computing resource (e.g., a CPU or GPU of at least one server) for evaluating a quality (or another such aspect) of a trained model. A model is evaluated to determine whether this model will provide at least a minimum acceptable or threshold level of performance in predicting a target on new and future data. If not, training manager 1504 can continue to train this model. Since future data instances will often have unknown target values, it can be desirable to check an accuracy metric of machine learning on data for which a target answer is known, and use this assessment as a proxy for predictive accuracy on future data.
[0129] In at least one embodiment, a model is evaluated using a subset of training data 1502 that was provided for training. This subset can be determined using a shuffle and split approach as discussed above. This evaluation data subset will be labeled with a target, and thus can act as a source of ground truth for evaluation. Evaluating a predictive accuracy of a machine learning model with same data that was used for training is not useful, as positive evaluations might be generated for models that remember training data instead of generalizing from it. Once training has completed, evaluation data subset is processed using trained model 1508 and evaluator 1510 can determine accuracy of this model by comparing ground truth data against corresponding output (or predictions/observations) of this model. Evaluator 1510 in at least one embodiment can provide a summary or performance metric indicating how well predicted and true values match. If a trained model does not satisfy at least a minimum performance criterion, or other such accuracy threshold, then training manager 1504 can be instructed to perform further training, or in some instances try training a new or different model. If trained model 1508 satisfies relevant criteria, then a trained model can be provided for use by classifier 1514.
[0211] In at least one embodiment, DLA may be used to run any type of network to enhance control and driving safety, including for example and without limitation, a neural network that outputs a measure of confidence for each object detection. In at least one embodiment, confidence may be represented or interpreted as a probability, or as providing a relative “weight” of each detection compared to other detections. In at least one embodiment, confidence enables a system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections. For example, In at least one embodiment, a system may set a threshold value for confidence and consider only detections exceeding threshold value as true positive detections. In an embodiment in which an automatic emergency braking (“AEB”) system is used, false positive detections would cause vehicle to automatically perform emergency braking, which is obviously undesirable. In at least one embodiment, highly confident detections may be considered as triggers for AEB. In at least one embodiment, DLA may run a neural network for regressing confidence value. In at least one embodiment, neural network may take as its input at least some subset of parameters, such as bounding box dimensions, ground plane estimate obtained (e.g. from another subsystem), output from IMU sensor(s) 1966 that correlates with vehicle 1900 orientation, distance, 3D location estimates of object obtained from neural network and/or other sensors (e.g., LIDAR sensor(s) 1964 or RADAR sensor(s) 1960), among others.
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 1-20 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.
Claims 1, 8 and 16 recite the limitation ‘the view of view of the operator’ in the last line of each respective claim. This limitation lacks antecedent basis. This limitation is believed to be a typographical error that should read ‘the field of view of the operator’. Correction is required.
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.
The factual inquiries 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.
Claims 1-10, 12-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hanna (US Pub 20180086339, see IDS) in view of Katz (US Pub 20200207358, see IDS).
Re claims 1 and 8, Hanna discloses a method and the associated system, comprising:
One or more processing circuits (Fig 1A-B, Par 23-25, 27-28), to perform:
obtaining, from one or more internal sensors (Fig 2A eye tracking sensor/camera, Par 39-40, 50), gaze direction information (Par 39-41, 50) associated with an operator (Fig 2A Driver/user, Par 38, 50) of a machine (Fig 2A, Par 38, 48 vehicle);
obtaining, from one or more external sensors (Fig 2A ADAS sensor/camera, Par 38), object tracking information (Par 38-41, 49) associated with an object outside of the machine (Fig 2A potential obstacle shown outside of vehicle, Par 38, 48-49);
determining, by at least one system (Par 40-43; Fig 2D combination processor; Fig 2F Activation engine; Par 51, 53, 60) based on the gaze direction information (Par 40-41, user’s gaze angle; Fig 2E Par 47, 49-52) and the object tracking information (Par 40-41, position V of potential obstacle; Fig 2E Par 47, 49-52), that the object has at least a minimum probability of being outside a field of view of the operator (Par 40-41, probabilities resulting in determination that user has not seen the potential obstacle; Fig 2E Par 47, 49-52, 56); and
initiating an automated maneuvering action (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56), with respect to the object (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56), based at least in part on the object being determined to be outside the view of view of the operator (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56); however Hanna fails to explicitly disclose wherein the at least one system comprises a neural network.
This design is however disclosed by Katz. Katz discloses wherein the at least one system comprises a neural network (Fig 4 el 450/460, Par 108-110; Fig 7 el 750/760, Par 158-161, 164, 169-172).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Hanna in order to incorporate the neural network integration of Katz based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by incorporating a neural network in the data review and processing allows for a higher volume of data to be reviewed in a shorter period of time with excepted quality metrics and functionality to improve the speed and accuracy of determinations made and improve the overall safety protocols while further reducing false positive reactions.
Re claim 16, Hanna discloses a processor (Fig 1A-B, Par 23-25, 27-28) configured to:
determine, via at least one system (Par 40-43; Fig 2D combination processor; Fig 2F Activation engine; Par 51, 53, 60) based on one or more gaze direction information (Par 40-41, user’s gaze angle; Fig 2E Par 47, 49-52) associated with an operator (Fig 2A Driver/user, Par 38, 50) of a machine (Fig 2A, Par 38, 48 vehicle) and one or more object tracking information (Par 40-41, position V of potential obstacle; Fig 2E Par 47, 49-52) associated with an object outside of the machine (Fig 2A potential obstacle shown outside of vehicle, Par 38, 48-49), that the object has at least a minimum probability of being outside a field of view of the operator (Par 40-41, probabilities resulting in determination that user has not seen the potential obstacle; Fig 2E Par 47, 49-52, 56); and
initiate an automated maneuvering action (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56), with respect to the object (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56), based at least in part on the object being determined to be outside the view of view of the operator (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56); however Hanna fails to explicitly disclose wherein the at least one system comprises a neural network.
This design is however disclosed by Katz. Katz discloses wherein the at least one system comprises a neural network (Fig 4 el 450/460, Par 108-110; Fig 7 el 750/760, Par 158-161, 164, 169-172).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Hanna in order to incorporate the neural network integration of Katz based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by incorporating a neural network in the data review and processing allows for a higher volume of data to be reviewed in a shorter period of time with excepted quality metrics and functionality to improve the speed and accuracy of determinations made and improve the overall safety protocols while further reducing false positive reactions.
Re claim 9 the combined disclosure of Hanna and Katz as a whole discloses the system of claim 8, Hanna further discloses wherein the object outside of the machine comprises at least one or more pedestrians (Par 38-39, 41-42), one or more additional machines (Par 38-39, 41-42), or one or more stationary obstacles (Par 38-39, 41-42).
Re claim 10, the combined disclosure of Hanna and Katz as a whole discloses the system of claim 8, Hanna further discloses wherein further configured to initiate the automated driving maneuvering (Par 40-41, system actuates brakes; Fig 2E Par 47, 53-56) upon determining that the minimum probability reaches a predetermined threshold (Par 40-41; Fig 2E Par 47, 53-56).
Re claim 12, the combined disclosure of Hanna and Katz as a whole discloses the system of claim 8, Katz further discloses wherein the neural network is trained on training data (Par 36-39) generated at least by one or more additional machines (Par 36-39) or by one or more simulations (Par 36-39).
Re claim 13, the combined disclosure of Hanna and Katz as a whole discloses the system of claim 8, Katz further discloses wherein further configured to obtain a second set of object tracking information (Par 59, 117, 123, 160-164).
Re claims 3 and 15, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1 and the system of claim 8, Katz further discloses wherein the at least one neural network is trained (par 36-43) using one or more learned characteristics (par 36-43) or one or more learned particularities of the operator (par 36-43).
Re claim 2, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1, Katz further discloses wherein:
the gaze direction information of the operator is determined (Par 27-29, 46-49, 177-179) independently of an angle from which the operator is detected by the internal sensors (Par 27-29, 46-49, 177-179); and
the gaze direction information is determined (Par 27-29, 46-49, 177-179) based, at least in part, on the at least one neural network trained using position data known for a machine coordinate system (Par 27-29, 46-49, 177-179).
Re claim 4, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1, Katz further discloses wherein the gaze direction information is determined based at least in part on an intersection of an operator gaze vector (Par 46, 167-169, 177-178) with an internal region of the machine (Par 46, 167-169, 177-178).
Re claim 5, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1, Hanna further discloses wherein the one or more external sensors comprise at least one or more cameras (Par 38, 48), one or more LiDAR sensors (Par 48), or one or more radar sensors (Par 38, 48).
Re claim 6, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1, Hanna further discloses wherein further comprising generating (Par 55-56, 58-60), in response to at least the determining (Par 55-56, 58-60), one or more alerts (Par 55-56, 58-60), the alerts comprising at least one or more sounds (Par 60), one or more visual warnings (Par 60), or one or more haptics (Par 60).
Re claim 7, the combined disclosure of Hanna and Katz as a whole discloses the method of claim 1, Katz further discloses wherein the object tracking information comprises at least three-dimensional information associated with the object outside of the machine (Par 21, 42, 174, 176, 178).
Re claim 17, the combined disclosure of Hanna and Katz as a whole discloses the processor of claim 16, Katz further discloses wherein:
the gaze direction information of the operator is determined (Par 27-29, 46-49, 177-179) independently of an angle from which the operator is detected by the internal sensors of the machine (Par 27-29, 46-49, 177-179); and
the gaze direction information is determined (Par 27-29, 46-49, 177-179) based, at least in part, on the at least one neural network trained using position data known for a machine coordinate system (Par 27-29, 46-49, 177-179).
Re claim 18, the combined disclosure of Hanna and Katz as a whole discloses the processor of claim 16, Katz further discloses wherein the at least one neural network is trained (par 36-43) using one or more learned characteristics (par 36-43) or one or more learned particularities of the operator (par 36-43).
Re claim 19, the combined disclosure of Hanna and Katz as a whole discloses the processor of claim 16, Katz further discloses wherein the gaze direction information is determined based at least in part on an intersection of an operator gaze vector (Par 46, 167-169, 177-178) with an internal region of the machine (Par 46, 167-169, 177-178).
Re claim 20, the combined disclosure of Hanna and Katz as a whole discloses the processor of claim 16, Katz further discloses wherein the object tracking information comprises at least three-dimensional information associated with the object outside of the machine (Par 21, 42, 174, 176, 178).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL R NEFF whose telephone number is (571)270-1848. The examiner can normally be reached Mon-Fri 5:30am-2:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hannah S. Wang can be reached at (571) 272-9018. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R NEFF/ Primary Examiner, Art Unit 2631
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