DETAILED ACTION
Drawings
The drawings are objected to under 37 CFR 1.83(a) because FIGs 2-4 fail to show the content in the white boxes as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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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.
Claim(s) 1-3, 6-13, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WEE ( US 20200249637) in view of ZHAN ( CN 108229479).
Regarding claim 1, WEE teaches a method for providing an explainable artificial intelligence-based representation for at least partially autonomous driving applications comprising:
receiving, by a processing circuit, environment information relating to an environment in which a vehicle is present ( [0050], First, at S101, the predictors 101 and subcontrollers receive state measurements such as position, speed and other observations from the plant 106 (e.g., vehicle)) ;
detecting, by the processing circuit, a plurality of discrete elements in the environment( [0051], At S102, the predictors 101 compute … output predictions, such as forecasts of traffic participants' behaviors);
generating, by the processing circuit, resource allocation information relating to the plurality of discrete elements( [0051], At S102, the predictors 101 compute and transmit the necessary output predictions, such as forecasts of traffic participants' behaviors) ;
based on the generating step, selecting, by the processing circuit, an artificial intelligence resource for processing a selected discrete element of the plurality of discrete elements, wherein the artificial intelligence resource is trained to identify a collection of detectable objects or characteristics in the driving environment([0031], The predictors 101 may employ any machine learning technique such as kernel methods or deep neural networks, and each predictor 101 computes state predictions that are required by each type of subcontroller. The predictors 101 can also be classifiers or detectors depending on the needs of the algorithms used in the subcontrollers)
WEE does not expressly teach
wherein the collection of detectable objects or characteristics includes the selected discrete element, and wherein the artificial intelligence resource is associated with a semantic element;
identifying, by the artificial intelligence resource, the discrete element as one of the detectable objects or characteristics; and
producing, using the semantic element, the explainable artificial intelligence-based representation of the selected discrete element or an action relating to the selected discrete element.
However, ZHAN teaches
wherein the collection of detectable objects or characteristics includes the selected discrete element, and wherein the artificial intelligence resource is associated with a semantic element( Page 6, para 5, schematic diagram of the … semantic segmentation);
identifying, by the artificial intelligence resource, the discrete element as one of the detectable objects or characteristics(Page 6, para 5, selecting at least one sub-image by selecting frame 211 in any one of the known category image 21 that has a characteristic … , in the image layer feature generally representing some parts information of the object (such as a vehicle wheel. the nose of the face, etc.)) ; and
producing, using the semantic element, the explainable artificial intelligence-based representation of the selected discrete element or an action relating to the selected discrete element( 21 and 22 in Fig. 2 (图2)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of WEE and ZHAN, by implementing the detection step in WEE with the neural network taught by ZHAN, with motivation to “obtain higher accuracy on the semantic segmentation” ( ZHAN, Abstract).
Regarding claim 2, WEE in view of ZHAN teaches the method according to claim 1, wherein the explainable artificial intelligence-based representation is a human-interpretable explainable representation or a machine- interpretable explainable representation ( ZHAN, 21 and 22 in Fig. 2 (图2)) .
Regarding claim3, WEE in view of ZHAN teaches the method according to claim 1, wherein the explainable artificial intelligence-based representation is a graphical indicator( ZHAN, 21 and 22 in Fig. 2 (图2)).
Regarding claim 6, WEE in view of ZHAN teaches the method according to claim 1, wherein the artificial intelligence resource is a narrow artificial intelligence agent (WEE, [0033]-[0035], the subcontrollers may include different model predictive controllers, without any learning-based controller).
Regarding claim 7, WEE in view of ZHAN teaches the method according to claim 1, wherein the resource allocation information pertains to multi-domain information associated with a given point in time and generated by a group of perception modules each associated with a dedicated domain ( WEE, [0033]-[0036], for autonomous driving, the learned subcontrollers 102 can be deep neural networks, and several learned subcontrollers 102 can be constructed by using open-source data, data that are part of trade secrets of automakers, and data collected from an open or proprietary network of cars with the same built or model. A separate learned subcontroller 102 can also be trained which focuses on a specific driver of the car. In this way, the learned subcontroller 102 predicts based on predictive machine learning models).
Regarding claim 8, WEE in view of ZHAN teaches the method according to claim 1, wherein the resource allocation information pertains to a specified point in time and includes predictive resource allocation information pertaining to a next point in time, and the explainable artificial intelligence-based representation pertains to an artificial intelligence agent allocation indicator at the next point in time( WEE, Fig. 3; [0050], subcontrollers receive state measurements such as position, speed and other observations ) .
Regarding claim 9, WEE in view of ZHAN teaches the method according to claim l, wherein the resource allocation information pertains to a specified point in time and includes predictive resource allocation information pertaining to a next point in time, and the explainable artificial intelligence-based representation pertains to a driving related operation indicator at the next point in time( WEE, Fig. 3; [0051], compute and transmit the necessary output predictions, such as forecasts of traffic participants' behaviors, that are required by the subcontrollers.).
Regarding claim 10, WEE in view of ZHAN teaches the method according to claim 1, wherein the producing of the explainable artificial intelligence-based representation comprises triggering a generation of the explainable artificial intelligence-based representation( ZHAN, 21 and 22 in Fig. 2 (图2)).
Claims 11-13 and 16-20 recite the medium for the method in claims 1-3 and 6-10, thus are also rejected.
Claim(s) 4-5, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over WEE in view of ZHAN, further in view of LEE ( US 20110242318)
Regarding claim 4, WEE in view of ZHAN teaches the method according to claim 1.
WEE in view of ZHAN does not expressly teach wherein the explainable artificial intelligence-based representation is an audio indicator.
However, LEE teaches the explainable artificial intelligence-based representation is an audio indicator ( [0015], Upon detecting the moving object exists in a blind spot … the audio device 42 is set to make a warning sound ) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of WEE in view of ZHAN with that of LEE, by setting a sound as taught by LEE after detection/segmentation is done in WEE in view of ZHAN, with motivation to “ alert the driver “ ( LEE, [0015]).
Regarding claim 5, WEE in view of ZHAN teaches the method according to claim 1.
WEE in view of ZHAN does not expressly teach wherein the explainable artificial intelligence-based representation comprises instructions executable by a computerized device that is onboard the vehicle, and wherein the producing of the explainable artificial intelligence-based representation is followed by transmitting the explainable artificial intelligence-based representation to the computerized device.
However, LEE teaches the explainable artificial intelligence-based representation comprises instructions executable by a computerized device that is onboard the vehicle, and wherein the producing of the explainable artificial intelligence-based representation is followed by transmitting the explainable artificial intelligence-based representation to the computerized device ( [0015], detecting the moving object exists in a blind spot …. a display device to display the image of the blind spot)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of WEE in view of ZHAN with that of LEE, by setting a sound as taught by LEE after detection/segmentation is done in WEE in view of ZHAN, with motivation “that the driver may be aware of the moving object existing in the blind spot” ( LEE, [0015]).
Claims 14 and 15 recite the medium for the method in claims 4-5, thus are also rejected.
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671