DETAILED ACTION
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.
Claim(s) 1-3, 5, 9, 10-13, 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ye (WO 2022104296).
Regarding claim 1, Ye teaches a method comprising:
obtaining ultrasonic data corresponding to an area( 445 in Fig. 4C);
obtaining image data corresponding to the area( infrared image data in 450 in Fig. 4C);
obtaining RADAR data corresponding to the area( radar data in 450 in Fig. 4C);
processing, using one or more neural networks, an aggregation of ultrasonic data, image data, and RADAR data to generate output data corresponding to one or more objects located in the area( 425 a’’ in Fig. 4C); and
causing a machine to perform one or more operations based at least on the output data ([0090], implementing ADAS with a camera(s) on a windshield of a vehicle and a mobile device).
Regarding claim 2, Ye teaches the method of claim 1, wherein the generating of the output data is based at least on the one or more neural networks processing ( [0092], a deep learning system, a neural network, a convolutional neural network ("CNN"), a deep neural network ("DNN"), or a fully convolutional network ("FCN"), and/or the like ) an input data set that is generated based at least on:
first input data that is generated based at least on the ultrasonic data`( 445 in Fig. 4C);
second input data that is generated based at least on the image data( infrared image data in 450 in Fig. 4C); and
third input data that is generated based at least on the RADAR data( radar data in 450 in Fig. 4C).
Regarding claim 3, Ye teaches the method of claim 2, wherein the third input data includes one or more of:
one or more first RADAR data sets that respectively correspond to one or more individual RADAR scans( [0067], radar-based tracking may be performed …, not frame by frame, but rather using doppler analysis to track movements); or
one or more second RADAR data sets that are respectively aggregated from two or more first RADAR data sets.
Regarding claim 5, Ye teaches the method of claim 2, wherein the input data set is further generated based at least on fourth input data that is generated based at least on fusing the ultrasonic data, the image data, and the RADAR data( 445b in Fig. 4E; 460 inf Fig. 4F).
Regarding claim 9, Ye teaches the method of claim 1, wherein the output data includes one or more of:
an occupancy map;
an evidence grid map;
a height map; or
a distance map ([0028], performing object detection and tracking may comprise … simultaneous location and mapping ("SLAM") or depth estimation).
Claims 10-13, 17 recite the system for the method in claims 1-3, 9. Since Ye also teaches a system( Fig. 5), those claims are also rejected.
Regarding claim 18, Ye teaches the system of claim 10, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine ( Abstract, advanced driver assistance system ("ADAS") );
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system for presenting at least one of augmented reality content, virtual reality content, or mixed reality content;
a system for hosting one or more real-time streaming applications;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs);
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
Claims 19-20 recite the processor(s) for the method in claims 1-2. Since Ye also teaches a processor( 510 in Fig. 5), those claims are also rejected.
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) 6-8, 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye.
Regarding claim 6, Ye teaches the method of claim 1, wherein the generating of the output data includes:
generating a combined feature data set ( 465d in Fig. 4G) based at least on fourth feature processing performed( 465c in Fig. 4G) with respect to the first feature data set, the second feature data set( 465b), and the third feature data set as combined( 465a in Fig. 4G).
Ye does not expressly teach
extracting a first feature data set based at least on first processing performed with respect to the ultrasonic data using a first feature extractor of the one or more neural networks;
extracting a second feature data set based at least on second processing performed with respect to the image data using a second feature extractor of the one or more neural networks;
extracting a third feature data set based at least on third processing performed with respect to the RADAR data using a third feature extractor of the one or more neural networks.
However, official notice is taken that it conventional in the art to use extractors to extract features from different modalities of images.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use extractors to extract features for different modalities of images, with motivation to carry out the image processing steps in Ye.
Regarding claim 7, Ye teaches the method of claim 6, wherein the fourth feature processing is performed using one or more of the first feature extractor, the second feature extractor, the third feature extractor ( those extractors implement via neural networks is also well known in the art), or a fourth feature extractor of the one or more neural networks.
Regarding claim 8, Ye teaches the method of claim 6.
It is not well known in the art wherein the extracting of the first feature data set, the second feature data set, and the third feature data set is performed in parallel.
However, there are only three options in carry out those extractions since they are independent of each other:
i. in sequence
ii. in parallel
iii. two in parallel and one in sequence
Therefore, it would have been obvious to try, by one of ordinary skill before the effective filing date of claimed invention, to run extractors in parallel, or a combination thereof, since there are a finite number of identified, predictable potential solutions to the recognized need and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
Claims 14-16 recite the system for the method in claims 4-6. Since Ye also teaches a system( Fig. 5), those claims are also rejected.
Claim(s) 4, 12is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye in view of Buras (US 20210327304 ).
Regarding claim 4, Ye teaches the method of claim 2, wherein:
the second input data corresponds to a second map indicating respective locations in the area of one or more second objects as indicated by the image data([0067], image-based … tracking may be performed); and
the third input data corresponds to a third map indicating respective locations in the area of one or more third objects as indicated by the RADAR data( [0067], radar-based tracking may be performed …, not frame by frame, but rather using doppler analysis to track movements).
Ye does not expressly
the first input data corresponds to a first map indicating respective locations in the area of one or more first objects as indicated by the ultrasonic data;
However, Buras teaches
the first input data corresponds to a first map indicating respective locations in the area of one or more first objects as indicated by the ultrasonic data ( [0131], provide spatial data (e.g., 3D position and rotation data) of tracked objects (e.g., the ultrasound probe))
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 Ye and Buras, by using the ultrasound data in Ye as a way to provide object tracking data, with motivation of “providing procedure instruction data to a user using an equipment system” ( Buras, Abstract).
Claims 12 recites the system for the method in claim 4. Since Ye also teaches a system( Fig. 5), this claim is also rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANGENG SUN whose telephone number is (571)272-3712. The examiner can normally be reached 8am to 5pm, EST, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Randolph Vincent can be reached at 571 272 8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
Prosecution Insights