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 .
This is in response to Applicant’s communication filed on 5/8/24, wherein:
Claims 1-34 are currently pending;
Claims 27-28 overcome the prior of record.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 23-28 are directed towards “A machine readable readable medium” having stored thereon a set of instructions. These claims are not found in one of the four statutory categories of invention. The broadest reasonable interpretation of a claim drawn to a computer or machine readable medium typically covers forms of non transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of "computer readable medium" and therefore is directed to non-statutory subject matter. See in In re Nuijten, 500 F.3d 1346, 1356-57.
The Examiner suggests in this situation to overcoming this rejection under 35 US.C. § 101, the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 US.C. § 101 by adding the limitation "non-transitory" to the preamble of the claim.
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) 1-4, 8, 12-15, 19, 23-26, 29-32 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GOPALKRISHMA ET AL (US 12,511,876). Herein after GOPALKRISHMA.
As for claim 1, GOPALKRISHMA discloses a processor comprising: one or more arithmetic logic units (ALUs) configured to perform, using one or more neural networks, motion planning for an autonomous vehicle {see at least figures 1-3, 6, at least col. 1, lines 38-57}, the one or more neural networks comprising:
a tokenizer configured to: identify, based on visual data obtained from an environment of the autonomous device, a plurality of objects in the environment; and generate, for the plurality of identified objects, a plurality of object level visual tokens in a latent token embedding space {see at least figures 2-3, tokenizer 211, 213; figures 4-6; col 1, lines 11-30; col. 6, lines 20-60; and at least figure 4-5, col. 10, lines 56-67, col. 11, lines 11-15}
an adapter configured to align the object level visual tokens to a text embedding space to generate aligned tokens corresponding to the object level visual tokens {see at least figures 4- 5, and col. 11, lines 15-45}
a large language model (LLM) configured to: determine, based on the aligned tokens, critical objects from the plurality of objects identified in the environment; and generate, based on the critical objects, a planning output for the autonomous vehicle {see at least figure 5-6, col. 11, lines 40-53 and claim 1}.
As for claim 2, GOPALKRISHMA discloses , wherein the tokenizer is further configured to generate, based on the visual data, a plurality of scene level tokens in the latent token embedding space, each scene level token of the plurality of scene level tokens providing scene information {see at least figures 2, 4-5, at least col. 6, lines 20-60; col. 11, lines 1-30}; wherein the adapter is further configured to align the scene level tokens to the text embedding space to generate aligned tokens corresponding to the scene level tokens; and wherein the aligned tokens comprise the aligned tokens corresponding to the object level visual tokens and the aligned tokens corresponding to the scene level tokens {see at least figures 4-5, col. 11, lines 22-53}.
As for claim 3, GOPALKRISHMA discloses wherein the tokenizer is further configured to generate a traffic agent token based on past state history from one or more traffic agents; and wherein generating the planning output for the autonomous vehicle is further based on the aligned tokens and the traffic agent token {see at least figures 6, at least col. 11, lines 1-67, col. 12, lines 1-25}.
As for claim 4, GOPALKRISHMA discloses wherein the visual data comprises at least one of: multi-view video frames; or high-definition (HD) maps, wherein the tokenizer is configured to identify the plurality of objects in the environment based on the visual data and symbolic representations that are obtained from the visual data {see at least figures 6, abstract, col. 3, lines 10-33; col. 11, lines 1-67, col. 12, lines 1-25}.
As for claim 8, GOPALKRISHMA discloses wherein the tokenizer comprises: a first querying transformer for extracting features of object tracking; a second querying transformer for extracting features of map elements; and a third querying transformer for extracting features of object motion {see at least col. 5, lines 65-67; col. 6, lines 1-29; col. 6, lines 38-61}
As for claims 12-15, 19, 23-26, 29-32, the limitations of these claims have been noted in the rejection above. They are therefore rejected for the same reason sets forth above.
Claims 5-7, 9-11, 16-18, 20-22, 33-34 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jang (US 2025/0145174): The method of learning a driving-decision algorithm for a vehicle includes: receiving a dataset related to driving from a database in which a driving scenario is stored; obtaining a first output by inputting the dataset into a first large language model (LLM), wherein the first output includes an inference of the first LLM based on the dataset; based on the first output, training a decision-trainer and a behavior-and-trajectory planner.
Li et al (US 2023/0237773): Vision-language models are configured to match an image with a proper caption. Vision-language pre-training (VLP) has been used to improve performance of downstream vision and language tasks by pretraining models on large-scale image-text pairs.
Wu (US 2023/0135659): techniques to facilitate financial natural language processing (NLP) training and tasks, such as sentiment analysis, machine reading comprehension, question answering, and causal inferencing.
Chaudhury et al (US 2022/0172080): A computer-implemented method is provided for learning multimodal feature matching. The method includes training an image encoder to obtain encoded images. The method further includes training a common classifier on the encoded images by using labeled images. The method also includes training a text encoder while keeping the common classifier in a fixed configuration by using learned text embeddings and corresponding labels for the learned text embeddings.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kira Nguyen whose telephone number is (571)270-1614. The examiner can normally be reached on Monday to Friday 9:00-5:00 ET.
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, Khoi Tran can be reached on 571-272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIRA NGUYEN/Primary Examiner, Art Unit 3656
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