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 .
Status of Claims
This communication is in response to Application 18/585,609 filed on 05/08/2024. Claims 1-3, 5-10, 17-25, 27-28, 31-32 and 34-37 are pending and examined below.
Priority
Acknowledgment is made of applicant’s claim for foreign priority for Application No. GB2314928.9 and GB 2317029.3, filed on 09/28/2023 and 11/06/2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an image encoder and a driving decoder in claims 1, 3, 6-7, 17-19 and 28.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 34 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a “model” is not a statutory category of invention, i.e. it is not a method and not a tangible thing (article, composition, apparatus). Products that do not have a physical or tangible form, such as information (often referred to as "data per se").
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-3, 5-10, 17-25, 27-28, 31-32 and 34-37 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-3, 5-10, 17-25, 27-28, 31-32 and 34-37 of copending Application No. 19/523,095 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Instant Application: 18/585,609
Copending Application: 19/523,095
Claim 1. An autonomous driving system comprising: an image encoder configured to receive video input data, said video input data comprising a plurality of time-varying image frames, wherein the image encoder is further configured to encode the image frames as a sequence of input tokens; a pre-trained world model having a set of pre-trained world model weights, the world model being configured to receive the sequence of input tokens and to generate therefrom a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events; and a driving decoder configured to receive the world state from the world model and to generate in a single forward pass a driving plan based on the implicit representation of candidate future events.
Claim 1. An autonomous driving system comprising: an image encoder configured to receive video input data, said video input data comprising a plurality of time-varying image frames, wherein the image encoder is further configured to encode the image frames as a sequence of input tokens; a pre-trained world model having a set of pre-trained world model weights, the world model being configured to receive the sequence of input tokens and to generate therefrom a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events; and a driving decoder configured to receive the world state from the world model and to generate in a single forward pass a driving plan based on the implicit representation of candidate future events.
Claim 2. wherein the driving plan comprises one or more of: a waypoint, a speed, a velocity, a curvature, a trajectory, an indicator signal, an acceleration value, a braking value, a parking brake value, a steering angle, a lighting setting, and a horn setting.
Claim 2. wherein the driving plan comprises one or more of: a waypoint, a speed, a velocity, a curvature, a trajectory, an indicator signal, an acceleration value, a braking value, a parking brake value, a steering angle, a lighting setting, and a horn setting.
Claim 3. wherein the driving decoder is further configured to receive a route plan input, wherein the driving decoder is further configured to generate the driving plan based on the route plan input.
Claim 3. wherein the driving decoder is further configured to receive a route plan input, wherein the driving decoder is further configured to generate the driving plan based on the route plan input.
Claim 5. wherein the pre-trained world model is trained by: i) receiving video training data, said video training data comprising a plurality of time-varying image frames; ii) encoding the image frames to generate image encodings; iii) generating a sequence of input tokens from the image encodings; and iv) using an autoregressive transformer to model the sequence of input tokens, thereby generating the pre-trained world model and the set of learned pre-trained world model weights.
Claim 5. wherein the pre-trained world model is trained by i) receiving video training data, said video training data comprising a plurality of time-varying image frames; ii) encoding the image frames to generate image encodings; iii) generating a sequence of input tokens from the image encodings; and iv) using an autoregressive transformer to model the sequence, thereby generating the pre-trained world model and the set of learned world model weights.
Claim 6. wherein the driving decoder is trained by: i) initialising a set of driving decoder weights; ii) receiving further video training data, said further video training data comprising a plurality of time-varying further image frames; iii) encoding the further image frames to generate further image encodings; iv) generating a sequence of further input tokens from the further image encodings; v) inputting the further image encodings to the trained world model, and using the trained world model to generate a world state from said further image encodings; vi) inputting the world state to the driving decoder, and using the driving decoder to generate a new driving plan based on the world state and a current set of driving decoder weights; and vii) updating the set of driving decoder weights based on a difference between the new driving plan and a driving plan prior corresponding to the further video training data.
Claim 6. wherein the driving decoder is trained by: i) initialising a set of driving decoder weights; ii) receiving further video training data, said further video training data comprising a plurality of time-varying further image frames; iii) encoding the further image frames to generate further image encodings; iv) generating a sequence of further input tokens from the further image encodings; v) inputting the further image encodings to the trained world model, and using the trained world model to generate a world state from said further image encodings; vi) inputting the world state to the driving decoder, and using the driving decoder to generate a new driving plan based on the world state and a current set of driving decoder weights; and vii) updating the set of driving decoder weights based on a difference between the new driving plan and a driving plan prior corresponding to the further video training data.
Claim 7. wherein training the driving decoder further comprises receiving a training route plan input corresponding to the further video training data, wherein the driving decoder is further configured to generate the new driving plan based on the training route plan input.
Claim 7. wherein training the driving decoder further comprises receiving a training route plan input corresponding to the further video training data, wherein the driving decoder is further configured to generate the new driving plan based on the training route plan input.
Claim 8. configured to update the set of world model weights based on the difference between the new driving plan and the driving plan prior corresponding to the further video training data.
Claim 8. configured to update the set of world model weights based on the difference between the new driving plan and the driving plan prior corresponding to the further video training data.
Claim 9. comprising one or more cameras configured to capture the video input data and to provide said video input data to the image encoder.
Claim 9. comprising one or more cameras configured to capture the video input data and to provide said video input data to the image encoder.
Claim 10. A method of operating an autonomous driving system, the method comprising: i) receiving video input data, said video input data comprising a plurality of time- varying image frames; ii) encoding the image frames as a sequence of input tokens; iii) inputting the sequence of input tokens into a pre-trained world model having a set of pre-trained world model weights; iv) using the pre-trained world model to generate a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events; and v) in a single forward pass, generating from the world state a driving plan based on the implicit representation of candidate future events.
Claim 10. A method of operating an autonomous driving system, the method comprising: i) receiving video input data, said video input data comprising a plurality of time- varying image frames; ii) encoding the image frames as a sequence of input tokens; iii) inputting the sequence of input tokens into a pre-trained world model having a set of pre-trained world model weights; iv) using the pre-trained world model to generate a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events; and v) in a single forward pass, generating from the world state a driving plan based on the implicit representation of candidate future events.
Claim 17. A method of training an autonomous driving system, the method comprising: a) training a world model to generate a world state from video input data, wherein the world state comprises an implicit representation of candidate future events, the step of training the world model comprising: i) receiving video training data, said video training data comprising a plurality of time-varying image frames; ii) encoding the image frames to generate image encodings; iii) generating a sequence of input tokens from the image encodings; and iv) using an autoregressive transformer to model the sequence, thereby generating a trained world model having a set of learned world model weights; and b) training a machine learning driving decoder to generate driving plans based on world states received from the world model, the step of training the driving decoder comprising: i) initialising a set of driving decoder weights; ii) receiving further video training data, said further video training data comprising a plurality of time-varying further image frames; iii) encoding the further image frames to generate further image encodings; iv) generating a sequence of further input tokens from the further image encodings; v) inputting the further image encodings to the trained world model, and using the trained world model to generate a world state from said further image encodings; vi) inputting the world state to the driving decoder, and using the driving decoder to generate a new driving plan based on the world state and a current set of driving decoder weights; and vii) updating the set of driving decoder weights based on a difference between the new driving plan and a driving plan prior corresponding to the further video training data.
Claim 17. A method of training an autonomous driving system, the method comprising: a) training a world model to generate a world state from video input data, wherein the world state comprises an implicit representation of candidate future events, the step of training the world model comprising: i) receiving video training data, said video training data comprising a plurality of time-varying image frames; ii) encoding the image frames to generate image encodings; iii) generating a sequence of input tokens from the image encodings; and iv) using an autoregressive transformer to model the sequence, thereby generating a trained world model having a set of learned world model weights; and b) training a machine learning driving decoder to generate driving plans based on world states received from the world model, the step of training the driving decoder comprising: i) initialising a set of driving decoder weights; ii) receiving further video training data, said further video training data comprising a plurality of time-varying further image frames; iii) encoding the further image frames to generate further image encodings; iv) generating a sequence of further input tokens from the further image encodings; v) inputting the further image encodings to the trained world model, and using the trained world model to generate a world state from said further image encodings; vi) inputting the world state to the driving decoder, and using the driving decoder to generate a new driving plan based on the world state and a current set of driving decoder weights; and vii) updating the set of driving decoder weights based on a difference between the new driving plan and a driving plan prior corresponding to the further video training data.
Claim 18. wherein a trainable image encoder is used for encoding the image frames to generate the image encodings, wherein the method comprises training a set of image encoder weights of the image encoder.
Claim 18. wherein a trainable image encoder is used for encoding the image frames to generate the image encodings, wherein the method comprises training a set of image encoder weights of the image encoder.
Claim 19. wherein the trained set of image encoder weights of the image encoder are used for encoding the further image frames to generate further image encodings.
Claim 19. wherein the trained set of image encoder weights of the image encoder are used for encoding the further image frames to generate further image encodings.
Claim 20. wherein the step of training the world model further comprises:i) receiving action training data, said action training data comprising a plurality of time-varying driving actions; ii) encoding the driving actions to generate action encodings; iii) temporally aligning the image encodings and action encodings; and iv) generating the sequence of input tokens from the temporally aligned image encodings and action encodings.
Claim 20. wherein the step of training the world model further comprises:i) receiving action training data, said action training data comprising a plurality of time-varying driving actions; ii) encoding the driving actions to generate action encodings; iii) temporally aligning the image encodings and action encodings; and iv) generating the sequence of input tokens from the temporally aligned image encodings and action encodings.
Claim 21. wherein a trainable action encoder is used for encoding the driving actions to generate the action encodings, wherein the method comprises training a set of action encoder weights of the action encoder.
Claim 21. wherein a trainable action encoder is used for encoding the driving actions to generate the action encodings, wherein the method comprises training a set of action encoder weights of the action encoder.
Claim 22. wherein the step of training the world model further comprises: i) receiving textual training data, said textual training data comprising a plurality of time-varying text data; ii) encoding the text data to generate text encodings; iii) temporally aligning the image encodings and text encodings; and iv) generating the sequence of input tokens from the temporally aligned image encodings and text encodings.
Claim 22. wherein the step of training the world model further comprises: i) receiving textual training data, said textual training data comprising a plurality of time-varying text data; ii) encoding the text data to generate text encodings; iii) temporally aligning the image encodings and text encodings; and iv) generating the sequence of input tokens from the temporally aligned image encodings and text encodings.
Claim 23. wherein a trainable text encoder is used for encoding the text data to generate the text encodings, wherein the method comprises training a set of text encoder weights of the text encoder.
Claim 23. wherein a trainable text encoder is used for encoding the text data to generate the text encodings, wherein the method comprises training a set of text encoder weights of the text encoder.
Claims 24-25, 27-28, 31-32 and 34-37
Claims 24-25, 27-28, 31-32 and 34-37
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-3,9-10, 32 and 34 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ANTHONY HU ET AL: "Model-Based Imitation Learning for Urban Driving", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 3 November 2022 (2022-11-03), XP091359070.
Regarding claim 1, Hu discloses an autonomous driving system comprising:
an image encoder configured to receive video input data, said video input data comprising a plurality of time-varying image frames (Fig. 1; section 3.3.1, p.5 “Lifting image features to 3D”: observations o1..2, observations are image input ot at step t),
wherein the image encoder is further configured to encode the image frames as a sequence of input tokens (Fig. 1 section 3.3.1, p5, “Mapping to a 1D vector”: the observation embeddings xt equates the claimed tokens);
a pre-trained world model having a set of pre-trained world model weights (Fig. 1; section 3.4 and 3.5, p6: the generative network parametrized by θ models the latent dynamics and the generative process. It comprises a gated recurrent cell fθ, a prior network (µθ,σθ). When the model of D1 is used in inference, the model must have been pre-trained), the world model being configured to receive the sequence of input tokens and to generate therefrom a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events (fig.1 1; section 3.4 and 3.5, p.6: the model comprises the latent representations corresponding to the observations h₁..T and S1..T, as well as the future latent representations hT+i and ST+i. Although fig. 1 represents only 1 future state, p.6, explicitly discloses "to generate sequences of longer futures in latent space", i.e., a plurality of events); and
a driving decoder configured to receive the world state from the world model and to generate in a single forward pass a driving plan based on the implicit representation of candidate future events (fig. 1: the policy πθ generates the actions based on the latent representation. Fig.1 and equation 1. Fig.1; section 3.5, p.6: h₃).
Regarding claim 2, Hu discloses wherein the driving plan comprises one or more of: a waypoint, a speed, a velocity, a curvature, a trajectory, an indicator signal, an acceleration value, a braking value, a parking brake value, a steering angle, a lighting setting, and a horn setting (See at least - section 4 Experimental Setting “Dataset”).
Regarding claim 3, Hu discloses wherein the driving decoder is further configured to receive a route plan input, wherein the driving decoder is further configured to generate the driving plan based on the route plan input (See at least – p.6, section 3.3.1, “Route map and speed”).
Regarding claim 9, Hu discloses one or more cameras configured to capture the video input data and to provide said video input data to the image encoder (See at least – Fig. 1, section 4).
Regarding claim 10, Hu discloses a method of operating an autonomous driving system, the method comprising:
receiving video input data, said video input data comprising a plurality of time- varying image frames (Fig. 1; section 3.3.1, p.5 “Lifting image features to 3D”: observations o1..2, observations are image input ot at step t);
ii) encoding the image frames as a sequence of input tokens (Fig. 1 section 3.3.1, p5, “Mapping to a 1D vector”: the observation embeddings xt equates the claimed tokens);
iii) inputting the sequence of input tokens into a pre-trained world model having a set of pre-trained world model weights (Fig. 1; section 3.4 and 3.5, p6: the generative network parametrized by θ models the latent dynamics and the generative process. It comprises a gated recurrent cell fθ, a prior network (µθ,σθ). When the model of D1 is used in inference, the model must have been pre-trained);
iv) using the pre-trained world model to generate a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events (fig.1 1; section 3.4 and 3.5, p.6: the model comprises the latent representations corresponding to the observations h₁..T and S1..T, as well as the future latent representations hT+i and ST+i. Although fig. 1 represents only 1 future state, p.6, explicitly discloses "to generate sequences of longer futures in latent space", i.e., a plurality of events); and
v) in a single forward pass, generating from the world state a driving plan based on the implicit representation of candidate future events (fig. 1: the policy πθ generates the actions based on the latent representation. Fig.1 and equation 1. Fig.1; section 3.5, p.6: h₃).
Regarding claim 32, Hu discloses a non-transitory computer-readable medium comprising instructions which, when executed by a processor, cause the processor to carry out the method of claim 10 (Model-based methods have mostly been explored in a reinforcement learning setting and have been shown to be extremely successful (See at least – pg. 3 “world model”).
Regarding claim 34, Hu discloses a world model for use in an autonomous driving system, said world model having a set of pre-trained world model weights, wherein the world model is configured to:
receive a sequence of input tokens comprising image encodings (Fig. 1 section 3.3.1, p5, “Mapping to a 1D vector”: the observation embeddings xt equates the claimed tokens); and
ii) generate a world state based on said sequence, wherein the world state comprises an implicit representation of candidate future events (fig.1 1; section 3.4 and 3.5, p.6: the model comprises the latent representations corresponding to the observations h₁..T and S1..T, as well as the future latent representations hT+i and ST+i. Although fig. 1 represents only 1 future state, p.6, explicitly discloses "to generate sequences of longer futures in latent space", i.e., a plurality of events).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
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