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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/1/2025 and 3/2/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Alice type rejection – Abstract Idea Mental Process
As to claim 1-20 the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Analysis – Step 1
Claim(s) 1-20 is/are directed to a mental process of determining a motion trajectory (Process claims 1-15 and 18-20 and apparatus for claim 16-17).
101 Analysis – Step 2A, Prong 1
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 16 includes limitations that recite an abstract idea – mental process (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 16 recites:
A system comprising:
one or more processors configured by machine-readable instructions to:
receive, from a camera mounted to or in a vehicle, a sequence of images of an environment surrounding the vehicle or within the vehicle;
generate a first plurality of tokens based on the sequence of images;
execute a machine learning model using the first plurality of tokens to generate a second plurality of tokens, the second plurality of tokens representing a prediction of a future state of the environment surrounding the vehicle or within the vehicle; and
generate a driver alert based on the prediction of the future state of the environment surrounding the vehicle or within the vehicle. (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “generate…”, “execute” in the context of this claim encompasses a person forming a simple judgement. Accordingly, the claim recites at least one abstract idea – mental process.
101 Analysis – Step 2A, Prong 2
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”) See above.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Claim 16 includes a processing apparatus. Regarding the additional limitations of “processor” that merely describes how to generally “apply” the otherwise mental judgements in a generic or general-purpose processing environment. The processing is recited at a high level of generality and merely automates the determining process steps.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the mental process into a practical application, the additional element of using a processor to perform the determining amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity (data gathering and transmitting) in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of processing with a processing apparatus are well-understood, routine, and conventional activities because the specification does not provide any indication that the processing apparatus is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claim(s) 2-15, 17, and 19-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application because they merely add to the mental processing. Therefore, dependent claims 2-15, 17, and 19-20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 16, and 18.
Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101. Examiner recommends a controlling step.
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 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.
(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) 1-10 and 12-20 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US 20240101157 A1.
As to claim 1, Pronovost discloses a method comprising:
receiving, by one or more processors [Pronovost: processors #816 #836] from a camera mounted to or in a vehicle, a sequence of images of an environment surrounding the vehicle or within the vehicle; [Pronovost: 0124 inputs may include camera data]
generating, by the one or more processors, a first plurality of tokens based on the sequence of images; [Pronovost: Fig. 7 #304 first machine learning model forms tokens based on sensor input/camera]
executing, by the one or more processors, a machine learning model using the first plurality of tokens to generate a second plurality of tokens, [Pronovost: Fig. 7 #314 second machine learning model 0021-0023] the second plurality of tokens representing a prediction of a future state of the environment surrounding the vehicle or within the vehicle; and [Pronovost: Fig. 7 #318 third machine learning model, 0044, 0095, 0067]
generating, by the one or more processors, a driver alert based on the prediction of the future state of the environment surrounding the vehicle or within the vehicle. [Pronovost: 0125, 0019, 0153, 0038]
As to claim 2, Pronovost discloses wherein generating the first plurality of tokens comprises executing, by the one or more processors, a neural network to generate the first plurality of tokens based on the sequence of images. [Pronovost: Fig. 7 #304, 0020]
As to claim 3, Pronovost discloses wherein executing the machine learning model using the first plurality of tokens comprises: executing, by the one or more processors, [Pronovost: processors #816] a neural network comprising a transformer model using the first plurality of tokens to generate the second plurality of tokens. [Pronovost: 0020]
As to claim 4, Pronovost discloses wherein executing the neural network comprising the transformer model comprises: executing, by the one or more processors, [Pronovost: processors #816] a large language model using the first plurality of tokens as input. [Pronovost: 0021, 0060, 0062, 0159 RNN]
As to claim 5, Pronovost discloses wherein generating the driver alert comprises generating, by the one or more processors, an audible or visual alert within the vehicle. [Pronovost:0019, 0153, 0038, 0125 ]
As to claim 6, Pronovost discloses wherein the driver alert causes the vehicle to adjust operation based on a location of one or more objects indicated by the second plurality of token representing the prediction of the future state of the environment. [Pronovost: 0019, 0153, 0191]
As to claim 7, Pronovost discloses wherein the driver alert causes the vehicle to adjust a velocity of the vehicle based on a proximity to the vehicle of one or more objects indicated by the second plurality of token representing the prediction of the future state of the environment. [Pronovost: 0019, 0153, 0191]
As to claim 8, Pronovost discloses wherein executing the machine learning model comprises: executing, by the one or more processors, the machine learning model using an identification of a driver of the vehicle in combination with the first plurality of tokens to generate the second plurality of tokens representing the prediction of the future state of one or more objects within the environment surrounding the vehicle or within the vehicle. [Pronovost: 0038, 0019, 0153, 0191 ]
As to claim 9, Pronovost discloses wherein executing the machine learning model comprises: executing, by the one or more processors, the machine learning model using an identification of a vehicle type of the vehicle in combination with the first plurality of tokens to generate the second plurality of tokens representing the prediction of the future state of one or more objects within the environment surrounding the vehicle or within the vehicle. [Pronovost: 0106, bike and car]
As to claim 10, Pronovost discloses further comprising: training, by the one or more processors, the machine learning model using a training dataset comprising a sequence of images captured by the camera and a label image captured by the camera subsequent to the sequence of images as a ground truth of an expected output for the machine learning model based on the sequence of images. [Pronovost: 0032, 0047, 0074, 0076, 0089]
As to claim 12, Pronovost discloses comprising: executing, by the one or more processors, the machine learning model using the first plurality of tokens to generate a third plurality of tokens representing a first prediction of a first future state of the environment surrounding the vehicle or within the vehicle; and executing, by the one or more processors, the machine learning model using third plurality of tokens to generate the second plurality of tokens representing the prediction of the future state of the environment surrounding the vehicle or within the vehicle. [Pronovost: ]
As to claim 13, Pronovost discloses wherein generating the first plurality of tokens comprises: executing, by the one or more processors, a neural network to detect one or more objects in the sequence of images; converting, by the one or more processors, the detected one or more objects into text-based objects in a text-based format; and converting, by the one or more processors, one or more objects in the text-based format to the first plurality of tokens according to a look-up table that maps text-based objects to tokens. [Pronovost: Fig. 7 #318 third machine learning model, 0044, 0095, 0067]
As to claim 14, Pronovost discloses further comprising: executing, by the one or more processors, a neural network to detect one or more objects in the environment surrounding or in the vehicle depicted in the sequence of images; and converting, by the one or more processors, the detected one or more objects into the first plurality of tokens. [Pronovost: 0092 see example of pedestrian]
As to claim 15, Pronovost discloses wherein the first plurality of tokens is one or more text-based objects in a text-based format. [Pronovost: 0021, 0060, 0062, 0159 RNN]
As to claim 16, Pronovost discloses a system comprising:
one or more processors [Pronovost: processors #816 #836, 0138] configured by machine-readable instructions to:
receive, from a camera mounted to or in a vehicle, a sequence of images of an environment surrounding the vehicle or within the vehicle; [Pronovost: 0124 inputs may include camera data]
generate a first plurality of tokens based on the sequence of images; [Pronovost: Fig. 7 #304 first machine learning model forms tokens based on sensor input/camera]
execute a machine learning model using the first plurality of tokens to generate a second plurality of tokens, [Pronovost: Fig. 7 #314 second machine learning model 0021-0023] the second plurality of tokens representing a prediction of a future state of the environment surrounding the vehicle or within the vehicle; and [Pronovost: Fig. 7 #318 third machine learning model, 0044, 0095, 0067]
generate a driver alert based on the prediction of the future state of the environment surrounding the vehicle or within the vehicle. [Pronovost: 0125, 0019, 0153, 0038]
As to claim 17, Pronovost discloses wherein the one or more processors are configured to generate the first plurality of tokens by executing a neural network to generate the first plurality of tokens based on the sequence of images. [Pronovost: Fig. 7 #304, 0020]
As to claim 18, Pronovost discloses a method comprising:
receiving, by one or more processors from a camera mounted to or in a vehicle, a sequence of images of an environment surrounding the vehicle or within the vehicle; [Pronovost: 0124 inputs may include camera data]
generating, by the one or more processors, a first plurality of tokens based on the sequence of images; [Pronovost: Fig. 7 #304 first machine learning model forms tokens based on sensor input/camera]
executing, by the one or more processors, a machine learning model using the first plurality of tokens to generate a second plurality of tokens, [Pronovost: Fig. 7 #314 second machine learning model 0021-0023] the second plurality of tokens comprising a textual representation of the sequence of images; and [Pronovost: Fig. 7 #318 third machine learning model, 0044, 0095, 0067]
generating, by the one or more processors, a driver alert based on the textual representation of the sequence of images. [Pronovost: 0125, 0019, 0153, 0038 RNN]
As to claim 19, Pronovost discloses wherein generating the first plurality of tokens comprises executing, by the one or more processors, a neural network to generate the first plurality of tokens based on the sequence of images. [Pronovost: Fig. 7 #304, 0020]
As to claim 20, Pronovost discloses wherein executing the machine learning model using the first plurality of tokens comprises: executing, by the one or more processors, [Pronovost: processors #816 #836] a neural network comprising a transformer model using the first plurality of tokens to generate the second plurality of tokens. [Pronovost: 0020]
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost in view of Milton US 2021/0312725A1.
As to claim 11, Pronovost is silent on using the well-known ASCII standard for data format but does disclose using the data in the claimed way and the flow of data. However, Milton discloses using ASCII format data in vehicle data analytics wherein generating the first plurality of tokens comprises: detecting, by the one or more processors, one or more objects within the environment surrounding the vehicle or within the vehicle using a neural network; converting the detected one or more objects into an American Standard Code for Information Interchange (ASCII) format, and wherein executing the machine learning model using the first plurality of tokens comprises executing, by the one or more processors, the machine learning model using the detected one or more objects in the ASCII format to generate the second plurality of tokens representing the prediction of the future state of the one or more objects. [Milton: 0167] It would have been obvious to on of ordinary skill in the art at the time of filing to modify the data structure of Pronovost to be the ASCII standard as disclosed in Milton because they are similar field , it is a data standard it is merely using a known device in a known way with predictable results with a good likelihood of success for the benefit of using a standard and all the benefits that come with standardization.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12128923 B2 Machine learning model optimization systems and methods are disclosed. A system receives sensor data captured by one or more sensors of a vehicle during a first time period. The vehicle uses a first trained machine learning (ML) model for one or more decisions of a first decision type during the first time period. The system generates a second trained ML model at least in part by using the sensor data to train the second trained ML model. The system identifies an optimal trained ML model from a plurality of trained ML models. The plurality of trained ML models includes the first trained ML model and the second trained ML model. The system causes the vehicle to use the optimal trained ML model for one or more further decisions of the first decision type during a second time period after the first time period.
US 20240160888 A1 In various examples, systems and methods are disclosed relating to neural networks for realistic and controllable agent simulation using guided trajectories. The neural networks can be configured using training data including trajectories and other state data associated with subjects or agents and remote or neighboring subjects or agents, as well as context data representative of an environment in which the subjects are present. The trajectories can be determining using the neural networks and using various forms of guidance for controllability, such as for waypoint navigation, obstacle avoidance, and group movement.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK M BRUSHABER whose telephone number is (313)446-4839. The examiner can normally be reached Monday-Friday 8am-5pm.
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, Hunter Lonsberry can be reached at (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FREDERICK M BRUSHABER/
Primary Examiner
Art Unit 3665
/FREDERICK M BRUSHABER/Primary Examiner, Art Unit 3665
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