DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Amended claims 1 thru 22 have been entered into the record.
Response to Amendment
The amendments to the specification overcome the specification objection from the previous office action (5/18/2026). The specification objection is withdrawn.
The interpretation under 25 U.S.C. 112(f) remains as recited in the previous office action (5/18/2026), and is recited below in this office action.
The amendments to independent claims 1 and 15 overcomes the 35 U.S.C. 101 rejections of claims 1 thru 8 and 15 thru 20 from the previous office action (5/18/2026). The 35 U.S.C. 101 rejections of claims 1 thru 8 and 15 thru 20 are withdrawn.
The approved terminal disclaimer (submitted and approved on 8/14/2026) overcomes the double patenting rejection from the previous office action (5/18/2026). The double patenting rejection is withdrawn.
Response to Arguments
Applicant's arguments filed 8/17/2026 regarding the 35 U.S.C. 101 rejection of claims 9 thru 10 have been fully considered but they are not persuasive. The applicant’s argues that the limitations of claim 9 cannot be practically performed in the human mind (argument page 11). The examiner respectfully disagrees. A person can identify a plurality of tracks that are taken over time (from the obtained training images – additional element of claim 9). A person can look at a series of images, and then interpret the direction and speed of any of the objects is the images. Then, from those identified tracks, a person can generate costs associated with each of the tracks as compared to other tracks. A person can then use those costs for the tracks to identify the final track mapping. A person is merely observing an environment, assigning values to different options and then selecting the best option. The examiner has considered the claim limitations as a whole, and has determined that they amount to the processing of data (identifying tracks, generating costs, and determining a final mapping). The use of the neural networks merely speeds up a process that can be performed by a person. The claim provides no details of the use of the neural network, merely being used for training. The use of the neural network is recited at a high level of generality. The specification recites that the processing may be performed by a general-purpose processing devices such as a microprocessor, central processing unit, or the like (PGPub P[0084]). There is nothing in the claims that is directed to anything beyond a general purpose computer.
The applicant further argues that the claimed adjusting of the parameters of the neural networks is a practical application of the abstract idea (argument page 12). The examiner respectfully disagrees. The applicant references the number parameters that could be adjusted (thousands, billions) in the neural network. But this number of adjustments is not required by the claim limitations. A single adjustment to the neural network would read on the claimed adjusting parameters. The adjustment may be something as simple as changing the weighted values a parameter.
The applicant further provides an example of a claim that is 101 eligible related to training a neural network and adjusting values (argument pages 12 and 13). The example provided in the argument includes more details of the training and the adjusting than is claimed in the limitations of claim 9. Claim 9 merely obtains training images, and adjusts parameters based on a cost value. While the example specifically recites, “training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task”. These limitations provide detailed requirements for the training and adjusting (“adjusting the first values of the plurality of parameters comprises adjusting the first values of the plurality of parameters to optimize an objective function that depends in part on a penalty term that is based on the determined measures of importance of the plurality of parameters to the first machine learning task”). The claim 9 limitations are recited at a more general level, without details, such as merely using a network, obtaining training images, and adjusting parameters. In order to be eligible under 101, there must be claims recited beyond using the network and making adjustments.
The applicant further argues that claim 9 constitutes “an improvement to how the machine learning model itself operates” (argument page 14). Claim 9 does not include any reference to a model, it merely recites processing by a neural network, and adjusting parameters of the neural network. In response to applicant's argument that limitations of the claims provide the eligibility requirement to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., improvement to the model) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Based on the above responses to the arguments, the below 101 rejections of claims 9 thru 14 are maintained.
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: a sensing system configured to obtain, and a processing device configured to generate, modify and process in claim 15.
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. The claimed sensing system is interpreted as including any combination of lidar, radar, sonar, cameras, IR sensors (Figure 1), and other sensors recited in the specification. The processing device is interpreted as central processing units (CPUs), graphics processing units (GPUs), etc. P[0049] and/or a microprocessor, central processing unit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like P[0084].
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.
Claims 9 thru 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claim 9 is directed to a method (i.e., a process). Accordingly, claim 9 is within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 update issued by the USPTO as now incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Claims 9 recites,
A method comprising:
obtaining a plurality of training images of an environment, wherein each training image of the plurality of training images is associated with a corresponding time of a plurality of times and depicts a plurality of objects in the environment;
processing the plurality of training images by one or more neural networks to identify a plurality of tracks, wherein each track of the plurality of tracks characterizes a trajectory of a respective object of the plurality of objects across the plurality of times;
generating a plurality of cost values, each cost value of the plurality of cost values associated with a respective track-to-track (TT) mapping of a plurality of TT mappings, wherein an individual TT mapping of the plurality of TT mappings maps each of the plurality of tracks to one of a plurality of ground truth tracks;
identifying, based on the plurality of cost values, a final TT mapping from the plurality of TT mappings; and
adjusting parameters of at least one of the one or more neural networks based on a cost value associated with the final TT mapping.
The above underlined limitation constitutes “a mental process” because it is an observation/evaluation/judgment/analysis that can, at the currently claimed high level of generality, be practically performed in the human mind (e.g., with pen and paper, or a general purpose computer). For instance, a person could look at images and identify tracks of objects, generate cost values for the track mapping, and identify a final track mapping from the cost values. Much of this is what a person does mentally while performing manual driving of a vehicle. See P[0024] and P[0025] of the present application which also disclose a human being aware of the vehicle's surroundings and supervises the driving operations. Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), 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 such as 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.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”): Claim 9 recites,
A method comprising:
obtaining a plurality of training images of an environment, wherein each training image of the plurality of training images is associated with a corresponding time of a plurality of times and depicts a plurality of objects in the environment (extra-solution activity (data gathering) as noted below, see MPEP § 2106.05(g));
processing the plurality of training images by one or more neural networks (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) to identify a plurality of tracks, wherein each track of the plurality of tracks characterizes a trajectory of a respective object of the plurality of objects across the plurality of times;
generating a plurality of cost values, each cost value of the plurality of cost values associated with a respective track-to-track (TT) mapping of a plurality of TT mappings, wherein an individual TT mapping of the plurality of TT mappings maps each of the plurality of tracks to one of a plurality of ground truth tracks;
identifying, based on the plurality of cost values, a final TT mapping from the plurality of TT mappings; and
adjusting parameters (extra-solution activity (data outputting) as noted below, see MPEP § 2106.05(g)) of at least one of the one or more neural networks (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) based on a cost value associated with the final TT mapping.
Regarding the additional limitation neural networks, this limitation amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of obtaining a plurality of training images of an environment, wherein each training image of the plurality of training images is associated with a corresponding time of a plurality of times and depicts a plurality of objects in the environment, and adjusting parameters, these additional limitations merely add insignificant extra-solution activity (data gathering, and data outputting) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, claim 9 does not recite additional elements that integrate the judicial exception into a practical application. Accordingly, claim 9 is directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, claim 9 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding claim 9 the additional limitation of neural networks, this limitation amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of obtaining a plurality of images of an environment, wherein each training image of the plurality of images is associated with a corresponding time of a plurality of times and depicts a plurality of objects in the environment; and adjusting parameters, these additional limitations have been reevaluated, and it has been determined that such limitations are not unconventional as they merely consist of data gathering, and data processing, which are recited at a high level of generality. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); or buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Further, adding a preliminary step of gathering data to a process that only recites obtaining and processing data that is in the environment of the vehicle (a mental process) does not add a meaningful limitation to the process of identifying tracks of objects. Similarly, adding a final step of adjusting parameters of a neural network mapping only recites a decision made to change tracking parameters (a mental process) does not add a meaningful limitation to the process of identifying tracks of objects. See MPEP 2106.05(d)(II) and 2106.05(g).
Therefore, at least claim 9 is rejected under 35 U.S.C. 101. Additionally, dependent claims 10 thru 14 are also rejected under 35 U.S.C. 101.
Claim 10 is directed to:
generating, using an object detection network of the one or more neural networks (using computers or machinery as mere tools to perform the abstract idea, see MPEP § 2106.05(f)) (recites merely using a network – high level of generality), a plurality of object vectors based on the plurality of training images, each of the plurality of object vectors representing one of the plurality of objects in one of the plurality of training images;
modifying, using an encoder network of the one or more neural networks (using computers or machinery as mere tools to perform the abstract idea, see MPEP § 2106.05(f)) (recites merely using a network – high level of generality), the plurality of object vectors to obtain a plurality of learned object vectors, each of the plurality of object vectors modified using a plurality of self-attention scores characterizing an association of a respective object vector with the plurality of object vectors;
generating, using a decoder network of the one or more neural networks (using computers or machinery as mere tools to perform the abstract idea, see MPEP § 2106.05(f)) (recites merely using a network – high level of generality), a plurality of track vectors, each of the plurality of track vectors generated using a plurality of cross-attention scores characterizing an association of a respective track vector with the plurality of learned object vectors; and
processing the plurality of track vectors to identify a plurality of tracks.
These limitations are recited in a more general manner, and are not recited in the detail as claimed in the parent application 17/715838. The claim 10 limitations are recited in a much broader manner and are therefore subject to the 101 rejection.
Claims 11 is directed to each object vector of the plurality of object vectors represents: a bounding shape for a respective object of the plurality of objects; and a timestamp associated with the respective object. Defining the object vectors.
Claim 12 is directed to each object vector of the plurality of object vectors further represents: one or more visual characteristics of the respective object. Defining the object vectors.
Claim 13 is directed to processing, using a network comprising one or more transformer layers (using computers or machinery as mere tools to perform the abstract idea, see MPEP § 2106.05(f)) (recited at a high level of generality, the network requires only one transformer layer), an input, the input comprising: a plurality of seed track vectors as queries, and the plurality of learned object vectors as keys.
Claim 14 is directed to processing, using one or more classification heads, the plurality of track vectors, the one or more classification heads comprising at least one of: a first classification head outputting at least a bounding shape for each of the plurality of objects across the plurality of times, a second classification head outputting at least a velocity of each of the plurality of objects across the plurality of times, or a third classification head outputting at least a type of each of the plurality of objects. Defining the processing of the track vectors.
For the following reasons, the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of processing devices, neural networks, encoder network, and decoder network, these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Allowable Subject Matter
Claims 1 thru 8 and 15 thru 22 have been indicated as allowable.
The following is a statement of reasons for the indication of allowable subject matter: The reasons for indicating allowable subject matter over the prior art of record are based on the claim amendments to independent claims 1 and 15, and the approved terminal disclaimer of 8/14/2026. The closest prior art of record is Jang Patent Application Publication Number 2020/0013157 A1. Jang discloses an automotive around view image providing method using a machine learning model. The method includes: receiving, by an image processing device, a plurality of images obtained by a plurality of cameras mounted on a car; inputting, by the image processing device, the plurality of images to a neural network encoder to generate a feature vector for each of the plurality of images; and combining, by the image processing device, feature vectors for the plurality of images into one image form and inputting the one image form to the neural network decoder to generate a matched one image. The neural network decoder includes a filter for performing matching on adjacent images having an overlapping area among the plurality of images based on the feature vector for the plurality of images.
In regard to claims 1 and 15, Jang taken either individually or in combination with other prior art, fails to teach or render obvious a method and system for use in an autonomous vehicle having a sensor system to perform the method. The method comprising obtaining, by one or more sensors of an autonomous vehicle, a plurality of images of an environment. Each image of the plurality of images is associated with a corresponding time of a plurality of times and depicts a plurality of objects in the environment. The method further comprising generating a plurality of object vectors, each of the plurality of object vectors representing one of the plurality of objects in one of the plurality of images, and modifying the plurality of object vectors to obtain a plurality of learned object vectors. Each of the plurality of object vectors modified using a plurality of self-attention scores characterizing an association of a respective object vector with the plurality of object vectors. The method further comprising generating a plurality of track vectors, each of the plurality of track vectors generated using a plurality of cross-attention scores characterizing an association of a respective track vector with the plurality of learned object vectors, and processing the plurality of track vectors to identify a plurality of tracks. Each track of the plurality of tracks characterizes a trajectory of a respective object of the plurality of objects. The method further comprising causing a path of the autonomous vehicle to be modified based on the identified plurality of tracks.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DALE W HILGENDORF whose telephone number is (571)272-9635. The examiner can normally be reached Monday - Friday 9-5:30.
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, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DALE W HILGENDORF/Primary Examiner, Art Unit 3662
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