DETAILED ACTION
This Office action is drafted in response to the Response to Election/Restriction Requirement dated 01/04/2026. Claims 1-15 are pending. Claims 5-14 have been withdrawn by the Applicant, and claims 1-4 and 15 are rejected as cited below.
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 .
Election/Restrictions
Applicant's election with traverse of claims 1-4 and 15 in the reply filed on 01/04/2026 is acknowledged. The traversal is on the ground(s) that “withdrawn claims 5- 14 are sufficiently related to claims 1-4 and 15 that an undue burden would not be presented to the Examiner by maintaining all of the claims in this application.”. This is not found persuasive because Examiner maintains the reasons for restriction set out in Restriction Requirement dated 11/04/2025. Examiner finds that the non-elected species are drawn to subject matter which is not encompassed within the elected species (e.g. static environment data, algorithm training, etc.). Additionally, claims 11-14 present a system and vehicle which define a trajectory of the host vehicle, which is not described in any of the other species. Examiner believes that additional search terms, queries, and cpc classifications will be required. This creates a serious search burden for the application as originally presented.
The requirement is still deemed proper and is therefore made FINAL.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: “METHOD FOR PREDICTING TRAJECTORIES OF ROAD USERS BY ALLOCATING LATENT FEATURES TO A DYNAMIC OCCUPANCY MAP.”
Claim Objections
Claim 15 is objected to because of the following informalities: “The non-transitory…” should be “A non-transitory…”. Appropriate correction is required.
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 1-4 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method for predicting trajectories (i.e. a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized in bold below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer implemented method for predicting respective trajectories of a plurality of road users, the method comprising:
determining, for each road user, a respective set of characteristics detected by a perception system of a host vehicle, the set of characteristics including specific characteristics associated with a predefined class of road users;
transforming, for each of the road users, the set of characteristics to a respective set of input data for a prediction algorithm via a processing unit of the host vehicle, wherein each set of input data comprises the same predefined number of data elements; and
determining, via the processing unit, at least one respective trajectory for each of the road users by applying the prediction algorithm to the input data.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind.
“transforming, for each of the road users, the set of characteristics …” in the context of this claim may encompasses a person assigning a predefined numerical value to a specific characteristic (e.g. speed > 30 mph = “2”).
“determining, via the processing unit, at least one respective trajectory …” in the context of this claim may encompass a person solving an equation using the numerical variable determined in the previous step, examining the numerical answer, and forming a simple judgement (e.g. a calculated result >10 indicates the trajectory of the target vehicle will take a left turn).
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
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”):
A computer implemented method for predicting respective trajectories of a plurality of road users, the method comprising:
determining, for each road user, a respective set of characteristics detected by a perception system of a host vehicle, the set of characteristics including specific characteristics associated with a predefined class of road users;
transforming, for each of the road users, the set of characteristics to a respective set of input data for a prediction algorithm via a processing unit of the host vehicle, wherein each set of input data comprises the same predefined number of data elements; and
determining, via the processing unit, at least one respective trajectory for each of the road users by applying the prediction algorithm to the input data.
For the following reason, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “determining, for each road user, a respective set of characteristics detected by a perception system of a host vehicle, the set of characteristics including specific characteristics associated with a predefined class of road users;” the examiner submits that this limitation is insignificant extra-solution activity that merely use a computer (perception system) to perform the process. This step, which acquires data from external sources, is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Secondly, “via the processing unit” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The processing unit is recited at a high level of generality and merely automates the transforming and trajectory determination steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
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 abstract idea into a practical application, the additional elements of using computer hardware components (e.g. “processing unit”) to transform data and determine a trajectory 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. And as discussed above, the additional limitation of “determining, for each road user, a respective set of characteristics detected by a perception system of a host vehicle, the set of characteristics including specific characteristics associated with a predefined class of road users;” the examiner submits that this limitation is insignificant extra-solution activity.
Dependent claims 2-4 do not recite any further limitations that cause the claim 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. These claims merely further narrow down the mental process (claims 2-4), none of which integrate the judicial exception into a practical application. Therefore, dependent claims 2-4 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 2-4 are ineligible under 35 USC §101.
Claim 15 recites a computer-readable medium containing instructions used to perform the method detailed in claim 1, therefore it is rejected for the same reason.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1:
The term “data elements” is recited in lines 8 and 9. It is unclear what should comprise a “data element.” Applicant specification ¶ [0075] assigns “elements” the label of “N”, however, FIG. 4 seems to designate “N” as a numerical variable. One or ordinary skill in the art would not have the requisite information to discern the true definition of “data element.” The scope of the claim is unclear, and thus indefinite. For the purpose of examination, Examiner will interpret “data element” to mean any single binary value.
Claims 2-4 are rejected by virtue of their dependency on claim 1 and not fixing the deficiencies stated above.
Claim 15 recites a computer-readable medium containing instructions which perform the method detailed in claim 1, thus is rejected on the same basis.
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)(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.
Claims 1 and 15 are rejected under 35 U.S.C. 102(a)2 as being anticipated by Pronovost (US Pub. 2024/0101157 A1; hereafter Pronovost).
Regarding claim 1, Pronovost teaches:
A computer implemented method for predicting respective trajectories of a plurality of road users, the method comprising:
determining, for each road user, a respective set of characteristics detected by a perception system of a host vehicle (At least ¶ [0032] “stored sensor data (or perception data derived therefrom) may be retrieved by a model and be used as input data to identify cues of an object (e.g., identify a feature, an attribute, or a pose of the object).” And ¶ [0040] “a vehicle computing device associated with the vehicle 102 may be configured to detect one or more objects (e.g., objects 108 and 110) in the environment 100, such as via a perception component. And ¶ [0062] “input data 302 representing object trajectories associated with one or more objects, object state data, and scene data can be input into an encoder 304.”), the set of characteristics including specific characteristics associated with a predefined class of road users (At least ¶ [0041] “the vehicle computing device can receive the sensor data and can semantically classify the detected objects (e.g., determine an object type), such as, for example, whether the object is a pedestrian, such as object 108, a vehicle such as object 110, a building, a truck, a motorcycle, a moped, or the like.” And ¶ [0118] “characteristics associated with each object type may be used by the model component 830 to determine a trajectory, a velocity, or an acceleration associated with the object. Examples of characteristics of an object type may include, but not be limited to: a maximum longitudinal acceleration, a maximum lateral acceleration, a maximum vertical acceleration, a maximum speed, maximum change in direction for a given speed, and the like.”);
transforming, for each of the road users, the set of characteristics (input data 302) to a respective set of input data (token sequence 316) for a prediction algorithm (Decoder 318) via a processing unit of the host vehicle, wherein each set of input data comprises the same predefined number of data elements (At least ¶ [0062] “The encoder 304 can represent a machine learned model such as a GNN, RNN, CNN, and the like, and output one or more feature vectors 306 which can be sent to a codebook 308 and a quantizer 310.” And ¶ [0063] “the quantizer 310 can receive the feature vectors 306 output by the encoder 304, and discretize the feature vectors 306 to output the discretized feature vectors 312 “ and ¶ [0064] “the codebook 308 can receive the discretized feature vectors 312 and determine the token sequence 316.” And ¶ [0067] “a decoder 318 can receive the token sequence 316 and determine the output data 320.” In this case, the token sequence 316, which is derived from the input data 302, is used itself as input data for the decoder 318 (i.e. prediction algorithm).); and
determining, via the processing unit, at least one respective trajectory for each of the road users by applying the prediction algorithm to the input data (At least ¶ [0067] “a decoder 318 can receive the token sequence 316 and determine the output data 320. The decoder 318 can represent a machine learned model such as a GNN, a GAN, an RNN, another Transformer model, etc. The output data 320 can, for example, be similar to the output data 106 or the output data 210 and represent an object trajectory, scene data, simulation data, and so on.”).
Claim 15 recites a computer-readable medium containing instructions which perform the method detailed in claim 1, thus is rejected on the same basis. Additionally, Pronovost teaches a non-transitory computer readable medium (At least ¶ [0139] “Memory 818 and memory 838 are examples of non-transitory computer-readable media.”).
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.
The factual inquiries 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.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost in view of H. Bi (“Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes”, hereafter Bi).
Bi was cited in the IDS dated 05/15/2024
Regarding claim 2, Pronovost teaches The method according to claim 1.
Pronovost does not teach:
each set of input data includes a set of latent features;
transforming the respective set of characteristics for each of the road users to the respective set of input data comprises applying an embedding algorithm to the respective set of characteristics in order to generate the corresponding set of latent features;
the latent features are allocated to a dynamic grid map of a predefined region of interest in the environment of the host vehicle, the dynamic grid map including a predefined number of pixels;
the prediction algorithm is applied to the allocated latent features for determining respective occupancy information for each class of the road users for each pixel of the grid map; and
the at least one respective trajectory for each of the road users is determined by using the respective occupancy information for each pixel.
However, Bi, within the same filed of endeavor, teaches:
each set of input data includes a set of latent features (At least section 3.2 “We adopt
this pooling scheme in our network to collect the latent motion representations of vehicles and pedestrians in the neighborhood.”);
transforming the respective set of characteristics for each of the road users to the respective set of input data comprises applying an embedding algorithm to the respective set of characteristics in order to generate the corresponding set of latent features (At least section 3.2 “For any pedestrian p 1 and any vehicle vj, we first use separate embedding functions ¢ ( ·) with Re LU nonlinearity to embed x!, P{ as follows:) …”);
the latent features are allocated to a dynamic grid map of a predefined region of interest in the environment of the host vehicle (At least section 3.2 “The positions of all the neighbors, including pedestrians and vehicles, are pooled on the occupancy map.”), the dynamic grid map including a predefined number of pixels (At least section 3.2 “We use a similar grid of N-0 x N0 cells in [2], called occupancy map, which is centered at the position of a pedestrian or vehicle.” A grid of N0 X N0 is a predefined size.);
the prediction algorithm is applied to the allocated latent features for determining respective occupancy information for each class of the road users for each pixel of the grid map (At least section 3.2 “The hidden states of pi and vj, denoted as ht (p,i) and ht (v,j) respectively, carry their latent representations. Through the occupancy map, pedestrians and vehicles share the latent representations with hidden states. As shown in Fig. 3, the occupancy map VO and PO are built respectively for both vehicles and pedestrians.”. Building an occupancy map is analogous to determining respective occupancy information.); and
the at least one respective trajectory for each of the road users is determined by using the respective occupancy information for each pixel (At least section 3.4 “Through the occupancy maps respectively for pedestrians and vehicles, frame-by-frame heterogeneous interactions are pooled. The predicted kinematic trajectories of pedestrians and vehicles at t are respectively given …”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pronovost with Bi. This modification would have been obvious as both Pronovost and Bi contain subject matter within the same field of endeavor (vehicle control) and Pronovost ¶ [0002] notes that “…Accurately predicting future object trajectories may be necessary to safely operate the vehicle in the vicinity of the object...”. Introducing Bi to Pronovost may help accomplish the goal of more accurately predicting future object trajectories. Bi notes that predicting the orientation together with the position simultaneously will produce more accurate kinematic trajectories. One of ordinary skill in the art would recognize that the method described by Bi would help create a more accurate dataset. This increased accuracy may lead to increased safety for those proximate to the ego vehicle.
Regarding claim 3, the combination of Pronovost and Bi teaches The method according to claim 2. Bi further teaches wherein transforming the set of characteristics to the respective set of input data is performed separately for each class of road users by applying a separated embedding algorithm being defined for the respective class to the respective set of characteristics (At least section 3.2 “For any pedestrian p 1 and any vehicle vj, we first use separate embedding functions ¢ ( ·) with Re LU nonlinearity to embed x!, P{ as follows:) …”).
Regarding claim 4, Pronovost teaches The method according to claim 1, Bi further teaches wherein a number and a type of the specific characteristics is different for the different classes of road users (At least section 3.3 “VP-LSTM estimates separate d-variate conditional distributions for pedestrians and vehicles, respectively.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan E Reinert whose telephone number is (571)272-1260. The examiner can normally be reached Mon - Thurs 7AM - 5PM EST.
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/J.E.R./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668