DETAILED ACTION
This action is in response to the claims filed 01/02/2026. Claims 1-31 are pending in the case. Claims 1, 12, 22, 27 are independent claims.
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 .
Response to Arguments
Applicant's arguments filed 01/02/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 U.S.C. 101
Applicant argues the claim 1 is not directed to a mental process because “a human mind isn’t equipped to use neural network to perform the above steps”.
Examiner disagrees with the framing of the argument and the conclusion.
The rejection under 101 is made according to the flow chart in the MPEP. First, it is asked whether the claims recite an abstract idea, for example a mental step. Then, the flow chart requires consideration of any additional elements in the claim which integrate the abstract idea such that the claim is not directed to the recited abstract idea.
The claim clearly recites abstract ideas as noted in the rejection, identification of characteristics, determinations of roles, and predicting paths are all decisions about abstract data which can be formed in the mind.
The fact that a claimed “neural network” performs the abstract idea does not make the claim eligible, rather the guidance requires consideration of the claimed neural network as an additional element because the neural network indeed is not part of the recited abstract idea. As noted in the MPEP merely adding the words “apply it” to implement an abstract idea on a computer or technology does not integrate the recited abstract idea. No details about the functioning or inner workings of the technology are described to consider as further additional elements which integrate the abstract idea. Instead, the thrust of the claim is related to a description of the recited abstract idea.
Examiner notes the rejection is maintained and has been updated accordingly.
With respect to the rejection under 35 U.S.C. 102 of claim 1
Applicant simply states that Alahi does not teach the claim limitation without any substantive argument.
Examiner disagrees at least as evidenced by the updated rejection.
With respect to the remaining rejections in view of the art
Applicant simply states that cited art does not teach the claim limitation without any substantive argument.
Examiner disagrees at least as evidenced by the updated rejection.
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 1-31 are rejected under 35 U.S.C 101 because the claimed invention is directed to abstract idea without significantly more.
Regarding Claim 1, 12, 22, and 27
Under step 1, Claim 1 is directed to a process. Claim 12 is directed to a machine. Claim 22 is directed to an article of manufacture.. Claim 27 is directed to machine. Each of which are a statutory category.
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations “identify one or more characteristics of a set of objects to determine different roles corresponding to the set of objects based, at least in part, on video frames of a scene identify a first model that corresponds to a first subset of objects from the set of objects, and a second model that corresponds to a second subset of objects from the set of objects, wherein the first subset of objects and the second subset of objects comprise objects with the different roles: and predict one or more paths of at least one object in common between the first subset of objects and the second subset of objects by at least comparing outputs generated by the first model and the second model.”
These limitations describe a mental evaluation about movement of objects in a scene. While the limitations recite a “model of motion” this broadly includes mental models and as such does not explicitly recite any particular technology.
Furthermore, under step 2A Prong 2 and 2B the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer or computer machinery as a tool to perform an abstract idea. (“using one or more neural networks”, “at least one non-transitory memory to store instructions, and one or more processors in communication with the at least one non-transitory memory, wherein the one or more processors execute the instructions to cause the apparatus to: use one or more neural networks”, “storing computer instructions that, when executed by one or more processors, cause one or more processors to: use one or more neural networks”, “the one or more processors are to use a selected neural network of the one or more neural networks”, “one or more circuits to use one or more neural networks” See MPEP 2106.05(f). Using a neural network amounts to merely applying a generic computer machinery to the above cited abstract idea. Accordingly, the recited additional elements, when taken alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 2-7, 9, 11, 13-18, 20, 23-26, 28- 31
The claim(s) 2-7, 9 and 11 are directed to a process. Each of the limitations recited in these claims only serve to describe the abstract idea addressed in the independent claim Under Step 2A Prong 1.
The claim(s) 13-18, 20 are directed to a machine. Each of the limitations recited in these claims only serve to describe the abstract idea addressed in the independent claim Under Step 2A Prong 1.
The claim(s) 23-26 are directed to an article of manufacture. Each of the limitations recited in these claims only serve to describe the abstract idea addressed in the independent claim Under Step 2A Prong 1.
The claim(s) 28- 31 are directed to machine. Each of the limitations recited in these claims only serve to describe the abstract idea addressed in the independent claim Under Step 2A Prong 1.
Furthermore, under step 2A Prong 2 and 2B, the claim(s) does not recite additional elements to consider other than those considered in the independent claim. Accordingly, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 8
The claim is directed to a process. The claim recites the judicial exceptions recited in the independent claim. Under Step 2A Prong 1, these limitations only serve to describe the abstract idea addressed in the independent claim.
Furthermore, under step 2A Prong 2 and 2B the claims recite the additional element(s) “wherein the one or more neural networks utilize long short-term memory (LSTM) system.” that generally link the use of the judicial exception to a particular field of use, See MPEP 2106.05(h). Accordingly, the recited additional elements, when taken alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 10
The claim is directed to a process. The claim recites the judicial exceptions recited in the independent claim. Under Step 2A Prong 1, these limitations only serve to describe the abstract idea addressed in the independent claim.
Furthermore, under step 2A Prong 2 and 2B the claims recite the additional element(s). In particular, the claim recites additional element(s) “wherein the predicted one or more paths are displayed utilizing a map” that generally link the use of the judicial exception to a particular field of use, See MPEP 2106.05(h). Accordingly, the recited additional elements, when taken alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 19
Claim 19 is rejected for the reasons set forth in claim 8 in connection with claim 12
Regarding Claim 21
Claim 21 is rejected for the reasons set forth in claim 10 in connection with claim 12
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.
Claim 7 is 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.
Claim 7 recites the limitation "the selection". There is insufficient antecedent basis for this limitation in the claim. Examiner notes this claim would appear to be intended to depend on claim 6 as it further describes the selection of models described in the claim.
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.
Claim(s) 1, 3-5, 8, 10, 11-12, 14-16, 19, 21-24 and 27-29 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Alahi et al. “Social LSTM: Human Trajectory Prediction in Crowded Spaces” hereinafter Alahi.
Regarding claim 1
Alahi teaches, A method, comprising: using one or more neural networks (Section 3.2 ¶01 pg 965 “The Social-LSTM model was trained on a single GPU”)
identify one or more characteristics of a set of objects (pg 7 “In Figure 5,we illustrate the prediction results of our Social-LSTM” the prediction results are the path characteristics of a set of objects in the frame) to determine different roles corresponding to the set of objects ( pg 7 “When people walk in a group or as e.g. a couple , our model is able to jointly predict their trajectories.” Pg 8 “In addition, we qualitatively show that our Social-LSTM successfully predicts various non-linear behaviors arising from social interactions, such as a group of individuals moving together.” The model qualitatively determines roles such as group movement.)based, at least in part, on video frames of a scene ( pg 5 “This synthetic data contained trajectories for hundreds of scenes with an average crowd density of 30 per frame.” The scenes are frames of videos, clearly shown in pg 8 Figure 5.) identify a first model that corresponds to a first subset of objects from the set of objects, and a second model that corresponds to a second subset of objects from the set of objects, ( pg 3 Figure 2 caption “Figure 2. Overview of our Social-LSTM method. We use a separate LSTM network for each trajectory in a scene” each person which is a subset of objects from the set of objects is used for a separately identified LSTM network.) wherein the first subset of objects and the second subset of objects comprise objects with the different roles: Pg 3 Section 3.1 “Every person has a different motion pattern: they move with different velocities, acceleration and have different gaits…” each person has different movement styles, i.e roles. Pg 7 “At time-step 3 and 4, we notice that the Social-LSTM predicts a “halt” for person (3) in order to yield for person (1).” Qualitatively the model predicts different movement roles for different objects) and predict one or more paths of at least one object in common between the first subset of objects and the second subset of objects by at least comparing outputs generated by the first model and the second model. ( pg 6 “In these scenes, we observe three people(2,3,4) walking close to each other and a fourth person(1) walking farther away from them.” the models predict common paths for at least one objects and divergent paths for at least one other. This is according to a comparison of the differences between the outputs.)
Regarding claim 3
Alahi teaches claim 1
Further Alahi teaches, wherein the set of objects include one or more dynamic objects (Section 3.1 pg 963 “Every person has a different motion pattern: they move with different velocities, acceleration and have different gaits.” The persons in the scene correspond to the objects, depicted in at least figure 2, each with different movement patterns, thus dynamic objects.)
Regarding claim 4
Alahi teaches claim 1
Further Alahi teaches, wherein comparing the outputs comprise determining the least amount of error of the outputs ( pg 6 Table 1 caption “Table 1. Quantitative results of all the methods on all the datasets. We present the performance metrics as follows: First 6 rows are the Average displacement error, row 7 to 12 are the Average displacement error for non-linear regions, and the final 6 rows are the Final displacement error. All methods forecast trajectories for a fixed period of 4.8 seconds” pg 6 “Our Social pooling based LSTM and O-LSTM outperform the heavily engineered Social Force and IGP models in almost all datasets. In particular, the error reduction is more significant in the case of the UCY datasets as compared to ETH.” The tables show the models with the least amount of error of the outputs.)
Regarding claim 5
Alahi teaches claim 1
Further Alahi teaches, wherein the different roles are used to predict the one or more paths ( pg 3 “Every person has a different motion pattern: they move with different velocities, acceleration and have different gaits. We need a model which can understand and learn such person-specific motion properties from a limited set of initial observations corresponding to the person. Long Short-Term Memory (LSTM) networks have been shown to successfully learn and generalize the properties… In particular, we have one LSTM for each person in a scene. This LSTM learns the state of the person and predicts their future positions” each person has different movement and motion patterns or roles, used by the LSTM to predict the path.)
Regarding claim 8
Alahi teaches claim 1
Further Alahi teaches, wherein the one or more neural networks utilize long short-term memory (LSTM) system. (abstract “we propose an LSTM model which can learn general human movement and predict their future trajectories” the system makes predicts of multiple objects including a second object as claimed)
Regarding claim 10
Alahi teaches claim 1
Further Alahi teaches, wherein the predicted one or more paths are displayed utilizing a map. (Caption 1 pg 961 Figure 1 “The predicted distribution of their future trajectories is shown in the heat-map” as shown in the figure the path of all the objects in the scene are displayed on a heat map.)
Regarding claim 11
Alahi teaches claim 1
Further Alahi teaches, wherein the first model determines relationships between objects within the first subset of objects. ( pg 6 “In these scenes, we observe three people(2,3,4) walking close to each other and a fourth person(1) walking farther away from them.” the models predict common paths for at least one objects and divergent paths for at least one other. This is a qualitative description of the first subset of objects, namely that the distance between the paths defines the relationship.)
Regarding Claim 12
Alahi teaches, An apparatus, comprising: at least one non-transitory memory to store instructions, and one or more processors in communication with the at least one non-transitory memory, wherein the one or more processors to execute the instructions to cause the apparatus to: (Section 3.2 ¶01 pg 965 “The Social-LSTM model was trained on a single GPU” The examiner notes that the instant claim limitation is inherent to the prior art (MPEP 2112), a neural network implemented on a generic computer necessarily requires non-transitory medium which is in communication with processors to execute the method taught by Alahi)
The remaining limitations are addressed in the rejection of claim 1
Regarding claim 14, 15, 16, 19 and 21
Alahi teaches claim 12.
Claim 14, 15, 16, 19 and 21 is rejected for the reasons set forth in the rejection of claim 3, 4, 5, 8 and 10, respectively.
Regarding Claim 22
Alahi teaches, A non-transitory computer-readable media medium storing computer instructions that, when executed by one or more processors, cause one or more processors to: (The examiner notes that the instant claim limitation is inherent to the prior art (MPEP 2112), a neural network implemented on a generic computer necessarily requires non-transitory medium which is in communication with processors to execute the method taught by Alahi)
The remaining limitations are addressed in the rejection of claim 1
Regarding claim 23 and 24
Alahi teaches claim 22.
Claim 23 and 24 is rejected for the reasons set forth in the rejection of claim 4 and 5, respectively.
Regarding claim 27
Alahi teaches, one or more processors, circuitry (The examiner notes that the instant claim limitation is inherent to the prior art (MPEP 2112), a neural network implemented on a generic computer necessarily requires one or more circuits which is in communication with processors to execute the method taught by Alahi)
The remaining limitations are addressed in the rejection of claim 1
Regarding claim 28 and 29
Alahi teaches claim 27.
Claim 28 and 29 is rejected for the reasons set forth in the rejection of claim 4 and 5, respectively.
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.
Claim(s) 2, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alahi, further in view of Hoermann “Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling”
Regarding claim 2
Alahi teaches claim 1
Alahi teaches does not explicitly teach, wherein the set of objects include one or more static objects.
Hoermann however, when addressing issues related to path prediction of a plurality of static and dynamic objects teaches, wherein the set of objects include one or more static objects. (pg 7 Section D ¶02 “The scene also illustrates an example of pedestrian-vehicle Interactions… In the first prediction, the approaching vehicle is still about 3 s away from the pedestrian, and a possible crossing trajectory is predicted by the neural network… Starting the prediction 1.8 s later (bottom row), the pedestrian still didn’t move… Appropriately, the neural network prediction for the pedestrian adapted and no crossing trajectory is predicted anymore” the cited text describes the situation in Figure 7. In the scene there are at least two objects whose paths are predicted. One is a static pedestrian not moving at a cross walk, the other is an approaching vehicle, which corresponds to a dynamic object. Therefore, the first and second objects include at least one static object, the pedestrian.)
It would have been obvious for one of ordinary skill in the arts before the effective filling date of the claimed invention to incorporate a neural network system that predicts paths for both static and dynamic objects as taught by Hoermann to the disclosed invention of Alahi.
One of ordinary skill in the arts would have been motivated to make this modification in order to implement a “network [that] can predict highly complex scenarios with various road users of different class up to 3 s. It can be seen, that the network is able to consider different maneuver classes, e.g., turn right or go straight, and interactions between road users reduce the prediction uncertainty. Experiments with an artificially added pedestrian emphasize intrinsically modeled interactions.” ( Hoermann Conclusion pg 8)
Regarding claim 13
Alahi teaches claim 12.
Claim 13 is rejected for the reasons set forth in the rejection of claim 2
Claim 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Alahi, and further in view of Rashid et al. “Risk behavior-based trajectory prediction for construction site safety monitoring”, hereinafter Rashid.
Regarding claim 9
Alahi teaches the method of claim 1.
Alahi further teaches, wherein the set of objects within scene (As shown in figure 5 the objects whose trajectory is predicted are objects within an image or scene)
Alahi does not appear to explicitly teach, for use in connection with a construction site.
However, Rashid, when addressing issues related to path prediction in a construction site, teaches is in connection with a construction site. (Header: Training Data Collection and Processing “The goal was to replicate the randomness of workers’ walking paths in a typical construction site”)
It would have been obvious for one of ordinary skill in the arts before the effective filling date of the claimed invention to use the path prediction method disclosed by Alahi in a construction site taught by Rashid.
One of ordinary skill in the arts would have been motivated to make this modification in order to remedy “a congested workspace [that] can lead to potential hazardous and life-threatening situations” (Introduction, Rashid)
Regarding claim 20
Alahi teaches claim 12.
Claim 20 is rejected for the reasons set forth in the rejection of claim 9
Claims 6, 7, 17, 18, 25, 26, 30 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Alahi, and further in view of Hernandez “Data-driven Aircraft Trajectory Predictions using Ensemble Meta-Estimators”
Regarding claim 6
Alahi teaches claim 1
Further Alahi does not explicitly teach, wherein the outputs are compared to select either the first model or the second model to predict the one or more paths.
However, Hernandez, when addressing selecting among and ensemble of estimators , teaches, wherein the outputs are compared to select either the first model or the second model to predict the one or more paths. (pg 4 “Gradient Boosting (GBM) is a very popular boosting algorithm that works by sequentially adding predictors to an ensemble, each one correcting its predecessor. GBM attempts to fit the new predictor to the residual errors made by the previous predictor. The ultimate goal is to create a ‘strong’ learner from an ensemble of ‘weak’ learners. Formally, GBM uses a loss function, MSE (Mean Squared Error) and tries to minimize it over a set of iterations.” Pg 8 “Checking the results for the longitude λ, GBM outperforms DRF in terms of prediction accuracy” Boosting selects a final model from a set of prior models to predict a path by minimizing error over iterations. Longitude is a prediction of the path of an object.)
It would have been obvious for one of ordinary skill in the arts before the effective filling date of the claimed invention to modify the trajectory estimator of Alahi to use an ensemble of estimators as taught by Hernandez.
One of ordinary skill in the arts would have been motivated to make this modification because “The underlying reasoning for the development of ensemble-meta estimators is that, empirically, the use of multiple and diverse predictive models trying to predict a particular feature will perform better than a single model trying to predict the same feature” (Hernandez pg 3-4)
Regarding claim 7
Alahi teaches claim 2
Further Alahi does not explicitly teach, wherein the selection of either the first model or the second model is based, at least in part, on an accuracy of the outputs
However, Hernandez, when addressing selecting among and ensemble of estimators , teaches, wherein the selection of either the first model or the second model is based, at least in part, on an accuracy of the outputs (pg 4 “Gradient Boosting (GBM) is a very popular boosting algorithm that works by sequentially adding predictors to an ensemble, each one correcting its predecessor. GBM attempts to fit the new predictor to the residual errors made by the previous predictor. The ultimate goal is to create a ‘strong’ learner from an ensemble of ‘weak’ learners. Formally, GBM uses a loss function, MSE (Mean Squared Error) and tries to minimize it over a set of iterations.” Pg 8 “Checking the results for the longitude λ, GBM outperforms DRF in terms of prediction accuracy” Boosting selects a final model from a set of prior models to predict a path by minimizing error over iterations. Longitude is a prediction of the path of an object.)
It would have been obvious for one of ordinary skill in the arts before the effective filling date of the claimed invention to modify the trajectory estimator of Alahi to use an ensemble of estimators as taught by Hernandez.
One of ordinary skill in the arts would have been motivated to make this modification because “The underlying reasoning for the development of ensemble-meta estimators is that, empirically, the use of multiple and diverse predictive models trying to predict a particular feature will perform better than a single model trying to predict the same feature” (Hernandez pg 3-4)
Regarding claim 17/25/30
Alahi teaches claim 12/22/27.
Claim 17/25/30 is rejected for the reasons set forth in the rejection of claim 6 in connection with the parent claim.
Regarding claim 18/26/31
Alahi teaches claim 18/26/31.
Claim 18/26/31is rejected for the reasons set forth in the rejection of claim 7 in connection with the parent claim.
Conclusion
Prior art:
Sarkar et al “Trajectory Prediction of Traffic Agents at Urban Intersections Through Learned Interactions” describes prediction of multiple agents with a neural network includes both vehicles and pedestrians.
Cheng et al “Mixed Traffic Trajectory Prediction Using LSTM–Based Models in Shared Space” describes an improved adaptation to Alahi which handles not only human agents but also pedestrians, cyclists and vehicles.
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 extension fee 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 JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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