DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendment filed on ----12/2/2025 for application 18/397,971. Claim 1 – 20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/2/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Respond to Amendment
This action is in response to the amendment filed on ----12/2/2025 has been entered.
Claim 1, 10, 19 are amended.
Respond to Argument
The applicant’s argument filed on 12/2/2025 has been fully considered but they are not persuasive.
Regarding claim rejection under 35 U.S.C. 101 section, applicant stated in page 6 that “the claim, as a whole, does not merely recite observation, evaluation, and judgment as would be performed in the human mind. Instead, it recites a specialized computer-implemented method for trajectory planning in HD mapping platforms:”:
“Multi-agent vehicle trajectory data are digital data streams (e.g., synchronized GPS and sensor data) collected automatically from multiple vehicles, which are not obtainable or processable by human minds absent specialized equipment and computation.”
“Constructing the trajectory value-based flow field is explicitly performed ‘by a neural network,’ requiring substantial computation and data-driven modeling that categorically cannot be conducted by a human mind. This is not a generic instruction to ‘apply it on a computer,’ but rather mandates the construction of a deep learning model tailored to trajectory value assessment within map segments.”
“Configuring a driving policy layer based on the constructed trajectory value-based flow field, where the driving policy layer ‘indicates respective values of trajectories’ for the map segment, is a technical implementation that outputs actionable data for downstream vehicle control systems, not a mental step or mere evaluation.”
Examiner respectfully disagrees. The claims are recited in high generality in steps of obtaining (observing) environment data, constructing (observing/evaluating) trajectory of the observed data, and determining/configuring (evaluating/judging) the trajectory of the ego vehicle based on the observed environmental data.
Applicant argued in point a) that the data are not obtainable or processable by human mind. However, the argued GPS or digital data stream are not in the claim. In Berkheimer v. HP INC., 881 F. 3d 1360 (Fed. Cir. 2018), the federal circuit held that improvements are only considered “to the extent they are captured in the claims.” Berkheimer at 1369.
Applicant argued in point b) that the steps are “explicitly performed by a neural network” and “not a generic instruction to ‘apply it on a computer’”. Examiner respectfully disagrees. “Trajectory value-based flow field” as point out in 0018 of instant application “indicates values or costs of various possible trajectories in the map segment”. Evaluating a value (for example risk, award, distance, curvature) associated with a trajectories can be easily done by human mind based on its experience. Using neural network to perform a task, within BRI, adding mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Applicant argued in point c) that the claimed limitation “is a technical improvement.” Examiner notes that the improvement is in the abstract idea not the technological field. Examiner further notes that the actionable data and the downstream vehicle control are not in the claim. Examiner notes that in order to overcome 101 rejection, applicant can choose to include positive steps/limitations that integrate independent claims into practical application. For example provide driving control and navigation instructions for the autonomous vehicle such as described in paragraph 0002.
Regarding claim rejection under 35 U.S.C. 103 section, applicant stated in page 9 – 10 that the cited reference do not render these feature of", “constructing, by a neural network, a trajectory value-based flow field for the map segment based on the multi-agent vehicle trajectory data." Examiner respectfully disagrees. “trajectory value-based flow field”, as described in the specification 0018, are trajectories data with a value or cost. Ding teaches clustered trajectory data as shown in fig. 1 – 6. The trajectories can be drawn on a map, thus having values associated with the trajectories. Fang teaches “Deep trajectory clustering … via self-training … which fully utilizes the data mining capabilities of neural networks” (Fang, page 697). The combination of Ding and Fang renders obviousness of the limitation: “constructing, by a neural network, a trajectory value-based flow field for the map segment based on the multi-agent vehicle trajectory data.”
Applicant further stated in page 11 that Ding reference has “no architectural layer, such as the claimed driving policy layer, providing values for multiple trajectories in a map segment independently of search output.” Examiner notes that architectural layer is not in the claim nor in the specification of the instant application. Rather, the “driving policy layer” is described by its function (for example specification 0018) that “implement a value-based flow field that indicates values or costs of various possible trajectories in the map segment”. Ding teaches at least in Fig. 4, “Path smoothing is conducted to ensure comfort”. As illustrated in fig. 4(d), a range of trajectories are considered, each with a comfort level (value/cost). Thus, Ding teaches the cited limitation.
The remaining arguments are essentially the same as those addressed above and/or below and are unpersuasive for at least the same reasons. Therefore, Examiner is unpersuaded and maintains the corresponding rejections.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Step 1 Analysis
Claim 1 is directed to a method, which is one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites the abstract ideas in the following limitations:
obtaining multi-agent vehicle trajectory data associated with a map segment of an HD map;
constructing a trajectory value-based flow field for the map segment based on the multi-agent vehicle trajectory data
configuring, for the map segment, trajectory planning parameters of a driving policy layer of the HD mapping platform based on the trajectory value-based flow field, wherein the driving policy layer indicates respective values of trajectories in the map segment .
The steps of obtaining, constructing and configuring recite observation, evaluation and judgement mental processes and can practically be performed in human mind with or without physical aid and thus falls under the mental processes group of abstract idea. Thus, the claim falls within judicial exception of abstract idea and requires further analysis under Step 2A Prong Two.
Step 2A Prong Two Analysis:
Claim 1 recites the following additional elements along with the abstract ideas:
by a neural network,
The additional element of neural network is recited in high generality and amounts to no more than a recitation of the words "apply it" (or an equivalent), or no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Claim 1 does not integrate the abstract idea into a practical application. Claim 1 directs to abstract idea.
Step 2B Analysis:
The additional element of neural network is recited in high generality and amounts to no more than a recitation of the words "apply it" (or an equivalent), or no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Claim 1 does not contribute inventive concept. Claim 1 is not eligible.
Regarding Claim 2 – 20,
Claim 2 – 20 fails to remedy these deficiencies and thus rejected with the same reason.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 – 6, 8 – 15 and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al., (hereinafter Ding), “FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow”, in view of Fang et al., (hereinafter Fang), “E2DTC: An End to End Deep Trajectory Clustering Framework via Self-Training”.
Claim 1, Ding discloses: A trajectory planning method for a high-definition (HD) mapping platform, comprising:
obtaining multi-agent vehicle trajectory data associated with a map segment of an HD map (sec I & fig. 3 (a), “The traffic flows consist of a number of vehicle trajectories which are produced by an onboard tracking module”, “traffic flows are easy to obtain. They can be obtained by extending RoadMap with an additional traffic flow layer”; the map with additional detail/layer of information, thus HD map; sec. III “It takes sparse semantic point clouds from vehicles (multi-agents) as input and fuses them into a global lightweight semantic map.”);
constructing a trajectory value-based flow field for the map segment based on the multi-agent vehicle trajectory data; and configuring, for the map segment, trajectory planning parameters of a driving policy layer of the HD mapping platform based on the trajectory value-based flow field (fig. 3, alg. 1 & sec. IV.A., “The traffic flow generation is lightweight and only relies on onboard tracking.”; traffic flow are clustered and generated/constructed for the use of trajectory planning. Sec. V.C, “we have two weighted costs, namely, the density cost and the direction cost, which punish the paths that enter low-density areas or do not match the field direction.”, “Another dynamic programming search is in Line 11, which is built on the graph formed by the sampled stations and lateral clusters, where Gz is used for cost evaluation”; i.e., the cost/value calculation function Gz is created and integrated into the traffic flow fields, thus the step also set the calculation function (as parameter) for online path generation (driving policy) ), wherein the driving policy layer indicates respective values of trajectories in the map segment (Fig. 4, “Path smoothing is conducted to ensure comfort”. As illustrated in fig. 4(d), a range of trajectories are considered, each with a comfort level (value/cost)).
Ding do not explicitly teach:
constructing, by a neural network,
Fang, in the same field of endeavor, explicitly teach:
constructing, by a neural network (Fang, sec. I, “GPS-enable devices and mobile computing services, massive volumes of trajectory data are collected to capture the mobility of vehicles … Trajectory clustering, an essential and popular trajectory data analytics task to discover similar trajectory groups”, “trajectory via deep learning representation based on neural networks can be used”).
Ding and Fang both teach trajectory clustering among multiple vehicles trajectories and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the neural network approach of Fang’s teaching in the system of Ding to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to “achieves superior accuracy and efficiency compared with classical clustering methods” (Fang, abs.).
Claim 2, Ding and Fang combination renders obviousness of all the limitation of Claim 1. The combination further teach: wherein constructing the trajectory value-based flow field for the map segment based on the multi-agent vehicle trajectory data includes assessing values of potential trajectories associated with positions within the map segment according to a trajectory value function (Fang, sec. III, “Equation 5 is the loss function (trajectory value function) designed for one input data sequence x.”; sec. IV., “we first convert each trajectory T with continuous GPS points to a sequence of discrete tokens, where we call this process as trajectory discretization.” i.e., the value/loss is calculated based on the positions on the map by a value function).
Claim 3, Ding and Fang combination renders obviousness of all the limitation of Claim 1. The combination further teach: generating an obstacle potential field for the map segment based on the trajectory value-based flow field; and updating an implicit obstacle layer of the HD mapping platform based on the obstacle potential field (Ding, sec. IV.B. & fig. 2, “once a significant change in traffic flow fields is observed, which is typically caused by road construction and road re-routing, A map update trigger will be generated by the traffic flow field generation process, which is then sent to RoadMap. We find that the traffic-flow-based update trigger is very useful for detecting the change in road course and can serve as an important complement for lightweight mapping”; i.e., based on the observed change on the flow field, the road section (obstacle potential field) has potential new obstacles, the RoadMap with new obstacle is updated and the change with obstacle (implicit obstacle layer) is sent to cloud database;).
Claim 4, Ding and Fang combination renders obviousness of all the limitation of Claim 3. The combination further teach: generating the obstacle potential field for the map segment based on analysis of flow field divergence in the trajectory value-based flow field (refer to the mapping in Claim 3, sec. IV.B. “significant change is observed”; i.e., the new vehicle trajectory/flow field is significant difference/divergence from the previously collected traffic flow field trajectory).
Claim 5, Ding and Fang combination renders obviousness of all the limitation of Claim 1. The combination further teach: the multi-agent vehicle trajectory data includes, for each of one or more vehicles: ego trajectory data associated with that vehicle; and neighboring agent trajectory data associated with one or more neighboring vehicles (Ding, sec. III.B., “For each channel, we construct a 2D grid, where each cell contains a tuple fi,j = (d,δi,δj). d denotes the density (number of vehicles passing the cell)”; fig. 3.a., multiple vehicle’s trajectories are collected in near/neighboring location).
Claim 6, Ding and Fang combination renders obviousness of all the limitation of Claim 1. The combination further teach: training the neural network based on driven trajectories associated with the map segment (Fang, sec. IV., “raw trajectory generated by a moving object is usually represented as a time ordered sequence of sample GPS points, i.e., T = p1 p2 pT . Each point pi T(1 i T) consists of a pair of spatial coordinates (i.e., latitude and longitude) and its observed timestamp. Here, T denotes the length of a trajectory, i.e., the number of sampled points.”; i.e., the training is based on the past trajectories on the map segment).
Claim 8, Ding and Fang combination renders obviousness of all the limitation of Claim 1. The combination further teach: the multi-agent vehicle trajectory data includes global navigation satellite system (GNSS) navigation data associated with a plurality of driven trajectories in the map segment (Fang, sec. IV., “GPS points” ).
Claim 9, Ding and Fang combination renders obviousness of all the limitation of Claim 8. The combination further teach: the GNSS navigation data includes global positioning system (GPS) navigation data (refer to the mapping in Claim 8, the GPS data).
Claim 10 – 15 and 17 – 18 are the corresponding apparatus claim of Claim 1 – 6 and 8 – 9. Ding further teaches at least one memory; and at least one processor communicatively coupled with the at least one memory (Ding, sec. III. & Fig. 2, “The framework consists of three major components: a lightweight semantic mapping module with an additional traffic flow management function, a traffic flow field generation module, and an online path planning and smoothing module.”; the framework provide a mobile signal processing and computation function which inherently include data processor and memory to store data). Claims 10 – 15 and 17 – 18 are rejected with same reason.
Claim 19 – 20 are the corresponding non-transitory computer-readable medium claim of Claim 10 – 11. At least algorithm 1 and 2 of Ding are instructions stored in memory for the processor to process data. Claim 19 – 20 are rejected with same reason.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al., (hereinafter Ding), “FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow”, in view of Fang et al., (hereinafter Fang), “E2DTC: An End to End Deep Trajectory Clustering Framework via Self-Training” as applied to claim 6 above, and further in view of Lu et a., (hereinafter Lu), “Learning under Concept Drift: A Review”.
Claim 7, Ding and Fang combination renders obviousness of all the limitation of Claim 6. The combination does not explicitly teach: training the neural network based on the driven trajectories associated with the map segment includes identifying anomalous trajectories among the driven trajectories and training the neural network based on the anomalous trajectories.
Lu, in the same field of endeavor, explicitly teach:
training the neural network based on the driven trajectories associated with the map segment includes identifying anomalous trajectories among the driven trajectories and training the neural network based on the anomalous trajectories (Lu, Fig. 2 & sec. 1, “the three major aspects of concept drift: concept drift detection, understanding and adaptation, as shown in Fig. 2”; sec. 2.1, “’concept drift’ as the problem in which
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”; Fig. 13, “new model is trained with latest data to replace the old model when a concept drift is detected.”; Ding and Fang combination teaches to train a neural network model for clustering trajectories and to detect significant change in the trajectory/clustering pattern. Lu teaches when the concept drift (significant change) happens to the model’s input and output, use new data to train a new model. The combination renders obviousness of the limitation.).
Ding (in view of Fang) and Lu both teach the training and inference stage of machine learning and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the retraining strategy of Lu’s teaching to the system of Ding (in view of Fang) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order “to provide more reliable data-driven predictions and decision” (Lu, sec. 1).
Claim 16 is the corresponding apparatus claim of Claim 7. The claims is rejected with same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Ding et al., “Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor” which teaches the generation of trajectories based on the trajectories of obstacles/vehicles in a map section.
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 SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HELAL ALGAHAIM can be reached on (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIEN MING CHOU/Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3645