DETAILED ACTION
Status of the Application
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 the Claims
This action is in response to the applicant’s filing on January 06, 2025. Claims 1 – 20 are pending and examined below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on January 29, 2025 and October 29, 2025 have been considered by the Examiner.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. § 119(a)-(d), which papers have been placed of record in the file.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in The Republic of China on July 06, 2022.
Claim Rejections - 35 USC § 101
6. 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 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
35 U.S.C. § 101 –Analysis – Step 1
Claim 1 is directed to a method of acquiring historically based target vehicle movements to determine collision avoidance trajectory of the target vehicle in a preset future time period (i.e., a process). Claim 16 is to a device / system claim with instructions executed by a processor (i.e. an article of manufacture). Claim 12 is directed to a non-transitory computer readable storage medium with instructions executed by a processor (i.e. an article of manufacture). Therefore, claims 1, 12, and 16 are within at least one of the four statutory categories.
35 U.S.C. § 101 – Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. See MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. See MPEP 2106.04(a)(2), subsection III.
A claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below in bold) and will be used as a representative claim for the remainder of the 35 U.S.C. §101 rejections. Claim 1 recites:
A vehicle trajectory prediction method, applied to an electronic device, wherein the method comprises:
obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle;
predicting location distribution information of the target vehicle and location distribution information of the associated vehicle in a preset future time period
based on the historical trajectory information and map information corresponding to a traveling environment of the target vehicle and the associated vehicle [recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)];
determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information [recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)]; and
determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information. [Recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)].
The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind.
For example, “predicting location distribution information of the target vehicle… and the associated vehicle… in… future…” in the context of this claim encompasses a person(s) forecasting the relative positions of the target vehicle and the associated vehicle (surrounding vehicle(s)) at a series of time point(s) with respect to their historical positions, whereupon “determining an interaction feature…” in the context of this claim encompasses those person(s) applying observations, evaluations, and judgment to calculate the probability / likelihood of the target vehicle and the associated vehicle (surrounding vehicle) colliding (impacting) with one another during their traveling process.
Further calculation is performed comprising “determining a traveling trajectory of the target vehicle… in… future time period based on the interaction feature…” in the context of this claim encompasses those person(s) applying observations, evaluations, and judgment to establish the path of the target vehicle based upon surrounding vehicle(s) interactions including but not limited to, the surrounding vehicle cutting in front of, or giving way to the target vehicle.
Accordingly, the claim recites at least one abstract idea.
35 U.S.C. § 101 – 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 idea 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 (application) activity and post-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 vehicle trajectory prediction method, applied to an electronic device, wherein the method comprises:
obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle [pre-solution activity (data gathering) using generic / conventional sensors];
predicting location distribution information of the target vehicle and location distribution information of the associated vehicle in a preset future time period
based on the historical trajectory information and map information corresponding to a traveling environment of the target vehicle and the associated vehicle [recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)];
determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information [recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)]; and
determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information. [Recites an abstract idea belonging to the grouping of mental processes. (Observation, Evaluation, Judgement, Opinion) established by performing in the human mind(s); or by human(s) conceptualizing, representing, or visualizing using a pen and paper; or through thoughtful discussion and collaboration with several human(s)].
For the following reasons, 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 “obtaining…” means acquiring, for instance, GPS sensor and/or LIDAR data of the surrounding vehicle(s) to the target vehicle; in order to establish historical trajectory (path) of the target vehicle and the surrounding vehicle (the associated vehicle), but nevertheless does not integrate the abstract idea into a practical application and does not amount to significantly more than the judicial exception for the same reasons to those discussed above. The Examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (i.e. the claimed processor) to perform the process.
In particular, the obtaining steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering data sequences to positions, locations, orientations, and / or accelerations determining (evaluating) and predicting (evaluating ~ estimating) steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the execution of the instructions by the ”processor” is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of forecasting information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The Examiner submits that these limitations are insignificant extra solution (application) activity and post-solution activity that merely comprise an insignificant application of the results of acquiring data from generic / conventional sensors and computer components. Lastly, the “vehicle controller” (i.e. the one or more processors) merely describes applying the abstract idea using generic computer components.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations 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 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). Accordingly, the additional limitations do NOT integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
35 U.S.C. § 101 – 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 element of using a vehicle processor (computer) controller to perform the “predicting location distribution information of the target vehicle… and the associated vehicle… in… future…” in the context of this claim encompasses a person(s) forecasting the relative positions of the target vehicle and the associated vehicle (surrounding vehicle(s)) at a series of time point(s) with respect to their historical positions, whereupon “determining an interaction feature…” in the context of this claim encompasses those person(s) applying observations, evaluations, and judgment to calculate the probability / likelihood of the target vehicle and the associated vehicle (surrounding vehicle) colliding (impacting) with one another during their traveling process.
Furthermore, the calculation comprising “determining a traveling trajectory of the target vehicle… in… future time period based on the interaction feature…” in the context of this claim encompasses those person(s) applying observations, evaluations, and judgment to establish the path of the target vehicle based upon surrounding vehicle(s) interactions including but not limited to, the surrounding vehicle cutting in front of, or giving way to the target vehicle, along with the other aforementioned abstract ideas constituted by these limitations… amount to nothing more than applying the exception using a generic computer component and performing insignificant application of the results of the mental process. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field.
Using additional sensors and using generic computers to perform determining and calculating steps are well-understood, routine, and conventional activities of automating a mental process, because the background recites conventional sensors and the vehicle controller is a conventional computer within a vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claims 2 – 11, 13 – 15, and 17 - 20 do not recite any further limitations that cause the claims 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. Therefore, it can clearly be seen that dependent claims 2 – 11, 13 – 15, and 17 - 20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 12, and 16.
Therefore, claims 1 – 20 are ineligible under 35 USC §101.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 5, 9, 12 - 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chinese Patent Application No. CN 114092751 A to HE HAO et al. (herein after "Hao") in view of Chinese Patent Application No. CN 111046919 A to ZHAO WANZHONG et al. (herein after "Zhao").
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 1,
Hao’s vehicle Trajectory Prediction Method and Device discloses a vehicle trajectory prediction method, applied to an electronic device (see Fig. 1 ~ outlines a process flow of neural network based vehicle trajectory prediction,
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Figs. 4 ~ outlines a process flowchart of process method steps 104, see Fig. 5a ~ illustrates dynamic objects in a target vehicle environment including but not limited to, other vehicles, cyclists, and pedestrians,
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see Fig. 11 ~ outlines a process flowchart of a trajectory prediction method,
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and ¶0064 ~ multiple trajectory data comprises multiple trajectory points of the moving subject and surrounding moving objects (surrounding / associated vehicles) relative to spatial positions in time), wherein the method comprises:
obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle (see Fig. 1 ~ process method step 102 -- historical trajectory data and ¶0066 ~ historical map data is acquired from vehicle(s) movement);
predicting location distribution information of the target vehicle and location distribution information of the associated vehicle in a preset future time period (see Fig. 6 ~ process method step 106, ¶0070 ~ "extract global scene features based on the first trajectory point set and the historical map point set… include map features and trajectory features", and ¶0147 ~ "trajectory prediction in real-time")
based on the historical trajectory information and map information corresponding to a traveling environment of the target vehicle and the associated vehicle (see Fig. 6 ~ process method step 1108 and ¶0152 ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period").
Zhao is then relied upon to disclose determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information (see Fig. 6; Zhao ~ process method step 1108, ¶0054; Zhao ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058; Zhao ~ behavior intention teach interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle and ¶0152; Zhao ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period"); and
determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information. (See Fig. 6, ¶0054 - ¶0055; Zhao ~ "LSTM regression neural network… output states… for the trajectory"; thereby teaching calculating a traveling path of the target vehicle in the predetermined future time period; ¶0058; Zhao, and ¶0152; Zhao).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the historically based target vehicle movement determination with the behavior intention sensor fusion detection, as taught by Zhao, where the resultant combination would successfully provide LSTM network based interaction feature prediction calculated by vehicle to vehicle interaction and surrounding traffic information states of the target vehicle, thereby enabling benefits, including but not limited to: eliminating and/or minimizing the influence of dynamic traffic environment on vehicle trajectory prediction.
As to Claim 2,
Hao/Zhao discloses the method according to claim 1, wherein the associated vehicle comprises
at least one of a vehicle on a lane adjacent to a lane on which the target vehicle is located in the traveling environment of the target vehicle or a vehicle on the same lane as the target vehicle. (See ¶0047; Zhao, ¶0054; Zhao ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058; Zhao ~ behavior intention, thereby teaching interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle, and ¶0063; Zhao ~ vehicle sensors acquire x,y positions, speed, and acceleration of both target and surround vehicles over a historical time period, resulting in historical trajectory data).
As to Claim 3,
Hao/Zhao discloses the method according to claim 2, wherein the historical trajectory information comprises
at least one of location information of the target vehicle and the associated vehicle obtained by the target vehicle by using a sensor, or location information of the target vehicle and the associated vehicle in a map corresponding to the traveling environment. (See ¶0047, ¶0054, ¶0058, and ¶0063; Zhao).
As to Claim 4,
Hao/Zhao discloses the method according to claim 3, wherein the obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle further comprises:
obtaining, in a first processing manner, a feature vector of the historical trajectory information by using the historical trajectory information as input. (See Fig. 2, ¶0044, and ¶0047; Zhao ~ multimodal LSTM trajectory prediction module facilitates behavioral intent based vehicle trajectory prediction).
As to Claim 5,
Hao/Zhao discloses the method according to claim 4, wherein the first processing manner comprises:
encoding the historical trajectory information by using a long short-term memory (LSTM) algorithm (see Fig. 2, ¶0044, and ¶0047; Zhao),
to obtain the feature vector indicating the historical trajectory information. (See Fig. 2, ¶0044, and ¶0047; Zhao ~ multimodal LSTM trajectory prediction module facilitates behavioral intent based vehicle trajectory prediction).
As to Claim 9,
Hao/Zhao discloses the method according to claim 1,
wherein the determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information comprises:
fusing the location distribution information of the target vehicle and the location distribution information of the associated vehicle in the preset future time period
based on a location relationship between a current location of the target vehicle and a current location of the associated vehicle in the traveling environment,
to obtain the interaction feature between the target vehicle and the associated vehicle in the preset future time period. (See Fig. 6 ~ process method step 1108, ¶0054 - ¶0055, ¶0058, and ¶0152; Zhao).
As to Claim 12,
Hao discloses a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores instructions (see ¶0014 ~ non-volatile storage medium (non-transitory computer-readable storage medium); and
when the instructions are executed on an electronic device (see ¶0012 - ¶0013 ~ computer processing device), the electronic device is enabled to perform operations comprising:
obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle (see Fig. 1 ~ process method step 102 -- historical trajectory data and ¶0066 ~ historical map data is acquired from vehicle(s) movement);
predicting location distribution information of the target vehicle and location distribution information of the associated vehicle in a preset future time period (see Fig. 6 ~ process method step 106, ¶0070 ~ "extract global scene features based on the first trajectory point set and the historical map point set… include map features and trajectory features", and ¶0147 ~ "trajectory prediction in real-time")
based on the historical trajectory information and map information corresponding to a traveling environment of the target vehicle and the associated vehicle (see Fig. 6 ~ process method step 1108 and ¶0152 ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period");
However, Hao does not explicitly disclose determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information; and
determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information.
Zhao, on the contrary, discloses determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information (see Fig. 6; Zhao ~ process method step 1108, ¶0054 ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058 ~ behavior intention teach interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle and ¶0152; Zhao ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period"); and determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information. (See Fig. 6, ¶0054 - ¶0055; Zhao ~ "LSTM regression neural network… output states… for the trajectory"; thereby teaching calculating a traveling path of the target vehicle in the predetermined future time period; ¶0058; Zhao, and ¶0152; Zhao).
Consequently, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the historically based target vehicle movement determination with the behavior intention sensor fusion detection, as taught by Zhao, where the resultant combination would successfully provide LSTM network based interaction feature prediction calculated by vehicle to vehicle interaction and surrounding traffic information states of the target vehicle, thereby enabling benefits, including but not limited to: eliminating and/or minimizing the influence of dynamic traffic environment on vehicle trajectory prediction.
As to Claim 13,
Hao/Zhao discloses the storage medium according to claim 12,
wherein the associated vehicle comprises
at least one of a vehicle on a lane adjacent to a lane on which the target vehicle is located in the traveling environment of the target vehicle or a vehicle on the same lane as the target vehicle. (See ¶0047, ¶0054; Zhao ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058; Zhao ~ behavior intention, thereby teaching interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle, and ¶0063; Zhao ~ vehicle sensors acquire x,y positions, speed, and acceleration of both target and surround vehicles over a historical time period, resulting in historical trajectory data).
As to Claim 14,
Hao/Zhao discloses the storage medium according to claim 13, wherein the historical trajectory information comprises
at least one of location information of the target vehicle and the associated vehicle obtained by the target vehicle by using a sensor, or location information of the target vehicle and the associated vehicle in a map corresponding to the traveling environment. (See ¶0047, ¶0054, ¶0058, and ¶0063; Zhao).
As to Claim 15,
Hao/Zhao discloses the storage medium according to claim 14, wherein the obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle further comprises:
obtaining, in a first processing manner, a feature vector of the historical trajectory information by using the historical trajectory information as input. (See Fig. 2, ¶0044, and ¶0047; Zhao ~ multimodal LSTM trajectory prediction module facilitates behavioral intent based vehicle trajectory prediction).
As to Claim 16,
Hao discloses an electronic device (see ¶0012 - ¶0015), comprising:
at least one processor (see ¶0012 - ¶0013 ~ computer processing device); and
at least one memory storing instructions for execution by the at least one processor to cause the electronic device to perform operations (see ¶0012 - ¶0014 ~ non-volatile storage medium (non-transitory computer-readable storage medium) comprising:
obtaining historical trajectory information of a target vehicle and an associated vehicle of the target vehicle (see Fig. 1 ~ process method step 102 -- historical trajectory data and ¶0066 ~ historical map data is acquired from vehicle(s) movement);
predicting location distribution information of the target vehicle and location distribution information of the associated vehicle in a preset future time period (see Fig. 6 ~ process method step 106, ¶0070 ~ "extract global scene features based on the first trajectory point set and the historical map point set… include map features and trajectory features", and ¶0147 ~ "trajectory prediction in real-time")
based on the historical trajectory information and map information corresponding to a traveling environment of the target vehicle and the associated vehicle (see Fig. 6 ~ process method step 1108 and ¶0152 ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period").
However, Hao does not explicitly disclose determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information; and
determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information.
Conversely, Zhao discloses determining an interaction feature between the target vehicle and the associated vehicle in the preset future time period based on the location distribution information (see Fig. 6; Zhao ~ process method step 1108, ¶0054 ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058 ~ behavior intention teach interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle and ¶0152; Zhao ~ "predicted trajectory point set includes spatial location of the moving subject at multiple time points within a predetermined future time period"); and determining a traveling trajectory of the target vehicle in the preset future time period based on the interaction feature, the location distribution information of the target vehicle in the preset future time period, and the map information. (See Fig. 6, ¶0054 - ¶0055; Zhao ~ "LSTM regression neural network… output states… for the trajectory"; thereby teaching calculating a traveling path of the target vehicle in the predetermined future time period; ¶0058; Zhao, and ¶0152; Zhao).
To that end, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the historically based target vehicle movement determination with the behavior intention sensor fusion detection, as taught by Zhao, where the resultant combination would successfully provide LSTM network based interaction feature prediction calculated by vehicle to vehicle interaction and surrounding traffic information states of the target vehicle, thereby enabling benefits, including but not limited to: eliminating and/or minimizing the influence of dynamic traffic environment on vehicle trajectory prediction.
As to Claim 17,
Hao/Zhao discloses the device according to claim 16, wherein the associated vehicle comprises
at least a vehicle on a lane adjacent to a lane on which the target vehicle is located in the traveling environment of the target vehicle or a vehicle on the same lane as the target vehicle. (See ¶0047, ¶0054 ~ behavior intention is apportioned as lane change, lane keeping, etc., ¶0058 ~ behavior intention, thereby teaching interaction features wherein the behaviors of surrounding vehicles are assessed for their likelihood to impact the target vehicle, and ¶0063 ~ vehicle sensors acquire x,y positions, speed, and acceleration of both target and surround vehicles over a historical time period, resulting in historical trajectory data).
As to Claim 18,
Hao/Zhao discloses the device according to claim 17, wherein the historical trajectory information comprises
at least one of location information of the target vehicle and the associated vehicle obtained by the target vehicle by using a sensor, or location information of the target vehicle and the associated vehicle in a map corresponding to the traveling environment. (See ¶0047, ¶0054, ¶0058, and ¶0063; Zhao).
As to Claim 19,
Hao/Zhao discloses the device according to claim 18, wherein the obtain historical trajectory information of a target vehicle and an associated vehicle of the target vehicle further comprises:
obtain, in a first processing manner, a feature vector of the historical trajectory information by using the historical trajectory information as input. (See Fig. 2, ¶0044, and ¶0047; Zhao ~ multimodal LSTM trajectory prediction module facilitates behavioral intent based vehicle trajectory prediction).
As to Claim 20,
Hao/Zhao discloses the device according to claim 19, wherein the first processing manner comprises:
encoding the historical trajectory information by using a long short-term memory (LSTM) algorithm,
to obtain the feature vector indicating the historical trajectory information. (See Fig. 2, ¶0044, and ¶0047;Zhao ~ multimodal LSTM trajectory prediction module facilitates behavioral intent based vehicle trajectory prediction).
Allowable Subject Matter
Claims 6 – 8 and 10 - 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
In particular, the available prior art appears to be silent in disclosing the method wherein the semantic feature of the map information comprises a lane and a travelable area in the traveling environment; and
performing encoding by using a convolutional neural network (CNN) algorithm by using the location distribution information of the target vehicle and the location distribution information of the associated vehicle in the preset future time period as input,
to obtain feature vectors corresponding to the location distribution information of the target vehicle and the location distribution information of the associated vehicle; and
fusing, by using a fusion model, the feature vectors corresponding to the location distribution information of the target vehicle and the location distribution information of the associated vehicle.
The prior art does not appear to explicitly teach or disclose the above recited claim limitations.
To that end and although further search and consideration would always need to be performed based upon any submitted amendments by the Applicant, it is the Examiner’s position that incorporating these above recited claim limitations into independent claims 1, 12, and 16 may/might possibly advance prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ASHLEY L. REDHEAD, JR. whose telephone number is (571) 272 - 6952. The Examiner can normally be reached on weekdays, Monday through Thursday, between 7 a.m. and 5 p.m.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Peter Nolan can be reached Monday through Friday, between 9 a.m. and 5 p.m. at (571) 270 – 7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY L REDHEAD JR./Primary Examiner, Art Unit 3661