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 . This office action is in response to an amendment filed on 9/17/25.
Claims 1, 3-4, 6-7, and 9-10 are pending.
Response to Amendments
Claims 2, 5, and 8 have been cancelled. Claims 1, 6, 7, and 9 have been amended. Hybrid claim rejection under 35 U.S.C. 112(b) has been removed upon correction.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are comprises a vehicle state acquisition unit and a positioning unit; the vehicle state acquisition unit is configured to acquire the state information of the vehicle… and the positioning unit is configured to acquire the position information of the vehicle as cited in claim 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. See at least,
vehicle state acquisition unit and a positioning unit;
Pg. 16- [lines 27-28] – “the vehicle-mounted driving intention perception system 10
includes a vehicle state acquisition unit 11 and a positioning unit 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.
Regarding claim 7, it discloses claim limitations: “comprises a vehicle state acquisition unit and a positioning unit; the vehicle state acquisition unit is configured to acquire the state information of the vehicle … and the positioning unit is configured to acquire the position information of the vehicle.” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. As seen in the specification – Pg. 16- [lines 27-28] – “the vehicle-mounted driving intention perception system 10 includes a vehicle state acquisition unit 11 and a positioning unit 12. yet the disclosure is devoid of any structure that performs the function in the claim as no definite structure is given to the “vehicle-mounted driving intention perception system 10”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 6, 7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Huasheng et al (CN108919803A). in view of Xue -Wu (“Intention Recognition and Trajectory Prediction for Vehicles Using LSTM Network MT”) and in view of Xiaofeng (CN110843789A).and in further view of Guang (CN109050537A) and in further view of Maeng (US 2019/0351918 Al)
Regarding Claim 1 Huasheng teaches each of the vehicles through a vehicle-mounted driving intention perception system disposed on each of the vehicles, (Pg. 4 – [41] – “Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” & See Also Pg. 8 – [88] – “the data sending process between the central server and the plurality of mining unmanned vehicles may be… binding the communication port and the listening port; and calling the receiving function to read the data into the receiving buffer.” (equates to each of the vehicles through a vehicle-mounted driving intention perception system disposed on each of the vehicles as the vehicle can communicate the driving path to the server, as well as, include a transmitting and receiving port as seen by the last quote equivalent to the output and receiving port of this application, and a plurality of vehicle have the capability of having path conflict data sent to them. ), and the position information comprises GPS longitude information and GPS latitude information of each of the vehicles; (Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”) connecting the vehicle-mounted driving intention perception system (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.”(equates vehicle-mounted driving intention perception system as the first quote shows the ability to collect the vehicle state by way of sensor and the second shows position information collection by way of gps.)) to a vehicle-mounted driving intention control system disposed on each of the vehicles, (Pg. 4 – [41] – “Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” & See Also Pg. 8 – [88] – “the data sending process between the central server and the plurality of mining unmanned vehicles may be… binding the communication port and the listening port; and calling the receiving function to read the data into the receiving buffer.” (equates to a vehicle-mounted driving intention control system disposed on a vehicle as the quote shows a functionality of the path conflict and thus driving intention as the vehicle can communicate the driving path to the server, as well as, include a transmitting and receiving port as seen by the last quote equivalent to the output and receiving port of this application. )) and transmitting the state information and position information of each of the vehicles to the vehicle-mounted driving intention control system via the vehicle-mounted driving intention perception system; (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.”) recognizing driving intention information of each of the vehicles based on the state information and position information of each of the vehicles; (Pg. 14 – [143] – “The central server can determine the position of the unmanned mine car in the grid map and the respective driving speed according to the vehicle position information of the plurality of unmanned mining vehicles, if at least two mining unmanned vehicles A and B are predicted to pass at the same time At the same grid, it is determined that there is a path conflict between at least two of the mine unmanned vehicles.” (equates to recognizing driving intention information of each of the vehicles based on the state information and position information of each of the vehicles; as the speed and position are used to see if the paths the vehicles will take conflict and thus the intention is establish through the path being taken then compared.)) (Pg. 14 – [143] – “The central server can determine the position of the unmanned mine car in the grid map and the respective driving speed according to the vehicle position information of the plurality of unmanned mining vehicles, if at least two mining unmanned vehicles A and B are predicted to pass at the same time At the same grid, it is determined that there is a path conflict between at least two of the mine unmanned vehicles.” (equates to acquiring driving intention information of the vehicles recognized by the state information of the vehicles as the speed and position are used to see if the paths the vehicles will take conflict and thus the intention is establish through the path being taken then compared.)) generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range, (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range as a grid in this art is formed showing each position of each vehicle relative to one another where their paths based on intention are utilized in the grid to determine if they’ll collide.)) the state information of the vehicles and the position information of the vehicles; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds,” (Equates to the state information of the vehicles and the position information of the vehicles as the grid map stores position information and speed information to determine collision instances.)) constructing a dispatching area occupancy grid map model; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map…The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds” (equates to constructing a dispatching area occupancy grid map model as a grid is formed between the plurality of vehicles where driving paths and thus intentions are recorded by the centrals server.)) and co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, (Pg. 8 – [88] – “…the data sending process between the central server and the plurality of mining unmanned vehicles may be: setting a communication IP; binding the communication port; and calling a sending function to send the data.” & See Also Pg. 9 – [89] [90] [91] – “The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation tasks.
The central server sends the path plan to each of the multiple mine unmanned vehicles. Path planning refers to the insertion of a sequence of intermediate points for control between a given path starting point and a target point based on a certain environmental model, given the starting point and the target point of the driverless car. Collision, a valid path that can safely reach the target point.
The central server may plan a first path for a plurality of mining unmanned vehicles according to the improved artificial potential field method, avoiding static obstacles in the environment, and proceeding toward the target.” ( equates to co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model as the quote show a communication established between the server and the intelligent vehicles wherein the server ensures that the paths taken by each within the grid allow for safe transit of each.)) and generating a global dispatching result of driving intentions of dispatching area vehicles, (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to generating a global dispatching result of driving intentions of dispatching area vehicles as the vehicles in this mentioned driving area have communicated their path intention and the control commands are dispatched according to the driving intentions of the vehicles.)) and- transmitting the global dispatching result to the vehicle-mounted driving intention control system; (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to and- transmitting the global dispatching result to the vehicle-mounted driving intention control system as the quote shows a transmission of a result of path conflict between two vehicles)) and guiding driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result through a vehicle driving decision system disposed on each of the vehicles, (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to and guiding driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result through a vehicle driving decision system disposed on each of the vehicles, as the quote shows a command sent to at least one or any of the vehicles in the area based on the path conflict.)) and transmitting the driving intention information, the state information and position information of each of the vehicle; (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.”) thereby controlling each of the vehicles to travel in sequence and avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles, the second control command including collision avoidance The second control instruction is used to instruct at least one mining unmanned vehicle to automatically travel according to a conflict avoidance strategy.” (equates to thereby controlling each of the vehicles to travel in sequence and avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously as the quote shows driving intention of each vehicles being reported and then based on the paths each vehicles takes the driving decisions are then altered to avoid collision, wherein the sequence of control is seen through a first and second control command. ))
Huasheng fails to teach A method for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode, comprising an accelerator pedal state, steering wheel angle, brake pedal state, and absolute speed of the vehicle; through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network, wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, and a recognition vector w =(wl, w2,w3,w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, where wl, w2, w3, w4, and w5 are probabilities of driving intention categories: traveling to a left lane, keeping unchanged, traveling to a right lane, speeding up, and slowing down, respectively: and setting confidence thresholds for various driving intention categories, and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold, determining that the vehicle has a driving intention C corresponding to the category, where CE{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane, Cd: speeding up, Cf: slowing down}; wherein the confidence thresholds for the driving intention categories: traveling to the left lane and traveling to the right lane are set as 80%, the confidence threshold for the driving intention category: keeping unchanged is set as 70%, and the confidence thresholds for the driving intention categories: speeding up and slowing down are set as 80%;connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, to the cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Xu-Wu teaches A method for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode (Pg. 2 – Introduction – “The classification algorithm aims to identify the driver's intention, and generally divides the trajectory into categories such as changing lanes to the left, changing lanes to the right, and driving in a straight line” & See Also Pg. 2 – {introduction} – “At the same time, trajectory prediction is also conducive to the optimization of advanced driver assistance systems” (equates to A method for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode as the quote shows the classification of driver intention used for driver assistance or human vehicle do driving mode being employed)) steering wheel angle , (Pg. 11 – Fig. 4 & See Also Pg. 10-11 – “The vehicle's heading angle 0 = arctan ( (1) -x (1-3) y(t) - y(-3) -) is calculated from the vehicle position parameters (x, y); then the heading angle of each sampling point is traversed from the lane change point to the reverse direction of the time axis. If the 0 of three consecutive sampling points in the trajectory sequence is ≤0. (the heading angle threshold of the lane change starting point), the position where the threshold is reached for the first time is located as the lane change starting point; finally, a similar method is used to judge |0|≤0. (the heading angle threshold of the lane change ending point) to determine the lane change end point” (equates to steering angle as probability of the lane change includes the vehicle heading angle as described above which is equivalent to the steering angle as the steering operation correlates to the vehicles heading.)) and absolute speed of the vehicle (Pg. 5 – “Specifically, the state information of the predicted vehicle includes S = (x, y)… y is the absolute speed of the predicted vehicle” ) wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, (Pg. 16 – “it can be seen that only on the basis of the input trajectory position information, adding vehicle speed information, surrounding vehicle information and driving intention information” (equates to wherein vehicle state information is taken as an input quantity I of the driving intention recognition model as the quote shows both surrounding vehicle position and speed data being used as an input. )) and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, (Pg. 4 – 1 Model Framework – “The Softmax function is used to calculate the probability of the driving intention to change lanes to the left, drive straight, and change lanes to the right” & See Also Pg. 4 – 1 Model Framework – “Set as the input of the model; C=(C1,C2,C3), is the intention category vector output by the intention recognition module, C1, C2, C3 represent the three intention categories of changing lanes to the left, driving straight, and changing lanes to the right;” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model as the quote shows the various changing lanes to the left, driving straight, and changing lanes to the right that re computed by the Softmax layer. Wherein the speeding up and slowing down are shown to be input to the predicted vehicle behavior as seen from the last quote to enhance the model they are building gup to in the paper.)) where wl, w2, w3, w4, and w5 are probabilities of driving intention categories. (Pg. 4 – 1 Model Framework – “is a vector composed of the probabilities of each intention category,; (i=1,2,3) represent the probabilities of changing lanes to the left, driving straight, and changing lanes to the right, respectively” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to where wl, w2, w3, w4, and w5 are probabilities of driving intention categories as the first quote shows the lane changing an then later in the paper the insertion of speeding up and down is included in the model to further the predictive capabilities.)) traveling to a left lane, keeping unchanged, traveling to a right lane, (Pg. 4 – 1 model framework – “change lanes to the left, drive straight, and change lanes to the right”) speeding up, and slowing down, respectively (Pg, 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.”) (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%, and the confidence threshold for driving in a straight line is set to 70%.”) and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold (Pg. 7 – “When the assumption of a certain type of intention is greater than the corresponding confidence threshold”) determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane,} (Pg. 7 - “it is determined that this is the correct type, so the probability of this category is adjusted to 100%, and the probabilities of the other two categories are 0, which becomes a one-hot vector at this time; in addition, the original probabilities of the three categories are output.” (equates to determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane,} as the quote shows how once the confidence threshold is eclipsed a determination about the vehicles ability to drive straight, or changes lanes is set as the one hot vector or that the driving intention is set.)) Cd: speeding up, Cf slowing down. (Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.”) wherein the confidence thresholds for the driving intention categories: traveling to the left lane and traveling to the right lane are set as 80%, the confidence threshold for the driving intention category: keeping unchanged is set as 70%, (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%, and the confidence threshold for driving in a straight line is set to 70%.”) and the confidence thresholds for the driving intention categories: are set as 80%; (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%...” (equates to and the confidence thresholds for the driving intention categories: are set as 80%; as the quote shows the confidence threshold for driving intention being set at 80%))
Huasheng & Xue - Wu fails to teach comprising an accelerator pedal state, brake pedal state, ; through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network, the driving intention categories: speeding up and slowing down connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, to the cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Guang teaches comprising an accelerator pedal state, brake pedal state. (Pg. 2 – [20] – “obtain a sample value in the form of an analog quantity, and the lower layer data includes an accelerator pedal position signal, a brake pedal position signal,”) the driving intention categories: speeding up and slowing down (Pg. 2 – [0024] – “the lower layer simple driving behavior recognition model includes a braking/acceleration type operation model” (equates to the driving intention categories: speeding up and slowing down as the quote shows the model including braking and accelerating whish is directly correlated to the vehicle speeding up and slowing down.))
Huasheng & Xue - Wu & Guang fails to teach through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network, connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, to the cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Xiaofeng teaches through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network (Pg. 2 – “Using a time-series convolutional network as a skeleton network, inputting a multi-frame feature matrix obtained from step S2, predicting the intended lane and lane change time” & See Also Pg. 2 – [17] – “the tracking trajectory data of the target vehicle and surrounding vehicles are generated by the driverless perception system during driving test, including a map with road information, vehicle physical information, vehicle position, lane, speed, and vehicle direction. information.” (equates to through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network as the quote shows a convolution network being utilized for understanding driver intention, and the second quote showing the perception system of the art used to take in input data.) )
Huasheng & Xue - Wu & Guang & Xiaofeng fails to teach connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Maeng teaches connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner (Pg. 19 – [0082] – “The robot 100a, the self-driving vehicle 100b, the XR device 100c, the smartphone 100d, or the home appliance l00e, to which the AI technology is applied, may be referred to as AI apparatuses 100a to l00e.” & See Also Pg. 19 – [0086] – “cloud network 10, and may assist at least part of AI processing of the connected AI apparatuses 100a to l00e” & see also Pg. 20 – [0104] – “In particular, the self-driving vehicle 100b may recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices” & See Also Pg. 23 – [0160] – “The AI apparatus 100 and the vehicle 300 may make communication with the external vehicle 400 through a wired/wireless communication technology” (equates to the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner as the quotes show the cloud system connected to the robotic self-driving car which has the ability for driving intention recognition and thus the control system and cloud system are connected. Also the wireless connection can be seen as the cloud system is connected to the AI apparatus and the ai apparatus is connected to the vehicle wirelessly.)) cloud dispatching system; performing, by the cloud dispatching system, the steps of:. (Pg. 19 – [0083] – “The cloud network 10 may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure.” & see Also Pg. 1 – Abstract – “An AI apparatus for providing a notification related to a lane-change of a vehicle includes a sensor unit including at least one of an image sensor, a radar sensor or a LiDAR sensor, and a processor to receive, from the sensor unit, sensor information on a surrounding road and each of at least one external vehicle,” & See Also Pg. 19 – [0086] – “cloud network 10, and may assist at least part of AI processing of the connected AI apparatuses 100a to l00e.”) . It would have been an advantageous addition to the system disclosed by Huasheng & Xue - Wu & Guang & Xiaofeng to include connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, cloud dispatching system; performing, by the cloud dispatching system, the steps of: as these limitations allow for a database to be ran far and away from the vehicle and allows for digitized storage of all information to be made away from the vehicle allowing for reduced storage to be needed on the vehicle.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, cloud dispatching system; performing, by the cloud dispatching system, the steps of: as this allows for a lower general expense to be needed in regards to hardware needed for software storage.
Regarding Claim 3 Huasheng & Xu-Wu & Xiaofeng & Guang & Maeng (Huasheng teaches the following:) teaches The method for regionally co-dispatching driving intentions of intelligent vehicles according to claim 1, wherein the generating a global driving intention graph of all the vehicles within a dispatching area range is. (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to wherein the generating a global driving intention graph of all the vehicles within a dispatching area range is as a range is made within viewing the map bounds to further determine path conflicts between vehicles.)) G=[
g
i
], 1< i <N , N is a total number of vehicles within the dispatch area range, (Pg. 2 – [12] – “the area in which the plurality of mining unmanned vehicles travel is divided into a plurality of grids, and the plurality of mines are determined according to the vehicle information. At least two mining unmanned vehicles in an unmanned vehicle have path conflicts, including: if at least two mining unmanned vehicles are predicted to pass the same grid at the same time, determine multiple mines There are path conflicts in at least two mining unmanned vehicles in the driving vehicle.” (equates to G=[
g
i
], 1< i <N , N is a total number of vehicles within the dispatch area range as the quote shows a plurality of vehicles within a grid map model in which driving intention is stored and viewed wherein this quote has i=2 as the path conflict is determined by at least two vehicles but may be more. )) of an ith vehicle within the dispatching area range, respectively. (, Pg. 2 - [12] – “including: if at least two mining unmanned vehicles are predicted to pass the same grid at the same time, determine multiple mines There are path conflicts in at least two mining unmanned vehicles in the driving vehicle.” & See Also Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed, acceleration, and angular velocity” & See Also Pg. 4 – [41] – “Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” (equates to of an ith vehicle within the dispatching area range, respectively as the vehicle information is used to determine driving intention within the range of the vehicle of each vehicle within the plurality as disclosed above. ))
Yet Huasheng fails to teach: where
g
i
= (
C
i
,
V
i
,
W
i
,
P
i
) are the driving intention, absolute speed, steering angle, and vehicle position information
Xue-Wu teaches where
g
i
= (
C
i
,
V
i
,
W
i
,
P
i
) are the driving intention, (Pg. 14 – “the intention recognition module can make a prediction before the driver performs the lane change action” (equates to driving intention as the model is predicting a driving or lane change intention in this example.)) absolute speed, (Pg. 5 – “Specifically, the state information of the predicted vehicle includes S = (x, y)… y is the absolute speed of the predicted vehicle” ) steering angle, (Pg. 11 – Fig. 4 & See Also Pg. 10-11 – “The vehicle's heading angle 0 = arctan ( (1) -x (1-3) y(t) - y(-3) -) is calculated from the vehicle position parameters (x, y); then the heading angle of each sampling point is traversed from the lane change point to the reverse direction of the time axis. If the 0 of three consecutive sampling points in the trajectory sequence is ≤0. (the heading angle threshold of the lane change starting point), the position where the threshold is reached for the first time is located as the lane change starting point; finally, a similar method is used to judge |0|≤0. (the heading angle threshold of the lane change ending point) to determine the lane change end point” (equates to steering angle as probability of the lane change includes the vehicle heading angle as described above which is equivalent to the steering angle as the steering operation correlates to the vehicles heading.)) and vehicle position information (Pg. 13 – “In addition, the intention recognition module needs to recognize the lane change intention as early as possible. The prediction time is defined as the time interval from the current position of the vehicle” (equates to vehicle position information as the recognition module uses vehicle position as seen above to recognize lane change. )). It would have been an advantageous addition to the system disclosed by Huasheng to include where
g
i
= (
C
i
,
V
i
,
W
i
,
P
i
) are the driving intention, absolute speed, steering angle, and vehicle position information as these variables allow for better prediction to take place as understanding each allows for the target vehicles intention to be better understood.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include where
g
i
= (
C
i
,
V
i
,
W
i
,
P
i
) are the driving intention, absolute speed, steering angle, and vehicle position information as each variable inclusion allows for a more robust model that is conducive to better understand the intention of target vehicles.
Regarding Claim 6 Huasheng teaches comprising: and a vehicle-mounted driving intention perception system each of the vehicles (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.”(equates vehicle-mounted driving intention perception system as the first quote shows the ability to collect the vehicle state by way of sensor and the second shows position information collection by way of gps.)) a vehicle-mounted driving intention control system disposed on each of the vehicles, (Pg. 4 – [41] – “Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” & See Also Pg. 8 – [88] – “the data sending process between the central server and the plurality of mining unmanned vehicles may be… binding the communication port and the listening port; and calling the receiving function to read the data into the receiving buffer.” (equates to and a vehicle-mounted driving intention control system disposed on each of the vehicles as the quote shows a functionality of the path conflict and thus driving intention as the vehicle can communicate the driving path to the server, as well as, include a transmitting and receiving port as seen by the last quote equivalent to the output and receiving port of this application. )) and a vehicle driving decision system disposed on each of the vehicles (Pg. 4 – [41] – “Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” & See Also Pg. 8 – [88] – “the data sending process between the central server and the plurality of mining unmanned vehicles may be… binding the communication port and the listening port; and calling the receiving function to read the data into the receiving buffer.” (equates to each of the vehicles through a vehicle-mounted driving intention perception system disposed on each of the vehicles as the vehicle can communicate the driving path to the server, as well as, include a transmitting and receiving port as seen by the last quote equivalent to the output and receiving port of this application, and a plurality of vehicle have the capability of having path conflict data sent to them. )) wherein the vehicle-mounted driving intention perception system is connected to the vehicle- mounted driving intention control system, (Pg. 4 – [40] – “information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.” & See Also Pg. 4 – [43] – “Transmitting, to the at least one mine unmanned vehicle of the at least two mining driverless vehicles, a second control command, the second control command including a conflict avoidance” & See Also Pg. 16 – [166] – “the coupling or direct coupling or communication connection shown or discussed herein may be an indirect coupling or communication connection through some interface,” (equates to wherein the vehicle-mounted driving intention perception system is connected to the vehicle- mounted driving intention control system as the above quotes show position information, and control commands being transmitted to vehicles and thus a connection is established between each disclosed module as the server and vehicles can communicate with the variety of information used to locate and determine driving intention. )) and is configured to acquire state information and position information of each of the vehicles and transmit the state information and position information of each of the vehicles to the vehicle-mounted driving intention control system; (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed” & See Also Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle to the center server.”) and is configured to recognize a driving intention of the vehicle according to the state information of the vehicle, (Pg. 4 – [40] – “The vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed,” & See Also Pg. 4 – [41] – “130，Determining, according to the vehicle information, that there is a path conflict between at least two of the plurality of mining unmanned vehicles” (equates to configured to recognize a driving intention of the vehicle according to the state information of the vehicle as the first quote shows the vehicle information being the state of the vehicle by way of keep track of vehicle speed and the second quote shows the driving intention of the vehicle is determined via the aforementioned vehicle information. ) ) and the position information comprises GPS longitude information and GPS latitude information of each of the vehicles; ((Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”) and transmit the driving intention information of the vehicle and the state information and position information of the vehicle (Pg. 4 – [39] - “the vehicle information transmitted by the mining driverless vehicle to the center server may include: a vehicle state and a vehicle location”) construct a dispatching area occupancy grid map model; (Pg. 6 – [56] – “The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map…The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles. The central server can predict whether two unmanned vehicles will pass the same grid at the same time according to the grid distance between the two unmanned mine cars and their respective travel speeds” (equates to constructing a dispatching area occupancy grid map model as a grid is formed between the plurality of vehicles where driving paths and thus intentions are recorded by the centrals server.)) co-dispatch global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model, (Pg. 8 – [88] – “…the data sending process between the central server and the plurality of mining unmanned vehicles may be: setting a communication IP; binding the communication port; and calling a sending function to send the data.” & See Also Pg. 9 – [89] [90] [91] – “The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation tasks. The central server sends the path plan to each of the multiple mine unmanned vehicles. Path planning refers to the insertion of a sequence of intermediate points for control between a given path starting point and a target point based on a certain environmental model, given the starting point and the target point of the driverless car. Collision, a valid path that can safely reach the target point. The central server may plan a first path for a plurality of mining unmanned vehicles according to the improved artificial potential field method, avoiding static obstacles in the environment, and proceeding toward the target.” ( equates to co-dispatching global driving intentions of the vehicles within the area range according to the constructed dispatching area occupancy grid map model as the quote show a communication established between the server and the intelligent vehicles wherein the server ensures that the paths taken by each within the grid allow for safe transit of each.)) and generate a global dispatching result of driving intentions of dispatching area vehicles, (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to generating a global driving intention graph of all the vehicles within a dispatching area range according to the driving intention information of the vehicles within the dispatching area range as a grid in this art is formed showing each position of each vehicle relative to one another where their paths based on intention are utilized in the grid to determine if they’ll collide.)) and transmit the global dispatching result to the vehicle-mounted driving intention control system; (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to and- transmitting the global dispatching result to the vehicle-mounted driving intention control system as the quote shows a transmission of a result of path conflict between two vehicles)) and the vehicle driving decision system is configured to: guide driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result; (Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles,” (equates to and guiding driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result through a vehicle driving decision system disposed on each of the vehicles, as the quote shows a command sent to at least one or any of the vehicles in the area based on the path conflict.)) and control each of the vehicles to travel in sequence(Pg. 1 – [9] – “The control instruction includes a first path plan for instructing the mining unmanned vehicle to automatically travel according to the first path plan; receiving vehicle information transmitted by the plurality of mining unmanned vehicles; determining a plurality of mining unmanned according to the vehicle information At least two mining unmanned vehicles in the driving vehicle have a path conflict; transmitting a second control command to at least one of the at least two mining unmanned vehicles, the second control command including collision avoidance The second control instruction is used to instruct at least one mining unmanned vehicle to automatically travel according to a conflict avoidance strategy.” (equates to thereby controlling each of the vehicles to travel in sequence and avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously as the quote shows driving intention of each vehicles being reported and then based on the paths each vehicles takes the driving decisions are then altered to avoid collision, wherein the sequence of control is seen through a first and second control command. ))
Yet Huasheng fails to teach A system for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode wherein the state information comprises an accelerator pedal state, steering wheel angle, brake pedal state, and absolute speed of each of the vehicles constructing a driving intention recognition model based on a convolutional neural network, wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, and a recognition vector w =(wl, w2,w3,w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, where wl, w2, w3, w4, and w5 are probabilities of driving intention categories: traveling to a left lane, keeping unchanged, traveling to a right lane, speeding up, and slowing down, respectively ;and setting confidence thresholds for various driving intention categories, and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold, determining that the vehicle has a driving intention C corresponding to the category, where CE{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane, Cd: speeding up, Cf slowing down};wherein the confidence thresholds for the driving intention categories:traveling to the left lane and traveling to the right lane are set as 80%, the confidence threshold for the driving intention category: keeping unchanged is set as 70%, and the confidence thresholds for the driving intention categories: speeding up and slowing down are set as 80%;- a cloud dispatching system, the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner, to the cloud dispatching system; and the cloud dispatching system is configured to perform.
Xu-Wu teaches A system for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode (Pg. 2 – Introduction – “The classification algorithm aims to identify the driver's intention, and generally divides the trajectory into categories such as changing lanes to the left, changing lanes to the right, and driving in a straight line” & See Also Pg. 2 – {introduction} – “At the same time, trajectory prediction is also conducive to the optimization of advanced driver assistance systems” (equates to A method for regionally co-dispatching driving intentions of intelligent vehicles, in a human-vehicle co-driving mode as the quote shows the classification of driver intention used for driver assistance or human vehicle do driving mode being employed)) steering wheel angle , (Pg. 11 – Fig. 4 & See Also Pg. 10-11 – “The vehicle's heading angle 0 = arctan ( (1) -x (1-3) y(t) - y(-3) -) is calculated from the vehicle position parameters (x, y); then the heading angle of each sampling point is traversed from the lane change point to the reverse direction of the time axis. If the 0 of three consecutive sampling points in the trajectory sequence is ≤0. (the heading angle threshold of the lane change starting point), the position where the threshold is reached for the first time is located as the lane change starting point; finally, a similar method is used to judge |0|≤0. (the heading angle threshold of the lane change ending point) to determine the lane change end point” (equates to steering angle as probability of the lane change includes the vehicle heading angle as described above which is equivalent to the steering angle as the steering operation correlates to the vehicles heading.)) and absolute speed of the vehicle (Pg. 5 – “Specifically, the state information of the predicted vehicle includes S = (x, y)… y is the absolute speed of the predicted vehicle” ) wherein vehicle state information is taken as an input quantity I of the driving intention recognition model, (Pg. 16 – “it can be seen that only on the basis of the input trajectory position information, adding vehicle speed information, surrounding vehicle information and driving intention information” (equates to wherein vehicle state information is taken as an input quantity I of the driving intention recognition model as the quote shows both surrounding vehicle position and speed data being used as an input. )) and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model, (Pg. 4 – 1 Model Framework – “The Softmax function is used to calculate the probability of the driving intention to change lanes to the left, drive straight, and change lanes to the right” & See Also Pg. 4 – 1 Model Framework – “Set as the input of the model; C=(C1,C2,C3), is the intention category vector output by the intention recognition module, C1, C2, C3 represent the three intention categories of changing lanes to the left, driving straight, and changing lanes to the right;” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to and a recognition vector w=(wl, w2,w3 w4,w5) for the driving intentions is output by a Softmax layer of the driving intention recognition model as the quote shows the various changing lanes to the left, driving straight, and changing lanes to the right that re computed by the Softmax layer. Wherein the speeding up and slowing down are shown to be input to the predicted vehicle behavior as seen from the last quote to enhance the model they are building gup to in the paper.)) where wl, w2, w3, w4, and w5 are probabilities of driving intention categories. (Pg. 4 – 1 Model Framework – “is a vector composed of the probabilities of each intention category,; (i=1,2,3) represent the probabilities of changing lanes to the left, driving straight, and changing lanes to the right, respectively” & See Also Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.” (equates to where wl, w2, w3, w4, and w5 are probabilities of driving intention categories as the first quote shows the lane changing an then later in the paper the insertion of speeding up and down is included in the model to further the predictive capabilities.)) traveling to a left lane, keeping unchanged, traveling to a right lane, (Pg. 4 – 1 model framework – “change lanes to the left, drive straight, and change lanes to the right”) speeding up, and slowing down, respectively (Pg, 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.”) (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%, and the confidence threshold for driving in a straight line is set to 70%.”) and when an output probability of a certain driving intention category is greater than the corresponding confidence threshold (Pg. 7 – “When the assumption of a certain type of intention is greater than the corresponding confidence threshold”) determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane,} (Pg. 7 - “it is determined that this is the correct type, so the probability of this category is adjusted to 100%, and the probabilities of the other two categories are 0, which becomes a one-hot vector at this time; in addition, the original probabilities of the three categories are output.” (equates to determining that a vehicle has a driving intention C corresponding to the category, where C
∈
{Ca: traveling to a left lane, Cb: keeping unchanged, Cc: traveling to a right lane,} as the quote shows how once the confidence threshold is eclipsed a determination about the vehicles ability to drive straight, or changes lanes is set as the one hot vector or that the driving intention is set.)) Cd: speeding up, Cf slowing down. (Pg. 15 – “E_LSTM: Based on XY_LSTM, predict vehicle speed information.”) wherein the confidence thresholds for the driving intention categories: traveling to the left lane and traveling to the right lane are set as 80%, the confidence threshold for the driving intention category: keeping unchanged is set as 70%, (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%, and the confidence threshold for driving in a straight line is set to 70%.”) and the confidence thresholds for the driving intention categories: are set as 80%; (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%...” (equates to and the confidence thresholds for the driving intention categories: are set as 80%; as the quote shows the confidence threshold for driving intention being set at 80%))
Huasheng & Xue - Wu fails to teach comprising an accelerator pedal state, brake pedal state, ; through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network, the driving intention categories: speeding up and slowing down connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, to the cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Guang teaches comprising an accelerator pedal state, brake pedal state. (Pg. 2 – [20] – “obtain a sample value in the form of an analog quantity, and the lower layer data includes an accelerator pedal position signal, a brake pedal position signal,”) the driving intention categories: speeding up and slowing down (Pg. 2 – [0024] – “the lower layer simple driving behavior recognition model includes a braking/acceleration type operation model” (equates to the driving intention categories: speeding up and slowing down as the quote shows the model including braking and accelerating whish is directly correlated to the vehicle speeding up and slowing down.))
Huasheng & Xue - Wu & Guang fails to teach through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network, connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, to the cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Xiaofeng teaches through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network (Pg. 2 – “Using a time-series convolutional network as a skeleton network, inputting a multi-frame feature matrix obtained from step S2, predicting the intended lane and lane change time” & See Also Pg. 2 – [17] – “the tracking trajectory data of the target vehicle and surrounding vehicles are generated by the driverless perception system during driving test, including a map with road information, vehicle physical information, vehicle position, lane, speed, and vehicle direction. information.” (equates to through the vehicle-mounted driving intention control system, comprising: constructing a driving intention recognition model based on a convolutional neural network as the quote shows a convolution network being utilized for understanding driver intention, and the second quote showing the perception system of the art used to take in input data.) )
Huasheng & Xue - Wu & Guang & Xiaofeng fails to teach connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner, cloud dispatching system; performing, by the cloud dispatching system, the steps of:
Maeng teaches a cloud dispatching system (Pg. 19 – [0083] – “The cloud network 10 may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure.” & see Also Pg. 1 – Abstract – “An AI apparatus for providing a notification related to a lane-change of a vehicle includes a sensor unit including at least one of an image sensor, a radar sensor or a LiDAR sensor, and a processor to receive, from the sensor unit, sensor information on a surrounding road and each of at least one external vehicle,” & See Also Pg. 19 – [0086] – “cloud network 10, and may assist at least part of AI processing of the connected AI apparatuses 100a to l00e.” (equates to cloud dispatching system as the quote shows a cloud system assisting the AI model present in the cited art wherein the ai model is used for lane changing recognition and transmitting and receiving said data in relation to.)) the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner (Pg. 19 – [0082] – “The robot 100a, the self-driving vehicle 100b, the XR device 100c, the smartphone 100d, or the home appliance l00e, to which the AI technology is applied, may be referred to as AI apparatuses 100a to l00e.” & See Also Pg. 19 – [0086] – “cloud network 10, and may assist at least part of AI processing of the connected AI apparatuses 100a to l00e” & see also Pg. 20 – [0104] – “In particular, the self-driving vehicle 100b may recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices” & See Also Pg. 23 – [0160] – “The AI apparatus 100 and the vehicle 300 may make communication with the external vehicle 400 through a wired/wireless communication technology” (equates to the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner as the quotes show the cloud system connected to the robotic self-driving car which has the ability for driving intention recognition and thus the control system and cloud system are connected. Also the wireless connection can be seen as the cloud system is connected to the AI apparatus and the ai apparatus is connected to the vehicle wirelessly. )) to the cloud dispatching system; and the cloud dispatching system is configured to perform. (Pg. 19 – [0083] – “The cloud network 10 may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure.” & see Also Pg. 1 – Abstract – “An AI apparatus for providing a notification related to a lane-change of a vehicle includes a sensor unit including at least one of an image sensor, a radar sensor or a LiDAR sensor, and a processor to receive, from the sensor unit, sensor information on a surrounding road and each of at least one external vehicle,” & See Also Pg. 19 – [0086] – “cloud network 10, and may assist at least part of AI processing of the connected AI apparatuses 100a to l00e.”) It would have been an advantageous addition to the system disclosed by Huasheng to include cloud dispatching system, the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner, to the cloud dispatching system; and the cloud dispatching system is configured to perform as these limitations allow for a database to be ran far and away from the vehicle and allows for digitized storage of all information to be made away from the vehicle allowing for reduced storage to be needed on the vehicle.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include a cloud dispatching system, the vehicle-mounted driving intention control system is connected to the cloud dispatching system in a wireless manner, to the cloud dispatching system; and the cloud dispatching system is configured to perform as this allows for a lower general expense to be needed in regards to hardware needed for software storage.
Regarding Claim 7 Huasheng & Xu-Wu & Xiaofeng & Guang & Maeng teaches (Huasheng Discloses the following limitations:) The system for regionally co-dispatching driving intentions of intelligent vehicles according to claim 6, wherein the vehicle-mounted driving intention perception system (Pg. 15 – [165] – “the device and the unit described above can refer to the corresponding process in the foregoing method embodiment”) comprises a vehicle state acquisition unit (Pg. 4 – [40] – “…vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed…”) and a positioning unit; (Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”) the vehicle state acquisition unit is configured to acquire the state information of the vehicle, (Pg. 4 – [40] – “…vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed…” (equates to the vehicle state acquisition unit is configured to acquire the state information of the vehicle as the quote shows the state being captured being speed)); and the positioning unit is configured to acquire the position information of the vehicle, (Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”)
Regarding Claim 9 Huasheng & Xu-Wu & Xiaofeng & Guang & Maeng (Huasheng discloses the following limitations: ) teaches A non-transitory computer readable storage medium storing a program which, (Pg. 15 – [163] – “A non-transitory computer readable storage medium ” & See Also Pg. 16 – [169] – “can store a program”)when executed by a processor, (Pg. 15 – [163] – “executed by a processor of the apparatus 700”) implements the method for regionally co-dispatching driving intentions of intelligent vehicles according to Claim 1. (Pg. 15 – [163] – “when the instructions in the storage medium are executed by a processor of the apparatus 700, enabling the apparatus 700 to perform a cooperative control method for a mine unmanned vehicle”)
Regarding Claim 10 Huasheng teaches A computing device, (Pg. 4 – [35] – “…can be performed by a computing device…”) comprising a processor and a memory for storing a program executable by the processor, (Pg. 15 – [161] – “processing component 710 that further includes one or more processors, and memory resources” & See Also Pg. 15 – [163] – “executed by a processor of the apparatus 700” & See Also Pg. 16 – [169] – “…can store a program…”) wherein the processor, when executing the program stored in the memory, implements the method for regionally co-dispatching driving intentions of intelligent vehicles according to Claim 1 . (Pg. 15 – [163] – “when the instructions in the storage medium are executed by a processor of the apparatus 700, enabling the apparatus 700 to perform a cooperative control method for a mine unmanned vehicle”)
Regarding Claim 7 Huasheng & Xu-Wu & Xiaofeng & Guang & Maeng teaches (Huasheng discloses the following limitations: ) The system for regionally co-dispatching driving intentions of intelligent vehicles according to claim 6, wherein the vehicle-mounted driving intention perception system (Pg. 15 – [165] – “the device and the unit described above can refer to the corresponding process in the foregoing method embodiment”) comprises a vehicle state acquisition unit (Pg. 4 – [40] – “…vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed…”) and a positioning unit; (Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”) the vehicle state acquisition unit is configured to acquire the state information of the vehicle, (Pg. 4 – [40] – “…vehicle status may be vehicle status information measured by onboard sensors such as mine unmanned vehicle speed…” (equates to the vehicle state acquisition unit is configured to acquire the state information of the vehicle as the quote shows the state being captured being speed)); and the positioning unit is configured to acquire the position information of the vehicle, (Pg. 4 – [40] – “The vehicle position may also be at least one of latitude and longitude information collected by the vehicle-mounted high-precision differential GPS transmitted by the mine unmanned vehicle…”)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Huasheng and in view of Xue-Wu and in further view of Jiang (US 2021/0118286 Al), Widmann (US 8,457,844 B2), Fukaya (US 2018/0281791 Al), Druger (“3.4 Motion with Constant Acceleration - University Physics Volume 1 ”)
Regarding Claim 4 Huasheng & Xu-Wu & Xiaofeng & Guang & Maeng teaches The method for regionally co-dispatching driving intentions of intelligent vehicles according to claim 3, wherein the process of generating the global dispatching result of driving intentions of dispatching area vehicles (Pg. 6 – [56] – “travel areas, or may be a preset range of the map. The central server can determine whether there are path conflicts between the two mining unmanned vehicles based on the grid map. The central server can determine the grid position of the two mining unmanned vehicles in the grid map based on the vehicle position information of the two mining unmanned vehicles, thereby determining the grid between the two mining unmanned vehicles. The grid distance and the speed information of the two mine unmanned vehicles are obtained based on the speed information in the vehicle state sent by the two mine unmanned vehicles.” (equates to wherein the process of generating the global dispatching result of driving intentions of dispatching area vehicles as the quotes shows paths being generated and thus intention is based on the path the vehicles will take)) comprises: Sa, representing position information P of each vehicle i within the dispatching area range with point coordinates based on the dispatching area occupancy grid map model, (Pg. 9 – [93] - “Assume that the two-dimensional working space of the mining unmanned vehicle is W=[x,y]T” & See Also Pg. 9 – “…given the starting point and the target point of the driverless car …” & See Also Pg. 9 – “The central server can set target speeds for multiple mine unmanned vehicles based on terrain, terrain and different loads of different mine vehicles. The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation task” (equates to Sa, representing position information P of each vehicle i within the dispatching area range with point coordinates based on the dispatching area occupancy grid map model as the first quote shows a dispatching area with coordinate information provided within. Quote two shows the unmanned vehicles being given position points, and quote three shows multiple vehicles being able to be accounted for within the prior art method. )) 1< i < N, (Pg. 2 – [12] – “the area in which the plurality of mining unmanned vehicles travel is divided into a plurality of grids, and the plurality of mines are determined according to the vehicle information. At least two mining unmanned vehicles in an unmanned vehicle have path conflicts, including: if at least two mining unmanned vehicles are predicted to pass the same grid at” (equates to 1<i<N as the quote shows a plurality of vehicles , N, wherein at least two , 1<i<N, are actually determined to conflict within the grid.)) mapping into a corresponding cell of an occupancy grid, (Pg. 13 – [140] – “grid method can be used to decompose the working environment of a mine unmanned vehicle into a series of grid cells with binary information…if there is no obstacle in a grid range, this grid can be defined as a free grid; otherwise it can be defined as a barrier grid ” (equates to mapping into a corresponding cell of an occupancy grid as the working area is shown to be decomposed into a grid where barrier and free grid spaces determine the grids occupancy. )) identifying a cell mapped with vehicle position information as occupancy, (Pg. 13 – [140] – “if there is no obstacle in a grid range, this grid can be defined as a free grid; otherwise it can be defined as a barrier grid ” (equates to identifying a cell mapped with vehicle position information as occupancy as the term used in this art is mapped as a barrier grid. ))and identifying a cell not mapped with vehicle position information as vacancy; (Pg. 13 – [140] – “grid method can be used to decompose the working environment of a mine unmanned vehicle into a series of grid cells with binary information…if there is no obstacle in a grid range, this grid can be defined as a free grid;” (equates to identifying a cell not mapped with vehicle position information as vacancy as a free grid space is seen to be used as the vacant label. )) each vehicle i (Pg. 1 – [9] – “to each of the plurality of mining unmanned vehicles ”) of each vehicle i within the dispatching area (Pg. 2 – [12] – “the area in which the plurality of mining unmanned vehicles travel is divided into a plurality of grids, and the plurality of mines are determined according to the vehicle information.” (equates to of each vehicle i within the dispatching area as the quote shows how each vehicle within the given range has their vehicular information used to determined intention.)) and determining predicted position information
P
i
t
s
(Pg. 9 – [90] – “Path planning refers to the insertion of a sequence of intermediate points for control between a given path starting point and a target point based on a certain environmental model…” (equates to determining predicted position information
P
i
t
s
as a target position is determined I this cited art equivalent to the predicted position.))1< i<N, (Pg. 2 – [12] – “the area in which the plurality of mining unmanned vehicles travel is divided into a plurality of grids, and the plurality of mines are determined according to the vehicle information. At least two mining unmanned vehicles in an unmanned vehicle have path conflicts, including: if at least two mining unmanned vehicles are predicted to pass the same grid at” (equates to 1<i<N as the quote shows a plurality of vehicles , N, wherein at least two , 1<i<N, are actually determined to conflict within the grid.)) Sc, acquiring a prediction state according to the predicted position information
P
i
t
s
of each vehicle i within the dispatching area for each cell in the dispatching area; (Pg. 13 – [140] – “According to the grid numbering rule, the sequence number of this position and its difference from the current position grid number can be predicted” & See Also Pg. 14 – [143] – “…if at least two mining unmanned vehicles A and B are predicted to pass at the same time At the same grid, it is determined that there is a path conflict between at least two of the mine unmanned vehicles…” & See Also Pg. 13 – [140] – “a grid method can be used to decompose the working environment of a mine unmanned vehicle into a series of grid cells with binary information… if there is no obstacle in a grid range, this grid can be defined as a free grid; otherwise it can be defined as a barrier grid.” (equates to acquiring a prediction state according to the predicted position information
P
i
t
s
of each vehicle i within the dispatching area for each cell in the dispatching area as the last quote shows each cell in the grid being decomposed into occupied or vacant cells and wherein the other quotes show the vehicles path being predicted and whether or not the vehicles will collide within the given grid.)) Sd, predicting, based on the prediction state of each cell, whether the driving intentions of the vehicles conflict in the cell, (Pg. 14 – [143] – “…if at least two mining unmanned vehicles A and B are predicted to pass at the same time At the same grid, it is determined that there is a path conflict between at least two of the mine unmanned vehicles…” & See Also Pg. 13 – [140] – “a grid method can be used to decompose the working environment of a mine unmanned vehicle into a series of grid cells with binary information… if there is no obstacle in a grid range, this grid can be defined as a free grid; otherwise it can be defined as a barrier grid.” (equates to Sd, predicting, based on the prediction state of each cell, whether the driving intentions of the vehicles conflict in the cell as the quote shows binary occupant vs vacant assignment to each cell and wherein the paths of the vehicles based on the grid map may occupy one another at the same time based on the grid data. )) specifically: determining whether the prediction state of each cell is occupancy by multiple vehicles, (Pg. 2 – [12] – “At least two mining unmanned vehicles in an unmanned vehicle have path conflicts, including: if at least two mining unmanned vehicles are predicted to pass the same grid at the same time” (equates to specifically: determining whether the prediction state of each cell is occupancy by multiple vehicles as the art shows a path conflict being defined by occupancy of a grid cell at the same time. )) when the prediction state of a cell is occupancy by a vehicle, (Pg. 13 – [140] – “space occupied by the mine unmanned vehicle on the site may be a small rectangle. The rectangular field can be divided evenly into a plurality of small rectangular grids to ensure that the mining unmanned vehicle is free to move therein”) namely, when the cell is predicted to be occupied by a vehicle, (Pg. 13 – [140] – “space occupied by the mine unmanned vehicle on the site may be a small rectangle. The rectangular field can be divided evenly into a plurality of small rectangular grids to ensure that the mining unmanned vehicle is free to move therein”) predicting that the driving intentions of the vehicle do not conflict in the cell, (Pg. 7 – [65] – “low-priority mining unmanned vehicle adopts a temporary retreat mode, the low-priority mining unmanned vehicle does not change the original planning path, so the first plan can still be followed after the conflict is eliminated” (equates to predicting that the driving intentions of the vehicle do not conflict in the cell as the quote shows the unmanned vehicles adopting a retreat plan and allows the other vehicle to continue along the original route and thus it is predicted that from the retreat that the intention wont conflict in the cell.)) and controlling the vehicle predicted to occupy the cell to travel according to the driving intentions thereof; (Pg. 7 – [65] – “Pg. 7 – [65] – “low-priority mining unmanned vehicle adopts a temporary retreat mode, the low-priority mining unmanned vehicle does not change the original planning path, so the first plan can still be followed after the conflict is eliminated” (equates to controlling the vehicle predicted to occupy the cell to travel according to the driving intentions thereof as the first vehicle mentioned is seen to occupy the space and is controlled by implementing a retreat mode to ensure the other vehicle safe passage. )) when the prediction state of a cell is occupancy by multiple vehicles, namely, when the cell is predicted to be occupied by multiple vehicles, (Pg. 13 – [140] – “It can be assumed that the limited activity site of the mine unmanned vehicle is rectangular, and the space occupied by the mine unmanned vehicle on the site may be a small rectangle” & See Also Pg. 14 – [150]- “ that there is a path conflict between at least two of the plurality of mining unmanned vehicles. ” (equates to when the prediction state of a cell is occupancy by multiple vehicles, namely, when the cell is predicted to be occupied by multiple vehicles as the quote shows the grid being implemented where cells are the size of vehicles and the second quote shows a conflict occurring that would be based on the grid map, hence multiple vehicles occupying the same grid.) ) predicting that the driving intentions of the vehicle conflict in the cell, ( Pg. 13 – [140] – “It can be assumed that the limited activity site of the mine unmanned vehicle is rectangular, and the space occupied by the mine unmanned vehicle on the site may be a small rectangle” & See Also Pg. 14 – [150]- “ that there is a path conflict between at least two of the plurality of mining unmanned vehicles. ”) and proceeding to step Se; Se, determining whether the multiple vehicles predicted to occupy the cell have a driving intention: (Pg. 1 – [9] – “At least two mining unmanned vehicles in the driving vehicle have a path conflict” & See Also Pg. 2 – [12] – “including: if at least two mining unmanned vehicles are predicted to pass the same grid at the same time, ” (equates to proceeding to step Se; Se, determining whether the multiple vehicles predicted to occupy the cell have a driving intention as the method described has moved to the step of determining if there a path conflict and thus a cell where multiple vehicles occupy the same cell as the grid map is in use wherein each vehicle has a driving intention as they have predefined paths.)) keeping unchanged; if yes, setting the driving intentions of all the vehicles predicted to occupy the cell as: keeping unchanged; (Pg. 7 – [65] – “mining unmanned vehicle does not change the original planning path, so the first plan can still be followed after the conflict is eliminated” (equates to keeping unchanged; if yes, setting the driving intentions of all the vehicles predicted to occupy the cell as: keeping unchanged as the vehicle is shown to maintain the same path and the driving intention is set to keep the vehicle path. )) if no, predicted to occupy the cell to travel according to the driving intention thereof, and setting the driving intentions of the other vehicles as: keeping unchanged; (Pg. 7 – [65] – “mining unmanned vehicle does not change the original planning path, so the first plan can still be followed after the conflict is eliminated” & See Also Pg. 9 – [89] – “The mission planning may be a mission plan that the central server separately formulates for multiple mining unmanned vehicles according to transportation tasks.” (equates to predicted to occupy the cell to travel according to the driving intention thereof, and setting the driving intentions of the other vehicles as: keeping unchanged as the first quote shows how the path of the vehicle can remain unchanged once the conflict has been avoided and the second quote shows how the mission planning unit of this art can be used to control multiple vehicles.) )Sf, determining the driving intentions of the vehicles within the dispatching area based on the above operations, (Pg. 1 – [10] – “cooperative control method for a mine unmanned vehicle further includes: at least one mining unmanned vehicle when determining a path conflict between at least two mining unmanned vehicles”) and generating a dispatching result. (Pg. 1 – [10] – “…where the third control instruction includes a second path plan for instructing the at least one mining unmanned vehicle to automatically travel according to the second path plan.” (equates to generating a dispatching result. As a control instruction is generated and subsequently given to a vehicle))
Yet Huasheng fails to teach Sb, calculating a longitudinal displacement
S
i
'
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
cos
W
i
and a lateral displacement
S
i
*
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
sin
W
i
within a safe acceleration
a
s
and a safe time
t
s
according to the driving intention
C
i
absolute speed
V
i
and steering wheel angle
W
i
of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
, randomly selecting a vehicle from the multiple vehicles.
Xue-Wu teaches calculating a longitudinal displacement (Pg. 6 – “△y is the longitudinal relative distance between the position vehicle and the predicted vehicle;”) and a lateral displacement (Pg. 6 – “where is the lateral relative distance between the position vehicle and the predicted vehicle”) according to the driving intention
C
i
(Pg. 14 – “the intention recognition module can make a prediction before the driver performs the lane change action” (equates to driving intention as the model is predicting a driving or lane change intention in this example.)) absolute speed
V
i
(Pg. 5 – “Specifically, the state information of the predicted vehicle includes S = (x, y)… y is the absolute speed of the predicted vehicle” ) and steering wheel angle
W
i
(Pg. 11 – Fig. 4 & See Also Pg. 10-11 – “The vehicle's heading angle 0 = arctan ( (1) -x (1-3) y(t) - y(-3) -) is calculated from the vehicle position parameters (x, y); then the heading angle of each sampling point is traversed from the lane change point to the reverse direction of the time axis. If the 0 of three consecutive sampling points in the trajectory sequence is ≤0. (the heading angle threshold of the lane change starting point), the position where the threshold is reached for the first time is located as the lane change starting point; finally, a similar method is used to judge |0|≤0. (the heading angle threshold of the lane change ending point) to determine the lane change end point” (equates to steering wheel angle
W
i
as the heading angle of the vehicle is defined by the steering wheel angle of the vehicle as the steering wheel guides the heading of the vehicle. ))
Yet both Huasheng & Xue-Wu fail to disclose
S
i
'
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
cos
W
i
S
i
*
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
sin
W
i
within a safe acceleration
a
s
and a safe time
t
s
of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
, randomly selecting a vehicle from the multiple vehicles.
Jiang Discloses randomly selecting a vehicle from the multiple vehicles. (Pg. 11 – [0054] – “As an example, the above executing body may randomly select one vehicle from the above target number of vehicles.”)
Yet Huasheng-Xue Wu-Jiang fail to teach disclose
S
i
'
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
cos
W
i
S
i
*
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
sin
W
i
within a safe acceleration
a
s
and a safe time
t
s
of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
Druger discloses
S
i
'
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
(Pg. 15 - Example 3.12 -
x
-
x
0
=
v
0
t
+
1
/
2
a
t
2
& See Also Pg. 1 - Notation – “It also simplifies the expression for x displacement, which is now Δx=
x
-
x
0
”)
S
i
*
=
(
V
i
t
s
+
1
2
a
s
t
s
2
)
(Pg. 15 - Example 3.12 -
x
-
x
0
=
v
0
t
+
1
/
2
a
t
2
& See Also Pg. 1 - Notation – “It also simplifies the expression for x displacement, which is now Δx=
x
-
x
0
”).
Yet all Huasheng, Xue-Wu, Jiang, Druger fail to disclose
cos
W
i
sin
W
i
of within a safe acceleration
a
s
and a safe time
t
s
of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
Widmann teaches
cos
W
i
(Pg. 4 – Fig. 4 & See Also Pg. 11 – Col. 6 – lines – 49-51 – “a displacement resulting from the substantially straight backing maneuver Y_L” & See Also Pg. 11 – Col. 6 – lines 62-63 – “displacement resulting from a substantially straight backing maneuver X_L” & See Also Pg. 12 – Col. 7 – Eq. 7 -
Y
L
=
L
L
c
o
s
(
θ
T
1
)
& See Also Pg. 12 – col. 7 – line 14 – “a small angle displacement
θ
” (equates to Cos Wi as the figure shows a displacement the vehicle makes from one point to the next where a y and x or lateral and longitudinal displacement are calculated based on the vehicle’s heading or the steering angle. The equation 7 Then shows the calculation of cos multiplied by the measured displacement giving the longitudinal displacement.))
sin
W
i
(Pg. 4 – Fig. 4 & See Also Pg. 11 – Col. 6 – lines – 49-51 – “a displacement resulting from the substantially straight backing maneuver Y_L” & See Also Pg. 11 – Col. 6 – lines 62-63 – “displacement resulting from a substantially straight backing maneuver X_L” & See Also Pg. 12 – Col. 7 – Eq. 10 -
X
L
=
L
L
s
i
n
(
θ
T
1
)
& See Also Pg. 12 – col. 7 – line 14 – “a small angle displacement
θ
” (equates to Sin Wi as the figure shows the vehicle making a displacement over time wherein the lateral and longitudinal components as each calculated over the L_L region as the equation 10 shows the displacement multiplied by the sin function yielding a lateral displacement.)) of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
(Pg. 10 – Col. 3 – lines 41-46 – “wherein a distance traveled during the first, second, and third reversing distances is a function of the determined distances, a longitudinal displacement of the vehicle 102, V H during the first, second, and third commands, and a lateral displacement of the vehicle 102, V H during the first, second, and third commands, as set forth in greater detail herein.” (equates to of the vehicle i according to the longitudinal displacement
S
i
'
and the lateral displacement
S
i
*
as the quote shows the vehicle 102 being displaceable and a lateral and longitudinal displacement can be calculated.))
Yet all fail to teach a safe acceleration
a
s
and a safe time
t
s
.
Fukaya teaches a safe acceleration
a
s
(Pg. 24 – [0142] – “When there exists a preceding vehicle, the ECU 10 calculates a target acceleration for trailing the preceding vehicle at a vehicle speed less than or equal to the set vehicle speed, maintaining the set inter-vehicular distance every time the predetermined calculation interval elapses during the engine on period” (equates to safe acceleration as the first quote shows a target acceleration that keeps the vehicle within a safe distance of the surrounding vehicle.)) and a safe time
t
s
(Pg. 22 – [0117] – “The brake avoidance limit time TB herein is a limit time for the driver to be able to avoid a collision with the other vehicle by the brake operation.” (equates to a safe time
t
s
as the quote shows how collision is avoided within the limit time. )) It would have been an advantageous addition to the method disclosed by Huasheng -Xue Wu-Jiang- Druger-Widmann to include a safe acceleration
a
s
and a safe time
t
s
as these limitations allow for the calculation of the various displacements to be within a time and acceleration range that allow for safe transit of vehicles being controlled within the method.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include a safe acceleration
a
s
and a safe time
t
s
as these inclusions ensure the displacement calculated for each and every vehicle under control or viewing external vehicles allows for safe passage for all within the range of the host vehicle.
Response to Arguments
Response to 35 U.S.C. § 101 rejection of claims 1, 3-4, 6-7,and 9-10 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered and are persuasive.
Applicant argues on page 1- “Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non- statutory subject matter. This rejection is respectfully traversed. Claim 5 has been cancelled. Therefore, the rejection is moot. Withdrawal of the 35 USC 101 rejection of claim 5 is respectfully requested. Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non- statutory subject matter. This rejection is respectfully traversed. Claim 9 is amended to limit the storage medium to non-transitory computer readable storage medium.” – In response to point A the Examiner agrees with the arguments set forth by the Applicant and removes the 35 U.S.C. § 101 rejection to claims 5 and 9 as claim is cancelled and the amendment to claim 9 including “non-transitory” brings the claim into patentable subject matter as a manufacture.
Applicant argues on pages 2-3 , “Claim 1 now recites: acquiring state information and position information of each of the vehicles through a vehicle-mounted driving intention perception system disposed on each of the vehicles; connecting the vehicle-mounted driving intention perception system to a vehicle- mounted driving intention control system disposed on each of the vehicles; transmitting the state information and position information of each of the vehicles to the vehicle-mounted driving intention control system via the vehicle-mounted driving intention perception system; recognizing driving intention information of each of the vehicles based on the state information and position information of each of the vehicles through the vehicle-mounted driving intention control system; connecting the vehicle-mounted driving intention control system to a cloud dispatching system in a wireless manner; performing, by the cloud dispatching system, the steps of... guiding driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result through a vehicle driving decision system disposed on each of the vehicles; controlling each of the vehicles to travel in sequence; avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously. It is respectfully submitted that the steps of claim 1 are performed by a cloud dispatching system, as well as a vehicle-mounted driving intention perception system, a vehicle-mounted driving intention control system, and a vehicle driving decision system installed in each vehicle Claim | recites “guiding driving decisions of each of the vehicles within the dispatching area range based on the global dispatching result through a vehicle driving decision system disposed on each of the vehicles, thereby controlling each of the vehicles to travel in sequence and avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously”.” - In response to point B the Examiner agrees with the arguments set forth by the Applicant and removes the 35 U.S.C. § 101 rejection to claims 1, 3-4, 6-7, and 9-10 as the inclusion of the claim limitation, “controlling each of the vehicles to travel in sequence and avoiding conflicts between driving intentions of the vehicles within the dispatching area range when drivers change driving intentions simultaneously” adds an element of control that cannot be performed by the human mind and therefor is now considered to be patentable subject matter rather than a mental process with a transaction of data.
Response to 35 U.S.C. § 102 and 103 rejection of claims 1, 3-4, 6-7,and 9-10 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are nto considered persuasive.
Applicant argues on pages 5 - 6, “claim 1 requires the vehicle operates in a human-vehicle co-driving mode, which indicates that the vehicle under claim 1 is not an autonomous vehicle, but rather a vehicle with a human driver. Huasheng is regarded as the closest prior art. However, Huasheng pertains to mine unmanned vehicles, whereas the present application involves human-driven vehicles. These two belong to distinct technical fields. A person of ordinary skill in the art would have no motivation to modify the autonomous mining vehicles described in Huasheng into human-driven vehicles.
Xue-Wu also relates to human-driven vehicles (see pages 2 and 3). The Examiner has provided no evidence as to how or why a person of ordinary skill in the art would combine the mine unmanned vehicles of Huasheng with the human-driven vehicles of Xue-Wu or other cited references. Moreover, the problem addressed by the present application is to avoid conflicts between vehicles when multiple human drivers simultaneously change their driving intentions. However, the mine unmanned vehicles disclosed in Huasheng do not encounter such an issue at all, precisely because Huasheng’s vehicles operate without human drivers - making it impossible for conflicts between driving intentions of multiple human drivers to arise.” – As to point C the examiner respectfully disagrees with the Applicant as Xue – Wu teaches art pertaining to prediction of trajectories of surrounding vehicles and a host vehicle wherein the environment is analyzed to best guide the host vehicle with levels of certainty through the environment. Similarly Huasheng teaches control of [a] vehicle(s) within a network of vehicle(s) while calculating driving intentions and trajectories of surrounding vehicles. Though Huasheng is relating to driverless vehicles the control of a host vehicle and calculation of surrounding vehicle trajectories exists in both pieces of art and can be readily combined together to ensure an ascertained level of certainty regarding safe passage of a host vehicle.
Applicant argues on pages 5-6, “Consequently, starting from Huasheng, a person skilled in the art would not recognize this problem, and would therefore have no motivation to seek a technical solution from Xue-Wu. In addition, Xue-Wu teaches that the Softmax function is used to calculate the probability of the driving intention to change lanes to the left, drive straight, and change lanes to the right. Xue-Wu makes no mention of scenarios involving vehicle acceleration and deceleration. (speeding up and slowing down). Xue-Wu also fails to disclose that the confidence thresholds for the driving intention categories: speeding up and slowing down are set as 80%.
Xue-Wu does not take into account driving intentions related to vehicle acceleration and deceleration. As a result, the vehicle trajectories derived by the model of Xue-Wu are not influenced by current speed or speed change trends, leading to outcomes that do not fully align with real-world conditions and compromising the accuracy of the results. In contrast, the present application considers vehicle acceleration and deceleration, enabling more accurate prediction of vehicle motion trajectories and improving the computational precision of the model. Consequently, this invention can effectively guide driving decisions, thereby not only avoiding potential vehicle conflicts and enhancing driving safety, but also improving overall traffic efficiency within the area and alleviating congestion caused by unreasonable route planning.”
In response to point D the Examiner respectfully disagrees with the applicant. Applicant asserts that Huasheng-Xue-Wu- Guang does not teach “and the confidence thresholds for the driving intention categories: speeding up and slowing down are set as 80%”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned limitation is to have a 80% calculation of calculation in regards to the detection go of the surrounding vehicle speeding up or slowing down. Xue-Wu teaches a prescribed 80% confidence interval for other detected driving intentions and sets other confidence thresholds to ensure the surrounding vehicle movement is understood by the perception system of the art. Guang disclosed a surrounding vehicle intention recognition system wherein speeding up and slowing down of the surrounding vehicles is considered (as mapped above in claim 1 and 6). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Xue-Wu & Guang teaches “and the confidence thresholds for the driving intention categories: speeding up and slowing down are set as 80%”. Xue-Wu discloses the following limitations: (and the confidence thresholds for the driving intention categories: are set as 80%; (Pg. 7 – “the confidence threshold for changing lanes to the left and right is set to 80%...” (equates to and the confidence thresholds for the driving intention categories: are set as 80%; as the quote shows the confidence threshold for driving intention being set at 80%)))
Guang discloses the following limitations: the driving intention categories: speeding up and slowing down (Pg. 2 – [0024] – “the lower layer simple driving behavior recognition model includes a braking/acceleration type operation model” (equates to the driving intention categories: speeding up and slowing down as the quote shows the model including braking and accelerating whish is directly correlated to the vehicle speeding up and slowing down.))
Applicant argues on page 6, “It is noted that the Examiner has cited multiple references in combination with Huasheng. However, some of these references, such as Xue-Wu, relate to human-driven vehicles, while others, like Xiaofeng, involve unmanned vehicles. The Examiner has provided no reasonable explanation as to how or why a person of ordinary skill in the art would combine Huasheng’s mine unmanned vehicles with these references - particularly those pertaining to human-driven vehicles - to arrive at every limitation of claim 1. Furthermore, the Examiner has offered no justification for why one of ordinary skill in the art would recognize the problem of conflicting driving intentions among multiple human drivers in the context of Huasheng’s mine unmanned vehicles, and consequently seek to combine it with other references to remedy this purported deficiency.
For the reasons set forth above, it is believed that independent claims 1 and 6 are not obvious. The other claims are also not obvious due to dependency.
In view of the foregoing amendments and remarks, reconsideration and withdrawal of the 35 USC 102 and 103 rejections are respectfully requested.” – As to point E See point C.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
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/R.A.W./ Examiner, Art Unit 3667
/Hitesh Patel/ Supervisory Patent Examiner, Art Unit 3667
12/19/25
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