Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the Examiner.
Claim Objections
Claims 7 and 18 are objected to because of the following informalities: the claims
recite the limitation “min imize J” appearing to be a typo in the claim presentation. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of
matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions
and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical concept without significantly more. The claims recite “calculating a gathering wave speed and a dissipation wave speed at an intersection based on a shock wave evolution theory, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time when a farthest queue is formed; predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed; predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle;”, “wherein the calculating a gathering wave speed w1 and a dissipation wave speed w2 at an intersecti0on comprises: acquiring a saturation flow qs, a saturation flow density
ρ
s
and a congestion flow density
ρ
j
at the intersection according to the historical vehicle state information; acquiring an arrival flow qa and an arrival flow density
ρ
a
according to the real-time vehicle state information; calculating the gathering wave speed w1 at the intersection, which is expressed as:
w
1
=
0
-
q
a
ρ
j
-
ρ
a
; calculating the dissipation wave speed w2 at the intersection, which is expressed as:
w
2
=
q
s
-
0
ρ
s
-
ρ
j
;”, “calculating a distance Lmq between the farthest queue point and the stop line, in which an expression is as follows:
L
m
q
=
m
i
n
ω
2
*
ω
1
t
g
'
-
t
0
-
L
0
ω
2
-
ω
1
,
N
0
p
j
,
wherein
ω
1
is the gathering wave speed,
ω
2
is the dissipation wave speed,
t
g
'
indicates an adjusted green light start time,
t
0
is a start time when a first connected and autonomous vehicle enters the intersection,
p
j
is a congestion flow density, and
N
0
is a total number of vehicles between the first connected and autonomous vehicle and the stop line; calculating the farthest queue point position
L
m
q
of the connected and autonomous vehicle at the downstream intersection, which is expressed as
L
m
q
=
L
s
-
L
m
q
;
where
L
s
indicates a position of the stop line; calculating a time
t
m
q
=
-
L
m
q
ω
2
;
wherein the adjusted green light start time comprises
t
g
'
=
t
g
,
t
g
<
t
r
t
g
-
C
,
t
g
>
t
r
; where
t
g
indicates a time when a traffic signal light turns green,
t
r
indicates a time when the traffic signal light turns red, and c indicates a signal period length “predicting a vehicle state and time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed comprises: … in which the expression is as follows
t
~
f
=
t
g
'
+
L
m
q
p
j
+
1
*
1
q
s
acquiring the vehicle state
x
~
when the connected and autonomous vehicle passes through the stop line, which is expressed as
x
~
=
[
l
s
,
v
s
]
T
where
v
s
indicates a speed at which the connected and autonomous vehicle passes through the stop line, that is, a speed of a saturation flow” “wherein the predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle comprises: an expression of the longitudinal acceleration of the manual-driving vehicle is as follows:
a
f
t
=
m
a
x
(
a
m
i
n
,
m
i
n
(
a
m
a
x
,
g
t
)
;
g
t
=
l
i
-
1
t
+
v
i
-
1
t
[
T
p
-
η
λ
1
r
d
-
1
]
-
l
i
t
-
v
i
t
T
p
-
1
λ
1
r
d
-
1
-
L
i
+
L
i
-
1
2
; where
a
f
t
indicates the longitudinal acceleration of the manual-driving vehicle,
g
t
indicates an acceleration expected by a driver,
a
m
i
n
indicates a minimum acceleration of the manual-driving vehicle,
a
m
a
x
indicates a maximum acceleration of the manual-driving vehicle,
T
p
indicates a preview time of the driver,
r
d
indicates a risk expected by the driver,
η
indicates an influence factor of a speed on the risk,
λ
indicates an influence factor of a distance on the risk, l indicates a position of the vehicle, v indicates a speed of the manual-driving vehicle, L indicates a length of the manual-driving vehicle, t indicates a time, and I indicates a vehicle serial number, in which an (i-1)th vehicle is in front of an i-th vehicle”, “wherein taking an instantaneous feul consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle, comprises: taking the instantaneous fuel consumption of the connected and autonomous vehicle and the penalty for the state of passing through the stop line as the objective function, which is expressed as:
m
i
n
i
m
i
z
e
j
=
|
|
x
t
f
~
-
x
~
f
|
|
Q
f
2
+
a
1
∫
t
0
t
f
~
L
(
v
t
,
a
t
,
t
)
d
t
where
x
t
f
~
indicates the vehicle state of the connected and autonomous vehicle at the time
t
f
~
when the vehicle passes through the stop line,
x
~
f
indicates the vehicle state that the connected and autonomous vehicle passes through the stop line;
|
|
x
t
f
~
-
x
~
f
|
|
Q
f
2
indicates the penalty for the state that the connected and autonomous vehicle passes through the stop line;
L
(
v
t
,
a
t
,
t
)
indicates the instantaneous fuel consumption of the vehicle;
v
t
indicates a speed of the connected and autonomous vehicle at time t, and
a
t
indicates a acceleration of the connected and autonomous vehicle at time t; each of
Q
f
and
a
1
indicates a penalty weight; setting dynamic constraints of the connected and autonomous vehicle based on a road speed limit, a minimum acceleration and a maximum acceleration of the connected and autonomous vehicle; the setting trajectory constraints comprises: a trajectory constraint corresponding to the first connected and autonomous vehicle passing through the stop line being expressed as
l
t
m
q
≤
l
m
q
;
where
l
t
m
q
indicates a position of the connected and autonomous vehicle at time
t
m
q
; a trajectory constraint corresponding to a j-th connected and autonomous vehicle passing through the stop line being expressed as:
l
(
t
)
≤
l
p
(
t
)
; where j is a positive integer greater than or equal to 2, l(t) indicates a position of the connected and autonomous vehicle, and
l
p
t
indicates a trajectory of the manual-driving vehicle in front of the connected and autonomous vehicle” and “wherein the setting the risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle comprises: setting a risk factor
ξ
i
f
(
x
k
+
1
)
of a following vehicle according to a predicted trajectory of the manual-driving vehicle behind the connected and autonomous vehicle; setting a risk factor
ξ
i
p
x
k
+
1
of a preceding vehicle according to a predicted trajectory of the manual-driving vehicle in front of the connected and autonomous vehicle; setting a risk factor
ξ
i
s
(
x
k
+
1
)
of a traffic signal light; … where an expression of the tracking target is:
minimize
a
k
,
a
k
+
1
…
a
k
+
p
-
1
J
=
∑
i
=
1
p
|
|
x
k
+
i
-
x
k
+
i
r
e
f
|
|
Q
r
e
f
2
+
∑
i
=
1
p
|
|
ξ
i
f
(
x
k
+
i
|
|
β
2
2
+
|
|
ξ
i
p
(
x
k
+
i
|
|
β
3
2
+
|
|
ξ
i
s
(
x
k
+
i
|
|
β
4
2
)
where x(k+i) indicates a state of the connected and autonomous vehicle at step k+i, k indicates a current time step, i indicates a number of predicted steps, x(k+i)ref indicates an ecological reference trajectory of the connected and autonomous vehicle, each of
Q
r
e
f
,
β
2
,
β
3
, and
β
4
indicates a penalty weight, and P indicates a prediction time domain” which calculates vehicle operations based on fuel economy and vehicle information using gathered information, without actually controlling the vehicle based on the calculations.
Step 1: These claims are directed to a method of calculating a eco mode driving of an autonomous vehicle.
Step 2A, Prong One:
The limitations of taking a predetermined vehicle characteristic like vehicle states, wave speeds, vehicle locations, fuel consumptions, and risk factors, comparing information to other vehicles to make a determination is a process that, under its broadest reasonable interpretation, covers performance of mathematic relationships without any inventive concepts. That is, other than reciting “a central controller and a plurality of local controllers deployed on the connected and autonomous vehicle” or “the gathering wave speed … dissipation wave speed” or “the distance between the farthest queue point” or “the longitudinal acceleration of the manual-driving vehicle” or “the fuel consumption” or “the speed of the connected and autonomous vehicle” nothing in the claim element precludes the step from relating to a purely mathematical concept with elements that relate to variables in the claimed formulas. For example, the “autonomous vehicle speed, distances, and accelerations” relates to variables utilized in the equations in order to allow for the corrected optimal ecological trajectory and risk. If a claim limitation, under its broadest reasonable interpretation, covers and abstract idea of using mathematical equations for determining updated vehicle characteristics like a wheel radius or corrected wheel speed, then it falls within the “Mathematical concepts” grouping of abstract ideas. Accordingly, these claims recite an abstract idea.
Step 2A, Prong Two:
This judicial exception is not integrated into a practical application because the claims recite additional elements: “acquiring signal phase and timing information, historical vehicle state information, and real-time vehicle state information” and “taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function,” and “setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle; and setting a risk factor according to the risk field model and the predicted trajectories of the manual- driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle” and “acquiring a current queue length L0 according to the signal phase and timing information and the real-time vehicle state information,” and “acquiring the time
t
~
f
when the connected and autonomous vehicle passes through the stop line,” and “setting the tracking target and the dynamic constraints of the manual-driving vehicle for solving, to obtain the control input of the connected and autonomous vehicle;”. The step of using a device to control this method and the physical vehicle components such as the wheels are all recited in a means for merely applying the exception using generic components. Further stated, using a computer or device to apply an abstract mathematical concept is recited as “apply it” additional elements recited here in the MPEP 2106.05(f) “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).” The acquisition step is recited at a high level of generality (i.e., as a general means of gathering information of a vehicle on a roadway for comparison), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Additionally, the gathering or acquiring steps are recited at a high level of generality (i.e., as a general means of gathering information for trajectories), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, this additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, this has been re-evaluated in Step 2B and determined whether there is significantly more than the abstract idea. The rationale for prong 2 of step 2A applies equally here. Furthermore, the specification does not provide any indication that the device is anything other than a generic, off-the-shelf vehicle control components such as a vehicle movement control system and electric steering actuators as described in the specification [0096]. As such, the claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2023/0138163 A1 discloses an intersection monitoring device for autonomous driving vehicles
US 2021/0125076 A1 discloses a driving prediction system for a vehicle using a training model and scores of the risk level and driving behavior
CN 117636628 A discloses a system for determining traffic conditions based on a shock wave upstream, and risks and fuel consumption determinations
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyle T Johnson whose telephone number is (303)297-4339. The examiner can normally be reached Monday-Thursday 7:00-5:00 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE T JOHNSON/Examiner, Art Unit 3656