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 01/15/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Para. [0066] grammar issue: states “in least amount of time”
Para. [0089]: states from center 322, when it should state from center 332
Para. [0094]: binary matrix
x
i
j
is not illustrated in equation 9
Para. [0095]: element 600 is not in fig. 4
Para. [0126]: element 600 is not in fig. 5
Para. [0130]: refers to steps 510-518 as being continuous when in fact steps 511, 513, 515 and 517 are not shown
Para. [0136]: refers to steps 510-518 as being continuous when in fact steps 511, 513, 515 and 517 are not shown
Appropriate correction is required.
Claim Objections
Claim 4 is objected to because of the following informalities:
Recites the plurality of elements but does not recite the plurality of elements of the binary matrix
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, and 14-26 are rejected under 35 U.S.C. 103 as being unpatentable over Tambunan et al., (March 2022) “Quantum Annealing for Vehicle Routing Problem with Weighted Segment,” arXiv:2203.13469v1(“Tambunan”) in view of Daugherty, Greyson et al., "Optimized multiagent routing for a class of guidepath-based transport systems." IEEE Transactions on Automation Science and Engineering 16.1 (2018)(“Greyson”) and further in view of Feld, Sebastian, et al. "A hybrid solution method for the capacitated vehicle routing problem using a quantum annealer." Frontiers in ICT 6 (2019)(“Sebastian”).
Independent Claims:
Regarding claim 1, Tambunan teaches a method for determining an optimized quantity of agents and routes for the agents using quantum computing, the method comprising:
determining, [using a classical computing system], a plurality of locations within a geographic region to be visited by a plurality of agents(Tambunan, pg., 3, see also fig. 1, “The road conditions in Fig. 1 show vehicle traffic from point A to point B, consisting of 9 nodes and 12 edges. Each vehicle[by a plurality of agents] is given three choices of alternative routes, but only one route is chosen by a vehicle as a road solution that can be passed. The choice of route will have a minimum of 4 segments to pass from point A to point B[determining, a plurality of locations within a geographic region to be visited].”).1
generating, [using the classical computing system], a sequence objective function to identify a sequence with which the plurality of locations are to be visited that minimizes a total distance traveled(Tambunan, pgs., 2-4, “The objective of the traffic flow for vehicle routing optimization problem is to minimize the time for a given set of cars to travel between their individual sources and destinations[to identify a sequence with which the plurality of locations are to be visited that minimizes a total distance traveled]… [t]he QUBO formula for the vehicle route selection optimization problem model is obtained from combining the cost function components ( equation 5) of each segment[generating, a sequence objective function] with the constraint function (equation 7).
O
b
j
=
∑
s
m
∈
S
(
∑
q
i
j
∈
B
s
m
w
i
j
q
i
j
)
2
+
K
∑
i
=
1
n
(
∑
j
=
1
3
q
i
j
-
1
)
2
”);2
inputting the sequence objective function and a set of sequence conditions into a quantum computing system to determine a sequence solution comprising the identified sequence(Tambunan, pgs., 2-4, see also figs. 2,3 and 4, “[T]he QUBO model (Fig.3) consisting
of a cost[the sequence objective function and a set of sequence conditions] and constraint matrix will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[inputting into a quantum computing system]… [t]hese results (Fig.4) get the lowest energy (E=-1184) value in the choice of vehicle route combination with the lowest cost value[to determine a sequence solution comprising the identified sequence].”).
and inputting [the agent quantity objective function, the temporal duration objective function, and] a set of agent-quantity conditions to the quantum computing system to determine an optimized solution comprising [the minimum quantity of agents], the subset of locations to be traveled to [by each of the minimum quantity of agents in the minimized amount of time], and an order with which the agent is to travel to the subset of locations(Tambunan, pgs., 2-5, see also figs. 2,3 and 4, “[T]he QUBO model (Fig.3) consisting of a cost and constraint matrix[a set of agent-quantity conditions] will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[and inputting to the quantum computing system to determine an optimized solution]… [t]he selection of the optimal route is carried out in the case of the number of vehicles n = 3, n = 4, and n = 5. These results indicate the effect of alternative routes generated… the road segment weight model allows overlapping
vehicles to select a specific road segment, ensuring that all vehicles have exactly one route (valid result)[comprising the subset of locations to be traveled to and an order with which the agent is to travel to the subset of locations].”).3
While Tambunan teaches the quantum computing system and the sequence solution, Tambunan does not teach:
using the classical computing system;
generating, using the classical computing system, an agent quantity objective function to identify a minimum quantity of agents from the plurality of agents to visit the plurality of locations based on the sequence solution;
generating, using the classical computing system, a temporal duration objective function to identify a plurality of subsets of locations to be visited by the minimum quantity of agents based on the sequence solution, wherein each of the plurality of subsets of locations is assigned to one of the minimum quantity of agents to minimize an amount of time for that agent to visit the subset of locations;
the agent quantity objective function, the temporal duration objective function, and; the minimum quantity of agents; by each of the minimum quantity of agents in the minimized amount of time
However, Greyson teaches:
generating, [using the classical computing system], an agent quantity objective function to identify a minimum quantity of agents from the plurality of agents to visit the plurality of locations based on the sequence solution(Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w[based on the sequence solution]… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[generating, an agent quantity objective function to identify a minimum quantity of agents from the plurality of agents to visit the plurality of locations].”)4
generating, [using the classical computing system], a temporal duration objective function to identify a plurality of subsets of locations to be visited by the minimum quantity of agents based on the sequence solution, wherein each of the plurality of subsets of locations is assigned to one of the minimum quantity of agents to minimize an amount of time for that agent to visit the subset of locations(Greyson, pgs., 371-373, see also fig. 4, “According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[generating, a temporal duration objective function to identify a plurality of subsets of locations to be visited by the minimum quantity of agents based on the sequence solution]… [t]he considered method will identify all the zero-cost paths by formulating and solving the corresponding shortest-path problem, and eventually, it will select as the new route
σ
^
a
^
for agent
a
^
any of these zero-cost paths that takes agent
a
^
to
its destination as soon as possible[wherein each of the plurality of subsets of locations is assigned to one of the minimum quantity of agents to minimize an amount of time for that agent to visit the subset of locations].”);5
the agent quantity objective function, the temporal duration objective function, and; the minimum quantity of agents; by each of the minimum quantity of agents in the minimized amount of time(Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[the agent quantity objective function and; the minimum quantity of agents]. According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[temporal duration objective function] ]… [t]he considered method will identify all the zero-cost paths by formulating and solving the corresponding shortest-path problem, and eventually, it will select as the new route
σ
^
a
^
for agent
a
^
any of these zero-cost paths that takes agent
a
^
to its destination as soon as possible[by each of the minimum quantity of agents in the minimized amount of time].”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the teachings of Greyson the motivation to do so would be to implement an iterative local search scheme that subsequently improves each solution by adding additional agents to the routing problem so long as a feasible routing solution that is more optimal than the previous solution is feasible (Greyson, pg., 365, “[T]he presented algorithm can be perceived as a []local-search[] scheme that starts with the construction of a feasible routing schedule, and subsequently, it searches for improved solutions over pertinently defined []neighborhoods[] of the underlying solution space.”).
Tambunan in view of Greyson do not teach: using the classical computing system.
However, Sebastian teaches:
using the classical computing system(Sebastian, pg., 10, see also table 4, “As already mentioned, QBSolv can be used as a pure classical solver (called Local)… [i]n Listing 1 and Listing 2 the measured CPU times for the locally executed QBSolv are shown… [t]he dataset has been run on a Dell 2.8 GHz i7 with 16 GB RAM Notebook[using the classical computing system].”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the teachings of Sebastian the motivation to do so would be to take advantage of a hybrid approach that incorporates classical computing to overcome the limitations regarding the number of qubits available in quantum hardware(Sebastian, pg., 2, “[Q]uantum computation compared to classical
computation is still in its infancy and one of the major problems is that quantum hardware is limited regarding the number of quantum bits (qubits) and their connectivity on the chip. Generally, this leads to difficulties in mapping large QUBO problems to the hardware. With this paper we present an intuitive way to split the CVRP into smaller optimization problems by taking advantage of a classical 2-Phase-Heuristic… that divides the CVRP into two phases, the
clustering phase and the routing phase.”).
Regarding claim 22, Tambunan teaches a method for determining an optimized quantity of agents and routes for the agents using quantum computing, the method comprising:
receiving, [using a classical computing system], geographic data comprising a collection of locations within a geographic region to be visited by a plurality of agents(Tambunan, pg., 3, see also fig. 1, “The road conditions in Fig. 1 show vehicle traffic from point A to point B, consisting of 9 nodes and 12 edges. Each vehicle[by a plurality of agents] is given three choices of alternative routes, but only one route is chosen by a vehicle as a road solution that can be passed. The choice of route will have a minimum of 4 segments to pass from point A to point B[receiving, geographic data comprising a collection of locations within a geographic region to be visited].”).6
for each of the plurality of sets of locations: generating, [using the classical computing system], a sequence objective function to identify a sequence with which the set of locations are to be visited that minimizes a total distance traveled(Tambunan, pgs., 2-4, “The objective of the traffic flow for vehicle routing optimization problem is to minimize the time for a given set of cars to travel between their individual sources and destinations[to identify a sequence with which the plurality of locations are to be visited that minimizes a total distance traveled]… [t]he QUBO formula for the vehicle route selection optimization problem model is obtained from combining the cost function components ( equation 5) of each segment[for each of the plurality of sets of locations: generating, a sequence objective function] with the constraint function (equation 7).
O
b
j
=
∑
s
m
∈
S
(
∑
q
i
j
∈
B
s
m
w
i
j
q
i
j
)
2
+
K
∑
i
=
1
n
(
∑
j
=
1
3
q
i
j
-
1
)
2
”);7
inputting the sequence objective function and a set of sequence conditions to a quantum computing system to determine a sequence solution comprising the identified sequence (Tambunan, pgs., 2-4, see also figs. 2,3 and 4, “[T]he QUBO model (Fig.3) consisting
of a cost[the sequence objective function and a set of sequence conditions] and constraint matrix will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[inputting to a quantum computing system]… [t]hese results (Fig.4) get the lowest energy (E=-1184) value in the choice of vehicle route combination with the lowest cost value[to determine a sequence solution comprising the identified sequence].”);
and inputting [the agent quantity objective function, the temporal duration objective function, and] a set of agent-quantity conditions to the quantum computing system to determine an optimized solution comprising [the minimum quantity of agents], the subset of locations to be traveled to by each of the agents in the minimized amount of time, and an order with which the agent is to travel to each of subset of locations (Tambunan, pgs., 2-5, see also figs. 2,3 and 4, “[T]he QUBO model (Fig.3) consisting of a cost and constraint matrix[a set of agent-quantity conditions] will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[and inputting to the quantum computing system to determine an optimized solution]… [t]he selection of the optimal route is carried out in the case of the number of vehicles n = 3, n = 4, and n = 5. These results indicate the effect of alternative routes generated… the road segment weight model allows overlapping
vehicles to select a specific road segment, ensuring that all vehicles have exactly one route (valid result)[ comprising the subset of locations to be traveled to by each of the agents in the minimized amount of time, and an order with which the agent is to travel to each of subset of locations].”).8
While Tambunan teaches the quantum computing system, the sequence solution, and the optimized solution Tambunan does not teach:
generating, using the classical computing system, an agent quantity objective function to identify a minimum quantity of agents to visit the set of locations based on the sequence solution;
generating, using the classical computing system, a temporal duration objective function to identify a plurality of subsets of locations of the set of locations to be visited by one or more of the plurality of agents based on the sequence solution, wherein each of the plurality of subsets of locations is assigned to one of the plurality of agents to minimize an amount of time for that agent to visit the subset of locations;
the agent quantity objective function, the temporal duration objective function, and; the minimum quantity of agents
using a classical computing system
determining, using the classical computing system, that a quantity of locations included within the collection of locations fails to satisfy a threshold location quantity condition;
splitting, using the classical computing system, the collection of locations into a plurality of sets of locations, wherein a quantity of locations included within each of the plurality of sets of locations satisfies the threshold location quantity condition;
and generating, using the classical computing system, a global solution based on determined for each of the plurality of sets of locations.
However, Greyson teaches:
generating, [using the classical computing system], an agent quantity objective function to identify a minimum quantity of agents to visit the set of locations based on the sequence solution(Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w[based on the sequence solution]… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[generating, an agent quantity objective function to identify a minimum quantity of agents from the plurality of agents to visit the plurality of locations].”)9
generating, [using the classical computing system], a temporal duration objective function to identify a plurality of subsets of locations of the set of locations to be visited by one or more of the plurality of agents based on the sequence solution, wherein each of the plurality of subsets of locations is assigned to one of the plurality of agents to minimize an amount of time for that agent to visit the subset of locations(Greyson, pgs., 371-373, see also fig. 4, “According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[generating, a temporal duration objective function to identify a plurality of subsets of locations to be visited by the minimum quantity of agents based on the sequence solution]… [t]he considered method will identify all the zero-cost paths by formulating and solving the corresponding shortest-path problem, and eventually, it will select as the new route
σ
^
a
^
for agent
a
^
any of these zero-cost paths that takes agent
a
^
to its destination as soon as possible[wherein each of the plurality of subsets of locations is assigned to one of the minimum quantity of agents to minimize an amount of time for that agent to visit the subset of locations].”);10
the agent quantity objective function, the temporal duration objective function, and; the minimum quantity of agents (Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[agent quantity objective function and; the minimum quantity of agents]. According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[temporal duration objective function]….”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the teachings of Greyson the motivation to do so would be to implement an iterative local search scheme that subsequently improves each solution by adding additional agents to the routing problem so long as a feasible routing solution that is more optimal than the previous solution is feasible (Greyson, pg., 365, “[T]he presented algorithm can be perceived as a []local-search[] scheme that starts with the construction of a feasible routing schedule, and subsequently, it searches for improved solutions over pertinently defined []neighborhoods[] of the underlying solution space.”).
Sebastian teaches:
using a classical computing system(Sebastian, pg., 10, see also table 4, “As already mentioned, QBSolv can be used as a pure classical solver (called Local)… [i]n Listing 1 and Listing 2 the measured CPU times for the locally executed QBSolv are shown… [t]he dataset has been run on a Dell 2.8 GHz i7 with 16 GB RAM Notebook[using the classical computing system].”)
determining, using the classical computing system(Sebastian, pg., 10, see also table 4, “As already mentioned, QBSolv can be used as a pure classical solver (called Local)… [i]n Listing 1 and Listing 2 the measured CPU times for the locally executed QBSolv are shown… [t]he dataset has been run on a Dell 2.8 GHz i7 with 16 GB RAM Notebook[using the classical computing system].”),
that a quantity of locations included within the collection of locations fails to satisfy a threshold location quantity condition(Sebastian, pgs., 2-3, “Let
G
=
(
V
,
E
)
be a graph with
V
=
{
1
,
…
,
n
}
being a set of vertices representing n customer location with the depot located at vertex 1 and E being a set of undirected edges…[f]urthermore, assume there are m vehicles stationed at the depot that have the same capacity Q…the size of the resulting QUBO problem may exceed the limited number of available qubits on the QPU[determining, that a quantity of locations included within the collection of locations fails to satisfy a threshold location quantity condition] and the problem cannot be put on the chip altogether anymore.”);
splitting, using the classical computing system, the collection of locations into a plurality of sets of locations, wherein a quantity of locations included within each of the plurality of sets of locations satisfies the threshold location quantity condition(Sebastian, pgs., 6-7, see also figs. 1 and 3, “The third approach (HS in Figure 3) as a candidate for a
CVRP solution method combines the positive aspects of the previous mentioned approaches. To achieve this, the clustering phase (KP) is solved using a classical algorithm[using the classical computing system]…[w]ithin the cluster generation the core stop of a cluster, i.e., the first customer in a cluster, is chosen… [t]he motivation behind choosing the customer with the largest distance to the depot is the assumption that this one is the most critical customer in relation to the routes’ length constraint and that other customers may be supplied while approaching or receding that particular customer. Once the core stop v of a cluster has been selected, the geometric center
C
C
(
m
k
)
of cluster
m
k
is calculated… [n]ow, the customer with the smallest distance to the cluster center is selected from the set of unclustered customers and added to the cluster. After the cluster center is recalculated the steps are repeated until the demand of a customer to be added would exceed the vehicle’s capacity[wherein a quantity of locations included within each of the plurality of sets of locations satisfies the threshold location quantity condition]. If this is the case, a new core stop is selected based on the previously explained criteria and the still unclustered customers are assigned to the new cluster.
This procedure stops when each customer has been assigned to a cluster[splitting, the collection of locations into a plurality of sets of locations,].” );
and generating, using the classical computing system, a global solution based on [the optimized solution] determined for each of the plurality of sets of locations(Sebastian, pgs., 10-11, see also table 4, “The total runtime of the locally executed algorithm consists of two parts, the main procedure (clustering phase, QUBO construction, I/O)… [i]n Table 4 can be seen, that the total runtime of the classically executed hybrid solution algorithm took…[the] main procedure of 1.24 s[and generating, using the classical computing system, a global solution based on determined for each of the plurality of sets of locations]”).11
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the teachings of Sebastian the motivation to do so would be to take advantage of a hybrid approach that incorporates classical computing to overcome the limitations regarding the number of qubits available in quantum hardware(Sebastian, pg., 2, “[Q]uantum computation compared to classical
computation is still in its infancy and one of the major problems is that quantum hardware is limited regarding the number of quantum bits (qubits) and their connectivity on the chip. Generally, this leads to difficulties in mapping large QUBO problems to the hardware. With this paper we present an intuitive way to split the CVRP into smaller optimization problems by taking advantage of a classical 2-Phase-Heuristic… that divides the CVRP into two phases, the
clustering phase and the routing phase.”).
Regarding claim 25, Sebastian teaches a system for determining an optimized quantity of agents and routes for the agents using quantum computing, the system comprising: memory storing computer program instructions; and one or more processors programmed with the computer program instructions(Sebastian, pg., 10, see also table 4, “In Listing 1 and Listing 2 the measured CPU times for the locally executed QBSolv are shown… [t]he dataset has been run on a Dell 2.8 GHz i7[and one or more processors programmed with the computer program instructions] with 16 GB RAM[memory storing computer program instructions] Notebook.”) and for all other claim limitations of claim 25 they are rejected on the same basis as independent claim 1 since they are analogous claims.
Regarding claim 26, Sebastian teaches a non-transitory computer-readable medium storing computer program instructions that, when executed by one or more processors, effectuate operations(Sebastian, pg., 10, see also table 4, “In Listing 1 and Listing 2 the measured CPU times for the locally executed QBSolv are shown… [t]he dataset has been run on a Dell 2.8 GHz i7with 16 GB RAM Notebook[computer-readable medium storing computer program instructions that, when executed by one or more processors, effectuate operations].”) and for all other claim limitations of claim 26 they are rejected on the same basis as independent claim 1 since they are analogous claims.
Dependent Claims:
Regarding claim 2, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein the quantum computing system comprises a plurality of qubits, wherein a quantity of qubits included within the plurality of qubits used by the quantum computing system to determine the optimized solution is based on a quantity of locations included within the plurality of locations(Tambunan, pgs., 3, see also table II and fig. 1, “Vehicles are represented by variable
i
=
{
1,2
,
3
,
…
,
n
}
and route choices are given for each vehicle with three choices of route
j
=
{
1,2
,
3
}
. The combination of each route choice from the vehicle will be modeled in the form of a variable
q
i
j
which represents qubits.” & Tambunan, pg., 6, As Table II details:
PNG
media_image1.png
221
548
media_image1.png
Greyscale
For 3 vehicles with 3 route combinations, 9 qubit values are required to represent each combination of vehicle/route choice of the plurality of locations. For 4 vehicles with 3 route combinations, 12 qubit values are required to represent each combination of vehicle/route choice of the plurality of locations. And for 5 vehicles with 3 route combinations, 15 qubit values are required to represent each combination of vehicle/route choices of the plurality of locations[wherein the quantum computing system comprises a plurality of qubits, wherein a quantity of qubits included within the plurality of qubits used by the quantum computing system to determine the optimized solution is based on a quantity of locations included within the plurality of locations].).
Regarding claim 3, Tambunan in view of Greyson and Sebastian teaches the method of claim 2, wherein the quantity of qubits comprises 1,000 or more qubits, 10,000 or more qubits, 20,000 or more qubits, 50,000 or more qubits, or 100,000 or more qubits(Sebastian, pg., 3, “In this paper we used the D-Wave 2000Q model located in Vancouver, Canada, and we accessed the machine using D-Wave’s cloud interface. The instance at hand has got a working graph with 2,038 qubits and 5,955 couplers out of the full graph with 2,048 qubits and 6,016 couplers[wherein the quantity of qubits comprises 1,000 or more qubits].”).12
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Regarding claim 4, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, further comprising:
defining a cost function comprising a binary matrix, the binary matrix comprising a plurality of elements; and mapping the plurality of elements of the binary matrix respectively to a plurality of qubits of the quantum computing system wherein: each row of the binary matrix corresponds an agent of a plurality of agents and each column of the binary matrix corresponds to a position within the sequence, each of the plurality of elements has a first value or a second value (Tambunan, pgs., 3-4, see also table II and fig. 1, “Vehicles are represented by variable
i
=
{
1,2
,
3
,
…
,
n
}
[each row of the binary matrix corresponds an agent of a plurality of agents]and route choices are given for each vehicle with three choices of route
j
=
{
1,2
,
3
}
[ and each column of the binary matrix corresponds to a position within the sequence]. The combination of each route choice from the vehicle will be modeled in the form of a variable
q
i
j
[comprising a binary matrix, the binary matrix comprising a plurality of elements] which represents qubits[and mapping the plurality of elements of the binary matrix respectively to a plurality of qubits of the quantum computing system]…[t]he following equation is the cost function for the road segment traversed by the vehicle.
c
o
s
t
s
m
=
(
∑
q
i
j
∈
B
s
m
w
i
j
q
i
j
)
2
[defining a cost function]…the problem of vehicle traffic is defined by a condition rule where each vehicle (i) must take exactly one route choice (j) only. So that in the combination of choices, each vehicle will only be worth (1), which means that there is only one binary variable q selected[each of the plurality of elements has a first value or a second value].”),
the first value corresponds to a first state of a qubit from the plurality of qubits indicating that, for the optimized solution, an agent of the plurality of agents travels to that location at that position within the sequence, and the second value corresponds to a second state of a qubit from the plurality of qubits indicating that, for the optimized solution, the agent does not travel to that location at that position within the sequence(Tambunan, pg., 4, As fig. 4 details:
PNG
media_image2.png
276
576
media_image2.png
Greyscale
After executing the annealing process the binary matrix is represented by 12 rows and 12 columns consisting of qubit values of 1 or 0 where 1 indicates that the segment attached to the location is taken by the vehicle in the route and 0 indicates that the segment attached to the location is not taken by the vehicle in the route[the first value corresponds to a first state of a qubit from the plurality of qubits indicating that, for the optimized solution, an agent of the plurality of agents travels to that location at that position within the sequence, and the second value corresponds to a second state of a qubit from the plurality of qubits indicating that, for the optimized solution, the agent does not travel to that location at that position within the sequence].).
Regarding claim 5, Tambunan in view of Greyson and Sebastian teaches the method of claim 4, wherein generating the agent quantity objective function(Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[wherein generating the agent quantity objective function].”)
comprises: summing the binary matrix across the plurality of agents to determine whether each row includes at least one element has the first value(Tambunan, pgs., 3-4, “The constraint function in the above equation will get 0 when the condition is true when only one vehicle route option is active (
q
i
j
is 1)…[t]he QUBO formula for the vehicle route selection…
K
∑
i
n
(
∑
j
3
q
i
j
-
1
)
2
[summing the binary matrix across the plurality of agents to determine whether each row includes at least one element has the first value]”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the above teachings of Greyson for the same rationale stated at Claim 1.
Regarding claim 6, Tambunan in view of Greyson and Sebastian teaches the method of claim 5,
wherein generating the temporal duration objective function(Greyson, pgs., 371-373, see also fig. 4, “According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[wherein generating the temporal duration objective function]….”) comprises:
summing the binary matrix across the plurality of agents to determine(Tambunan, pgs., 3-4, “The constraint function in the above equation will get 0 when the condition is true when only one vehicle route option is active (
q
i
j
is 1)…[t]he QUBO formula for the vehicle route selection…
K
∑
i
n
(
∑
j
3
q
i
j
-
1
)
2
[summing the binary matrix across the plurality of agents to determine]):
a first amount of time for each of the plurality of agents to travel from a center of the geographic region to a given location of the plurality of locations; a second amount of time for each of the plurality of agents to travel from an immediately previous location to the given location; and a third amount of time for each of the plurality of agents to travel from the immediately previous location to the center(Greyson, pgs., 368, see also fig.6, “The system consists of a guidepath graph G… that is traversed by a set of agents… edge h, which models a “storage” (or “home”) location that can hold an arbitrary number of agents that either have not initiated[a first amount of time for each of the plurality of agents to travel from a center of the geographic region]… a trip for some agent a is defined by a sequence of edges[to to a given location of the plurality of locations]… that must be visited by a in the specified order[a second amount of time for each of the plurality of agents to travel from an immediately previous location to the given location], before the agent eventually retires in edge h[and a third amount of time for each of the plurality of agents to travel from the immediately previous location to the center]… we shall assume that each agent a ∈ A is associated with a single destination edge and the posed problem is to transfer each agent a from its current edge to its destination edge while minimizing the required transfer time w, i.e., w denotes the “makespan” of the corresponding traffic schedule[amount of time]….”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the above teachings of Greyson for the same rationale stated at Claim 1.
Regarding claim 7, Tambunan in view of Greyson and Sebastian teaches the method of claim 6, wherein the temporal duration objective function comprises: a first component for determining the first amount of time based on the binary matrix and a first distance matrix comprising distances from the center to each of the plurality of locations; a second component for determining the second amount of time based on the binary matrix and a second distance matrix comprising distances from the immediately previous location to the given location; and a third component for determining the third amount of time based on the binary matrix and a third distance matrix comprising distances from the immediately previous location to the center(Sebastian, pgs., 4-5, “The QUBO formulation of the clustering phase, however, is an adaption of the Knapsack Problem…[i]t is composed of
H
=
H
A
+
H
B
+
H
C
with
PNG
media_image3.png
262
576
media_image3.png
Greyscale
Examiner Remarks: Examiner is interpreting equation
H
A
as a first component for determining the first amount of time based on the binary matrix and a first distance matrix comprising distances from the center to each of the plurality of locations;
H
B
as a second component for determining the second amount of time based on the binary matrix and a second distance matrix comprising distances from the immediately previous location to the given location and
H
C
as and a third component for determining the third amount of time based on the binary matrix and a third distance matrix comprising distances from the immediately previous location to the center ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Regarding claim 14, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein the agent quantity objective function and the temporal duration objective function each include a matrix comprising rows representing a plurality of candidate agents and columns representing candidate locations to be visited by a corresponding agent(Tambunan, pgs., 3-4, see also table II and fig. 1, “Vehicles are represented by variable
i
=
{
1,2
,
3
,
…
,
n
}
[ rows representing a plurality of candidate agents] and route choices are given for each vehicle with three choices of route
j
=
{
1,2
,
3
}
[ and columns representing candidate locations to be visited by a corresponding agent]. The combination of each route choice from the vehicle will be modeled in the form of a variable
q
i
j
[a matrix].”).
Regarding claim 15, Tambunan in view of Greyson and Sebastian teaches the method of claim 14, wherein the minimum quantity of agents is determined from the plurality of candidate agents, the method further comprising: removing, from the plurality of candidate agents, any agents determined to not travel to at least one of the plurality of locations(Greyson, pgs., 373-374, see also fig. 4, “First, the algorithm will seek to identify among the paths of the DAGs that were computed in iteration k, one of minimal total cost
(i.e., minimal conflict with respect to the remaining fixed routes of schedule[wherein the minimum quantity of agents is determined from the plurality of candidate agents]…the algorithm will try to obtain a new feasible traffic schedule from schedule
S
1
k
-
1
by eliminating incrementally the various conflicts that are present in this schedule. This is done by starting with the new schedule
S
1
k
-
1
and trying to identify an agent
a
2
∈
A
\
{
a
1
}
that possesses a minimal-cost path in the corresponding DAG[removing, from the plurality of candidate agents, any agents determined to not travel to at least one of the plurality of locations].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the above teachings of Greyson for the same rationale stated at Claim 1.
Regarding claim 16, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein the agent quantity objective function and the temporal duration objective function (Greyson, pgs., 371-373, see also fig. 4, “[S]uppose that we have already computed a feasible schedule
S
=
{
σ
a
:
a
∈
A
}
with makespan w… [t]he proposed improving step seeks to find an agent
a
^
∈
A
˙
and a new route
σ
^
a
^
for this agent: 1) presents no conflicts with the routes
σ
a
that are specified by the original schedule S for all the other agents
a
∈
A
\
{
a
^
}
and 2) places agent
a
^
to its destination location
d
a
^
at a period earlier than w[agent quantity objective function and; the minimum quantity of agents]. According to the relevant definitions that were provided in that section, DAG
D
(
a
^
,
s
a
^
,
0
,
w
-
1
)
encodes all the possible routes that take agent
a
^
from its initial location
s
a
^
to its destination location
d
a
^
no later than period w − 1, and each node of this DAG carries a label (e, t) indicating that agent
a
^
is located at edge e at period t[temporal duration objective function]….”)
are solved, via the quantum computing system, together(Tambunan, pgs., 2-5, see also figs. 2,3 and 4, “[T]he QUBO model (Fig.3) consisting of a cost and constraint matrix will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[are solved, via the quantum computing system, together].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan with the above teachings of Greyson for the same rationale stated at Claim 1.
Regarding claim 17, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein generating the sequence objective function comprises: calculating a plurality of distances, wherein each distance of the plurality of distances is between two of the plurality of locations; formulating a distance matrix comprising the plurality of distances, wherein the sequence objective function comprises the distance matrix(Sebastian, pgs., 6-7, see also fig. 4, “In order to find the Hamiltonian Cycle with the shortest length, the following minimization function is needed:
PNG
media_image4.png
132
367
media_image4.png
Greyscale
Here
D
u
i
is the euclidean distance between the customers u and i. The minimization function sums all costs of the edges between successive customers.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Regarding claim 18, Tambunan in view of Greyson and Sebastian teaches the method of claim 17, wherein generating the sequence objective function comprises: defining a cost function comprises a product of the distance matrix and a binary matrix, the binary matrix comprising a plurality of elements; and mapping the plurality of elements of the binary matrix respectively to a plurality of qubits of the quantum computing system, wherein: each row of the binary matrix corresponds an agent of a plurality of agents and each column of the binary matrix corresponds to a position within the sequence, each of the plurality of elements has a first value or a second value(Tambunan, pgs., 3-4, see also table II and fig. 1, “Vehicles are represented by variable
i
=
{
1,2
,
3
,
…
,
n
}
[each row of the binary matrix corresponds an agent of a plurality of agents]and route choices are given for each vehicle with three choices of route
j
=
{
1,2
,
3
}
[ and each column of the binary matrix corresponds to a position within the sequence]. The combination of each route choice from the vehicle will be modeled in the form of a variable
q
i
j
[comprising a binary matrix, the binary matrix comprising a plurality of elements] which represents qubits[and mapping the plurality of elements of the binary matrix respectively to a plurality of qubits of the quantum computing system]… [t]he weights on the segments have different values and can
be assumed as parameters of distance…[t]he following equation is the cost function for the road segment traversed by the vehicle.
c
o
s
t
s
m
=
(
∑
q
i
j
∈
B
s
m
w
i
j
q
i
j
)
2
[wherein generating the sequence objective function comprises: defining a cost function comprises a product of the distance matrix and a binary matrix]…the problem of vehicle traffic is defined by a condition rule where each vehicle (i) must take exactly one route choice (j) only. So that in the combination of choices, each vehicle will only be worth (1), which means that there is only one binary variable q selected[each of the plurality of elements has a first value or a second value].”),
the first value corresponds to a first state of a qubit from the plurality of qubits indicating that, for the optimized solution, an agent of the plurality of agents travels to that location at that position within the sequence, and the second value corresponds to a second state of a qubit from the plurality of qubits indicating that, for the optimized solution, the agent does not travel to that location at that position within the sequence(Tambunan, pg., 4, As fig. 4 details:
PNG
media_image2.png
276
576
media_image2.png
Greyscale
After executing the annealing process the binary matrix is represented by 12 rows and 12 columns consisting of qubit values of 1 or 0 where 1 indicates that the segment attached to the location is taken by the vehicle in the route and 0 indicates that the segment attached to the location is not taken by the vehicle in the route).
Regarding claim 19, Tambunan in view of Greyson and Sebastian teaches the method of claim 18, wherein the cost function comprises a constrained quadratic model(Tambunan, pg., 4, see also fig. 3, “[t]he QUBO formula for the vehicle route selection optimization problem model is obtained from combining the cost function components ( equation 5) of each segment with the constraint function (equation 7).
O
b
j
=
∑
s
m
∈
S
(
∑
q
i
j
∈
B
s
m
w
i
j
q
i
j
)
2
+
K
∑
i
=
1
n
(
∑
j
=
1
3
q
i
j
-
1
)
2
…the QUBO model (Fig.3) consisting of a cost and constraint matrix will be processed by quantum annealing using access with Leap programming to the D-Wave annealer machine[wherein the cost function comprises a constrained quadratic model].”).
Regarding claim 20, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein the sequence solution comprising the sequence represents a shortest distance to travel to each of the plurality of locations(Sebastian, pg., 6, “After the clustering phase is completed, the goal is now to find the shortest route inside each cluster. Thus, the Travelling Salesman Problem (TSP) is executed for every generated cluster. The TSP can be reduced to the Hamiltonian Cycle Problem (HPC), which can be formulated as QUBO problem.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Regarding claim 21, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, wherein the set of sequence conditions comprise: a first condition that each location of the plurality of locations is traveled to a single time; and a second condition that each location of the plurality of locations occupies a single position within the sequence(Tambunan, pg., 2-4, see also fig.1, “Each vehicle is given three choices of alternative routes, but only one route s chosen by a vehicle as a road solution that can be passed. The choice of route will have a minimum of 4 segments to pass from point A to point B[a first condition that each location of the plurality of locations is traveled to a single time]….[t]he constraint function in the above equation will get 0 when the condition is true when only one vehicle route option is active (
q
i
j
is 1). The problem of vehicle traffic flow is defined by a condition rule where each vehicle (i) must take exactly one route choice (j) only[and a second condition that each location of the plurality of locations occupies a single position within the sequence].”).
Regarding claim 23, Tambunan in view of Greyson and Sebastian teaches the method of claim 22, wherein the quantum computing system comprises a plurality of qubits, the threshold location quantity condition is determined based on a quantity of the plurality of qubits(Sebastian, pgs., 2-3, see also fig. 2, “Let
G
=
(
V
,
E
)
be a graph with
V
=
{
1
,
…
,
n
}
being a set of vertices representing n customer location with the depot located at vertex 1 and E being a set of undirected edges…[f]urthermore, assume there are m vehicles stationed at the depot that have the same capacity Q…the size of the resulting QUBO problem may exceed the limited number of available qubits on the QPU[the threshold location quantity condition is determined based on a quantity of the plurality of qubits]…[i]n this paper we used the D-Wave 2000Q model located in Vancouver, Canada, and we accessed the machine using D-Wave’s cloud interface. The instance at hand has got a working graph with 2,038 qubits and 5,955 couplers out of the full graph with 2,048 qubits and 6,016 couplers[wherein the quantum computing system comprises a plurality of qubits].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Regarding claim 24, Tambunan in view of Greyson and Sebastian teaches the method of claim 23, wherein the threshold location quantity condition being satisfied comprises the quantity of locations being less than or equal to a threshold quantity of locations, the threshold quantity of locations being determined based on the quantity of the plurality of qubits(Sebastian, pgs., 7-8, “The QUBO problem matrix increases with the problem size, i.e., with the number of problem variables used. For the TSP
n
2
logical variables, with n being
the number of customers, have to be used to describe it as a QUBO problem. These variables have to be mapped to the qubits and the logical links between them to the couplers of the physical QPU. Because of the almost fully meshed dependencies between the logical variables it is not possible that the logical problem structure matches the physical one. For such issues D-Wave provides a minor embedding technique to find a valid embedding to the hardware. We have used this technique in combination with DWave’s QBSolv tool to fit our large QUBO problems to the physical hardware… QBSolv also takes care of the unembedding and the merging of the subproblems’ solutions. We use the default configuration of D-Wave’s QBSolv including the auto_scale function that automatically scales the values of the QUBO matrix to the
allowed range of values for the biases and strengths of qubits and couplers. The single-shot annealing time is set to the default value of 20 μs[wherein the threshold location quantity condition being satisfied comprises the quantity of locations being less than or equal to a threshold quantity of locations, the threshold quantity of locations being determined based on the quantity of the plurality of qubits].”).
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Tambunan et al., (March 2022) “Quantum Annealing for Vehicle Routing Problem with Weighted Segment,” arXiv:2203.13469v1(“Tambunan”) in view of Daugherty, Greyson et al., "Optimized multiagent routing for a class of guidepath-based transport systems." IEEE Transactions on Automation Science and Engineering 16.1 (2018)(“Greyson”) and in view of Feld, Sebastian, et al. "A hybrid solution method for the capacitated vehicle routing problem using a quantum annealer." Frontiers in ICT 6 (2019)(“Sebastian”) and further in view of Haghighi, Hassan, Davood Asadi, and Daniel Delahaye. "Multi-objective cooperated path planning of multiple unmanned aerial vehicles based on revisit time." Journal of Aerospace Information Systems 18.12 (2021)(“Hassan”)
Regarding claim 10, Tambunan in view of Greyson and Sebastian teaches the method of claim 1, but does not teach: wherein determining the plurality of locations comprises: receiving geographic data comprising a plurality of longitudes-latitudes pairs describing the plurality of locations.
However, Hassan teaches:
wherein determining the plurality of locations comprises: receiving geographic data comprising a plurality of longitudes-latitudes pairs describing the plurality of locations(Hassan, pg., 2, see also fig.1, “Therefore, an approximate 3D cell decomposition of the Zagros forest protected area” is applied using a 2Dgrid, as shown in Fig. 1. Each element of the matrix (cells) represents the elevation of the terrain. This representation allows for applying digital elevation maps repository with no further processing…[l]et X be a set of position vectors including a nominal sequence of position values of longitude x, latitude y, and altitude h.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson and Sebastian with the teachings of Hassan the motivation to do so would be to allow agents to cooperatively plan together to determine the most optimal path rather than treating each agent as an isolated entity(Hassan, pg., 2, “Because of the limited capabilities of a single UAV in large areas and long-range missions, more than one UAV is mostly required to be applied to accomplish the mission. Thus, cooperative algorithms should control the overall framework.”).
Regarding claim 11, Tambunan in view of Greyson, Sebastian and Hassan teach the method of claim 10, further comprising: calculating, based on the geographic data, a center of the geographic region(Sebastian, pg., 6, “Once the core stop v of a cluster has been selected, the geometric center CC(mk) of cluster mk is calculated….”), wherein the center comprises a center longitude and a center latitude(Hassan, pg., 8, As fig. 7(a) details:
PNG
media_image5.png
386
704
media_image5.png
Greyscale
The latitude center is approximately 6000 meters, and the longitude center is approximately 12000 meters).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson and Sebastian with the above teachings of Hassan for the same rationale stated at Claim 10.
Regarding claim 12, Tambunan in view of Greyson, Sebastian and Hassan teach the method of claim 11, wherein the temporal duration objective function comprises: a first component representing, for each of the plurality of agents, an amount of time for the agent to travel from the center to each of the plurality of locations; a second component representing, for each of the plurality of agents, an amount of time for the agent to travel from an immediately prior location of the plurality of locations to each of the plurality of locations; and a third component representing, for each of the plurality of agents, an amount of time for the agent to travel from the immediately prior location to the center(Sebastian, pgs., 4-5, “The QUBO formulation of the clustering phase, however, is an adaption of the Knapsack Problem…[i]t is composed of
H
=
H
A
+
H
B
+
H
C
with
PNG
media_image3.png
262
576
media_image3.png
Greyscale
Examiner Remarks: Examiner is interpreting equation
H
A
as a first component representing, for each of the plurality of agents, an amount of time for the agent to travel from the center to each of the plurality of locations;
H
B
as a second component representing, for each of the plurality of agents, an amount of time for the agent to travel from an immediately prior location of the plurality of locations to each of the plurality of locations and
H
C
as and a third component representing, for each of the plurality of agents, an amount of time for the agent to travel from the immediately prior location to the center ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
.
Regarding claim 13, Tambunan in view of Greyson, Sebastian and Hassan teach the method of claim 12, wherein generating the temporal duration objective function comprises: combining, for each of the plurality of agents, the first component, the second component, and the third component(Sebastian, pgs., 4-5, “The QUBO formulation of the clustering phase, however, is an adaption of the Knapsack Problem…[i]t is composed of
H
=
H
A
+
H
B
+
H
C
….”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tambunan in view of Greyson with the above teachings of Sebastian for the same rationale stated at Claim 1.
Allowable Subject Matter
Claims 8-9 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Thuravakkath et al. US 2025/0124405 Al
Yarkoni, US 2021/0248489 Al
Howard et al. US 12,518,228 B2
Schuetz et al. US 12,447,617 Bl
Toyota et al. US 11,714,419 B2
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
2 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
3 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
4Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Greyson.
5 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Greyson.
6 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
7 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
8 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Tambunan.
9Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are claim limitations that are not taught by the prior art of Greyson.
10 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Greyson.
11 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are taught by the prior art of Tambunan.
12 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
Prosecution Insights