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 .
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.
Status of Claims
The following is an office action in response to the communication filed on 10/21/2025.
Claim 16 has been cancelled.
Claims 6, 9, 13-14, and 17 are amended.
Claims 1-15 and 17 are currently pending.
Claims 1-15 and 17 have been examined.
Priority
Applicant' s claim for the benefit of prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Information Disclosure Statement
The Information Disclosure Statement received on 10/30/2025 has been reviewed and considered.
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 1-15 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite the judicial exception of mental process. This judicial exception is not integrated into a practical application, nor do the claims include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1: Yes, the claims are drawn to one or more statutory categories.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject
matter falls within the four statutory categories of patentable subject matter identified by 35
U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-14 are directed toward a method (i.e., process) with at least one step and claim 15 is directed toward a server computer (i.e., a machine).
Step 2A Prong 1: Yes, the claims recite an abstract idea.
If the claim recites a statutory category of invention, the claim requires further analysis
in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One,
examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those in bold below.
A method for predicting a path taken by a vehicle for a transport task comprising:
obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network;
representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network;
converting the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value;
augmenting each outgoing edge of each decision node with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements; and
predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task.
These limitations, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance in the mind. For example, the claim encompasses observing the path of a vehicle, using the observations to create a Boolean formula representation of the paths (e.g., for location points A, B, and C,
A
B
=
T
R
U
E
1
,
A
C
=
F
A
L
S
E
(
0
)
, where TRUE (1) is a valid path and FALSE (0) is an invalid path), mentally converting the formula to a decision tree (e.g., for location points A, B, and C,
A
→
B
→
T
R
U
E
,
A
→
C
→
F
A
L
S
E
), assigning a probability to each arrow of the decision tree based on observed historic preference for location nodes (e.g., for location points A, B, and C,
A
→
B
75
%
c
h
a
n
c
e
,
A
→
C
(
25
%
c
h
a
n
c
e
)
), and predicting a path from a starting location to an ending location by repeatedly navigating the decision tree mentally or with pencil and paper to find a valid path. The claim does not recite anything that precludes it from the mental process grouping.
Step 2A Prong 2: No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
If the claim recites a judicial exception in step 2A Prong One, the claim requires further
analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim
recites additional elements that integrate the exception into a practical application of that
exception.
Claim 1 recites additional element limitations, including those underlined below.
A method for predicting a path taken by a vehicle for a transport task comprising:
obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network;
representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network;
converting the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value;
augmenting each outgoing edge of each decision node with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements; and
predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task.
The obtaining training data step is recited at a high level of generality and amounts to no more than insignificant pre-solution activity.
Step 2B: No, the additional elements of these claims do not amount to significantly more than the judicial exception.
If the additional elements do not integrate the exception into a practical application in
step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires
further analysis under Step 2B to determine whether they provide an inventive concept (i.e.,
whether the additional elements amount to significantly more than the exception itself).
As discussed above, the obtaining training data step amounts to no more than insignificant extra-solution activity and is recited at a high level of generality. The obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network as described in the claim is well-understood, routine, and conventional in the art.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The limitations of claim 2 merely adds a mental process step. For example, the claim limitations encompass mentally navigating the nodes of a layered decision tree with probabilities to compute the probability of a path being traveled (e.g., for location points A, B, and C, determining the probability of traveling from A (layer 1) to B (layer 2) to C (layer 3) to be 75% because the probability from traveling from A to B is 75% and the probability of traveling from B to C is 100%). For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application or significantly more than the abstract idea.
The limitations of claims 3-8 merely serve to further characterize the path prediction step of the mental process. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application or significantly more than the judicial exception.
The limitations of claims 9-14 merely serve to further characterize the variables and Boolean formulas utilized in predicting the path. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application or significantly more than the abstract idea.
The limitations of claims 15 and 17 merely serve as generic means to “apply” the otherwise mental process. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application or significantly more than the abstract idea.
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.
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 1-5, 9-10, and 14are rejected under 35 U.S.C. 103 as being unpatentable over George (CN 112534488 A; hereinafter George) in view of Mahulea et al. (Cristian Mahulea, Marius Kloetzer, Jean-Jacques Lesage. Multi-robot Path Planning with Boolean Specifications and Collision Avoidance. 15th Workshop on Discrete Event Systems, (WODES’20), Nov 2020, Rio de Janeiro, Brazil. pp. 101-108.; hereinafter Mahulea) and further in view of Mehta et al. (Mehta, D., & Raghavan, V. (2002). Decision tree approximations of Boolean functions. Theoretical Computer Science, 270(1-2), 609-623.; hereinafter Mehta).
Regarding claim 1, George discloses the subject matter indicated in bold below:
A method for predicting a path taken by a vehicle for a transport task comprising (see George at least pg. 2, paragraph 6 “. . . the present invention provides a method for providing reroute information . . .”):
obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network (see George at least pg. 4, paragraph 3 “This probability allows the system to calculate the expected savings for a given route change candidate according to one embodiment: E(S)＝Pα×S_DWR wherein [Pα is] the probability of acceptance [(i.e., probability)], S is the time-of-flight savings, S _ DWR is the time-of-flight savings estimated by DWR, and [E](S) is the expected value of the time-of-flight savings.”; pg. 6, paragraph 1 “Another factor in the acceptability of the ATC for a route change request is the historical usage of the route. Route changes using routes that are unclear or rarely used are less likely to be accepted than route changes using familiar, commonly used routes. Historical monitoring and flight plan data are mined to build a frequent routes database. The route legs and combinations of route legs to be flown, and the frequency of use in terms of the number of flights that have flown each leg [(i.e., outgoing edges)] or combination of legs, are determined according to the historical data for each month.”); . . .
augmenting each outgoing edge of each decision node with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements (see George at least pg. 4, paragraph 3 “This probability allows the system to calculate the expected savings for a given route change candidate according to one embodiment: E(S)＝Pα×S_DWR wherein [Pα is] the probability of acceptance [(i.e., probability)], S is the time-of-flight savings, S _ DWR is the time-of-flight savings estimated by DWR, and [E](S) is the expected value of the time-of-flight savings.”; pg. 6, paragraph 1 “Another factor in the acceptability of the ATC for a route change request is the historical usage of the route. Route changes using routes that are unclear or rarely used are less likely to be accepted than route changes using familiar, commonly used routes. Historical monitoring and flight plan data are mined to build a frequent routes database. The route legs [(i.e., outgoing edges)] and combinations of route legs to be flown, and the frequency of use in terms of the number of flights that have flown each leg [(i.e., outgoing edges)] or combination of legs, are determined according to the historical data for each month.”; pg. 6, paragraph 2 “This data is used to assign a total route frequency metric RF for each candidate route change. The metric RF is the average frequency value of the routes taken over the legs that make up the route.”; pg. 10, paragraph 3 “. . . each segment in the tree has an associated probability.”); and
predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task (see George at least pg. 9, paragraph 7 “. . . the system next builds a probabilistic binary decision tree, as shown in FIG. 7 (which shows the probabilistic binary decision tree for the candidate route change R1). ‘yes’ and ‘no’ indicate a change in course of flight or non-flight, respectively.”; pg. 9, paragraph 8 “However, not all paths are feasible. A path is not feasible if the route change initiation time point overlaps with the ongoing route change. For example, from fig. 6, if the flying route changes R1, the flying route cannot be changed R2. Fig. 8 (which shows a probabilistic binary decision tree for the candidate route change R1 with infeasible paths framed) shows the infeasible portion of the decision tree framed.”; pg. 10, paragraph 2 “. . . the resulting nodes without decisions, may be removed, leaving a fully feasible decision tree [(i.e., only elect a path that is valid)] . . .”; pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations and find the decision sequence that yields the highest expected savings [(i.e., system samples an assignment of values to the variables by traversing the ordered binary decision diagram to predict a path)].”; Figure 8- probabilistic binary decision tree with binary decision nodes connected by outgoing edges to subsequent decision nodes that are both valid and non-valid routes).
While George discloses a method for predicting a path taken by a vehicle for a transport task comprising obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network, augmenting each outgoing edge of each decision node in an ordered binary decision diagram with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements, and predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task, it does not appear to explicitly disclose representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network, nor converting the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value.
Mahulea teaches the subject matter underlined below:
. . . representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network (see Mahulea at least pg. 4, col. 2, paragraph 2 “The team of robots is required to move so that it fulfils a Boolean formula over the set of regions of interest. The formula can include requirements on both the robot trajectories and on their final (stopping) positions. Formally, the formula is given over set Yi ∪ Yf , where Yi = {Y1, Y2, . . . , Y|Y|} and Yf = Y = {y1, y2, . . . , y|Y|}, with the following meaning:
• Set Yi refers to intermediate requirements on robot trajectories, i.e., it is used to specify which regions of interest are to be visited or avoided during the robot motion, excluding their stopping positions;
• Set Yf refers to final requirements and it is used for indicating regions in which the robots should or should not remain at the end of their movement.”; pg. 9, col. 1, paragraph 1 “We now consider the following new formula:
φ
3
=
⋀
i
=
1
10
¬
Y
i
∧
⋀
i
=
11
20
Y
i
∧
⋀
i
=
1
10
y
i
, meaning that during the trajectories the robots should avoid the regions at the middle of the environment, should reach the regions at the right part, and should finally stop in the regions in the middle [(i.e., Boolean formula operating on a set of variables representing locations in network, outputs a valid path)].”); . . .
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the method for predicting a path taken by a vehicle for a transport task comprising obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network, augmenting each outgoing edge of each decision node in an ordered binary decision diagram with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements, and predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task of George with the representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network as taught by Mahulea to represent a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network. Doing so would distill the complexity of path-planning with high-level specifications down to a compact model, as recognized by Mahulea (see Mahulea at least pg. 2, col. 1, paragraph 2 "Path-planning with high-level specifications is a problem extensively studied in literature, both for single robot . . . or for multi-robot systems . . .").
While George and Mahulea disclose a method for predicting a path taken by a vehicle for a transport task comprising obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network, representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables and the output of the Boolean formula for an assignment of values to the variables outputs whether the assignment of values to the variables represents a valid path through the road network, augmenting each outgoing edge of each decision node in an ordered binary decision diagram with a probability depending on the number of times the location represented by the decision node was visited in the paths specified by the training data elements, and predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task, they do not appear to explicitly disclose converting the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value.
Mehta teaches the subject matter indicated with dashed underline below:
. . . converting the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value (see Mehta at least pg. 611, paragraph 4 “We first show that in the case of some well-known representation schemes, small ε-approximating decision trees can be obtained in quasi-polynomial time. (More precisely, a polynomial factor of the size |f| of the input function f is multiplied by a factor which involves an exponent logarithmic in 1/ε, where ε is the desired error tolerance, and the size m of the smallest decision tree which can represent f.) These schemes are: . . . Ordered binary decision diagrams . . .”; pg. 613, paragraph 1 “Let f be a Boolean function over a set V= {v1; v2; . . . ;vn} of n variables.”; pg. 617, paragraph 5 “Given f, a Boolean function over n variables, a height parameter h and a size parameter m, it builds precisely one tree . . .”; pg. 617, paragraph 6 “The algorithm employs a two-dimensional array P[α; k] to hold a tree in Tα;k. A tree in the P array will be represented by a triple of the form (root, left sub[-]tree, right sub-tree), unless it consists of a single-leaf node, in which case it will be represented by the leaf’s value [(i.e., conjunction nodes comprising decision nodes for each variable (i.e., roots), and each decision node has for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value (i.e., left and right sub-trees))].”); . . .
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the Boolean formulation and ordered binary decision diagram approaches to path-planning of George and Mahulea with the Boolean formula conversion to ordered binary decision diagram as taught by Mehta to convert the Boolean formula into an ordered binary decision diagram augmented with conjunction nodes comprising, for each variable of the set of variables, a decision node representing the variable, wherein the decision node has, for each assignment of a value to the variable represented by the decision node, an outgoing edge associated with the value. Doing so would make the Boolean representation more amenable to manipulation downstream, as recognized by Mehta (see Mehta at least pg. 609, paragraph 1 "The popularity of decision trees for representing Boolean functions may be attributed to the following reasons: – Universality: Decision trees can represent all Boolean functions. – Amenability to manipulation: Many useful operations on Boolean functions can be performed efficiently in time polynomial in the size of the decision tree representation. In contrast, most such operations are intractable under other popular representations.").
Regarding claim 2, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . comprising calculating the probability of a path by traversing the ordered binary decision diagram augmented with conjunction nodes in a layer- wise manner (see George at least pg. 9, paragraph 8 “Fig. 8 (which shows a probabilistic binary decision tree for the candidate route change R1 with infeasible paths framed) shows the infeasible portion of the decision tree framed.”; pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations and find the decision sequence that yields the highest expected savings [(i.e., system samples an assignment of values to the variables by traversing the ordered binary decision diagram in a layer-wise manner to predict a path)].”; Figure 8- probabilistic binary decision tree with binary decision nodes arranged layer-wise).
Regarding claim 3, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein predicting the path comprises determining an output assignment for each node of the ordered binary decision diagram augmented with conjunction nodes (see George at least see George at least pg. 9, paragraph 8 “Fig. 8 (which shows a probabilistic binary decision tree for the candidate route change R1 with infeasible paths framed) shows the infeasible portion of the decision tree framed.”; pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations and find the decision sequence that yields the highest expected savings.”; Figure 8- output assigned for each node of the ordered binary decision diagram augmented with conjunction nodes), . . .
While George discloses predicting the path comprises determining an output assignment for each node of the ordered binary decision diagram augmented with conjunction nodes, it does not appear to explicitly disclose each output assignment specifying a partial assignment of values to the variables of the Boolean formula.
Mehta discloses the subject matter indicated with dashed underline below:
. . . wherein each output assignment specifies a partial assignment of values to the variables of the Boolean formula (see Mehta at least pg. 613, paragraph 2 “A partial assignment is obtained when only a subset of variables in V is assigned values. A partial assignment may be represented by a vector of length n each of whose elements is either 0; 1, or *. A vector element is * if the corresponding variable was not assigned a value.”; pg. 617, paragraph 5 “Given f, a Boolean function over n variables, a height parameter h and a size parameter m, it builds precisely one tree from the set Tα;k, for each partial vector α . . .”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the predicting the path comprises determining an output assignment for each node of the ordered binary decision diagram augmented with conjunction nodes of George with the each output assignment specifying a partial assignment of values to the variables of the Boolean formula as taught by Mehta to predict the path comprises determining an output assignment for each node of the ordered binary decision diagram augmented with conjunction nodes, wherein each output assignment specifies a partial assignment of values to the variables of the Boolean formula. Doing so would allow the ordered binary decision diagram to additionally capture uncertain results.
Regarding claim 4, George, Mahulea, and Mehta disclose the subject matter of claim 3 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein determining the output assignment at a conjunction node comprises combining the assignments output by the child nodes of the conjunction node (see George at least pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations [(i.e., all child node results)] and find the decision sequence that yields the highest expected savings.”).
Regarding claim 5, George, Mahulea, and Mehta disclose the subject matter of claim 3 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein determining the output assignment at a decision node comprises combining the assignment output of the child node of the outgoing branch corresponding to the selected assignment of the variable represented by the decision node with the selected assignment of the variable represented by the decision node (see George at least pg. 4, paragraph 3 “This probability allows the system to calculate the expected savings for a given route change candidate according to one embodiment: E(S)＝Pα×S_DWR wherein [Pα is] the probability of acceptance [(i.e., probability)], S is the time-of-flight savings, S _ DWR is the time-of-flight savings estimated by DWR, and [E](S) is the expected value of the time-of-flight savings.”; pg. 6, paragraph 1 “Another factor in the acceptability of the ATC for a route change request is the historical usage of the route. Route changes using routes that are unclear or rarely used are less likely to be accepted than route changes using familiar, commonly used routes. Historical monitoring and flight plan data are mined to build a frequent routes database. The route legs and combinations of route legs to be flown, and the frequency of use in terms of the number of flights that have flown each leg [(i.e., outgoing edges)] or combination of legs [(i.e., child and selected nodes considered)], are determined according to the historical data for each month.”; pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations [(i.e., all child node results)] and find the decision sequence that yields the highest expected savings.”).
Regarding claim 9, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein the set of variables is a first set of variables and the binary logic further operates on a second set of variables, wherein each location of the location network is associated with a respective variable of the second set of variables whose value indicates, for a path, whether the location is an end location of the path (see George at least pg. 11, paragraph 4 “. . . identifying, for each probabilistic binary decision tree, each possible path or sequence of route changes through the tree [(i.e., all points are mapped until an end point is reached)] . . .”).
While George discloses the set of variables being a first set of variables and the binary logic further operating on a second set of variables, wherein each location of the location network is associated with a respective variable of the second set of variables whose value indicates, for a path, whether the location is an end location of the path, it does not appear to explicitly disclose the binary logic being a Boolean formula.
Mahulea teaches using Boolean formulas to represent path-planning problems.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the set of variables being a first set of variables and the binary logic further operating on a second set of variables, wherein each location of the location network is associated with a respective variable of the second set of variables whose value indicates, for a path, whether the location is an end location of the path of George with the Boolean formulas to represent path-planning problems as taught by Mahulea to have the set of variables be a first set of variables and the Boolean formula further operator on a second set of variables, wherein each location of the location network is associated with a respective variable of the second set of variables whose value indicates, for a path, whether the location is an end location of the path. The examiner supplies the same rationale for the combination of these references as supplied in claim 1 above.
Regarding claim 10, George, Mahulea, and Mehta disclose the subject matter of claim 9 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein the output of the binary logic only indicates for a path that it is a valid path if it contains at least one end location (see George at least pg. 10, paragraph 2 “Given the designation of infeasible portions in FIG. 8, those portions, and the resulting nodes without decisions, may be removed, leaving a fully feasible decision tree . . .”; pg. 11, paragraph 4 “. . . identifying, for each probabilistic binary decision tree, each possible path or sequence of route changes through the tree [(i.e., all points are mapped until an end point is reached)] . . .”).
Regarding claim 14, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, George discloses the subject matter indicated in bold below:
. . . wherein, in an assignment of the variables, each variable is assigned true if the location represented by the variable is part of a path represented by the assignment or false if the location represented by the variable is not part of the path represented by the assignment (see George at least pg. 9, paragraph 7 “’yes’ and ‘no’ indicate a change in course of flight or non-flight, respectively [(i.e., true if it is on a re-route path, false if it is not on a re-route path)].”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Brito et al. (Brito, M. P., Smeed, D. A., & Griffiths, G. (2014). Analysis of causation of loss of communication with marine autonomous systems: A probability tree approach. Methods in Oceanography, 10, 122-137.; hereinafter Brito).
Regarding claim 8, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above.
While George discloses predicting the path comprising navigating a probabilistic ordered binary decision diagram with decision and conjunction nodes, it does not appear to explicitly disclose predicting the path comprising generating, for each layer, a decision node matrix which has a column for each decision node containing the probabilities of the outgoing edges of the decision node and comprises generating, for each layer, a conjunction node matrix which has a column for each conjunction node containing identifications of child nodes of the conjunction node and processing the decision node matrix and the conjunction matrix.
Brita teaches the subject matter underlined below:
. . . wherein predicting the path comprises generating, for each layer, a decision node matrix which has a column for each decision node containing the probabilities of the outgoing edges of the decision node and comprises generating, for each layer, a conjunction node matrix which has a column for each conjunction node containing identifications of child nodes of the conjunction node and processing the decision node matrix and the conjunction matrix (see Brito at least Figure 1- decision tree shown with columns for each layer for each decision node containing the probabilities of the outgoing edges of the decision node and for each layer (i.e., path probabilities) and a conjunction node matrix which has a column for each conjunction node containing identifications of child nodes of the conjunction node and processing the decision node matrix and the conjunction matrix (i.e., decision))).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the predicting the path comprising navigating a probabilistic ordered binary decision diagram with decision and conjunction nodes of George with the predicting the path comprising generating, for each layer, a decision node matrix which has a column for each decision node containing the probabilities of the outgoing edges of the decision node and comprises generating, for each layer, a conjunction node matrix which has a column for each conjunction node containing identifications of child nodes of the conjunction node and processing the decision node matrix and the conjunction matrix as taught by Brito to have predicting the path comprise generating, for each layer, a decision node matrix which has a column for each decision node containing the probabilities of the outgoing edges of the decision node and comprises generating, for each layer, a conjunction node matrix which has a column for each conjunction node containing identifications of child nodes of the conjunction node and processing the decision node matrix and the conjunction matrix. Doing so would organize the decision diagram information in an easily accessible way.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Anderson (Andersen, H. R. (1998). An introduction to binary decision diagrams. Lecture notes for, 49285, 36.; hereinafter Anderson).
Regarding claim 11, George, Mahulea, and Mehta disclose the subject matter of claim 9 as recited in the claim and applied above.
While George discloses using a Boolean-based binary decision diagram for path planning (see George at least pg. 9, paragraph 7 “. . . the system next builds a probabilistic binary decision tree, as shown in FIG. 7 (which shows the probabilistic binary decision tree for the candidate route change R1). ‘yes’ and ‘no’ indicate a change in course of flight or non-flight, respectively.”; pg. 9, paragraph 8 “However, not all paths are feasible. A path is not feasible if the route change initiation time point overlaps with the ongoing route change. For example, from fig. 6, if the flying route changes R1, the flying route cannot be changed R2. Fig. 8 (which shows a probabilistic binary decision tree for the candidate route change R1 with infeasible paths framed) shows the infeasible portion of the decision tree framed.”; pg. 10, paragraph 2 “. . . the resulting nodes without decisions, may be removed, leaving a fully feasible decision tree [(i.e., only elect a path that is valid)] . . .”; pg. 10, paragraph 6 “To do these calculations, when those nodes in the tree are reached, the system needs to determine whether to request R2, R3, and R4. For the last node in the tree (the R4 node in this example), the savings are expected to be consistently higher (i.e., no compromise) when a route change [(i.e., path for a transport task)] is requested. However, for earlier internal nodes (R2 and R3 nodes in our example), the system will exhaust all possible decision combinations and find the decision sequence that yields the highest expected savings [(i.e., system samples an assignment of values to the variables by traversing the ordered binary decision diagram to predict a path)].”; Figure 8- probabilistic binary decision tree with binary decision nodes connected by outgoing edges to subsequent decision nodes that are both valid and non-valid routes), it does not appear to explicitly disclose the Boolean formula only indicating for a path that it is a valid path if it contains at most two end locations.
Anderson teaches the subject matter underlined below:
. . . wherein the output of the Boolean formula only indicates for an output that it is a valid output if it contains at most two end locations (see Anderson at least pg. 9, paragraph 1 “An If-then-else Normal Form (INF) is a Boolean expression built entirely from the if-then-else operator and the constants 0 and 1 such that all tests are performed only on variables.”; pg. 9, paragraph 4 “We have proved: . . . Any Boolean expression is equivalent to an expression in INF.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the Boolean-based binary decision diagram for path planning of George with the output of the Boolean formula only indicating for an output that it is a valid output if it contains at most two end locations as taught by Anderson to output of the Boolean formula only indicates for a path that it is a valid path if it contains at most two end locations. Doing so would ensure that the output paths could be modeled as a binary decision diagram, as recognized by Anderson (see Anderson at least pg. 9, paragraph 6 “If we in fact identify all equal subexpressions we end up with what is known as a binary decision diagram (a BDD).”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Verma et al. (Verma, J.P.V., Mankad, S.H. & Garg, S. GeoHash tag based mobility detection and prediction for traffic management. SN Appl. Sci. 2, 1385 (2020).; hereinafter Verma).
Regarding claim 13, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above.
While George discloses using a binary decision tree to determine navigational routing of a vehicle (see George at least pg. 9, paragraph 7 “. . . the system next builds a probabilistic binary decision tree, as shown in FIG. 7 (which shows the probabilistic binary decision tree for the candidate route change R1). ‘yes’ and ‘no’ indicate a change in course of flight or non-flight, respectively.”), it does not appear to explicitly disclose each location being a geographical area corresponding to a geohash of a predetermined level.
Verma teaches the subject matter underlined below:
. . . wherein each location is a geographical area corresponding to a geohash of a predetermined level (see Verma at least Section 5.2 “Extracting Data combine these multiple data files into one single file containing all the record of taxi-id, latitude, longitude and timestamp. Here transformation of latitude and longitude are concatenated together to form a unique area called GeoHash tag. The Geohash is the method for encoding regions with specific precision of latitude and longitude.”; Section 5.3 “Edges between these Geohash tags show the link between the vertexes.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the binary decision tree to determine navigational routing of a vehicle of George with each location being a geographical area corresponding to a geohash of a predetermined level as taught by Verma to have each location be a geographical area corresponding to a geohash of a predetermined level. Doing so would enable size reduction for improved processing times, as recognized by Verma (see Verma at least Section 5.2 “Geohashes offer properties like arbitrary precision and the possibility of gradually removing characters from the end of the code to reduce its size.”).
Claims 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Dori et al. (WO 2020178639 A1; hereinafter Dori).
Regarding claim 15, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above.
While George discloses a method with steps (see George at least pg. 2, paragraph 6 “. . . the present invention provides a method for providing reroute information . . .”), it does not appear to explicitly disclose a server computer comprising a radio interface, a memory interface and a processing unit configured to perform the method.
Dori teaches the subject matter underlined below:
A server computer comprising a radio interface, a memory interface and a processing unit configured to perform the method (see Dori at least [0297] “FIG. 15 is an exemplary functional block diagram of memory 140 and/or 150, which may be stored/programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory 140, one of skill in the art will recognize that instructions may be stored in memory 140 and/or 150.”; [0309] “. . . process 1700 may be performed by at least one processing device of a server remote from a host vehicle.”; [0311] “In some embodiments, the navigational information may be received over a computer network (e.g., cellular, the Internet, etc.) by use of a radio frequency . . .”) . . .
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the method with steps of George with the server computer comprising a radio interface, a memory interface and a processing unit configured to perform the method as taught by Dori to have a server computer comprising a radio interface, a memory interface and a processing unit configured to perform the method. Doing so would provide a means for executing the method.
Regarding claim 17, George, Mahulea, and Mehta disclose the subject matter of claim 1 as recited in the claim and applied above.
While George discloses a method with steps (see George at least pg. 2, paragraph 6 “. . . the present invention provides a method for providing reroute information . . .”), it does not appear to explicitly disclose a computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method.
A computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method (see Dori at least [0006] “In an embodiment, non-transitory, computer-readable medium may store instructions that, when executed by at least one processor . . .”; [0297] “FIG. 15 is an exemplary functional block diagram of memory 140 and/or 150, which may be stored/programmed with instructions for performing one or more operations consistent with the disclosed embodiments. Although the following refers to memory 140, one of skill in the art will recognize that instructions may be stored in memory 140 and/or 150.”) . . .
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the method with steps of George with the computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method as taught by Dori to have a computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method. Doing so would provide a means for executing the method.
Response to Arguments
Applicant's arguments filed 10/21/2025 with regard to the rejection of claims 1-15 and 17 under 35 U.S.C. §101 have been fully considered and are partially persuasive.
(A) Applicant argues, “Claim 16 has been cancelled. Further, claim 17 has been amended to recite ‘a non-transitory computer readable medium,’ which falls within at least one of the four categories of patent eligible subject matter. Therefore, the amended claim 17 is not directed to non-statutory subject matter under Step 1,” (from remarks pg. 5).
As to Point (A), examiner agrees. The amendment made to claim 17 directs it toward a statutory category under step 1 of the two-prong 35 U.S.C. §101 analysis.
(B) Applicant argues, “Independent claim 1 is not directed to an abstract idea of mental processes.
“’Software can make non-abstract improvements to computer technology just as hardware improvements can, and sometimes the improvements can be accomplished through either route. We thus see no reason to conclude that all claims directed to improvements in computer-related technology, including those directed to software, are abstract.’ Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327.
“Further, Enfish explained:
“In this case, however, the plain focus of the claims is on an improvement to computer functionality itself, not on economic or other tasks for which a computer is used in its ordinary capacity. Accordingly, we find that the claims at issue in this appeal are not directed to an abstract idea within the meaning of Alice. Rather, they are directed to a specific improvement to the way computers operate, embodied in the self-referential table ... In sum, the self-referential table recited in the claims on appeal is a specific type of data structure designed to improve the way a computer stores and retrieves data in memory.
“Id.
“The USPTO, in its May 19, 2016 Memorandum, acknowledging Enfish, asserts, ‘[t]he Federal Circuit in Enfish stated that certain claims directed to improvements in computer-related technology, including claims directed to software, are not necessarily abstract (Step 2A) ... the Enfish claims were not ones in which general-purpose computer components are added after the fact to fundamental economic practice or mathematical equation, but were directed to a specific implementation of a solution to a problem in the software art, and concluded that the Enfish claims were directed to an improvement in the operation of a computer technology, thus not directed to an abstract idea (under Step 2A).’
“Similar to Enfish, independent claim 1 includes computations that are not abstract, but are instead applied in a structured and integrated way that results in an improvement to computer-related technology. In particular, independent claim 1 describes a computer-implemented method for predicting a path taken by a vehicle for a transport task. The method obtains training data which includes a multiplicity of training data elements that specify a path taken in a location network. Further, a set of valid paths are represented as a Boolean formula operating on a set of variables. A variable of the set of variables represents each location of the location network and the Boolean formula for an assignment of values to the variable outputs a valid path through the road network. Further, the method predicts a path for a given transport task by sampling the assignment of values to the variable. Further, the method traversed the assignment of values to the variable by an ordered binary decision diagram augmented with conjunction nodes. Further, the method represents the assignment of a value to the variable by a decision node and selects a decision node from multiple decision nodes for path prediction with the probability of the outgoing edge having a value that leads to a valid path which is in line with the transport task. This enables accurate prediction of vehicle routes by representing valid paths as Boolean formulas, thereby improving trip duration estimates and optimizing driver allocation in e-hailing systems.
“As a vehicle navigates through a location network, its position continuously changes which makes accurate path prediction essential to complete an associated transport task. Accordingly, training data is obtained which includes multiple training elements representing paths that a vehicle has taken in a location network. Based on the training data, the method identifies which paths are valid and represents them as a Boolean formula. The Boolean formula operates on a set of variables, where each variable represents a specific location in the location network. Further, the Boolean formula is converted into an ordered binary decision diagram (OBBD). Each variable is represented as a decision node. Each decision node has outgoing edges that correspond to possible values assigned to that variable. To make the OBDD reflect real-world vehicle movement, each outgoing edge is given a probability. This probability depends on how often the location represented by the decision node was visited in the training data. As a result, the method predicts a path for a transport task by sampling values for the variables. The method does the path prediction by traversing the OBBD and selecting values at each decision node based on the assigned probabilities. This results in a predicted path that aligns with actual travel patterns.
“The human mind cannot obtain training data which includes a multiplicity of training data elements specifying a path taken by a vehicle in a location network. The human mind cannot evaluate such Boolean formulas or traverse the decision node in real time to predict the valid path from a set of valid paths, especially as the location of the vehicle changes dynamically with vehicle movement. Accordingly, the human mind even with a help of pen and paper cannot employ a structured, algorithmic approach to predict an accurate path using an ordered binary decision diagram (OBDD) augmented with conjunction nodes in real time in line with the transport task. Thus, it is not possible for the human minds to accurately predict the valid path considering the complexity of calculation and processing involved.
“This multi-step method operates in a structured, algorithmic manner within a computerized environment, solving a concrete technical problem: predicting the route a vehicle takes for a given transport task to optimize the efficiency of vehicle dispatching systems to predict and reduce traffic Jams. Claim 1 recites an efficient method to predict accurate path for a specific transport task, by representing valid paths as Boolean formulas and sampling an assignment of values to variables through OBDD. As a result, trip duration estimates are improved, and driver allocation is optimized in ride-hailing systems. See Application paragraphs [0002]- [0004].
“Therefore, the steps described in independent claim 1 are a technological improvement in the domain of ride-haling systems and are not mere mental steps that can be performed accurately on pen and paper.
“Therefore, independent claim 1 is not directed to an abstract idea under Prong One of Step 2A,” (from remarks pg. 5-8).
As to Point (B), the examiner respectfully disagrees. Applicant appears to argue that the claims do not recite abstract ideas because the claims provide an improvement to a technology and further recite limitations that cannot be performed by the human mind. In support of the former, Applicant references Enfish, noting that the claimed methods are applied in a structured and integrated way that results in an improvement to a computer-related technology and are therefore eligible at step 2A of the two-prong analysis. MPEP §2106.05(a) provides guidance on determining an improvement to the functioning of a computer when analyzing claims under 35 U.S.C. §101. In paragraph [0004] of the specification of the present application, the purpose of the claimed invention relative to the context of its technology is outlined as predicting routes efficiently for transportation hailing applications. The manner in which a path is predicted in the claims can be done mentally, as described in the analysis above. Using an example in the context of hailing transportation, the provided methodology is akin to a taxi service provider receiving a call, contemplating routes to the location based on previous experience servicing the area, and then providing a time estimate. For location points A, B, and C, A to C might be a direct path available to the taxi servicer for servicing a request and A to B to C may represent an alternative route. Based on historical experience, the taxi servicer may know that A to C directly is interrupted by a railroad, and route A to B to C avoids railroads. Because A to B to C avoids the railroad but is a longer distance, the taxi servicer may have a preference for taking A to B to C 10% of the time, such in cases when a train can audibly be heard nearby or if crossings are visibly closed. Under the currently claimed methodology, a taxi servicer is able to perform the same analysis mentally for the different routes, as described in more detail above, and is able to provide a time estimate to the customer. The claimed methodology does not provide improved functioning to a computer, such as the case with Enfish, but rather uses generic computing components in their ordinary capacity to digitize a manual process. Mere automation of manual processes is not eligible for streamlined analysis nor eligible under step 2A of the 35 U.S.C. § 101 two-prong test, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Regarding the latter argument, Applicant indicates that the human mind cannot obtain the training data required by the claims and cannot dynamically adapt the algorithmic approach outlined in the claims in real-time. As provided with the numerous examples above, the training data as described in the claims may be historical data, which a human is capable of acquiring and interpreting; the training data limitations provided by the claimed invention are akin to experience in the simplest format but may also be pen and paper data recording and analysis. Dynamic, real-time implementation of the steps outlined are neither required by the claims nor described in the specification of the present application. Given even a short duration in the simple examples provided above, the human mind, with or without pen and paper, is capable of performing the steps described in the claims. As such, the claims are not eligible at step 2A prong 1 and require further analysis.
(C) Applicant argues, “Notwithstanding the above remarks, arguendo, that independent claim 1 is directed to an abstract idea/judicial exception as the Office Action contends, it is submitted that independent claim 1 recites additional elements that integrate the judicial exception into a practical application.
“It is submitted that some steps of independent claim 1 is similar to at least claim 3 of Example 47 provided in ‘AI-related Subject Matter Eligibility Examples,’ effective as of July 17, 2024 . . .
“The USPTO considers Claim 3 of Example 47 to be eligible at Step 2A Prong 2, stating: ‘According to the background section, existing systems use various detection techniques for detecting potentially malicious network packets and can alert a network administrator to potential problems. The disclosed system detects network intrusions and takes real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses... Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception.’
“Claim 3 of Example 47 not only detects an anomaly and alerts a user but also takes real-time remedial actions to enhance network security upon detecting an anomaly. Similarly, independent claim 1 goes beyond mere mental steps that can be performed on pen and paper. Independent claim 1 introduces a structured, computer-implemented method for predicting the path a vehicle takes for a transport task, using training data. The training data is used to predict paths within a location network. Each location is represented as a variable, and valid paths are represented as Boolean formulas operating on the variables that enables whether a given assignment of values corresponds to a valid route through the road network in line with the transport task, thereby optimizing the efficiency of vehicle dispatching systems to predict and reduce traffic jams.
“Independent claim 1 reflects ‘an improvement in the functioning of a computer, or an improvement to other technology or technical field.’ The features, ‘obtaining training data comprising a multiplicity of training data elements...; representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables...; predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node,... the variable leads to a valid path which is in line with the transport task,’ of independent claim 1 demonstrate that the claimed apparatus is not merely using a computer as a passive tool to perform a mental process. Instead, the claim elements, when considered as a whole, reflect a specific and structured computer-implemented technique that facilitates accurate and efficient prediction of vehicle paths for transport tasks by sampling valid paths through a probabilistic decision diagram derived from Boolean logic.
“Independent claim 1 is not limited to processing and generating data, nor does it merely apply generic computer components as abstract concepts. Instead, independent claim 1 recites a detailed workflow that enables accurate prediction of vehicle paths for transport tasks by obtaining training data. The method represents a set of valid paths as a Boolean formula operating on a set of variables and predicts a path for a given transport task by sampling an assignment of values to the variables by traversing OBDD augmented with conjunction nodes. The assignment of the value to the variable leads to a valid path which is in line with the transport task, enabling accurate prediction of vehicle routes by representing valid paths as Boolean formulas and sampling an assignment of values to variables through OBDD. As a result, trip duration estimates are improved, and driver allocation is optimized in ride-hailing systems. See Application paragraphs [0003], [0004], [0033], and [0044].
“Thus, independent claim 1 integrates the purported judicial exception into a practical application under Prong Two of Step 2A.
“Therefore, independent claim 1 recites additional elements that integrate the judicial exception into a practical application under Prong Two of Step 2A.
“Claims 2-15 and 17 are also allowable through their dependency on independent claim 1,” (from remarks pg. 8-10).
As to Point (C), examiner respectfully disagrees. Applicant appears to argue that the claims recite additional elements that integrate the abstract ideas of the claims into a practical application. Applicant relies upon example 47 claim 3 provided in ‘AI-related Subject Matter Eligibility Examples,’ effective as of July 17, 2024 to illustrate how the claimed invention integrates abstract ideas into a practical application. Example 47 claim 3 is deemed eligible under 35 U.S.C. § 101 because it performs corrective actions in response to a determination, as reflected in steps (d)-(f) of the example claim. Unlike example 47 claim 3, the claimed invention does not take any additional actions after making a determination that would integrate it into a practical application.
(D) Applicant argues, “Notwithstanding the above remarks under Step 2A, arguendo, that independent claim 1 is directed to an abstract idea/judicial exception as the Office Action contends, it is submitted that independent claim 1 amounts to ‘significantly more’ than an abstract idea.
According to the MPEP, ‘[e]valuating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself ... Consideration of the elements in combination is particularly important, because even if an additional element does not amount to significantly more on its own, it can still amount to significantly more when considered in combination with the other elements of the claim’ (emphasis added). See MPEP 2106.05(I).
“Reconsideration and withdrawal of the rejection of independent claim 1 under 35 U.S.C. § 101 are requested in view of the decision of the Court of Appeals for the Federal Circuit regarding subject matter eligibility as acknowledged by the USPTO in its Memorandum issued on November 2, 2016, addressing ‘Recent Subject Matter Eligibility Decisions (BASCOM Global Internet Services v. AT&TMobility LLC).’ The USPTO, in the Memorandum, instructs ‘[t]he BASCOM court agreed that the additional elements were generic computer, network, and Internet components that did not amount to significantly more when considered individually, but explained that the district court erred by failing to recognize that when combined, an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements ... (note that the term inventive concept' is often used by the courts to describe additional element(s) that amount to significantly more than a judicial exception)’ (emphasis in original). The USPTO, in the Memorandum, further describes ‘[i]n Step 2B of the USPTO's SME guidance, examiners should consider the additional elements in combination, as well as individually, when determining whether a claim as a whole amounts to significantly more, as this may be found in the non-conventional and non-generic arrangement of known, conventional elements.’
“Independent claim 1 amounts to ‘significantly more’ at least because the ‘additional elements’ add ‘a specific limitation other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application.’
“The elements claimed in independent claim 1 are not routine or conventional in the field of ‘path prediction for a transport task" because before this application, the following elements have not been suggested: "obtaining training data comprising a multiplicity of training data elements...; representing a set of valid paths as a Boolean formula operating on a set of variables, wherein each location of the location network is represented by a variable of the set of variables...; predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node,... the variable leads to a valid path which is in line with the transport task.’ The claimed additional elements qualify as ‘significantly more’ not only because they are not generic computer functions, but also because they add specific limitations that are unconventional. As the Federal Circuit elaborated in BASCOM, ‘[t]he inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art... [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.’
“Conventionally, predicting the route a vehicle takes for a trip relies on static mapping techniques or heuristic-based estimations that fail to account for dynamic factors such as real-time traffic conditions, driver behaviour, and road patterns. As a result, the conventional path prediction system often produces inaccurate route predictions, which in turn lead to unreliable estimates of trip duration and driver availability. This lack of precision hampers the ability of e-hailing platforms to efficiently reallocate drivers to subsequent transport tasks, causing delays and suboptimal resource utilization. Moreover, conventional systems do not incorporate location data, such as geohash-level segmentation, nor do the probabilistic models to simulate realistic travel paths. Therefore, there exists a need for more efficient and accurate path prediction techniques. See Application paragraphs [0002]-[0004].
“In contrast, independent claim 1 describes non-conventional computational steps for predicting a path taken by a vehicle for a transport task using a structured and probabilistic approach. The method obtains training data which includes a multiplicity of training data elements, such that each training data element specifies a path taken in a location network. Further, the method represents variables as Boolean formulas such that the variables represent a path taken by a vehicle, which are then converted into an ordered binary decision diagram (OBDD) augmented with conjunction nodes. The method assigns probabilities to each decision node in the diagram corresponding to a variable based on how frequently that location was visited in the location network. By traversing the OBDD, the method samples variable assignments to generate valid paths aligned with the transport task. These computational steps represent ‘significantly more’ than known practices and are thus not a mere mental process of organizing human activity that can be performed by a human mind with physical aids. This unconventional arrangement of operations, as emphasized in BASCOM, amounts to ‘significantly more’ than an abstract idea of mental processes. It provides a specific, technical solution that enables real-time, accurate route prediction without relying on manual estimation or static mapping. As a result, the system achieves reduced latency and improved prediction accuracy, beneficial in applications such as ride-hailing platforms, fleet dispatching, autonomous vehicle routing, and traffic flow optimization.
“In view of the foregoing remarks, independent claim 1, when taken as a whole, qualifies as significantly more than an abstract idea.
“Therefore, independent claim 1 amounts to ‘significantly more’ than an abstract idea under Step 2B.
“Claims 2-15 and 17 are also allowable through their dependency on independent claim 1,” (from remarks pg. 10-12).
As to Point (D), the examiner respectfully disagrees. The additional elements of claim 1 being considered in step 2B the two prong analysis under 35 US.C. § 101 are “. . . obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network . . .” which amounts to insignificant pre-solution activity because the way the data is obtained and the specificity of the data itself are both recited at a high level of generality. The obtaining training data comprising a multiplicity of training data elements, wherein each training data element specifies a path taken in a location network as described in the claim is well-understood, routine, and conventional in the art. Even in combination, this data collection step does not amount to significantly more than the judicial exception.
Applicant's arguments filed 10/21/2025 with regard to the rejection of claims 1-15 and 17 under 35 U.S.C. §103 have been fully considered but are not persuasive.
(E) Applicant argues, “Independent claim 1 recites, ‘predicting a path for a given transport task by sampling an assignment of values to the variables by traversing the ordered binary decision diagram augmented with conjunction nodes wherein at each decision node, an assignment of a value to the variable represented by the decision node is selected with the probability of the outgoing edge associated with the value if the assignment of the value to the variable leads to a valid path which is in line with the transport task’ which is not taught or suggested, either alone or in combination, George, Mahulea, and Mehta.
“George describes aircraft rerouting due to factors such as weather changes, airspace congestion, and traffic conditions. The rerouting system calculates route acceptance probability, which estimates the likelihood that air traffic control will approve a proposed alternative route. This probability is based on route complexity (RC), controller workload (CW), constraint proximity (CP), and route usage frequency (RF). Once the acceptance probability is determined, the system builds a decision tree to compare the outcomes of requesting versus not requesting a reroute modelling all feasible sequences of route changes (e.g., R1 through R4), with each node representing a decision point. If a route change is requested, a ‘yes’ branch reflects its acceptance probability, while a ‘no’ branch reflects the complement. See George, Abstract, page 6, paragraph 2, and page 9, paragraph 7.
“George merely describes a system for rerouting an aircraft based on calculating the probability that a predetermined proposed route will be accepted by air traffic control. However, George fails to teach or suggest any path prediction. In fact, the path rerouting described in George is different from the claimed path prediction because rerouting is reactive and path prediction is proactive. George's rerouting system determines whether a route change of the aircraft should be requested based on a calculated (i.e., predetermined) route acceptance probability. In contrast, independent claim 1 presents a proactive method for predicting the path a ground vehicle will take for a transport task by analyzing training data.
“Thus, George fails to teach or suggest the above-mentioned features recited in independent claim 1.
“The Office Action does not rely on Mahulea to teach the above-mentioned claim features of independent claim 1, nevertheless, Mahulea describes a collision-free multi-robot path planning process under Boolean specifications using Mixed Integer Linear Programming (MILP). The process translates Boolean formula which defines regions to be visited or avoided by a robot, into a set of linear inequalities. This is done by constructing a binary vector that represents each region to be visited during a trajectory. The trajectory is computed at first, middle, and last positions. Marking vector mj tracks the robot positions at each step. Constraints are added to the trajectory to ensure that the robots do not enter negated regions and that no two robots occupy the same cell simultaneously. See Mahulea, Abstract, page 101, column 1; 104, column 2, paragraphs 2-4; and page 5, column 1.
“First, Mahulea merely describes that the trajectory of each robot is computed using the Boolean formula φ which is followed by the robots in an environment to avoid collision. However, Mahulea fails to teach or suggest any form of prediction of a path for any transport task.
“Second, Mahulea merely describes that the Boolean formula # defines regions to be visited or avoided by a robot. However, Mahulea fails to teach or suggest that the Boolean formula # represents a set of valid paths taken by a vehicle in a location network.
“Thus, Mahulea fails to teach or suggest the above-mentioned features recited in independent claim 1.
“The Office Action does not rely on Mehta to teach the above-mentioned claim features of independent claim 1, nevertheless, Mehta describes representing Boolean functions as compact decision trees. Mehta constructs e-approximations: decision trees that differ from an original Boolean function on not more than an c fraction of inputs. The process defines approximation goal and analyzes a structure of the original Boolean function. The method expresses Boolean function in compact decision trees by sampling input-output functions. The resulting decision tree mimics the original Boolean function while remaining computationally tractable. See Mehta, Abstract, page 611, paragraphs 3 and 4.
“Mehta, at best, describes a method for approximating Boolean functions using decision trees. However, Mehta fails to teach or suggest any form of prediction of a path for any transport task. Thus, Mehta fails to teach or suggest the above-mentioned features recited in independent claim 1 . . .
“The Office Action allegedly relies on a combination of George's aircraft rerouting system, Mahulea's multi-robot path planning process, and Mehta's decision tree approximations of Boolean functions to suggest the features of independent claim 1. However, George's system is designed for aircraft rerouting based on factors such as weather and air traffic, using decision tree analysis. Mahulea's method focuses on collision-free planning for multiple robots using MILP formulations derived from Boolean specifications, without any probabilistic sampling or predictive modelling of transport paths. Mehta's decision tree approximation method, while involving a decision tree, is limited to approximating Boolean functions and does not address path prediction or transport tasks. Therefore, the combination of these references fails to teach or suggest prediction of a path for a transport task by sampling an assignment of values to variables through traversal of an ordered binary decision diagram (OBDD) augmented with conjunction nodes.
“Therefore, George, Mahulea, and Mehta, either alone or in combination, fail to teach or suggest the above-mentioned features of independent claim 1.
“Therefore, the rejection of independent claim 1 under 35 U.S.C. § 103 should be withdrawn.
“Dependent claims 2-5, 9-10, 14, and 16
“Claims 2-5, 9-10, and 14 are also allowable through their dependency on independent claim
“Further, claim 16 has been cancelled, rendering the rejection moot.
“Rejection of claim 8 under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Brito et al. (Brito, M. P., Smeed, D. A., & Griffiths, G. (2014). Analysis of causation of loss of communication with marine autonomous systems: A probability tree approach. Methods in Oceanography, 10, 122-137; hereinafter Brito).
“Brito is not contended to cure the deficiencies in the rejection of independent claim 1 above. Accordingly, claim 8 is also allowable through their dependency on independent claim 1.
“As a result, the rejection of dependent claim 8 under 35 U.S.C. § 103 should be withdrawn.
“Rejection of claim 11 under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Anderson (Andersen, H. R. (1998). An introduction to binary decision diagrams. Lecture notes for, 49285, 36; hereinafter Anderson).
“Anderson is not contended to cure the deficiencies in the rejection of independent claim 1 above. Accordingly, claim 11 is also allowable through its dependency on independent claim 1.
“As a result, the rejection of dependent claim 11 under 35 U.S.C. § 103 should be withdrawn.
“Rejection of claim 13 under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Verma et al. (Verma, J.P.V., Mankad, S.H. & Garg, S. GeoHash tag based mobility detection and prediction for traffic management. SN Appl. Sci. 2, 1385 (2020); hereinafter Verma).
“Verma is not contended to cure the deficiencies in the rejection of independent claim 1 above. Accordingly, claim 13 is also allowable through its dependency on independent claim 1.
“As a result, the rejection of dependent claim 13 under 35 U.S.C. § 103 should be withdrawn.
“Rejection of claims 15 and 17 under 35 U.S.C. 103 as being unpatentable over George in view of Mahulea and further in view of Mehta and Dori et al. (WO 2020178639 A1; hereinafter Dori).
“Dori is not contended to cure the deficiencies in the rejection of independent claim 1 above.
“Accordingly, claims 15 and 17 are also allowable through their dependency on independent claim 1.
“As a result, the rejection of dependent claims 15 and 17 under 35 U.S.C. § 103 should be withdrawn,” (from remarks pg. 12-16).
As to Point (E), the examiner respectfully disagrees. Applicant appears to argue that the combination of claims provided in the rejection of claims under 35 U.S.C. § 103 does not teach or suggest prediction of a path for a transport task by sampling an assignment of values to variables through traversal of an ordered binary decision diagram (OBDD) augmented with conjunction nodes. Specifically, Applicant states, “George's system is designed for aircraft rerouting based on factors such as weather and air traffic, using decision tree analysis. Mahulea's method focuses on collision-free planning for multiple robots using MILP formulations derived from Boolean specifications, without any probabilistic sampling or predictive modelling of transport paths. Mehta's decision tree approximation method, while involving a decision tree, is limited to approximating Boolean functions and does not address path prediction or transport tasks.” MPEP § 2141.01(a) indicates that references relied upon in a rejection under 35 U.S.C. § 103 must be analogous art to the claimed invention, and MPEP § 2143 provides examples of motivations to combine when establishing a prima facie case of obviousness. George and Mahulea are both in the field of path planning for vehicles, and Mahulea provides a motivation for combining. That is, to distill the complexity of path-planning with high-level specifications down to a compact model, as recognized by Mahulea (see Mahulea at least pg. 2, col. 1, paragraph 2 "Path-planning with high-level specifications is a problem extensively studied in literature, both for single robot . . . or for multi-robot systems . . ."). Mehta, while not in the same field of endeavor, is reasonably pertinent because it is directed toward a methodology for representing Boolean functions as decision trees agnostic of context and further provides a motivation for wishing to do so (i.e., to make the Boolean representation more amenable to manipulation downstream; see Mehta at least pg. 609, paragraph 1 "The popularity of decision trees for representing Boolean functions may be attributed to the following reasons: – Universality: Decision trees can represent all Boolean functions. – Amenability to manipulation: Many useful operations on Boolean functions can be performed efficiently in time polynomial in the size of the decision tree representation. In contrast, most such operations are intractable under other popular representations."). Since the combination of George and Mahulea presents Boolean formulation and ordered binary decision diagram approaches to path-planning, Mehta, given its explicitly stated advantages and context-agnostic approach, provides a desirable and pertinent methodology for maximizing the benefits presented by both George and Mahulea. In combination, these references would yield the claimed invention.
Allowable Subject Matter
Claims 6-7 and 12 are rejected under 3.5. U.S.C. 101 above but would be allowable if rewritten in such a way that they overcome the 35 U.S.C. 101 rejection and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TABITHA KRESS whose telephone number is (703) 756-1763. The examiner can normally be reached MTWR 06:30-16:30 CST.
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, Hitesh Patel can be reached at (571) 270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TABITHA KRESS/ Examiner, Art Unit 3667
/Hitesh Patel/ Supervisory Patent Examiner, Art Unit 3667
6/17/26
Prosecution Insights