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 .
Response to Amendment
This Final Office action is responsive to the communication filed under 37 C.F.R. § 1.111 on March 6, 2026 (hereafter “Response”). The amendments to the claims are acknowledged and have been entered.
Claims 1–14 are now amended.1
New claims 16 and 17 are now added.
Claims 1–17 are pending in the application.
Response to Arguments
In response to the amendments, the objection to the specification and rejections under 35 U.S.C. § 112 are hereby withdrawn.
Claims 1–15 stand rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. 2016/0205509 A1 (hereafter “Hopcraft”) in view of Haoyu Han et al., STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation, 2020 IEEE International Conference on Data Mining (ICDM) (Nov. 17, 2020), available at https://doi.org/10.1109/ICDM50108.2020.00124 (hereafter “Han”). The Applicant’s remarks have been considered, but are not persuasive.
The Applicant’s remarks are not persuasive because they are incorrect. According to the Applicant, “in Hoperaft, the edge of the directed graph is determined only based on whether the edge corresponds to a valid travel option,” and that “there is no disclosure of ‘wherein the edge connecting the plurality of nodes is identified based on at least one of information on a number of times of movement of the at least one user between the plurality of identified nodes or information on a physical distance between the plurality of nodes.’” (Response 12). However, this is not true.
To the contrary, as shown in FIG. 2B, Hopcraft explicitly teaches that the vertices in the graph 280 are connected by edges that represent the distances between the vertices. “For example, in FIG. 2B, a vertex 220 has vertices 210, 218, 235 and 252 as adjacent vertices, and the distances to the adjacent vertices are 5, 15, 14 and 12, respectively.” Hopcraft ¶ 35. Accordingly, this argument is not persuasive, and the rejection is maintained.
Regarding new claims 16 and 17, the Examiner agrees that Hopcraft and Han do not explicitly disclose the subject matter therein, but the newly cited Liu reference does teach the additional subject matter of those claims, and even provides explicit evidence as to why and how one of ordinary skill in the art could apply Liu’s technique to an existing graph convolutional neural network.
Accordingly, claims 16 and 17 are rejected under 35 U.S.C. § 103, as are claims 1–15, and with all claims rejected, the Applicant’s request for a notice of allowance is respectfully denied.
Information Disclosure Statement
The specification and foreign priority documents appear to make reference to an algorithm called “DynaPosGNN,” and a research paper thereof, but such reference(s) do not seem to appear in any of the Applicant’s information disclosure statements. As a reminder, a listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
The amendment to claims 1 and 9 tack a limitation about the originally obtained region mobility graph’s edges onto the ends of each claim, despite both claims introducing edges much earlier in the recitations of the claims (e.g., line 6 of claim 1). There does not appear to be any substantive reason to do this, other than the convenience of the amendment’s drafter. Moreover, by separating the initial recitation of the “edge connecting the plurality of nodes” and its new wherein clause with several intermediate processing steps, the narrative of the claim is difficult to follow.
The claims should be amended to move the last wherein clause to pair with the “an edge connecting the plurality of nodes” language earlier in the claim. For example, claim 1 should be amended as follows:
A method for controlling a server, the method comprising:
obtaining road information in a region having a predetermined range and information on a plurality of places in the region;
obtaining a region mobility graph corresponding to the region, based on movement information of at least one user between the plurality of places, the region mobility graph including a plurality of nodes corresponding to the plurality of places and an edge connecting the plurality of nodes, wherein the edge connecting the plurality of nodes is identified based on at least one of information on a number of times of movement of the at least one user between the plurality of identified nodes or information on a physical distance between the plurality of nodes;
generating a learned region mobility graph, by using a graph convolutional network (GCN) model, the GCN model being configured to learn the region mobility graph based on the plurality of nodes, feature information included in the plurality of nodes, and edges connecting the plurality of nodes, and predict a relationship between the plurality of nodes in the region mobility graph; and
providing the learned region mobility graph to an external apparatus[[,]]
Appropriate correction is required.
Claim Rejections – 35 U.S.C. § 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
I. Hopcraft and Han teach claims 1–15.
Claims 1–15 are rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. 2016/0205509 A1 (hereafter “Hopcraft”) in view of Haoyu Han et al., STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation, 2020 IEEE International Conference on Data Mining (ICDM) (Nov. 17, 2020), available at https://doi.org/10.1109/ICDM50108.2020.00124 (hereafter “Han”).
Claim 1
Hopcraft teaches a method for controlling a server, the method comprising:
obtaining road information in a region having a predetermined range and information on a plurality of places in the region;
“According to an embodiment of the present invention, geolocation application 120 determines the historical motion patterns of a mobile device user based on map data including a road network of the geographical region in which the mobile device user is located. Thus, at 402, geolocation application 120 may load road network data from one or more geospatial data sources.” Hopcraft ¶ 40.
obtaining a region mobility graph corresponding to the region, based on movement information of at least one user between the plurality of places, the region mobility graph including a plurality of nodes corresponding to the plurality of places and an edge connecting the plurality of nodes;
“Next, at step 404, geolocation application 120 may transform at least a portion of the loaded road network into a directed graph of valid travel options. During this transformation, road intersections in the road network become vertices in the generated graph data structure. Additionally, in one embodiment of the present invention, any two nodes in the graph are connected by edges if: the corresponding segments in the road network are connected at a common vertex and if there is no turn restriction prohibiting traversal between the corresponding segments in the road network.” Hopcraft ¶ 41.
generating a learned region mobility graph,
“At 410, geolocation application 120 may generate a mobility model based on the graph generated at step 404. The mobility model defines the law under which the mobile device positions evolve over time. In other words, the mobility model mathematically defines transition probabilities between the pluralities of states.” Hopcraft ¶ 44.
by using a graph
“Examples of the mobility modeling for making a movement pattern of the mobile device of interest may include, but is not limited to, a fluid model, a random movement model, a Markov model, etc.” Hopcraft ¶ 44.
the relationship between the plurality of nodes in the region mobility graph; and
The “states” and their “transition probabilities” in model represent “any path travelled by an object along any portion of the road network” via “a series of events causing the road network model to transition from the state of one starting edge through a graph node to which the graph edge in question is connected to the state of another edge.” Hopcraft ¶ 36.
Accordingly, the model used in step 410 learns and predicts the relationships of nodes in the graph generated at step 404 using (1) the plurality of nodes, (2) feature information included in the plurality of nodes, and (3) edges connecting the plurality of nodes.
and providing the learned region mobility graph to an external apparatus,
The state transition matrix may be stored on database 124 for use at a later time. Hopcraft ¶ 45.
wherein the edge connecting the plurality of nodes is identified based on at least one of information on a number of times of movement of the at least one user between the plurality of identified nodes or information on a physical distance between the plurality of nodes.
“FIG. 2B shows the arrangement of vertices in the graph 280. The vertices in the graph 280 represent corresponding nodes illustrated in FIG. 2A. For example, in FIG. 2B, a vertex 220 has vertices 210, 218, 235 and 252 as adjacent vertices, and the distances to the adjacent vertices are 5, 15, 14 and 12, respectively.” Hopcraft ¶ 35. Since Hopcraft at least discloses that the edges include information on a physical distance between their corresponding nodes, Hopcraft necessarily teaches that the edges are identified “based on at least one of information on a number of times of movement of the at least one user between the plurality of identified nodes or information on a physical distance between the plurality of nodes.”
Hopcraft does not explicitly disclose using a graph convolutional network (GCN) to learn its mobility model. Han however, teaches a method comprising:
obtaining . . . information on a plurality of places in the region;
Han’s model (Fig. 4) receives, as input, a multigraph comprising several different pieces of information, including information about a plurality of points of interest (“POIs”). Han 1053–1054.
obtaining a region mobility graph corresponding to the region, based on movement information of at least one user between the plurality of places, the region mobility graph including a plurality of nodes corresponding to the plurality of places and an edge connecting the plurality of nodes;
“To fuse the spatial-temporal context information, we propose the user record multigraph, as shown in Figure 3.” Han 1053. In this graph, there are a plurality of nodes
P
=
p
1
,
p
2
,
…
,
p
N
, Han 1053, and one or more edges
e
i
,
j
that connect POI nodes
p
i
and
p
j
if users visit POI
p
i
and POI
p
j
during a time period t.
generating a learned region mobility graph, by using a graph convolutional network (GCN) model, the GCN model being configured to learn the region mobility graph based on the plurality of nodes, feature information included in the plurality of nodes, and edges connecting the plurality of nodes,
“After getting the multi-hops sampled neighbors of each node at time t, we design STGCN Layer to learn the representations of each node. By exploiting the idea of Relational Graph Convolution Network (RGCN) [16], we assign different transformation matrix to different relations of edges.” Han 1054.
Han’s STGCN learns based on the plurality of nodes themselves, because it takes as input a graph comprising a plurality of nodes, including nodes that represent the physical locations (“POIs,” i.e.,
P
=
p
1
,
p
2
,
…
,
p
N
). See Han 1054–1055 and Fig. 4(a).
Han’s STGCN further learns based on the feature information included in the plurality of nodes, such as information about the amount of time t each user
u
i
spends at the different POI nodes. Han 1054. “[T]he user node interacted POIs provide direct evidence on the user’s preference, [and] the POI node gathers the representations of similar POIs, users and its own region.” Han 1054. Note that this is only one example of feature information included in the nodes that the STGCN uses to learn the set of transformation matrices. In addition to the foregoing, the nodes of the multigraph aggregate a diverse set of information as features for consideration, per equation (3). Han 1054.
And finally, Han’s STGCN also learns based on the edges connecting the plurality of nodes, at least because it uses the edges to propagate the feature information. See Han 1054.
and predict a relationship between the plurality of nodes in the region mobility graph;
After propagating L layers, we obtain the representations of users, POIs and regions at time t. To utilize the user periodic patterns for POIs and regions, while taking advantage of previous work, we propose the following score function:
y
p
r
e
d
u
,
p
,
t
=
e
u
t
⊤
e
p
t
+
α
e
u
t
⊤
e
l
p
+
β
e
p
t
⊤
e
l
u
+
γ
e
l
u
⊤
e
l
p
,” where “[t]he first part can represent user temporal POI interests, the second part can represent user temporal region interests, the third part is POI influence areas similar to [9], and the last part represent user and POI distance.” Han 1054.
wherein the edge connecting the plurality of nodes is identified based on at least one of information on a number of times of movement of the at least one user between the plurality of identified nodes or information on a physical distance between the plurality of nodes.
In the multigraph, an “edge
e
i
,
j
connect[s] POI nodes
p
i
and
p
j
if users visit POI
p
i
and POI
p
j
during a time period
t
. The weight
w
of the edge
e
i
,
j
is the number of co-visits
p
i
and
p
j
in a time period
t
. The POI-POI edges point the proximity between the POIs.” Han 1054.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Hopcraft’s model with Han’s learning method and architecture of a semantic-time based graph convolutional network. One would have been motivated to use Han’s GCN because “the flexible propagation mechanism of GCNs” make them well suited “to learn the representations of each node.” Han 1052.
Claim 2
Hopcraft, as combined with Han, teaches the method according to claim 1, wherein the obtaining the region mobility graph comprises:
obtaining a place mobility
“At step 406, geolocation application 120 may retrieve network traffic data associated with the mobile device of interest,” such as “location update or total location update (which includes different types of location update such as location area update), periodic location update, attaching location update,” each of which “may help reflect a specific aspect of mobility behavior of the subscribers.” Hopcraft ¶ 42.
obtaining a road network graph including a plurality of second nodes corresponding to intersections and a second edge connecting the plurality of second nodes, based on the road information in the region;
“[G]eolocation application 120 may transform at least a portion of the loaded road network into a directed graph of valid travel options. During this transformation, road intersections in the road network become vertices in the generated graph data structure. Additionally, in one embodiment of the present invention, any two nodes in the graph are connected by edges if: the corresponding segments in the road network are connected at a common vertex and if there is no turn restriction prohibiting traversal between the corresponding segments in the road network.” Hopcraft ¶ 41.
Hopcraft does not explicitly disclose formatting its network traffic data (which corresponds to at least some of the claimed “place mobility” data) as a “graph” data structure.
Han, however, teaches a method comprising:
obtaining a place mobility graph corresponding to the region, the place mobility graph including a plurality of first nodes corresponding to the plurality of places, respectively, and a first edge connecting the plurality of nodes, the place mobility graph further including the movement information of the at least one user between the plurality of places;
“To fuse the spatial-temporal context information, we propose the user record multigraph, as shown in Figure 3,” Han 1053, which includes a plurality of POI nodes
P
(e.g.,
p
i
and
p
j
), and a plurality of edges connecting the POI nodes, e.g., “edge
e
i
,
j
connects
p
i
and
p
j
if users visit POI
p
i
and POI
p
j
during a time period.” Han 1054.
obtaining a road network graph including a plurality of second nodes
“Region-Region edges connect region nodes if two regions are geographically close to each other.” Han 1054. Each region is represented in the graph as nodes
L
=
l
1
,
l
2
,
…
,
l
Z
. Han 1053; see also Fig. 3.
The multigraph shown in Fig. 3 is then created, by fusing the POI-POI graph to the Region-Region graph via POI-Region edges
e
i
,
j
, each of which represents POI
p
i
located in region
l
j
. Han 1054.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to represent the mobility data in Hopcraft’s method as a graph, in order to fuse that known graph with Hopcraft’s known road network graph, as taught by Han. One would have been motivated to represent the mobility data in Hopcraft’s method as a graph because unifying all of the data in the same format allows us to model spatio-temporal patterns together, which is important, because “different users may prefer the same POI in different time periods.” Han Abstract.
Claim 3
Hopcraft and Han teach the method according to claim 2, wherein the obtaining the region mobility graph further comprises:
identifying, as a node of the region mobility graph, an area defined by the plurality of second nodes and the second edge of the road network graph; and
“[G]eolocation application 120 may transform at least a portion of the loaded road network into a directed graph of valid travel options. During this transformation, road intersections in the road network become vertices in the generated graph data structure. Additionally, in one embodiment of the present invention, any two nodes in the graph are connected by edges if: the corresponding segments in the road network are connected at a common vertex and if there is no turn restriction prohibiting traversal between the corresponding segments in the road network.” Hopcraft ¶ 41.
identifying, as an edge of the region mobility graph, movement information of the at least one user between a plurality of identified nodes and information on a physical distance between the plurality of identified nodes.
“FIG. 2B shows the arrangement of vertices in the graph 280,” and as shown, “a vertex 220 has vertices 210, 218, 235 and 252 as adjacent vertices, and the distances to the adjacent vertices are 5, 15, 14 and 12, respectively.” Hopcraft ¶ 35.
Claim 4
Hopcraft and Han teach the method according to claim 3,
wherein, in the region mobility graph, each of the plurality of identified nodes comprises information on a place, with respect to at least one place located in an area corresponding to each of the plurality of nodes, as the feature information, and wherein the information on the place comprises location information of the place and category information corresponding to the place.
Each of the POIs in
P
include both information about the POI itself, as well as its location. Han 1053. In some instances—e.g., when using the Gowalla dataset—the POIs include data about “POI, time, and POI location,” as well as “seven categories of POIs.” Han 1055.
Claim 5
Hopcraft and Han teach the method according to claim 2, wherein the obtaining the region mobility graph further comprises:
identifying, as a node of the region mobility graph, the plurality of second nodes of the road network graph; and
[G]eolocation application 120 may transform at least a portion of the loaded road network into a directed graph of valid travel options. During this transformation, road intersections in the road network become vertices in the generated graph data structure
identifying, as an edge of the region mobility graph, movement information of the at least one user between a plurality of identified nodes
“Additionally, in one embodiment of the present invention, any two nodes in the graph are connected by edges if: the corresponding segments in the road network are connected at a common vertex and if there is no turn restriction prohibiting traversal between the corresponding segments in the road network.” Hopcraft ¶ 41.
The above rule on turn restrictions falls within the broadly claimed scope of “movement information of the at least one user between a plurality of identified nodes” because the rules provide information about all of the users—they define where and how any user (and thus “at least one”) is allowed to travel within the graph.
and the information on a physical distance between the plurality of identified nodes.
“FIG. 2B shows the arrangement of vertices in the graph 280,” and as shown, “a vertex 220 has vertices 210, 218, 235 and 252 as adjacent vertices, and the distances to the adjacent vertices are 5, 15, 14 and 12, respectively.” Hopcraft ¶ 35.
Claim 6
Hopcraft and Han teach the method according to claim 3,
wherein in the region mobility graph, each of the plurality of identified nodes comprises information on a place, with respect to at least one place related to a location corresponding to each of the plurality of identified nodes, as feature information.
Each of the POIs in
P
include both information about the POI itself, as well as its location. Han 1053. In some instances—e.g., when using the Gowalla dataset—the POIs include data about “POI, time, and POI location,” as well as “seven categories of POIs.” Han 1055.
Claim 7
Hopcraft and Han teach the method according to claim 1,
wherein the learning comprises obtaining an embedding vector for predicting an edge included in the region mobility graph by learning the region mobility graph through the GCN model.
Han uses the STGCN to learn representations of each node, allowing us to obtain embeddings of
e
u
t
and
e
p
t
for user u with POI p respectively, at time t,
e
l
u
and
e
l
p
for the user with POI location. These embeddings are then combinable into a single embedding
e
u
t
⊤
+
α
e
u
t
⊤
e
l
p
+
β
e
p
t
⊤
e
l
u
+
γ
e
l
u
⊤
e
l
p
that is usable to obtain predictions
y
p
r
e
d
u
,
p
,
t
about a given user u with respect to a given POI p at time t. Han 1054. The first term of the embedding represents user temporal POI interests, the second term represents user temporal region interests, the third term is for POI influence areas, and the last term represents user and POI distance. Han 1054. (α, β, and γ are tunable weights).
Claim 8
Hopcraft and Han teach the method according to claim 7,
wherein the obtaining the embedding vector comprises obtaining the embedding vector by using the GCN model configured to learn the region mobility graph by giving a weight to the physical distance between the plurality of nodes of the region mobility graph.
As a reminder, in Han’s multigraph, there are nodes for every user, POI, and region, and when building the STGCN, information propagates across the nodes of the multigraph such that each node aggregates information from its neighbors according to equation (3). Han 1054. Since the “Region-Region edges connect region nodes if two regions are geographically close to each other,” Han 1054, information from a given region’s neighbors has a greater influence over the given region’s node representation than information propagated from more distant regions, that are more than one hop away from the given region.
Claims 9–15
Claims 9–15 recite a server with a memory and processor that performs exactly the same server control method set forth in corresponding claims 1–7. Therefore, claims 9–15 are rejected over the same findings and rationale as provided above for those claims.
II. Hopcraft, Han, and Liu teach claims 16 and 17
Claims 16 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Hopcraft and Han as applied to claims 1 and 9 above, and further in view of Chang Liu, Chen Gao, Depeng Jin, and Yong Li, Improving Location Recommendation with Urban Knowledge Graph (Nov. 1, 2021) https://doi.org/10.48550/arXiv.2111.01013
Claim 16
Hopcraft and Han teach the method according to claim 7, but neither give a greater weight to a movement between two nodes of the region mobility graph having a greater physical distance.
Liu, however, teaches a method for obtaining an embedding vector including:
obtaining the embedding vector by using the GCN model configured to learn the region mobility graph by giving a greater weight to a movement between two nodes of the region mobility graph having a greater physical distance.
As shown in Figure 2 (page 4), Liu proposes obtaining embeddings
u
i
from a graph describing both the geography and functions of POIs in the real world, but then weighting them using a counterfactual learning model, to “eliminate the geographical bias” in the initial embeddings that result from users biasing POIs that are nearby, even if those POIs would not ordinarily be their first choice. Liu page 5. Specifically, using equation (16), Liu calculates a debiased interaction prediction score between user
u
i
and POI
p
j
as the difference between the total effect (“TE”) (expressed as
Y
u
i
,
p
j
,
g
j
) and the natural direct effect (“NDE”) (expressed as
Y
u
i
,
p
*
j
,
g
j
) of the geography of a POI on the probability that a user will interact with a POI. By subtracting the second part, the model effectively removes the “bonus points” a POI gets just for being close to the user, such that a very distant POI will have a very small value for
Y
u
i
,
p
*
j
,
g
j
, and yield a relatively higher final score if the user actually interacts with it, as opposed to a closer POI of the same nature.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to improve Hopcraft and Han’s combined GCN model with Liu’s counterfactual learning model, so as to give greater weight to POIs that are further from a user in the embeddings. There would have been a reasonable expectation of success in the combination because Liu explicitly instructs the person of ordinary skill to apply the counterfactual learning model to a GCN layer. And such a person would have been motivated to combine Liu with Hopcraft because the close proximity of POIs in a model sometimes “serves as a confounder” of the users’ true intentions or desires, which “will not only make it difficult to accurately match users’ interests but drastically damage the performance of recommender systems.” Liu 1.
Claim 17
Claim 17 is rejected over the same findings and rationale as provided above for claim 16.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 Justin R. Blaufeld whose telephone number is (571)272-4372. The examiner can normally be reached M-F 9:00am - 4:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James K Trujillo can be reached at (571) 272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Justin R. Blaufeld
Primary Examiner
Art Unit 2151
/Justin R. Blaufeld/Primary Examiner, Art Unit 2151
1 Claim 7 bears a status identifier of “Original,” but contains amendment notation. Given its context among the rest of the amendments, the Examiner is exercising his discretion to treat claim 7 as amended.
Prosecution Insights