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 .
Status of Claims
This Office Action is in response to the communication filed on 17 November 2023.
Claims 1-30 are being considered on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 16 Feb 2026 and 13 May 2024 have been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, initialed and dated copies of Applicant's IDS forms 1499 are attached to the instant Office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101
Regarding Claim 1:
Step 1: Independent claim 1 recites a method and therefore falls under one of the four statutory categories of patent-eligible subject matter.
Step 2A Prong 1:
wherein the urban graph data comprises a node set, an edge set, and a feature set, the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region, the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprises other nodes in the node set that are connected to the central node via an edge, the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region, and the feature set comprises node features of nodes in the node set; (Mental process: Creating node, edge, and feature sets is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can create graph data by writing down nodes, edges, and features corresponding to urban entities using a pen and paper.)
partitioning the target region into at least two sub-regions to obtain a region partition set; (Mental process: partitioning a region into at least two sets is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can partition a target region by drawing line down a drawing of the region to create a partition.)
updating a node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data. (Mental process: Updating a node feature is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by crossing out a feature label on a drawn central node graph and writing a new one.)
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
A data generation method comprising: obtaining urban graph data of a predetermined region, (insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network – See MPEP § 2106.05(g))
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network – See MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
A data generation method comprising: obtaining urban graph data of a predetermined region, (Insignificant Extra Solution Activity: Receiving or transmitting data over a network is well-understood, routine, conventional activity – see Berkheimer evidence MPEP § 2106.05(d))
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Insignificant Extra Solution Activity: Receiving or transmitting data over a network is well-understood, routine, conventional activity – see Berkheimer evidence MPEP § 2106.05(d))
Regarding claim 2:
Step 2A Prong 1: See the rejection of claim 1 above. The same rationale applies to this dependent claim.
The data generation method according to claim 1, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including to result in one-to-one correspondence.)
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including corresponding to different heads of an M-head region.)
the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines. (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including between different partition lines.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claim 3:
Step 2A Prong 1: See the rejection of claim 2 above. The same rationale applies to this dependent claim.
The data generation method according to claim 2, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in a fan-shape.)
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in ring shapes.)
wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region, (Mental process: partitioning a region with a target position is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including pre-determining a street in a neighborhood in a city.)
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including into a first and second region groups.)
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Mental process: partitioning a region with different parameters is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different parameters.)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition. (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different distance parameters.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claim 4:
Step 2A Prong 1: See the rejection of claim 3 above. The same rationale applies to this dependent claim.
The data generation method according to claim 3, wherein the updating the node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain the target feature data comprises: fusing the regional features of the sub-regions in each first region group to obtain M first feature data in one-to-one correspondence with the M region partition subsets; (Mental process: updating a node representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can erase partition lines in a region to fuse features.)
fusing the regional features of the sub-regions in each second region group to obtain M second feature data in one-to-one correspondence with the M region partition subsets; and (Mental process: updating a node representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can erase partition lines in a region to fuse features.)
updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data. (Mental process: updating a node feature representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by crossing out a feature label on a labeled node and writing a new label.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claim 5:
Step 2A Prong 1: See the rejection of claim 4 above. The same rationale applies to this dependent claim.
The data generation method according to claim 4, wherein the fusing the regional features of the sub-regions in each first region group to obtain the M first feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each first region group to obtain the M first feature data. (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claim 6:
Step 2A Prong 1: See the rejection of claim 1 above. The same rationale applies to this dependent claim.
The data generation method according to claim 4, wherein the fusing the regional features of the sub-regions in each second region group to obtain the M second feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each second region group to obtain the M second feature data. (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claim 7:
Step 2A Prong 1: See the rejection of claim 1 above. The same rationale applies to this dependent claim.
The data generation method according to claim 4, wherein the updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data comprises: performing a feature concatenation on the M first feature data to obtain a first updated feature; (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
performing a feature concatenation on the M second feature data to obtain a second updated feature; and (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
performing a weighted summation on the first updated feature and the second updated feature to obtain the target feature data (Mental process: Performing weighted summation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can perform weighted summation entirely in their minds or with the assistance of their pen and paper.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements.
Regarding claims 8:
Step 1: Independent claim 8 recites a system and therefore falls under one of the four statutory categories of patent-eligible subject matter.
Step 2A Prong 1:
A model training method comprising: obtaining urban graph data of a predetermined region, wherein the urban graph data comprises a node set, an edge set, and a feature set, wherein the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region, the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprising other nodes in the node set that are connected to the central node via an edge, and the feature set comprises node features of nodes in the node set, and wherein the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region; (Mental process: Creating node, edge, and feature sets is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can create graph data by writing down nodes, edges, and features corresponding to urban entities using a pen and paper.)
updating the node feature of each central node in the feature set to obtain the target feature set, the target feature set comprising target feature data of each node in the node set, wherein the updating the node feature of each central node in the feature set to obtain the target feature set comprises: partitioning the target region into at least two sub-regions to obtain a region partition set; (Mental process: updating a node representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can erase partition lines in a region to fuse features.)
updating a node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain the target feature data; and (Mental process: updating a node feature representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by crossing out a feature label on a labeled node and writing a new label.)
wherein the target model is used for generating a score value of a predetermined urban indicator, and the predetermined urban indicator is an urban indicator associated with the predetermined urban entities (Mental process: generating a score value is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a “model”, nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can arbitrarily assign a numerical score to an indicator entirely in their mind or with a pen and paper.)
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network – See MPEP § 2106.05(g))
training a pre-constructed initial urban indicator generation model based on the node set, the edge set, and the target feature set to obtain a target model, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Insignificant Extra Solution Activity: Receiving or transmitting data over a network is well-understood, routine, conventional activity – see Berkheimer evidence MPEP § 2106.05(d))
training a pre-constructed initial urban indicator generation model based on the node set, the edge set, and the target feature set to obtain a target model, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Regarding claims 9:
Step 2A Prong 1: See the rejection of claim 8 above. The same rationale applies to this dependent claim.
The model training method according to claim 8, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including to result in one-to-one correspondence.)
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including corresponding to different heads of an M-head region.)
the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including between different partition lines.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements
Regarding claims 10:
Step 2A Prong 1: See the rejection of claim 9 above. The same rationale applies to this dependent claim.
The model training method according to claim 9, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in a fan-shape.)
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in ring shapes.)
wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region, (Mental process: partitioning a region with a target position is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including pre-determining a street in a neighborhood in a city.)
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including into a first and second region groups.)
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Mental process: partitioning a region with different parameters is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different parameters.)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition. (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different distance parameters.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements
Regarding claims 11:
Step 2A Prong 1: See the rejection of claim 10 above. The same rationale applies to this dependent claim.
The model training method according to claim 10, wherein the updating the node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain the target feature data comprises: fusing the regional features of the sub-regions in each first region group to obtain M first feature data in one-to-one correspondence with the M region partition subsets; (Mental process: updating a node representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can erase partition lines in a region to fuse features.)
fusing the regional features of the sub-regions in each second region group to obtain M second feature data in one-to-one correspondence with the M region partition subsets; and (Mental process: updating a node representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can erase partition lines in a region to fuse features.)
updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data (Mental process: updating a node feature representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by crossing out a feature label on a labeled node and writing a new label.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements
Regarding claims 12:
Step 2A Prong 1: See the rejection of claim 11 above. The same rationale applies to this dependent claim.
The model training method according to claim 11, wherein the fusing the regional features of the sub-regions in each first region group to obtain the M first feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each first region group to obtain the M first feature data (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
Step 2A Prong 2 and step 2B: The claim does not include additional elements
Regarding claims 13:
Step 2A Prong 1: See the rejection of claim 9 above. The same rationale applies to this dependent claim.
The model training method according to claim 11, wherein the fusing the regional features of the sub-regions in each second region group to obtain the M second feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each second region group to obtain the M second feature data (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
Step 2A Prong 2 and step 2B: The claim does not include additional elements
Regarding claims 14:
Step 2A Prong 1: See the rejection of claim 11 above. The same rationale applies to this dependent claim.
The model training method according to claim 11, wherein the updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data comprises: performing a feature concatenation on the M first feature data to obtain a first updated feature; (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
performing a feature concatenation on the M second feature data to obtain a second updated feature; and (Mental process: Concatenation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by concatenation.)
performing a weighted summation on the first updated feature and the second updated feature to obtain the target feature data (Mental process: Performing weighted summation of features representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can perform weighted summation entirely in their minds or with the assistance of their pen and paper)
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
wherein one or more computing devices train the first one or more neural networks in response to a change in computing resources identified to perform inference operations of the second one or more neural networks. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
wherein one or more computing devices train the first one or more neural networks in response to a change in computing resources identified to perform inference operations of the second one or more neural networks. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Regarding claims 15:
Step 1: Independent claim 15 recites a device and therefore falls under one of the four statutory categories of patent-eligible subject matter.
Step 2A Prong 1:
partitioning the target region into at least two sub-regions to obtain a region partition set; (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including to result in one-to-one correspondence.)
updating a node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data (Mental process: updating a node feature representing a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can update a node feature by crossing out a feature label on a labeled node and writing a new label.)
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
An electronic device comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform a data generation method, comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
obtaining urban graph data of a predetermined region, wherein the urban graph data comprises a node set, an edge set, and a feature set, the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region, the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprises other nodes in the node set that are connected to the central node via an edge, the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region, and the feature set comprises node features of nodes in the node set; (Insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network – See MPEP § 2106.05(g))
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Insignificant extra-solution activity to the judicial exception: Receiving or transmitting data over a network – See MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
An electronic device comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform a data generation method, comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
obtaining urban graph data of a predetermined region, wherein the urban graph data comprises a node set, an edge set, and a feature set, the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region, the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprises other nodes in the node set that are connected to the central node via an edge, the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region, and the feature set comprises node features of nodes in the node set; (Insignificant Extra Solution Activity: Receiving or transmitting data over a network is well-understood, routine, conventional activity – see Berkheimer evidence MPEP § 2106.05(d))
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Insignificant Extra Solution Activity: Receiving or transmitting data over a network is well-understood, routine, conventional activity – see Berkheimer evidence MPEP § 2106.05(d))
Regarding claims 16:
Step 2A Prong 1: See the rejection of claim 15 above. The same rationale applies to this dependent claim.
The electronic device according to claim 15, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including to result in one-to-one correspondence.)
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including corresponding to different heads of an M-head region.)
the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines. (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including between different partition lines.)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements
Regarding claims 17:
Step 2A Prong 1: See the rejection of claim 16 above. The same rationale applies to this dependent claim.
The electronic device according to claim 16, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in a fan-shape)
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region, Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including in ring shapes
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including into a first and second region groups)
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Mental process: partitioning a region with different parameters is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different parameters)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition (Mental process: partitioning a region is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind; nothing in this claim element precludes the step from practically being performed in the mind. For example, a person can draw lines across a region to partition it in any manner including using different distance parameters)
Step 2A Prong 2 and Step 2B: The claim does not include additional elements
Regarding claims 18:
Step 2A Prong 1: See the rejection of claim 8 above. The same rationale applies to this dependent claim.
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
An electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform the steps of the method according to claim 8 ((Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
An electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform the steps of the method according to claim 8 (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Regarding claims 19:
Step 2A Prong 1: See the rejection of claim 1 above. The same rationale applies to this dependent claim.
Step 2A Prong 2: The additional elements integrate the judicial exception into practical application.
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 1 (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 1 (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Regarding claims 20:
Step 2A Prong 1: See the rejection of claim 8 above. The same rationale applies to this dependent claim.
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 8. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 8 (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8-9,15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Schloegel, et. al. (US 2013/0142438 A1; hereinafter, “Schloegel”) in view of Hanna, Sean. (“Comparative Analysis of Neighbourhoods using local graph spectra”, 2012, https://sss8.cl/8034.pdf; hereinafter, “Hanna”).
Regarding claim 1, Schoegel and Hanna teach:
A data generation method comprising: obtaining urban graph data of a predetermined region, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
wherein the urban graph data comprises a node set, an edge set, (Schoegel, para. 0026 and fig. 3: “ FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.”) and a feature set, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region (Hanna, pg. 8024-2: “a method is proposed to incorporate geometry through street intersection angles, and rather than using entire cities of arbitrary size, subgraphs with a fixed node count are sampled from the whole, corresponding to small neighbourhoods within a circular region around a central node.”), the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprises other nodes in the node set that are connected to the central node via an edge (Schoegel, para. 0027: “Each of the edges 304a-304g may include constraints that define conditions associated with the relationship of one node to another. For example, a particular edge between two nodes may represent a physical barrier, such as a wall or other obstacle that would normally hinder movement of a target from one sub-region 206 to another” Examiner notes Schoegel teaches connections of edges from one node to another, including any central node as taught by Hanna), the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region (Hanna, pg. 8034-8 and fig. 4: “Figure 4: London neighbourhood samples (ρ = 200) evaluated as being most characteristic (top) and least characteristic (bottom) of the class of London spectra as opposed to all other cities.” Examiner notes Hanna teaches a target region a neighborhood in London and urban entities as neighborhoods of cities as compared to those in London), and the feature set comprises node features of nodes in the node set; (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
partitioning the target region into at least two sub-regions to obtain a region partition set; (Schoegel, para. 0017: “ The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.”)
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions. An example of an aggregated sensor result may include the average number of tracks 106 detected in a region over a certain time period. Another example of an aggregated sensor result is the change in velocity of the tracks 106 detected in a region for a certain time period”)
updating a node feature of the central node (Hanna, pg. 8024-2, above) based on the regional features of the sub-regions in the region partition set to obtain target feature data. (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel. Schoegel teaches a system and method for geospatial partitioning of a geographical region; Hanna teaches a method for determining existing Space Syntax measures such as choice. One of ordinary skill would have been motivated to combine the teachings of Hanna into Schoegel in order to allow comparisons to be made at a greater level of detail, both between and within cities (Hanna, abstract).
Regarding claim 2, Schoegel and Hanna teach:
The data generation method according to claim 1, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.” Examiner notes Schoegel teaches a partition manner based on similarity of sensor results and/or domain-specific constraints)
wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Schoegel, para. 0026: “FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.” Examiner notes Schoegel teaches multiple sub-regions represented by each node i.e. “M-head” where each sub-region is distinctly represented by one node in an one-to-one correspondence).
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines. (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
Regarding Claim 8, Schoegel as modified teaches:
A model training method comprising: obtaining urban graph data of a predetermined region, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
wherein the urban graph data comprises a node set, an edge set, (Schoegel, para. 0026 and fig. 3: “ FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.”) and a feature set, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
wherein the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region (Hanna, pg. 8024-2: “a method is proposed to incorporate geometry through street intersection angles, and rather than using entire cities of arbitrary size, subgraphs with a fixed node count are sampled from the whole, corresponding to small neighbourhoods within a circular region around a central node.”), the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprising other nodes in the node set that are connected to the central node via an edge (Schoegel, para. 0027: “Each of the edges 304a-304g may include constraints that define conditions associated with the relationship of one node to another. For example, a particular edge between two nodes may represent a physical barrier, such as a wall or other obstacle that would normally hinder movement of a target from one sub-region 206 to another” Examiner notes Schoegel teaches connections of edges from one node to another, including any central node as taught by Hanna), and the feature set comprises node features of nodes in the node set, and (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
wherein the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region; (Hanna, pg. 8034-8 and fig. 4: “Figure 4: London neighbourhood samples (ρ = 200) evaluated as being most characteristic (top) and least characteristic (bottom) of the class of London spectra as opposed to all other cities.” Examiner notes Hanna teaches a target region a neighborhood in London and urban entities as neighborhoods of cities as compared to those in London)
updating the node feature of each central node (Hanna, pg. 8024-2, above) in the feature set to obtain the target feature set, the target feature set comprising target feature data of each node in the node set, (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered)
wherein the updating the node feature of each central node in the feature set to obtain the target feature set comprises: partitioning the target region into at least two sub-regions to obtain a region partition set; (Schoegel, para. 0017: “ The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.”)
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions. An example of an aggregated sensor result may include the average number of tracks 106 detected in a region over a certain time period. Another example of an aggregated sensor result is the change in velocity of the tracks 106 detected in a region for a certain time period”)
updating a node feature of the central node (Hanna, pg. 8024-2, above) based on the regional features of the sub-regions in the region partition set to obtain the target feature data; and (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered)
training a pre-constructed initial urban indicator generation model based on the node set, the edge set, and the target feature set to obtain a target model, (Schoegel, para. 0007 and 0020: “The computer program also includes computer readable program code for generating a graph having multiple nodes and multiple edges. The nodes represent the sub-regions, and the edges couple related nodes. The computer program further includes computer readable program code for geospatially partitioning the geographical region using the graph.” “As another example, although one example geographical region 104 having a particular terrain is shown, other types of terrains, such as wilderness regions, undeveloped regions, urban regions, or sub-urban regions may be partitioned according to the teachings of this disclosure” Examiner notes Schoegel teaches urban regions i.e. an urban indicator)
wherein the target model is used for generating a score value of a predetermined urban indicator, and the predetermined urban indicator is an urban indicator associated with the predetermined urban entities. (Schoegel, para. 0027: “In the former case, the edge representing a physical barrier could have a relatively high constraint value, while the edge representing the pathway could have a relatively low constraint value.” Examiner notes Schoegel teaches a constraint value which is a value scoring the movement from one sub-region to another in a region i.e. a score value associated with an urban region and urban entities).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as modified, as set forth above with respect to claim 1.
Regarding Claim 9, Schoegel as modified teaches:
The model training method according to claim 8, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.” Examiner notes Schoegel teaches a partition manner based on similarity of sensor results and/or domain-specific constraints)
wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Schoegel, para. 0026: “FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.” Examiner notes Schoegel teaches multiple sub-regions represented by each node i.e. “M-head” where each sub-region is distinctly represented by one node in an one-to-one correspondence).
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines. (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
Regarding Claim 15, Schoegel as modified teaches:
An electronic device comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform a data generation method, comprising: (Schoegel, para. 0039: “As shown in FIG. 4, the partitioning apparatus 400 includes at least one processing unit 402, at least one memory unit 404, at least one interface 406, a display 408, and at least one input device 410. The processing unit 402 represents any suitable processing device(s), such as a microprocessor, microcontroller, digital signal processor, application-specific integrated circuit, field programmable gate array, or other logic device”)
obtaining urban graph data of a predetermined region, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
wherein the urban graph data comprises a node set, an edge set, (Schoegel, para. 0026 and fig. 3: “ FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.”) and a feature set, (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”)
the node set comprises a central node corresponding to a predetermined urban entity in the predetermined region, the edge set comprises a neighborhood corresponding to the central node, the neighborhood comprises other nodes in the node set that are connected to the central node via an edge (Schoegel, para. 0027: “Each of the edges 304a-304g may include constraints that define conditions associated with the relationship of one node to another. For example, a particular edge between two nodes may represent a physical barrier, such as a wall or other obstacle that would normally hinder movement of a target from one sub-region 206 to another” Examiner notes Schoegel teaches connections of edges from one node to another, including any central node as taught by Hanna), the neighborhood corresponds to one target region in the predetermined region, and predetermined urban entities corresponding to the nodes in the neighborhood are located in the target region, (Hanna, pg. 8034-8 and fig. 4: “Figure 4: London neighbourhood samples (ρ = 200) evaluated as being most characteristic (top) and least characteristic (bottom) of the class of London spectra as opposed to all other cities.” Examiner notes Hanna teaches a target region a neighborhood in London and urban entities as neighborhoods of cities as compared to those in London), and the feature set comprises node features of nodes in the node set; (Schoegel, para. 0018: “For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104.”);
partitioning the target region into at least two sub-regions to obtain a region partition set; (Schoegel, para. 0017: “ The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.”)
obtaining a regional feature of each sub-region by performing a feature aggregation on node features corresponding to all nodes located in the same sub-region; and (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions. An example of an aggregated sensor result may include the average number of tracks 106 detected in a region over a certain time period. Another example of an aggregated sensor result is the change in velocity of the tracks 106 detected in a region for a certain time period.)
updating a node feature of the central node (Hanna, pg. 8024-2, above) based on the regional features of the sub-regions in the region partition set to obtain target feature data (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as modified, as set forth above with respect to claim 1.
Regarding Claim 16, Schoegel as modified teaches:
The electronic device according to claim 15, wherein the partitioning the target region into the at least two sub-regions to obtain the region partition set comprises: performing an M-head region partition on the target region based on a target partition manner to obtain the region partition set, (Schoegel, para. 0017: “The system 100 may geospatially partition sub-regions of the geographical region 104 based upon similarity of aggregated sensor results and/or domain-specific constraints that are associated with these sub-regions.” Examiner notes Schoegel teaches a partition manner based on similarity of sensor results and/or domain-specific constraints)
wherein the region partition set comprises M region partition subsets in one-to-one correspondence with M heads of the M-head region partition, and each of the M region partition subsets comprises at least two sub-regions, and (Schoegel, para. 0026: “FIG. 3 illustrates an example graph 300 that may be generated by the geographical partitioning system 100 according to this disclosure. The graph 300 includes nodes 302a-302f representing sub-regions 206 of the geographical region 104 and edges 304a-304g defining relationships between adjacent sub-regions 206.” Examiner notes Schoegel teaches multiple sub-regions represented by each node i.e. “M-head” where each sub-region is distinctly represented by one node in an one-to-one correspondence).
wherein partition parameters corresponding to different heads of the M-head region partition are different, where M is an integer greater than 1, and the partition parameters comprise at least one of: a position parameter of a partition line in the target region, and a distance parameter between different partition lines. (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
Regarding Claim 18, Schoegel as modified teaches:
An electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to perform the steps of the method according to claim 8. (Schoegel, para. 0039 and 0050: “As shown in FIG. 4, the partitioning apparatus 400 includes at least one processing unit 402, at least one memory unit 404, at least one interface 406, a display 408, and at least one input device 410. The processing unit 402 represents any suitable processing device(s), such as a microprocessor, microcontroller, digital signal processor, application-specific integrated circuit, field programmable gate array, or other logic device” “The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code)”)
Regarding Claim 19, Schoegel as modified teaches:
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 1. (Schoegel, para. 0039 and 0050: “As shown in FIG. 4, the partitioning apparatus 400 includes at least one processing unit 402, at least one memory unit 404, at least one interface 406, a display 408, and at least one input device 410. The processing unit 402 represents any suitable processing device(s), such as a microprocessor, microcontroller, digital signal processor, application-specific integrated circuit, field programmable gate array, or other logic device” “The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code)”)
Regarding Claim 20, Schoegel, as modified teaches:
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause a computer to perform the steps of the method according to claim 8. (Schoegel, para. 0050: “The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code)”)
Claims 3-4, 10-11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Schloegel, in view of Hanna, in view of Tu, et. al., (“A spatial parallel heuristic approach for solving very large-scale vehicle routing problems”, 17, March 2017, Transactions in GIS. Doi: 10.1111/tgis.12267; hereinafter, “Tu”) and further in view of Shi, et. al. (“Visual Analysis of Steady-State Human Mobility in Cities” Hum. Cent. Comput. Inf. Sci. (2021) 11:31. 18 Jul 2021. https://doi.org/10.22967/HCIS.2021.11.031; hereinafter, “Shi”)
Regarding Claim 3, Schoegel, as modified, teaches:
The data generation method according to claim 2, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions).
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”).
wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”).
wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region, (Hanna, pg. 8024-2: “a method is proposed to incorporate geometry through street intersection angles, and rather than using entire cities of arbitrary size, subgraphs with a fixed node count are sampled from the whole, corresponding to small neighbourhoods within a circular region around a central node.”),
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions where each region is an i-th region and a subset of the M region).
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Schoegel, para. 0003: “Reconnaissance has become an important military defense activity for determining threats that may exist in a theater of battle. In many cases, reconnaissance may be provided by one or more types of sensors, such as video cameras, synthetic aperture radars (SARs), forward-looking infrared light (FLIR) devices, and other devices that detect movement or positions of potential targets in a particular geographical region.”)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition. (Hanna, pg. 8034:4: “To do so, a given street segment Va is defined as the centre of the subgraph, and distances measured to all other segments in the graph, where D(i,j) is the Euclidian distance in (x, y) coordinates from the midpoint of the segment associated with Vi to that of Vj.” Examiner notes Hanna teaches a distance parameter for each segment where each distance is measured individually and therefore different for each sub-partition)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as modified, as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Tu into Schoegel, as modified. Tu teaches a spatial partitioning strategy to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. One of ordinary skill would have been motivated to combine the teachings of Tu into Schoegel, as modified, in order to improve route segments across spatial cells to overcome the border effect (Tu, abstract).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Shi into Schoegel, as modified. Shi teaches a steady-state visual analysis of human mobility that abstracts the longitudinal trajectory of each city resident into five information-theoretic metrics. One of ordinary skill would have been motivated to combine the teachings of Shi into Schoegel, as modified, in order to efficiently visualize, profile, and compare human mobility from their long-term stay distributions (Shi, sec. 8).
Regarding Claim 4, Schoegel teaches:
The data generation method according to claim 3, wherein the updating the node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain the target feature data comprises: fusing the regional features of the sub-regions in each first region group to obtain M first feature data in one-to-one correspondence with the M region partition subsets; (Schoegel, para. 0029: “Provided with this information, the geospatial partitioning system 100 partitions the image 202 such that sub-regions 206 joined by edges 304b, 304d, 304f, and 304g having a relatively high correlation value may be combined with one another into a contiguous region having a relatively high degree of similarity. The geospatial partitioning system 100 also partitions the image 202 such that nodes 302a, 302c, and 302e having a relatively low correlation value are separated from one another to indicate a boundary between their respective sub-regions 206. All of the sub-regions 206 of the geographical region 104 may be processed with other contiguous sub-regions 206 in a similar manner to partition the image 202.”)
fusing the regional features of the sub-regions in each second region group to obtain M second feature data in one-to-one correspondence with the M region partition subsets; and (Schoegel, para. 0031: “For example, a vector of weights may be associated with each edge 304a-304g. Each element of a vector may specify the similarity of a different subset of aggregated sensor results between any sub-regions that are associated with the two incident nodes. A user may select which subset of aggregated sensor results is represented. A second vector of weights that quantifies a subset of the domain-specific constraints between two sub-regions associated with the nodes may be associated with each edge.”)
updating the node feature of the central node (Hanna, pg. 8024-2, above) based on the M first feature data and the M second feature data to obtain the target feature data. (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered).
Regarding Claim 10, Schoegel as modified:
The model training method according to claim 9, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions).
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”).
wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”)
wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region (Hanna, pg. 8024-2: “a method is proposed to incorporate geometry through street intersection angles, and rather than using entire cities of arbitrary size, subgraphs with a fixed node count are sampled from the whole, corresponding to small neighbourhoods within a circular region around a central node.”),
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions where each region is an i-th region and a subset of the M region).
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Schoegel, para. 0003: “Reconnaissance has become an important military defense activity for determining threats that may exist in a theater of battle. In many cases, reconnaissance may be provided by one or more types of sensors, such as video cameras, synthetic aperture radars (SARs), forward-looking infrared light (FLIR) devices, and other devices that detect movement or positions of potential targets in a particular geographical region.”)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition. (Hanna, pg. 8034:4: “To do so, a given street segment Va is defined as the centre of the subgraph, and distances measured to all other segments in the graph, where D(i,j) is the Euclidian distance in (x, y) coordinates from the midpoint of the segment associated with Vi to that of Vj.” Examiner notes Hanna teaches a distance parameter for each segment where each distance is measured individually and therefore different for each sub-partition)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as modified, as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Tu into Schoegel, as modified, as set forth above with respect to claim 3.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Shi into Schoegel, as modified, as set forth above with respect to claim 3.
Regarding Claim 11, Schoegel as modified teaches:
The model training method according to claim 10, wherein the updating the node feature of the central node based on the regional features of the sub-regions in the region partition set to obtain the target feature data comprises: fusing the regional features of the sub-regions in each first region group to obtain M first feature data in one-to-one correspondence with the M region partition subsets; (Schoegel, para. 0029: “Provided with this information, the geospatial partitioning system 100 partitions the image 202 such that sub-regions 206 joined by edges 304b, 304d, 304f, and 304g having a relatively high correlation value may be combined with one another into a contiguous region having a relatively high degree of similarity. The geospatial partitioning system 100 also partitions the image 202 such that nodes 302a, 302c, and 302e having a relatively low correlation value are separated from one another to indicate a boundary between their respective sub-regions 206. All of the sub-regions 206 of the geographical region 104 may be processed with other contiguous sub-regions 206 in a similar manner to partition the image 202.”)
fusing the regional features of the sub-regions in each second region group to obtain M second feature data in one-to-one correspondence with the M region partition subsets; and (Schoegel, para. 0031: “For example, a vector of weights may be associated with each edge 304a-304g. Each element of a vector may specify the similarity of a different subset of aggregated sensor results between any sub-regions that are associated with the two incident nodes. A user may select which subset of aggregated sensor results is represented. A second vector of weights that quantifies a subset of the domain-specific constraints between two sub-regions associated with the nodes may be associated with each edge.”)
updating the node feature of the central node (Hanna, pg. 8024-2, above) based on the M first feature data and the M second feature data to obtain the target feature data (Schoegel, para. 0018: “That is, once geospatial partitioned regions of the geographical region 104 have been identified, the system 100 may determine anomalies in the acquired information. For example, information associated with an urban geographical region may be gathered over a period of time to determine one or more features, such as parking lots, roadways, or other constraining features like creeks, rivers, or streams that may limit movement of targets 108 in the geographical region 104. Once these features are determined, abnormal behavior of a particular target 108 may be detected and further mined to determine potential threats, such as placement of improvised explosive devices (IEDs) or other types of activities.” Examiner notes that Schoegel teaches information being gathered such that the features are updated as more information is gathered).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as modified, as set forth above with respect to claim 1.
Regarding Claim 17, Schoegel as modified:
The electronic device according to claim 16, wherein the target partition manner comprises a first sub-partition manner and a second sub-partition manner, (Schoegel, para. 0022: “The sub-regions 206 are contiguously aligned and form potential boundaries that may be determined when the geographical region 104 is partitioned by the system 100. In some cases, the grid may be formed over the image 202 such that the sub-regions 206 have a relatively equal size and shape. In other cases, the grid may be formed over the image 202 such that the sub-regions 206 are individually sized according to objectives of the system 100”)
and an i-th head of the M-head region partition performed on the target region based on the target partition manner comprises: partitioning the target region into at least two fan-shaped sub-regions centered at a target position point based on the first sub-partition manner to obtain a first region group, wherein the first region group comprises the at least two fan-shaped sub-regions and a central sub-region, the central sub-region being a region where the target position point is located; and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions).
partitioning the target region into at least two ring-shaped sub-regions centered at the target position point based on the second sub-partition manner to obtain a second region group, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”)
wherein the second region group comprises the at least two ring-shaped sub-regions and the central sub-region, (Shi, pg. 14: “The region defines the metropolitan area of Beijing (metro in short) within the 6th ring road (the blue enclosure in Fig. 1(a), enlarged in Fig. 1(b)). The metro is centered at Tiananmen and divided into the northern and southern city by the Chang’an avenue and into five ring-shaped regions by the 2nd to 6th ring roads (Fig. 1(b)”).
wherein the target position point is a position point of the predetermined urban entity corresponding to the central node in the target region (Hanna, pg. 8024-2: “a method is proposed to incorporate geometry through street intersection angles, and rather than using entire cities of arbitrary size, subgraphs with a fixed node count are sampled from the whole, corresponding to small neighbourhoods within a circular region around a central node.”),
wherein an i-th region partition subset comprises the first region group and the second region group, and the i-th region partition subset is one of the M region partition subsets corresponding to the i-th head, and (Tu, pg. 6: “Definition 7: A fan partitioning splits region R into fan-shaped cells with many rays starting from the depot. Each ray is defined as in Equation 6, where r is the maximum distance between the customers and the depot. Two sequential rays and the arc between them form a fan-shaped area. Figure 3d illustrates an example of a six-fan division with rays every 60˚.” Examiner notes figure 3d illustrates a center with 6 fan-shaped regions where each region is an i-th region and a subset of the M region).
wherein the position parameter of the first sub-partition manner is different for different heads of the M-head region partition, and (Schoegel, para. 0003: “Reconnaissance has become an important military defense activity for determining threats that may exist in a theater of battle. In many cases, reconnaissance may be provided by one or more types of sensors, such as video cameras, synthetic aperture radars (SARs), forward-looking infrared light (FLIR) devices, and other devices that detect movement or positions of potential targets in a particular geographical region.”)
the distance parameter of the second sub-partition manner is different for different heads of the M-head region partition. (Hanna, pg. 8034:4: “To do so, a given street segment Va is defined as the centre of the subgraph, and distances measured to all other segments in the graph, where D(i,j) is the Euclidian distance in (x, y) coordinates from the midpoint of the segment associated with Vi to that of Vj.” Examiner notes Hanna teaches a distance parameter for each segment where each distance is measured individually and therefore different for each sub-partition)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Hanna into Schoegel, as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Tu into Schoegel, as modified, as set forth above with respect to claim 3.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Shi into Schoegel, as modified, as set forth above with respect to claim 3.
Claims 5-7, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Schloegel, in view of Hanna, in view of Tu, in view of Shi, and further in view of Zhou, Y., et al. (“Multi-directional feature refinement network for real-time semantic segmentation in urban street scenes.” IET Comput. Vis. 17(4), 431–444 (2023). https://doi.org/10.1049/cvi2.12178; hereinafter “Zhou”)
Regarding Claim 5, Schoegel, as modified, teaches:
The data generation method according to claim 4, wherein the fusing the regional features of the sub-regions in each first region group to obtain the M first feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each first region group to obtain the M first feature data. (Zhou, sec. 4.2.5: “The performance achieves to 74.7% mIoU when we use ordinary summation to aggregate the output feature of detail branch and semantic branch. We simply concatenate the feature information of the two paths, our method achieves the mIoU 74.9%.” Examiner notes Zhou teaches concatenating feature information of two paths i.e. where each path may belong to a sub-region in region as taught by where Schoegel)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified. Zhou teaches a Multi-directional Feature Refinement Module (MFRM) which has three sub-paths to capture information at different scales and directions is proposed. One of ordinary skill would have been motivated to combine the teachings of Zhou into Schoegel, as modified, in order to guide extraction of feature maps in a more precise direction (Zhou, abstract).
Regarding Claim 6, Schoegel, as modified, teaches:
The data generation method according to claim 4, wherein the fusing the regional features of the sub-regions in each second region group to obtain the M second feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each second region group to obtain the M second feature data. (Zhou, sec. 4.2.5: “The performance achieves to 74.7% mIoU when we use ordinary summation to aggregate the output feature of detail branch and semantic branch. We simply concatenate the feature information of the two paths, our method achieves the mIoU 74.9%.” Examiner notes Zhou teaches concatenating feature information of two paths i.e. where each path may belong to a sub-region in region as taught by where Schoegel)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified, as set forth above with respect to claim 5.
Regarding Claim 7, Schoegel as modified teaches:
The data generation method according to claim 4, wherein the updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data comprises: performing a feature concatenation (Zhou, sec. 4.2.5) on the M first feature data to obtain a first updated feature; (Shoegel, sec. 0036: “ In a dynamic scenario in which sensor data is streamed to the system 100, the graph 300 may be dynamically repartitioned by continually updating certain nodes, edge weights, and constraints of edges. After the graph 300 is updated, a multi-objective, multi-constraint graph repartitioning algorithm can be used to compute an updated geospatial partitioning while attempting to minimize the difference between the previous partitioning and the new partitioning” Examiner notes Zhao teaches feature concatenation and Schoegel teaches continuous updating, including constraints of edges and repartitioning i.e. continuous updating after such concatenation).
performing a feature concatenation (Zhou, sec. 4.2.5) on the M second feature data to obtain a second updated feature; and (Shoegel, sec. 0036: “ In a dynamic scenario in which sensor data is streamed to the system 100, the graph 300 may be dynamically repartitioned by continually updating certain nodes, edge weights, and constraints of edges. After the graph 300 is updated, a multi-objective, multi-constraint graph repartitioning algorithm can be used to compute an updated geospatial partitioning while attempting to minimize the difference between the previous partitioning and the new partitioning” Examiner notes Zhao teaches feature concatenation and Schoegel teaches continuous updating, including constraints of edges and repartitioning i.e. continuous updating after such concatenation).
performing a weighted summation on the first updated feature and the second updated feature to obtain the target feature data. (Schoegel, para. 0046: “In some embodiments, nodes may be associated with weighting values according to one or more criteria. For example, nodes associated with certain sub-regions 206 may be weighted according to a priori knowledge about these sub-regions 206. As another example, the nodes representing certain sub-regions 206 may be weighted according to information about these sub-regions 206 or tracks acquired from other sources. Additionally, weighting values may be adjusted according to objectives or desired information to be obtained from the process. In some embodiments, additional weighting may be applied based on characteristics of the tracks, such as the speed, acceleration, and/or direction of the tracks.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified, as set forth above with respect to claim 5.
Regarding Claim 12, Schoegel as modified teaches:
The model training method according to claim 11, wherein the fusing the regional features of the sub-regions in each first region group to obtain the M first feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each first region group to obtain the M first feature data. (Zhou, sec. 4.2.5: “The performance achieves to 74.7% mIoU when we use ordinary summation to aggregate the output feature of detail branch and semantic branch. We simply concatenate the feature information of the two paths, our method achieves the mIoU 74.9%.” Examiner notes Zhou teaches concatenating feature information of two paths i.e. where each path may belong to a sub-region in region as taught by where Schoegel)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified, as set forth above with respect to claim 5.
Regarding Claim 13, Schoegel as modified teaches:
The model training method according to claim 11, wherein the fusing the regional features of the sub-regions in each second region group to obtain the M second feature data comprises: performing a feature concatenation on the regional features of the sub-regions in each second region group to obtain the M second feature data. (Zhou, sec. 4.2.5: “The performance achieves to 74.7% mIoU when we use ordinary summation to aggregate the output feature of detail branch and semantic branch. We simply concatenate the feature information of the two paths, our method achieves the mIoU 74.9%.” Examiner notes Zhou teaches concatenating feature information of two paths i.e. where each path may belong to a sub-region in region as taught by where Schoegel)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified, as set forth above with respect to claim 5.
Regarding Claim 14, Schoegel as modified teaches:
The model training method according to claim 11, wherein the updating the node feature of the central node based on the M first feature data and the M second feature data to obtain the target feature data comprises: performing a feature concatenation (Zhou, sec. 4.2.5) on the M first feature data to obtain a first updated feature; (Shoegel, sec. 0036: “ In a dynamic scenario in which sensor data is streamed to the system 100, the graph 300 may be dynamically repartitioned by continually updating certain nodes, edge weights, and constraints of edges. After the graph 300 is updated, a multi-objective, multi-constraint graph repartitioning algorithm can be used to compute an updated geospatial partitioning while attempting to minimize the difference between the previous partitioning and the new partitioning” Examiner notes Zhao teaches feature concatenation and Schoegel teaches continuous updating, including constraints of edges and repartitioning i.e. continuous updating after such concatenation).
performing a feature concatenation (Zhou, sec. 4.2.5) on the M second feature data to obtain a second updated feature; and (Shoegel, sec. 0036: “ In a dynamic scenario in which sensor data is streamed to the system 100, the graph 300 may be dynamically repartitioned by continually updating certain nodes, edge weights, and constraints of edges. After the graph 300 is updated, a multi-objective, multi-constraint graph repartitioning algorithm can be used to compute an updated geospatial partitioning while attempting to minimize the difference between the previous partitioning and the new partitioning” Examiner notes Zhao teaches feature concatenation and Schoegel teaches continuous updating, including constraints of edges and repartitioning i.e. continuous updating after such concatenation)
performing a weighted summation on the first updated feature and the second updated feature to obtain the target feature data. (Schoegel, para. 0046: “In some embodiments, nodes may be associated with weighting values according to one or more criteria. For example, nodes associated with certain sub-regions 206 may be weighted according to a priori knowledge about these sub-regions 206. As another example, the nodes representing certain sub-regions 206 may be weighted according to information about these sub-regions 206 or tracks acquired from other sources. Additionally, weighting values may be adjusted according to objectives or desired information to be obtained from the process. In some embodiments, additional weighting may be applied based on characteristics of the tracks, such as the speed, acceleration, and/or direction of the tracks.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zhou into Schoegel, as modified, as set forth above with respect to claim 5.
Prior Art
Xie, et al (Z. Xie et al., "Using Remote Sensing Data and Graph Theory to Identify Polycentric Urban Structure," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 3000505, doi: 10.1109/LGRS.2023.3235943.) teaches multisource remote sensing data and graph as an effective method for polycentric structure identification.
Hillier, B.; Turner, A.; Yang, T.; Park, H.-T.; ((2009) Metric and topo-geometric properties of urban street networks: some convergences, divergences and new results. Journal of Space Syntax Studies) teaches metric measures used to create a new urban phenomenon: the partitioning of the background network of urban space into a network of semi-discrete patches by applying metric universal distance measures at different metric radii, suggesting a natural spatial area-isation of the city at all scales
Search Notes
PE2E search notes most relevant: L12. CPC with Keywords “urban” and “sub-regions”
Conclusion
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/STL/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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