DETAILED ACTION
Claims 1, 3-15, and 17-21 are pending. Claims 2 and 16 have been canceled. Claim 21 has been added. Claim 1, 15, and 18 have been amended. Claims 1, 3-15, and 17-21 are rejected.
The instant application has PRO 63/597,800 filed on 11/10/2023.
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 .
Allowable Subject Matter
Claim 21 is allowable over the prior art of record. Claim 21 is still rejected under 35 U.S.C. 101.
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, 3-15, and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (All Claims)
According to the first part of the analysis, in the instant case, claims 1, 3-14, and 21 are directed to a method, claims 15 and 17 are directed to a non-transitory computer-readable medium, and claims 18-20 are directed to a system comprising memory and processing circuitry. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2A, Prong 1 (Claims 1, 15, and 18)
Regarding claim 1, the following limitations are abstract ideas:
transforming, by an input formatting engine of a server, load data from a plurality of load data sources into a standardized load format; is a step that can be performed as a mental process, with the aid of pen and paper.
transforming, by the input formatting engine, truck data from a plurality of truck data sources into a standardized truck format; is a step that can be performed as a mental process, with the aid of pen and paper.
generating, load-truck matches matching at least one load to at least one truck based on the load data, the truck data, and a set of global constraints stored in a constraint data repository, wherein generating the load-truck matches comprises: is a step that can be performed as a mental process, with the aid of pen and paper.
generating a graph data structure representing the load data and the truck data, the graph data structure comprising nodes corresponding to individual loads and trucks, and edges representing potential matches between loads and trucks, is a step that can be performed as a mental process, with the aid of pen and paper.
evaluating each edge based on learned dependencies among features in the load data, the truck data, and the global constraints, is a step that can be performed as a mental process, with the aid of pen and paper.
iteratively adjusting edge weights to optimize at least one numeric value by selecting a first subset of the edges and rejecting a second subset of the edges, wherein the first subset and the second subset are mutually exclusive and collectively exhaustive; is a step that can be performed as a mental process, with the aid of pen and paper.
transforming, by a match formatting engine, the load-truck matches into a match format for storage in a match data repository; is a step that can be performed as a mental process, with the aid of pen and paper.
The above analysis applies to each independent claim as they contain similar limitations.
Step 2A, Prong 2 (Claims 1, 15, and 18)
Regarding claim 1, the following limitations are additional elements:
obtaining, by a multilayered graph neural network of the server, the load data in the standardized load format and the truck data in the standardized truck format; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or typed of data to be manipulated as identified in MPEP 2106.05(g).
transmitting, by the server, the load-truck matches in the match format to the match data repository. is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or typed of data to be manipulated as identified in MPEP 2106.05(g).
Regarding claim 15, the following limitations are additional elements:
A non-transitory computer-readable medium storing instructions operable to cause one or more processors to perform operations comprising: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application;
Regarding claim 18, the following limitations are additional elements:
a memory subsystem storing instructions; is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application;
processing circuitry configured to execute the instructions to: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application;
The above analysis for claim 1 applies to each independent claim as they contain similar limitations. The analysis for claims 15 and 18 are to show the different limitations not in claim 1.
Step 2B (Claims 1, 15, and 18)
Regarding claim 1, the following limitations are additional elements:
obtaining, by a multilayered graph neural network of the server, the load data in the standardized load format and the truck data in the standardized truck format; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
transmitting, by the server, the load-truck matches in the match format to the match data repository. when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
Regarding claim 15, the following limitations are additional elements:
A non-transitory computer-readable medium storing instructions operable to cause one or more processors to perform operations comprising: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
Regarding claim 18, the following limitations are additional elements:
a memory subsystem storing instructions; ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
processing circuitry configured to execute the instructions to: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
The above analysis for claim 1 applies to each independent claim as they contain similar limitations. The analysis for claims 15 and 18 are to show the different limitations not in claim 1.
The dependent claims are directed to the same abstract ideas as their parent claims. The dependent claims add further limitations directed to generating a data structure, displaying, or further clarify certain aspects of the claim limitations. Generating a data structure or clarifying certain aspects of other limitations are similar to the above identified abstract ideas. The displaying of data is directed to the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g) “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”. Therefore, the dependent claims are still rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 3-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al., Patent Application Publication No. 2019/0120640 (hereinafter Ho) in view of Cummings, Patent Application Publication No. 2021/0264520 (hereinafter Cummings).
Regarding claim 1, Ho teaches:
transforming (Ho Paragraph [0116], The normalizing data schema 314 translates data from a plurality of first formats to a normalized second format that is independent of the first format (e.g., independent of the source of the data)), by an input formatting engine of a server, load data from a plurality of load data sources into a standardized load format (Ho Paragraph [0116], The normalizing data schema 314 translates data from a plurality of first formats to a normalized second format that is independent of the first format (e.g., independent of the source of the data));
transforming (Ho Paragraph [0116], The normalizing data schema 314 translates data from a plurality of first formats to a normalized second format that is independent of the first format (e.g., independent of the source of the data)), by the input formatting engine (Ho Paragraph [0116], The normalizing data schema 314 translates data from a plurality of first formats to a normalized second format that is independent of the first format (e.g., independent of the source of the data)), truck data from a plurality of truck data sources into a standardized truck format (Ho Paragraph [0191], The autonomous vehicle 1410-2 (e.g., a car, truck, or motorcycle) includes non-transitory memory 1404 (e.g., non-volatile memory) that stores instructions for one or more client routing applications 1406);
generating a graph data structure representing the load data and the truck data (Ho Paragraph [0043], generating a graph representation of a geographic map that includes the requested pick-up locations and drop-off locations), the graph data structure comprising nodes corresponding to individual loads and trucks (Ho Paragraph [0043], generating a graph representation of a geographic map that includes the requested pick-up locations and drop-off locations), and edges representing potential matches between loads and trucks (Ho Paragraph [0032], In some of the embodiments of (C1), the cost model is represented as a graph of nodes and edges, the respective edges having respective edge weights that represent costs);
evaluating each edge based on learned dependencies among features in the load data (Ho Paragraph [0032], In some of the embodiments of (C1), the cost model is represented as a graph of nodes and edges, the respective edges having respective edge weights that represent costs), the truck data (Ho Paragraph [0190], The non-autonomous vehicle 1410-1 is a representative human-driven vehicle (e.g., a car, truck, or motorcycle)), and the global constraints (Ho Paragraph [0092], customized based on the constraints of a particular autonomous vehicle); and
iteratively adjusting edge weights to optimize at least one numeric value by selecting a first subset of the edges and rejecting a second subset of the edges (Ho Paragraph [0134], During traversal, a cost model may be used to evaluate the costs of the edges that are explored (e.g., weights are assigned as the costs of the edges based on tags or attributes associated with the edges)), wherein the first subset and the second subset are mutually exclusive and collectively exhaustive (Ho Paragraph [0134], During traversal, a cost model may be used to evaluate the costs of the edges that are explored (e.g., weights are assigned as the costs of the edges based on tags or attributes associated with the edges));
transforming, by a match formatting engine, the load-truck matches into a match format for storage in a match data repository (Ho Paragraph [0190], The non-autonomous vehicle 1410-1 is a representative human-driven vehicle (e.g., a car, truck, or motorcycle), Paragraph [0117], The map matching/positioning module 316 receives the location data from a respective vehicle (e.g., through the fleet manager 303, which interfaces with the first server system 300)); and
transmitting, by the server, the load-truck matches in the match format to the match data repository (Ho Paragraph [0117], The map matching/positioning module 316 receives the location data from a respective vehicle (e.g., through the fleet manager 303, which interfaces with the first server system 300), Paragraph [0329], routing the fleet vehicles in accordance with the generated set of routes includes sending each vehicle's route to the vehicle).
Ho does not expressly disclose:
obtaining, by a multilayered graph neural network of the server, the load data in the standardized load format and the truck data in the standardized truck format;
generating, by the multilayered graph neural network, load-truck matches matching at least one load to at least one truck based on the load data, the truck data, and a set of global constraints stored in a constraint data repository, wherein generating the load-truck matches comprises:
However, Cummings teaches:
obtaining, by a multilayered graph neural network of the server (Cummings Paragraph [0326], output of a computational graph (i.e., directed acyclic graph, or DAG) representing the neural network), the load data in the standardized load format and the truck data in the standardized truck format (Cummings Paragraph [0105], format the data payload by serializing an object representation (e.g., JavaScript object representation) of hierarchical portfolio 300 to a formatted character stream using a standard character-based data-exchange format (Ho teaches a neural network in Paragraph 183 and truck data while Cummings teaches the multilayered graph));
generating, by the multilayered graph neural network (Cummings Paragraph [0326], output of a computational graph (i.e., directed acyclic graph, or DAG) representing the neural network), load-truck matches matching at least one load to at least one truck based on the load data (Cummings Paragraph [0264], If the compared values match, then the integrity of AI view (t.sub.2) stored within block N(t.sub.2)), the truck data, and a set of global constraints stored in a constraint data repository (Cummings Paragraph [0083], restructuring the hierarchical portfolio subject to user and global financial data constraints), wherein generating the load-truck matches comprises (Cummings Paragraph [0083], restructuring the hierarchical portfolio subject to user and global financial data constraints):
The claimed invention and Cummings are from the analogous art of systems using neural networks. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Ho in view of Cummings to have combined Ho in view of Cummings. Cummings teaches enabling continuous improvement in the performance of the underlying machine learning models by using network-based AI-enhanced investment systems with an immutable source of invaluable private training data (Paragraph 80).
Regarding claim 3, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), of a graphical user interface representing the load-truck matches, wherein the graphical user interface comprises a dashboard layout that includes multiple interactive panels displaying key performance indicators related to at least one of the load-truck matches, wherein the key performance indicators comprise at least one of (Ho Paragraph [0230], the set of one or more autonomous driving capabilities of the autonomous vehicle includes a limitation on the autonomous vehicle determined from historical performance data for at least one of the autonomous vehicle and vehicles of a same type as the autonomous vehicle): a profit, a revenue per mile, a fuel cost, a mileage travelled (Ho Paragraph [0148], cost is a scalar value, but it can also have multiple costs (e.g., time and distance) and/or confidence probabilities. Pareto/multi-value optimization has been used in the former and stochastic routing in the latter), or a deadhead percentage.
Regarding claim 4, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), of a graphical user interface representing the load-truck matches, wherein the graphical user interface comprises a visual map interface that graphically represents a route associated with at least one load-truck match, the visual map interface including at least one of (Ho Fig. 1, shows a graphical user interface representing elements such as a route): an origin location (Ho Paragraph [0105], Bidirectional Dijkstra explore the weighted graph to find paths which minimize a cost function between an origin and destination), a destination location (Ho Paragraph [0105], Bidirectional Dijkstra explore the weighted graph to find paths which minimize a cost function between an origin and destination), a route path (Ho Paragraph [0142], Different routing modes thus may use different cost models and functions, resulting in different routes. Routing therefore may be preceding by selection of a mode in accordance with some embodiments), or an estimated travel time (Ho Paragraph [0195], FIGS. 15A-15B are a flow diagram illustrating a method 1500 for routing a vehicle using a cost model with costs other than travel time), the visual map interface comprising a visually indicated line to indicate a status of at least one route segment (Ho Paragraph [0142], Different routing modes thus may use different cost models and functions, resulting in different routes. Routing therefore may be preceding by selection of a mode in accordance with some embodiments).
Regarding claim 5, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), of a graphical user interface representing the load-truck matches, wherein the graphical user interface comprises a filter and search toolbar (Ho Paragraph [0051], In some of the embodiments of any of (E1)-(E8), performing the graph search includes limiting a number of states explored in the state graph representation using a lowest-bound heuristic by forgoing subsequent exploration from any nodes in the state graph representation for which a lowest-bound of the cost function exceeds a cost of an already-determined set of routes for the fleet vehicles), the filter and search toolbar enabling a user of the client device to sort and filter load-truck matches based on criteria, the criteria comprising at least one of a distance (Ho Paragraph [0088], To that end, some embodiments provide autonomous-vehicle (AV)-focused methods and devices that account for (e.g., optimize for) costs other than time and distance), driver availability, compliance status, or load type.
Regarding claim 6, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server, of a graphical user interface representing the load-truck matches (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), wherein the graphical user interface comprises a load-truck matching matrix indicating the load-truck matches in cell of the matrix (Ho Paragraph [0279], In some embodiments, the planner 1908 creates cost matrices for each of the plurality of FRP problem instances generated by the grouper 1906);
receiving, from the client device, an indication of a selection of a cell of the matrix (Cummings Paragraph [0357], each selected element of the user data vector of the k.sup.th financial client and each selected element of the global financial data matrix);
causing display, at the client device and in response to the indication of the selection, of additional data related to a load-truck match of the cell (Ho Paragraph [0279], The planner 1908 is a service that interfaces with the router 1902 and solver 1910 to service plan request and updates (e.g., additions of pick-up/drop-off requests to existing routes, as described below). In some embodiments, the planner 1908 creates cost matrices for each of the plurality of FRP problem instances generated by the grouper 1906).
Regarding claim 7, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server, of a graphical user interface representing the load-truck matches (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), wherein the graphical user interface comprises a dashboard comprising at least one graph indicating at least one of a truck utilization rate, a mileage rate, a profit rate, a revenue rate, or a match rate of loads to trucks (Ho Paragraph [0114], the geospatial silo-ed databases 312 store locations (e.g., map matched locations) of the vehicles in the various fleets).
Regarding claim 8, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server, of a graphical user interface representing the load-truck matches (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), wherein the graphical user interface comprises a truck view interface indicating loads assigned to at least one truck (Ho Paragraph [0277], The number of cluster pairs assigned to a vehicle is a function of the vehicle's capacity and load on the cluster pair); and
transmitting, to a computing device associated with the at least one truck, an indication the loads assigned to the at least one truck and a proposed route for the at least one truck (Ho Paragraph [0279], The planner 1908 is a service that interfaces with the router 1902 and solver 1910 to service plan request and updates (e.g., additions of pick-up/drop-off requests to existing routes, as described below)).
Regarding claim 9, Ho in view of Cummings further teaches:
The method of claim 1, further comprising: causing display, at a client device communicating with the server, of a graphical user interface representing the load-truck matches (Ho Paragraph [0111], the second server system 302 acts as a client of the first server system 300, while riders, consumers (e.g., riders/consumers 304), and vehicles (e.g., non-autonomous vehicles 306 and/or autonomous vehicles 308) act as clients of the second server system 302 (Cummings teaches displaying on a client device (Paragraph 11) while Ho teaches the rest of the limitation)), wherein the graphical user interface comprises a modification interface for a user of the client device to manually assign a first load to a first truck or to manually remove an assignment of a second load to a second truck (Ho Paragraph [0047], updating the route for the respective vehicle of the fleet vehicles includes inserting a pick-up location and a drop-off location for the second passenger into an existing route for the respective vehicle without modifying pick-up and drop-off locations for passengers already assigned to the respective vehicle).
Regarding claim 10, Ho in view of Cummings further teaches:
The method of claim 1, wherein the features comprise at least one of a load location, a truck location, a load departure time range, a load delivery time range, a truck location constraint, a revenue potential, or a compliance constraint of the global constraints (Ho Paragraph [0086], the present disclosure provides routing devices and methods that take into account AV-focused requirements and constraints (e.g., requirements and constraints that are particularly, although possibly not exclusively, important to autonomous vehicles)).
Regarding claim 11, Ho in view of Cummings further teaches:
The method of claim 1, wherein the at least one numeric value comprises or is determined based on at least one of a revenue value, a cost value, a profit value, a carbon emission value (Ho Paragraph [0196], Routing autonomous vehicles in a way that is tailored to their capabilities increases passenger comfort and safety, conserves resources (e.g., cuts down on carbon emissions and/or battery usage from autonomous vehicles), reduces traffic), or a mileage value.
Regarding claim 12, Ho in view of Cummings further teaches:
The method of claim 1, wherein the load data comprises a first set of mandatory loads and a second set of optional loads (Ho Paragraph [0186], To lessen the amount of memory needed on an onboard device, some embodiments shard the routing graph to load only the necessary graph components into memory, Paragraph [0242], e.g., edge weights are assigned in response to the request and optionally include costs associated with a particular vehicle's constraints), wherein the load-truck matches include the first set of mandatory loads and a subset of the second set of optional loads (Ho Paragraph [0186], To lessen the amount of memory needed on an onboard device, some embodiments shard the routing graph to load only the necessary graph components into memory).
Regarding claim 13, Ho in view of Cummings further teaches:
The method of claim 1, wherein the multilayered graph neural network comprises
at least one input layer, one or more hidden layers, and an output layer, wherein (Cummings Paragraph [0326], The forward pass step may further include batch-normalization operations between hidden layers of the network, where the outputs from a previous layer are normalized (e.g., adjusted by the mean and standard deviation of the batch sample)):
the input layer receives node features representing attributes of loads from the load data and attributes of trucks from the truck data (Ho Paragraph [0277], The number of cluster pairs assigned to a vehicle is a function of the vehicle's capacity and load on the cluster pair);
the hidden layers perform graph convolutional operations that aggregate information from neighboring nodes and edges to capture relationships between loads and trucks (Cummings Paragraph [0326], The forward pass step may further include batch-normalization operations between hidden layers of the network, where the outputs from a previous layer are normalized (Cummings teaches the hidden layer while Ho teaches the truck data)); and
the output layer generates a matching score for each load-truck pairing based on the aggregated node and edge features (Cummings Paragraph [0202], return the specific event logs that a) are associated with the addView event of the RecordViews contract and b) match the value (i.e., hash of AI view 1200) of the viewHash argument specified in query), the load-truck matches being generated based on the matching score (Cummings Paragraph [0202], return the specific event logs that a) are associated with the addView event of the RecordViews contract and b) match the value (i.e., hash of AI view 1200) of the viewHash argument specified in query (Ho teaches the truck data)).
Regarding claim 14, Ho in view of Cummings further teaches:
The method of claim 1, wherein the multilayered graph neural network is configured to iteratively adjust the edge weights by: using a message-passing sub-engine to transmit information between connected nodes, allowing each node to update its state based on a state of a neighboring node or an attribute of an edge connecting to the node (Cummings Paragraph [0128], More specifically, the point of the tail of the message bubble of AI suggestion 810 may be placed directly at the center of AI suggestion location 812 and the tail of the message bubble may be directed to point toward the right side of hierarchical portfolio 300), and
using a gradient-based optimization sub-engine to adjust weights of the edges based on optimizing the at least one numeric value (Ho Paragraph [0032], representing the intersection as a plurality of nodes and a plurality of edges having edge weights. Each edge of the representation of the intersection represents a distinct path through the intersection).
Claims 15 and 17-20 are rejected in the same manner as claims 1 and 3-14 but are merely directed to a different embodiment of the same invention (method, computer-readable medium, and system). Ho further teaches a computer system with non-transitory memory for storing instructions and a processor for executing instructions (Paragraph 250).
Response to Arguments
Applicant's arguments filed 07/20/2026 have been fully considered but they are not persuasive. A detailed explanation is provided below.
On pages 9-16, Applicant argues against the rejection under 35 U.S.C. 101.
Applicant argues that the claim limitations encompassing AI in a way that cannot practically be performed in the human mind, the Examiner disagrees. The claim appears to be using AI to perform steps that a human mind can perform. The human mind can generate graph data based on loads and trucks with edges representing potential matches. The human mind can further adjust the weights and constraints. Merely using a computer or artificial intelligence system to perform a task that a human mind could perform but perform it hundreds of times does not overcome the 101 rejection. Applicant further states that dependencies are derived from training the multilayered graph neural network on historical data rather than from any pre-specified formula. The claims do not provide any detail on how the multilayered graph neural network is trained, the claims do not even mention the word training in any of the claims.
Applicant argues against the analysis under step 2A, Prong 2, the Examiner disagrees. Similar to the above response, it is not clear how the multilayered graph neural network reaches this improvement. The claims merely state that the multilayered graph neural network performs the steps of generating the load-truck matches by performing multiple other steps but these steps have all been identified as mental processes. Applicant further mentions the learned dependencies from GNN training rather than manually specified rules, the Examiner disagrees. Similar to the above response, the claims do not go into detail on how the training is performed or used after the training is performed. Are the steps of generating the load-truck matches when the training is being performed or are those steps taken after the training is complete? Therefore, the claims are still rejected under 35 U.S.C. 101.
Applicant argues against the analysis under step 2B, the Examiner disagrees. The primary reference does disclose the use of machine learning and neural networks (Paragraphs 167 and 158). The primary reference merely does not disclose the particular multilayered graph neural network. Furthermore, the Ho reference not disclosing the claimed GNN does not mean that there is not a primary art reference that discloses GNN architecture in a logistics system. Therefore, the claims are still rejected under 35 U.S.C. 101.
Applicant argues against the analysis of claim 2, the Examiner disagrees. The rejection under 35 U.S.C. 101 has been updated to show that the steps of claim 2 have been identified as mental processes. A human mind can generate a graph data structure representing certain data such as trucks, can evaluate the edges of the graph, and adjust weights to optimize at least one numeric value. The claim does not provide any detail on how the multilayered graph neural network is performing these steps or how it is used in training or how the trained GNN is performing these steps. Regarding the remarks of Electric Power Group, that was directed at claims such as 3-9 where information is displayed. Therefore, the claims are still rejected under 35 U.S.C. 101.
Applicant argues against the analysis for claim 14, the Examiner disagrees. Similar to the above responses, the hidden layer is a mental process where a human mind can aggregate information from nodes and edges. The input and output layers are additional elements similar to the above identified additional elements of sending or receiving data. Using a multilayered graph neural network to perform these steps does not appear to overcome the rejection under 35 U.S.C. 101. Furthermore, an improve to matching accuracy is an improvement over the prior art of record rather than an improvement to the functioning of a computer or to any other technology or technical field. Therefore, the claims are still rejected under 35 U.S.C. 101.
On pages 16-26, Applicant argues against the rejection under 35 U.S.C. 103.
Applicant argues against the use of the Ho reference for generating the graph, the Examiner disagrees. The claim states that the nodes represent the load data and truck data. The nodes of Ho represent the pick-up and drop-off locations where the pick-up can be considered where the truck would be loaded (load data) and the drop-off is where the truck has to drop-off the goods that were picked up (truck data). The claim is not specific on what the data of the truck data and load data are. Should the claims be amended to clarify what the specifics of the truck and load data include, then it could overcome the prior art of record.
Applicant further argues that the claim requires learned dependencies which are learned by the multilayered GNN through training, the examiner disagrees. Similar to the above responses for the rejection under 35 U.S.C. 101, the claims do not provide detail on when or how the training is done. If the GNN is already trained then the claims are merely using the GNN to perform the limitations. Therefore, it is not clear if these learned dependencies have been previously established by some previous training or are learned during the limitations of the claims.
Applicant argues that the constraints as cited by Ho are different from the claimed constraints, the Examiner disagrees. The claims merely state that there are global constraints and do not specific or limit what the constraints can be. Looking to the specification, paragraphs such as 24 merely give examples of what the constraints can be but do not explicitly limit the constraints to being certain elements. One of these constraints mentioned in paragraph 24 is cost factors. Ho teaches costs associated with a particular vehicle’s constraints (Paragraph 242). This shows that the constraints of Ho are similar to the constraints of the claimed invention as the claims of the claimed invention do not limit the constraints to any particular constraint.
Applicant argues against Ho disclosing the iteratively adjusting edge weights limitation, the Examiner disagrees. Ho teaches evaluating the costs of edges that are explored and assigning weights as the cost of the edges based on attributes (Paragraph 134). The claim merely states that the weights are optimized by selecting one set and rejecting another. Using the cost model of Ho can be considered using it to select the weights while rejecting the other evaluations. Therefore, Ho does disclose this limitation.
Applicant argues that there is no motivation to combine the references, the Examiner disagrees. As mentioned in an above response, Ho does disclose machine learning and neural networks but does not use them in the specific way as shown by the claim limitations. Cummings teaches neural networks used in a more similar manner to the claims. The claimed invention, Ho, and Cummings are directed to optimizing costs related to their specific invention. Therefore, one of ordinary skill in the art would find the teachings of Cummings as analogous art and see motivation to combine them. Regarding the argument that the combination would render Ho inoperable, the Examiner disagrees. Cummings teaches a computational graph representing the neural network (Paragraph 326). Ho teaches a graph representing truck data. It is not clear how Cummings would render Ho inoperable when both are directed to graphs, the neural network of Cummings could be applied to the graph of Ho. It is not clear how that would destroy the very system of Ho.
Applicant argues that dependent claim 14 is allowable, the Examiner disagrees. Cummings teaches a message bubble of AI suggestion (Paragraph 128). This shows that Cummings teaches the message-passing sub-engine. Ho has already been shown to teach adjusting the weights based on values. As stated above, Ho does disclose machine learning systems. One of ordinary skill in the art would recognize that a neural network could be used for the system of Ho. Applicant argues that Ho does not teach either sub-engine, the Examiner disagrees. Cummings teaches the message-passing sub-engine. The claim states that the invention uses a gradient-based optimization sub-engine but does not provide detail on the specifics of the sub-engine, it merely adjusts weights of the edges based on optimizing the at least one numeric value. Ho has already been shown to disclose creating weights based on numeric values. Therefore, the combination of the prior art does disclose this limitation.
Regarding claim 21, the prior art of record does not disclose this limitation but the claims are still rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dyk, Patent Application Publication No. 2019/0370735 (hereinafter Dyk). Dyk teaches an output of the carrier optimizer may be an optimized carrier assignment data package that matches specific carriers and trucks with loads to be delivered (Paragraph 25). This shows that the claimed invention and Dyk are analogous art as both are directed to truck data systems.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DUSTIN D EYERS/ Examiner, Art Unit 2164
/AMY NG/ Supervisory Patent Examiner, Art Unit 2164
