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 action is in response to the reply filed January 21, 2026.
Claim 2 has been amended.
Claims 1-15 are currently pending and have been amended.
Response to Arguments
The previous rejection under 35 USC 112(b) to claim 2 has been withdrawn in response to the submitted amendments.
Applicant’s remarks filed January 21, 2026 have been fully considered but they are not persuasive.
Regarding the previous rejection under 35 USC 112(b), Applicant presented the following remarks:
With respect to claim 12, Applicant contends that the recited computer program product claim is a well-established acceptable format that is not indefinite. Claim 12 recites "a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method according to claim 1." The claim defines a product (the storage device with code) by what the code is capable of doing.
Examiner respectfully disagrees. Examiner contends that the explicit recital of “a method” in a claim for a manufacture is indefinite. A process and a manufacture are distinct statutory categories enumerated under 35 USC 101. Applicant appears to claim both categories in a way that fails to particularly point out which category is claimed. Examiner recognizes that a computer readable hardware storage containing executable instructions without explicitly claiming “a method” may be an acceptable format directed toward a manufacture.
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
Claim 1 requires "providing, by a graph neural network model receiving the knowledge graph as input, a node embedding for each entity node and an edge embedding for each delivery edge based on the respective node embeddings."2 This operation requires iteratively applying each layer of the graph neural network model to the node embeddings across large graphs, and cannot practically be performed in the human mind. Claim 1 further requires "calculating for each entity node, by a first neural network receiving as input the respective node embedding, a probability that the entity node lies on an optimal path" and "calculating for each delivery edge, by a second neural network receiving as input the respective edge embedding, a probability that the delivery edge lies on an optimal path." These neural network calculations involve complex mathematical transformations that are fundamentally incapable of being performed mentally. As described in the specification, "[i]n another embodiment of the method, the first neural network and the second neural network are multilayer perceptrons." Multilayer perceptrons involve multiple layers of weighted connections and non-linear activation functions applied to high-dimensional embeddings, which are operations that cannot be practically performed in the human mind. The claims do not merely recite "observing a graph database, evaluating path probabilities, and outputting an optimal path" as the Examiner has alleged. Rather, claim 1 recites specific neural network architectures that perform these calculations through machine learning operations.
Examiner respectfully disagrees. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
The claimed use of a neural-network for calculating probability and a neural network for providing embeddings is merely the use of a computer as a tool. Improving the performance of the abstract idea by using a computer as a tool does not materially alter the patent eligibility of the claimed subject matter. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)). Regarding claim 1, the claim only requires a high-level recitation of calculating probability which does not involve complex mathematical transformations that are fundamentally incapable of being performed mentally. Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper")
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
While neural networks involve mathematical operations, claim 1 recites a specific technical architecture rather than merely abstract mathematics. Claim 1 specifies a particular system comprising a knowledge graph with a specific structure including "entity nodes each representing an entity in the logistics network" and "incident nodes each representing an incident, wherein each incident is an event that negatively impacts at least one entity," connected by "delivery edges between the entity nodes" and "impact edges between each incident node and one or more entity nodes," a graph neural network model that receives this specific knowledge graph structure as input, a first neural network for calculating node probabilities, and a second neural network for calculating edge probabilities. This specific technical architecture is not a mere mathematical concept but rather a concrete implementation for solving a technical problem in logistics route planning.
Examiner respectfully disagrees. Examiner maintains that the claims recite a mathematical concept under Step 2A1 because the identified idea is a mathematical calculation by reciting a graph neural network providing a node embedding, calculating a probability the node lies on an optimal path, calculating a probability the edge lies on an optimal path, and computing the optimal path. See MPEP 2106.04(a)(2)(I)(C). Under the Alice/Mayo framework the questions of practical application under prong 2A2 and significantly more under step 2B are addressed after determining whether or not the claims recite a mathematical concept.
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
Furthermore, the claims provide specific technical improvements over prior approaches and are therefore integrated into a practical application. First, the claimed method provides flexibility without retraining. As described in the specification, "[t]his embodiment provides flexibility as it can adapt to changes in the graph structure without retraining. The graph neural network model can handle new nodes/edges as well as removed nodes/edges. This is advantageous as in logistics networks, frequent changes might occur, e.g., when a new incident affects an airport so that no deliveries to the airport can be made on this day." The specification further explains that "[t]his embodiment allows to add new logistics and incident nodes to the knowledge graph without the need of retraining the graph neural network model. As a result, the graph neural network model can flexibly adapt to structural changes in the logistics network and/or to new incidents."'
Examiner respectfully disagrees. First, Examiner notes that there is no claimed training so the significance of not re-training when new nodes/edges are added/removed is not apparent. Second, the only claimed function of the graph neural network model is to provide embeddings, therefore adding/removing nodes appears to be performing in the function of updating embedding data rather than changing the underlying functionality of how a graph neural network provides embeddings. Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014) ("Without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible."). Finally, the improvement to the graph neural network model handling new nodes/edges as well as removed nodes/edges appears to be an improvement to the underlying mathematical concept rather than an improvement to the underlying graph neural network itself. See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245, 1251 (Fed. Cir. 2021) (“[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological process; rather, it is merely an enhancement to the abstract mathematical calculation … itself.”); Parker v. Flook, 437 U.S. 584, 591-92 (1978) ("the novelty of the mathematical algorithm is not a determining factor at all"). In other words, the claims do not appear to change the graph neural network model but instead changes the way a graph neural network model is used.
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
Second, the claimed method provides scalability. The specification explains that "[i]n embodiments, the method and system provide scalability since they have constant computational complexity, and the computation time does not depend on the number of hops. In contrast to many shortest path algorithms and manual path finding, they are applicable to large graphs." This is a specific technical improvement over traditional shortest path algorithms which have high computational complexity making them unsuitable for large logistics networks.
Examiner respectfully disagrees. An improvement to scalability through constant computational complexity is an improvement to the underlying mathematical concept that does not qualify as an improvement to a technological process. See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245, 1251 (Fed. Cir. 2021) (“[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological process; rather, it is merely an enhancement to the abstract mathematical calculation … itself.”); Parker v. Flook, 437 U.S. 584, 591-92 (1978) ("the novelty of the mathematical algorithm is not a determining factor at all"). Additionally, the scale of the graphs is not claimed and is therefore immaterial. Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1242 (Fed. Cir. 2016) ("The difficulty of the programming details for this functionality is immaterial because these details are not recited in the actual claims. The degree of difficulty in implementing an abstract idea in this circumstance does not itself render an abstract idea patentable.")
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
Third, the claimed system enables real-time incident handling through the specific knowledge graph structure with incident nodes and impact edges. The specification explains that "[i]n embodiments, the method and system provide a graph neural network-based approach that learns node and edge embeddings while taking the impact of real-time incidents into account." The specification further explains that "[a]ccording to this embodiment, the model can even give predictions in real-time. Since the knowledge graph is updated in real-time to reflect the dynamically changing status of the logistics network, the model can be used to provide changing optimal path options in real time as well.""
Examiner respectfully disagrees. Performance of the identified abstract idea in real time does not make the identified abstract idea any less abstract. See Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1368-69 (Fed. Cir. 2015) ("Indeed, the budgeting calculations at issue here are unpatentable because they could still be made using a pencil and paper with a simple notification device, even in real time as expenditures were being made.”).
Regarding the previous rejection under 35 USC 101, Applicant presented the following remarks:
These are not generic computer implementations but specific technical solutions to technical problems in logistics route planning systems. The claimed combination of a knowledge graph with incident nodes and impact edges, processed by a graph neural network model with separate neural networks for node and edge probability calculations, provides concrete technical improvements including adaptability without retraining, constant computational complexity for scalability, and real-time incident handling.
Examiner respectfully disagrees. The claimed knowledge graph appears to be a definition of the data stored within a graph database rather than describing a new graph database. In re TLI Communications LLC Patent Litigation, 823 F.3d 607, 612 (Fed. Cir. 2016) (The specification does not describe a new telephone, a new server, or a new physical combination of the two. The specification fails to provide any technical details for the tangible components, but instead predominately describes the system and methods in purely functional terms.)
Regarding the previous rejection under 35 USC 102, Applicant presented the following remarks:
Applicant contends that claim 1 is not anticipated by Shao because Shao does not teach each and every element of claim 1. For example, Shao does not teach "incident nodes each representing an incident, wherein each incident is an event that negatively impacts at least one entity" and "impact edges between each incident node and one or more entity nodes." Claim 1 requires a knowledge graph having nodes that include "entity nodes each representing an entity in the logistics network" and "incident nodes each representing an incident, wherein each incident is an event that negatively impacts at least one entity." As described in the specification, the subgraph contains entity nodes EN representing the entities in the logistics network, incident nodes IN representing incidents in the logistics network, delivery edges DE between the entity nodes EN, and impact edges between each incident node IN and one or more entity nodes EN. The delivery edges are of edge type "delivers to" and the impact edges are of edge type "impacts."
Examiner respectfully disagrees. Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The presumption that a term is given its ordinary and customary meaning may be rebutted by the applicant by clearly setting forth a different definition of the term in the specification. MPEP 2173.01(I). Specification [0049] cited to by Applicant does not present a definition because it only provides examples (i.e. “an exemplary subgraph”) that do not amount to clearly redefining claim terms. Therefore, the entity nodes, incident nodes, delivery edges, and impact edges do not have a special definition in the specification that can be read into the claims. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) ("We have cautioned against reading limitations into a claim from the preferred embodiment described in the specification, even if it is the only embodiment described, absent clear disclaimer in the specification.") The claims require storing nodes and edges with different characteristics but do not require pre-labeling the nodes or edges in accordance with those characteristics. A entity node representing an entity that is having an incident with negative impact on the entity may be considered an incident node. In other words, there is nothing that prevents an incident node from being associated with a location or entity because the only difference between the nodes, as claimed, is the “current status” of whether there is an associated incident. The same reasoning applies to directed edges where the difference between the edges is the current status.
Regarding the previous rejection under 35 USC 102, Applicant presented the following remarks:
In contrast, Shao's directed graph structure contains only physical location nodes representing points in a workshop (such as material start points, material end points, and AGV route intersection points) connected by AGV route edges, without any separate incident nodes representing discrete events that negatively impact entities or impact edges connecting such incident nodes to entity nodes. Shao discloses that "the basic layout directed graph consists of nodes and edges, the nodes comprise the intersection points of the AGV routes, the material startpoint and the material end point. Shao's nodes are "points where materials are loaded or unloaded and where the edges intersect during material transportation, such as the material start point, the material end point, the intersection points of the AGV routes, etc.." " Shao's nodes represent only physical locations in a workshop but not discrete incident events. Shao's edges are AGV routes connecting two nodes, classified as "a start path, a terminal path, intermediate paths, etc. based on the different types of the connected nodes." There are no "impact edges" connecting incident nodes to entity nodes in Shao.
Examiner respectfully disagrees. As explained above, the claims are broader than Applicant alleges and the specificity of discrete incidents and separate incident nodes is not required by the claim. Examiner notes that in Shao node attributes include congestion and traffic where congestion and traffic read on Applicants incidents.
Regarding the previous rejection under 35 USC 102, Applicant presented the following remarks:
The Examiner asserts that Shao's congestion degree attribute teaches incident nodes. However, Shao's "congestion degree of a node refers to a value that can reflect the number of AGV transport vehicles passing through the node in unit time. " This is merely a traffic volume metric (i.e. an attribute of a physical location node) and not a separate incident node representing a discrete event that negatively impacts entities. In contrast, claim 1 requires incident nodes representing incidents as distinct node types separate from entity nodes, and further requires edge types including "impacts," which connects an incident with one or several entities in the logistics network. While Shao does mention obstacles, Shao's "material transportation obstacles refer to situations that affect the normal operation of material transportation" such as "a situation where the transport vehicle drops goods" or "the presence of obstacles is identified by monitoring." However, these obstacles are not represented as separate incident nodes in Shao's graph structure with directed impact edges connecting them to entity nodes. Rather, Shao merely determines "whether the second connected nodes have material transportation obstacles" and sets "the connected node transition probability" to 0 in response. A comparison of FIG. 3 of the present application with FIG. 4 of Shao clearly illustrates this distinction. Fig. 3 of the present application shows incident nodes IN as distinct node types connected to entity nodes EN via impact edges:
[[Application Fig. 3]]
In contrast, Fig. 4 of Shao shows only physical location nodes (material start point A, material end point J, intersection points B, C, E, F, G, I) connected by AGV route edges:
[[Shao Fig. 4]]
As can be seen, Shao's FIG. 4 contains no incident nodes and no impact edges. Because Shao does not disclose "incident nodes each representing an incident, wherein each incident is an event that negatively impacts at least one entity" or "impact edges between each incident node and one or more entity nodes" as recited by claim 1, Shao cannot anticipate claim 1.
Examiner respectfully disagrees. As explained above, the broadest reasonable interpretation has been applied because the claims do not invoke 35 USC 112(f) and the specification does not provide a special definition, therefore Application Fig. 3 cannot be read into the claims. The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 12-13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 12-13 are directed toward a "computer program product" and “a provisioning device” however also require activities or steps to take place. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112, second paragraph. IPXL Holdings v. Amazon.com, Inc., 430 F.2d 1377, 1384, 77 USPQ2d 1140, 1145 (Fed. Cir. 2005); Ex parte Lyell, 17 USPQ2d 1548 (BPAI 1990). The claims as drafted do not properly apprise the public as to what would constitute infringement (i.e., creation of the claimed system or the act of using it), and are therefore indefinite under § 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Alice/Mayo Framework Step 1:
Claims 1-10 and 14 recite a series of steps and therefore recite a process.
Claims 11-12 and 15 recite a tangible article given properties through artificial means and therefore recite a manufacture.
Claims 13 recite a combination of devices and therefore recite a machine.
Alice/Mayo Framework Step 2A – Prong 1:
Claims 1 and 11-13, as a whole, are directed to the abstract idea of using a knowledge graph to determine an optimal path, which is a mathematical concept and a mental process. The claims recite a mathematical concept because the identified idea is a mathematical calculation by reciting a graph neural network providing a node embedding, calculating a probability the node lies on an optimal path, calculating a probability the edge lies on an optimal path, and computing the optimal path. See MPEP 2106.04(a)(2)(I)(C). The claims recite a mental process because the identified idea contains limitations that can practically be performed in the human mind (including an observation, evaluation, judgement, or opinion) by reciting observing a graph database, evaluating path probabilities, and outputting an optimal path based on the probabilities. See MPEP 2106.04(a)(2)(III). The mathematical concept and mental process of “using a knowledge graph to determine an optimal path,” is recited by claiming the following limitations: representing a transportation network with a knowledge graph having nodes and directed edges, receiving a selection of a sender and receiver node, determining an optimal path option, providing a node embedding and an edge embedding by a graph neural network, calculating a probability a node lies on an optimal path, calculating the probability an edge lies on an optimal path, computing an optimal path, and outputting an optimal path. The mere nominal recitation of a processor, a graph database, a logistics network, a user interface, a provisioning device, a computer readable hardware storage device, and a graph neural network does not take the claim of the mathematical concept or mental process grouping. Thus, the claim recites an abstract idea.
With regards to Claims 2 and 5, the claims further recite the above-identified judicial exception (the abstract idea) by reciting the following limitations: changing the knowledge graph based on the logistics network and greedily appending edges and nodes with a highest probability to the optimal path.
With regards to Claims 8-9, the claims further recite a mathematical concept because the identified idea is a mathematical formula or equation by reciting a node embedding formula and an edge weighting formula. See MPEP 2106.04(a)(2)(I)(B).
Alice/Mayo Framework Step 2A – Prong 2:
Claims 1 and 11-13 recite the additional elements: a processor, a graph database, a logistics network, a provisioning device, a computer readable hardware storage device, and a user interface. These a processor, a graph database, a user interface, a provisioning device, and a computer readable hardware storage device limitations are no more than mere instructions to apply the exception using a generic computer component. The user interface step is recited at a high level of generality (i.e., as a general means of gathering selection data for use in the path determination step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The logistics network limits the field of use by generally linking the identified abstract idea to the logistics field. Taken individually these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Considering the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception does not amount to a practical application of the abstract idea. The claim as a whole does not improve the functioning of a computer or improve other technology or improve a technical field. The claim as a whole is not implemented with a particular machine. The claim as a whole does not effect a transformation of a particular article to a different state. The claim as a whole is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The claim as a whole merely describes how to generally “apply” the concept of route planning in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing route planning process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim is directed to the abstract idea.
Alice/Mayo Framework Step 2B:
Claims 1 and 11-13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite a generic computer performing generic computer function by reciting a processor, a graph database, a provisioning device, a computer readable hardware storage device, and a user interface. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1341 (describing a “processor” as a generic computer component); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347–48 (discussing the same with respect to “data” and “memory”). The claims recite the following computer functions recognized by the courts as generic computer functions by reciting receiving information (See MPEP 2106.05(d)(II) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec; TLI Communications LLC; OIP Techs.; buySAFE, Inc.), processing information (See MPEP 2106.05(d)(II) performing repetitive calculations, Flook; Bancorp Services), presenting information (See MPEP 2106.05(d)(II), MPEP 2106.05(g) presenting offers gathering statistics, OIP Technologies), storing and retrieving information (See MPEP 2106.05(d)(II) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Technologies). The specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a processor (Specification [0036], [0083]), a graph database (Specification [0075]), a logistics network (Specification [0004], [0065]), a user interface (Specification [0036]), a provisioning device (Specification [0040]), and a computer readable hardware storage device (Specification [0035], [0085]). See MPEP 2106.05(d)(I)(2). The claims add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a processor, a graph database, a provisioning device, and a computer readable storage device. See MPEP 2106.05(f). The claims recite instructions to implement the abstract idea on a computer by providing a user interface, and responding to a user interface using the computer's ordinary ability to display and process data inputs. (See MPEP 2106.05(f) accessing information through a mobile interface Intellectual Ventures v. Erie Indem. Co.; Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc.) The claims recite insignificant extrasolution activity (i.e. mere data gathering) by reciting receiving selections in a user interface. See MPEP 2106.05(g). The claims limit the field of use by reciting a logistics network. See MPEP 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements alone, and in combination, that are sufficient to amount to significantly more than the recited judicial exception.
With regards to Claims 4, the additional elements do not amount to significantly more than the judicial exception. Regarding claims 4, the specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a greedy pathfinding algorithm to compute optimal path (Specification [0061]). See MPEP 2106.05(d)(I)(2). Claims 4 add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a greedy pathfinding algorithm computing optimal path. See MPEP 2106.05(f). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exception.
Remaining Claims:
With regards to Claims 3, 6, 10, and 14-15, these claims merely add a degree of particularity to the limitations discussed above rather than adding additional elements capable of transforming the nature of the claimed subject matter. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims as a whole do not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 11-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shao et al. (U.S. P.G. Pub. 2023/0244219 A1), hereinafter Shao.
Claim 1.
Shao discloses a computer implemented method for logistics route planning, wherein the following operations are performed by components, and wherein the components are hardware components and/or software components executed by one or more processors, the method comprising:
storing, by a graph database, a knowledge graph having nodes and directed edges representing a current status of a logistics network (Shao [0023], [0026], [0073], [0091] generate graph with nodes and edges; [0034], [0043] information storage), wherein the nodes include:
entity nodes each representing an entity in the logistics network, and incident nodes each representing an incident, wherein each incident is an event that negatively impacts at least one entity (Shao [0093] node attributes include congestion degree and the traffic correlation degree; [0094] congestion degree; [0124] weighting based on the impact of the traffic correlation degree),
and wherein the directed edges include:
delivery edges between the entity nodes (Shao [0023], [0026], [0073] [0091] generate graph with nodes and edges), and
impact edges between each incident node and one or more entity nodes, receiving, by a user interface, a selection of a sender node and a receiver node among the entity nodes (Shao [0145] initialization based on user input),
determining at least one optimal path option between the sender node and the receiver node by:
providing, by a graph neural network model receiving the knowledge graph as input, a node embedding for each entity node and an edge embedding for each delivery edge based on the respective node embeddings (Shao [0023], [0026], [0073], [0091] generate graph with nodes and edges; [0122] neural network),
calculating for each entity node, by a first neural network receiving as input the respective node embedding, a probability that the entity node lies on an optimal path (Shao [0142] the higher the pheromone concentration is the higher the probability that the node or the edge is selected to be included in the optimal AGV path),
calculating for each delivery edge, by a second neural network receiving as input the respective edge embedding, a probability that the delivery edge lies on an optimal path (Shao [0142] the higher the pheromone concentration is the higher the probability that the node or the edge is selected to be included in the optimal AGV path),
computing the at least one optimal path option between the sender node and the receiver node using the calculated probabilities (Shao [0146] optimal AGV path is determined based on the final pheromone of all nodes and the final pheromone of all edges),
outputting, by the user interface, the at least one optimal path option (Shao [0042], [0079], [0184] display the path).
Claim 2.
Shao discloses all the elements of claim 1, as shown above. Additionally, Shao discloses:
wherein before determining the at least one optimal path option, the knowledge graph is configured due to a current change in the logistics network by: removing or adding entity nodes or delivery edges, or removing or adding incident nodes or impact edges (Shao [0027] nodes may be extracted; [0091] generate graph with nodes and edges; [0093] node attributes include congestion degree and the traffic correlation degree; [0094] congestion degree; [0124] weighting based on the impact of the traffic correlation degree; [0151] transportation obstacles).
Claim 3.
Shao discloses all the elements of claim 2, as shown above. Additionally, Shao discloses:
wherein the knowledge graph is configured in real time to reflect a dynamically changing status of the logistics network (Shao [0188] processing of each node and each edge is adjusted in real time according to the changes in the specific transportation situation).
Claim 4.
Shao discloses all the elements of claim 1, as shown above. Additionally, Shao discloses:
wherein a greedy pathfinding algorithm computes the at least one optimal path option between the sender node and the receiver node (Shao [0179]-[0180] determine whether interaction ends).
Claim 5.
Shao discloses all the elements of claim 4, as shown above. Additionally, Shao discloses:
wherein the greedy pathfinding algorithm initializes each optimal path option with the sender node and greedily appends delivery edges and entity nodes with a highest probability to the optimal path option until the receiver node is reached (Shao [0142] the higher the pheromone concentration, the higher the probability that the node or edge is selected to be included in the optimal AGV path; [0179]-[0180] determine whether interaction ends).
Claim 6.
Shao discloses all the elements of claim 1, as shown above. Additionally, Shao discloses:
wherein the first neural network and the second neural network are multilayer perceptrons (Shao [0037] intermediate paths; [0158]-[0159] determining the connected node with the highest transition probability; [0172] transition along a route from kth node to the (k+1)th node, until the (k+1)th node corresponds to the material end point; [0122] neural network).
Claim 7.
Shao discloses all the elements of claim 1, as shown above. Additionally, Shao discloses:
wherein the graph neural network model is structured to:
receive an initialization of each node embedding, wherein each node embedding is initialized with a one-hot encoded vector stating if the node represents the sender node, the receiver node, or another node (Shao [0099] types of connected nodes), and
iteratively apply each layer of the graph neural network model to the node embedding (Shao [0179]-[0180] determined whether the iteration ends).
Claim 11.
Shao discloses all the elements of claim 11 as shown above in claim 1.
Claim 12.
Shao discloses a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein (Shao [0043]),
Shao discloses the remaining limitations of claim 12 as shown above in claim 1.
Claim 13.
Shao discloses all the elements of claim 12 as shown above. Additionally, Shao discloses:
a provisioning device for the computer program product according to claim 12, wherein the provision device stores and/or provides the computer program product (Shao [0043]).
Claim 14.
Shao discloses all the elements of claim 1, as shown above. Additionally, Shao discloses:
wherein the entity is a supplier, a distribution center, a port, an airport, or production site (Shao [0024]).
Claim 15.
Shao discloses all the elements of claim 15 as shown above in claim 14.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT M TUNGATE whose telephone number is (571)431-0763. The examiner can normally be reached Monday - Friday, 9:00 - 4:30 EST.
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/SCOTT M TUNGATE/Primary Examiner, Art Unit 3628
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