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 Application
This office action is in response to the most recent filings filed by applicants on 12/30/2025.
Claims 1, 9 and 17 are amended
Claims 7 and 15 are cancelled
Claims 18-22 are added
Claims 1-6, 8-14, 16-22 are pending
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 1-6, 8-14, 16-22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-6, 8 and 18-22 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 9-14 and 16 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 17 is/are directed to a device/apparatus which is a statutory category.
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Claim 1 is directed to an abstract idea, as evidenced by claim limitations “for an input of a graph structure dataset comprising a plurality of nodes and a plurality of edges, each of the plurality of nodes representative of an object, each of the plurality of edges representative of an interaction between each of the plurality of nodes: defining types and attributes for the plurality of nodes and the plurality of edges to generate a heterogenous graph from the input graph structure dataset; associating the plurality of nodes with each other based on similarity; defining a scheme for a meta-path between the plurality of nodes and the plurality of edges based on defined flows; sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process; projecting the associated plurality of nodes into low dimensional embedded vectors using a spatial-based Graph Neural Network (GNN) that aggregates neighbor node features based on heterogeneous graph topology; tracing the sampled positive meta-paths and negative meta-paths from the heterogenous graph by computing similarity scores between the embedded vectors and selecting ones of the sampled positive meta-paths and the negative meta-paths of the associated nodes with most similar embedded vectors; outputting the selected ones of the sampled positive meta-paths and the negative meta- paths through an interface; identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption.”
Applicants originally submitted specification describes the claimed invention in [0010]: Example implementations described herein involve a method, system, and software for tracing paths in graph structured data. Such tracing of paths can involve tracing the physical flow or cashflow of each product in a supply chain, or tracing the propagation path of fake news in a social network. However, in these graph structured graphs, there exist multiple interactions between entities. For example, in a supply chain, each company produces multiple types of product, using a variety of intermediates and materials. In a social network, each user receives and send multiple posts. Furthermore, the correlation between the incoming and outgoing interactions (material and product, incoming and outgoing post) is unknown. Tracing paths in such graph structured data is the problem to be addressed by the example implementations described herein.
In the claim limitations above, “sampling positive meta-paths and negative meta- paths to a spatial based graph neural network”, “nodes into embedded vectors”, “spatial based graph neural network (GNN)”, and “wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process, projecting the associated plurality of nodes into low dimensional embedded vectors using a spatial-based Graph Neural Network (GNN) that aggregates neighbor node features based on heterogeneous graph topology; tracing the sampled positive meta-paths and negative meta-paths from the heterogenous graph by computing similarity scores between the embedded vectors and selecting ones of the sampled positive meta-paths and the negative meta-paths of the associated nodes with most similar embedded vectors; outputting the selected ones of the sampled positive meta-paths and the negative meta- paths through an interface; identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption”. Here, these claims offer further descriptive limitations of elements found in the claim limitations surrounding the above limitations which are similar to the abstract idea noted in the independent claims above. The limitation discussed are still just data represented on the graph.
Additionally, claim limitations such as “identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption” is improving a business process by identifying supply chain disruptions.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to tracing the flow of data on a graph for either a supply chain or social network environment. As discussed in the spec. above, the claimed invention is tracing paths in graph structured data in a supply chain environment for tracing the physical flow or cashflow of each product in a supply chain. While in a social network, it is tracing the propagation path of fake news. In either environment, the tracing paths in graph structured data is simply tracing the flow of data on a graph for either a supply chain or social network environment.
Managing tracing the flow of data on a graph for either a supply chain or social network environment for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
In addition, claim limitations such as “sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph using random walk with restart, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process …. identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption” belong to the grouping of “Mathematical concepts” because the claims are related to solving supply chain disruptions using random walk with restart algorithm. The court have used the phrase “Mathematical concepts” as — mathematical relationships, mathematical formulas or equations, mathematical calculations.
Independent Claims 1 is/are recite substantially similar limitations to independent claims 9 and 17 and is/are rejected under 2A for similar reasons to claims 9 and 17 above.
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A method to trace flow of a node in a graph structure dataset through meta-paths, comprising: through an interface; A non-transitory computer readable medium, storing instructions to trace flow of a node in a graph structure dataset through meta-paths, the instructions comprising: An apparatus configured to trace flow of a node in a graph structure dataset through meta- paths, the apparatus comprising: a processor, configured to: using a spatial-based Graph Neural Network (GNN),” at a high level of generality such that it amounts to no more than: 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, as discussed in MPEP 2106.05(f).
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 9 and 17 does not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Similarly dependent claims 2-6, 8, 10-14 and 16-22 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 4 recite “wherein the associating the plurality of nodes with each other based on similarity comprises classifying the plurality of nodes based on the types and attributes of the physical flow of input or output goods and determining a similarity score from the classifying” and dependent claims 6 recite “wherein the associating the plurality of nodes with each other based on similarity comprises classifying the social media post as real news or fake news based on similarity of the types and attributes of the social media account and social media post to real news or fake news, and clustering the plurality of nodes and the plurality of edges based on the similarity of the types and attributes to determine weights for the plurality of edges.”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 18 recites “wherein sampling the positive meta-paths using random walk with restart comprises: generating each step reversely along a direction of the edges, and restarting to the selected node after reaching a length of the meta-path, and wherein when there are multiple nodes, randomly selecting one node with a probability p, wherein p is a weight of an edge connecting to the node.” Dependent claims 19 recites “wherein sampling the negative meta-paths using random walk with restart comprises: at each step, choosing a type of node defined by the meta-path and one hop from a current node, wherein a probability of moving to an unconnected node is a predefined value q, and a probability of moving to a connected node is q*(1-p), wherein p is a weight of an edge connecting to the connected node.” Dependent claims 20 recites “further comprising: selecting procurement edges and production edges for each company in the heterogeneous graph; and calculating weights for the procurement edges and production edges based on price and quantity data, wherein the weights determine probability used in the random walk with restart”. In this claim, “using random walk with restart” is an additional element, but it is still being recited such that it amounts to no more than: 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, as discussed in MPEP 2106.05(f).
As a result, Examiner asserts that dependent claims, such as dependent claims 2-6, 8, 10-14 and 16-22 are also directed to the abstract idea identified above.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A method to trace flow of a node in a graph structure dataset through meta-paths, comprising: through an interface; using random walk with restart, A non-transitory computer readable medium, storing instructions to trace flow of a node in a graph structure dataset through meta-paths, the instructions comprising: An apparatus configured to trace flow of a node in a graph structure dataset through meta- paths, the apparatus comprising: a processor, configured to: using a spatial-based Graph Neural Network (GNN)” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0048]-[0052], [0099]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 9 and 17 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-6, 8, 10-14 and 16-22 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 9 and 17. As a result, Examiner asserts that dependent claims, such as dependent claims 2-6, 8, 10-14 and 16-22 are also directed to the abstract idea identified above.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103 - withdrawn
In the most recent filings, applicants have amended independent claims 1-6, 8-14, and 16-22. Please see the Remarks dated 12/30/25, especially on pages 12-15 from applicants why the 103 rejection is overcome are persuasive.
In light of applicants’ arguments and the amendments filed by applicants to previously presented claims in light of the originally filed disclosure the previously made rejection under 35 U.S.C. 103 has been withdrawn for following reasons:
None of the references cited - (US 2017/0140285) Dotan-Cohen et al., further in view of (US 2019/0018904) Russell et al., (US 2022/0374812) Riedl and (US 2005/0171826) Denton et al. show the claim limitations discussed above in light of the specification.
Even though, Reference Dotan-Cohen shows in : the term “word generation model” is used to refer to a trained model that receives input text and a target characteristic token and generates a summary tuned to target characteristics identified by the target characteristic token. In an example, the word generation model includes a convolutional neural network-based sequence-to-sequence framework. [0029] FIG. 2 depicts an example of a process 200 for using the word generation model 104 to generate the tuned summary 102 of the input text 110. One or more processing devices implement operations depicted in FIG. 2 by executing suitable program code (e.g., the word generation model 104). For illustrative purposes, the process 200 is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. [0021] Accordingly, at a high level, in one embodiment, user data is received from one or more data sources. The user data may be received by collecting user data with one or more sensors or components on user device(s) associated with a user. Examples of user data, also described in connection to component 210 of FIG. 2, may include information about the user device(s), user activity associated with the user devices (e.g., app usage, online activity, searches, calls, usage duration, and other user-interaction data), network-related data (such as network ID, connection data, or other network-related information), application data, contacts data, calendar and social network data, or nearly any other source of user data that may be sensed or determined by a user device or other computing device. The received user data may be monitored and information about the user activity may be stored in a user profile, such as user profile 240 of FIG. 2. [0027] For example, features associated with an activity event may be categorized (such as by type, similar time frame or location, for example), and related features may be identified for use in determining a similarity or relational proximity to other activity events. [0107] Some embodiments of user accounts and devices 244 may store information across one or more databases, knowledge graphs, or data structures. As described previously, the information stored in user accounts and devices 244 may be determined from user-data collection component 210 or user activity monitor 280; see also [0107] social network accounts and data, such as news feeds; online activity; and calendars, appointments, application data, other user accounts, or the like. [0084] Additionally, in embodiments where user activity filtering may be performed such that each predictor 267 determines a subset of historical user actions with features that correspond to its prediction criteria; see also [0085] The user actions from the subset of historical user actions that are most similar ( or similar enough) to the particular current or recent user action may be selected. In some embodiments, the selection process uses a similarity threshold, such as described above, to determine those historical user actions that satisfy the similarity threshold. [0084] The differences may include, for example, differences in the time-related features, usage duration, sequence distances, etc.
Even though Dotan-Cohen shows in [0084]-[0086] that data can be traced using historical actions against newer dataset to determine similar enough to understand user data pattern from a group of data sets. However, Dotan-Cohen does not take it far enough to show that datasets are used to further identify fake news or click baits or any of these tactics used in social media networks to propagate false information. As such, Dotan-Cohen does not explicitly show “sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph random walk with restart, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process”, “identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption” and “using a spatial-based Graph Neural Network (GNN)”. However, the reference does not show the above claim limitations.
Reference Russell et al. shows how datasets are used to further identify fake news or click baits or any of these tactics used in social media networks to propagate false information at least in [0005]: As fake websites, news sites, journals, and accounts grow across connected networks, assuring the quality of datasets has become paramount to any search process. In any new approaches, traditional back end processes to identify and block fake sites, questionable metadata or malicious code, should be enhanced with a hybrid method of pattern recognition algorithms and human quality assurance editing to block or identify unsubstantiated claims, questionable evidence or data collection methodologies, or controversial or “biased” content to fully inform users of the choices before them, [0015]: The system may highlight paths to complete coverage based on network algorithms showing the shortest path between two nodes, the path reflecting the most shared metadata attributes or the least costly path, among other embodiments, [0046]: Further embodiments relate to the application of network theory familiar to those skilled in the art to the analysis of links and identification of disparate potential paths between nodes that are not directly linked in a graph 190. Further embodiments relate to enabling the user's ability to compare and contrast the metaset attributes and benefits of multiple datasets in context of each other, and to weigh the inputs of other system users and system recommendations for alternate datasets before committing to any financial, licensing, or other data restrictions as defined by the owner of an externally held dataset. [0057] Once uploaded, the system verification process 350 compares the metasets of unverified dataset metadata held in a temporary data store 375 to the most recently updated version of the system's crowdsourced index 360 of datasets and data sources that have already been flagged by users or system administrators as “bad” or as needing a dataset “warning”. The system also compares the temporarily held metasets to the system's updated list of known bad IP and URL sites compiled and maintained by external sources. Datasets, IP addresses, and URLs that have been previously associated with verified claims of “fake” source data, unsubstantiated evidence or data claims, questionable evidence or data collection methodologies, concerns over bias, or other controversies are tagged with “warning” tags for display in the system's user interface. “Bad” datasets 355—defined as: 1) violating the system's data privacy, ethics, or code of conduct standards; 2) containing malware, Trojans or other malicious code designed to do harm to the system or its users; or 3) identified as fake aggregator, “click bait”, or other advertising-oriented sites lacking original content—are tagged, deleted and blocked from further processing within the system, according to an example embodiment. Reference Russell does not show “sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph random walk with restart, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process”, “identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption”. Reference Russell does not explicitly show “using a spatial-based Graph Neural Network (GNN)”. However, the reference does not show the above claim limitations.
Reference Reidl shows Riedl shows “using a spatial-based Graph Neural Network (GNN)” at least in [0033]: skill representation graph 112 is generated by generating a plurality of nodes, generating a plurality of interconnections, and generating a plurality of interrelations. Computing device 104 assembles skill representation graph 112 using plurality of interrelations. In an embodiment, each node of skill representation graph 112 may represent a skill wherein the skill may include any skill described in the entirety of this disclosure. An edge connecting two nodes of a skill representation graph 112 may represent a skill interrelation between two skills represented by the two nodes; where skill interrelation requires greater than a threshold effort to advance from a first skill to a second skill, and/or where path traversing additional nodes represents interrelations having an aggregate or node-to-node degree of difficulty easier by some threshold amount than a direct interrelation between the first skill and the second skill, no edge may be constructed between the first skill and the second skill. In other words, edges of skill representation graph 112 may represent realistic paths for progression from one skill to another. In an embodiment, lengths of edges may represent quantities of skill interrelations 116. For instance, a long skill representation graph 112 edge may represent a greater degree of difficulty in proceeding from one skill to another, while a smaller edge may represent a lesser degree of difficulty. A person of ordinary skill in the art and in view of the entirety of this disclosure would appreciate the skill representation graph and further representations of the nodes and interconnections in the context of progression in view of activities and skills. The generating of the skill representation graph 112 may include the use of neural networks as described in the entirety of this disclosure.[0038]: Network represented in skill representation graph 112 may be hierarchical. Beginner skills may not be directly connected with expert skills; a user may have to go through intermediate skills first. Skill representation graph 112 may apply Louvain community detection to reveal a hierarchy and to communicate goals to users. Hierarchical skill representation graph 112 may be used to represent the fact that skills are a hierarchy in which one cannot go from bottom to top without going through intermediate steps. Skills that are not directly connected, and/or have many steps in between, may lack sufficient complementarity such that acquiring such skills is very difficult. In an alternative embodiment, similarity such as cosine similarity of Euclidean distance from embeddings of the neural network algorithm may be used to construct such a weighted skill-skill network. Clustering and thresholding and/or backboning may be applied as before to generate connections. A further alternative that does not utilize pre-defined skills may include performing community detection or clustering on a latent space to define artificial centroids. Such centroids may then be used as nodes in skill representation graph 112, with calculation of their weighted edges through similarity in the latent space, followed by application of clustering and thresholding and/or backboning. This approach as the benefit that it could identify higher-level skills such as those extracted from semantic latent spaces. A person of ordinary skill in the art, after reviewing the entirety of this disclosure, would appreciate various components of generating a graph in the context of generating interconnections, generation nodes, and assembling of nodes and interconnections for a skill representation graph. [0071]: heterogeneous exercise network such as graph 112 and of neural network architectures for embedding this network in a latent space are depicted, along with and illustrative application scenarios based on node-to-node similarities. Neural network architectures may include any neural network described herein. Heterogenous exercise network may include a family of machine-learning methods, algorithms, and/or models wherein the machine-learning may include any machine-learning described herein. At Step 1, a heterogeneous exercise network as input data is illustrated. The heterogenous exercise network may include skill representation graph 112. Skill representation graph may include any skill representation graph as described herein. The heterogeneous network of Step 1 may include any nodes and interconnection as described herein. A person of ordinary skill in the art after reviewing the entirety of this disclosure would appreciate the details of the heterogenous network in the context of exercises. [0081]: method 800 may include step 815 which includes generating a skill representation graph 112. The skill representation graph 112 may include any skill representation graph as described in the entirety of this disclosure. Step 815 may include generating a graph representing a plurality of skill interrelations 116. Skill interrelation 116 may include any skill interrelation as described in the entirety of this disclosure. Generating of the skill representation graph of method 815 may further comprise using a machine-learning model to generate a plurality of interrelations comprises a configuration of a neural network. Generating of the plurality of interrelations may further include the use of at least a revealed comparative advantage. Step 815 may include generating a plurality of nodes. The nodes may represent, but not limited to, a skill, an individual, an attribute, or the like. Nodes may include any nodes as described herein. Step 815 may further include generating a plurality of interconnections wherein each interconnection represents a process and/or path to master a subsequent skill of a first skill for an individual. Interconnection may include any interconnection as described herein. Reference Reidl shows “random walk” [0070] Still referring to FIG. 4, latent embeddings determined using a neural network may be determined using a metapath2vec algorithm. A metapath2vec algorithm, as used in this disclosure, determines embeddings by performing meta-path-based random walks in heterogenous networks. A person of ordinary skill in the art after reviewing the entirety of this disclosure would appreciate the algorithms performed in the determining of a neural network. Reference Reidl does not explicitly show “sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph random walk with restart, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process”, “identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption”. However, the reference does not show the above claim limitations.
Reference Denton shows a supply chain network in [0012]-[0016], [0068]. Reference Denton does not explicitly show “sampling positive meta-paths and negative meta-paths for a selected node from the heterogenous graph random walk with restart, wherein the positive meta-paths trace physical flow paths from a company supplying raw material through intermediate companies to final products, and the negative meta-paths indicate product outputs produced by companies that do not require the input material in their production process”, “identifying, based on the traced positive meta-paths and negative meta-paths, that a disruption in production of an input material affects product outputs along the positive meta-paths while product outputs along the negative meta-paths are not affected by the disruption”. However, the reference does not show the above claim limitations.
*Additionally, the prior art made of record and not relied upon is considered pertinent to applicant's disclosure; however, the reference does not show the above claim limitations:
NPL Reference:
Reference Y. Zhang, X. Yang and L. Wang, "Clustering via Meta-path Embedding for Heterogeneous Information Networks," 2020 IEEE International Conference on Knowledge Graph (ICKG), Nanjing, China, 2020, pp. 188-194, doi: 10.1109/ICBK50248.2020.00036.
https://ieeexplore.ieee.org/document/9194498
A low-dimensional embedding of multiple nodes is very convenient for clustering, which is one of the most fundamental tasks for heterogeneous information networks (HINs). On the other hand, the random walk-based network embedding is proved to be equivalent to the method of matrix factorization whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a meta-path embedding based clustering method (called MPEClus) is proposed in this paper. Firstly, the original network is transformed into several subnetworks with independent semantics specified by meta-paths to solve the incompatibility problem. Secondly, an approximate commute embedding method, bypassing eigen-decomposition to reduce computational cost, is leveraged to the representation learning of the nodes in each subnetwork. At last, a unified probabilistic generation model is designed to aggregate the vectorized representations learned in different metric spaces for clustering. Experiment results show that MPEClus is effective in HIN clustering and outperforms the state-of-the-art baselines on two real-world datasets. However, the reference does not show the claim limitations above.
Foreign Reference:
Reference (CA 3115640 A1) Altshuler et al. There is provided a method for adapting components of a network, comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, wherein nodes of the graphs denote entities, and edges of the graphs denote interactions between the entities over a network, computing community graphs according to the graphs, computing meta-community graphs according to the community graphs, analyzing dynamics of the community graphs to detect changes between two temporally adjacent community graphs, analyzing dynamics of the meta-community graphs to detect changes between two temporally adjacent meta-community graphs, identifying at least one entity corresponding to node(s) of the dynamic graph according to a predicted likelihood of performing an anomalous action during a future time interval, and generating instructions for adapting component(s) of the network for ensuring availability of network resources for interactions between entities during the future time interval. However, the reference does not show the claim limitations above.
None of the prior art of record, taken individually or in combination, teach, interalia, the claimed invention as detailed in independent claims 1, 9 and 17, wherein the novelty of the claimed invention is in the combination of limitations and not in any single limitation.
Response to Arguments
Applicants’ arguments have been fully considered; however, they are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
Y. Zhang, X. Yang and L. Wang, "Clustering via Meta-path Embedding for Heterogeneous Information Networks," 2020 IEEE International Conference on Knowledge Graph (ICKG), Nanjing, China, 2020, pp. 188-194, doi: 10.1109/ICBK50248.2020.00036.
https://ieeexplore.ieee.org/document/9194498
A low-dimensional embedding of multiple nodes is very convenient for clustering, which is one of the most fundamental tasks for heterogeneous information networks (HINs). On the other hand, the random walk-based network embedding is proved to be equivalent to the method of matrix factorization whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a meta-path embedding based clustering method (called MPEClus) is proposed in this paper. Firstly, the original network is transformed into several subnetworks with independent semantics specified by meta-paths to solve the incompatibility problem. Secondly, an approximate commute embedding method, bypassing eigen-decomposition to reduce computational cost, is leveraged to the representation learning of the nodes in each subnetwork. At last, a unified probabilistic generation model is designed to aggregate the vectorized representations learned in different metric spaces for clustering. Experiment results show that MPEClus is effective in HIN clustering and outperforms the state-of-the-art baselines on two real-world datasets.
Foreign Reference:
(CA 3115640 A1) Altshuler et al. There is provided a method for adapting components of a network, comprising: providing graphs each indicative of a respective sequential snapshot of a dynamic graph obtained over a historical time interval, wherein nodes of the graphs denote entities, and edges of the graphs denote interactions between the entities over a network, computing community graphs according to the graphs, computing meta-community graphs according to the community graphs, analyzing dynamics of the community graphs to detect changes between two temporally adjacent community graphs, analyzing dynamics of the meta-community graphs to detect changes between two temporally adjacent meta-community graphs, identifying at least one entity corresponding to node(s) of the dynamic graph according to a predicted likelihood of performing an anomalous action during a future time interval, and generating instructions for adapting component(s) of the network for ensuring availability of network resources for interactions between entities during the future time interval.
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