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 .
This communication is in response to Amendment filed on January 23, 2026. Claims 1, 2, 4-10, 12-18, and 21-24 are pending. Claims 1, 7-9, and 15-18 are amended. Claims 3, 11, 19 and 20 are cancelled. Claims 21-24 are newly added.
Response to Arguments
Referring to the 35 USC 101 rejection of claims 1-20, Applicant’s amendments are acknowledged. However, only the 101 rejections of claims 1, 2, 4-8, 21 and 22 are overcome by the amendment. The 35 USC 101 rejection of claims 9, 10, 12-18, 23 and 24 are maintained.
Referring to the 35 USC 112(b) rejection of claim 20, Applicant’s cancellation of the claim is acknowledged. However, Applicant’s amendments to the independent claims now raise new 35 USC 112(b) issues as addressed below.
Applicant’s arguments with respect to claims 1, 2, 4-10, 12-18, and 21-24, as amended, have been considered but are moot in view of the new grounds of rejection.
Claim Objections
Claims 9 and 17 are objected to because of the following informalities: the terms ‘metadata descriptive of to the computation resources’ in lines 22 and 18 respectively, should read ‘terms ‘metadata descriptive of the computation resources’. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: the terms ‘the updated the metadata descriptive’ in lines 23-24 should read ‘the updated metadata descriptive’. Appropriate correction is required.
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 9, 10, 12-18, 23 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 9 recites:
generate a domain-specific knowledge graph related to a network of computation resources by a machine learning model applied to domain-specific data descriptive of the computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships, wherein the machine learning model assigns weights to the plurality of domain-specific relationships;
responsive to receiving an input tag, locate in the domain-specific knowledge graph, an input node corresponding to the input tag, extract a subset of domain-specific features of the plurality of domain-specific features connected to the input node via a subset of domain-specific relationships of the plurality of domain-specific relationships, and extract a subset of weights assigned to the subset of domain-specific relationships;
generate a metadata score algorithm for the input tag that weights the subset of domain-specific features according to the subset of weights;
compute metadata tag scores corresponding to the input tag for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources and obtained from the computation resources via an application programming interface (API);
tagging the computation resources with the input tag by updating the metadata descriptive of the computation resources with the input tag and the metadata tag scores; and
configure the network of computation resources by clustering the computation resources according to the updated metadata descriptive of the computation resources and determining an optimal configuration of the network of computation resources from the clusters of computation resources.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 9 recites limitations that are abstract ideas.
The limitations “generate a domain-specific knowledge graph related to a network of computation resources applied to domain-specific data descriptive of the computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships, assigns weights to the plurality of domain-specific relationships” are mental steps. One can mentally or using pen and paper generate a knowledge graph related to selected computing resources by domain specific data pertaining to the resources and mentally assign weights indicating an importance to the relationships between the resources in the graph.
Thus, the claimed limitations can be performed by the human mind.
Furthermore, the limitations “locate in the domain-specific knowledge graph, an input node corresponding to the input tag, extract a subset of domain-specific features of the plurality of domain-specific features connected to the input node via a subset of domain-specific relationships of the plurality of domain-specific relationships, and extract a subset of weights assigned to the subset of domain-specific relationships” are also mental steps. One can look at the graph and visually locate and extract selected data. Thus, the claimed limitations can be performed by the human mind.
The limitations “generate a metadata score algorithm for the input tag that weights the subset of domain-specific features according to the subset of weights” and “compute metadata tag scores corresponding to the input tag for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources and obtained from the computation resources” are also mental steps. A person can generate a metadata scoring function or algorithm in the human mind or using pen and paper and can mentally perform tag score calculations and input scores within the metadata scoring function using pen and paper.
The limitation “tagging the computation resources with the input tag by updating the metadata descriptive of the computation resources with the input tag and the metadata tag scores” is a mental step because a person can choose to associate or tag data within the graph with descriptive metadata and tag scores. Thus, the claimed limitation can be performed by the human mind.
The limitations “configure the network of computation resources by clustering the computation resources according to the updated metadata descriptive of the computation resources and determining an optimal configuration of the network of computation resources from the clusters of computation resources” are mental steps because a user can mentally configure network resources by grouping like data together and mentally determine a desired configuration from the groups of clustered data. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2: Claim 9 recites the limitation “receiving an input tag”. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claim 9 recites the following additional elements “a system”, “at least one processor”, “memory”, “machine learning model” and “an application programming interface (API)”, note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "receiving” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim 9 as a whole does not change this conclusion and the claim is ineligible.
Claim 17 recites:
constructing a domain-specific knowledge graph by one or more Large Language Models (LLMs) applied to domain-specific data descriptive of a network of computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships, wherein the LLM assigns weights to the plurality of domain-specific relationships;
generating a metadata score algorithm for one or more input tags, received from a user device, based on the domain-specific knowledge graph, wherein generating the metadata score algorithm comprises locating, in the domain-specific knowledge graph, input nodes corresponding to the one or more input tags, extracting subsets of domain-specific features of the plurality of domain-specific features connected to the input nodes via subsets of domain-specific relationships of the plurality of domain-specific relationships, and extracting subsets of weights assigned to the subsets of domain-specific relationships, wherein the metadata score algorithm weights the subset of domain-specific features according to the subset of weights;
determine metadata tag scores for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources and obtained from the computation resources via an application programming interface (API); and
update the metadata descriptive of the computation resources to include the input tag and the metadata tag scores; and
configure the network of computation resources by determining an optimal configuration of the network of computation resources from the updated the metadata descriptive of the computation resources.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 17 recites limitations that are abstract ideas.
The limitations “constructing a domain-specific knowledge graph by one or more Large Language Models (LLMs) applied to domain-specific data descriptive of a network of computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships” and “assigns weights to the plurality of domain-specific relationships” are mental steps. One can mentally or using pen and paper generate a knowledge graph related to selected computing resources by domain specific data pertaining to the resources and mentally assign weights indicating an importance to the relationships between the resources in the graph. Thus, the claimed limitations can be performed by the human mind.
The limitations “generating a metadata score algorithm for one or more input tags, based on the domain-specific knowledge graph, wherein generating the metadata score algorithm comprises locating, in the domain-specific knowledge graph, input nodes corresponding to the one or more input tags, extracting subsets of domain-specific features of the plurality of domain-specific features connected to the input nodes via subsets of domain-specific relationships of the plurality of domain-specific relationships, and extracting subsets of weights assigned to the subsets of domain-specific relationships, wherein the metadata score algorithm weights the subset of domain-specific features according to the subset of weights” are also mental steps. A person can generate a metadata scoring function or algorithm in the human mind or using pen and paper and can look at a graph and visually locate and extract selected data. Thus, the claimed limitations can be performed by the human mind.
The limitations “determine metadata tag scores for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources and obtained from the computation resources via an application programming interface (API); and update the metadata descriptive of the computation resources to include the input tag and the metadata tag scores” are also mental steps. A person can generate a metadata scoring function or algorithm in the human mind or using pen and paper and can mentally perform tag score calculations and input scores within the metadata scoring function using pen and paper can choose to associate or tag data within the graph with descriptive metadata and tag scores. Thus, the claimed limitations can be performed by the human mind.
The limitation “configure the network of computation resources by determining an optimal configuration of the network of computation resources from the updated the metadata descriptive of the computation resources” is a mental step because a user can mentally configure network resources by determining a desired configuration from selected data criteria. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2: Claim 17 recites the limitation “receiving input tags from a user device”. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claim 17 recites the following additional elements “a processor”, “computer readable medium”, “one or more LLMs”, and “an application programming interface (API)”, note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "receiving” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim 17 as a whole does not change this conclusion and the claim is ineligible.
Claims 10, 12 and 13 depend from claim 9 and thus include all the limitations of claim 9, therefore claims 10, 12 and 13 recite the same abstract ideas of "mental processes".
Claims 10, 12 and 13 furthermore recite:
(claim 10) wherein the machine learning model comprises a Large Language Model (LLM);
(claim 12): wherein the computation resources comprise one or more virtual machines; and
(claims 13): inputting data descriptive of the computation resources into the machine learning model as the domain-specific data.
Step 1: Claims 10, 12 and 13 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claim 12 recite limitations that are abstract ideas because they depend from claim 9 which recite mental steps.
Claim 12 further defines the computation resources as being one or more virtual machines. Since the computation resources are recited in the generating a domain specific knowledge graph step in claim 9 as a mental step, this limitation also recites a mental step.
Step 2A prong 2: Claim 13 recite the limitations
“inputting data descriptive of the computation resources into the machine learning model as the domain-specific data”. This step is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claim 10 recites the following additional elements “a LLM” note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "inputting” limitation identified as insignificant extra-solution activity above, when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, claims 10, 12 and 13 as a whole do not change this conclusion and the claims are ineligible.
Claims 14-16 depend from claim 9 and thus include all the limitations of claim 9, therefore claims 14-16 recite the same abstract ideas of "mental processes".
Claims 14-16 furthermore recite:
(claim 14) wherein the domain-specific knowledge graph comprises a plurality of nodes representing the plurality of domain-specific features and a plurality of connections between the plurality of nodes representing the plurality of domain-specific relationships;
(claim 15): the input tag corresponding to the input node of the domain-specific knowledge graph and the subset of domain-specific features corresponding to a subset of nodes of the domain-specific knowledge graph connected to the input node; and
(claim 16): wherein extracting the subset of domain-specific features and the subset of domain-specific relationships comprises: identifying the subset of nodes of the plurality of nodes connected to the input node, wherein the subset of domain-specific relationships correspond to connectors connecting the input node to each of the subset of nodes.
Step 1: Claims 14-16 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 14-16 recite limitations that are abstract ideas because they depend from claim 9 which recite mental steps.
Claim 14 further defines the domain-specific knowledge graph as comprising a plurality of nodes representing the plurality of domain-specific features and a plurality of connections between the plurality of nodes representing the plurality of domain-specific relationships. Since the domain-specific knowledge graph is recited in the generating step in claim 9 as a mental step, this limitation also recites a mental step.
Claim 16 recites “identifying the subset of nodes of the plurality of nodes connected to the input node, wherein the subset of domain-specific relationships correspond to connectors connecting the input node to each of the subset of nodes”. This is a mental step as a human could visually identify nodes of a graph to see connecting nodes linked to it. As such, the limitation can be performed in the human mind.
Step 2A prong 2: Claim 15 recite the limitation “the input tag corresponding to the input node of the domain-specific knowledge graph and the subset of domain-specific features corresponding to a subset of nodes of the domain-specific knowledge graph connected to the input node”. This step is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g), as addressed in the ‘receiving an input tag’ step in claim 9, and does not provide integration into a practical application.
Step 2B:
With respect to the " receiving” limitation identified as insignificant extra-solution activity above, when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, claims 14-16 as a whole do not change this conclusion and the claims are ineligible.
Claim 18 depends from claim 17 and thus include all the limitations of claim 17, therefore claim 18 recites the same abstract ideas of "mental processes".
Claim 18 furthermore recites:
“the domain-specific knowledge graph comprises a plurality of nodes connected via a plurality of connectors, wherein the plurality of nodes are based on a plurality of domain-specific features and the plurality of connectors are based on a plurality of domain-specific relationships between the plurality of nodes, wherein the plurality of domain specific features and the plurality of domain-specific relationships are determined by the one or more LLMs from the domain specific data.
Step 1: Claim 18 as a whole falls within one or more statutory categories.
Step 2A prong 1: Claim 18 recites limitations that are abstract ideas because they depend from claim 17 which recite mental steps.
Claim 18 further defines domain-specific knowledge graph comprises a plurality of nodes connected via a plurality of connectors, wherein the plurality of nodes are based on a plurality of domain-specific features and the plurality of connectors are based on a plurality of domain-specific relationships between the plurality of nodes, wherein the plurality of domain specific features and the plurality of domain-specific relationships are determined from the domain specific data. Since the domain-specific knowledge graph is recited in the generating step in claim 17 as a mental step, this limitation also recites a mental step. Furthermore the determining of features and relationships can be done in the human mind.
Step 2A prong 2: Claim 18 recites the following additional elements “one or more LLMs” note that these recited additional elements are a high-level recitation of generic computer hardware and software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
Therefore, claim 18 as a whole does not change this conclusion and the claim is ineligible.
Claims 23 and 24 depend from claims 9 and 17 and thus include all the limitations of claims 9 and 17, therefore claims 23 and 24 recite the same abstract ideas of "mental processes".
Claims 23 and 24 furthermore recite: “grouping the computation resources according to the updated metadata descriptive” and “backing up traffic data to computation resources associated with a group having higher metadata tag scores”
Step 1: Claims 23 and 24 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 23 and 24 recite limitations that are abstract ideas because they depend from claims 9 and 17 which recite mental steps.
The limitation “grouping the computation resources according to the updated metadata descriptive” is a mental step. A human could mentally group together data based on desired criteria. As such, the limitation can be performed in the human mind.
Step 2A prong 2: Claims 23 and 24 recite “backing up traffic data to computation resources associated with a group having higher metadata tag scores”. This is an additional element and is using of a computer or other machinery in its ordinary capacity for tasks such as storing data does not integrate a judicial exception into a practical application or provide significantly more.
Step 2B:
Furthermore, the “backing up traffic data to computation resources associated with a group having higher metadata tag scores” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), “iv. 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” and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole does not change this conclusion and the claims are ineligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
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.
Claims 1, 2, 4-10, 12-18 and 21-24 are 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.
Referring to claims 1, 9 and 17, the term ‘optimal configuration’ is a relative term which renders the claims indefinite. The term “optimal configuration” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
All claims depending from the aforenoted claims are also rejected by virtue of their dependencies.
Due to the 35 USC 112 rejections, the claims have been examined as best understood by the Examiner.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0297851 by Sbodio et al (hereafter Sbodio), in view of US 2025/0094835 by Lowinger et al (hereafter Lowinger), in view of US 2023/0196242 by Kumar et al (hereafter Kumar), and further in view of US 2024/0112473 by Ding et al (hereafter Ding).
Referring to claim 1, Sbodio discloses a method [Abstract] comprising:
generating a domain-specific knowledge graph related to a network of computation resources comprising domain-specific data descriptive of the computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships [wherein an initial knowledge graph is created with one or more curated and focused datasets of elements and relationships to a subject (e.g. type of vehicle), para 12-14, 20; Fig 3, element 302, para 51; knowledge graph is a semantic network, para 1], wherein a machine learning model assigns weights to the plurality of domain-specific relationships [controller assigns weights to crawled data that matches knowledge graph data, para 20, 28, 34];
responsive to receiving an input tag, locating in the domain-specific knowledge graph, an input node corresponding to the input tag, extracting a subset of domain-specific features of the plurality of domain-specific features connected to the input node via a subset of domain-specific relationships of the plurality of domain-specific relationships [second set of data on one or more domains of entity is received and public repositories or the like within the domain are crawled to identify data that matches nodes of the initial knowledge graph and underlying data detailing the association (i.e. relationships), para 52, Fig 3, element 304; wherein the nodes of the initial knowledge graph are analyzed to identify domains of the entity as a potential domain for searching within, after which the crawling takes place, para 52], and extracting a subset of weights assigned to the domain-specific relationships of the plurality of domain-specific relationships corresponding to the subset of domain-specific features [controller crawls across databases to identify data that may match the knowledge graph. If the controller finds any data that appears to match (whether as a node or an edge or both), the controller may input this into the knowledge graph along with the respective score/weights for this data, para 20];
generating a metadata score algorithm for the input tag that weights the subset of domain-specific features according to the subset of weights [confidence scores are calculated for each probabilistic edge and pertaining to each element, para 53, Fig 3, element 306; the generation of probabilistic knowledge graphs is performed by machine learning algorithms that are generated and applied, para 48];
compute metadata tag scores corresponding to the input tag for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources [confidence scores are calculated for each probabilistic edge and pertaining to each element, para 53, Fig 3, element 306; machine learning algorithm is applied to enable controller to identify certain types of data within domain data 140 that is found to be more predictive and/or useful for different types of entities, para 48];
tagging the computation resources with the input tag by updating the metadata descriptive of the computation resources with the input tag and the metadata tag scores [probabilistic KG is generated and stored such that confidence scores are stored as metadata associated with each respective edge as a combined confidence score/weight and/or sub scores/weights relating to individual factors, para 34]; and
configuring the network of computation resources by clustering the computation resources according to the updated metadata descriptive of the computation resources [confidence scores are normalized in clusters within a given probabilistic KG, para 54; downstream evaluations using probabilistic KG, para 55, Fig 3, element 308].
While Sbodio discloses all of the above claimed subject matter and also discloses that controller 110 can query domain knowledge 140 for data [para 26] and that the probabilistic KG is used to execute downstream evaluations [para 54-55], it remains silent as to the query received via an API; the domain-specific knowledge graph being generated by a machine learning model; and directing data traffic within the network.
Lowinger discloses that LLMs are used to extract information identifying constructors, constructs and categories of constructs, which are used by the data layer to store a knowledge graph with the extracted data [para 164, 296].
Sbodio and Lowinger are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the initial knowledge graph of Sbodio to include the use of the LLMs of Lowinger in generating the initial knowledge graph because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification since both Sbodio and Lowinger are directed to the use of machine learning analysis for data with the knowledge graphs. The use of the LLMs in Lowinger provides a refinement to the type of machine learning model used.
Still referring to claim 1, while Sbodio/Lowinger discloses all of the above claimed subject matter, also discloses that controller 110 can query domain knowledge 140 for data [Sbodio, para 26] and that the probabilistic KG is used to execute downstream evaluations [Sbodio, para 54-55], it remains silent as to the query received via an API; and directing data traffic within the network.
Kumar discloses a data insight layer 220 that includes an API for supporting interactive data queries [para 69].
Sbodio, Lowinger and Kumar are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the input queries by the controller in Sbodio to include the query API within the data insight layer of Kumar because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the query API of Kumar refines the source that the query of Sbodio comes from.
Still referring to claim 1, while Sbodio/Lowinger/Kumar discloses all of the above claimed subject matter, also discloses that the probabilistic KG is used to execute downstream evaluations [Sbodio, para 54-55], it remains silent as to directing data traffic within the network.
Ding discloses object-trajectory clustering with hybrid reasoning for machine learning applications that include intelligent traffic monitoring to improve traffic flow [para 1-2].
Sbodio, Lowinger, Kumar and Ding are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the downstream evaluations applications in Sbodio to include the intelligent traffic monitoring of Ding because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the traffic monitoring application of Ding further refines the type of downstream evaluations taught by Sbodio.
Referring to claim 9, the limitations of the claim are similar to those of claim 1 in the form of a system [Sbodio, para 2, Fig 1, environment 100] comprising a memory storing instructions [Sbodio, para 23, Fig 1]; and at least one processor [Sbodio, para 23, Fig 1]. As such, claim 9 is rejected for the same reasons as claim 1.
Referring to claim 17, Sbodio discloses a non-transitory computer-readable storage medium storing instructions that, when executed by a processor [memory 230, processor 220, para 44, Fig 2], cause the processor to:
construct a domain-specific knowledge graph comprising domain-specific data descriptive of a network of computation resources, the domain-specific knowledge graph comprising a plurality of domain-specific features connected via a plurality of domain-specific relationships [wherein an initial knowledge graph is created with one or more curated and focused datasets of elements and relationships to a subject (e.g. type of vehicle), para 12-14, 20; Fig 3, element 302, para 51; knowledge graph is a semantic network, para 1], wherein the LLM assigns weights to the plurality of domain-specific relationships [controller assigns weights to crawled data that matches knowledge graph data, para 20, 28, 34];
generate a metadata score algorithm for one or more input tags, received from a user device, based on the domain-specific knowledge graph [second set of data on one or more domains of entity is received, para 52, Fig 3, element 304; generation of probabilistic KG 120 includes probabilities, para 53, Fig 3, element 306], wherein generating the metadata score algorithm comprises locating, in the domain-specific knowledge graph, input nodes corresponding to the one or more input tags, extracting subsets of domain-specific features of the plurality of domain-specific features connected to the input nodes via subsets of domain-specific relationships of the plurality of domain-specific relationships [second set of data on one or more domains of entity is received and public repositories or the like within the domain are crawled to identify data that matches nodes of the initial knowledge graph and underlying data detailing the association (i.e. relationships), para 52, Fig 3, element 304; wherein the nodes of the initial knowledge graph are analyzed to identify domains of the entity as a potential domain for searching within, after which the crawling takes place, para 52], and extracting subsets of weights assigned to the subsets of domain-specific relationships, wherein the metadata score algorithm weights the subset of domain-specific features according to the subset of weights [controller crawls across databases to identify data that may match the knowledge graph. If the controller finds any data that appears to match (whether as a node or an edge or both), the controller may input this into the knowledge graph along with the respective score/weights for this data, para 20];
determine metadata tag scores for each of the computation resources by populating the metadata score algorithm with metadata descriptive of to the computation resources [confidence scores are calculated for each probabilistic edge and pertaining to each element, para 53, Fig 3, element 306; machine learning algorithm is applied to enable controller to identify certain types of data within domain data 140 that is found to be more predictive and/or useful for different types of entities, para 48]; and
update the metadata descriptive of the computation resources to include the input tag and the metadata tag scores [probabilistic KG is generated and stored such that confidence scores are stored as metadata associated with each respective edge as a combined confidence score/weight and/or sub scores/weights relating to individual factors, para 34]; and
configure the network of computation resources by determining a configuration of the network of computation resources from the updated the metadata descriptive of the computation resources [confidence scores are normalized in clusters within a given probabilistic KG, para 54; downstream evaluations using probabilistic KG, para 55, Fig 3, element 308].
While Sbodio discloses all of the above claimed subject matter and also discloses that controller 110 can query domain knowledge 140 for data [para 26] and that the probabilistic KG is used to execute downstream evaluations [para 54-55], it remains silent as to the query received via an API; the domain-specific knowledge graph being generated by a machine learning model; and determining optimal configuration of network resources.
Lowinger discloses that LLMs are used to extract information identifying constructors, constructs and categories of constructs, which are used by the data layer to store a knowledge graph with the extracted data [para 164, 296].
Sbodio and Lowinger are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the initial knowledge graph of Sbodio to include the use of the LLMs of Lowinger in generating the initial knowledge graph because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification since both Sbodio and Lowinger are directed to the use of machine learning analysis for data with the knowledge graphs. The use of the LLMs in Lowinger provides a refinement to the type of machine learning model used.
Still referring to claim 17, while Sbodio/Lowinger discloses all of the above claimed subject matter, also discloses that controller 110 can query domain knowledge 140 for data [Sbodio, para 26] and that the probabilistic KG is used to execute downstream evaluations [Sbodio, para 54-55], it remains silent as to the query received via an API; and determining optimal configuration of network resources.
Kumar discloses a data insight layer 220 that includes an API for supporting interactive data queries [para 69].
Sbodio, Lowinger and Kumar are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the input queries by the controller in Sbodio to include the query API within the data insight layer of Kumar because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the query API of Kumar refines the source that the query of Sbodio comes from.
Still referring to claim 17, while Sbodio/Lowinger/Kumar discloses all of the above claimed subject matter, also discloses that the probabilistic KG is used to execute downstream evaluations [Sbodio, para 54-55], it remains silent as to determining optimal configuration of network resources.
Ding discloses object-trajectory clustering with hybrid reasoning for machine learning applications that include intelligent traffic monitoring to improve traffic flow [para 1-2].
Sbodio, Lowinger, Kumar and Ding are analogous art because they are directed to the same field of endeavor- analysis of data within knowledge graphs. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the downstream evaluations applications in Sbodio to include the intelligent traffic monitoring of Ding because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the traffic monitoring application of Ding further refines the type of downstream evaluations taught by Sbodio.
Referring to claims 2 and 10, Sbodio/Lowinger/Kumar/Ding discloses that the machine learning model comprises a Large Language Model (LLM) [Lowinger, para 164, 296].
Referring to claims 4 and 12, Sbodio/Lowinger/Kumar/Ding discloses that the computation resources comprising one or more virtual machines [Kumar, para 112, Fig 9].
Referring to claims 5 and 13, Sbodio/Lowinger/Kumar/Ding discloses inputting data descriptive of the computation resources into the machine learning model as the domain-specific data [Lowinger, LLMs extract information identifying constructors, constructs and categories of constructs, which are used by the data layer to store a knowledge graph with the extracted data, para 164, 296].
Referring to claims 6 and 14, Sbodio/Lowinger/Kumar/Ding discloses that the domain-specific knowledge graph comprises a plurality of nodes representing the plurality of domain-specific features and a plurality of connections between the plurality of nodes representing the plurality of domain-specific relationships [Sbodio, knowledge graph has nodes and edges, para 11-12, 20; Lowinger, LLM, para 164, 296].
Referring to claims 7 and 15, Sbodio/Lowinger/Kumar/Ding discloses that the input tag corresponds to an input node of the domain-specific knowledge graph and the subset of domain-specific features corresponds to a subset of nodes of the domain-specific knowledge graph connected to the node [Sbodio, nodes of initial knowledge graph, para 52].
Referring to claims 8 and 16, Sbodio/Lowinger/Kumar/Ding discloses that extracting the subset of domain-specific features and the subset of domain-specific relationships comprises: locating the input node, on the domain-specific knowledge graph, corresponding to the input tag; and identifying the subset of nodes of the plurality of nodes connected to the input node, wherein the subset of domain-specific relationships correspond to connectors connecting the input node to each of the subset of nodes [Sbodio, second set of data on one or more domains of entity is received and public repositories or the like within the domain are crawled to identify data that matches nodes of the initial knowledge graph and underlying data detailing the association (i.e. relationships), para 52, Fig 3, element 304].
Referring to claim 18, Sbodio/Lowinger/Kumar/Ding discloses that the domain-specific knowledge graph comprises a plurality of nodes connected via a plurality of connectors, wherein the plurality of nodes are based on a plurality of domain-specific features and the plurality of connectors are based on a plurality of domain-specific relationships between the plurality of nodes, wherein the plurality of domain-specific features and the plurality of domain-specific relationships are determined by the one or more LLMs from the domain-specific data [Sbodio, knowledge graph has nodes and edges, para 11-12, 20; Lowinger, LLM, para 164, 296].
Referring to claim 19, Sbodio/Lowinger/Kumar/Ding discloses that for each item of the plurality of items, populate the metadata score algorithm with metadata descriptive of the item, wherein the metadata tag scores are determined by executing the metadata score algorithm populated with the metadata descriptive of the item [Sbodio, reliability (trust) scores run through schema used to generate probabilities and probabilistic edges, para 28].
Referring to claim 20, Sbodio/Lowinger/Kumar/Ding discloses obtaining weights for the metadata descriptive of the item from the domain-specific knowledge graph, wherein the metadata tag scores are determined based on the weights [Sbodio, highest relation type weight, para 31,34].
Referring to claim 21, Sbodio/Lowinger/Kumar/Ding grouping the computation resources according to the updated metadata descriptive and backing up traffic data to computation resources associated with a group having higher metadata tag scores [Sbodio, clustering;, Ding, traffic flow improving, para 1-2].
Referring to claims 22-24, Sbodio/Lowinger/Kumar/Ding discloses classifying the computation resources into categories according to the metadata tag scores; and updating the metadata descriptive of the computation resources by reordering the metadata descriptive of the computation resources according to the categories [Sbodio, para 48].
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.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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