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 .
Claims 1-20 are currently pending for examination.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. "a computer program comprising instructions..", as claimed, is merely a set of instructions - i.e. software code itself - which is not a process, machine, manufacture or composition of matter. Per MPEP 2106.03, a claim must fall within at least one statutory category to be eligible under 101. The MPEP further clarifies: "Products that do not have a physical or tangible form, such as a computer program per se (often referred to as "software pe se") when claimed without any structural recitation, do not fall within any statutory category (See MPEP 2106.03(I)). "A computer program" which is not embodied on a non-transitory computer-readable medium (an article of manufacture) is not directed to any of the four statutory categories. Therefore, claim 20 is directed to software per se, which does not fall within any of the four statutory categories of patentable subject matter under 35 USC 101 and is therefore non-statutory.
The applicant is advised to amend this claim to "A computer program product comprising a non-transitory computer-readable medium having computer instructions to....", or something similar including “non-transitory” to overcome this 101 rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2).
As per claim 1, Martinez Ayala discloses:
A method for generating an ontological dataset using cloud data for development operations, the method comprising: receiving cloud data from a plurality of sources; generating an ontology dataset for the cloud data from the plurality of sources based on parsing the cloud data from the plurality of sources ("The system includes an input/output unit, a memory unit, an ontology generation unit, and a knowledge retrieval system. The input/output unit is configured to receive a plurality of documents from one or more document sources. The memory unit is configured to store the plurality of documents received from the one or more document sources. The ontology generation system is operatively coupled to the input/output unit and the memory unit and is configured to generate the organization level ontology. ", 0016 ; " For example, the ontology system 102, in some embodiments, may be a cloud environment incorporating the operations of the input/output unit 114, the communication interface 115, the memory unit 116, the ontology generation system 118, and the knowledge retrieval system 120, and various other operating modules to serve as a software as a service model for the document sources 104 and the organization.", 0030 ; “ Further, the generation unit 204 may be configured to generate an intermediate document ontology, such as one of the intermediate document ontologies 302 (shown in FIG. 3) for every document received from the document sources 104. The intermediate document ontologies 302, such as the intermediate document ontology 302-N, may be represented in the form of a directed graph including each of the extracted nodes and the directed relationships among the extracted nodes, wherein each node represents an edge while the directed relationship represents a link between two nodes in the directed graph.”, 0046 ; Examiner Note: extracting nodes and relationships equates to parsing the cloud data from a plurality of sources)
initiating provisioning of an electronic dashboard on a display device based on a first user input, wherein: the electronic dashboard includes one or more display elements associated with the ontology dataset, and the one or more display elements of the electronic dashboard are activatable to load a computing resource associated with the cloud data. ("In an embodiment, the knowledge retrieval system 120 may be operatively coupled to the ontology generation system 118 and configured to process one or more search queries, received from one or more users, such as via the one or more user devices 106, using the generated organization level ontology. The knowledge retrieval system 120 may further be configured to provide for display, via the input/output unit 114 on a display, such as a display of the user device 106, one or more search results, and a traversable pathway to access the one or more documents corresponding to the search results in response to the received search queries.", 0036)
Martinez Ayala discloses the above limitations of claim 1, but does not disclose the determination of which analysis operations were applied to the cloud data, or subsequent processes.
However, Mizrahy discloses:
evaluating the cloud data to determine which analysis operations have been applied to the cloud data; confirming corresponding outputs generated based on the analysis operations executed on the cloud data; generating data categories for one or more of data elements of the cloud data or the outputs generated based on the analysis operations; and linking the data categories to generate the ontology dataset having an ontological structure that provides relationships between one or more of: the data elements of the cloud data, the outputs generated based on the analysis operations, or a combination of the data elements of the cloud data and the outputs generated based on the analysis operations ("The method further may include querying a queried dataset using multiple clusters of key subunits, to provide multiple query results; consolidating the multiple query results to provide a consolidated dataset; and extracting clusters of key subunits from the consolidated dataset.", 0012 ; "The method may include generating a concise representation of the content of the query results and of the content of the sample of the first dataset", 0013 ; "The method may include consolidating the multiple query results to provide a consolidated dataset; and generating a concise representation of the consolidated dataset.", 0022 ; "(b) determining the similarities (distance) between the locations associated with different key subunits, and (c) clustering the key subunits based on these distances (similarities)--lower distances indicate of closer semantic relationships", 0122 ; "The subgroups are meaningful in the sense that they are expected to reflect the content of the first dataset. Additionally or alternatively, they can be meaningful in the sense that the key subunits that may form a subgroup are linked or related to each other.", 0103 ; Examiner Note: The analysis operations are either consolidation or clustering, it is necessary that the extraction module has knowledge of this, as it generates a representation of the content of the query results ; the query results being the outputs. The clustered key subunits equate to data categories, and are linked.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala with those of Mizrahy in order to provide the the ontology generation system with an extraction module which can operate without using any a-priory knowledge about the content of the cloud data (Mizrahy, [0152]).
Martinez Ayala in view of Mizrahy discloses the above limitations of claim 1, but does not disclose the generation of ontological datasets in a development operation context.
However, Carpenter discloses:
generating an ontological dataset using cloud data for development operations ("In short, graphical representation allows for a highly flexible representation that may not be dependent on any given cloud template schema, and can be analyzed at large scale. DevOps integration allows analysis to occur at one or multiple points in the development and deployment chain. The Cloud Resource Ontology (CRO) is a unique representation that can be enriched from other sources (i.e. security scans) and used for other purposes (i.e. Cyber situational awareness).”, col.7, lines 32-40)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala and Mizrahy with those of Carpenter in order to integrate the ontological dataset generation system into a DevOps context, thereby allowing the effective analysis of cloud data at multiple points in the development and deployment chain (Carpenter, [col.7, lines 32-40]).
As per claim 12, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1.
Furthermore, Martinez Ayala discloses:
the ontological structure is based on a graph data structure, the graph data structure including: one or more nodes indicating at least one of the data elements of the cloud data or the outputs generated based on the analysis operations, and one or more vertices indicating at least one relationship between: the data elements of the cloud data, the outputs generated based on the analysis operations, or the data elements of the cloud data and the outputs generated based on the analysis operations. (“The extraction unit is configured to extract from each of the plurality of documents, one or more nodes and one or more directed relationships among the extracted one or more nodes. For each of the plurality of documents, the generation unit is configured to generate an intermediate document ontology including the extracted one or more nodes and the one or more directed relationships.”, 0016 ; "In an exemplary embodiment, the organization level ontology 306 is also a directed graph including nodes and directed relationships among the nodes, wherein each node represents an edge while the directed relationship represents a link between two nodes in the directed graph", 0086 ; Examiner Note: the directed relationships/links between nodes equate to vertices)
As per claim 17, it is a system claim with substantially the same limitations as claim 1, and as such, it is rejected for substantially the same reasons.
Claims 2, 3, 4, 13, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Koomullil (US 20120233160 A1).
As per claim 2, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not disclose loading a visual indicator of the computing resource on a graphical user interface device associated with the cloud data.
However, Koomullil discloses:
the one or more display elements of the electronic dashboard are electronically linked to the computing resource such that activating the one or more display elements loads a visual indicator of the computing resource on a graphical user interface device associated with the cloud data. ("Every topic has an underlying ontology which includes multiple topics and their relations. In the graph window 530, the user is allowed to navigate in the graphical representation of the context or topic or topics relation. If the user clicks or selects one topic in the representation, the system takes the user to a different part of ontology, where the selected topic becomes the central theme.", 0046)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Koomullil in order to provide a user interface which helps the user to make a decision quickly and easily by avoiding unnecessary searching (Koomullil, [0038]).
As per claim 3, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not disclose the display of picture data associated with the ontology dataset.
However, Koomullil discloses:
the one or more display elements comprise at least one of: picture data associated with the ontology dataset, video data associated with the ontology dataset, audio data associated with the ontology dataset, and textual data including tabular or non-tabular data associated with the ontology dataset. ("Every topic has an underlying ontology which includes multiple topics and their relations. In the graph window 530, the user is allowed to navigate in the graphical representation of the context or topic or topics relation. If the user clicks or selects one topic in the representation, the system takes the user to a different part of ontology, where the selected topic becomes the central theme.", 0046)
As per claim 4, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not disclose a file associated with the ontology dataset.
However, Koomullil discloses:
the computing resource comprises one or more of: a file associated with the ontology dataset, an application associated the ontology dataset, configuration parameters of an electronic equipment associated with the ontology dataset, or configuration parameters of an electro-mechanical equipment associated with the ontology dataset. (“According to one aspect of the invention there is provided a method of providing assistance to a user to identify at least one context of search results of a search query, the method comprising: receiving an input related to the search query, providing a plurality of first contexts related to the received input, wherein the first contexts include one of the keywords and concepts, displaying a set of results from a web search or a file search based on the selection of the first context, each search result containing a link to a web page or file location”, 0011)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Koomullil in order to provide a method for assisting the user in identifying the context of search results or selection (Koomullil, [0011]).
As per claim 13, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not disclose an electronic dashboard which includes a search field.
However, Koomullil discloses:
the electronic dashboard includes a search field for receiving the first user input or a second user input, the one or more display elements being generated and displayed on the electronic dashboard based on the first user input or the second user input. (“FIG. 3 shows a third aspect of an exemplary user interface to identify at least one context of search results of a search query according to one embodiment of the present invention. FIG. 3 illustrates the User Interface includes query window 310, a topic window 320, a graph window 330, a concepts window 340, a keyword window 350 and a search results window 360. The query window 310 is capable of accepting one or more query strings or query sequences from one or more users.”, 0035)
As per claim 18, it is a system claim with substantially the same limitations as claim 2, and as such, it is rejected for substantially the same reasons.
As per claim 19, it is a system claim with substantially the same limitations as claim 3, and as such, it is rejected for substantially the same reasons.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Mene (US 20240085892 A1).
As per claim 5, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not explicitly disclose the receipt of report data associated with energy development operations.
However, Mene discloses:
the plurality of sources includes one or more of: workflow data native to multiple domains associated with the cloud data; report data associated with energy development operations or energy exploration operations; report data associated with a resource site, report data associated with a site different from or similar to the resource site, or simulation data associated with the resource site, the site different from or similar to the resource site, or the energy development operations or the energy exploration operations ("The ontology adaptation module may derive the workflow using the data received and/or accessed at step 202 with the one or more linguistic analysis techniques and/or receive the workflow directly from the user on the EUD 103. The workflow may be a specific business process, safety procedure, evacuation procedure, activity, and/or other process performed on the industrial floor. The ontology adaptation module may extract one or more concepts from the workflow and identify how the workflow will be executed according to the business process ontology (e.g., process ontology). Accordingly, the ontology adaptation module may identify one or more inefficiencies within the business process ontology (e.g., process ontology) and/or the plurality of machines on the industrial floor. The ontology adaptation module 150 may identify the one or more inefficiencies by comparing the business process ontology (e.g., process ontology) with the performance of the digital twin for the workflow and/or measuring one or more performance metrics. The one or more performance metrics may include, but are not limited to including, bottlenecks in the workflow, long durations and/or wait times in activity executions, lagging KPIs, process outcomes, possible replacement machines and/or equipment, consumption, output produced (e.g., throughput), amongst other performance metrics. ", 0042 ; “; "The ontology adaptation module 150 may simulate each of the one or more new business process ontologies (e.g., new process ontologies) using at least the one or more machine learning models and/or one or more simulation methods described at step 206. The ontology adaptation module 150 may rank the one or more new business process ontologies (e.g., new process ontologies) based on their respective performances in the digital twin simulation.”, 0046)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Mene, in order to provide the system with the ability to identify inefficiencies within an existing process ontology based on a workflow performance of a digital twin (Mene, [0014]).
As per claim 14, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not disclose the ontology dataset is updated based on configuration data.
However, Mene discloses:
the ontology dataset is updated based on configuration data from an entity that has access to the cloud data, the entity comprising one of a user computing device or a computing device associated with an organization ("In an embodiment, the user may also input one or more potential changes and/or potential updates to the industrial floor using the EUD 103. In this embodiment, the ontology adaptation module 150 may generate one or more new business process ontologies (e.g., new process ontologies) which incorporate the one or more potential changes and/or potential updates.", 0047; Examiner Note: changes to the industrial floor correspond to changes in configuration data)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Mene, in order to provide the system with the ability to maintain an updated ontological dataset at all times (Mene, [0047]).
Claims 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Mene (US 20240085892 A1) in further view of Seibel (US 20230273663 A1).
As per claim 6, Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Mene fully discloses the limitations of claim 5, but does not disclose the multiple domains associated with the cloud data comprise one or more operations comprised in the energy development operations.
However, Seibel discloses:
the multiple domains associated with the cloud data comprise one or more operations comprised in the energy development operations or the energy exploration operations. ("For example, in one embodiment, a method comprises receiving energy consumption data corresponding to operation of a plurality of edge devices from a plurality of edge service providers. In the method, a plurality of energy efficiency scores are computed based, at least in part, on the energy consumption data. The plurality of energy efficiency scores correspond to operation of one or more edge devices of the plurality of edge devices associated with respective ones of the plurality of edge service providers. The method further comprises receiving one or more energy consumption parameters from at least one user device for the operation of the one or more edge devices, and identifying based, at least in part, on the plurality of energy efficiency scores, a subset of the plurality of edge devices corresponding to the one or more energy consumption parameters", 0005)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, Carpenter, and Mene with those of Seibel in order to provide techniques for defining and configuring energy efficiency and environmental impact parameters (e.g., settings and/or requirements) in connection with edge devices- and enable automatic generation of energy efficiency and environmental impact ratings (Seibel, [0007]).
As per claim 8, Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Mene fully discloses the limitations of claim 5.
Furthermore, Mene discloses:
the workflow data native to the multiple domains associated with the cloud data includes metadata associated with the workflow data ("At 202, the ontology adaptation module 150 constructs a business process ontology (e.g., process ontology) for an industrial floor. The ontology adaptation module 150 may construct the business process ontology (e.g., process ontology) for the industrial floor based on data received and/or accessed. The data received and/or accessed may include, but is not limited to including, metadata received from the equipment, machines, and/or other assets of the industrial floor, documentation received from a user, amongst other data.", 0035)
Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Mene fully discloses the above limitation of claim 8, but does not disclose the report data associated with the energy development operations or the energy exploration operations comprises metadata associated with the energy development operations or the energy exploration operations.
However, Seibel discloses:
the report data associated with the energy development operations or the energy exploration operations comprises metadata associated with the energy development operations or the energy exploration operations ("Alternatively, the metering and logging logic 207 can be part of another edge device, other computing device or a cloud service capable of retrieving data from one or more edge devices and transmitting the retrieved data to the edge resource platform 110. In one or more embodiments, the metering and logging logic 207 captures input-output (IO) event metadata, and other metadata pertaining to energy efficiency such as, but not necessarily limited to, power consumption levels.", 0030)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, Carpenter, and Mene with those of Seibel in order to provide further detail and context to the energy development report data through the inclusion of metadata (Seibel, [0030]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Mene (US 20240085892 A1) in further view of Symons (US 12511947 B1).
As per claim 7, Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Mene fully discloses the limitations of claim 5.
Furthermore, Mene discloses:
the simulation data associated with the resource site comprises analysis data associated with exploring a resource at the resource site including metadata associated with the analysis data. ("The ontology adaptation module 150 may construct the business process ontology (e.g., process ontology) for the industrial floor based on data received and/or accessed. The data received and/or accessed may include, but is not limited to including, metadata received from the equipment, machines, and/or other assets of the industrial floor, documentation received from a user, amongst other data.", 0035)
Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Mene fully discloses the above limitation of claim 7, but does not disclose the report data associated with the resource site including data captured by one or more sensors disposed about the resource site.
However, Symons discloses:
the report data associated with the resource site includes data captured by one or more sensors disposed about the resource site including metadata associated with the data captured by the one or more sensors disposed at the resource site ("FIG. 2 is a block diagram of the example site visibility system (or “local” site visibility system) in communication with a site monitor device and with a cloud site visibility system. In this example, the local site visibility system 110 communicates sensor data, such as one or more video streams acquired from cameras 104, to the site monitor device 220”, col.12, lines 45-51 ; “Event Data: data associated with an event, such as a set of sensor data (e.g., metadata and/or asset data), such as photographs, video files, etc., associated with a detected safety event.", col.10, lines 11-14)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, Carpenter, and Mene with those of Symons in order to provide a download system which increases the efficiency and cost-effectiveness of the system, and reduces the time required to obtain data from a gateway device (Symons, [col.1-2, lines 52-7]).
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Engelberg (US 20230021961 A1).
As per claim 9, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not explicitly disclose merging the operations data with the report data.
However, Engelberg discloses:
the ontology dataset in conjunction with the electronic dashboard are configured, to: track operations data including workflow data native to multiple domains associated with the cloud data; merge the operations data with one or more of: report data associated with a resource site, report data associated with energy development operations or energy exploration operations, report data associated with a site different from or similar to the resource site, or simulation data associated with the resource site, the site different from or similar to the resource site, or the energy development operations or the energy exploration operations ("In some implementations, the digital twin builder 202 ingests specification data from the specifications module 204 and operational data from data module 206. In some examples, the specification data is at least partially provided as an ontology that specifies domains of energy consumption, process, and risk. The digital twin builder 202 builds an instance of a knowledge graph using the specification data and the operational data. The knowledge graph instance is loaded to a graph database (e.g., in the store 212).", 0025)
execute one or more opportunity assessment operations based on the merging; generate, based on the one or more opportunity assessment operations, decisions data indicating one or more of: a resource model associated with the resource site, the site different from or similar to the resource site, the energy development operations, or the energy exploration operations, contextual data associated with: the energy development operations or the energy exploration operations, the resource site or the site different from or similar to the resource site, audit trail data that links one or more output data from the opportunity assessment operations with one or more data elements of the cloud data (“In some implementations, actions include receiving data representative of a physical entity, generating an initial knowledge graph representative of a process that is executed by the physical entity based on the data, enriching the initial knowledge graph to provide a process aware energy consumption (PAEC) digital twin of the process as an enriched knowledge graph, providing at least two permutations based on the PAEC digital twin, executing analytics at least partially based on the at least two permutations to provide one or more recommendations", 0004 ; "A digital twin is unique, because it captures and models the aspect of the energy consumption of a particular physical entity. In short, the digital twin, here, the PAEC digital twin, can be described as an inferencing model of the physical entity.", 0023)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Engelberg in order to provide the system with a means for building a knowledge graph wherein all information is connected in a way that it is time- and resource-efficient to extract (Engelberg, [0057]).
As per claim 10, Martinez Ayala in view of Mizrahy in further view of Carpenter in further view of Engelberg fully discloses the limitations of claim 9.
Furthermore, Engelberg discloses:
the decisions data comprise data associated with opportunity assessment operations, the opportunity assessment operations including one or more of: generating knowledge data indicating at least one relationship between: the data elements of the cloud data, the outputs generated based on the analysis operations, or a combination thereof, sequencing or structuring the knowledge data to generate at least an optimized set of computing operations associated with the energy development operations ("In general, a knowledge graph can be described as a collection of data and related based on a schema representing entities and relationships between entities. The data can be logically described as a graph (even though also provided in table form), in which each distinct entity is represented by a respective node, and each relationship between a pair of entities is represented by an edge between the nodes. Each edge is associated with a relationship and the existence of the edge represents that the associated relationship exists between the nodes connected by the edge.", 0026)
executing, using the optimized set of computing operations, one or more of: configuring an electronic or mechanical device associated with the energy development operations, or generating one or more computing models associated with the energy development operations ("In some examples, a permutation can be selected and a configuration delta can be provided. In some examples, the configuration delta represents a difference between a baseline configuration and a configuration permutation of the selected permutation. In some examples, the baseline configuration can be adjusted based on the configuration delta to achieve the configuration permutation. For example, and without limitation, parameters of one or more physical entities (e.g., energy consuming machines) can be adjusted based on the configuration delta. In this context, the configuration delta can be representative of one or more recommendations for configuration of the physical entity to optimize energy consumption of the physical entity within constraints (e.g., outcome, SLA, risk) of an underlying process.", 0052)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Engelberg in order to optimize energy consumption of the system (Engelberg, [0052]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Sezgin (US 11709892 B2).
As per claim 11, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not explicitly disclose the ontology dataset comprising a library of data that connects information across multiple domains associated with the cloud data.
However, Sezgin discloses:
the ontology dataset comprises a library of data that connects information across multiple domains associated with the cloud data. ("According to a second aspect, this disclosure describes a system comprising: a user interface; a query builder module; a data repository comprising a plurality of datasets; and an ontology comprising metadata indicative of relationships between the plurality of datasets", col.2, lines 30-34 ; "The data repository 102 may be a distributed data repository, with the datasets 104 comprising the data repository 102 being stored at a plurality of locations. One or more of the datasets 104 may be under control of one or more different entities. The datasets 104 may be edited by the entities that control them, for example to update the data in the dataset in light of new measurements and/or surveys. The datasets 104 may relate to/originate from one or more systems that generate data.", col. 4, lines 1-9)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Sezgin in order to allow for metadata in the metadata layer to be associated with a plurality of objects/object types/object properties in the datasets without the need to explicitly associate the objects/object types/object properties with that metadata- thus reducing the amount of memory required to store the datasets and the associated metadata and allowing for aggregation across datasets/tables with the same global property (Sezgin, [col.5, lines 43-50]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Kapkowski (US 20140012820 A1).
As per claim 15, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not explicitly disclose evaluating the cloud data to determine which analysis operations have been applied to the cloud data based on the source data.
However, Kapkowski discloses:
the parsing comprises: determining source data indicating at least one source from which one or more data elements of the cloud data originated, and evaluating the cloud data to determine which analysis operations have been applied to the cloud data based on the source data. (“Each metadata cluster may have been formed using metadata that originated from a plurality of different data sources. The metadata from the different data sources may relate to the same multimedia content. Mappings, or correlations, between chunks of the metadata that originated from a particular data source and the metadata clusters may be determined, and, using these mappings, inconsistencies in the correlated metadata may be detected.”, 0013 ; Examiner Note: the clustering equates to an analysis operation)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Kapkowski in order to provide the ontology generation system with the ability to pre-process (parse) received data, thus avoiding the need to load large amounts of data into memory (Kapkowski, [0025]).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Carpenter (US 10255370 B2) in further view of Adami (US 20180032327 A1).
As per claim 16, Martinez Ayala in view of Mizrahy in further view of Carpenter fully discloses the limitations of claim 1, but does not explicitly disclose storing the ontology dataset into a database such that the database preserves the ontological structure of the ontology dataset.
However, Adami discloses:
storing the ontology dataset into a database such that the database preserves the ontological structure of the ontology dataset. ("As shown in FIG. 1, the DSB 100 comprises a relational database DBMS 101 to store datasets semantic descriptions produced and used/consumed by the devices operating in the deployment environment and interacting with the M2M Marketplace. The Resource Description Framework (RDF) converter 102 describes the dataset ontology in a format that is compatible to be stored in the relational database DBMS 101. ", 0074)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala, Mizrahy, and Carpenter with those of Adami in order to provide the system with a novel approach to automatic dataset linking, thus improving the interoperability between devices (Adami, [0010]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Martinez Ayala (US 20230030086 A1) in view of Mizrahy (US 20120284301 A1) in further view of Mene (US 20240085892 A1).
As per claim 20, Martinez Ayala discloses:
A computer program comprising instructions, that when executed by a computer processor of a computing device, causes the computing device to: receive cloud data from a plurality of sources…generate an ontology dataset for the cloud data from the plurality of sources based on parsing the cloud data from the plurality of sources("The system includes an input/output unit, a memory unit, an ontology generation unit, and a knowledge retrieval system. The input/output unit is configured to receive a plurality of documents from one or more document sources. The memory unit is configured to store the plurality of documents received from the one or more document sources. The ontology generation system is operatively coupled to the input/output unit and the memory unit and is configured to generate the organization level ontology. ", 0016 ; " For example, the ontology system 102, in some embodiments, may be a cloud environment incorporating the operations of the input/output unit 114, the communication interface 115, the memory unit 116, the ontology generation system 118, and the knowledge retrieval system 120, and various other operating modules to serve as a software as a service model for the document sources 104 and the organization.", 0030 ; “ Further, the generation unit 204 may be configured to generate an intermediate document ontology, such as one of the intermediate document ontologies 302 (shown in FIG. 3) for every document received from the document sources 104. The intermediate document ontologies 302, such as the intermediate document ontology 302-N, may be represented in the form of a directed graph including each of the extracted nodes and the directed relationships among the extracted nodes, wherein each node represents an edge while the directed relationship represents a link between two nodes in the directed graph.”, 0046 ; Examiner Note: extracting nodes and relationships equates to parsing the cloud data from a plurality of sources)
initiate provisioning of an electronic dashboard on a display device based on a first user input, wherein: the electronic dashboard includes one or more display elements associated with the ontology dataset, and the one or more display elements of the electronic dashboard are activatable to load a computing resource associated with the cloud data. ("In an embodiment, the knowledge retrieval system 120 may be operatively coupled to the ontology generation system 118 and configured to process one or more search queries, received from one or more users, such as via the one or more user devices 106, using the generated organization level ontology. The knowledge retrieval system 120 may further be configured to provide for display, via the input/output unit 114 on a display, such as a display of the user device 106, one or more search results, and a traversable pathway to access the one or more documents corresponding to the search results in response to the received search queries.", 0036)
Martinez Ayala discloses the above limitations of claim 20, but does not disclose the evaluation of the cloud data to determine which analysis operations have been applied to the data.
However, Mizrahy discloses:
evaluating the cloud data to determine which analysis operations have been applied to the cloud data; confirming corresponding outputs generated based on the analysis operations executed on the cloud data, generating data categories for one or more of data elements of the cloud data or the outputs generated based on the analysis operations, and linking the data categories to generate the ontology dataset having an ontological structure that provides relationships between one or more of: the data elements of the cloud data, the outputs generated based on the analysis operations, or a combination of the data elements of the cloud data and the outputs generated based on the analysis operations("The method further may include querying a queried dataset using multiple clusters of key subunits, to provide multiple query results; consolidating the multiple query results to provide a consolidated dataset; and extracting clusters of key subunits from the consolidated dataset.", 0012 ; "The method may include generating a concise representation of the content of the query results and of the content of the sample of the first dataset", 0013 ; "The method may include consolidating the multiple query results to provide a consolidated dataset; and generating a concise representation of the consolidated dataset.", 0022 ; "(b) determining the similarities (distance) between the locations associated with different key subunits, and (c) clustering the key subunits based on these distances (similarities)--lower distances indicate of closer semantic relationships", 0122 ; "The subgroups are meaningful in the sense that they are expected to reflect the content of the first dataset. Additionally or alternatively, they can be meaningful in the sense that the key subunits that may form a subgroup are linked or related to each other.", 0103 ; Examiner Note: The analysis operations are either consolidation or clustering, it is necessary that the extraction module has knowledge of this, as it generates a representation of the content of the query results ; the query results being the outputs. The clustered key subunits equate to data categories, and are linked.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala with those of Mizrahy in order to provide the ontology generation system with an extraction module which can operate without using any a-priory knowledge about the content of the cloud data (Mizrahy, [0152]).
Martinez Ayala in view of Mizrahy fully discloses the above limitations of claim 20, but does not explicitly disclose the received cloud data being report data associated with energy development operations.
However, Mene discloses:
receive cloud data from a plurality of sources, the plurality of sources including one or more of: workflow data native to multiple domains associated with the cloud data; report data associated with energy development operations or energy exploration operations; report data associated with a resource site, report data associated with a site different from or similar to the resource site, or simulation data associated with the resource site, the site different from or similar to the resource site, or the energy development operations or the energy exploration operations ("The ontology adaptation module may derive the workflow using the data received and/or accessed at step 202 with the one or more linguistic analysis techniques and/or receive the workflow directly from the user on the EUD 103. The workflow may be a specific business process, safety procedure, evacuation procedure, activity, and/or other process performed on the industrial floor. The ontology adaptation module may extract one or more concepts from the workflow and identify how the workflow will be executed according to the business process ontology (e.g., process ontology). Accordingly, the ontology adaptation module may identify one or more inefficiencies within the business process ontology (e.g., process ontology) and/or the plurality of machines on the industrial floor. The ontology adaptation module 150 may identify the one or more inefficiencies by comparing the business process ontology (e.g., process ontology) with the performance of the digital twin for the workflow and/or measuring one or more performance metrics. The one or more performance metrics may include, but are not limited to including, bottlenecks in the workflow, long durations and/or wait times in activity executions, lagging KPIs, process outcomes, possible replacement machines and/or equipment, consumption, output produced (e.g., throughput), amongst other performance metrics. ", 0042 ; “; "The ontology adaptation module 150 may simulate each of the one or more new business process ontologies (e.g., new process ontologies) using at least the one or more machine learning models and/or one or more simulation methods described at step 206. The ontology adaptation module 150 may rank the one or more new business process ontologies (e.g., new process ontologies) based on their respective performances in the digital twin simulation.”, 0046)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Martinez Ayala and Mizrahy with those of Mene, in order to provide the system with the ability to identify inefficiencies within an existing process ontology based on a workflow performance of a digital twin (Mene, [0014]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lindberg (US 20200159747 A1)- discloses a method of provisioning and utilizing a code based ontology to index a repository of container images, and then using the ontology as a mechanism to search the repository to find the best container image to use for a given set of needs.
Stuart (US 20220188344 A1) – discloses a method comprising providing a first graph ontology, collecting sample values of a plurality of concept attributes, clustering the sample values. Additional concepts and associated relations may be determined and the first ontology may be updated with them.
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/R.M.V./
Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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