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 .
DETAILED ACTION
This Office Action corresponds to application 19/328,593 which was filed on 9/15/2025. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method (claim 1), a system (claim 9), and a non-transitory computer-readable medium (claim 16). These claims fall within at least one of the four categories of patentable subject matter.
Step 2A, Prong One
Claim 1 recites receiving data, extracting metadata, enriching metadata, storing the data, searching the metadata, and returning the results.
The recited steps for retrieving data, extracting and storing, and searching the data, are acts of information evaluation and retrieval that can be practically performed in the human mind with the virtual machines and repository being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can retrieve documents, add context, and search the documents. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 2-8 recite additional elements of determining data attributes and specifying what the metadata and enriched metadata are comprised of . These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 2 a person can determine quality attributes of data from documents; or with claim 4 a person can determine an object name from a document.
Claim 9 recites receiving data, extracting metadata, storing the data, and searching the metadata.
The recited steps for retrieving data, extracting and storing, and searching the data, are acts of information evaluation and retrieval that can be practically performed in the human mind with the processor, memory, virtual machines, and repository being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can retrieve documents, add context, and search the documents. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent 10-15 recite additional elements of enriching the metadata, returning the results, determining data attributes and specifying what the data, metadata, and enriched metadata are comprised of . These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 11 a person can determine quality attributes of data from documents; or with claim 13 a person calculate supplemental/enriched metadata based on the metadata of a document.
Claim 16 recites receiving data, extracting metadata, enriching metadata, storing the data, searching the metadata, and returning the results.
The recited steps for retrieving data, extracting and storing, and searching the data, are acts of information evaluation and retrieval that can be practically performed in the human mind with the processor, memory, virtual machines and repository being interepted as generic computer components to apply the instructions of the abstract idea. For example, a person can retrieve documents, add context, and search the documents. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 17-20 recite additional elements of determining data attributes and specifying what the metadata and enriched metadata are comprised of . These are all further extensions of the abstract idea or mere extra-solution activity. For example, with claim 17 a person can determine quality attributes of data from documents; or with claim 19 a person can determine an object name from a document.
Step 2A, Prong Two
This judicial exception is not integrated into a practical application because the combination of additional elements includes only generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
For claims 1-8, the additional elements include the virtual machines and repository.
For claims 9-15, the additional elements include the processor, memory, virtual machines and repository.
For claims 16-20, the additional elements include the non-transitory computer-readable storage medium, processor, memory, virtual machines and repository.
The non-transitory computer-readable storage medium, the processor, memory, virtual machines, and repository are all recited at a high-level of generality (i.e., as a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the non-transitory computer-readable storage medium, the processor, memory, virtual machines, and repository to perform the steps or the additional elements from the dependent claims amounts to no more than part of the abstract idea, mere extra-solution activity, and mere instructions to apply the exception using a generic computer component. The claims are not patent eligible.
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.
Claim(s) 1, 4-6, 8, 16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US2019/0138345), hereinafter Singh, in view of Luo et al. (US2015/0253445), hereinafter Luo.
Regarding Claim 1:
Singh teaches:
A method, comprising: receiving data representing one or more oilfield projects (Singh, figures 1-2 and 4A-4B, [0024, 0030, 0035], note receiving information and extracting metadata. When combined with the reference below, this would be for data representing an oilfield project as taught by Luo below; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
extracting the metadata from the data representing the one or more oilfield projects using one or more virtual machines (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035], note receiving information and extracting metadata; note the systems may be virtual, e.g., virtual machines. When combined with the reference below, this would be for data representing an oilfield project as taught by Luo below; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
determining enriched metadata based on the metadata (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted);
storing the data, including the metadata and the enriched metadata, in one or more repositories (Singh, figures 1-2 and 4A-4B, [0049-0051], note storing the metadata in the metadata repository);
searching the metadata in the one or more repositories to find data representing a subterranean feature of interest within the data (Singh, figures 1-2 and 4A-4B, [0027, 0049], note query interfaces; note search services and querying the metadata in the repository. When combined with the reference below, this would be for data representing a subterranean feature of interest as taught by Luo below; note a “subterranean” feature is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight); and
returning results of the searching, the results based at least in part on the metadata and the enriched metadata (Singh, figures 1-2 and 4A-4B, [0027, 0049], note search services and querying the metadata int eh repository; note returning query results).
While Singh teaches enriching metadata and searching the metadata, Singh does not state that the data is representing an oilfield project or subterranean features. However, Luo is in the same field of endeavor, data analysis and management, and Luo teaches:
data representing one or more oilfield projects (Luo, abstract, figures 1A-1D, [0005, 0033, 0034, 0038], note data representing an oilfield project is stored in a database. When combined with the previously cited references this would be for the data received as taught by Singh; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
data representing a subterranean feature of interest within the data (Luo, abstract, [0005, 0038], note the data represents at least a portion of a subterranean volume, e.g., a subterranean feature of interest. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note a “subterranean” feature is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 4:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the metadata represents at least one of: a history of changes of a subterranean object, or information about the subterranean object comprising at least one of a name of the object, a description of the object, or user-defined comments relating to the object; or a combination thereof (Singh, figures 10A-10D, [0024, 0043, 0047], note metadata may comprise a name node (e.g., name of the object), data lineage (e.g., history of changes)) (Luo, abstract, [0005, 0038], note the data represents at least a portion of a subterranean volume, e.g., a subterranean feature of interest. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note the object being a “subterranean” object is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 5:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the enriched metadata represents a subterranean volume including a plurality of objects, and wherein the plurality of objects are represented by data in a plurality of projects, respectively (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data, which is interpreted as a plurality of projects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subterranean” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 6:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the enriched metadata is calculated based on the metadata (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted).
Regarding Claim 8:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the data represents an individual feature, wherein the metadata represents one or more aspects of the data, and wherein the enriched metadata represents data about a subsurface region based on metadata representing multiple objects (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035, 0049-0051], note receiving information and extracting metadata; note the received information is from the services which may represent an individual feature; note the extracted metadata represents an aspect of the data and the enriched metadata may represent entities and other relationships with the metadata. When combined with the other cited references this would be for the subterranean volume, e.g. subsurface region as taught by Luo) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subsurface” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 16:
Singh teaches:
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, (Singh, figure 11, note processor and memory) the operations comprising:
receiving data representing one or more oilfield projects (Singh, figures 1-2 and 4A-4B, [0024, 0030, 0035], note receiving information and extracting metadata. When combined with the reference below, this would be for data representing an oilfield project as taught by Luo below; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
extracting the metadata from the data representing the one or more oilfield projects using one or more virtual machines (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035], note receiving information and extracting metadata; note the systems may be virtual, e.g., virtual machines. When combined with the reference below, this would be for data representing an oilfield project as taught by Luo below; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
determining enriched metadata based on the metadata (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted);
storing the data, including the metadata, in a repository (Singh, figures 1-2 and 4A-4B, [0049-0051], note storing the metadata in the metadata repository);
searching the metadata in the repository to find data representing a subterranean feature of interest within the data (Singh, figures 1-2 and 4A-4B, [0027, 0049], note query interfaces; note search services and querying the metadata in the repository. When combined with the reference below, this would be for data representing a subterranean feature of interest as taught by Luo below; note a “subterranean” feature is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight); and
returning results of the searching, the results based at least in part on the metadata and the enriched metadata (Singh, figures 1-2 and 4A-4B, [0027, 0049], note search services and querying the metadata int eh repository; note returning query results),
wherein the data represents an individual feature, wherein the metadata represents one or more aspects of the data, and wherein the enriched metadata represents data about a subsurface region based on metadata representing multiple objects (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035, 0049-0051], note receiving information and extracting metadata; note the received information is from the services which may represent an individual feature; note the extracted metadata represents an aspect of the data and the enriched metadata may represent entities and other relationships with the metadata. When combined with the other cited references this would be for the subterranean volume, e.g. subsurface region as taught by Luo)
While Singh teaches enriching metadata and searching the metadata, Singh does not state that the data is representing an oilfield project or subterranean features. However, Luo is in the same field of endeavor, data analysis and management, and Luo teaches:
data representing one or more oilfield projects (Luo, abstract, figures 1A-1D, [0005, 0033, 0034, 0038], note data representing an oilfield project is stored in a database. When combined with the previously cited references this would be for the data received as taught by Singh; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
data representing a subterranean object or feature of interest within the data (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subterranean” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 19:
Singh as modified shows the non-transitory computer-readable medium as disclosed above;
Singh as modified further teaches:
wherein the metadata represents at least one of: a history of changes of a subterranean object, information about the subterranean object comprising at least one of a name of the object, a description of the object, or user-defined comments relating to the object; a combination thereof (Singh, figures 10A-10D, [0024, 0043, 0047], note metadata may comprise a name node (e.g., name of the object), data lineage (e.g., history of changes)) (Luo, abstract, [0005, 0038], note the data represents at least a portion of a subterranean volume, e.g., a subterranean feature of interest. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note the object being a “subterranean” object is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 20:
Singh as modified shows the non-transitory computer-readable medium as disclosed above;
Singh as modified further teaches:
wherein the enriched metadata represents a subterranean volume including a plurality of objects, wherein the plurality of objects are represented by data in a plurality of projects, respectively (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data, which is interpreted as a plurality of projects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subterranean” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Claim Rejections - 35 USC § 103
Claim(s) 2-3, 7, 9-15, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh in view of Luo and Palrecha (US2018/0373781).
Regarding Claim 2:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the results are based at least partially on the quality attributes (Singh, figures 1-2 and 4A-4B, [0027, 0049-0051], note storing the metadata and enhanced metadata in the metadata repository; note search services and querying the metadata in the repository. When combined with the Palrecha teachings below, the results would be partially based on the quality attributes as taught by Palrecha)
While Singh as modified teaches enriching metadata and searching the metadata, Singh does not teach determining one or more quality attributes of the data based at least in part on the metadata, and wherein the results are based at least partially on the quality attributes. However, Palrecha is in the same field of endeavor, data analysis and management, and Palrecha teaches:
determining one or more quality attributes of the data based at least in part on the metadata, and wherein the results are based at least partially on the quality attributes (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking accesses quality of the data elements, e.g., determining quality attributes, which impact the data processing/search results; note metadata repository. When combined with the previously cited references the data would include the metadata as taught by Singh).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data
Regarding Claim 3:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
wherein the one or more quality attributes comprise completeness, accuracy, consistency, integrity, unicity, and efficiency of data representing an object represented in the data representing the one or more oilfield projects (Luo, abstract, figures 1A-1D, [0005, 0033, 0034, 0038], note data representing an oilfield project is stored in a database. When combined with the previously cited references this would be for the data received as taught by Singh; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight) (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking accesses quality of the data elements, e.g., quality attributes; note the quality attributes include completeness, accuracy, consistency/unicity, integrity, and efficiency; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight. When combined with the previously cited references this would be for the data representing the one or more oilfield projects as taught by Singh and Luo).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data.
Regarding Claim 7:
Singh as modified shows the method as disclosed above;
Singh as modified further teaches:
calculating a quality attribute for the subterranean object, based on at least one of accuracy, completeness, consistency, efficiency, interpretability, or temporality of the object (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data, which is interpreted as a plurality of projects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note a “subterranean” object is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
While Singh as modified teaches enriching metadata and searching the metadata, Singh does not teach calculating a quality attribute for the subterranean object, based on at least one of accuracy, completeness, consistency, efficiency, interpretability, or temporality of the object. However, Palrecha is in the same field of endeavor, data analysis and management, and Palrecha teaches:
calculating a quality attribute for the subterranean object, based on at least one of accuracy, completeness, consistency, efficiency, interpretability, or temporality of the object (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking accesses quality of the data elements, e.g., quality attributes; note the quality attributes include completeness, accuracy, consistency/unicity, integrity, and efficiency; note a “subterranean” object is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight. When combined with the previously cited references this would be for the data representing the one or more oilfield projects as taught by Singh and Luo).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data.
Regarding Claim 9:
Singh teaches:
A computing system, comprising: one or more processors (Singh, figure 11, note processor); and
a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, (Singh, figure 11, note memory) the operations comprising:
receiving data representing one or more oilfield projects, the data comprising metadata representing at least one of: a history of changes of a subterranean object, or a statistic representing the object, a quality attribute comprising at least one of completeness, unicity, or accuracy of the data, or a combination thereof (Singh, figures 1-2, 4A-4B, 10A-10D, [0024, 0030, 0035, 0043, 0047], note receiving information and extracting metadata; note metadata may comprise a name node (e.g., name of the object), data lineage (e.g., history of changes). When combined with the reference below, this would be for data representing an oilfield project and subterranean objects as taught by Luo below; note the data representing one or more oilfield projects and “subterranean” objects are nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
extracting the metadata from the data representing the one or more oilfield projects using one or more virtual machines (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035], note receiving information and extracting metadata; note the systems may be virtual, e.g., virtual machines. When combined with the reference below, this would be for data representing an oilfield project as taught by Luo below; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight);
storing the data, including the metadata, in a repository (Singh, figures 1-2 and 4A-4B, [0049-0051], note storing the metadata in the metadata repository); and
searching, using one or more filters, the metadata in the repository to find data representing a subterranean feature of interest within the data (Singh, figures 1-2 and 4A-4B, [0027, 0049], note query interfaces; note search services and querying the metadata in the repository. When combined with the reference below, this would be for data representing a subterranean feature of interest as taught by Luo below; note a “subterranean” feature is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
While Singh teaches enriching metadata and searching the metadata, Singh does not state that the data is representing an oilfield project or subterranean object/features. However, Luo is in the same field of endeavor, data analysis and management, and Luo teaches:
data representing one or more oilfield projects (Luo, abstract, figures 1A-1D, [0005, 0033, 0034, 0038], note data representing an oilfield project is stored in a database. When combined with the previously cited references this would be for the data received as taught by Singh; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
data representing a subterranean object or feature of interest within the data (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subterranean” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
While Singh teaches enriching metadata and searching the metadata, Singh does not teach using filters for searching. However, Palrecha is in the same field of endeavor, data analysis and management, and Palrecha teaches:
searching using one or more filters (Palrecha, figure 2, [0047, 0063-0066], note data filters. When combined with the previously cited references this would be included in the searching as taught by Singh).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data.
Regarding Claim 10:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
determining enriched metadata based on the metadata (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted); and
returning results of the searching, the results based at least in part on the metadata and the enriched metadata (Singh, figures 1-2 and 4A-4B, [0027, 0049], note search services and querying the metadata int eh repository; note returning query results).
Regarding Claim 11:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
determining the quality attribute of the data based at least in part on the metadata, and wherein the results are based at least partially on the quality attributes (Singh, figures 1-2 and 4A-4B, [0027, 0049-0051], note storing the metadata and enhanced metadata in the metadata repository; note search services and querying the metadata in the repository. When combined with the Palrecha teachings, the results would be partially based on the quality attributes as taught by Palrecha) (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking accesses quality of the data elements, e.g., determining quality attributes, which impact the data processing/search results; note metadata repository. When combined with the previously cited references the data would include the metadata as taught by Singh).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data
Regarding Claim 12:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
wherein the enriched metadata represents a subterranean volume including a plurality of objects, and wherein the plurality of objects are represented by data in a plurality of projects, respectively ((Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects represented by data, which is interpreted as a plurality of projects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subterranean” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 13:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
wherein the enriched metadata is calculated based on the metadata (Singh, figures 1-2 and 4A-4B, [0049-0051], note determining supplemental, e.g., enriched, metadata, based on the metadata extracted).
Regarding Claim 14:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
wherein the data represents a geological feature and the enriched metadata represents a region that includes the geological feature (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035, 0049-0051], note receiving information and extracting metadata; note the received information is from the services which may represent an individual feature; note the extracted metadata represents an aspect of the data and the enriched metadata may represent entities and other relationships with the metadata. When combined with the other cited references this would be for the subterranean volume, e.g. geological feature and region, as taught by Luo) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects, e.g. geological feature and region. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note geological feature and region are nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 15:
Singh as modified shows the system as disclosed above;
Singh as modified further teaches:
wherein the data represents an individual feature, wherein the metadata represents one or more aspects of the data, and wherein the enriched metadata represents data about a subsurface region based on metadata representing multiple objects (Singh, figures 1-2 and 4A-4B, [0024-0025, 0030, 0035, 0049-0051], note receiving information and extracting metadata; note the received information is from the services which may represent an individual feature; note the extracted metadata represents an aspect of the data and the enriched metadata may represent entities and other relationships with the metadata. When combined with the other cited references this would be for the subterranean volume, e.g. subsurface region as taught by Luo) (Luo, abstract, [0005, 0011, 0015, 0038, 0065-66], note the data represents at least a portion of a subterranean volume which includes a plurality of objects. When combined with the previously cited references this would be for the data that is searched as taught by Singh; note “subsurface” is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
Regarding Claim 17:
Singh as modified shows the non-transitory computer-readable medium as disclosed above;
Singh as modified further teaches:
wherein the results are based at least partially on the quality attributes (Singh, figures 1-2 and 4A-4B, [0027, 0049-0051], note storing the metadata and enhanced metadata in the metadata repository; note search services and querying the metadata in the repository. When combined with the Palrecha teachings below, the results would be partially based on the quality attributes as taught by Palrecha)
While Singh as modified teaches enriching metadata and searching the metadata, Singh does not teach determining one or more quality attributes of the data based at least in part on the metadata, and wherein the results are based at least partially on the quality attributes. However, Palrecha is in the same field of endeavor, data analysis and management, and Palrecha teaches:
determining one or more quality attributes of the data based at least in part on the metadata, and wherein the results are based at least partially on the quality attributes (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking accesses quality of the data elements, e.g., determining quality attributes, which impact the data processing/search results; note metadata repository. When combined with the previously cited references the data would include the metadata as taught by Singh).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data
Regarding Claim 18:
Singh as modified shows the non-transitory computer-readable medium as disclosed above;
Singh as modified further teaches:
wherein the one or more quality attributes comprise calculating a quality attribute for the subterranean object, based on at least one of accuracy, completeness, consistency, efficiency, interpretability, or temporality of the data representing an object in one of the one or more oilfield projects (Luo, abstract, figures 1A-1D, [0005, 0033, 0034, 0038], note data representing an oilfield project is stored in a database. When combined with the previously cited references this would be for the data received as taught by Singh; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight) (Palrecha, figures 2-4 and 9, [0046, 0053, 0057, 0065, 0068], note data quality checking calculates quality attributes of the data elements, e.g., quality attributes; note the quality attributes include completeness, accuracy, consistency/unicity, integrity, and efficiency; note the data representing one or more oilfield projects is nonfunctional descriptive material as explained in section 2111.05 of the MPEP and does not hold patentable weight. When combined with the previously cited references this would be for the data representing the one or more oilfield projects as taught by Singh and Luo).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Luo because all references are directed to data analysis and management and because Luo would expand upon the teachings of the previously cited references data analysis which would improve the useability of the system by utilizing data for oilfield projects.
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Palrecha because all references are directed to data analysis and management and because Palrecha would expand upon the teachings of the previously cited references data analysis which would improve the efficiency of the system by improving the management and quality of the data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Arora et al. (US2019/0138654) teaches data analysis with enriched metadata.
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/JOHN J MORRIS/Examiner, Art Unit 2151 6/17/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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