DETAILED ACTION
Claims 1-20 are pending in the present invention. A preliminary amendment was filed 24 June 2025 claims 1-20 were cancelled, and new claims 21-40 were added.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 21 October 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,361,001 B2 Yu et al.. Although the claims at issue are not identical, they are not patentably distinct from each other because it is well settled that omission of elements and their functioning is an obvious expedient if the remaining elements perform the same function as before. See In re Karlson, 136 USPQ 184 (CCPA 1963).
19/248,449
21. A system for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph, the system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: determine a set of semantically similar phrases corresponding to a user-specified query associated with a set of data objects; accessing a metadata graph to determine a node corresponding to the set of semantically similar phrases, wherein the metadata graph comprises (i) a set of nodes indicating (a) metadata of internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating a data lineage between a first node and a second node of the set of nodes; determining a data silo storing at least one data object of the set of data objects using a location identifier, of the location identifiers of the data silos, corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating, for display, on a graphical user interface (GUI), a visual representation of the at least one data object.
22. The system of claim 21, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifier, respectively; generating a metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
23. The system of claim 21, further comprising the instructions to: receiving, via a second the GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
24. The system of claim 23, further comprising the instructions to: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the second set of data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to the one or more computing systems, applying the second artificial intelligence model to generate the intended result.
25. A method for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph, the method comprising: determining a set of phrases corresponding to a user-specified query associated with a set of data objects; accessing a metadata graph to determine a node corresponding to the set of phrases, wherein the metadata graph comprises (i) a set of nodes comprising (a) metadata indicating internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating data lineages of the set of nodes; determining a data silo storing at least one data object of the set of data objects using a location identifier, of the location identifiers of the data silos, corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating a representation of the at least one data object.
26. The method of claim 25, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifiers, respectively; generating a metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
27. The method of claim 25, further comprising: receiving, via a second GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
28. The method of claim 27, further comprising: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the second set of data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to the one or more computing systems, applying the second artificial intelligence model to generate the intended result.
29. The method of claim 25, wherein determining the set of phrases corresponding to the user-specified query further comprises: parsing the user-specified query for a set of keywords, wherein each keyword of the set of keywords is associated with the set of data objects; for each keyword of the set of keywords associated with the set of data objects, determining a set of semantically similar phrases corresponding to a respective keyword of the set of keywords; and determining the set of phrases corresponding to the user-specified query using the set of semantically similar phrases corresponding to each keyword of the set of keywords.
30. The method of claim 29, wherein determining a semantically similar phrase corresponding to the respective keyword of the set of keywords further comprises: accessing a database indicating a mapping between first keywords and a set of second keywords; and in response to accessing the database, determining the set of semantically similar phrases corresponding to the respective keyword using the respective keyword.
31. The method of claim 25, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; and in response to determining that the metadata identifier matches the at least one phrase of the set of phrases, determining the node corresponding to the set of phrases.
32. The method of claim 25, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; in response to determining that the metadata identifier matches the at least one phrase of the set of phrases, determining a first node corresponding to the set of phrases; in response to determining the first node corresponds to the set of phrases, performing a second traversal of the nodes of the set of nodes using an edge indicating a first data lineage of the first node, wherein the first data lineage of the first node indicates a second node that comprises information that is a source of information associated with the first node; determining a second data silo storing a second data object of the set of data objects using the location identifier corresponding to the second node to obtain the second data object of the set of data objects via the second data silo; and generating a second representation of the second data object.
33. The method of claim 25, wherein the representation of the at least one data object comprises lineage information of the at least one data object.
34. One or more non-transitory, computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising: determine a set of phrases corresponding to a user-specified query associated with a set of data objects; accessing a metadata graph to determine a node corresponding to the set of phrases, wherein the metadata graph comprises (i) a set of nodes comprising (a) metadata indicating internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating data lineages of the set of nodes; determining a data silo storing at least one data object of the set of data objects using a location identifier, of the location identifiers of the data silos, corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating a representation of the at least one data object.
35. The one or more non-transitory, computer-readable media of claim 34, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifiers, respectively; generating a metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
36. The one or more non-transitory, computer-readable media of claim 34, wherein the instructions, when executed by the one or more processors, further cause operations comprising: receiving, via a second GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
37. The one or more non-transitory, computer-readable media of claim 36, wherein the instructions, when executed by the one or more processors, further cause operations comprising: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the second set of data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to the one or more computing systems, applying the second artificial intelligence model to generate the intended result.
38. The one or more non-transitory, computer-readable media of claim 34, wherein determining the set of phrases corresponding to the user-specified query further comprises: parsing the user-specified query for a set of keywords, wherein each keyword of the set of keywords is associated with the set of data objects; for each keyword of the set of keywords associated with the set of data objects, determining a set of semantically similar phrases corresponding to a respective keyword of the set of keywords; and determining the set of phrases corresponding to the user-specified query using the set of semantically similar phrases corresponding to each keyword of the set of keywords.
39. The one or more non-transitory, computer-readable media of claim 38, wherein determining a semantically similar phrase corresponding to the respective keyword of the set of keywords further comprises: accessing a database indicating a mapping between first keywords and a set of second keywords; and in response to accessing the database, determining the set of semantically similar phrases corresponding to the respective keyword using the respective keyword.
40. The one or more non-transitory, computer-readable media of claim 34, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; and in response to determining that the metadata identifier matches the at least one phrase of the set of phrases, determining the node corresponding to the set of phrases.
US Patent 12,361,001 B2
1. A system for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph, the system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: identifying a set of keywords associated with a request to access a set of data objects; performing natural language processing on the set of keywords to determine a set of semantically similar phrases corresponding to each keyword of the set of keywords; accessing a metadata graph to determine a node corresponding to the set of semantically similar phrases, wherein the metadata graph comprises (i) a set of nodes indicating (a) metadata of internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating a data lineage between a first node and a second node of the set of nodes, wherein the metadata graph is generated using a metadata data structure that is based on file-level and container-level metadata identifiers; determining a data silo storing at least one data object of the set of data objects using the location identifier corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating, for display, on a graphical user interface (GUI), a visual representation of the at least one data object, wherein the visual representation of the at least one data object comprises lineage information of the at least one data object.
2. The system of claim 1, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifiers, respectively; generating the metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
3. The system of claim 1, further comprising the instructions to: receiving, via a second the GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
4. The system of claim 3, further comprising the instructions to: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the set of second data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to one or more computing systems, applying the second artificial intelligence model to generate the intended result.
5. A method for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph, the method comprising: identifying a set of keywords associated with a user-specified query to access a set of data objects; performing natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query; accessing a metadata graph to determine a node corresponding to the set of phrases, wherein the metadata graph comprises (i) a set of nodes comprising (a) metadata indicating internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating data lineages of the set of nodes, wherein the metadata graph is generated using a metadata data structure that is based on file-level and container-level metadata identifiers; determining a data silo storing at least one data object of the set of data objects using the location identifier corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating a representation of the at least one data object.
6. The method of claim 5, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifiers, respectively; generating the metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
7. The method of claim 5, further comprising: receiving, via a second GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
8. The method of claim 7, further comprising: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the set of second data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to the one or more computing systems, applying the second artificial intelligence model to generate the intended result.
9. The method of claim 5, wherein determining the set of phrases corresponding to the user-specified query further comprises: parsing the user-specified query for a set of keywords, wherein each keyword of the set of keywords is associated with the set of data objects; for each keyword of the set of keywords associated with the set of data objects, determining a set of semantically similar phrases corresponding to the respective keyword of the set of keywords; and determining the set of phrases corresponding to the user-specified query using the set of semantically similar phrases corresponding to each keyword of the set of keywords.
10. The method of claim 9, wherein determining a semantically similar phrase corresponding to the respective keyword of the set of keywords further comprises: accessing a database indicating a mapping between first keywords and a set of second keywords; and in response to accessing the database, determining the set of semantically similar phrases corresponding to the respective keyword using the respective keyword.
11. The method of claim 5, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; and in response to determining that the metadata identifier matches the at least one phrase of the set of phrases, determining the node corresponding to the set of phrases.
12. The method of claim 5, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; in response to determining that the metadata identifier matches at least one phrase of the set of phrases, determining a first node corresponding to the set of phrases; in response to determining the first node corresponds to the set of phrases, performing a second traversal of the nodes of the set of nodes using an edge indicating a first data lineage of the first node, wherein the first data lineage of the first node indicates a second node that comprises information that is a source of information associated with the first node; determining a second data silo storing a second data object of the set of data objects using the location identifier corresponding to the second node to obtain the second data object of the set of data objects via the second data silo; and generating a second representation of the second data object.
13. The method of claim 5, wherein the representation of the at least one data object comprises lineage information of the at least one data object.
14. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising: performing natural language processing on a user-specified query to determine a set of phrases corresponding the user-specified query; accessing a metadata graph to determine a node corresponding to the set of phrases, wherein the metadata graph comprises (i) a set of nodes comprising (a) metadata indicating internal data objects stored in data silos and (b) location identifiers of the data silos, and (ii) edges indicating data lineages of the set of nodes, wherein the metadata graph is generated using a metadata data structure that is based on file-level and container-level metadata identifiers; determining a data silo storing at least one data object of the set of data objects using the location identifier corresponding to the determined node to obtain the at least one data object of the set of data objects via the data silo; and generating a representation of the at least one data object.
15. The media of claim 14, wherein the metadata graph is generated by: retrieving (i) a set of file-level metadata identifiers and (ii) a set of container-level metadata identifiers from a second set of data silos, wherein each file-level metadata identifier of the set of file-level metadata identifiers indicates metadata of a given data object stored within a respective data silo, and wherein each container-level metadata identifier of the set of container-level metadata identifiers indicates metadata of the respective data silo of the second set of data silos; generating a set of semantically similar metadata identifiers corresponding to each file-level and container-level metadata identifiers, respectively; generating the metadata data structure to map each semantically similar metadata identifier of the set of semantically similar metadata identifiers to normalized file-level metadata identifiers and normalized container-level metadata identifiers; and generating the metadata graph using the generated metadata data structure.
16. The media of claim 14, wherein the instructions, when executed by the one or more processors, further cause operations comprising: receiving, via a second GUI, a second user-specified query indicating a request to generate an intended result; providing the second user-specified query to an artificial intelligence model to generate a recommendation, wherein the recommendation comprises (i) a second artificial intelligence model to be used to generate the intended result and (ii) a second set of data objects to be used when training the second artificial intelligence model; in response to receiving a user selection indicating acceptance of the recommendation, (i) accessing a database to obtain the second artificial intelligence model and (ii) obtaining the second set of data objects using the metadata graph; training the second artificial intelligence model using the set of data objects; and applying the second artificial intelligence model to generate the intended result.
17. The media of claim 16, wherein the instructions, when executed by the one or more processors, further cause operations comprising: accessing a governance database to obtain a set of policies indicating usage criteria corresponding to the second set of data objects; determining whether the second set of data objects are approved to be used to train the second artificial intelligence model using the set of policies indicating usage criteria corresponding to the set of second data objects; determining whether an output of the second artificial intelligence model is approved to be provided to one or more computing systems using a second set of policies indicating usage criteria corresponding to artificial intelligence model predictions; and in response to (i) the second set of data objects being approved to be used to train the second artificial intelligence model and (ii) the output of the second artificial intelligence model is approved to be provided to the one or more computing systems, applying the second artificial intelligence model to generate the intended result.
18. The media of claim 14, wherein determining the set of phrases corresponding to the user-specified query further comprises: parsing the user-specified query for a set of keywords, wherein each keyword of the set of keywords is associated with the set of data objects; for each keyword of the set of keywords associated with the set of data objects, determining a set of semantically similar phrases corresponding to the respective keyword of the set of keywords; and determining the set of phrases corresponding to the user-specified query using the set of semantically similar phrases corresponding to each keyword of the set of keywords.
19. The media of claim 18, wherein determining a semantically similar phrase corresponding to the respective keyword of the set of keywords further comprises: accessing a database indicating a mapping between first keywords and a set of second keywords; and in response to accessing the database, determining the set of semantically similar phrases corresponding to the respective keyword using the respective keyword.
20. The media of claim 14, wherein accessing the metadata graph further comprises: traversing each node of the set of nodes to identify a metadata identifier matching at least one phrase of the set of phrases; and in response to determining that the metadata identifier matches the at least one phrase of the set of phrases, determining the node corresponding to the set of phrases.
Conclusion
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/GRETA L ROBINSON/Primary Examiner, Art Unit 2163
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