DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are presented for examination.
The claims and only the claims form the metes and bounds of the invention. “Office personnel are to give claims their broadest reasonable interpretation in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969)” (MPEP p 2100-8, c 2, I 45-48; p 2100-9, c 1, l 1-4). The Examiner has full latitude to interpret each claim in the broadest reasonable sense. The Examiner will reference prior art using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,411,891.
Although the claims at issue are not identical, they are not patentably distinct from each other because following the rationale in In re Goodman, cited above, where applicant has once been granted a patent containing a claim for the specific or narrower invention, applicant may not then obtain a second patent with a claim for the generic or broader invention without first submitting an appropriate terminal disclaimer.
Instant Application
US Patent No. 12,411,891
1. A computing system, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the processor to:
determine, for each of a plurality of data assets, one or more data asset characteristics that are included in a data profile of each of the plurality data assets, the one or more data asset characteristics including input or output connections that connect the plurality of data assets to one or more machine learning models;
compare the input or output connections that connect the plurality of data assets to the one or more machine learning models, wherein the one or more machine learning models provide guidance on establishment of rules of the plurality of data assets;
perform, based on the comparing, a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data assets of the entity are to be interconnected in accordance with decision rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data assets that multiple data assets of the plurality of data assets have common input or output connections that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data assets have the common input or output connections;
generate, for the multiple data assets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections between the multiple data assets having common characteristics, the representation including nodes and lines indicating interconnections between the multiple data assets and the one or more machine learning models;
receive, from a user device, a request to access the generated data governance graph of the multiple data assets determined to have the common input or output connections; and
initiate display, via a graphical user interface associated with the user device, of the generated data governance graph.
1. A computing system, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the processor to:
receive a plurality of data assets from a plurality of data sources;
determine, for each of the plurality of data assets, one or more data asset characteristics that are included in a data profile of each of the plurality data assets, the one or more data asset characteristics including input or output connections that connect the plurality of data assets to one or more machine learning models;
compare the input or output connections that connect the plurality of data assets to the one or more machine learning models, wherein the one or more machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data assets;
perform, based on the comparing, a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data assets of the entity are to be interconnected in accordance with decision rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data assets that multiple data assets of the plurality of data assets have common input or output connections that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data assets have the common input or output connections;
generate, for the multiple data assets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections between the multiple data assets having common characteristics, the representation including nodes and lines indicating interconnections between the multiple data assets and the one or more machine learning models;
receive, from a user device, a request to access the generated data governance graph of the multiple data assets determined to have the common input or output connections;
initiate display, via a graphical user interface associated with the user device, of the generated data governance graph;
receive, from the user device, a user selection of at least one data asset of the multiple data assets determined to have the common input or output connections; and in response to receiving the user selection of the at least one data asset of the multiple data assets, initiate display, via the graphical user interface of the user device, the at least one data asset and at least one additional data asset determined to have common characteristics with the at least one data asset selected by the user.
2. The system of claim 1, wherein the one or more data asset characteristics further include at least one characteristic selected from the group consisting of user interactions, downstream reports, and presence in workflows.
3. The system of claim 1, wherein the representation further includes edges and labels.
4. The system of claim 1, wherein the representation further indicates a size of a network node of the nodes, and wherein the lines are between the nodes, a thickness of the lines between the nodes is indicative of a degree of the interconnections between the multiple data assets, a thicker line being representative of a greater degree of connection and a thinner line being representative of a lesser degree of connection, and respective lengths of the lines between the nodes varies for the multiple data assets.
5. The system of claim 1, wherein the representation comprises a first line between a first node and a second node and a second line between the second node and a third node, wherein the first line has a thickness greater than the second line.
6. The system of claim 5, wherein the first line thickness that is greater than the second line thickness indicates that the first node is more closely associated with the second node than the second node is with the third node.
7. The system of claim 1, wherein the one or more data asset characteristics further include either a direct connection or an inferred connection, wherein the determining of the one or more data asset characteristics further includes determining the direct connection or the inferred connection and the comparing of the input or output connections further includes comparing the direct connection or the inferred connection to identify whether there is a common characteristic associated with the direct connection or the inferred connection.
8. The system of claim 7, wherein the common characteristic comprises a first data asset of the one or more data assets having the common input or output connections being directly associated with a second data asset of the one or more data assets having the common input or output connections, wherein the first data asset and the second data asset are directly related based on common metadata fields.
9. The system of claim 8, wherein the common characteristic comprises the second data asset of the one or more data assets having the common input or output connections being indirectly associated with a third data asset of the one or more data assets having the common input or output connections, wherein the second data asset and the third data asset are indirectly associated based on the second data asset and the third data asset being commonly used together.
10. The system of claim 9, wherein changes made to the third data asset affect the first data asset and the second data asset.
2. The system of claim 1, wherein the one or more data asset characteristics further include at least one characteristic selected from the group consisting of user interactions, downstream reports, and presence in workflows.
3. The system of claim 1, wherein the representation further include edges and labels.
4. The system of claim 1, wherein the representation further indicates a size of a network node of the nodes, and wherein the lines are between the nodes, a thickness of the lines between the nodes is indicative of a degree of the interconnections between the multiple data assets, a thicker line being representative of a greater degree of connection and a thinner line being representative of a lesser degree of connection, and respective lengths of the lines between the nodes varies for the multiple data assets.
5. The system of claim 1, wherein the representation comprises a first line between a first node and a second node and a second line between the second node and a third node, wherein the first line has a thickness greater than the second line.
6. The system of claim 5, wherein the first line thickness that is greater than the second line thickness indicates that the first node is more closely associated with the second node than the second node is with the third node.
7. The system of claim 1, wherein the one or more data asset characteristics further include either a direct connection or an inferred connection, wherein the determining of the one or more data asset characteristics further includes determining the direct connection or the inferred connection and the comparing of the input or output connections further includes comparing the direct connection or the inferred connection to identify whether there is a common characteristic associated with the direct connection or the inferred connection.
8. The system of claim 7, wherein the common characteristic comprises a first data asset of the one or more data assets having the common input or output connections being directly associated with a second data asset of the one or more data assets having the common input or output connections, wherein the first data asset and the second data asset are directly related based on common metadata fields.
9. The system of claim 8, wherein the common characteristic comprises the second data asset of the one or more data assets having the common input or output connections being indirectly associated with a third data asset of the one or more data assets having the common input or output connections, wherein the second data asset and the third data asset are indirectly associated based on the second data asset and the third data asset being commonly used together.
10. The system of claim 9, wherein changes made to the third data asset affect the first data asset and the second data asset.
11. A computer-implemented method, comprising:
determining, for each of the plurality of data assets, one or more data asset characteristics that are included in a data profile of each of the plurality of data assets, the one or more data asset characteristics including input or output connections that connect the plurality of data assets to one or more machine learning models;
comparing the input or output connections that connect the plurality of data assets to the one or more machine learning models, wherein the one or more machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data assets;
performing, based on the comparing, a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data assets of the entity are to be interconnected in accordance with decisions rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data assets that multiple data assets of the plurality of data assets have common input or output connections that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data assets have the common input or output connections; generating, for the multiple data assets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections between the multiple data assets having common characteristics, the representation including nodes and lines indicating interconnections between the multiple data assets and the one or more machine learning models;
receiving, from a user device, a request to access the generated data governance graph of the multiple data assets determined to have the common input or output connections; and initiating display, via a graphical user interface associated with the user device, of the generated data governance graph.
11. A computer-implemented method for data governance implementation using a governance graph, the method comprising the steps of:
receiving a plurality of data assets from a plurality of data sources;
determining, for each of the plurality of data assets, one or more data asset characteristics that are included in a data profile of each of the plurality of data assets, the one or more data asset characteristics including input or output connections that connect the plurality of data assets to one or more machine learning models;
comparing the input or output connections that connect the plurality of data assets to the one or more machine learning models, wherein the one more machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data assets;
performing, based on the comparing, a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data assets of the entity are to be interconnected in accordance with decisions rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data assets that multiple data assets of the plurality of data assets have common input or output connections that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data assets have the common input or output connections; generating, for the multiple data assets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections between the multiple data assets having common characteristics, the representation including nodes and lines indicating interconnections between the multiple data assets and the one or more machine learning models;
receiving, from a user device, a request to access the generated data governance graph of the multiple data assets determined to have the common input or output connections; initiating display, via a graphical user interface associated with the user device, of the generated data governance graph;
receiving, from the user device, a user selection of at least one data asset of the multiple data assets determined to have the common input or output connections; and in response to receiving the user selection of the at least one data asset of the multiple data assets, initiating display, via the graphical user interface of the user device, the at least one data asset and at least one additional data asset determined to have common characteristics with the at least one data asset selected by the user.
12. The method of claim 11, wherein the representation further indicates a size of a network node of the nodes, and wherein the lines are between the nodes, a thickness of the lines between the nodes is indicative of a degree of the interconnections between the multiple data assets, a thicker line being representative of a greater degree of connection and a thinner line being representative of a lesser degree of connection, and respective lengths of the lines between the nodes varies for the multiple data assets.
13. The method of claim 11, wherein the representation comprises a first line between a first node and a second node and a second line between the second node and a third node, wherein the first line has a thickness greater than the second line.
14. The method of claim 13, wherein the first line thickness that is greater than the second line thickness indicates that the first node is more closely associated with the second node than the second node is with the third node.
15. The method of claim 11, wherein the one or more data asset characteristics further include at least one characteristic selected from the group consisting of user interactions, downstream reports, and presence in workflows.
12. The method of claim 11, wherein the representation further indicates a size of a network node of the nodes, and wherein the lines are between the nodes, a thickness of the lines between the nodes is indicative of a degree of the interconnections between the multiple data assets, a thicker line being representative of a greater degree of connection and a thinner line being representative of a lesser degree of connection, and respective lengths of the lines between the nodes varies for the multiple data assets.
13. The method of claim 11, wherein the representation comprises a first line between a first node and a second node and a second line between the second node and a third node, wherein the first line has a thickness greater than the second line.
14. The method of claim 13, wherein the first line thickness that is greater than the second line thickness indicates that the first node is more closely associated with the second node than the second node is with the third node.
15. The method of claim 11, wherein the one or more data asset characteristics further include at least one characteristic selected from the group consisting of user interactions, downstream reports, and presence in workflows.
17. A computing system, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the processor to:
determine, for each of a plurality of data sets, one or more data set characteristics that are included in a data profile of each of the plurality of data sets, the one or more data set characteristics including input or output connections that connect the plurality of data sets to the machine learning models,
wherein the machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data sets;
compare the input or output connections that connect the plurality of data sets to the machine learning models, wherein the machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data sets;
perform a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data sets of the entity are to be interconnected in accordance with decisions rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data sets that multiple data sets of the plurality of data sets have common input or output connections to the machine learning models that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data sets have the common input or output connections;
generate, for the multiple data sets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections, the representation including nodes and lines indicating interconnections between the multiple data sets and the machine learning models;
receive, from a user device, a request to access the generated governance graph of the multiple data assets determined to have the common input or output connections; and
in response to receiving the request to access the generated data governance graph, initiate display, via a graphical user interface associated with the user device, of the generated data governance graph, wherein the generated data governance graph that is displayed includes a node representing a first data set, another node representing a second data set, and a line indicating a connection between the first data set and the second data set.
16. A computing system, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the processor to: receive a plurality of data sets from a plurality of sources, the plurality of data sets each having a plurality of input or output connections that connect respective data sets of the plurality of data sets to respective machine learning models,
wherein the machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data sets; store the plurality of data sets into a data catalog;
determine, for each of the plurality of data sets, one or more data set characteristics that are included in a data profile of each of the plurality of data sets, the one or more data set characteristics including input or output connections that connect the plurality of data sets to the machine learning models;
compare the input or output connections that connect the plurality of data sets to the machine learning models, wherein the machine learning models provide guidance on establishment of rules on behalf of an owner of the plurality of data sets;
perform a data governance procedure to evaluate compliance with an entity's governance policy that defines how the plurality of data sets of the entity are to be interconnected in accordance with decisions rights granted to individuals associated with the entity, the data governance procedure including determining from metadata of the plurality of data sets that multiple data sets of the plurality of data sets have common input or output connections to the machine learning models that are similar to one another, the metadata indicating usage in workflows and data fields and the determining from the metadata including inferring from the usage in the workflows and the data fields that the multiple data sets have the common input or output connections;
generate, for the multiple data sets determined to have the common input or output connections, a data governance graph that includes a representation of the common input or output connections, the representation including nodes and lines indicating interconnections between the multiple data sets and the machine learning models;
receive, from a user device, a request to access the generated governance graph of the multiple data assets determined to have the common input or output connections; and
in response to receiving the request to access the generated data governance graph, initiate display, via a graphical user interface associated with the user device, of the generated data governance graph, wherein the generated data governance graph that is displayed includes a node representing a first data set, another node representing a second data set, and a line indicating a connection between the first data set and the second data set.
18. The system of claim 17, wherein first data set is directly related to the second data set based on the at least one common input or output connection to the machine learning models.
19. The system of claim 17, wherein the executable code, when executed, further causes the processor to: determine at least one tangentially related input or output connection to the machine learning models for the second data set and a third data from the plurality of data sets; and generate a representation of a second connection between the second data set and the third data set based on the at least one tangentially related input or output connection to the machine learning models.
20. The system of claim 17, wherein governance graph further includes a depiction of the second connection between the second data set and the third data set.
17. The system of claim 16, wherein first data set is directly related to the second data set based on the at least one common input or output connection to the machine learning models.
18. The system of claim 16, wherein the executable code, when executed, further causes the processor to: determine at least one tangentially related input or output connection to the machine learning models for the second data set and a third data from the plurality of data sets; and generate a representation of a second connection between the second data set and the third data set based on the at least one tangentially related input or output connection to the machine learning models.
19. The system of claim 18, wherein governance graph further includes a depiction of the second connection between the second data set and the third data set.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “at least one processor” and “… causes the processor to”. The processor lacks proper antecedent basis.
Claim 11 recites “determining, for each of the plurality of data assets, one or more data asset characteristics that are included in a data profile of each of the plurality of data assets”. “The plurality of data assets” lacks proper antecedent basis.
Claim 17 recites “at least one processor” and “… causes the processor to”. The processor lacks proper antecedent basis.
Claim 17 also recites “determine, for each of a plurality of data sets, one or more data set characteristics that are included in a data profile of each of the plurality of data sets, the one or more data set characteristics including input or output connections that connect the plurality of data sets to the machine learning models”. “The machine learning models lacks proper antecedent basis.
Claims 2-10, 12-16 and 18-20 depend from claim 1, 11 and 17, and are rejected for the same reason(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to HUEN WONG whose telephone number is (571)270-3426. The examiner can normally be reached on Monday to Friday (10-6:30 EST).
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Charles Rones can be reached on (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300 for regular communications and after final communications.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.W./
Examiner, AU 2168
20 August 2026
/ANHTAI V TRAN/ Primary Examiner, Art Unit 2168
Prosecution Insights