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 .
Summary
This action is a responsive to the application filed on 7/23/2024.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/23/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 7-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
As to claim 7, claim 7 is directed to “A system comprising: one or more processors; one or more communication interfaces; and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, from a first user device of a first user, a first diagram in a first format; determining, based at least on the first diagram, diagram data; generating, based at least in part on the diagram data, a second diagram in a second format, the second format preferred by an organization associated with the first user; and storing the second diagram or the diagram data at a location accessible by at least a second user associated with the organization, the second user different than the first user.” The specification is silent as to transitory media. Transitory media (signals) does not fit within recognized categories of statutory subject matter.
As to claim 12, claim 12 is directed to “One or more computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a request to access a first diagram from a user device, the first diagram associated with a first format and the request indicating a second format different than the first format; generating, based at least in part on stored diagram data and the second format, a second diagram corresponding to the first diagram, the second diagram in the second format; and providing the second diagram to the user device.” The specification is silent as to transitory media. Transitory media (signals) does not fit within recognized categories of statutory subject matter.
As to claims 8-11, claims 8-11 do not cure the deficiencies that are found in independent claim 7 and therefore are rejected under the same rationale.
As to claims 13-20, claims 13-20 do not cure the deficiencies that are found in independent claim 12 and therefore are rejected under the same rationale.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5, 7-9, 12, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over BHOOVARAGHAVAN et al. (US 20180349461 A1) and further in view of Khurana et al. (US 20240169055 A1).
As to claim 1, BHOOVARAGHAVAN et al. teaches a method comprising: receiving, from a first user device, a first diagram in a first format (See ¶ [0044], Teaches that The structured data may be accessed through the data connection manager 200 and the data synchronization manager 202 from one or more files stored in one or more data sources 150 (at block 302), and, in some embodiments, a copy of the accessed structured data is stored to the storage device 108 (at block 304));
determining, based at least on the first diagram, diagram data (See ¶ [0053], Teaches that Thus, to generate a diagram, the data binding engine 204 invokes one or more binders 208 to generate visual structures based on the generated expressions (at block 312). The binders 208 generate, edit, and organize the visual structures based on the applicable expressions. For example, the data binding engine 204 may invoke a shape binder to set a shape type of a visual structure, invoke a shape property binder to set a shape size, shape position, and shape description of the visual structure, and invoke a container binder to add the visual structure to a container.);
storing the diagram data (See ¶ [0044], Teaches that The structured data may be accessed through the data connection manager 200 and the data synchronization manager 202 from one or more files stored in one or more data sources 150 (at block 302), and, in some embodiments, a copy of the accessed structured data is stored to the storage device 108 (at block 304));
generating, based at least in part on the diagram data and the second format, a second diagram corresponding to the first diagram, the second diagram in the second format (See ¶ [0055], Teaches that Once these expressions are generated, the binders 208 implement the expressions to generate the applicable visual structures arranged within a diagram, The resulting diagram is then output (for display through the canvas 214 within the diagramming application 120) for user review and interaction (at block 314). Outputting a generated diagram may include displaying the diagram through the canvas 214 or transmitting the diagram to user device for display when the diagram is generated in a hosted or distributed environment.);
and providing the second diagram to the second user device (See ¶ [0055], Teaches that Once these expressions are generated, the binders 208 implement the expressions to generate the applicable visual structures arranged within a diagram, The resulting diagram is then output (for display through the canvas 214 within the diagramming application 120) for user review and interaction (at block 314). Outputting a generated diagram may include displaying the diagram through the canvas 214 or transmitting the diagram to user device for display when the diagram is generated in a hosted or distributed environment.).
However, it does not expressly teach the details of receiving a request to access the first diagram from a second user device, the request indicating a second format different than the first format.
Khurana et al., from analogous art, teaches receiving a request to access the first diagram from a second user device, the request indicating a second format different than the first format (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into BHOOVARAGHAVAN et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 2, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the method according to claim 1 above. BHOOVARAGHAVAN et al. further teaches wherein the diagram data includes object data, relationship data, and metadata associated with objects of the first diagram (See ¶¶ [0048], [0041], Teaches that the transformation engine 206 may generate a user interface 800 that prompts the user to specify what type of shape should be assigned to each type of step (or other component represented in the diagram). For example, as illustrated in FIG. 8, the user interface 800 may include, for each step type listed in the structured data representation 210, a shape-type input mechanism 802 (a dropdown list). Each shape-type input mechanism 802 allows a user to define what type of shape should be used in the resulting diagram to represent each type of step (or other component represented within the diagram). In some embodiments, the user interface may include a list 804 of available shape types to aid a user in making these selections. As illustrated in FIG. 9, the transformation engine 206 may also generate a user interface 900 that prompts the user to specify how to connect shapes (or other visual structures) included in a diagram, such as process steps represented in a cross-functional flowchart. For example, as illustrated in FIG. 9, the user interface 900 may include connection-definition input mechanism 904 (a dropdown list) that allows a user to define how connections will be defined. For example, in some embodiments, the structured data representation may include a column that indicates a next or previous process step for a particular process step. Accordingly, in this situation, the user may be able to select a “Connect using column” option with the connection-definition input mechanism 904. The user may then use a connection-column input mechanism 906 (a dropdown list) to select the particular column within the structured data representation 210. In other embodiments, a user may use the connection-definition input mechanism 904 to designate that an order or format of data records within the structured data representation 210 represents connections (for example, indented data records may indicate the indented records contain process steps that connect to the process step in the previous, non-indented data record). As visual structures are generated and displayed within the canvas 214 as a diagram, a user may modify the visual structures. These modifications may be tracked by a change tracker 216, which may communicate with an undo or reset manager associated with the diagramming application 120. The change tracker 216 may also store records representing tracked modifications to a diagram changes database 218.).
As to claim 3, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the method according to claim 1 above. However, it does not expressly teach the details of further comprising: receiving authentication data associated with a user of the second user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view.
Khurana et al., from analogous art, teaches further comprising: receiving authentication data associated with a user of the second user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 5, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the method according to claim 1 above. However, it does not expressly teach the details of further comprising: receiving input data from the second user device; and generating, based at least in part on the input data and the second diagram, a third diagram, code, or object data.
Khurana et al., from analogous art, teaches further comprising: receiving input data from the second user device; and generating, based at least in part on the input data and the second diagram, a third diagram, code, or object data (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 7, BHOOVARAGHAVAN et al. teaches a system comprising: one or more processors; one or more communication interfaces; and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, from a first user device of a first user, a first diagram in a first format (See ¶ [0044], Teaches that The structured data may be accessed through the data connection manager 200 and the data synchronization manager 202 from one or more files stored in one or more data sources 150 (at block 302), and, in some embodiments, a copy of the accessed structured data is stored to the storage device 108 (at block 304));
determining, based at least on the first diagram, diagram data (See ¶ [0053], Teaches that Thus, to generate a diagram, the data binding engine 204 invokes one or more binders 208 to generate visual structures based on the generated expressions (at block 312). The binders 208 generate, edit, and organize the visual structures based on the applicable expressions. For example, the data binding engine 204 may invoke a shape binder to set a shape type of a visual structure, invoke a shape property binder to set a shape size, shape position, and shape description of the visual structure, and invoke a container binder to add the visual structure to a container.);
generating, based at least in part on the diagram data, a second diagram in a second format (See ¶ [0055], Teaches that Once these expressions are generated, the binders 208 implement the expressions to generate the applicable visual structures arranged within a diagram, The resulting diagram is then output (for display through the canvas 214 within the diagramming application 120) for user review and interaction (at block 314). Outputting a generated diagram may include displaying the diagram through the canvas 214 or transmitting the diagram to user device for display when the diagram is generated in a hosted or distributed environment.).
However, it does not expressly teach the details of the second format preferred by an organization associated with the first user; and storing the second diagram or the diagram data at a location accessible by at least a second user associated with the organization, the second user different than the first user.
Khurana et al., from analogous art, teaches the second format preferred by an organization associated with the first user (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.);
and storing the second diagram or the diagram data at a location accessible by at least a second user associated with the organization, the second user different than the first user (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into BHOOVARAGHAVAN et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 8, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the system according to claim 7 above. However, it does not expressly teach the details of wherein the operations further comprise: receiving a request to access the first diagram from a second user device associated with a third user, the request indicating a third format different than the first format and the second format; generating, based at least in part on the diagram data and the third format, a third diagram corresponding to the first diagram, the third diagram in the third format; and providing the third diagram to the second user device.
Khurana et al., from analogous art, teaches wherein the operations further comprise: receiving a request to access the first diagram from a second user device associated with a third user, the request indicating a third format different than the first format and the second format; generating, based at least in part on the diagram data and the third format, a third diagram corresponding to the first diagram, the third diagram in the third format; and providing the third diagram to the second user device (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 9, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the system according to claim 8 above. However, it does not expressly teach the details of wherein the operations further comprise: receiving authentication data associated with a user of the second user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view.
Khurana et al., from analogous art, teaches wherein the operations further comprise: receiving authentication data associated with a user of the second user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 12, BHOOVARAGHAVAN et al. teaches one or more computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: generating, based at least in part on stored diagram data and the second format, a second diagram corresponding to the first diagram, the second diagram in the second format (See ¶ [0055], Teaches that Once these expressions are generated, the binders 208 implement the expressions to generate the applicable visual structures arranged within a diagram, The resulting diagram is then output (for display through the canvas 214 within the diagramming application 120) for user review and interaction (at block 314). Outputting a generated diagram may include displaying the diagram through the canvas 214 or transmitting the diagram to user device for display when the diagram is generated in a hosted or distributed environment.);
and providing the second diagram to the user device (See ¶ [0055], Teaches that Once these expressions are generated, the binders 208 implement the expressions to generate the applicable visual structures arranged within a diagram, The resulting diagram is then output (for display through the canvas 214 within the diagramming application 120) for user review and interaction (at block 314). Outputting a generated diagram may include displaying the diagram through the canvas 214 or transmitting the diagram to user device for display when the diagram is generated in a hosted or distributed environment.).
However, it does not expressly teach the details of receiving a request to access a first diagram from a user device, the first diagram associated with a first format and the request indicating a second format different than the first format.
Khurana et al., from analogous art, teaches receiving a request to access a first diagram from a user device, the first diagram associated with a first format and the request indicating a second format different than the first format (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into BHOOVARAGHAVAN et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 15, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 12 above. BHOOVARAGHAVAN et al. further teaches wherein the operations further comprise: receiving, from another user device, the first diagram in the first format (See ¶ [0044], Teaches that The structured data may be accessed through the data connection manager 200 and the data synchronization manager 202 from one or more files stored in one or more data sources 150 (at block 302), and, in some embodiments, a copy of the accessed structured data is stored to the storage device 108 (at block 304));
determining, based at least on the first diagram, the diagram data associated with the first diagram (See ¶ [0053], Teaches that Thus, to generate a diagram, the data binding engine 204 invokes one or more binders 208 to generate visual structures based on the generated expressions (at block 312). The binders 208 generate, edit, and organize the visual structures based on the applicable expressions. For example, the data binding engine 204 may invoke a shape binder to set a shape type of a visual structure, invoke a shape property binder to set a shape size, shape position, and shape description of the visual structure, and invoke a container binder to add the visual structure to a container.);
and storing the diagram data as stored diagram data (See ¶ [0044], Teaches that The structured data may be accessed through the data connection manager 200 and the data synchronization manager 202 from one or more files stored in one or more data sources 150 (at block 302), and, in some embodiments, a copy of the accessed structured data is stored to the storage device 108 (at block 304)).
As to claim 16, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 12 above. However, it does not expressly teach the details of wherein the operations further comprise: receiving authentication data associated with a user of the user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view.
Khurana et al., from analogous art, teaches wherein the operations further comprise: receiving authentication data associated with a user of the user device with the request to access the first diagram; determining, based at least in part on the authentication data, a portion of the first diagram the user is authorized to view; and wherein generation the second diagram includes limiting the second diagram to the portion of the first diagram that the user is authorized to view (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 17, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 16 above. However, it does not expressly teach the details of wherein determining the portion of the first diagram the user is authorized to view is based at least in part on organizational rule data associated with data represented by the first diagram.
Khurana et al., from analogous art, teaches wherein determining the portion of the first diagram the user is authorized to view is based at least in part on organizational rule data associated with data represented by the first diagram (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 18, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 16 above. However, it does not expressly teach the details of wherein limiting the second diagram to the portion of the first diagram that the user is authorized to view comprises generating the second diagram to include only the portion of the first diagram.
Khurana et al., from analogous art, teaches wherein limiting the second diagram to the portion of the first diagram that the user is authorized to view comprises generating the second diagram to include only the portion of the first diagram (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 19, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 12 above. However, it does not expressly teach the details of wherein the operations further comprise: determining the second format is not authorized by an organization associated with the first diagram; generating, based at least in part on the second format, organizational rule data, and the stored diagram data, a third format; and wherein generating the second diagram corresponding to the first diagram further comprises generating the second diagram in the third format.
Khurana et al., from analogous art, teaches wherein the operations further comprise: determining the second format is not authorized by an organization associated with the first diagram; generating, based at least in part on the second format, organizational rule data, and the stored diagram data, a third format; and wherein generating the second diagram corresponding to the first diagram further comprises generating the second diagram in the third format (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
As to claim 20, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 12 above. However, it does not expressly teach the details of wherein the operations further comprise: determining, based at least in part on the second format, organizational rule data, and the stored diagram data, a third format; generating, based at least in part on the diagram data, a third diagram corresponding to the first diagram in the third format; and providing the third diagram with the second diagram to the user device.
Khurana et al., from analogous art, teaches wherein the operations further comprise: determining, based at least in part on the second format, organizational rule data, and the stored diagram data, a third format; generating, based at least in part on the diagram data, a third diagram corresponding to the first diagram in the third format; and providing the third diagram with the second diagram to the user device (See ¶¶ [0047]-[0049], Teaches that a data access platform receives a request from a user to access a dataset into the sandbox. The user is associated with an access level for the dataset from a restricted database. In step 402, the data access platform retrieves at least a portion of the dataset from the database, which may be based on the user's associated access level. The data access platform may utilize a metadata service, data access service, or other module of the data access platform to retrieve the dataset according the access policies. In some examples, retrieving the dataset may include querying the database for the data. In step 403, the data access platform identifies at least one sandbox access policy associated with the user request and the dataset. In some examples, the user has access to a certain amount of data, such as a number of entries, from the dataset for a specified amount of time. Furthermore, restrictions may guide a metadata service to include or exclude certain cells, rows, columns, types of information, or other groups of content or combinations thereof. In step 404, the data access platform generates a view of the dataset in the sandbox environment with one or more enabled elements according to the access policy for the user.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Khurana et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to a virtual sandbox database through which a user may access a virtual version of a dataset associated with a data access system (See Khurana et al. ¶ [0002]).
Claims 4, 6, 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over BHOOVARAGHAVAN et al. (US 20180349461 A1) and Khurana et al. (US 20240169055 A1) and further in view of GOYAL et al. (US 20250371075 A1).
As to claim 4, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the method according to claim 1 above. However, it does not expressly teach the details of wherein determining the diagram data further comprises: inputting the first diagram into one or more first machine learning models trained to segment and classify objects of diagrams and receiving as an output of the one or more first machine learning models object data associated with the first diagram, the one or more first machine learning models trained on diagrams associated with various organizational functions and operable on various different tools; and inputting the object data into one or more second machine learning models trained to output relationship data associated with the objects of the object data, the one or more second machine learning models trained on object data of various types of objects associated with various types of diagrams operable on various different tools.
GOYAL et al., from analogous art, teaches wherein determining the diagram data further comprises: inputting the first diagram into one or more first machine learning models trained to segment and classify objects of diagrams and receiving as an output of the one or more first machine learning models object data associated with the first diagram, the one or more first machine learning models trained on diagrams associated with various organizational functions and operable on various different tools; and inputting the object data into one or more second machine learning models trained to output relationship data associated with the objects of the object data, the one or more second machine learning models trained on object data of various types of objects associated with various types of diagrams operable on various different tools (See ¶¶ [0040], [0034] Teaches that The pipeline 200 can use LLMs throughout the transformation pipeline 200. The transformation pipeline 200 involves interpreting these media forms into text when necessary, such as converting the image content 202 b into descriptions, converting the audio content 202 c into transcripts, dividing and converting the video content 202 d into transcripts, timing data, image frames, and the like. The interpreted data is assembled into content data 204 for processing. Continuing to FIG. 2B, the pipeline 200 assembles an intent prompt 206 based on any specified intent 206 a, level of detail 206 b, and/or diagram form 206 c (e.g., a timeline, a flowchart, a decision tree, or the like). The pipeline 200 then combines the content data 204 with the intent prompt 206 into a system prompt 208 for a generative model (e.g., the LLM 126 a). This system prompt 208 is processed by the LLM 126 a to generate a JSON structure 210. This JSON structure 210 is subsequently translated in a visual preview step 212 into a draft diagram 214 representing the interim stage of the diagram's development. The generative models 126 ground on the provided content to create a draft diagram for preview. For example, the natural language prompt calls a LLM 126 a to process different data type components of the content to get text and/or audio components of the content, and then call a LMM 126 b or a LVM 126 c to generate a diagram of the content based on the outputs from the LLM 126 a. A meta prompt for the LLM 126 a may imply or indicate that the user would like to have the different data type components of the content processed differently, as described in the AI-based content transformation, into the diagram pipeline 200 in FIGS. 2A-2B.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of GOYAL et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to semantically analyze and extract diagram data from at least one of the text data item, the text transcripts, or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data (See GOYAL et al. ¶ [0004]).
As to claim 6, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the method according to claim 1 above. However, it does not expressly teach the details of wherein generating the second diagram corresponding to the first diagram further comprises: inputting the diagram data and the second format into one or more machine learning models trained to generate diagrams in various formats and receiving as an output of the one or more machine learning models the second diagram in the second format, the one or more machine learning models trained on diagram data associated with various organizational functions and operable on various different tools.
GOYAL et al., from analogous art, teaches wherein generating the second diagram corresponding to the first diagram further comprises: inputting the diagram data and the second format into one or more machine learning models trained to generate diagrams in various formats and receiving as an output of the one or more machine learning models the second diagram in the second format, the one or more machine learning models trained on diagram data associated with various organizational functions and operable on various different tools (See ¶¶ [0040]-[0041], [0034] Teaches that The pipeline 200 can use LLMs throughout the transformation pipeline 200. The transformation pipeline 200 involves interpreting these media forms into text when necessary, such as converting the image content 202 b into descriptions, converting the audio content 202 c into transcripts, dividing and converting the video content 202 d into transcripts, timing data, image frames, and the like. The interpreted data is assembled into content data 204 for processing. Continuing to FIG. 2B, the pipeline 200 assembles an intent prompt 206 based on any specified intent 206 a, level of detail 206 b, and/or diagram form 206 c (e.g., a timeline, a flowchart, a decision tree, or the like). The pipeline 200 then combines the content data 204 with the intent prompt 206 into a system prompt 208 for a generative model (e.g., the LLM 126 a). This system prompt 208 is processed by the LLM 126 a to generate a JSON structure 210. This JSON structure 210 is subsequently translated in a visual preview step 212 into a draft diagram 214 representing the interim stage of the diagram's development. The draft diagram 214 can take the form of a timeline, flowchart, decision tree, and so on, depending on the requirements specified in the intent prompt 206 and/or the system prompt 208. The pipeline 200 is designed to be iterative, allowing for refinement of the output by revisiting and modifying the content generated by the LLM 126 a until the final diagram meets the expected standards and accurately represents the intended information. The generative models 126 ground on the provided content to create a draft diagram for preview. For example, the natural language prompt calls a LLM 126 a to process different data type components of the content to get text and/or audio components of the content, and then call a LMM 126 b or a LVM 126 c to generate a diagram of the content based on the outputs from the LLM 126 a. A meta prompt for the LLM 126 a may imply or indicate that the user would like to have the different data type components of the content processed differently, as described in the AI-based content transformation, into the diagram pipeline 200 in FIGS. 2A-2B.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of GOYAL et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to semantically analyze and extract diagram data from at least one of the text data item, the text transcripts, or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data (See GOYAL et al. ¶ [0004]).
As to claim 10, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the system according to claim 7 above. However, it does not expressly teach the details of wherein determining the diagram data further comprises: inputting the first diagram into one or more first machine learning models trained to segment and classify objects of diagrams and receiving as an output of the one or more first machine learning models object data associated with the first diagram, the one or more first machine learning models trained on diagrams associated with various organizational functions and operable on various different tools; and inputting the object data into one or more second machine learning models trained to output relationship data associated with the objects of the object data, the one or more second machine learning models trained on object data of various types of object associated with various types of diagrams operable on various different tools.
GOYAL et al., from analogous art, teaches wherein determining the diagram data further comprises: inputting the first diagram into one or more first machine learning models trained to segment and classify objects of diagrams and receiving as an output of the one or more first machine learning models object data associated with the first diagram, the one or more first machine learning models trained on diagrams associated with various organizational functions and operable on various different tools; and inputting the object data into one or more second machine learning models trained to output relationship data associated with the objects of the object data, the one or more second machine learning models trained on object data of various types of object associated with various types of diagrams operable on various different tools (See ¶¶ [0040], [0034] Teaches that The pipeline 200 can use LLMs throughout the transformation pipeline 200. The transformation pipeline 200 involves interpreting these media forms into text when necessary, such as converting the image content 202 b into descriptions, converting the audio content 202 c into transcripts, dividing and converting the video content 202 d into transcripts, timing data, image frames, and the like. The interpreted data is assembled into content data 204 for processing. Continuing to FIG. 2B, the pipeline 200 assembles an intent prompt 206 based on any specified intent 206 a, level of detail 206 b, and/or diagram form 206 c (e.g., a timeline, a flowchart, a decision tree, or the like). The pipeline 200 then combines the content data 204 with the intent prompt 206 into a system prompt 208 for a generative model (e.g., the LLM 126 a). This system prompt 208 is processed by the LLM 126 a to generate a JSON structure 210. This JSON structure 210 is subsequently translated in a visual preview step 212 into a draft diagram 214 representing the interim stage of the diagram's development. The generative models 126 ground on the provided content to create a draft diagram for preview. For example, the natural language prompt calls a LLM 126 a to process different data type components of the content to get text and/or audio components of the content, and then call a LMM 126 b or a LVM 126 c to generate a diagram of the content based on the outputs from the LLM 126 a. A meta prompt for the LLM 126 a may imply or indicate that the user would like to have the different data type components of the content processed differently, as described in the AI-based content transformation, into the diagram pipeline 200 in FIGS. 2A-2B.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of GOYAL et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to semantically analyze and extract diagram data from at least one of the text data item, the text transcripts, or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data (See GOYAL et al. ¶ [0004]).
As to claim 11, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the system according to claim 7 above. However, it does not expressly teach the details of wherein generating the second diagram corresponding to the first diagram further comprises: inputting the diagram data and the second format into one or more machine learning models trained to generate diagrams in various formats and receiving as an output of the one or more machine learning models the second diagram in the second format, the one or more machine learning models trained on diagram data associated with various organizational functions and operable on various different tools.
GOYAL et al., from analogous art, teaches wherein generating the second diagram corresponding to the first diagram further comprises: inputting the diagram data and the second format into one or more machine learning models trained to generate diagrams in various formats and receiving as an output of the one or more machine learning models the second diagram in the second format, the one or more machine learning models trained on diagram data associated with various organizational functions and operable on various different tools (See ¶¶ [0040]-[0041], [0034] Teaches that The pipeline 200 can use LLMs throughout the transformation pipeline 200. The transformation pipeline 200 involves interpreting these media forms into text when necessary, such as converting the image content 202 b into descriptions, converting the audio content 202 c into transcripts, dividing and converting the video content 202 d into transcripts, timing data, image frames, and the like. The interpreted data is assembled into content data 204 for processing. Continuing to FIG. 2B, the pipeline 200 assembles an intent prompt 206 based on any specified intent 206 a, level of detail 206 b, and/or diagram form 206 c (e.g., a timeline, a flowchart, a decision tree, or the like). The pipeline 200 then combines the content data 204 with the intent prompt 206 into a system prompt 208 for a generative model (e.g., the LLM 126 a). This system prompt 208 is processed by the LLM 126 a to generate a JSON structure 210. This JSON structure 210 is subsequently translated in a visual preview step 212 into a draft diagram 214 representing the interim stage of the diagram's development. The draft diagram 214 can take the form of a timeline, flowchart, decision tree, and so on, depending on the requirements specified in the intent prompt 206 and/or the system prompt 208. The pipeline 200 is designed to be iterative, allowing for refinement of the output by revisiting and modifying the content generated by the LLM 126 a until the final diagram meets the expected standards and accurately represents the intended information. The generative models 126 ground on the provided content to create a draft diagram for preview. For example, the natural language prompt calls a LLM 126 a to process different data type components of the content to get text and/or audio components of the content, and then call a LMM 126 b or a LVM 126 c to generate a diagram of the content based on the outputs from the LLM 126 a. A meta prompt for the LLM 126 a may imply or indicate that the user would like to have the different data type components of the content processed differently, as described in the AI-based content transformation, into the diagram pipeline 200 in FIGS. 2A-2B.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of GOYAL et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to semantically analyze and extract diagram data from at least one of the text data item, the text transcripts, or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data (See GOYAL et al. ¶ [0004]).
Claim 13 rejected under 35 U.S.C. 103 as being unpatentable over BHOOVARAGHAVAN et al. (US 20180349461 A1) and Khurana et al. (US 20240169055 A1) and further in view of Pavlin et al. (US 20210075887 A1).
As to claim 13, the combination of BHOOVARAGHAVAN et al. and Khurana et al. teaches the one or more computer-readable media according to claim 12 above. However, it does not expressly teach the details of wherein the operations further comprise: receiving, from the user device, input data associated with the second diagram; determining, based at least on the second diagram and the input data, configuration data for deployment on a network; and applying the configuration data to the network.
Pavlin et al., from analogous art, teaches wherein the operations further comprise: receiving, from the user device, input data associated with the second diagram; determining, based at least on the second diagram and the input data, configuration data for deployment on a network; and applying the configuration data to the network (See ¶¶ [0032]-[0033], Teaches that the processing logic checks to determine whether an update to the deployment configuration has been received. At times, a microservice team may make updates to the deployment configuration or to the services within the microservice system. The diagram-to-deployment converter 125 or any other suitable processing logic may periodically check to see if any updates have been made. These updates may be structured and fed into the diagram-to-deployment converter 125. The diagram-to-deployment converter 125 may then operate in reverse: if the updates to the deployment configuration warrant any changes to the architecture diagram, the diagram-to-deployment converter 125 may proceed to step 540, where the processing logic modifies the architecture diagram to reflect the update to the deployment configuration or to the services themselves. For example, the microservice team may add an additional microservice to the deployment configuration. In the example of FIG. 4, this new microservice may be a recommendation microservice that recommends products to users in a sidebar of the user interface. The diagram-to-deployment converter 125 may detect this and consequently output an update to the associated architecture diagram. The update may include any suitable number of new or modified design parameters. This way, anyone who works on this particular microservice system may have access to a substantially fully updated architecture diagram and deployment configuration. The update to the deployment configuration may be any suitable update, including an addition of a new microservice, an addition of a new programming language to the deployment configuration, a change in a size of a memory storage device, a change to a number of input/output ports in one or more components. At step 550, the processing logic may send the architecture diagram, the deployment configuration, or both to a user of the deployment configuration. This user may be a member of the microservice team for this particular microservice system. At step 560, the processing logic may store the architecture diagram in association with the deployment configuration. This may mean that the architecture diagram and its associated deployment configuration may be stored in such a manner that they each may be accessed by a user).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Pavlin et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. in order to continuously update its software in a piecemeal fashion (See Pavlin et al. ¶ [0002]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over BHOOVARAGHAVAN et al. (US 20180349461 A1) and Khurana et al. (US 20240169055 A1) and Pavlin et al. (US 20210075887 A1) and further in view of Young et al. (US 20250392518 A1).
As to claim 14, the combination of BHOOVARAGHAVAN et al. and Khurana et al. and Pavlin et al. teaches the one or more computer-readable media according to claim 13 above. However, it does not expressly teach the details of wherein the input data includes audio data representative of a verbal command of the user of the user device.
Young et al., from analogous art, teaches wherein the input data includes audio data representative of a verbal command of the user of the user device (See ¶ [0106], Teaches that The UI 604 can also include an audio system 612 that utilizes audio technology for conveying low volume audio (such as audio heard in proximity of a human ear) and high-volume audio (such as speakerphone for hands free operation). The audio system 612 can further include a microphone for receiving audible signals of an end user. The audio system 612 can also be used for voice recognition applications. The UI 604 can further include an image sensor 613 such as a charged coupled device (CCD) camera for capturing still or moving images).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Young et al. into the combination of BHOOVARAGHAVAN et al. and Khurana et al. and Pavlin et al. in order to facilitate network analysis and optimization using machine learning (See Young et al. ¶ [0024]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khan et al. (US 20250181572 A1) teaches Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop. A model architecture is trained, based on weights and calculations, using the training data in the iterative training and testing loop to detect data redundancies, the training including predicting a target variable and iteratively adjusting the weights and calculations during each subsequent iteration to improve predictability of the target variable, where the model architecture is trained to identify data similarities among the two or more datasets.
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James Hollister
/J.R.H./Examiner, Art Unit 2499 2/20/26
/PHILIP J CHEA/Supervisory Patent Examiner, Art Unit 2499