DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/12/2026 has been entered.
The application contains claims 1- 7, 9-13, 16-23, all examined and rejected.
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 .
Response to Amendment
The amendments have been acknowledged, claims 1, 10, 11, 19 and 20 were amended. Claims 8, 14-15 were canceled. Claims 23 were newly added.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 19, 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-13, 16-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wernick et al. (US 20230336438 A1) in view of Setlur et al. (US 11244006 B1) and Nixon et al. (US 20230394045 A1).
Regarding claim 1, Wernick et al. (US 20230336438 A1) discloses:
a method of reusing custom concepts in visual analytics workflows, at least by (paragraph [0140-0141] which describes reusing a stored user’s analysis sequence/workflow named VM Usage Analysis (e.g., custom concepts) in Liveboard (e.g., visual analytics workflows)
performed at a computer having a display, one or more processors, and memory storing one or more programs configured for execution by the one or more processors, the method comprising, at least by (paragraph [0161-0163])
receiving, via a user interface, a first natural language input directed to a first data source having a plurality of data fields and a plurality of data values corresponding to the plurality of data fields, at least by (paragraph [0050] which describes a first natural language input as “Which VM instances across all regions and availability zones are using the most CPU resources per day over the past 60 days?” and paragraph [0052] further describes the data sources the query is directed to as “the data sources queried are given in 220”, and paragraph [0061, 0071] which describes data sets as “data source” being selected and a corresponding graph representing the data set is displayed according to the a first natural language input, see Fig. 2 Ref. 256, as such at the very least the plurality of data fields in the data set correspond to Host, CPU resources {“traffic”, “network traffic”, “storage”, “storage metrics”, etc. }, Date, with corresponding values, such as for Host data field the values are: “splunkes”, “worksindexer1” etc.; for CPU resources the values are: measurement values corresponding to each data field: “traffic”, “network traffic”, “storage”, “storage metrics”, etc.; and for data field date, the values are dates corresponding to when the measurements were recorded.)
parsing the first natural language input at least by (paragraph [0055] “converting a natural language question into an executable database query that can be used to retrieve data from data store”)
executing one or more queries against a set of one or more data sources to retrieve one or more results, based on the one or more data fields and/or the one or more data values, at least by (paragraph [0042] “Query generator 119 interacts with natural language user input to provide queries for processing by a query processor 139 that retrieves responsive data from data store 151. The results processor 159 processes responsive data for display on the user computing device 175.” paragraph [0052] “date range (span) for the information and the data sources queried are given in 220. … The first answer, which includes panels 240 and 250, is given a title and data range in 240. A table-graph 250 includes a table 260 and a graph or chart 256, in panels or subpanels.”
returning, via the user interface, the one or more results, wherein the respective results is formatted as a respective text and chart response, at least by (Fig. 2, which depicts the answer to user’s question, formatted as text and chart)
generating and storing a first result, of the one or more results as a first named concept, at least by (paragraph [0052] which further describes “Analysis controls 237 include an analysis name, save” See also Fig. 2, which shows that the results are save with corresponding name “VM Usage Analysis”)
receiving a second natural language input at least by (paragraph [0073] describes receive a follow-up natural language question directed to the data visualization shown in Fig. 6; “ a follow-up question was entered, “What was the network traffic by host per day over the past 60 days?” This follow-up question is answered, within the context of selected rows “splunkes” and “worksindexer1”,” where network traffic with the context of splunkes and worksindexer1 was not part of the one or more results, but are one of many attributes from the data source)
in response to receiving the second natural language input, executing one or more updated queries against the set of one or more data sources to retrieve an update result, based on the data in the first named concept and data of the first attribute, at least by (paragraph [0074-0075] and Fig. 8, describes the retrieved and updated results based on the follow-up question received based on the data from the initial query and data of the first attribute)
and generating and storing a second named concept according to the updated results, at least by (paragraph [0078] “additional answers help the user answer the follow-up question or query. Each chart is shown on a separate panel that can be saved for future use or sharing via a save control” See Fig. 10A-D which shows named concepts to each of the updated results, for example, “Top Destination Hosts by Network Flow over time”)
But Wernick fails to specifically describe: parsing the natural language input to identify one or more data fields and/or one or more data values in the data source.
However, Setlur discloses the above limitation at least by (col. 8 lines 8-48, which describes extracting (e.g. parsing) keywords and phrases from the command (e.g., natural language input) and maps (e.g., identify) them to related data fields and parameter values from the data set)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Wernick with the ability to parse natual languae input as described by Setlur to resolve semantic ambiguities in the natural language query by mapping keywords/phrases to at least one data field and values from the data dataset (Setlur, col. 7 lines 53-61).
Wernick also fails to describe the above limitation (a) persisting the first names concept in the first data source, from which the first named concept is created, as a reusable custom data field; and (b) that the follow-up question includes that actual first named concept.
However, Nixon discloses the above limitation (a) at least by (paragraph [0241] “search tags may be stored in the contextual knowledge repository 49” where the search tags are the reusable custom data field and the contextual knowledge repository 49 is in the first data source, from which the first named concept is created. Nixon further describes the above limitation (b) at least by (paragraph [0046, 0239] describes a search tag that references previous search results, (e.g. search tag = named concept); and paragraph [0239-0240] describes a search query that includes the search tag and additional term(s), where the search query is in a natural language format (see para. 0095).
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Wernick with Nixon’s the ability to include the search tag referring previous search results in a subsequent NLQ to avoid the need to re-enter the process plant search query to retrieve previous sets of process plant search results as the search result to the search query may have changed (Nixon’s, para. [0238]).
As per claim 2, claim 1 is incorporated and Wernick discloses:
triggering one or more actions based on the one or more data fields and/or the one or more data values, wherein the one or more actions represent analytical operations that satisfy a user’s intent specified in the first natural language input, and the one or more actions subsequently trigger the one or more queries, at least by (paragraph [0069-0071] which describes triggered action related to provided follow-up query control, where suggested keywords for follow-up query is based on related concepts to initial query and results (e.g., represent analytical operations that satisfy a user’s intent specified in the natural language input), as such the action related to providing the follow-up query control subsequently triggers the one or more follow-up queries to be executed)
As per claim 3, claim 2 is incorporated and Wernick discloses:
wherein each action of the one or more actions is realized with a corresponding function that parameterizes entities recognized from the first natural language input and a current conversational state, at least by (paragraph [0071] “keyword “CPU resources” can be mapped to concept “computing resources” and this term can be assigned a high correlation with other concepts and their associated keywords like “traffic”, “network traffic”, “storage”, “storage metrics”; paragraph [0073] “a follow-up question was entered, “What was the network traffic by host per day over the past 60 days?” This follow-up question is answered, within the context of selected rows “splunkes” and “worksindexer1””, where parameterizes entities are like “traffic”, “network traffic”, “storage”, “storage metrics” and the current conversational state is within the context of selected rows “splunkes” and “worksindexer1” from the original query)
As per claim 4, claim 1 is incorporated and Wernick discloses:
wherein the current conversational state encompasses a current data source, a most recent query posed, a result for that query, any filters in play, and any previously saved named concepts, at least by (paragraph [0073] “a follow-up question was entered, “What was the network traffic by host per day over the past 60 days?” This follow-up question is answered, within the context of selected rows “splunkes” and “worksindexer1””, where parameterizes entities are like “traffic”, “network traffic”, “storage”, “storage metrics” and the current conversational state is within the context of selected rows “splunkes” and “worksindexer1”, data sources AWS, Azure, GCP, date filter, from named concept VM Usage Analyssis, from the original query)
As per claim 5, claim 1 is incorporated and Wernick discloses:
further comprising: subsequently using the named concept in one or more analytical queries that follow the first natural language input, at least by (paragraph [0140-0141] describes subsequent additional queries and re-running queries using the saved “VM Usage Analysis”)
As per claim 6, claim 1 is incorporated and Wernick discloses:
further comprising: subsequently using the first named concept in one or more analytical queries directed to other data sources with shared attributes, at least by (paragraph [0140-0141] describes subsequent additional queries and re-running queries using the saved “VM Usage Analysis” where the sequence of queries associated with VM Usage Analysis can be, “executed “live” using whatever data sources a user chooses”)
Wernick fails to describe specifically: first named concept.
However, Nixon discloses the above limitation at least by (paragraph [0046, 0239] describes a search tag that references previous search results, (e.g. search tag = first named concept); and paragraph [0239-0240] describes a search query that includes the search tag and additional term(s), where the search query is in a natural language format (see para. 0095) and further using the search tag for further search of the search results with respect to the additional search terms (e.g. attributes))
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Wernick with Nixon’s the ability to include the search tag referring previous search results in a subsequent NLQ to avoid the need to re-enter the process plant search query to retrieve previous sets of process plant search results as the search result to the search query may have changed (Nixon’s, para. [0238]).
As per claim 7, claim 1 is incorporated and Wernick discloses:
further comprising: subsequently using the first named concept in one or more analytical queries posed by one or more users that are different from a user who created the named concept, at least by (paragraph [0140-0141] describes sharing the saved “VM Usage Analysis” to other users)
Wernick fails to describe specifically: first named concept.
However, Nixon discloses the above limitation at least by (paragraph [0046, 0239] describes a search tag that references previous search results, (e.g. search tag = first named concept);
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Wernick with Nixon’s the ability to include the search tag referring previous search results in a subsequent NLQ to avoid the need to re-enter the process plant search query to retrieve previous sets of process plant search results as the search result to the search query may have changed (Nixon’s, para. [0238]).
As per claim 8, canceled.
As per claim 9, claim 1 is incorporated and Wernick discloses:
further comprising: in response to a user requesting, via a natural language utterance, to make the first named concept available in a specified data source: subsequently using the named concept in one or more analytical queries directed to the specified data source, at least by (paragraph [0140-0141] describes subsequent additional queries and re-running queries using the saved “VM Usage Analysis” where the sequence of queries associated with VM Usage Analysis can be “executed “live” using whatever data sources a user chooses. Thus, Liveboard results are real time, dynamic and specific to the environments and data sources for a given user. They are also portable and easily shared for reuse and collaboration among many users with their own data.”
Wernick fails to describe specifically: first named concept.
However, Nixon discloses the above limitation at least by (paragraph [0046, 0239] describes a search tag that references previous search results, (e.g. search tag = first named concept); and paragraph [0239-0240] describes a search query that includes the search tag and additional term(s), where the search query is in a natural language format (see para. 0095) and further using the search tag for further search of the search results with respect to the additional search terms (e.g. attributes))
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify Wernick with Nixon’s the ability to include the search tag referring previous search results in a subsequent NLQ to avoid the need to re-enter the process plant search query to retrieve previous sets of process plant search results as the search result to the search query may have changed (Nixon’s, para. [0238]).
As per claim 10, claim 1 is incorporated and Wernick discloses:
further comprising: subsequently using the first named concept in other visual analysis tools that import the data source for analysis, at least by (paragraph [0156] which describes reusing the same natural language query on other visual analysis tool such as Amazon.)
As per claim 11, claim 1 is incorporated and Wernick discloses:
further comprising: displaying the reusable custom field in the user interface and applying custom data field to filter values in a line chart, at least by (paragraph [0137] describes saved questions as reusable custom field, such as “Show me network traffic . . . ”, “Show me host logins . . . ”, and “Show me read and write bytes . . .” and paragraph [0139] Fig. 15B Ref. 1561, which displays follow-up questions subsequently using the initial question associated with VM Usage Analysis, which filters VM instances, CPU resources per day over past 60 days, to further extract read bytes using the filtered CPU resources per day over past 60 days and host VM instances from VM instances and providing results in corresponding line chart using the displayed “Show me read bytes…”)
As per claim 12, claim 1 is incorporated and Wernick discloses:
further comprising: associating a user with the first named concept; managing updates and/or refinements to the first named concept based on the association, at least by (paragraph [0141] “ As a user saves queries and panels, they are arranged sequentially to capture the user's workflow, … the original sequence can be edited or rearranged”)
As per claim 13, claim 1 is incorporated and Wernick discloses:
further comprising: associating a user with the first named concept; and performing at least one of: personalizing the named concept for a user, controlling access to the first named concept, and personalizing the saved concept based on a dataset, at least by (paragraph [0141] “As a user saves queries and panels, they are arranged sequentially to capture the user's workflow. This sequential arrangement can be done using a scrolling web page, multiple browser tabs, and similar alternatives. Additionally, in other implementations, the original sequence can be edited or rearranged. By storing a user's workflow in this fashion, a Liveboard acts at a higher level: it does not need to store the actual results of queries. Rather, it stores the sequence of queries so they may be executed “live” using whatever data sources a user chooses. Thus, Liveboard results are real time, dynamic and specific to the environments and data sources for a given user. They are also portable and easily shared for reuse and collaboration among many users with their own data.” Further controlling access to the named concept is describe by whether the names concept is shared to other users (see para. 0145))
As per claim 14, canceled.
As per claim 15, canceled.
As per claim 16, claim 11 is incorporated and Wernick discloses:
further comprising: building a concept map based on common attributes and determining relational dependencies to place the first named concept at an appropriate level in a concept hierarchy or a concept nesting; and based on concepts in the concept map, providing recommendations that are relevant scaffolding and/or guidance to support natural language interface users as they frame their natural language utterance while exploring data, at least by (paragraph [0071] “keyword “CPU resources” can be mapped to concept “computing resources” and this term can be assigned a high correlation with other concepts and their associated keywords like “traffic”, “network traffic”, “storage”, “storage metrics”, and so forth.” Where, “CPU resources” mapped “computing resources” is mapped to {“traffic”, “network traffic”, “storage”, “storage metrics”}, the recited concept map, dependencies, nesting, for the suggested follow-up natural language queries)
As per claim 17, claim 11 is incorporated and Wernick discloses:
further comprising: tracking frequency and commonality of a combination of attributes in input natural language utterances to automatically trigger the generation and storing of the first named concept without user input, at least by (paragraph [0070] " Suggested queries can be generated based on previous queries. For instance, in main query 710 in FIG. 7, the query keywords “VM instances” and “CPU resources” could be mapped to one or more associated queries. These associated queries can be obtained by examining previously saved query threads. By correlating the occurrence of keywords “VM instances” and “CPU resources” found in the saved query threads with subsequent queries in these threads and ranking them, for instance by frequency of occurrence, associated queries can be found. These associated queries can then be used as suggested queries when keywords “VM instances” and “CPU resources” are found in a user's current query.”)
As per claim 18, claim 1 is incorporated and Wernick discloses:
further comprising: parameterizing the first named concept based on attribute value and type; and reusing the named concept based on the parameterization, at least by (paragraph [0071] “keyword “CPU resources” can be mapped to concept “computing resources” and this term can be assigned a high correlation with other concepts and their associated keywords like “traffic”, “network traffic”, “storage”, “storage metrics”; paragraph [0073] “a follow-up question was entered, “What was the network traffic by host per day over the past 60 days?” This follow-up question is answered, within the context of selected rows “splunkes” and “worksindexer1””, where parameterizes entities are like “traffic”, “network traffic”, “storage”, “storage metrics” and the current conversational state is within the context of selected rows “splunkes” and “worksindexer1” from the original query)
As per claim 21, claim 1 is incorporated and Wernick discloses:
wherein the data source that includes the first attribute is different from the first data source, at least by (paragraph [0072-0076] describes attributes related to host where the data source are from different sources such as “context of selected rows “splunkes” and “worksindexer1”,… selected segment include “worksindexer1”, “worksindexer6”, “worksindexer2”, “worksindexer8”, “splunkworks2”, and“worksindexer7”.
As per claim 22, claim 19 is incorporated and Wernick discloses:
wherein the user interface is configured to: support analytical queries having aggregation, grouping, and/or filtering; and produce a text response for a single result and visualizations for results that return more than a single row, at least by (paragraph [0042” describes analytical queries resulting in, aggregation, statistical analysis including averages and ranges, reordering or grouping into data sets, paragraph [0047’ “. Individual rows of data in the table can be selected or deselected for display. The rows can be displayed in a single graph or individual graphs. Averages and other statistical measures can be calculated and graphed responsive to selectable controls, without formulas for series calculations.” See also results in Fig. 2-6, etc.)
As per claim 23, claim 1 is incorporated and Wernick discloses:
wherein executing the one or more updated queries against the set of one or more data sources to retrieve the updated result comprises: executing the one or more updated queries against a second data source that is different from the first data source, wherein the first data source and the second data source share one or more data fields and/or data values semantically without having a matching data column, at least by (paragraph [0137] “multiple natural language questions and selected answer views of data, saved from past experience by an origin organization, that can be shared between organizations even if they use different cloud platforms and have loaded different data from respective cloud platforms. The natural language questions are processed against the user organization's loaded data, without dependence on an origin organization's loaded data or on the origin organization's platform(s). ” paragraph [0146] “user accessing different data will also be able to invoke queries packaged in the Liveboard, with different results from accessing different data. The underlying database engine can automatically repopulate the displayed screens using their data.”)
Claims 19 and 20 recite equivalent claim limitations as claim 1 above, except that they set forth the claimed invention as a system and non-transitory computer readable storage medium, as such they are rejected for the same reasons as applied hereinabove.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230185798 A1: Para. 0047, describes saving workflow entries representing different recipe entries related to user’s interaction using natural language sequences.
US 20210141838 A1: Para. 0070, “a portion of a flow has been created that scrubs a certain type of input using a combination of 10 steps, that 10 step flow portion can be saved and reused, either in the same flow or in completely separate flows.”
US 11720595 B2: col. 15 lines 20-27, “Once the user receives related data queries, the user may have an option to download, share, or save the related data queries. The user action (i.e., if the user downloads, shares, or saves the related data queries) may be used to further train the machine learning model. In some examples, the user may provide a title or description for the received data query. In some cases, the title or description may be used to further train the machine learning model.”
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 10/08/2025