DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA and is in response to communications filed on 5/30/2025 in which claims 1-20 are presented for examination.
Priority
Acknowledgment is made of Provisional Application No. 63037511, filed on 6/10/2020.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12013850 in view of Jolley. It would have been obvious for one of ordinary skill in the art at the time of the effective filing date of the invention to modify the results to include metadata to display to the interface as part of the results in view of Jolley; this is advantageous to provide a summary for the user for further clarification of the result (Jolley, [0074]).
This is a provisional nonstatutory double patenting rejection.
A computer-implemented method, comprising:
receiving, by a user interface, an utterance directed to querying a data query engine;
identifying one or more previous steps of a data conversation that are indicated by the utterance, wherein the one or more previous steps were previously received by the user interface before a step of the data conversation having the utterance;
determining, by a machine learning model and based on the utterance and the one or more previous steps, an effective schema targeted by the utterance, wherein
(i) the machine learning model determines the effective schema by selecting a topic comprising one or more schema objects of a plurality of schema objects of the data query engine, and
(ii) the effective schema comprises the one or more schema objects; and
generating and executing, based on the effective schema, an executable structured query language statement for the data query engine against the effective schema, wherein the execution of the executable structure query language statement generates a result set.
8. The method of claim 1, further comprising:
generating, based on the utterance and the effective schema, an intermediate structured query language statement that is representative of the utterance.
9. The method of claim 8, wherein generating and executing the executable structured query language statement further comprises:
generating, based on the intermediate structured query language statement, an executable structured query language statement, wherein the executable structured query language statement is comprised of a query language dialect of the data query engine.
A computer-implemented method, comprising:
receiving, by a user interface, an utterance directed to querying a data query engine;
identifying one or more previous steps of a data conversation that are indicated by the utterance, wherein the one or more previous steps were previously received by the user interface before a step having the utterance;
determining, by a machine learning model and based on the utterance and the one or more previous steps, an effective schema targeted by the utterance, wherein
(i) the machine learning model determines the effective schema by selecting a topic comprising one or more schema objects of a plurality of schema objects of the data query engine, and
(ii) the effective schema comprises the one or more schema objects;
generating, based on the utterance and the effective schema, an intermediate structured query language statement that is representative of the utterance;
generating an executable structured query language statement based on the intermediate structured query language statement, wherein the executable structured query language statement is comprised of a query language dialect of the data query engine;
executing the executable structured query language statement for the data query engine; and
communicating, via the user interface, a result set and metadata, wherein the result set and the metadata correspond to the execution of the executable structured query language statement.
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 of this title, 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.
Claims 1-2, 8-9, 13-16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jolley et al. US 20170242886 A1 (hereinafter referred to as “Jolley”) in view of Bastide et al. US 20210150398 A1 (hereinafter referred to as “Bastide”) and further in view of Berstein et al. US 20190361897 A1 (hereinafter referred to as “Bernstein”).
As per claim 1, Jolley teaches:
A computer-implemented method, comprising:
receiving, by a user interface, an utterance directed to querying a data query engine (Jolley, [0029] – Intelligent agent and interface to provide enhanced search by conversing with a user, and finding the intent of the user. Paragraph [0059] – Uses Geo Location longitude/latitude in UTC format with a user utterance, wherein this is interpreted as at least an utterance, and could be interpreted as a query language statement in natural form. See also paragraph [0049] – Natural language statement);
identifying one or more previous steps of a data conversation that are indicated by the utterance, wherein the one or more previous steps were previously received by the user interface before a step of the data conversation having the utterance (Jolley, [0035] – The intelligent agent attempts to understand and answer with data from its knowledge database, based at least in part on the intelligent agent's understanding of the user's context, conversational state, and previous activity. See also paragraphs [0055] and [0057] with respect to previous conversation topics and entities);
Jolley doesn’t explicitly teach a machine learning model that identifies a topic corresponding to an effective schema, however, Bastide teaches:
determining, by a machine learning model and based on the utterance and the one or more previous steps, an effective schema targeted by the utterance, wherein
(i) the machine learning model determines the effective schema by selecting a topic comprising one or more schema objects of a plurality of schema objects of the data query engine (Bastide, [0058] – System may generate a list of topics in the conversations. Conversation topics may be determined by topic identifier using neural network techniques. Topic identifier can be implemented with machine learning. In one embodiment, topic identifier comprises a deep learning neural network that learns to classify conversation text (including textual renderings of verbal conversations) into one of N categories, each category corresponding to a predetermined topic. Text generated from prior conversations and archived in database. [0062] – Various schema can be used for indexing the data stored in the database. Conversations, for example, can be archived based on assigned conversation identifiers), and
(ii) the effective schema comprises the one or more schema objects (Bastide, [0058] – System may generate a list of topics in the conversations. Conversation topics may be determined by topic identifier using neural network techniques. Topic identifier can be implemented with machine learning); and
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention in view of Bastide in order to train the system with a machine learning model; this is advantageous because the system can achieve an acceptable level of accuracy after an appropriate amount of refinement through classifying samples of text of conversations (Bastide, paragraph [0058]).
Although Jolley in view of Bastide teaches topic identifiers as well as using schema for indexing the data, Jolley as modified with Bastide doesn’t explicitly teach generating and executing an SQL statement against the effective schema for generating a result set, however, Berstein teaches:
generating and executing, based on the effective schema, an executable structured query language statement for the data query engine against the effective schema, wherein the execution of the executable structure query language statement generates a result set (Bernstein, [0029] – The virtual schema may include fields that are present in a search index and/or database as well as virtual fields used to specify different behaviors in generating an SQL execution plan. The virtual field may be able to retrieve operational data, such as creation timestamps, deletion timestamps, lock timestamps, time series data, and/or other data. In some aspects, the query results with this metadata may be returned to the user).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Berstein in order to execute an SQL against the schema for results; this is advantageous because the system can use the schema to specify different behaviors in generating an SQL execution plan (Berstein, paragraph [0029]).
As per claim 2, Jolley as modified teaches:
The method of claim 1, wherein the user interface is configured to display a holistic view of one or more data conversations comprising the data conversation (Jolley, [0033] – Enhanced and/or interactive search comprises a search engine/service/experience that seeks to provide a highly precise result to the user by focusing the interface on helping the user to clarify or discover their actual search intention rather than focusing on the result. Paragraph [0035] – The intelligent agent attempts to understand and answer with data from its knowledge database, based at least in part on the intelligent agent's understanding of the user's context, conversational state, and previous activity, wherein this is interpreted as a holistic view of data conversations).
As per claim 8, Jolley as modified teaches:
The method of claim 1, further comprising:
generating, based on the utterance and the effective schema, an intermediate structured query language statement that is representative of the utterance (Jolley, [0082] – The intent system may derive meaning from the structure of the user utterance, statement, and/or query. Paragraph [0084] – The intent system extracts named entities from a user utterance which is interpreted as using the schema as well because the schema is an entity schema. See also paragraphs [0069], and [0080]-[0086]).
As per claim 9, Jolley as modified teaches:
The method of claim 8, wherein generating and executing the executable structured query language statement further comprises:
generating, based on the intermediate structured query language statement, an executable structured query language statement, wherein the executable structured query language statement is comprised of a query language dialect of the data query engine (Jolley, [0092] – The user’s textual input enters a decoder where the representation from the input is extracted so the machine can understand and execute the query to find results).
As per claim 13, Jolley as modified teaches:
The method of claim 1, further comprising:
converting the utterance directly to the executable structured query language statement based on one or more user intent keywords or key phrases included in the utterance (Jolley, [0211] – Once the ambiguity is resolved, the interpretation determined to reflect the user's intent is converted to a query plan, which is then executed to determine and return a set of results).
As per claim 14, Jolley as modified teaches:
The method of claim 1, further comprising:
causing communication of, via the user interface, an indication of one or more of:
the executable structured query language statement, a confidence level of a translation of the utterance to the executable structured query language statement, the result set, and zero or more query filters (Jolley, [0112] – The three words “New York City” might be tagged as a single “Place/Locality” with a confidence of 97%).
As per claim 15, Jolley as modified teaches:
The method of claim 1, further comprising:
causing communication of, via the user interface, a natural language explanation of actions performed by the executable structured query language statement (Jolley, [0216]-[0219] – The natural language component offers clarifying responses in natural language text, wherein this is interpreted as an explanation of actions).
As per claim 16, Jolley as modified teaches:
The method of claim 1, further comprising:
communicating, from the user interface accessed by a first computing device to a second user interface accessed by a second computing device, views of one or more data conversations comprising the data conversation (Jolley, [0035] – A group chat suggests that multiple computing devices are involved in the conversation).
As per claim 18, Jolley as modified teaches:
The method of claim 1, further comprising:
substituting one or more words included in the utterance for one or more alternate words (Jolley, [0105] – Recognition of underlying terms despite intentional and unintentional variations in spelling and morphology, including spelling errors, alternative spellings, abbreviations and shortcuts, emoji).
Claim 19 is directed to a system performing steps recited in claim 1 with substantially the same limitations. Therefore, the rejection made to claim 1 is applied to claim 19.
Claims 3-6, 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Jolley in view of Bastide in view of Berstein and further in view of Tankersley et al. US 20190095478 A1 (hereinafter referred to as “Tankersley”).
As per claim 3, Jolley doesn’t explicitly teach a schema view with data available in a particular column that is searchable, however, Tankersley teaches:
The method of claim 1, wherein the user interface is configured to display a schema view of the data query engine indicative of at least one of the plurality of schema objects stored by the data query engine (Tankersley, [0275]-[0276], fig. 8A – Shows an interface with an events list column where queryable data is shown).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Tankersley in order to show a schema in an interface; this is advantageous because it provides the user with a reference for what is being searched and how the data is being pulled from the storage (Tankersley, paragraph [0164]).
As per claim 4, although Jolley teaches calculations of geographic location from an utterance from a user, Jolley doesn’t explicitly teach a metric name or encoding the metric names and calculations in an interface, however, Tankersley teaches:
The method of claim 1, further comprising:
extracting one or more calculations and one or more metric names indicated by the utterance (Tankersley, [0297] and fig. 12 – Graphical user interface screen allows the user to filter search results and to perform statistical analysis on values extracted from specific fields in the set of events. Paragraph [0343] – If the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria); and
encoding the one or more calculations and the one or more metric names in the effective schema (Tankersley, [0297] and fig. 12 – Graphical user interface screen allows the user to filter search results and to perform statistical analysis on values extracted from specific fields in the set of events, wherein statistical analysis on the search results in an interface are interpreted as calculations associated with the metric names in the schema).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Tankersley in order to show a schema in an interface with calculations and metric names; this is advantageous because it provides the user with a reference for what is being searched and how the data is being pulled from the storage (Tankersley, paragraph [0164]).
As per claim 5, Jolley as modified with Tankersley teaches:
The method of claim 1, wherein the user interface is configured to display a topic view comprising the topic, wherein the topic comprises one or more entities corresponding to the one or more schema objects, wherein each entity of the one or more entities comprises one or more queryable attributes (Tankersley, [0277] – The sidebar which displays an interactive field picker with field names is interpreted as a topic view comprising a listing of topics).
As per claim 6, Jolley as modified with Tankersley teaches:
The method of claim 5, further comprising:
causing display, via the user interface, of the topic, the entities, and the queryable attributes, wherein the topic, the entities, and the queryable attributes are
(i) logically categorized based on a usage (Tankersley, [0357] – energy-usage. Also, [0488]) and
(ii) represented in one or more natural language terms (Tankersley, [0278] – A value type identifier identifies the type of value for the respective field, such as an “a” for fields that include literal values or a “#” for fields that include numerical values. Paragraph [0279] – Each field name in the field picker also has a unique value count to the right of the field name, such as unique value count).
As per claim 10, Jolley as modified teaches:
The method of claim 8, wherein the intermediate structured query language statement:
(i) indicates an intent of the utterance in an expression, wherein the expression indicates a functionality of the executable structured query language statement (Jolley, [0080] – The intent system provides language and understanding to a user utterance); and
Jolley as modified doesn’t explicitly teach SQL, however, Tankersley teaches:
(ii) is generated from a set of intermediate structured query language keywords, metadata of the effective schema, and zero or more utterance tokens (Tankersley, [0246] – Structured Query Language (“SQL”), can be used to create a query).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Tankersley in order to explicitly use SQL; this is a well-known technique for querying databases and would yield predictable results such as using the known language of SQL to generate a query language statement (Tankersley, paragraph [0164]).
As per claim 11, Jolley as modified teaches:
The method of claim 10, further comprising:
generating the set of intermediate structured query language keywords based on standard structured query language keywords that include one or more user intent keywords that manipulate or relate the one or more previous steps of the data conversation (Jolley, [0211] – Once the ambiguity is resolved, the interpretation determined to reflect the user's intent is converted to a query plan, which is then executed to determine and return a set of results. [0427]-[0430] – Queries/statements/utterances are expanded to include location and other information, wherein location is interpreted as including previous data conversation steps).
As per claim 12, Jolley as modified with Tankersley teaches:
The method of claim 1, further comprising:
generating the executable structured query language statement based on one or more previous executable structured query language statements corresponding to the one or more previous steps of the data conversation (Tankersley, [0734] – The search query can be an ad-hoc search or a saved search, wherein using a saved search is interpreted as generating an executable structured query language statement based on an intermediate SQL statement).
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jolley in view of Bastide in view of Berstein and further in view of Gopalakrishnan et al. US 20200117737 A1 (hereinafter referred to as “Gopalakrishnan”).
As per claim 7, Jolley as modified doesn’t explicitly teach how the machine learning model determines the effective schema using previous steps of the data conversation, however, Gopalakrishnan teaches:
The method of claim 1, wherein
(i) the machine learning model determines the effective schema by selecting one or more previous result sets generated based on the one or more previous steps of the data conversation (Gopalakrishnan, [0015] – Machine learning and logical reasoning may be used in combination to develop the translation capabilities between the different schema and recommend relationships between heterogenous data sets. [0024] – The query result 190, along with the results sets from previous and subsequent queries, are input into the knowledge graph 140 and stored in metadata repository 150 and used to drive the search index. The knowledge graph defining the nodes and edges of the metadata repository 150 allows for an efficient way to utilize the computational graph architecture to apply machine learning to the data contained in the federated data sources. The result sets are translated, based on similar translation rules as mentioned above, into a desired result set schema for the end user),
(ii) each of the previous result sets corresponds to a respective previous step of the previous steps (Gopalakrishnan, [0021] – The query-data source rules may be learned rules from previous queries and resulting data sets), and
(iii) the effective schema comprises the one or more previous result sets (Gopalakrishnan, [0024] – The result sets are translated, based on similar translation rules as mentioned above, into a desired result set schema for the end user).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Gopalakrishnan in order to train the system with a machine learning model to determine the schema; this is advantageous because a knowledge graph defining the nodes and edges of the metadata repository allows for an efficient way to utilize the computational graph architecture to apply machine learning to the data contained in the federated data sources (Gopalakrishnan, [0024]).
Claim 20 is directed to a system performing steps recited in claim 7 with substantially the same limitations. Therefore, the rejection made to claim 7 is applied to claim 20.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Jolley in view of Bastide in view of Berstein and further in view of Fox et al. US 20210073336 A1 (hereinafter referred to as “Fox”).
As per claim 17, Jolley as modified teaches:
The method of claim 1, wherein the user interface is configured to
(i) receive a save input configured to store one or more data conversations comprising the data conversation (Jolley, [0059] – Previous search history can be saved with preferences indicating a preferred “point of interest” for food),
Jolley as modified doesn’t go into detail about the historical conversations and all the parts they can contain, however, Fox teaches:
(ii) receive a replay input configured to replay at least part of the one or more data conversations comprising the data conversation (Fox, [0037] – Historical communications can be retrieved), and
(iii) display the result set, wherein the user interface comprises one or more of:
graphing options for the result set, data filters for the result set, a table view for the result set, table sorting for the result set, a naming capability, an annotation capability for the step and the previous steps of the data conversation, and a deletion capability for the step and the previous steps of the data conversation (Fox, [0037] – Retrieves all historical communications including, but limited to, queries, messages, conversations, discussions, utterances, and/or statements associated with a specified channel (e.g., chat room, sub-channel, moderated group, etc.), application (e.g., application 114), author (e.g., user), sets of authors, topics, and associated search terms (e.g., collocations and colligations), wherein sets of authors or topics are interpreted as data set filters).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Jolley’s invention as modified in view of Fox in order to include historical conversations and the various data associated with the conversations; this is advantageous because it provides the user with a reference to past conversations so that a conversation can continue until a threshold number of days is reached (Fox, paragraph [0037]).
Response to Arguments
Applicant’s arguments filed 5/30/2025 have been fully considered but they are not persuasive. Applicant’s arguments begin on page 6 of Remarks where there are a total of 2 specific arguments. Each specific argument is addressed below.
Argument: Applicant requests in Remarks on page 6 that the non-statutory double patenting be held in abeyance until the claims of the instant application are otherwise allowable condition.
In Response: The double patenting rejection is pending due to lack of arguments against said rejection.
Argument: Applicant argues in Remarks on page that Jolley in view of Bastide doesn’t adequately teach “determining, …, an effective schema targeted by the utterance, wherein
(i) the machine learning model determines the effective schema by selecting a topic comprising one or more schema objects of a plurality of schema objects of the data query engine, and
(ii) the effective schema comprises the one or more schema objects; and
generating and executing, …, an executable structured query language statement for the data query engine against the effective schema, ….”
In Response: Reference Bastide is specifically used with the initial primary reference as a combination to teach these first two limitations. Bastide has been used where “topics may be determined by topic identifier.” Paragraph [0007] of the specification describe(s) an “effective schema” as comprising at least one of “a topic schema from a data query engine schema or one or more columns of one or more previous result sets.” A “topic schema” doesn’t appear to be mentioned anywhere else in the specification for clarifying over Bastide’s teaching of determining a topic of a conversation. Therefore, based on a reasonable interpretation in view of the specification, the prior art of record teaches the claimed limitation.
However, Applicant can overcome Bastide by clearly articulating the effective schema by amending the claims to comprise either a topic schema form a data query engine as well as clearly describing how that overcomes Bastide or amending the claims to comprise a column of a previous result set. At least the column of a previous result set would clearly overcome the prior art of record without further arguments being necessary.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ranganathan et al. US 20220067037 A1 teaches a conversational interface for generating and executing controlled natural language queries on a relational database (Title). This has a later filing date than the provisional application.
Kuchmann-Beauger et al. US 8996555 B2 teaches defines a mapping of recognized semantics of user questions, to a well structured query model that can be executed on arbitrary data warehouses (Abstract).
Zhong et al. “Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Nov 9, 2017, Pgs. 1-7 teaches relational databases with a deep neural network for translating natural language questions to corresponding SQL queries (Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew Ellis whose telephone number is (571)270-3443. The examiner can normally be reached on Monday-Friday 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached on (571)270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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September 22, 2025
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2152