Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
2. 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
3. Claims 1, 8 and 15 are rejected under 35 U.S.C. 102(a)(1) being anticipated by Prabhugaonkar (US 2020/0279001).
Regarding Claim 1:
Prabhugaonkar discloses a computer-implemented method comprising: providing an enhanced grammar, a natural language utterance (Prabhugaonkar: ¶[0025]-[0026] receives a question in a natural language, ¶[0120] discloses enhanced grammar, by using vocabulary terms to express something meaningful within a specified domain. The vocabulary is used to make queries and assertions)
comprising a date-time interval (Prabhugaonkar: ¶[0025-[0026] receives a question in a natural language. ¶[0073]-[0079] discloses period and time handling),
and database schema information to a machine learning model that has been trained to convert natural language utterances to logical forms (Prabhugaonkar: ¶[0017]-[0019] converts intermediate queries into a database-aware structured query, i.e., a database schema information,, ¶[0029] and ¶[0105]-[0107] discloses a machine learning model for parsing user intent in the query, and discovering relationships between words in a phrase, in ¶[0115] the query, once it has identified entities, periods, filters, conditions and intents is converted into a database aware structured query, i.e., a logical form);
and using the machine learning model to convert the natural language utterance to an output logical form, wherein the output logical form comprises at least one of the date-time interval and an extraction function for extracting date-time information corresponding to the date-time interval from at least one date-time attribute of the database schema information (Prabhugaonkar: ¶[0063], ¶[0079] and ¶[0108]-[0115] discloses the interpretation, and later, the outputted structured query, will include a time period (which includes dates and times. It achieves this by extracting/identifying periods in the initial query, if one is not explicitly given, it infers a period).
Regarding Claim 8:
Claim 8 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of anticipation used above.
It is noted that Prabhugaonkar discloses one or more processors; and one or more computer-readable media storing instructions which, when executed by the one or more processors at least in ¶[0008] and ¶[0025].
Regarding Claim 15:
Claim 15 has been analyzed with regard to claim 1 (see rejection above) and is rejected for the same reasons of anticipation used above.
It is noted that Prabhugaonkar discloses one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations.
Claim Rejections - 35 USC § 103
4. 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.
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.
5. Claims 2-3, 9-10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Prabhugaonkar (US 2020/0279001) in view of Motik (US 20110320187) and further in view of Ang (US 2018/0210883).
Regarding Claim 2:
Prabhugaonkar further discloses the computer-implemented method of claim 1, wherein the output logical form comprises the extraction function (Prabhugaonkar: ¶[0073] time/period handling is explicitly part of the interpretation and ¶[0114] discloses it is carried into the database aware time field / query form), wherein the enhanced grammar comprises (Prabhugaonkar: ¶[0073] explicitly uses SUTime extraction/normalization ¶[0111] also discloses separate handling when no temporal pieces are explicitly present and inferencing must occur), and wherein the machine learning model converts the natural language utterance to the output logical form (Prabhugaonkar: ¶[0063], ¶[0079] and ¶[0108]-[0115] discloses the interpretation, and later, the outputted structured query )
Prabhugaonkar does not explicitly disclose wherein the enhanced grammar comprises a set of relational algebra operators. However, Motik discloses wherein the enhanced grammar comprises a set of relational algebra operators (Motik: ¶[0014]-[0016], ¶[0047]-[0048] and ¶[0052] discloses that the Natural Language Question Compiler (NLQC) maps the natural language (NL) question into logical forms (semantic hypergraphs in doctrine query language (DQL)), this is explicitly logical form).
Prabhugaonkar and Motik are combinable because they are from the same field of endeavor. Prabhugaonkar discloses receiving a natural language question and generating an intermediate structured representation and then converting to a database aware Structured Query Language (SQL0 query for retrieval/answering. Motik teaches mapping natural language questions into a deep semantic logical form (semantic hypergraphs) and transforming that into deductive database queries which translate into SQL. Prabhugaonkar does not disclose the formal logical form algebraic representation that Motik explicitly discloses. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose the enhanced grammar comprises a set of relational algebra operators. The suggestion/motivation for doing so is “Given the widespread usage of search engines and the growing size and importance of the search economy, the vast improvement in the information retrieval capabilities introduced by the present invention is likely to have a lasting social and economical impact” as disclosed in ¶[0012] of Motik.
The combination of Prabhugaonkar and Motik does not explicitly disclose based at least in-part on selecting the extracting function from the set of date-time extraction functions. However, Ang discloses based at least in-part on selecting the extracting function from the set of date-time extraction functions (Ang: ¶[0054]-[0057] discloses the system identifies and selects among time constraint operators (>=, <=, = / IN) based on phrases like since/from/between/to/until/in,” then extract time phrases).
Prabhugaonkar, Motik and Ang are combinable because they are from the same field of endeavor. Prabhugaonkar and Motik in combination discloses extracting/handling periods of time from natural language questions using SUTime and resolving time periods which are then converted into database aware queries and SQL commands. Ang teaches converting natural language questions into SQL by extracting time constraints and interpreting time operators, identifying time and using the extracting time constrains in SQL generation. Even though Prabhugaonkar clearly teaches time/period extraction and database aware time fields, Ang more explicitly teaches selecting/using the exact appropriate time-constraint operator/extraction handling based on the utterance (e.g., mapping since vs until vs in to different constraints) which directly supports selecting the extraction function from a set of date-time extraction functions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose selecting the extraction function from a set of date-time extraction functions. The suggestion/motivation for doing so is “Once all these phrases have been identified then the system will convert and compile them into compatible SQL-syntax based on set of rules or known as heuristic rules and run this newly generated SQL against user data warehouse/big-data platform” in ¶[0005] of Ang, i.e., identification of the exact metrics and functions allows SQL queries for very large databases.
Regarding Claim 3:
The proposed combination of Prabhugaonkar, Motik and Ang further discloses the computer-implemented method of claim 2, further comprising: prior to using the machine learning model to convert the natural language utterance to the output logical form: accessing a grammar comprising the set of relational algebra operators (Motik: ¶[0015]-[0017] discloses semantic parsing which is converted into semantic hypergraphs (logical form) which is then turned into deductive database queries. ¶[0163]-[0164] discloses intent detection builds deductive database queries from semantic hypergraphs, ¶[0298]-[0305] and ¶[0317]-[0318] discloses query language forms including conjunction, disjunction, union, negation, aggregate, exists, etc., and translation into SQL, i.e., formal operator based query representations (relational algebra operators));
and generating the enhanced grammar by adding the set of date-time extraction functions to the grammar (Ang: ¶[0054]-[0057] discloses date-time extraction/handling functions set being added to the base operator system because it identifies time constraint operators (since/from/between/until/in), applies chunking and named entity recognition to extract time phrases as <TIME> and translates the time phrase/operators into a query constraint. ¶[0058]-[0061] then discloses SQL generator collects extracted entities and uses them to construct SQL including WHERE clauses based on extracted time and operators, i.e., integrating the time extraction into the query generation).
Prabhugaonkar and Motik are combinable because they are from the same field of endeavor. Prabhugaonkar discloses receiving a natural language question and generating an intermediate structured representation and then converting to a database aware Structured Query Language (SQL0 query for retrieval/answering. Motik teaches mapping natural language questions into a deep semantic logical form (semantic hypergraphs) and transforming that into deductive database queries which translate into SQL. Prabhugaonkar does not disclose the formal logical form algebraic representation that Motik explicitly discloses. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose the enhanced grammar comprises a set of relational algebra operators. The suggestion/motivation for doing so is “Given the widespread usage of search engines and the growing size and importance of the search economy, the vast improvement in the information retrieval capabilities introduced by the present invention is likely to have a lasting social and economical impact” as disclosed in ¶[0012] of Motik.
Prabhugaonkar, Motik and Ang are combinable because they are from the same field of endeavor. Prabhungaonkar and Motik in combination discloses extracting/handling periods of time from natural language questions using SUTime and resolving time periods which are then converted into database aware queries and SQL commands. Ang teaches converting natural language questions into SQL by extracting time constraints and interpreting time operators, identifying time and using the extracting time constrains in SQL generation. Even though Prabhugaonkar clearly teaches time/period extraction and database aware time fields, Ang more explicitly teaches selecting/using the exact appropriate time-constraint operator/extraction handling based on the utterance (e.g., mapping since vs until vs in to different constraints) which directly supports selecting the extraction function from a set of date-time extraction functions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose generating the enhanced grammar by adding the set of date-time extraction functions to the grammar. The suggestion/motivation for doing so is “Once all these phrases have been identified then the system will convert and compile them into compatible SQL-syntax based on set of rules or known as heuristic rules and run this newly generated SQL against user data warehouse/big-data platform” in ¶[0005] of Ang, i.e., identification of the exact metrics and functions allows SQL queries for very large databases.
Regarding Claim 9:
Claim 9 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 10:
Claim 10 has been analyzed with regard to claim 3 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 16:
Claim 16 has been analyzed with regard to claim 2 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 17:
Claim 17 has been analyzed with regard to claim 3 (see rejection above) and is rejected for the same reasons of obviousness used above.
6. Claims 4-7, 11-14 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Prabhugaonkar in view of Ang.
Regarding Claim 4:
Prabhugaonkar further discloses the computer-implemented method of claim 1, wherein the machine learning model has been trained to convert natural language utterances to logical forms by:
accessing training data comprising a set of training examples (Prabhugaonkar ¶[0107] also discloses machine learning based parsers may be trained);
Prabhugaonkar does not explicitly disclose but Ang discloses:
generating a set of augmented training examples from training examples in the set of training examples by (Ang: discloses in ¶[0048]-[0049] discloses training phases for metric extraction, ¶[0056]-[0057] identifies time phrases via chunking + NER and extracts time phrases identified as <TIME> which it was trained to do):
identifying a subset of training examples in the set of training examples that include date-time intervals (Ang: ¶[0055] the system is taught to interpret timing and date based phrases as >=);
associating the date-time intervals with first extraction functions included in a set of extraction functions that are configured to extract date-time information from date-time attributes included in a database schema (Ang: ¶[0055] the system is taught to interpret timing and date based phrases as >=);
selecting second extraction functions included in the set of extraction functions that are different from the first extraction functions (Ang: ¶[0055] distinguishes different time-constraint operators/interpretations, directly teaching selecting a different extraction function/operator than the one initially associated);
and modifying logical forms and natural language utterances of training examples in the subset of training examples based on the second extraction functions to result in the set of augmented training examples (Ang: ¶[0048]-[0049] discloses additional training set via inside outside beginning (IOB) tagging annotations, this teaches creation of additional training sets in order to learn from the sets of annotations and IOB tags, i.e., augmented training examples are created by generating additional labeled instances. Ang ¶[0054]-[0055] discloses multiple ways to express time spans and that the system is trained to map them to different constraint forms, so alternative natural language time phrasing map to different formal constraints);
generating augmented training data by combining the set of augmented training examples and the set of training examples (Ang: ¶[0048]-[0049] building training sets and enhancing them with IOB tag derived annotations that can themselves be used as new training sets, Prabhugaonkar ¶[0107] discloses training set may be enhanced by receiving user intents derived by a rules based parser or another machine learning parser);
and using the augmented training data to train the machine learning model to convert natural language utterances to logical forms (Ang: ¶[0048]-[0049] supervised model training for NER extractions, trained model is then used to tag/extract entities from tokenized questions).
Prabhugaonkar and Ang are combinable because they are from the same field of endeavor. Prabhugaonkar discloses extracting/handling periods of time from natural language questions using SUTime and resolving time periods which are then converted into database aware queries and SQL commands. Ang teaches converting natural language questions into SQL by extracting time constraints and interpreting time operators, identifying time and using the extracting time constrains in SQL generation. Even though Prabhugaonkar clearly teaches time/period extraction and database aware time fields, Ang more explicitly teaches named entity recognition and selecting/using the exact appropriate time-constraint operator/extraction handling based on the utterance (e.g., mapping since vs until vs in to different constraints) which directly supports selecting the extraction function from a set of date-time extraction functions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose selecting a training process more closely catered to the date-time interval process. The suggestion/motivation for doing so is “there still exists a need for an accessible and easy-to-use business intelligence tool that facilitates the manner in which business users can derive analytical insights from data” as disclosed in ¶[0002] of Ang.
Regarding Claim 5:
Prabhugaonkar further discloses the computer-implemented method of claim 1, wherein the output logical form comprises the date-time interval, and the method further comprising:
processing the output logical form to generate a processed output logical form (Prabhugaonkar: ¶[0068], ¶[0073]-[0079], ¶[0108] and ¶[0114]-[0115] discloses periods/dates/times are part of the interpretation and carried into the query pipeline),
Prabhugaonkar does not explicitly disclose wherein the processing output logical form to generate the processed output logical form comprises identifying a portion of the output logical form that comprises a logical form operator and the date-time interval and replacing the portion with a replacement extraction function selected from a set of extraction functions that are configured to extract date-information from date-time attributes included in a database schema.
However, Ang discloses:
wherein the processing output logical form to generate the processed output logical form comprises identifying a portion of the output logical form that comprises a logical form operator and the date-time interval and replacing the portion with a replacement extraction function selected from a set of extraction functions that are configured to extract date-information from date-time attributes included in a database schema (Ang: ¶[0053]-[0057] discloses identifying time constrain operators to time phrases, which configures it to extract date information from date time attributes included in a database schema; Prabhugaonkar ¶[0114] discloses the period entity is converted into a database aware time field which is then converted into SQL).
Prabhugaonkar and Ang are combinable because they are from the same field of endeavor. Prabhugaonkar discloses extracting/handling periods of time from natural language questions using SUTime and resolving time periods which are then converted into database aware queries and SQL commands. Ang teaches converting natural language questions into SQL by extracting time constraints and interpreting time operators, identifying time and using the extracting time constrains in SQL generation. Even though Prabhugaonkar clearly teaches time/period extraction and database aware time fields, Ang more explicitly teaches selecting/using the exact appropriate time-constraint operator/extraction handling based on the utterance (e.g., mapping since vs until vs in to different constraints) which directly supports selecting the extraction function from a set of date-time extraction functions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose wherein the processing output logical form to generate the processed output logical form comprises identifying a portion of the output logical form that comprises a logical form operator and the date-time interval and replacing the portion with a replacement extraction function selected from a set of extraction functions that are configured to extract date-information from date-time attributes included in a database schema. The suggestion/motivation for doing so is “Once all these phrases have been identified then the system will convert and compile them into compatible SQL-syntax based on set of rules or known as heuristic rules and run this newly generated SQL against user data warehouse/big-data platform” in ¶[0005] of Ang, i.e., identification of the exact metrics and functions allows SQL queries for very large databases.
Regarding Claim 6:
The proposed combination of Prabhugaonkar and Ang further discloses the computer-implemented method of claim 5, wherein the processing the output logical form to generate the processed output logical form further comprises using a named entity recognizer to identify a type for the date-time interval and selecting the replacement extraction function based on the type for the date-time interval (Ang: ¶[0041]-[0042] discloses a named entity recognition and extractor modules including a time extractor for identifying time/timespan phrases from the user question, ¶[0054]-[0057] discloses selecting different time handling operators and behaviors based on the recognized time expression).
Prabhugaonkar and Ang are combinable because they are from the same field of endeavor. Prabhugaonkar discloses extracting/handling periods of time from natural language questions using SUTime and resolving time periods which are then converted into database aware queries and SQL commands. Ang teaches converting natural language questions into SQL by extracting time constraints and interpreting time operators, identifying time and using the extracting time constrains in SQL generation. Even though Prabhugaonkar clearly teaches time/period extraction and database aware time fields, Ang more explicitly teaches selecting/using the exact appropriate time-constraint operator/extraction handling based on the utterance (e.g., mapping since vs until vs in to different constraints) which directly supports selecting the extraction function from a set of date-time extraction functions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose wherein the processing output logical form to generate the processed output logical form comprises identifying a portion of the output logical form that comprises a logical form operator and the date-time interval and replacing the portion with a replacement extraction function selected from a set of extraction functions that are configured to extract date-information from date-time attributes included in a database schema. The suggestion/motivation for doing so is “professionals may become encumbered/burdened by administrative work while creating/building dashboards/analytical reports, resulting in potential time loss for business users to gain analytical insights as they wait for the dashboards/analytical reports.” in ¶[0002] of Ang.
Regarding Claim 7:
The proposed combination of Prabhugaonkar and Ang further discloses the computer-implemented method of claim 5, further comprising:
translating the processed output logical form to a query language output statement; providing the query language output statement to a cloud-based platform (Prabhugaonkar: ¶[0115] the interpretation is processed into intermediate queries and then converted into a database aware SQL query);
using the cloud-based platform to execute the query language output statement on a database associated with the database schema information to retrieve a result describing information corresponding to the date-time interval of the natural language utterance (Prabhugaonkar: ¶[0115]-[0116] identifies transaction table converts query into database aware SQL , executes query, retrieves relevant data values);
and providing the result to a user device (Prabhugaonkar: ¶[0025], ¶[0055]-[0058], [0117] user asks the question in natural language and answers are presented back to the user on the system of interaction).
Regarding Claim 11:
Claim 11 has been analyzed with regard to claim 4 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 12:
Claim 12 has been analyzed with regard to claim 5 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 13:
Claim 13 has been analyzed with regard to claim 6 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 14:
Claim 14 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 18:
Claim 18 has been analyzed with regard to claim 4 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 19:
Claim 19 has been analyzed with regard to claim 5 (see rejection above) and is rejected for the same reasons of obviousness used above.
Regarding Claim 20:
Claim 20 has been analyzed with regard to claim 7 (see rejection above) and is rejected for the same reasons of obviousness used above.
Conclusion
The prior art made of record made of record and not relied up is considered pertinent to applicant’s disclosure.
Anderson et al. (US 2018/0095962) discloses receiving the natural language query in computer-readable form. The entities are extracted (60) from the natural language query. The entities are correlated (64) with a generic variable. The entity in the natural language query is replaced (68) with the generic variable to generate a generic query. The generic query is associated (70) with a structured question type corresponding to generic variable. The specific data in the structured question type in relation is inserted to structured data variable based on entity to form structured data query.
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654