DETAILED ACTION
Introduction
This office action is in response to applicant’s claims filed 1/29/2026. Claims 1-20 are currently pending and have been examined. Applicant’s IDS have been considered. There is no claim to foreign priority.
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 Arguments
Applicant’s arguments, see remarks, filed 1/29/26, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the previously cited prior art, and further in view of Quamar et al. (Quamar, Natural Language Interfaces to Data).
The Examiner notes, the newly cited prior art is explicitly directed towards processing a natural language utterance by combining the natural language utterance with a class label and database schema information. The Examiner notes, this enhances the RATSQL and pertinent prior art, which concatenates the natural language utterance with a database schema and further provides this data to an machine learning model which produces a meaning representation. Therefore, in combination with the previous prior art, there is a clear motivation and corresponding combination, which allows for the processing of entities, utterance tokens and schema, from the query to generate a meaning representation in order to query a database (see rejection below).
Claim Objections
Claims 7 and 15 are objected to because of the following informalities:
In claims 7 and 15, “comprised a plurality of training meaning representations” should probably be - - comprised of a plurality of training meaning representations- - (or the like). Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gadde et al. (Gadde, US 2021/0390951) in view of Quamar et al. (Quamar, Natural Language Interfaces to Data) in view of Fujimoto et al. (Fujimoto, Us 2020/0402509), and further in view of Naganathan et al. (Naganathan, US 11,550,786).
As per claim 1, Gadde teaches a method comprising:
accessing a natural language utterance (Fig. 2, his input utterance, paragraph [0088, 0100]-as his utterance and natural language utterance);
predicting, by a machine learning model, a class label for a token in the natural language utterance, wherein the class label corresponds to an entity category of a plurality of entity categories (ibid, paragraphs [0091, 0030, 0046, 0116, 0117]-his named entity recognizer, and corresponding predicted entities thereof, his predicted entities to include date/time, entities, see his prediction model discussion, ML model);
[processing the natural language utterance to generate a processed natural language utterance, wherein processing the natural language utterance comprises combining the natural language utterance with the class label and database schema information;
providing the processed natural language utterance to a second machine learning model from the first machine learning model;]
predicting, [using the second machine learning model, and the processed natural language utterance,] a meaning representation for the natural language utterance, wherein the meaning representation for the natural language utterance comprises a value associated with the class label and an operator (ibid, paragraphs [0116, 0117, 0159], Fig. 4 items 425 and 410-his predicted intent, template and artificial utterance, the generated template and utterance as included a meaning, as a logical form representation of the utterance, and including a value associated with the class label (date_time) and an operator (on|at));
[detecting that the value matches a predetermined value type or that the operator matches a predetermined operator; and
in response to detecting that the value matches the predetermined value type or that the operator matches the predetermined operator, modifying at least one of the value and the operator and generating an executable statement for the meaning representation, wherein the executable statement for the meaning representation comprises a modified version of at least one of the value and the operator].
Gadde lacks explicitly teaching that which Quamar teaches, [processing the natural language utterance to generate a processed natural language utterance, wherein processing the natural language utterance comprises combining the natural language utterance with the class label and database schema information (pages 36-44, section 3.2, Fig. 3.4, page 37, see his concatenation discussion, including the utterance text query tokens, consisting of entity mappings and schema elements, his encoder as the first machine learning model);
providing the processed natural language utterance to a second machine learning model from the first machine learning model (ibid-Fig. 3.4-his passed information to his decoder, his decoder as the second machine learning model);]
predicting, using the second machine learning model, and the processed natural language utterance, a meaning representation for the natural language utterance (ibid, see also Fig. 3.4-his output from his decoder, see also SQL query discussion).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Gadde and Quamar to combine the prior art element of a user natural language user request/utterance with date/time entities converted into a logical form, using predictive machine learning techniques, as taught by Gadde with the enhanced RATSQL, including a concatenation of utterance text, entities and schema to be input into a second machine learning model to generate a meaning representation as taught by Quamar as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be generating a logical form or structured query, such as SQL, for a database (ibid, Quamar, see also Quamar abstract NLID discussion).
Gadde lacks explicitly teaching that which Fujimoto teaches,
detecting that the value matches a predetermined value type or that the operator matches a predetermined operator (Fujimoto, paragraph [0027-0041]-as his value type that is associated with a temporal ambiguity, time information types, from his time extraction unit, and corresponding entity categories, wherein the categories are associated with words in his sentence, including date and time, see his ISO8601, his representation of dates and times, his predetermined value types including, “time of the year, month, day, hour minute and second” as ambiguous, when matched, they are processed to disambiguate or resolve the ambiguities, see also paragraphs [0037-0039, 0025, 0033]);
in response to detecting that the value matches the predetermined value type or that the operator matches the predetermined operator (ibid-based on the input sentence and matching values), modifying at least one of the value and the operator; and generating an executable statement for the meaning representation (ibid-the time value information, is modified, based on “nearest past” or “nearest future time”), wherein the executable statement for the meaning representation comprises a modified version of at least one of the value and the operator (ibid-the new time value, generated, and included in a execution of the task command, paragraphs [0045)).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Gadde and Fujimoto to combine the prior art element of a user natural language user request/utterance with date/time entities converted into a logical form, using predictive machine learning techniques, as taught by Gadde with resolving the ambiguities of the date/time entities by modifying the executable command for the task as taught by Fujimoto as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be resolving date/time, based ambiguities in user utterances, wherein the natural language used for execution and having an updated instruction includes matching values and/or operands (ibid, Fujimoto, paragraph [0004], see Naganathan-abstract).
Gadde with Fujimoto lack explicitly teaching that which Naganathan teaches, in response to detecting that the value matches the predetermined value type or that the operator matches the predetermined operator (C.11 lines 23-34-his “over” operator, matching his predetermined “greater than” operator); wherein the executable statement for the meaning representation comprises a modified version of at least one of the value and the operator (ibid, C.11 lines 23-34, C.2 lines 44-53, as the executable and modified structured query statement generated).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Gadde and Fujimoto to combine the prior art element of a user natural language user request/utterance with date/time entities converted into a logical form, using predictive machine learning techniques, as taught by Gadde with resolving the ambiguities of the date/time entities by modifying the executable command for the task as taught by Fujimoto with detecting an operator matches a predetermined operator as taught by Naganathan as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be resolving date/time, based ambiguities in user utterances, wherein the natural language used for execution and having an updated instruction includes matching values and/or operands (ibid, Fujimoto, paragraph [0004], see Naganathan-abstract).
As per claims 2, 10 and 18, Gadde further makes obvious the method of claim 1, wherein the plurality of entity categories comprises at least one of a date category that is representative of a date entity in an utterance, a time category that is representative of a time entity in an utterance, and a datetime category that is representative of a date entity and a time entity in an utterance (ibid-see above entity categories, paragraphs [0030, 0091, 0116]-see date, time and datetime categories).
As per claims 3 and 11, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, wherein the predetermined value type corresponds to an entity category of the plurality of entity categories (ibid—Fujimoto, paragraph [0027-0041]-as his value type that is associated with a temporal ambiguity, time information types, from his time extraction unit, and corresponding entity categories, wherein the categories are associated with words in his sentence, including date and time, see his ISO8601, his representation of dates and times, his predetermined value types including, “time of the year, month, day, hour minute and second” as ambiguous, and not known, the Examiner notes, Fujimoto is similarly motivated and combined with Gadde and Naganathan, as seen in claim 1).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Gadde and Fujimoto to combine the prior art element of a user natural language user request/utterance with date/time entities converted into a logical form, using predictive machine learning techniques, as taught by Gadde with the predetermined value types for ambiguities associated with date/time as taught by Fujimoto as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be resolving date/time, based ambiguities in user utterances, having predetermined value types associated with entity categories, Naganathan value types associated with entity categories (C.5 lines 62-64-his date/time entities as tagged), in order to select and resolve the date/time values, wherein the natural language used for execution and having an updated instruction includes matching values and/or operands (ibid, Fujimoto, paragraph [0004], see Naganathan-abstract).
As per claims 4 and 12, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, wherein the predetermined operator corresponds to an operator for selecting a duration of time (Naganathan, C.5 lined 57-64, as operators as defining range(s) of time, see also C.11 lines 23-34-his “over” operator, matching his predetermined “greater than” operator, and “between” operator, as defining range(s) of time).
As per claims 5, 13 and 19, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, further comprising:
detecting that the value matches the predetermined value type (ibid-see claim 1, corresponding and similar limitation); and
modifying the value, wherein modifying the value comprises modifying a date based associated with the natural language utterance based on preference information included in database schema information (ibid-see claim 1, Fujimoto modification discussion, wherein the time value information, is modified, based on “nearest past” or “nearest future time”, which is preference information included in database schema information, paragraphs [0022-0044]-his time units, as including database schema information, Figs. 1 and 2, the new time value, generated, and included in a execution of the task command, paragraphs [0045], as similarly motivated and combined as seen in claim 1).
As per claims 6, 14 and 20, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, further comprising:
detecting that the operator matches the predetermined operator (ibid-see claim 1, corresponding and similar limitation); and
modifying the operator, wherein modifying the operator comprises replacing the operator with another operator (Naganathan, C.11 lines 23-34-his “over” operator, matching and replaced by his predetermined “greater than” operator, as similarly motivated and combined).
As per claims 7 and 15, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, wherein the second machine learning model is a trained machine learning model that was trained with training data comprised a plurality of training meaning representations, [wherein each training meaning representation of the plurality of training meaning representations comprises an operator] (ibid-see claim 1, Quamar, decoder as the second machine learning model, text-to-SQL training including operators-as similarly combined and motivated, Gadde, machine learning discussion, paragraphs [0091, 0102, 0111, 0115, 0116-0123, 0159 ]-his training algorithms, machine learning, using all labeled data, and prediction based intent/meaning classification, wherein the labeled data includes an operator, as seen in paragraph [0159, 0161]-Fig. 4 items 425 and 410).
Gadde with Quamar lack explicitly teaching that which Naganathan teaches, wherein each training meaning representation of the plurality of training meaning representations comprises an operator (C.18 line 49-C.19 line 6-his intent/meaning and corresponding training based on the query and operand, C.7 line 57-C.8 line 11-for each query, corresponding operator is created, thus intent/meaning training comprises the operator in training).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Gadde and Fujimoto to combine the prior art element of a user natural language user request/utterance with date/time entities converted into a logical form, using predictive machine learning techniques, as taught by Gadde with resolving the ambiguities of the date/time entities by modifying the executable command for the task as taught by Fujimoto with training data with respect to meaning representations which include an operator as taught by Naganathan as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be resolving date/time, based ambiguities in user utterances, wherein the natural language used for execution and having an updated instruction includes matching values and/or operands (ibid, Fujimoto, paragraph [0004], see Naganathan-abstract, and previously cited sections).
As per claims 8 and 16, Gadde with Quamar with Fujimoto with Naganathan further makes obvious the method of claim 1, further comprising: executing a query on a computing system based on the executable statement (ibid-Gadde, paragraph [0028-0031, 0041]-his user requests/statements, and corresponding action by the digital assistant, to carry out the user’s intent).
As per claim 9, claim 9 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the system is deemed to embody the method, such that Gadde with Quamar with Fujimoto with Naganathan make obvious a system comprising: one or more processors (Gadde, paragraphs [0013, 0014]-see his processors and non-transitory computer readable storage medium and instructions); and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform a method comprising (ibid): accessing a natural language utterance (ibid-see claim 1, corresponding and similar limitation); predicting, using a first machine learning model, and the natural language utterance, a class label for a token in the natural language utterance, wherein the class label corresponds to an entity category of a plurality of entity categories (ibid); processing the natural language utterance to generate a processed natural language utterance, wherein processing the natural language utterance comprises combining the natural language utterance with the class label and database schema information (ibid); providing the processed natural language utterance to a second machine learning model from the first machine learning model (ibid); predicting, using the second machine learning model, and the processed natural language utterance, a meaning representation for the natural language utterance (ibid); predicting, by a machine learning model, a meaning representation for the natural language utterance, wherein the meaning representation for the natural language utterance comprises a value associated with the class label and an operator (ibid); detecting that the value matches a predetermined value type or that the operator matches a predetermined operator (ibid); in response to detecting that the value matches the predetermined value type or that the operator matches the predetermined operator, modifying at least one of the value and the operator; and generating an executable statement for the meaning representation, wherein the executable statement for the meaning representation comprises a modified version of at least one of the value and the operator (ibid).
As per claim 17, claim 17 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the non-transitory computer-readable media storing instructions is deemed to embody the method, such that Gadde with Quamar with Fujimoto with Naganathan make obvious a one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform a method comprising (Gadde, paragraphs [0013, 0014]-see his processors and non-transitory computer readable storage medium and instructions): accessing a natural language utterance (ibid-see claim 1, corresponding and similar limitation); predicting, using a first machine learning model and the natural language utterance, a class label for a token in the natural language utterance, wherein the class label corresponds to an entity category of a plurality of entity categories (ibid); processing the natural language utterance to generate a processed natural language utterance, wherein processing the natural language utterance comprises combining the natural language utterance with the class label and database schema information (ibid); providing the processed natural language utterance to a second machine learning model from the first machine learning model (ibid); predicting, using the second machine learning model and the processed natural language utterance, a meaning representation for the natural language utterance, wherein the meaning representation for the natural language utterance comprises a value associated with the class label and an operator (ibid); detecting that the value matches a predetermined value type or that the operator matches a predetermined operator (ibid); in response to detecting that the value matches the predetermined value type or that the operator matches the predetermined operator, modifying at least one of the value and the operator (ibid); and generating an executable statement for the meaning representation, wherein the executable statement for the meaning representation comprises a modified version of at least one of the value and the operator (ibid).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAMONT M SPOONER whose telephone number is (571)272-7613. The examiner can normally be reached 8:00 AM -5:00 PM.
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/LAMONT M SPOONER/ Primary Examiner, Art Unit 2657
4/24/2026
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