DETAILED ACTION
Response to Amendments
This action is in response to the amendment filed on 12/15/2025.
In this Office Action, claims 1-28 are pending.
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
Regarding rejections 35 USC §101, Applicant respectfully disagrees with this analysis because the claims do not merely recite steps that can be performed mentally using pen and paper as alleged. See id. Further, the Office Action does not compare the actual language of the claims to the examples referenced in the MPEP. Instead, claim 1 recites "determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question; determining, based on the base question and one or more machine learning models, data for generating a query; and causing, based on generating the query using the data for generating the query, the query to be sent to a data store" (emphasis added), which are steps that clearly cannot practically be performed in the human mind and therefore do not recite a mental process. The applicant also argues that the claims are directed toward to an improvement for translating requests into data store queries.
In response to applicant's argument, the examiner disagrees and submits that claim 1 along with independent claims 8, 15 and 22 are directed to an abstract idea and not significantly more than the abstract idea itself.
The limitation of determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question; determining, based on the base question and one or more machine learning models, data for generating a query. For example, the “determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question; determining, based on the base question and one or more machine learning models, data for generating a query”, “determining” data for generating a query based on the base question and one or more machine learning models” in claim 1, is a process that under broadest interpretation, cover performance limitation in the in the mind, but for the recitation of generic computer components. For example, other than the " machine learning models ", the context of this claim encompasses in this limitation merely includes an individual being able to with the aid of paper and pen to write a code instructing to assign the machine learning model a role some task to perform the instructions accordingly, in which is a mental process. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (Concepts performed in the human mind including observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. The limitation " causing, based on generating the query using the data for generating the query, the query to be sent to a data store " recites an insignificant extra solution activity as mere data processing information such as 'sending query information to storage'. See MPEP 2106.05(g) and is well-understood, routine and conventional activities, (WURC), see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, OIP Techs., 788 F.3d at 1363."
With respect to Applicant's argument that" the elements of claim 1 directed to an improvement for translating requests into data store queries", Examiner respectfully disagrees.
The claim language does not reflect any improvement to the technology as discussed above and in the 101 rejection section below. Therefore, claims 1-28 remain rejected under 101.
Applicant arguments regarding claim 1 rejection relating to prior arts OH, Zheng and Harnek do not explicitly teach determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question, determining, based on the base question and one or more machine learning models, data for generating a query, and causing, based on generating the query using the data for generating the query, the query to be sent to a data store.
The examiner respectfully submits in particular that OH clearly teaches determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question, (see [0062], e.g., discloses wherein determining a characteristic of the query/a base question based on at least one of a table and a conditional clause associated with the received query, determining the query characteristic corresponds to identifying the base question being asked). Prior art Zheng teaches determining, based on the base question and one or more machine learning models, data for generating a query (see [0050], e.g., discloses wherein using the machine learning model, the database server may parse through the natural language query, identify what the data the natural language query is asking for and identify where that data is stored and what other data may be used to generate the corresponding data queries, parsing and identifying requested data constitutes determining information about the query and the natural language query submitted by the user corresponds to the base question), and prior art Harnesk teaches causing, based on generating the query using the data for generating the query, the query to be sent to a data store ([0016], e.g., discloses wherein generates the data query according to the translated characteristics and sends the generated query to the data store and further see [0067], e.g., states that a query generating module generates a data query according to translated characteristics, and that the generated query is then sent to the data store for processing).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-28 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
Claims 1, 8, 15 and 22:
At Step 1:
The claims are directed to “a method”, “a device” "a system" and “a non-transitory computer readable storage medium” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“ determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question” recites a mental process because human mind determine a question base on identifying conditional terms associated with data request from a user by evaluation and judgement of data.
-“ determining, based on the base question
At Step 2A, Prong Two:
The claim recites the following additional elements:
“one or more processor, memory storing instructions”, “a computing device, “a non-transitory computer readable medium storing computer executable instructions”, “one or more machine learning models” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application and/or is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP §2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“causing, based on generating the query using the data for generating the query, the query to be sent to a data store”, is insignificant extra-solution activity as mere processing information such as ‘sending information to storage’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ causing, based on generating the query using the data for generating the query, the query to be sent to a data store” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, ... Of P Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
The dependent claims 2-7, 9-14, 16-21 and 23-28 have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the above-mentioned groupings of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In view of applicant specification it is not clear if system include definitive hardware or physical components. Applicant is suggested to insert – “memory and processor” in the claim to obviate this rejection.
Claims 16-21 are also rejected under 35 U.S. C 101 because they fail to resolve the deficiencies of claim 15.
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 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, 3, 5-8, 10, 12-15, 17, 19-22, 24 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over OH et al (US 2016/0162546 A1) in view of Zheng et al (US 2021/0149886 A1) further in view of Harnesk (US 2015/0301875 A1).
As per claim 1, OH teaches a method comprising: determining, based on identifying conditional terms associated with data indicative of a request from a user, a base question ([0062], e.g., discloses wherein determining a characteristic of the query/a base question based on at least one of a table and a conditional clause associated with the received query);
OH does not explicitly teach determining, based on the base question and one or more machine learning models, data for generating a query;
However, Zheng teaches determining, based on the base question and one or more machine learning models, data for generating a query ([0050], e.g., discloses wherein using the machine learning model, the database server may parse through the natural language query, identify what the data the natural language query is asking for and identify where that data is stored and what other data may be used to generate the corresponding data queries);
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Zheng with the teachings of OH in order to efficiently enabling a system to utilize a tenant-specific machine learning model and tenant-specific data lineage map to interpret a natural language query (Zheng).
OH and Zheng do not explicitly teach causing, based on generating the query using the data for generating the query, the query to be sent to a data store.
However, Harnesk teaches causing, based on generating the query using the data for generating the query, the query to be sent to a data store ([0016], e.g., discloses wherein generates the data query according to the translated characteristics and sends the generated query to the data store and further see [0067], e.g., states that a query generating module generates a data query according to translated characteristics, and that the generated query is then sent to the data store for processing).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Harnesk with the teachings of OH and Zheng in order to efficiently enabling a system to generate user queries based on data characteristics provided by the user and sends the generated query to the data store (Harnesk).
As per claim 3, wherein determining the data for generating the query comprises inputting one or more of the base question or the indication of the conditional terms to the one or more machine learning models to generate an output, and determining the data for generating the query is based on the output (see rejection of claim 1 above).
As per claim 5, wherein the one or more machine learning models are trained based on data from the data store ([0020], Zheng).
As per claim 6, wherein the one or more machine learning models are trained to determine one or more of: a specific type of query clause, a specific function of a query clause, or a specific query parameter of a query clause ([0020], Zheng).
As per claim 7, wherein the one or more machine learning models are configured to determine, for the request, one or more of: columns of the data store, mathematical functions to apply to values of the data store, conditions to evaluate on the data store, columns to group results from the data store, ordering of results for values in a column of the data store, or a limit of a number or results from the data store ([0020], [0035], Zheng).
Regarding claims 8, 15, 22, claims 8, 15, 22 are rejected for substantially the same reason as claim 1 above.
Regarding claims 10, 12-14, 17, 19-21, 24, 26-28, claims 10, 12-14, 17, 19-21, 24, 26-28 are rejected for substantially the same reason as claims 3, 5-7 above.
Claims 2, 9, 16 and 23 are rejected under 35 U.S.C. 103(a) as being unpatentable over OH et al (US 2016/0162546 A1) in view of Zheng et al (US 2021/0149886 A1) in view of Harnesk (US 2015/0301875 A1) further in view of Kaku et al (US 2023/0195723 A1).
As per claim 2, OH, Zheng and Harnesk do not explicitly teach wherein the determining the data for generating the query comprises determining query data based on the one or more machine learning models and combining the query data with the base question.
However, Kaku teaches wherein the determining the data for generating the query comprises determining query data based on the one or more machine learning models and combining the query data with the base question ([0029], [0086], e.g., discloses wherein estimation is performed regarding whether or not two column names in an SQL for obtaining an answer to the given question sentence are joined by JOIN, by a deep learning model).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Kaku with the teachings of OH, Zheng and Harnesk in order to efficiently enabling a system to proposes a deep learning model that takes a question sentence relating to a DB and a DB schema as input and estimates an SQL query for acquiring an answer to the question sentence from the DB (Kaku).
Regarding claims 9, 16, 23, claims 9, 16, 23 are rejected for substantially the same reason as claim 2 above.
Claims 4, 11, 18 and 25 are rejected under 35 U.S.C. 103(a) as being unpatentable over OH et al (US 2016/0162546 A1) in view of Zheng et al (US 2021/0149886 A1) in view of Harnesk (US 2015/0301875 A1) further in view of Toper (US 2022/0019416 A1, which claiming priority of provisional app. No: 63/051864 filed on July 14, 2020).
As per claim 4, OH, Zheng and Harnesk do not explicitly teach wherein determining the base question comprises removing the conditional terms from the data indicative of the request.
However, Toper teaches wherein determining the base question comprises removing the conditional terms from the data indicative of the request ([0046], e.g., discloses wherein system removes an “if” statement or similar conditional statement within the jump instruction of the branch).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Toper with the teachings of OH, Zheng and Harnesk in order to efficiently enabling a system to remove conditional statement within the instruction (Toper).
Regarding claims 11, 18, 25, claims 11, 18, 25 are rejected for substantially the same reason as claim 4 above.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any wav. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]].
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Otaki discloses US 2019/0205388 A1 GENERATION METHOD, INFORMATION PROCESSING APPARATUS, AND STORAGE MEDIUM.
Gupta et al discloses US 2019/0156216 A1 MACHINE LEARNING MODEL INTERPRETATION.
Conclusion
THIS ACTION IS MADE FINAL. 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 Mohammad A Sana whose telephone number is (571)270-1753. The examiner can normally be reached Monday-Friday 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at 5712724098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mohammad A Sana/Primary Examiner, Art Unit 2166