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 .
This communication is in response to Preliminary amendment filed on January 14, 2025. Claims 1-20 are pending. Claims 5-15 are amended. Claims 17-20 are newly added.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 1/14/2025 and 10/28/2025 are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: ‘encoder’, ‘decoder’, ‘splitting module’, ‘correction module’, ‘combination module’ in claims 2, 5, 6, 16, 17 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Referring to claims 1, 14 and 16, the claims recite the limitation ‘combining the corrected target substatement’ in lines 6, 9 and 11 of the respective claims. However it is unclear as to what the corrected target substatement is combined with.
Referring to claims 2 and 17 recite the limitation "the at least one target statement without the nested relationship" in lines 6-7. There is insufficient antecedent basis for this limitation in the claims. Furthermore, it is unclear as to how the a target substatement can be generated without the nested relationship when the splitting of the target statement (that contains the target substatement) is performed according to the nested relationship.
Referring to claim 16, the claim recites the limitation “a database configured to execute a data query task based on the executable machine language” in line 13. However, it is unclear as to how the database which is software, able to execute a data query task without some claimed processing unit.
All claims depending from the aforenoted claims are also rejected by virtue of their dependencies.
Due to the 35 USC 112 rejections, the claims have been examined as best understood by the Examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 14 and 15 recite:
splitting a target statement acquired to obtain at least one target substatement;
determining a keyword type and a keyword clause contained in the target substatement;
correcting the keyword clause in the target substatement according to a semantic constraint rule corresponding to the keyword type contained in the target substatement; and
combining the corrected target substatement, and generating an executable machine language.
Step 1: The claims as a whole fall within one or more statutory categories.
Step 2A prong 1: At least claims 1, 14 and 15 recite limitations that are abstract ideas.
The limitation “splitting a target statement acquired to obtain at least one target substatement” is a mental step. One can mentally segment a statement into substatement sections. Thus, the claimed limitation can be performed by the human mind.
The limitation “determining a keyword type and a keyword clause contained in the target substatement” is a mental step. One can mentally identify visually identify a keyword clause within a sentence and determine the type of keyword referenced in the sentence. Thus, the claimed limitation can be performed by the human mind.
The limitation “correcting the keyword clause in the target substatement according to a semantic constraint rule corresponding to the keyword type contained in the target substatement” is a mental step. One can mentally make the correction or substitution of a given keyword with another version based on a given semantic rule for the keyword type. Thus, the claimed limitation can be performed by the human mind.
The limitation “combining the corrected target substatement” is a mental step. One can mentally combine the keywords within the sentence. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2: Claims 1 and 14 recite the limitation “generating an executable machine language”. This is an additional element and is using of a computer or other machinery in its ordinary capacity for tasks of outputting a result after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore, Claims 14 and 15 recite the following additional elements “an electronic device”, “a processor”, “memory”, and “non-transitory machine readable storage medium”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "generating” limitation identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … 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 thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claim 16 recites:
an encoder, configured to encode an acquired natural language to obtain a semantic feature vector;
a decoder, configured to decode the semantic feature vector output by the encoder to generate a target statement;
a splitting module, configured to split the target statement acquired to obtain at least one target substatement;
a correction module, configured to correct a keyword clause in the target substatement according to a semantic constraint rule corresponding to a keyword type contained in the target substatement;
a combination module, configured to combine the corrected target substatement, and generate an executable machine language; and
a database, configured to execute a data query task based on the executable machine language.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 16 recites limitations that are abstract ideas.
The limitation “split the target statement acquired to obtain at least one target substatement” is a mental step. One can mentally segment a statement into substatement sections. Thus, the claimed limitation can be performed by the human mind.
The limitation “correct a keyword clause in the target substatement according to a semantic constraint rule corresponding to a keyword type contained in the target substatement” is a mental step. One can mentally make the correction or substitution of a given keyword with another version based on a given semantic rule for the keyword type. Thus, the claimed limitation can be performed by the human mind.
The limitation “combine the corrected target substatement” is a mental step. One can mentally combine the keywords within the sentence. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2: Claim 16 recites the limitations “generate an executable machine language” and “execute a data query task based on the executable machine language”. These are additional elements and are using of a computer or other machinery in its ordinary capacity for tasks of outputting results and tasks after the fact to an abstract idea (mental process) does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore, Claim 16 recites the following additional elements “an encoder, configured to encode an acquired natural language to obtain a semantic feature vector”, “a decoder, configured to decode the semantic feature vector output by the encoder to generate a target statement”, “splitting module”, “correction module”, “combination module” and “database”, note that the recited “splitting module”, “correction module”, “combination module” and “database” additional elements are a high-level recitation of generic computer software components and the “encoder” and “decoder” are generic pretrained neural network models (para 31 of specification) used to perform the mental process and applied on a computer as in MPEP 2106.05(f), which do not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "generate” and “execute” limitations identified as insignificant extra-solution activity above when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … 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 thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole does not change this conclusion and the claim is ineligible.
Claims 2, 5, 6, 17 and 20 depend from claims 1 and 14 and thus include all the limitations of claims 1 and 14, therefore claims 2, 5, 6, 17 and 20 recite the same abstract ideas of "mental processes".
Claims 2, 5, 6, 17 and 20 furthermore recite:
(claims 2, 17) wherein the splitting the acquired target statement to obtain the at least one target substatement comprises:
decoding a semantic feature vector encoded based on a natural language provided by a user by using a decoder to obtain a target statement containing a keyword, an operation content and a predicted value; and
splitting the target statement according to a nested relationship to generate the at least one target substatement without the nested relationship;
(claims 5, 20): wherein in a condition that the keyword type is SELECT type;
the decoding the semantic feature vector encoded based on the natural language provided by the user by using the decoder to obtain the target statement containing the keyword, the operation content and the predicted value comprises:
acquiring a data table provided by a database;
performing analysis on a matching degree between an operation content associated with a SELECT keyword obtained by decoding the semantic feature vector and a data column in the data table;
determining a target data column matching the operation content from the data table according to the matching degree; and
generating a target statement containing the SELECT keyword, the operation content, and the predicted value based on data in the target data column; and
(claim 6) wherein in a condition that the keyword type is WHERE type;
the decoding the semantic feature vector encoded based on the natural language provided by the user by using the decoder to obtain the target statement containing the keyword, the operation content and the predicted value comprises:
acquiring a data table provided by a database;
screening out an operation content from the data table according to a predicted value associated to the a WHERE keyword obtained by decoding the-semantic feature information; and
according to a matching degree between the predicted value and the screened operation content, determining the screened operation content matched with the predicted value to obtain a target statement containing the WHERE keyword, the predicted value and the screened operation content.
Step 1: Claims 2, 5, 6, 17 and 20 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 2, 5, 6, 17 and 20 recite limitations that are abstract ideas.
The limitation “splitting the target statement according to a nested relationship to generate the at least one target substatement without the nested relationship” in claims 2 and 17, is a mental step. One can mentally split a statement into sub-statements based on a relationship criteria. Thus, the claimed limitation can be performed by the human mind.
The limitations “performing analysis on a matching degree between an operation content associated with a SELECT keyword obtained by decoding the semantic feature vector and a data column in the data table”, “determining a target data column matching the operation content from the data table according to the matching degree” and “generating a target statement containing the SELECT keyword, the operation content, and the predicted value based on data in the target data column” in claims 5 and 20 are mental steps. One can mentally perform an analysis that involves determining how similar two sets of data are, determine a data column that matches content from the table according to the similarity degree and generate a statement containing the keyword, data table content and a predicted value using pen and paper.
Thus, the claimed limitations can be performed in the mind.
The limitations “screening out an operation content from the data table according to a predicted value associated to a WHERE keyword obtained by decoding the-semantic feature information” and “according to a matching degree between the predicted value and the screened operation content, determining the screened operation content matched with the predicted value to obtain a target statement containing the WHERE keyword, the predicted value and the screened operation content” in claim 6 are mental steps. One can selectively choose operation content data from a table pertaining to a ‘WHERE’ keyword and determine that the operation content from the data table matched with the predicted value to obtain a target statement with all of these data items. Thus, the claimed limitations can be performed in the mind.
Step 2A prong 2: Claims 2, 5, 6, 17 and 20 recite the limitations
“obtain a target statement containing a keyword, an operation content and a predicted value” and “acquiring a data table provided by a database”. These obtaining and acquiring steps are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, claims 2, 5, 6, 17 and 20 recites the following additional element “a decoder”, which is a generic pretrained neural network model (para 31 of specification) used to perform the mental process and applied on a computer as in MPEP 2106.05(f), which do not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “obtaining” and “acquiring” limitations identified as insignificant extra-solution activity above, when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … 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 thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, claims 2, 5, 6, 17 and 20 as a whole do not change this conclusion and the claims are ineligible.
Claims 3, 4, 7-13, 18 and 19 depend from claims 1 and 14 and thus include all the limitations of claims 1 and 14, therefore claims 3, 4, 7-13, 18 and 19 recite the same abstract ideas of "mental processes".
Claims 3, 4, 7-13, 18 and 19 furthermore recite:
(claims 3, 18) wherein the splitting the target statement according to the nested relationship to generate the at least one target substatement without the nested relationship comprises:
determining a first target substatement and a second target substatement that have the nested relationship according to a nested identifier contained in the target statement;
wherein the second target substatement is nested within the first target substatement;
adding a location identifier at a nested location where the nested identifier is located in the first target substatement; and
generating a sequence consisting of the first target substatement and the second target substatement in a hierarchical order in the nested relationship.
(claims 4, 19) (Original) after the obtaining the at least one target substatement, further comprising: determining the corresponding semantic constraint rule according to the keyword type contained in the target substatement.
(claim 7) wherein the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the keyword type is HAVING type, and an execution result of performing the operation content is a numerical constant, replacing the predicted value in a HAVING clause by a constant.
(claim 8) wherein the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the predicted value is a non-nested value, calculating a first similarity value between a first feature vector of the predicted value and a second feature vector of any value in a first data column of the a data table; and
in a condition that the first similarity value is greater than a first threshold, replacing the predicted value by the any value to obtain the corrected target substatement.
(claim 9) in a condition that the similarity value is less than the first threshold, calculating a second similarity value between a third feature vector of any value in any column in the data table except the first data column and the first feature vector; and
in a condition that the second similarity value is greater than a second threshold, replacing the predicted value by the any value in the any column to obtain the corrected target substatement.
(claim 10) wherein the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the keyword type is ORDER BY type, and any clause in the target substatement other than an ORDER BY clause contains an aggregate function, converting the ORDER BY clause to a nested query statement.
(claim 11) wherein the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the keyword type is GROUP BY type, and a SELECT clause does not contain an aggregate function and a column name of a data column corresponding to a GROUPBY clause, replacing the column name of the GROUP BY clause by a primary key; and
copying a data column corresponding to the SELECT clause to the data column corresponding to the GROUP BY clause in a condition that the primary key is not found.
(claim 12) in a condition that the keyword type is FROM type, generating a directed graph based on a data table corresponding to the corrected target substatement and the data column therein; and
constructing a minimum spanning tree with the data column or a foreign key as a root node based on the data table and the data column in the directed graph.
(claim 13) wherein the combining the corrected target substatement comprises:
combining the corrected target substatement in a condition that a SELECT clause and a WHERE clause in the corrected target substatement have a same column name or a primary foreign key relationship; or,
copying a column name corresponding to the WHERE clause to the SELECT clause to combine the corrected target substatement in a condition that the SELECT clause and the WHERE clause in the corrected target substatement do not have the same column name or the primary foreign key relationship.
Step 1: Claims 3, 4, 7-13, 18 and 19 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 3, 4, 7-13, 18 and 19 recite limitations that are abstract ideas.
The limitations “determining a first target substatement and a second target substatement that have the nested relationship according to a nested identifier contained in the target statement, wherein the second target substatement is nested within the first target substatement”, “adding a location identifier at a nested location where the nested identifier is located in the first target substatement” and “generating a sequence consisting of the first target substatement and the second target substatement in a hierarchical order in the nested relationship”, in claims 3 and 18 are mental steps. One can mentally make a comparison between substatements, insert a location identifier within a substatement and generate a sequence of the substatements using pen and paper. Thus, the claimed limitations can be performed by the human mind.
The limitation “determining the corresponding semantic constraint rule according to the keyword type contained in the target substatement” in claims 4 and 19 is a mental step. One can mentally determine based on a type of keyword within the substatement which semantic constraint rule to apply. Thus, the claimed limitation can be performed by the human mind.
The limitation “in a condition that the keyword type is HAVING type, and an execution result of performing the operation content is a numerical constant, replacing the predicted value in a HAVING clause by a constant” in claim 7 is a mental step. One can mentally or using pen and paper replace a value with a constant within a substatement. Thus, the claimed limitation can be performed by the human mind.
The limitations “in a condition that the predicted value is a non-nested value, calculating a first similarity value between a first feature vector of the predicted value and a second feature vector of any value in a first data column of the a data table” and “in a condition that the first similarity value is greater than a first threshold, replacing the predicted value by the any value to obtain the corrected target substatement” in claim 8 are mental steps. One can mentally calculate a similarity value between vectors and mentally or using pen and paper replace a value within a substatement. Thus, the claimed limitations can be performed by the human mind.
The limitations “in a condition that the similarity value is less than the first threshold, calculating a second similarity value between a third feature vector of any value in any column in the data table except the first data column and the first feature vector” and
“in a condition that the second similarity value is greater than a second threshold, replacing the predicted value by the any value in the any column to obtain the corrected target substatement” in claim 9 are mental steps. One can mentally calculate a similarity value between vectors and mentally or using pen and paper replace a value within a substatement. Thus, the claimed limitations can be performed by the human mind.
The limitation “in a condition that the keyword type is ORDER BY type, and any clause in the target substatement other than an ORDER BY clause contains an aggregate function, converting the ORDER BY clause to a nested query statement” in claim 10 is a mental step. One can mentally or using pen and paper make changes to the format of a substatement. Thus, the claimed limitation can be performed by the human mind.
The limitations “in a condition that the keyword type is GROUP BY type, and a SELECT clause does not contain an aggregate function and a column name of a data column corresponding to a GROUPBY clause, replacing the column name of the GROUP BY clause by a primary key” and “copying a data column corresponding to the SELECT clause to the data column corresponding to the GROUP BY clause in a condition that the primary key is not found” in claim 11 are mental steps. One can mentally or using pen and paper replace data values within a substatement based on a specific condition being met. Thus, the claimed limitations can be performed by the human mind.
The limitations “in a condition that the keyword type is FROM type, generating a directed graph based on a data table corresponding to the corrected target substatement and the data column therein” and “constructing a minimum spanning tree with the data column or a foreign key as a root node based on the data table and the data column in the directed graph” in claim 12 are mental steps. One can mentally or using pen and paper generate a directed graph from a data set and construct a minimum spanning tree based on data within the directed graph. Thus, the claimed limitations can be performed by the human mind.
The limitations “combining the corrected target substatement in a condition that a SELECT clause and a WHERE clause in the corrected target substatement have a same column name or a primary foreign key relationship” or “copying a column name corresponding to the WHERE clause to the SELECT clause to combine the corrected target substatement in a condition that the SELECT clause and the WHERE clause in the corrected target substatement do not have the same column name or the primary foreign key relationship” in claim 13 are mental steps. One can mentally or using pen and paper combine substatements based on a given condition or make changes to data values within a substatement. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2: Claims 3, 4, 7-13, 18 and 19 do not recite any additional elements that would integrate the judicial exception into a practical application.
Step 2B: Claims 3, 4, 7-13, 18 and 19 do not recite any additional elements that would provide significantly more than the judicial exception.
Therefore, claims 3, 4, 7-13, 18 and 19 as a whole are ineligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 8, 9, and 14-16 are rejected under 35 U.S.C. 102(1) as being anticipated by US 2022/0067281 by Hu et al (hereafter Hu).
Referring to claim 1, Hu discloses a language escape method [Abstract], comprising:
splitting a target statement acquired to obtain at least one target substatement [natural language text input is processed to generate a plurality of encoded question tokens, para 34, 36, Fig 2, element 120];
determining a keyword type and a keyword clause contained in the target substatement [input natural language is a question formulated by a user and spoken into a microphone of smart phone/vehicle, para 35; question token is of a question type and is a keyword/token clause in the input natural language question received, para 34-36];
correcting the keyword clause in the target substatement according to a semantic constraint rule corresponding to the keyword type contained in the target substatement [semantic enrichment 120 of natural language question is performed, para 80, 81,83, Fig 2]; and
combining the corrected target substatement, and generating an executable machine language [SQL syntax is generated in ToSQL step 156 based on determined output table schema and BERT fine tuning 155, para 85; SQL execution layer used for outputting natural language string, para 86; Fig 2].
Referring to claim 14, the limitations of the claim are similar to those of claim 1 in the form of an electronic device [device 30, Fig 3], comprising a memory [para 91] and a processor [processor 34, Fig 3; para 91]; wherein the memory is configured to store a program; the processor is coupled with the memory and configured to execute the program stored in the memory [para 88, Fig 3]. As such, claim 14 is rejected for the same reasons as claim 1.
Referring to claim 16, Hu discloses a database system [Fig 3, device 30], comprising:
an encoder, configured to encode an acquired natural language to obtain a semantic feature vector [language model based encoder is applied on the natural language question to generate embedding vectors, para 36, 76, 83, Fig 1, element 120; Fig 2];
a decoder, configured to decode the semantic feature vector output by the encoder to generate a target statement [NL2SQL decoding layer (neural network—based decoder) used to output natural language string, para 85-86];
a splitting module, configured to split the target statement acquired to obtain at least one target substatement [BERT used for tokenization, para 4; natural language text input is processed to generate a plurality of encoded question tokens, para 34, 36, Fig 2, element 120];
a correction module, configured to correct a keyword clause in the target substatement according to a semantic constraint rule corresponding to a keyword type contained in the target substatement [semantic enrichment 120 of natural language question is performed, para 80, 81,83, Fig 2];
a combination module, configured to combine the corrected target substatement, and generate an executable machine language [SQL syntax is generated in ToSQL step 156 based on determined output table schema and BERT fine tuning 155, para 85; SQL execution layer used for outputting natural language string, para 86; Fig 2]; and
a database, configured to execute a data query task based on the executable machine language [NL2SQL used to translate natural language to an correct executable SQL query, database, para 2].
Referring to claim 8, Hu discloses that the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the predicted value is a non-nested value, calculating a first similarity value between a first feature vector of the predicted value and a second feature vector of any value in a first data column of the a data table [similarity between plurality of encoded question tokens and plurality of encoded table schema tokens is determined, para 6, 41-55]; and
in a condition that the first similarity value is greater than a first threshold, replacing the predicted value by the any value to obtain the corrected target substatement [table schema with highest similarity is selected as the output table schema which is used to generate the SQL query, para 6, 41-55].
Referring to claim 9, Hu discloses that in a condition that the similarity value is less than the first threshold, calculating a second similarity value between a third feature vector of any value in any column in the data table except the first data column and the first feature vector; and in a condition that the second similarity value is greater than a second threshold, replacing the predicted value by the any value in the any column to obtain the corrected target substatement [second similarity value calculated, para 48-55].
Referring to claim 15, Hu discloses a non-transitory machine-readable storage medium storing an executable code executed by a processor of an electronic device [para 21].
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-6, 10, 11, 13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0067281 by Hu et al (hereafter Hu), as applied to claims 1 and 14 above, and further in view of US 2020/0334252 by Lee.
Referring to claims 2 and 17, while Hu discloses all of the above claimed subject matter and also discloses: decoding a semantic feature vector encoded based on a natural language provided by a user by using a decoder to obtain a target statement containing a keyword, an operation content and a predicted value [Hu, NL2SQL decoding layer (neural network—based decoder) used to output natural language string, para 85-86; syntactic constraints including clause (keyword), predicted header of index from table e.g. sco11 (predicted value) and operands such as Select, From and Where (operation content), para 42-46], and tokenizing the natural language into a plurality of encoded question tokens [para 34, 36, Fig 2, element 120] it remains silent as to the target statement split according to a nested relationship to generate the at least one target substatement without the nested relationship.
Lee teaches that a nested query predictive model predicts whether there is a nested query within a statement and the nested query is recursively processed to be predicted by the same model architecture [para 18-19, 46-47, Fig 4C].
Hu and Lee are analogous art because they are directed to the same field of endeavor- text to SQL generation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the NL2SQL decoding layer to process the natural language input in Hu to incorporate the nested queries detected in Lee because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the nested queries in Lee further define the type of natural language input in Hu.
Referring to claims 3 and 18, Hu/Lee discloses that the splitting the target statement according to the nested relationship to generate the at least one target substatement without the nested relationship comprises: determining a first target substatement and a second target substatement that have the nested relationship according to a nested identifier contained in the target statement; wherein the second target substatement is nested within the first target substatement; adding a location identifier at a nested location where the nested identifier is located in the first target substatement; and generating a sequence consisting of the first target substatement and the second target substatement in a hierarchical order in the nested relationship [Lee, predicted presence of sub-query (nested query), temporal sub-query token 452 added to corresponding location in SQL output, para 46, Fig 4C].
Referring to claims 4 and 19, Hu/Lee discloses that after the obtaining the at least one target substatement, further comprising: determining the corresponding semantic constraint rule according to the keyword type contained in the target substatement [Lee, para 47].
Referring to claims 5 and 20, Hu/Lee discloses that in a condition that the keyword type is SELECT type; the decoding the semantic feature vector encoded based on the natural language provided by the user by using the decoder to obtain the target statement containing the keyword, the operation content and the predicted value comprises: acquiring a data table provided by a database; performing analysis on a matching degree between an operation content associated with a SELECT keyword obtained by decoding the semantic feature vector and a data column in the data table;
determining a target data column matching the operation content from the data table according to the matching degree; and generating a target statement containing the SELECT keyword, the operation content, and the predicted value based on data in the target data column [Hu, SELECT clause, para 66-69].
Referring to claim 6, Hu/Lee discloses that in a condition that the keyword type is WHERE type; the decoding the semantic feature vector encoded based on the natural language provided by the user by using the decoder to obtain the target statement containing the keyword, the operation content and the predicted value comprises:
acquiring a data table provided by a database; screening out an operation content from the data table according to a predicted value associated to the a WHERE keyword obtained by decoding the-semantic feature information; and according to a matching degree between the predicted value and the screened operation content, determining the screened operation content matched with the predicted value to obtain a target statement containing the WHERE keyword, the predicted value and the screened operation content [Lee, WHERE clause, para 16-17; Fig 2].
Referring to claim 10, Hu/Lee discloses that the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises:
in a condition that the keyword type is ORDER BY type, and any clause in the target substatement other than an ORDER BY clause contains an aggregate function, converting the ORDER BY clause to a nested query statement [Lee, ORDER BY clause with aggregate, para 16-17; recursively generated nested queries with this clause, para 33, Fig 2].
Referring to claim 11, Hu/Lee discloses that the correcting the keyword clause in the target substatement according to the semantic constraint rule corresponding to the keyword type contained in the target substatement comprises: in a condition that the keyword type is GROUP BY type, and a SELECT clause does not contain an aggregate function and a column name of a data column corresponding to a GROUPBY clause, replacing the column name of the GROUP BY clause by a primary key; and copying a data column corresponding to the SELECT clause to the data column corresponding to the GROUP BY clause in a condition that the primary key is not found [Lee, GROUPBY clause, para 16-17, 33, Fig 2].
Referring to claim 13, Hu/Lee discloses that the combining the corrected target substatement comprises: combining the corrected target substatement in a condition that a SELECT clause and a WHERE clause in the corrected target substatement have a same column name or a primary foreign key relationship; or copying a column name corresponding to the WHERE clause to the SELECT clause to combine the corrected target substatement in a condition that the SELECT clause and the WHERE clause in the corrected target substatement do not have the same column name or the primary foreign key relationship [Lee, SELECT, WHERE clauses with column name, para 16, 17, 33, Fig 2].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Lee, as applied to claim 4 above, and further in view of US 2007/0067262 by Ramesh et al (hereafter Ramesh).
Referring to claim 7, while Hu/Lee discloses all of the above claimed subject matter, and also discloses a condition that the keyword type is HAVING type [Lee, Fig 2], however it remains silent as to in a condition that the keyword type is HAVING type, and an execution result of performing the operation content is a numerical constant, replacing the predicted value in a HAVING clause by a constant. Ramesh teaches replacing the input relations in a SQL database query with a constant expression [para 12-13].
Hu, Lee and Ramesh are analogous art because they are directed to the same field of endeavor- modification of queries.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the NL2SQL decoding layer to process the natural language input in Hu to incorporate with the replacement of an input relations with a constant, as in Ramesh, because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the replacement with a constant further defines the NL2SQL conversion rules of Hu.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hu, in view of Lee, as applied to claim 5 above, and further in view of US 2013/0110820 by Gupta et al (hereafter Gupta).
Referring to claim 12, while Hu/Lee discloses all of the above claimed subject matter and also discloses a condition that the keyword type is FROM type [Lee, para 16]. However it remains silent as to: generating a directed graph based on a data table corresponding to the corrected target substatement and the data column therein; and constructing a minimum spanning tree with the data column or a foreign key as a root node based on the data table and the data column in the directed graph.
Gupta teaches generating a directed graph based on a hierarchy of pattern queries and determining an execution order of the pattern queries based on a determined minimum spanning tree of the directed graph [Abstract; Fig 2, elements 206-210; pattern queries, Fig 1].
Hu, Lee and Gupta are analogous art because they are directed to the same field of endeavor- query processing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the NL2SQL decoding layer in Hu to include the execution order of pattern queries according to the minimum spanning tree and directed graph of Gupta because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the execution order of pattern queries in Gupta further defines the NL2SQL SQL statement generation in Hu.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rahmfeld et al (US 11934392) directed to: generating an executable structured query language statement representative of an utterance based on an intermediate structured query language statement through translation layer comprising an encoder and decoder [Abstract; Fig 2 and corresponding portions of specification].
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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