DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant application having application No. 18/384,989 filed on October 30, 2023, presents claims 1-27 for examination. The instant application is a Continuation of International App. No. PCT/KR2022/005901, filed on April 26, 2022, which claims priority to Korean Patent App. No. KR10-2021-0123664, filed on September 16, 2021.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed 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: “a communication module configured to transmit and receive information to and from the terminal” in claim 15 and “an input/output module … receives input data including a text through the input/output module … displays the output data on the input/output module” in claim 21.
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 1-27 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.
With respect to claim 1, lines 10-12 recite, with emphasis added, “converting a text keyword into source code information corresponding to the text keyword when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the server”. This is ambiguous for two reasons. First, it is unclear whether “a text keyword” and “the text keyword including one or more words included in the text” are the same keyword. Second, it is unclear whether “source code information corresponding to the text keyword” is the same as “the source code information stored in the server”. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- converting a text keyword into source code information, wherein the source code information corresponds [[corresponding]] to the text keyword, when the text keyword [[including]] includes one or more words included in the text and has semantics corresponding to the source code information, wherein the source code information is stored in the server”.
With respect to claims 2-8 and 27, claims 2-8 inherit the 35 USC 112(b) deficiency identified above with respect to claim 1. Claim 27 incorporates the method of claim 1 and this includes the same 112(b) deficiency and has been similarly interpreted.
With respect to claim 9, lines 12-14 recite, with emphasis added, “converting a text keyword into source code information corresponding to the text keyword by using the chatbot when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the server”. This is ambiguous for two reasons. First, it is unclear whether “a text keyword” and “the text keyword including one or more words included in the text” are the same keyword. Second, it is unclear whether “source code information corresponding to the text keyword” is the same as “the source code information stored in the server”. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- converting a text keyword into source code information, wherein the source code information corresponds [[corresponding]] to the text keyword by using the chatbot, when the text keyword [[including]] includes one or more words included in the text and has semantics corresponding to the source code information, wherein the source code information is stored in the server”.
With respect to claims 10-14, each inherits the 35 USC 112(b) deficiency identified above with respect to claim 9.
With respect to claim 15, lines 5-7 recite, with emphasis added, “a memory storing a text semantics analysis program; and a processor configured to execute a text semantics analysis program stored in the memory, wherein, by executing the text semantics analysis program”. It is unclear whether these are all the same “text semantics analysis program”, which renders the scope of the claim indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- a memory storing a text semantics analysis program; and a processor configured to execute [[a]] the text semantics analysis program stored in the memory, wherein, by executing the text semantics analysis program --.
Additionally, lines 12-14 recite, with emphasis added, “when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the memory, the processor modifies the text by converting a text keyword into source code information corresponding to the text keyword”. This is ambiguous for four reasons. First, the is no prior recitation of “text keyword” prior to the first recitation of “the text keyword” and it is unclear what this might refer to. Second, it is unclear whether the subsequently recited “a text keyword” and the previously recited “the text keyword” are the same keyword. Third, there is no recitation of “source code information” prior to the first recitation of “the source code information stored in the memory” and it is unclear what this might refer to. Fourth, it is unclear whether the subsequently recited “source code information corresponding to the text keyword” is the same as “the source code information stored in the memory”. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- when [[the]] a text keyword including one or more words included in the text has semantics corresponding to [[the]] source code information stored in the memory, the processor modifies the text by converting [[a]] the text keyword into the source code information, wherein the source code information corresponds [[corresponding]] to the text keyword --.
With respect to claims 16-20, each inherits the 35 USC 112(b) deficiency identified above with respect to claim 15.
With respect to claim 21, lines 4-7 recite, with emphasis added, “a memory storing a text semantics extraction program; and a processor configured to execute a text semantics extraction program stored in the memory, wherein by executing the text semantics extraction program”. It is unclear whether these are all the same “text semantics extraction program”, which renders the scope of the claim indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- a memory storing a text semantics extraction program; and a processor configured to execute [[a]] the text semantics extraction program stored in the memory, wherein by executing the text semantics extraction program --.
Additionally, lines 12-14 recite, with emphasis added, “when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the memory, the processor modifies the text by converting a text keyword into source code information corresponding to the text keyword”. This is ambiguous for four reasons. First, the is no prior recitation of “text keyword” prior to the first recitation of “the text keyword” and it is unclear what this might refer to. Second, it is unclear whether the subsequently recited “a text keyword” and the previously recited “the text keyword” are the same. Third, there is no recitation of “source code information” prior to the first recitation of “the source code information stored in the memory” and it is unclear what this might refer to. Fourth, it is unclear whether the subsequently recited “source code information corresponding to the text keyword” is the same as “the source code information stored in the memory”. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted that claim as reciting -- when [[the]] a text keyword including one or more words included in the text has semantics corresponding to [[the]] source code information stored in the memory, the processor modifies the text by converting [[a]] the text keyword into the source code information, wherein the source code information corresponds [[corresponding]] to the text keyword --.
With respect to claims 22-26, each inherits the 35 USC 112(b) deficiency identified above with respect to claim 21.
With respect to claim 4, line 6 recites “the text keywords”, but there is no previous recitation of “text keywords” and it is unclear if this means the previously recited “text keyword” or something different. For purposes of compact prosecution only, Examiner has interpreted the claim as reciting -- [[the]] text keywords --.
With respect to claims 12, 18, and 24, each recites limitations similar to claim 4 and are similarly indefinite. For purposes of compact prosecution only, Examiner has interpreted these claims as indicated above with respect to claim 4.
With respect to claim 7, lines 3-6 recite, with emphasis added, “the text analysis result includes function information for generating an answer corresponding to the question information of the text, and the output data includes answer information generated according to the function information” and parent claim 2 at lines 4-5 recites “function information”. It is unclear if all of these are the same “function information”. For purposes of compact prosecution only, Examiner has interpreted claim 7 as reciting -- the text analysis result includes function information for generating an answer corresponding to the question information of the text, and the output data includes answer information generated according to the function information for generating an answer corresponding to the question information of the text --.
With respect to claim 8, lines 3-9 recite, with emphasis added, “providing, by the server, a plurality of pieces of source code information to the terminal when the text keyword including the one or more words included in the text has semantics corresponding to the plurality of pieces of source code information stored in the server; and receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal, converting the text keyword into the source code information received from the terminal, modifying the text, analyzing the modified text, and generating the text analysis result by using the server.” This is ambiguous for several reasons. First, it is unclear whether “a plurality of pieces of source code information” and “the plurality of pieces of source code information stored in the server” are the same. Second, it is unclear what “the source code information received from the terminal” as there is prior recitation of “source code information received from the terminal” and it unclear whether this means other previous recitations of “source code information.” Third, it is unclear whether “modifying the text, analyzing the modified text” is the same as “modifying, by the server, the text … generating, by the server, the text analysis result by performing analysis on the modified text”, as recited in parent claim 1. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted claim 8 as reciting -- providing, by the server, a plurality of pieces of source code information to the terminal when the text keyword including the one or more words included in the text has semantics corresponding to the plurality of pieces of source code information, where the plurality of pieces of source code information are stored in the server; and receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal, converting the text keyword into [[the]] source code information received from the terminal, the modifying the text, analyzing the modified text, and generating the text analysis result by using the server --.
With respect to claims 14, 20, and 26, each recites limitations similar to claim 8 and are similarly indefinite. For purposes of compact prosecution only, Examiner has interpreted these claims as indicated above with respect to claim 8.
With respect to claim 19, lines 2-3 recite “the intent of the text” and “the entity of the text”, but there are no previous recitations of these terms and it is unclear what they might refer to. For purposes of compact prosecution only, Examiner has interpreted claim 19 as reciting – [[the]] an intent of the text -- and – [[the]] an entity of the text --.
With respect to claim 25, the claim recites limitations similar to those identified above with respect to claim 19 and are likewise indefinite and have been interpreted similarly by Examiner for purposes of compact prosecution only.
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 1-27 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, specifically an abstract idea, as it has not been integrated into a practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-14 are directed to computer implemented methods and fall within the statutory category of processes; Claims 15-20 are directed to systems and fall within the statutory category of machines; Claims 21-26 are directed to devices and fall within the statutory category of machines; Claim 27 is directed to a non-transitory computer-readable media and falls within the statutory category of articles of manufacture. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon, or an abstract idea (see MPEP § 2106.04).
Step 2A Prong 1:
With respect to claims 1, 9, 15, 21, and 27, The limitations of “A text semantic analysis method using source code analysis based on … the text semantic analysis method comprising: … generating … a text analysis result including semantics of the text by performing natural language processing on the text based on the input data … generating … output data corresponding to the semantics of the text based on the text analysis result … wherein the generating of the text analysis result comprises modifying … the text by converting a text keyword into source code information corresponding to the text keyword when the text keyword including one or more words included in the text has semantics corresponding to the source code information … and generating … the text analysis result by performing analysis on the modified text”1, “A question-answer … providing method using source code analysis … the question-answer … providing method using source code analysis … providing method comprising: … performing … natural language processing on the text based on the question data … and generating … a text analysis result including semantics of the text … generating … answer data corresponding to the semantics of the text based on the text analysis result … wherein the performing of the natural language processing comprises modifying … the text by converting a text keyword into source code information corresponding to the text keyword … when the text keyword including one or more words included in the text has semantics corresponding to the source code information … and generating, … the text analysis result by performing analysis on the modified text …”2, “A text semantic analysis … using source code analysis … the text semantic analysis … comprising: … text semantics analysis …; and … a text semantics analysis … the text semantics analysis … … generates a text analysis result including semantics of the text by performing natural language processing on the text based on the input data, generates output data corresponding to the semantics of the text based on the text analysis result, …, and when the text keyword including one or more words included in the text has semantics corresponding to the source code information … modifies the text by converting a text keyword into source code information corresponding to the text keyword and generates the text analysis result by performing analysis on the modified text”3, “A text semantic analysis … using source code analysis, the text semantic analysis … comprising: … generates a text analysis result including semantics of the text by performing natural language processing on the text based on the input data, generates output data corresponding to the semantics of the text based on the text analysis result … and when the text keyword including one or more words included in the text has semantics corresponding to the source code information … modifies the text by converting a text keyword into source code information corresponding to the text keyword and generates the text analysis result by performing analysis on the modified text”4 is a process that, but for the recitation of generic computing components and under its broadest reasonable interpretation, covers performance of the limitation in the mind with no more than pen and paper. For example, a human developer could analyze a question for meaning and modify the question and futher analyze the modified question, and then produce an answer for the question.
Therefore, Yes, claims 1, 9, 15, 21, and 27 recite limitations that fall within the “Mental Processes” grouping of abstract ideas.
As the claims have been identified as reciting a judicial exception, Step 2A Prong 2 will evaluate whether the claim as a whole integrates the recited judicial exception into a practical application (see MPEP § 2106.04(d)).
Step 2A Prong 2:
With respect to claims 1, 9, 15, 21, and 27, The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements: “a communication connection between a terminal and a server … by the server … by the server … by the server … by the server,”5 “service … and a chatbot based on a communication connection between a terminal and a server … service … by the server … by using the chatbot … by the server … by the server … by using the chatbot … by the server … by using the chatbot … by the server … by using the chatbot”,6 “system … through a communication connection to a terminal … system … a communication module configured to transmit and receive information to and from the terminal; a memory storing a … program; and a processor configured to execute … a program stored in the memory, wherein, by executing … program, the processor”,7 “device …device … an input/output module; a memory storing a text semantics extraction program; and a processor configured to execute a text semantics extraction program stored in the memory, wherein by executing the text semantics extraction program … the processor,”8, which merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components to perform the abstract idea, which does not integrate a judicial exception into a practical application (see MPEP § 2106.05(f)).
The claims further recite the following additional element(s): “receiving, by the server, input data including a text from the terminal … and providing the output data to the terminal … stored in the server”9, “receiving, by the server, question data including a text from the terminal by using the chatbot … and providing the answer data to the terminal … stored in the server”10, “the processor receives input data including a text from the terminal through the communication module … and provides the output data to the terminal … stored in the memory”11, “the processor receives input data including a text through the input/output module … and displays the output data on the input/output module … stored in the memory”12, which is/are merely insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exception into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Lastly, the claims recite the following additional element(s): “A non-transitory computer-readable recording medium on which a computer program for performing … is recorded,”13 which is/are merely a recitation of a field of use/technological environment that does not integrate the judicial exception into a practical application (see MPEP § 2106.05(h)).
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 9, 15, 21, and 27 not only recite a judicial exception but are directed to the judicial exception as the judicial exception has not been integrated into a practical application. Accordingly, Step 2B will evaluate whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP § 2106.05.
Step 2B:
With respect to claims 1, 9, 15, 21, and 27, The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components applying the abstract idea, recitation of a field of use/technological environment, and insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which is well-understood, routine, and conventional (see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.).
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded the analysis within the provided framework, claims 1, 9, 15, 21, and 27 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claims 2, 10, 16, and 2214, the limitations recite “wherein the server includes a memory storing the source code information including information on one or more programming languages, and the source code information includes variable information, class information, function information, and relationship information thereof which are used for the one or more programming languages”15. Regarding “the source code information including information on one or more programming languages, and the source code information includes variable information, class information, function information, and relationship information thereof which are used for the one or more programming languages”, this merely recites additional details of the “source code information” that was identified as part of the Mental Process in parent claims 1, 9, 15, and 21 (see above) and under its broadest reasonable interpretation, could also be performed in the mind with no more than pen and paper. Thus, this limitation is also part of the same Mental Process. Furthermore, “wherein the server includes a memory”, this merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components to perform the abstract idea, which does not integrate a judicial exception into a practical application (see MPEP § 2106.05(f)), and “storing”, this is insignificant extra-solution activity (see MPEP § 2106.05(g)) that is well-understood, routine, and conventional (see § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional). Thus, the claims are directed to the judicial exception and do not have elements amounting to significantly more than the abstract idea itself. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claims 3, 11, 17, and 2316, the limitations recite “storing the source code information in which the server generates the source code information and stores the source code information in the memory, wherein the storing of the source code information comprises: extracting nouns included in the variable information, the class information, and the function information which are used for the one or more programming languages and setting the extracted nouns as source code keywords by using the server; and generating the source code information based on names of source codes corresponding to the source code keywords, abbreviations of the source codes, and relationships between translations of the source codes and the source code keywords and storing the source code information in the memory by using the server.” Regarding “setting the extracted nouns as source code keywords … and generating the source code information based on names of source codes corresponding to the source code keywords, abbreviations of the source codes, and relationships between translations of the source codes and the source code keywords ”, this could also be performed in the human mind and thus is part of the same Mental Process identified above with respect to independent claims 1, 9, 15, and 21. Regarding “storing the source code information in which the server generates the source code information and stores the source code information in the memory, wherein the storing of the source code information comprises: extracting nouns included in the variable information, the class information, and the function information which are used for the one or more programming languages … and storing the source code information in the memory by using the server”, this is insignificant extra-solution activity (see MPEP § 2106.05(g)) that is well-understood, routine, and conventional (see § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional). Thus, the claims are directed to the judicial exception and do not have elements amounting to significantly more than the abstract idea itself. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claims 4, 12, 18, and 2417, the limitations recite “wherein the generating of the text analysis result comprises: determining, by the server, whether a source code keyword corresponding to the text keyword is in the memory; and performing, by the server, semantic analysis on a text keyword including a corresponding source code keyword stored in the memory among the text keywords included in the text based on semantics of the corresponding source code keyword, and performing, by the server, semantic analysis on a text keyword which does not include a corresponding source code keyword based on dictionary semantics.” The limitations “wherein the generating of the text analysis result comprises: determining… whether a source code keyword corresponding to the text keyword is …; and performing…, semantic analysis on a text keyword including a corresponding source code keyword … among the text keywords included in the text based on semantics of the corresponding source code keyword, and performing, … semantic analysis on a text keyword which does not include a corresponding source code keyword based on dictionary semantics”, can be performed in the human mind with no more than pen and paper. Regarding “by the server”, and “stored in the memory”, these are merely generic computing components applying the abstract idea. Regarding “in the memory”, is merely a recitation of a field of use/technological environment. Thus, the claims are directed to the judicial exception and do not have elements amounting to significantly more than the abstract idea itself. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claims 5, 13, 19, and 2518, the limitations recite “wherein the generating of the text analysis result comprises analyzing intent of the text, and analyzing an entity of the text, the analyzing of the intent of the text comprises setting… the intent of the text to one of preset intent types based on the text analysis result including the semantics of the text, and the analyzing of the entity of the text comprises setting … the entity of the text to one of preset entity types based on the text analysis result including the semantics of the text”, which can be performed in the human mind with no more than pencil and paper and thus is part of the same abstract idea identified above with respect to the independent claims. Furthermore, “by the server” is merely a generic computing component applying the abstract idea. Thus, the claims are directed to the judicial exception and do not have elements amounting to significantly more than the abstract idea itself. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claim 6, the limitations recite “wherein the preset intent types include a function check type and an error report type, and the preset entity types include a function description type and an error description type” which merely provide additional detail of the mental process identified above with respect to claims 1 and 5 and can also be performed in the human mind with no more than pencil and paper and thus is part of the same abstract idea identified above with respect to the independent claims. Thus, the claim is directed to the judicial exception and does not have elements amounting to significantly more than the abstract idea itself. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claim 7, the limitations recite “wherein the text includes question information, the text analysis result includes function information for generating an answer corresponding to the question information of the text, and the output data includes answer information generated according to the function information”, which can be performed in the human mind with no more than pencil and paper and thus is part of the same abstract idea identified above with respect to the independent claims. Thus, the claim is directed to the judicial exception and does not have elements amounting to significantly more than the abstract idea itself. Therefore, the claim does not recite patent eligible subject matter under 35 U.S.C. § 101.
With respect to claims 8, 14, 20, and 26,19 the limitations recite “wherein the generating of the text analysis result comprises: providing … when the text keyword including the one or more words included in the text has semantics corresponding to the plurality of pieces of source code information … converting the text keyword into the source code information …, modifying the text, analyzing the modified text, and generating the text analysis result”, which can be performed in the human mind with no more than pencil and paper and thus is part of the same abstract idea identified above with respect to the independent claims. Furthermore “by the server, a plurality of pieces of source code information to the terminal … stored in the server; and receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal … received from the terminal”, this is insignificant extra-solution activity (see MPEP § 2106.05(g)) that is well-understood, routine, and conventional (see § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional) and “by using the server” is merely a generic computing functionality applying the abstract idea. Thus, the claims are directed to the judicial exception and do not have elements amounting to significantly more than the abstract idea itself. Therefore, the claims do not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 7, 8, 9, 10, 14, 15, 16, 20, 21, 22, 26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Dey et al. (US 20200167134 A1, hereinafter Dey) in view of Palani “Statistical Machine Translation of English Text to API Code Usages: A comparison of Word Map, Contextual Graph Ordering, Phrase-based, and Neural Network Translation” (hereinafter Palani).
With respect to claim 1, Dey discloses A text semantic analysis method using source code analysis based on a terminal , (e.g., Figs. 1-5 and associated text, e.g., [0019], augmenting a programming interface or other software development environment (e.g., an integrated development environment (IDE)) with an automated dialog system or chatbot. The chatbot enables a programmer with a reasonable skill level to interact with the chatbot using natural language, and eases the cognitive load on the programmer to create programming solutions; [0016], To provide assistance at the semantic level, the system needs to understand programming intricacies.) the text semantic analysis method comprising:
receiving, , input data including a text from the terminal (e.g., Figs. 1-5 and associated text, e.g., [0026], one or more explicit user queries [input data] 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response.);
generating, , a text analysis result including semantics of the text by performing natural language processing on the text based on the input data (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0016], [0029] and [0031].); and
generating, , output data corresponding to the semantics of the text based on the text analysis result and providing the output data to the terminal (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE; [0026], On classifying the given query, the reactive chat generation module 106 provides responses [answer data] by searching at appropriate levels or from appropriate information sources or repositories; see also [0016], [0029] and [0031].),
wherein the generating of the text analysis result comprises , and generating, , the text analysis result by performing analysis on the text (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].).
Dey does not appear to disclose the following, which is taught in analogous art, Palani: a communication connection between … and a server … by the server … by the server … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.) … modifying, by the server, the text by converting a text keyword into source code information corresponding to the text keyword when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the server (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture) … modified (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph [generating, by the server, the text analysis result by performing analysis on the modified text by using the chatbot]; see also pp. 44-45 and pp. 46-51, §6.3 Server Components.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani, such that the user can automatically create API template code, because it “is quite effective,” as suggested by Palani (see Abstract).
With respect to claim 9, Dey discloses A question-answer service providing method using source code analysis and a chatbot based on a terminal , the question-answer service providing method comprising:
receiving, , question data including a text from the terminal by using the chatbot (e.g., Figs. 1-5 and associated text, e.g., [0026], one or more explicit user queries [question] 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response.);
performing, , natural language processing on the text based on the question data by using the chatbot and generating, , a text analysis result including semantics of the text (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0016], [0029] and [0031].); and
generating, , answer data corresponding to the semantics of the text based on the text analysis result by using the chatbot and providing the answer data to the terminal (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE; [0026], On classifying the given query, the reactive chat generation module 106 provides responses [answer data] by searching at appropriate levels or from appropriate information sources or repositories; see also [0016], [0029] and [0031].),
wherein the performing of the natural language processing comprises by using the chatbot , and generating, , the text analysis result by performing analysis on the text by using the chatbot (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].).
Dey does not appear to disclose the following, which is taught in analogous art, Palani: a communication connection between … and a server … by the server … by the server … by the server … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.) … modifying, by the server, the text by converting a text keyword into source code information corresponding to the text keyword … when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the server (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.), … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture), … modified (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph [generating, by the server, the text analysis result by performing analysis on the modified text by using the chatbot]; see also pp. 44-45 and pp. 46-51, §6.3 Server Components.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani, such that the user can automatically create API template code, because it “is quite effective,” as suggested by Palani (see Abstract).
With respect to claim 15, Dey discloses A text semantic analysis system using source code analysis through a terminal (e.g., Figs. 1-5 and associated text, e.g., [0019], augmenting a programming interface or other software development environment (e.g., an integrated development environment (IDE)) with an automated dialog system or chatbot. The chatbot enables a programmer with a reasonable skill level to interact with the chatbot using natural language, and eases the cognitive load on the programmer to create programming solutions; [0016], To provide assistance at the semantic level, the system needs to understand programming intricacies; see also [0006].), the text semantic analysis system comprising:
a communication module configured to transmit and receive information to and from the terminal (e.g., Fig. 5 and associated text, e.g., [0075], computer system/server 512 can communicate with one or more networks such as a LAN, a general WAN, and/or a public network (e.g., the Internet) via network adapter 520.);
a memory storing a text semantics analysis program (e.g., Figs. 1-5 and associated text, e.g., [0069] One or more embodiments can make use of software running on a general-purpose computer or workstation; [0016], To provide assistance at the semantic level, the system needs to understand programming intricacies; [0019], augmenting a programming interface or other software development environment (e.g., an integrated development environment (IDE)) with an automated dialog system or chatbot. The chatbot enables a programmer with a reasonable skill level to interact with the chatbot using natural language, and eases the cognitive load on the programmer to create programming solutions; see also [0006].);
and a processor configured to execute a text semantics analysis program stored in the memory, wherein, by executing the text semantics analysis program, the processor (Id.; [0071], The components of computer system/server 512 may include, but are not limited to, one or more processors or processing units 516, a system memory 528, and a bus 518 that couples various system components including system memory 528 to processor 516.) receives input data including a text from the terminal , generates a text analysis result including semantics of the text by performing natural language processing on the text based on the input data (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0016], [0029] and [0031].), generates output data corresponding to the semantics of the text based on the text analysis result, and provides the output data to the terminal (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE; [0026], On classifying the given query, the reactive chat generation module 106 provides responses [answer data] by searching at appropriate levels or from appropriate information sources or repositories; see also [0016], [0029] and [0031].), and and generates the text analysis result by performing analysis on the text (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].).
Dey does not appear to disclose the following, which is taught in analogous art, Palani: a communication connection to … through the communication module (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.) … when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the memory, the processor modifies the text by converting a text keyword into source code information corresponding to the text keyword (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … modified (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph [generates the text analysis result by performing analysis on the modified text]; see also pp. 44-45 and pp. 46-51, §6.3 Server Components.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani, such that the user can automatically create API template code, because it “is quite effective,” as suggested by Palani (see Abstract).
With respect to claim 21, Dey discloses A text semantic analysis device using source code analysis (e.g., Figs. 1-5 and associated text, e.g., [0019], augmenting a programming interface or other software development environment (e.g., an integrated development environment (IDE)) with an automated dialog system or chatbot. The chatbot enables a programmer with a reasonable skill level to interact with the chatbot using natural language, and eases the cognitive load on the programmer to create programming solutions; [0016], To provide assistance at the semantic level, the system needs to understand programming intricacies; see also [0006].), the text semantic analysis device comprising:
an input/output module; a memory storing a text semantics extraction program (e.g., Figs. 1-5 and associated text, e.g., [0075] Computer system/server 512 may also communicate with one or more external devices 514 such as a … display 524, etc., one or more devices that enable a user to interact with computer system/server 512; [0069] One or more embodiments can make use of software running on a general-purpose computer or workstation; [0016], To provide assistance at the semantic level, the system needs to understand programming intricacies; [0019], augmenting a programming interface or other software development environment (e.g., an integrated development environment (IDE)) with an automated dialog system or chatbot. The chatbot enables a programmer with a reasonable skill level to interact with the chatbot using natural language, and eases the cognitive load on the programmer to create programming solutions; see also [0006].); and a processor configured to execute a text semantics extraction program stored in the memory, wherein by executing the text semantics extraction program, the processor receives input data including a text through the input/output module (e.g., Figs. 1-5 and associated text, e.g., [0026], one or more explicit user queries [input data including a text] 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response; see also [0075].), generates a text analysis result including semantics of the text by performing natural language processing on the text based on the input data (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0016], [0029] and [0031].), generates output data corresponding to the semantics of the text based on the text analysis result, and displays the output data on the input/output module (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE; [0026], On classifying the given query, the reactive chat generation module 106 provides responses [answer data] by searching at appropriate levels or from appropriate information sources or repositories; see also [0016], [0029] and [0031].), and and generates the text analysis result by performing analysis on the text (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].).
Dey does not appear to disclose the following, which is taught in analogous art, Palani: when the text keyword including one or more words included in the text has semantics corresponding to the source code information stored in the memory, the processor modifies the text by converting a text keyword into source code information corresponding to the text keyword (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … modified (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph [generates the text analysis result by performing analysis on the modified text]; see also pp. 44-45 and pp. 46-51, §6.3 Server Components.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani, such that the user can automatically create API template code, because it “is quite effective,” as suggested by Palani (see Abstract).
With respect to claim 27, Dey in view of Palani discloses A non-transitory computer-readable recording medium on which a computer program for performing the text semantics analysis method using the source code analysis according to claim 1 is recorded (see the rejection of claim 1 above; see also Dey, e.g., [0069] One or more embodiments can make use of software running on a general-purpose computer or workstation.).
With respect to claims 2, 10, 16, and 22, Palani further teaches wherein the server includes a memory storing the source code information including information on one or more programming languages (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 46, 1st full para., The figure 9 shows the architecture of T2API console. The ContextualExpansion, GrouMiner and GraphOrdering components were developed in Java programming language as part of the earlier research of T2API. Hence we decide to use Apache Tomcat framework [server] to host the Java web application and Jersey library to build REST interfaces on top of existing components and the new pipeline connecting those components. The web application use the translation probability table from Berkeley Aligner to translate English text to API elements using WordMapK and ContextualExpansion algorithms. The web application loads the trained graph database into the main memory during the initialisation; p. 15, § 3.3.3 Ordering the code into a graph, To re-order the output of these simple word mapping models, a code only language model is created by mining the graphs of 556 existing android projects using GrouMiner; p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; see also Figures 11-13 on pp. 52-54.), and the source code information includes variable information, class information, function information, and relationship information thereof which are used for the one or more programming languages (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 1, 1st para., Each API consists of set of named functionality in the form of named functions (i.e. actions) and classes (i.e. entities); p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; pp. 49-50, § 6.3.5 Code Generator, We develop the code generator component to translate the Groum (API usage graph) into a usable code template. The GraphOrdering outputs a ranked list of API usage graphs given the list of API elements. The Groum is an abstract representation of the source code with temporal and data dependencies … The code generator iterates each connected component of the graph and then identifies the control and data dependencies … It also does variable creation and assignment for the invocation of constructors or static methods which returns a specific type by inferring the type information of the API elements; see also p. 26, 2nd para, p. 46, 1st full para.; see also Figures 11-13 on pp. 52-54.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
With respect to claim 7, Dey also discloses wherein the text includes question information, the text analysis result includes function information for generating an answer corresponding to the question information of the text (e.g., Figs. 1-5 and associated text, e.g., [0026], one or more explicit user queries [question] 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine …whether the given query relates to achieving a goal within a programming function; [0049-50], The programmer may start to write the code for the HeadPoseEstimation( ) function in the code editing pane 206. While writing the code for this function, the programmer may be unsure of which facial landmarks should be saved, or may be unsure of how the facial landmarks are named in a particular environment (e.g., an OpenCV/Dlib environment) … With the code assistant chatbot 208, however, the programmer may instead submit a query to the chatbot using the input field pane 282 such as: “Which facial landmarks do I need to capture?” … The code assistant chatbot 208 will respond to the query in the dialog pane 280 with a message … along with a code snippet for possible insertion into the code file for the HeadPoseEstimation( ) function.), and the output data includes answer information generated according to the function information (Id., particularly, [0050], The code assistant chatbot 208 will respond to the query in the dialog pane 280 with a message … along with a code snippet for possible insertion into the code file for the HeadPoseEstimation( ) function.).
With respect to claim 8, Dey also discloses wherein the generating of the text analysis result comprises: providing, , a plurality of pieces of source code information to the terminal when the text keyword including the one or more words included in the text has semantics corresponding to the plurality of pieces of source code information ; and
receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal (e.g., Figs. 1-5 and associated text, e.g., [0056] In step 308, one or more user-activatable interface features are provided in the GUI of the automated dialog system. The one or more user-activatable interface features enable selection of respective ones of the one or more suggested additional actions in the natural language dialog generated in step 306.), , and generating the text analysis result by (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions; see also [0029] and [0031].) and Palani discloses by the server … stored in the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.) … converting the text keyword into the source code information received from the terminal, modifying the text, analyzing the modified text (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … using the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
With respect to claim 14, Dey also discloses wherein the performing of the natural language processing comprises: providing, , a plurality of pieces of source code information to the terminal by using the chatbot when the text keyword including the one or more words included in the text has semantics corresponding to the plurality of pieces of source code information (e.g., Figs. 1-5 and associated text, e.g., [0055], In step 306, a natural language dialog is generated in a GUI of an automated dialog system that is associated with the programming environment. The natural language dialog comprises one or more suggested additional actions to be taken in the programming environment based at least in part on the determined intent. The one or more suggested additional actions comprise one or more actions that affect code of one or more code files in the programming environment. Step 306 may utilize a natural language model adapted to a domain of the programming environment to wrap natural language phrases around a core response.); and receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal (e.g., Figs. 1-5 and associated text, e.g., [0056] In step 308, one or more user-activatable interface features are provided in the GUI of the automated dialog system. The one or more user-activatable interface features enable selection of respective ones of the one or more suggested additional actions in the natural language dialog generated in step 306.) , and generating the text analysis result, which are performed by using the chatbot (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions; see also [0029] and [0031].) and Palani discloses by the server … stored in the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.) … converting the text keyword into the source code information received from the terminal, modifying the text, analyzing the modified text (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
With respect to claim 20, Dey also disclose wherein by executing the text semantics analysis program, , the processor further performs a function of providing the plurality of pieces of source code information to the terminal (e.g., Figs. 1-5 and associated text, e.g., [0055], In step 306, a natural language dialog is generated in a GUI of an automated dialog system that is associated with the programming environment. The natural language dialog comprises one or more suggested additional actions to be taken in the programming environment based at least in part on the determined intent. The one or more suggested additional actions comprise one or more actions that affect code of one or more code files in the programming environment. Step 306 may utilize a natural language model adapted to a domain of the programming environment to wrap natural language phrases around a core response.), receiving an input for one piece of source code information among the plurality of pieces of source code information from the terminal (e.g., Figs. 1-5 and associated text, e.g., [0056] In step 308, one or more user-activatable interface features are provided in the GUI of the automated dialog system. The one or more user-activatable interface features enable selection of respective ones of the one or more suggested additional actions in the natural language dialog generated in step 306.), , and generating the text analysis result (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions; see also [0029] and [0031].).
Palani further discloses when the text keyword including the one or more words included in the text has semantics corresponding to a plurality of pieces of source code information stored in the memory (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … modifying the text by converting the text keyword into source code information received from the terminal, performing analysis on the modified text (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
With respect to claim 26, Dey also discloses wherein by executing the text semantics extraction program, the processor further performs a function of displaying the plurality of pieces of source code information on the input/output module (e.g., Figs. 1-5 and associated text, e.g., [0055], In step 306, a natural language dialog is generated in a GUI of an automated dialog system that is associated with the programming environment. The natural language dialog comprises one or more suggested additional actions to be taken in the programming environment based at least in part on the determined intent. The one or more suggested additional actions comprise one or more actions that affect code of one or more code files in the programming environment. Step 306 may utilize a natural language model adapted to a domain of the programming environment to wrap natural language phrases around a core response.), receiving an input for one piece of source code information among the plurality of pieces of source code information through the input/output module (e.g., Figs. 1-5 and associated text, e.g., [0056] In step 308, one or more user-activatable interface features are provided in the GUI of the automated dialog system. The one or more user-activatable interface features enable selection of respective ones of the one or more suggested additional actions in the natural language dialog generated in step 306.), , and generating the text analysis result (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions; see also [0029] and [0031].).
Palani further discloses when the text keyword including the one or more words included in the text has semantics corresponding to a plurality of pieces of source code information stored in the memory (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.) … modifying the text by converting the text keyword into the received source code information, performing analysis on the modified text (e.g., Figure 9 on p. 47 and associated text, p. 46, § 6.3.1 Stemming, 1st para., The raw query in English has to be converted to a common base form; p. 48, § 6.3.2 WordMapK Mapper, The WordMapK algorithm is a simple maximum likelihood mapping between each English input word and the most probable K code elements …The WordMapK mapping produces a set of API elements for the query in English text; p. 48, § 6.3.3 Contextual Expansion Mapper, The ContextualExpansion algorithm takes as input the stemmed words and does expansion of API elements. It outputs unordered API elements which are then used by the GraphOrdering component; § 6.3.4 Graph Synthesizer, To order the code elements from the word map model, we use Graph Based API Synthesis (GraSyn) which takes as input a set of code elements and produces a code graph; see also p. 13 and pp. 44-45.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Claims 3, 11, 17, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Dey in view of Palani, as applied to claim 2, 10, 16, and 22 above, and further in view of Kim et al. “Automatic identifier inconsistency detection using code dictionary” (hereinafter Kim) and Abouelsaadat (US 20060271920 A1, hereinafter Abouelsaadat).
With respect to claims 3 and 11, Palani further teaches storing the source code information in which the server generates the source code information and stores the source code information in the memory, wherein the storing of the source code information comprises (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 46, 1st full para., The figure 9 shows the architecture of T2API console. The ContextualExpansion, GrouMiner and GraphOrdering components were developed in Java programming language as part of the earlier research of T2API. Hence we decide to use Apache Tomcat framework [server] to host the Java web application and Jersey library to build REST interfaces on top of existing components and the new pipeline connecting those components. The web application use the translation probability table from Berkeley Aligner to translate English text to API elements using WordMapK and ContextualExpansion algorithms. The web application loads the trained graph database into the main memory during the initialisation; p. 15, § 3.3.3 Ordering the code into a graph, To re-order the output of these simple word mapping models, a code only language model is created by mining the graphs of 556 existing android projects using GrouMiner; p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; see also Figures 11-13 on pp. 52-54.):
included in the variable information, the class information, and the function information which are used for the one or more programming languages by using the server (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 1, 1st para., Each API consists of set of named functionality in the form of named functions (i.e. actions) and classes (i.e. entities); p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; pp. 49-50, § 6.3.5 Code Generator, We develop the code generator component to translate the Groum (API usage graph) into a usable code template. The GraphOrdering outputs a ranked list of API usage graphs given the list of API elements. The Groum is an abstract representation of the source code with temporal and data dependencies … The code generator iterates each connected component of the graph and then identifies the control and data dependencies … It also does variable creation and assignment for the invocation of constructors or static methods which returns a specific type by inferring the type information of the API elements; see also p. 26, 2nd para, p. 46, 1st full para.; see also Figures 11-13 on pp. 52-54); and
generating the source code information based on names of source codes and storing the source code information in the memory by using the server (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 46, 1st full para., The figure 9 shows the architecture of T2API console. The ContextualExpansion, GrouMiner and GraphOrdering components were developed in Java programming language as part of the earlier research of T2API. Hence we decide to use Apache Tomcat framework [server] to host the Java web application and Jersey library to build REST interfaces on top of existing components and the new pipeline connecting those components. The web application use the translation probability table from Berkeley Aligner to translate English text to API elements using WordMapK and ContextualExpansion algorithms. The web application loads the trained graph database into the main memory during the initialisation; p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; see also Figures 11-13 on pp. 52-54; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also pp. 49-50, § 6.3.5 Code Generator.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Kim: extracting nouns (e.g., Fig. 2 on p. 574 and caption, Domain word identification. nn, vb, adj, and adv denote a noun, verb, adjective and adverb, respectively; p. 573, top para., dictionary extracts the domain words with dominant POSes, idioms, and abbreviated words; p. 574, last para., Figure 2 shows an example of how domain words are collected) … and setting the extracted nouns as source code keywords … corresponding to the source code keywords, abbreviations of the source codes, and relationships between … the source codes and the source code keywords (Id.; p. 573, § 3.1, Based on the POS discovery results, the approach collects the idioms, domain, and abbreviated words to build a Code Dictionary … To build a Code Dictionary … this approach collects class, method; p. 575, § 3.1.4, 2nd para., To assist the NLP parsers, our approach takes a mapping from abbreviated identifiers to the original words; p. 576, § 3.2, our approach first scans the identifiers in the source code and figures out the POS of each of the words in an identifier. Then, it detects inconsistencies based on the Code Dictionary; p. 591, last para., Code Dictionary could bridge the gap between source code analysis and natural language analysis by using domain words POS, idioms, and abbreviations; see also Figs. 2-3 on pp. 574-575.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Kim, such that inconsistent identifiers are detected using a code dictionary, because “Inconsistent identifiers make it difficult for developers to understand source code,” as suggested by Kim (see Abstract).
Dey as modified does not appear to disclose the following, which is taught in analogous art Abouelsaadat: translations of (e.g., Figs. 2A-6B along with associated text, e.g., [0011], The present invention provides a novel method and system for creating multilingual computer programs. As used herein the term "human-language", is used to refer to written and spoken native languages by humans, for example, English, French, or Japanese; The invention comprises a bi-directional multilingual translator for translating an input source code program written in either a specific human-language-like representation or in a human-language-independent representation to a logically and semantically equivalent source code written in another human-language-like representation or in a human-language-independent representation; see also [0039].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Abouelsaadat, such that inconsistent identifiers are detected using a code dictionary, because “dependency on a single human-language, whether English or otherwise, creates an unnecessary barrier for programmers whose native languages are different”, as suggested by Abouelsaadat (see [0008]).
With respect to claim 17, Palani further teaches wherein by executing the text semantics analysis program, the processor included in the variable information, the class information, and the function information which are used in the one or more programming languages, (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 1, 1st para., Each API consists of set of named functionality in the form of named functions (i.e. actions) and classes (i.e. entities); p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; pp. 49-50, § 6.3.5 Code Generator, We develop the code generator component to translate the Groum (API usage graph) into a usable code template. The GraphOrdering outputs a ranked list of API usage graphs given the list of API elements. The Groum is an abstract representation of the source code with temporal and data dependencies … The code generator iterates each connected component of the graph and then identifies the control and data dependencies … It also does variable creation and assignment for the invocation of constructors or static methods which returns a specific type by inferring the type information of the API elements; see also p. 26, 2nd para, p. 46, 1st full para.; see also Figures 11-13 on pp. 52-54), generates the source code information based on names of source codes , and stores the source code information in the memory (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 46, 1st full para., The figure 9 shows the architecture of T2API console. The ContextualExpansion, GrouMiner and GraphOrdering components were developed in Java programming language as part of the earlier research of T2API. Hence we decide to use Apache Tomcat framework [server] to host the Java web application and Jersey library to build REST interfaces on top of existing components and the new pipeline connecting those components. The web application use the translation probability table from Berkeley Aligner to translate English text to API elements using WordMapK and ContextualExpansion algorithms. The web application loads the trained graph database into the main memory during the initialisation; p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; see also Figures 11-13 on pp. 52-54; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also pp. 49-50, § 6.3.5 Code Generator.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Kim: extracts nouns (e.g., Fig. 2 on p. 574 and caption, Domain word identification. nn, vb, adj, and adv denote a noun, verb, adjective and adverb, respectively; p. 573, top para., dictionary extracts the domain words with dominant POSes, idioms, and abbreviated words; p. 574, last para., Figure 2 shows an example of how domain words are collected) … sets the nouns as source code keywords … corresponding to the source code keywords, abbreviations of the source codes, and relationships between … the source codes and the source code keywords (Id.; p. 573, § 3.1, Based on the POS discovery results, the approach collects the idioms, domain, and abbreviated words to build a Code Dictionary … To build a Code Dictionary … this approach collects class, method; p. 575, § 3.1.4, 2nd para., To assist the NLP parsers, our approach takes a mapping from abbreviated identifiers to the original words; p. 576, § 3.2, our approach first scans the identifiers in the source code and figures out the POS of each of the words in an identifier. Then, it detects inconsistencies based on the Code Dictionary; p. 591, last para., Code Dictionary could bridge the gap between source code analysis and natural language analysis by using domain words POS, idioms, and abbreviations; see also Figs. 2-3 on pp. 574-575.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Kim, such that inconsistent identifiers are detected using a code dictionary, because “Inconsistent identifiers make it difficult for developers to understand source code,” as suggested by Kim (see Abstract).
Dey as modified does not appear to disclose the following, which is taught in analogous art Abouelsaadat: translations of (e.g., Figs. 2A-6B along with associated text, e.g., [0011], The present invention provides a novel method and system for creating multilingual computer programs. As used herein the term "human-language", is used to refer to written and spoken native languages by humans, for example, English, French, or Japanese; The invention comprises a bi-directional multilingual translator for translating an input source code program written in either a specific human-language-like representation or in a human-language-independent representation to a logically and semantically equivalent source code written in another human-language-like representation or in a human-language-independent representation; see also [0039].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Abouelsaadat, such that inconsistent identifiers are detected using a code dictionary, because “dependency on a single human-language, whether English or otherwise, creates an unnecessary barrier for programmers whose native languages are different”, as suggested by Abouelsaadat (see [0008]).
With respect to claim 23, Palani further teaches wherein by executing the text semantics extraction program, the processor further performs a function of included in the variable information, the class information, and the function information which are used in the one or more programming languages, st para., Each API consists of set of named functionality in the form of named functions (i.e. actions) and classes (i.e. entities); p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; pp. 49-50, § 6.3.5 Code Generator, We develop the code generator component to translate the Groum (API usage graph) into a usable code template. The GraphOrdering outputs a ranked list of API usage graphs given the list of API elements. The Groum is an abstract representation of the source code with temporal and data dependencies … The code generator iterates each connected component of the graph and then identifies the control and data dependencies … It also does variable creation and assignment for the invocation of constructors or static methods which returns a specific type by inferring the type information of the API elements; see also p. 26, 2nd para, p. 46, 1st full para.; see also Figures 11-13 on pp. 52-54), generating the source code information based on names of source codes , and storing the source code information in the memory (e.g., Figure 9 on p. 47 and Fig. 10 on p. 49, along with associated text, e.g., p. 46, 1st full para., The figure 9 shows the architecture of T2API console. The ContextualExpansion, GrouMiner and GraphOrdering components were developed in Java programming language as part of the earlier research of T2API. Hence we decide to use Apache Tomcat framework [server] to host the Java web application and Jersey library to build REST interfaces on top of existing components and the new pipeline connecting those components. The web application use the translation probability table from Berkeley Aligner to translate English text to API elements using WordMapK and ContextualExpansion algorithms. The web application loads the trained graph database into the main memory during the initialisation; p. 29, 3rd full para., WordMapK and ContextualExpansion algorithms are able to translate relevant code elements including MediaPlayer.setDataSource(), MediaPlayer.start(), MediaPlayer.stop() along with the class MediaPlayer; see also Figures 11-13 on pp. 52-54; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also pp. 49-50, § 6.3.5 Code Generator.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Kim: extracting nouns (e.g., Fig. 2 on p. 574 and caption, Domain word identification. nn, vb, adj, and adv denote a noun, verb, adjective and adverb, respectively; p. 573, top para., dictionary extracts the domain words with dominant POSes, idioms, and abbreviated words; p. 574, last para., Figure 2 shows an example of how domain words are collected) … setting the nouns as source code keywords … corresponding to the source code keywords, abbreviations of the source codes, and relationships between … the source codes and the source code keywords (Id.; p. 573, § 3.1, Based on the POS discovery results, the approach collects the idioms, domain, and abbreviated words to build a Code Dictionary … To build a Code Dictionary … this approach collects class, method; p. 575, § 3.1.4, 2nd para., To assist the NLP parsers, our approach takes a mapping from abbreviated identifiers to the original words; p. 576, § 3.2, our approach first scans the identifiers in the source code and figures out the POS of each of the words in an identifier. Then, it detects inconsistencies based on the Code Dictionary; p. 591, last para., Code Dictionary could bridge the gap between source code analysis and natural language analysis by using domain words POS, idioms, and abbreviations; see also Figs. 2-3 on pp. 574-575.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Kim, such that inconsistent identifiers are detected using a code dictionary, because “Inconsistent identifiers make it difficult for developers to understand source code,” as suggested by Kim (see Abstract).
Dey as modified does not appear to disclose the following, which is taught in analogous art Abouelsaadat: translations of (e.g., Figs. 2A-6B along with associated text, e.g., [0011], The present invention provides a novel method and system for creating multilingual computer programs. As used herein the term "human-language", is used to refer to written and spoken native languages by humans, for example, English, French, or Japanese; The invention comprises a bi-directional multilingual translator for translating an input source code program written in either a specific human-language-like representation or in a human-language-independent representation to a logically and semantically equivalent source code written in another human-language-like representation or in a human-language-independent representation; see also [0039].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Abouelsaadat, such that inconsistent identifiers are detected using a code dictionary, because “dependency on a single human-language, whether English or otherwise, creates an unnecessary barrier for programmers whose native languages are different”, as suggested by Abouelsaadat (see [0008]).
Claims 5, 13, 19, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Dey in view of Palani, as applied to claims 1, 9,15, and 21 above, and further in view Jones (US 20230245655 A1, hereinafter Jones).
With respect to claim 5, Dey also discloses wherein the generating of the text analysis result comprises analyzing of the text, and analyzing an of the text, the analyzing of the of the text comprises (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].) .
Palani further discloses by the server … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Jones: intent … entity … intent … setting, …, the intent of the text to one of preset intent types based on the text analysis result including the semantics of the text, and the analyzing of the entity of the text comprises setting, … , the entity of the text to one of preset entity types based on the text analysis result including the semantics of the text (e.g., [0013], The system may extract entities from … a string of text … for entities that are of a type associated with the determined user intent; [0014], search for patterns associated with the predefined [preset] user intents in the voice command, e.g. in a string of characters corresponding to the voice command. If a pattern is found … then the predefined user intent associated with that pattern may be determined to be the user intent; [0017], select [set] one of the plurality of predefined user intents as the user intent; [0024], store, for each of the plurality of predefined user intents, an entity type associated with that predefined user intent. The entity types associated with the predefined user intents may be stored [preset entity types] in a memory of the system; [0025], extract an entity of the first entity type … by matching … with one of the plurality of regular expressions associated with the first entity type; [0033], selecting [set] an entity from the one or more extracted entities; see also [0070-71] and [0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Jones because it “may improve the accuracy with which a meaning” is obtained (see [0004]).
With respect to claim 13, Dey also discloses wherein the performing of the natural language processing comprises analyzing of the text, and analyzing an of the text, the analyzing of the of the text comprises (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries, such as to determine whether a given one of the user queries 110 is about the programming IDE [text analysis result including semantics of the text] … whether the given query is about the programming language … whether the given query relates to achieving a goal within a programming function, whether the given query relates to the availability of ready-made solutions (e.g., one or more libraries) to meet one or more specified requirements, whether the given query relates to so-called philosophical understandings of programming (e.g., determining how one or more programming communities view and resolve problems similar to those being faced by the user submitting the given query), etc.; see also [0029] and [0031].) by the server … by the server (e.g., Figure 9 on p. 47 and associated text, e.g., p. 46, § 6.3, The server comprise of REST interface implementations for various URLS; p. 51, § 6.4, On the client [terminal] side AngularJS framework is used to communicate with the server as well as to handle user interactions; see also pp. 46-51, §6.3 Server Components for more details of the overall server client architecture; see also Figures 11-13 on pp. 52-54.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Jones: intent … entity … intent … setting, … , the intent of the text to one of preset intent types based on the text analysis result including the semantics of the text, and the analyzing of the entity of the text comprises setting, …, the entity of the text to one of preset entity types based on the text analysis result including the semantics of the text (e.g., [0013], The system may extract entities from … a string of text … for entities that are of a type associated with the determined user intent; [0014], search for patterns associated with the predefined [preset] user intents in the voice command, e.g. in a string of characters corresponding to the voice command. If a pattern is found … then the predefined user intent associated with that pattern may be determined to be the user intent; [0017], select [set] one of the plurality of predefined user intents as the user intent; [0024], store, for each of the plurality of predefined user intents, an entity type associated with that predefined user intent. The entity types associated with the predefined user intents may be stored [preset entity types] in a memory of the system; [0025], extract an entity of the first entity type … by matching … with one of the plurality of regular expressions associated with the first entity type; [0033], selecting [set] an entity from the one or more extracted entities; see also [0070-71] and [0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Jones because it “may improve the accuracy with which a meaning” is obtained (see [0004]).
With respect to claim 19, Dey also discloses wherein by executing the text semantics analysis program (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries; see also [0016], [0029] and [0031].), .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Dey with the invention of Palani for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Jones: the processor sets the intent of the text to one of preset intent types based on the text analysis result including the semantics of the text, and sets the entity of the text to one of preset entity types based on the text analysis result including the semantics of the text (e.g., [0013], The system may extract entities from … a string of text … for entities that are of a type associated with the determined user intent; [0014], search for patterns associated with the predefined [preset] user intents in the voice command, e.g. in a string of characters corresponding to the voice command. If a pattern is found … then the predefined user intent associated with that pattern may be determined to be the user intent; [0017], select [set] one of the plurality of predefined user intents as the user intent; [0024], store, for each of the plurality of predefined user intents, an entity type associated with that predefined user intent. The entity types associated with the predefined user intents may be stored [preset entity types] in a memory of the system; [0025], extract an entity of the first entity type … by matching … with one of the plurality of regular expressions associated with the first entity type; [0033], selecting [set] an entity from the one or more extracted entities; see also [0070-71] and [0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Jones because it “may improve the accuracy with which a meaning” is obtained (see [0004]).
With respect to claim 25, Dey also discloses wherein by executing the text semantics extraction program, the processor further performs a function of (e.g., Figs. 1-5 and associated text, e.g., [0026] one or more explicit user queries 110 are provided to the chatbot interface, and the reactive chat generation module 106 utilizes the chatbot natural language model 104 to analyze or resolve the queries to provide an appropriate response. The reactive chat generation module 106 uses the chatbot natural language model 104 to understand various types of queries; see also [0016], [0029] and [0031].) .
Dey as modified does not appear to disclose the following, which is taught in analogous art, Jones: setting the intent of the text to one of preset intent types based on the text analysis result including the semantics of the text and setting the entity of the text to one of preset entity types based on the text analysis result including the semantics of the text (e.g., [0013], The system may extract entities from … a string of text … for entities that are of a type associated with the determined user intent; [0014], search for patterns associated with the predefined [preset] user intents in the voice command, e.g. in a string of characters corresponding to the voice command. If a pattern is found … then the predefined user intent associated with that pattern may be determined to be the user intent; [0017], select [set] one of the plurality of predefined user intents as the user intent; [0024], store, for each of the plurality of predefined user intents, an entity type associated with that predefined user intent. The entity types associated with the predefined user intents may be stored [preset entity types] in a memory of the system; [0025], extract an entity of the first entity type … by matching … with one of the plurality of regular expressions associated with the first entity type; [0033], selecting [set] an entity from the one or more extracted entities; see also [0070-71] and [0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Jones because it “may improve the accuracy with which a meaning” is obtained (see [0004]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Dey in view of Palani and Jones, as applied to 5 above, and further in view Champlin-Scharff et al. (US 20150082277 A1, hereinafter Champlin).
With respect to claim 6, Jones further teaches wherein the preset intent types include , and the preset entity types include .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Jones for the same reason set forth above.
Dey as modified does not appear to disclose the following, which is taught in analogous art, Champlin: a function check type and an error report type … a function description type and an error description type (e.g., e.g., Figs. 1-5 and associated text, e.g., [0007], using a received a statement in natural language to perform a natural language search; [0027], NLP searching shall mean to receive an input phrase from a user that is expressed in natural language, to apply NLP to that input phrase to extract symbols from it, and then to search on those symbols in a corpus; [0029], A semantic analyzer may, based on the results of the syntactic analysis or interactively operating with syntactic analysis, determines the meaning of a phrase, statement or sentence; [0032], Embodiments may implement any one or more of the features in allowing pre-detection and advisory intervention notices responsive to changes in requirements, changes in check-in code, changes in released code, and changes in bug reports or feature requests; [0037], a natural language description of what functions would use the hashmap, and what data structures the hashmap would relate to each other, and to which particular method or code module the hashmap would be added, such as com.my.package; [0048], Description(s) of the bug(s) and the corrective action(s); claim 5, wherein the received statement comprises a description of an untested revision the software product, and wherein the impact prediction comprises a suggestion of one or more test cases to be employed to regression test and validate the revision of the software product.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Dey with the invention of Champlin because it provides an “improved software problem discussion search methodology and a tool that "pre-detects" potential coding issues” and it “provide[s] a means and mechanism to intelligently re-use test cases, abandoning those that are completely outdated or obsoleted, re-using those that are still applicable, and recommending modifications to those that are still partially applicable but partially inapplicable”, as suggested by Champlin (see [0019] and [0021]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Preston et al. US 20030046061 A1 discloses generating software from one or more predefined functions in accordance with an input statement entered in natural language.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN DAVID BERMAN whose telephone number is (571) 272-7206. The examiner can normally be reached M-F, 9-6 Eastern.
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/STEPHEN D BERMAN/ Examiner, Art Unit 2192
1 See claim 1 and 27.
2 See claim 9.
3 See claim 15.
4 See claim 21.
5 See claim 1.
6 See claim 9.
7 See claim 15
8 See claim 21.
9 See claim 1
10 See claim 9.
11 See claim 15.
12 See claim 21.
13 See claim 27.
14 Although the words of the claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
15 Although the words of the claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
16 Although the words of these claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
17 Although the words of the claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
18 Although the words of the claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
19 Although the words of the claims differ slightly, this does not impact the 35 USC 101 analysis and conclusion.
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