Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on January 17, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
3. The amendment filed on March 27, 2026 has been entered. Claims 1-20 remain pending in the application. Claims 1, 4-5, 7, 10-11, 15-16, and 19-20 are amended.
The applicant argues that in regards to the 35 USC 101 rejection, the claims are directed to the technological implementation described in the specification, in which “the system can more accurately suggest modifications to a user for pasted text, which allows for more efficient user interface functionality” and “improve user experience by allowing the user to adapt pasted text in an expedited manner without having to manually modify the text”. However, this is not disclosed in the claims. In other words, the invention discloses a pre-trained model, which must include explanation in the claims on how the training provides improvement to the model. Hence, the applicant’s arguments are not persuasive.
Applicant’s arguments with respect to the 35 U.S.C. 102 rejections for claims 1-20 have been considered but are moot because the arguments are directed towards amended claim language, addressed on new grounds of rejection below.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claim 1 recites “A computer-implemented method, comprising: receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 1 recites “A computer-implemented method, comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 1 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 2-10 are rejected for their dependence on claims 1, because they do not contain additional limitations that overcome the present rejection.
The independent claim 11 recites “A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising: receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface” as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 1 recites” A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 11 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 11 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 12-15 are rejected for their dependence on claims 11, because they do not contain additional limitations that overcome the present rejection.
The independent claim 16 recites “One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface”.
The limitations “receiving information comprising a code segment to be inserted into a body of code displayed in a user interface; identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code; generating an input that comprises the code segment and the context; processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both; and presenting the suggested modification to a user in the user interface”. as drafted, covers a mental process, as this could be done by mentally or by hand with pen and paper.
This judicial exception is not integrated into a practical application. Claim 16 recites ““One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:…”. This limitation directs towards using a computer for the method, and does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The addition of the generic computer components recited above with regard to claim 16 do not amount to more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 16 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 17-20 are rejected for their dependence on claims 16, because they do not contain additional limitations that overcome the present rejection.
Claim Rejections - 35 USC § 102
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gupta (U.S. Publication No. 20230315399).
Regarding claim 1, Gupta discloses a computer-implemented method, comprising:
receiving information comprising a code segment to be inserted into a body of code displayed in a user interface ([0024] - code repository 130 may be configured to receive information from, send information to, and/or otherwise exchange information with one or more devices as described herein);
identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code ([0036] - instinctive cipher computing platform 110 may analyze the requirements document and extract context data from the requirements document. In extracting the context data from the requirements document, instinctive cipher computing platform 110 may identify (e.g., via natural language processing engine 112d) inferences from text using natural language processing techniques. In some examples, instinctive cipher computing platform 110 may decompose and parse the requirements document into a set of linguistic distinctions (e.g., parts of speech, phrases, named entities, document categories, grammatical relationships of words, punctuation, sentence structures, etc.). For instance, instinctive cipher computing platform 110 may employ parts of speech tagging and relationship searching to identify sentence components (e.g., as nouns, verbs, adjectives, pronouns, etc.), and to recognize and disambiguate entities in text. The data may then be further processed using machine learning models to interpret the document);
generating an input that comprises the code segment and the context ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications… [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications…Various machine learning algorithms may be used without departing from the disclosure, such as…artificial neural network algorithms, and the like [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
and presenting the suggested modification to a user in the user interface ([0041] - instinctive cipher computing platform 110 may cause the identified modifications to be displayed on one or more user interfaces (e.g., on a display device of developer computing device 140 or other user computing device)).
Regarding claim 2, Gupta discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface is based at least in part on a confidence score of the suggested modification ([0048] - instinctive cipher computing platform 110 may compare the variance to a predetermined threshold (e.g., a variance cutoff), and accept or redeploy the updated code based on the comparison. In some examples, instinctive cipher computing platform 110 may identify the variance as being greater than (e.g., above) a predetermined threshold and in response, regenerate (e.g., make further modifications, remove modifications made previously, or the like) and redeploy one or more portions of the updated code. If the variance is less than (e.g., below) the predetermined threshold, the variance may be low enough that the code may be implemented or used in production).
Regarding claim 3, Gupta discloses the computer-implemented method, wherein presenting the suggested modification to the user in the user interface comprises:
determining that the confidence score of the suggested modification exceeds a threshold ([0048] - instinctive cipher computing platform 110 may compare the variance to a predetermined threshold (e.g., a variance cutoff), and accept or redeploy the updated code based on the comparison. In some examples, instinctive cipher computing platform 110 may identify the variance as being greater than (e.g., above) a predetermined threshold and in response, regenerate (e.g., make further modifications, remove modifications made previously, or the like) and redeploy one or more portions of the updated code. If the variance is less than (e.g., below) the predetermined threshold, the variance may be low enough that the code may be implemented or used in production);
and in response, presenting the suggested modification ([0048] - instinctive cipher computing platform 110 may compare the variance to a predetermined threshold (e.g., a variance cutoff), and accept or redeploy the updated code based on the comparison. In some examples, instinctive cipher computing platform 110 may identify the variance as being greater than (e.g., above) a predetermined threshold and in response, regenerate (e.g., make further modifications, remove modifications made previously, or the like) and redeploy one or more portions of the updated code. If the variance is less than (e.g., below) the predetermined threshold, the variance may be low enough that the code may be implemented or used in production).
Regarding claim 7, Gupta discloses the computer implemented method, wherein the body of text comprises code from a source document corresponding to the code segment, code from a clipboard of the user interface, or both ([0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data. In some examples, instinctive cipher computing platform 110 may automatically integrate with the code repository (e.g., code repository 130). For instance, instinctive cipher computing platform 110 may fetch code from the repository, push code into the repository, merge code in the repository, perform code conflict resolution, and/or the like, without manual intervention).
Regarding claim 8, Gupta discloses the computer implemented method, wherein the trained neural network is a language model ([0028] - memory 112 may have, store, and/or include an instinctive cipher compilation module 112a, an instinctive cipher compilation database 112b, a DevOps plug-in integrator 112c, a natural language processing (NLP) engine 112d, a machine learning (ML) engine 122e, and a notification generation engine 112f [0030] - instinctive cipher computing platform 110 may build and/or train one or more machine learning models.).
Regarding claim 9, Gupta discloses the computer implemented method, wherein the suggested modification is in a domain-specific language ([0039] - In identifying modifications to code, instinctive cipher computing platform 110 may detect a type of programming language and syntax being used).
Regarding claim 10, Gupta discloses the computer-implemented method, wherein the trained neural network has been trained on a plurality of training examples, each training example corresponding to an insertion event and comprising:
an original code segment that has been inserted into an original body of code ([0024] - code repository 130 may be configured to receive information from, send information to, and/or otherwise exchange information with one or more devices as described herein);
an original context comprising text surrounding the original code segment in the original body of code ([0036] - instinctive cipher computing platform 110 may analyze the requirements document and extract context data from the requirements document. In extracting the context data from the requirements document, instinctive cipher computing platform 110 may identify (e.g., via natural language processing engine 112d) inferences from text using natural language processing techniques. In some examples, instinctive cipher computing platform 110 may decompose and parse the requirements document into a set of linguistic distinctions (e.g., parts of speech, phrases, named entities, document categories, grammatical relationships of words, punctuation, sentence structures, etc.). For instance, instinctive cipher computing platform 110 may employ parts of speech tagging and relationship searching to identify sentence components (e.g., as nouns, verbs, adjectives, pronouns, etc.), and to recognize and disambiguate entities in text. The data may then be further processed using machine learning models to interpret the document);
and data identifying any edits that were made to the original code segment or the original context after the original code segment was inserted into the original body of code ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications…Various machine learning algorithms may be used without departing from the disclosure, such as…artificial neural network algorithms, and the like [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data).
Regarding claim 11, Gupta discloses a system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the operations comprising:
receiving information comprising a code segment to be inserted into a body of code displayed in a user interface ([0024] - code repository 130 may be configured to receive information from, send information to, and/or otherwise exchange information with one or more devices as described herein);
identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code ([0036] - instinctive cipher computing platform 110 may analyze the requirements document and extract context data from the requirements document. In extracting the context data from the requirements document, instinctive cipher computing platform 110 may identify (e.g., via natural language processing engine 112d) inferences from text using natural language processing techniques. In some examples, instinctive cipher computing platform 110 may decompose and parse the requirements document into a set of linguistic distinctions (e.g., parts of speech, phrases, named entities, document categories, grammatical relationships of words, punctuation, sentence structures, etc.). For instance, instinctive cipher computing platform 110 may employ parts of speech tagging and relationship searching to identify sentence components (e.g., as nouns, verbs, adjectives, pronouns, etc.), and to recognize and disambiguate entities in text. The data may then be further processed using machine learning models to interpret the document);
generating an input that comprises the code segment and the context ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications… [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications…Various machine learning algorithms may be used without departing from the disclosure, such as…artificial neural network algorithms, and the like [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
and presenting the suggested modification to a user in the user interface ([0041] - instinctive cipher computing platform 110 may cause the identified modifications to be displayed on one or more user interfaces (e.g., on a display device of developer computing device 140 or other user computing device)).
Dependent claims 12-13 are analogous in scope to claims 2-3, and are rejected according to the same reasoning.
Regarding claim 16, Gupta discloses one or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
receiving information comprising a code segment to be inserted into a body of code displayed in a user interface ([0024] - code repository 130 may be configured to receive information from, send information to, and/or otherwise exchange information with one or more devices as described herein);
identifying a context, wherein the context is based at least in part on code surrounding the inserted code segment in the body of code ([0036] - instinctive cipher computing platform 110 may analyze the requirements document and extract context data from the requirements document. In extracting the context data from the requirements document, instinctive cipher computing platform 110 may identify (e.g., via natural language processing engine 112d) inferences from text using natural language processing techniques. In some examples, instinctive cipher computing platform 110 may decompose and parse the requirements document into a set of linguistic distinctions (e.g., parts of speech, phrases, named entities, document categories, grammatical relationships of words, punctuation, sentence structures, etc.). For instance, instinctive cipher computing platform 110 may employ parts of speech tagging and relationship searching to identify sentence components (e.g., as nouns, verbs, adjectives, pronouns, etc.), and to recognize and disambiguate entities in text. The data may then be further processed using machine learning models to interpret the document);
generating an input that comprises the code segment and the context ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications… [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
processing the input using a trained neural network to generate a suggested modification to the inserted code segment, to the context, or both ([0030] - The data may be gathered and used to build and train one or more machine learning models executed by machine learning engine 112e to identify modifications to one or more portions of code, intelligently generate code in accordance with the identified modifications…Various machine learning algorithms may be used without departing from the disclosure, such as…artificial neural network algorithms, and the like [0038] - instinctive cipher computing platform 110 may scan a repository of code (e.g., application source code from code repository 130) to identify code to be modified based on the context data);
and presenting the suggested modification to a user in the user interface ([0041] - instinctive cipher computing platform 110 may cause the identified modifications to be displayed on one or more user interfaces (e.g., on a display device of developer computing device 140 or other user computing device)).
Dependent claims 17-18 are analogous in scope to claims 2-3, and are rejected according to the same reasoning.
Claim Rejections - 35 USC § 103
8. 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 taught 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.
9. Claims 4-6, 14-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (U.S. Publication No. 20230315399) in view of Guberman (U.S. Publication No. 20230123574).
Regarding claim 4, Gupta discloses all limitations of claim 1, above.
However, Gupta does not disclose the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: automatically inserting the suggested modification in the body of code, wherein the suggested modification is in bold form.
Guberman does teach the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: automatically inserting the suggested modification in the body of code, wherein the suggested modification is in bold form ([0057] - Target text 152 or text to which suggested modification 160 is directed to may be brought to user's attention by altering its appearance, for example and without limitation, by highlighting, bolding and/or underlining, or the like, among others).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gupta to incorporate the teachings of Guberman in order to implement presenting the suggested modification to the user in the user interface further comprises: automatically inserting the suggested modification in the body of code, wherein the suggested modification is in bold form. Doing so allows for reducing ambiguity and bias in documents being created by a user (Guberman [0016]).
Regarding claim 5, Gupta discloses all limitations of claim 1, above.
However, Gupta does not disclose the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: presenting the suggested modification in a color different than a color of the body of code, wherein the user can accept the suggested modification.
Guberman does teach the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: presenting the suggested modification in a color different than a color of the body of code, wherein the user can accept the suggested modification ([0057] - Target text 152 or text to which suggested modification 160 is directed to may be brought to user's attention by altering its appearance, for example and without limitation, by highlighting, bolding and/or underlining, or the like, among others).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gupta to incorporate the teachings of Guberman in order to implement presenting the suggested modification to the user in the user interface further comprises: presenting the suggested modification in a color different than a color of the body of code, wherein the user can accept the suggested modification. Doing so allows for reducing ambiguity and bias in documents being created by a user (Guberman [0016]).
Regarding claim 6, Gupta discloses all limitations of claim 1, above.
However, Gupta does not disclose the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: indicating the suggested modification to the user as an icon that the user can inspect and determine whether to accept the suggested modification.
Guberman does teach the computer-implemented method, wherein presenting the suggested modification to the user in the user interface further comprises: presenting the suggested modification in a color different than a color of the body of code, wherein the user can accept the suggested modification ([0058] - User 144 may have the ability to completely ignore, completely accept or partially accept suggested modification 160, as desired).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gupta to incorporate the teachings of Guberman in order to implement presenting the suggested modification to the user in the user interface further comprises: indicating the suggested modification to the user as an icon that the user can inspect and determine whether to accept the suggested modification. Doing so allows for reducing ambiguity and bias in documents being created by a user (Guberman [0016]).
Dependent claims 14-15 are analogous in scope to claims 4-5, and are rejected according to the same reasoning.
Dependent claims 19-20 are analogous in scope to claims 4-5, and are rejected according to the same reasoning.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alikaniotis (U.S. Publication No. 20230289529) teaches detecting the tone of text. Sampaio de Rezende (U.S. Publication No. 20230073843) teaches data compatibility for text-enhanced visual retrieval.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN DANIEL KIM whose telephone number is (571) 272-1405. The examiner can normally be reached on Monday - Friday 9:00 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached on (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETHAN DANIEL KIM/
Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights