DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/11/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
A new search was made and art was found to Fox which teaches systems and methods to enable generation of high-quality summaries of documents that have questions and answers, see abstract. Fox teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types (o better comprehend the text in a question-answer document, it helps to parse the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058]), the plurality of different candidate question types corresponding to different answer collecting manners (classifying each question and each answer according to a category based on dialog acts. Dialog Acts (dialog act) can represent the communicative intention behind a speaker's utterance in a conversation. Identifying the dialog act of each speaker utterance in a conversation thus can help to automatically determine intent and meaning. Specific rules can be developed for each dialog act type to process a conversation question-answer group and transform it into a suitable form for subsequent analysis, see par. [0061]); obtaining a question type template of each of the plurality of questions based on the question type of the respective question (create generalized templates from summary sentences and leverage the relationships between the summaries and their source conversation transcripts to generate abstract summaries, see par. [0046-0047]; ] Classification of questions and answers based on dialog acts can be done with machine learning methods and other methods as can be appreciated. In some embodiments, classification using machine learning can involve training a classifier and applying the resulting classifier with its trained model, see par. [0062].
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.
Claim(s) 1-2, 7-9, 11-15, 18, 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jenkins U.S. PAP 2014/0046947 A1 in view of Ross U.S. PAP 2012/0166457 A1 further in view of Fox U.S. PAP 2021/0174016 A1.
Regarding claim 1 Jenkins teaches a form generation method (method for question/answer creation for a document is described, see abstract; question/answer creation system QAC, see par. [0018), comprising:
displaying a text entry interface, the text entry interface being configured to receive text content of a plurality of questions to be included in a questionnaire form (the QAC system 100 may receive input from the network 102, a corpus of documents 106 or other data, a content creator 108, content users, and other possible sources of input, see par. [0019]);
receiving the text content of the plurality of questions to be included in the questionnaire form and entered in the text entry interface (The document 106 may include any file, text, article, or source of data for use in the QAC system 100, see par. [0020]);
generating the questionnaire form according to the text content, the questionnaire form including a plurality of questions that are determined based on parsing of the text content (The content creator may create 306 more questions based on the content, if applicable. The QAC system 100 also generates candidate questions 216 based on the content that may not have been entered by the content creator, see par. [0031]);
and displaying the questionnaire form (The questions, candidate questions 216, and answers 218 may then be presented 308 on an interface to the content creator for verification, see par. [0033]).
However Jenkins does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions; and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form.
In the same field of endeavor Ross teaches a system for enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001]. Ross teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions (A question parsing module 104 includes a process that parses text in a question and recognizes tag objects 106 that are stored in the tag store 216. These tag objects 106 are identified and associated with the question object 108 for the parsed question, see par. [0039]); and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form (he question store 208 maintains 302 question objects that are associated with hierarchically organized tags. Tag objects are hierarchically organized by topic and sub-topic. Relationships are recognized between topics and sub-topics when tags are created, see par. [0042]; Because tags are organized hierarchically, the questions that are selected include the selected topic as well as sub-topics that are related to the selected topic. The question store 208 executes 312 the request for questions and retrieves question objects based on the selected topic, see par. [0044]).
It would have been obvious to one of ordinary skill in the art to combine the Jenkins invention with the teachings of Ross for the benefit of enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001].
However Jenkins in view of Ross does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types, the plurality of different candidate question types corresponding to different answer collecting manners; obtaining a question type template of each of the plurality of questions based on the question type of the respective question.
In the same field of endeavor Fox teaches systems and methods to enable generation of high-quality summaries of documents that have questions and answers, see abstract. Fox teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types (o better comprehend the text in a question-answer document, it helps to parse the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058]), the plurality of different candidate question types corresponding to different answer collecting manners (classifying each question and each answer according to a category based on dialog acts. Dialog Acts (dialog act) can represent the communicative intention behind a speaker's utterance in a conversation. Identifying the dialog act of each speaker utterance in a conversation thus can help to automatically determine intent and meaning. Specific rules can be developed for each dialog act type to process a conversation question-answer group and transform it into a suitable form for subsequent analysis, see par. [0061]);
obtaining a question type template of each of the plurality of questions based on the question type of the respective question (create generalized templates from summary sentences and leverage the relationships between the summaries and their source conversation transcripts to generate abstract summaries, see par. [0046-0047]; ] Classification of questions and answers based on dialog acts can be done with machine learning methods and other methods as can be appreciated. In some embodiments, classification using machine learning can involve training a classifier and applying the resulting classifier with its trained model, see par. [0062].
It would have been obvious to one of ordinary skill in the art to combine the Jenkins in view of Ross invention with the teachings of Fox for the benefit of parsing the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058].
Regarding claim 2 Jenkins teaches the form generation method according to claim 1, wherein the text content of the plurality of questions is received in a single data field of the text entry interface (QAC system 100 may be configured to receive inputs from various sources. For example, the QAC system 100 may receive input from the network 102, a corpus of documents 106 or other data, a content creator 108, content users, and other possible sources of input, see par. [0019]).
Regarding claim 3 Jenkins teaches the form generation method according to claim 1, wherein the generating the questionnaire form comprises:
determining an answer format type for each of the plurality of questions in the questionnaire form from a plurality of predetermined formats (the processor 202 determines that one or more of the questions are answered by the content of the document 106 and lists or otherwise marks the questions that were answered in the document 106, see par. [0025]);
and generating the questionnaire to include the answer format types corresponding to the plurality of questions (The QAC system 100 may also attempt to provide answers 218 for the candidate questions 216. In one embodiment, the QAC system 100 answers 218 the set of questions 210 created by the content creator before creating the candidate questions 216. In another embodiment, the QAC system 100 answers 218 the questions and the candidate questions 216 at the same time, see par. [0025]).
Regarding claim 7 Jenkins teaches the form generation method according to claim 1, wherein the text entry interface is displayed based on a user selection of a first graphical element that is associated with the text entry interface (questions, candidate questions 216, and answers 218 may then be presented 308 on an interface to the content creator for verification. In some embodiments, the document text and metadata 212 may also be presented for verification, see par. [0033]).
Regarding claim 8 Jenkins teaches the form generation method according to claim 1, wherein the text entry interface is displayed based on at least one of a predetermined gesture operation, a predetermined audio signal input operation, or a predetermined vibration operation (interface may be configured to receive a manual input from the content creator for user verification of the questions, candidate questions 216, and answers 218, see par. [0033]).
Regarding claim 9 Jenkins teaches the form generation method according to claim 1, wherein the receiving the text content comprises: receiving the text content that is manually entered in the in the text entry interface ( "Importing a document into a requirements project." , see par. [0052]).
Regarding claim 11 Jenkins teaches the form generation method according to claim 1, wherein the receiving the text content comprises: copying the text content from another document based on an import document instruction ( "Importing a document into a requirements project." , see par. [0052]).
Regarding claim 12 Jenkins teaches the form generation method according to claim 1, further comprising:
displaying an object list based on a collaborative editing operation on the text content, the object list including an identifier of at least one collaborative editing object (determines that one or more of the questions are answered by the content of the document 106 and lists or otherwise marks the questions that were answered in the document 106, see par. [0025]);
selecting one or more identifiers from the object list, and providing the text content in the text entry interface to the selected collaborative editing object for collaborative editing (The author may adjust any of the questions created previously by the author or generated by the system. In one embodiment, editing is achieved by leveraging a user interface with regular expressions for alternatives or by checklists, see par. [0072]);
and updating the text content in the text entry interface according to the collaborative editing (editing is achieved by leveraging a user interface with regular expressions for alternatives or by checklists, see par. [0072]).
Regarding claim 13 Jenkins teaches the form generation method according to claim 1, further comprising: displaying an object list based on a collaborative editing operation on the questionnaire form, the object list including an identifier of at least one collaborative editing object (set of questions 210 shown in the viewable text 214 may be displayed in a list in the document 106 so that the content users may easily see specific questions answered by the document 106, see par. [0024]);
selecting one or more identifiers from the object list, and providing the questionnaire form to the selected collaborative editing object for collaborative editing (The author may adjust any of the questions created previously by the author or generated by the system. In one embodiment, editing is achieved by leveraging a user interface with regular expressions for alternatives or by checklists, see par. [0072]);
and updating the questionnaire form according to the collaborative editing (editing is achieved by leveraging a user interface with regular expressions for alternatives or by checklists, see par. [0072]).
Regarding claim 14 Jenkins teaches the form generation method according to claim 1, further comprising: at least one of increasing or decreasing a quantity of questions in the questionnaire form, modifying a question type of one of the plurality of questions in the questionnaire form, and adjusting a display position of the one of the plurality of questions in the questionnaire form based on user input (The author adjusts the question list, see par. [0072]).
Regarding claim 15 Jenkins teaches the form generation method according to claim 1, further comprising: adding a target question type template and updating the questionnaire form based on a question editing operation applied to the added target question type template(the QAC system 100 determines that additional questions may be used--the QAC system 100 may perform some or all of the steps again, see par. [0034]; editing is achieved by leveraging a user interface with regular expressions for alternatives or by checklists, see par. [0072]);
and updating the questionnaire form according to a question editing operation on the target question type template (In one embodiment, the QAC system 100 uses the verified document and/or the verified questions to create new metadata 212, see par. [0034]).
Regarding claim 18 Jenkins teaches an information processing apparatus (system includes: a memory device and a processor connected to the memory device, see par. [0003]), comprising: processing circuitry configured to:
display a text entry interface, the text entry interface being configured to receive text content of a plurality of questions to be included in a questionnaire form (the QAC system 100 may receive input from the network 102, a corpus of documents 106 or other data, a content creator 108, content users, and other possible sources of input, see par. [0019]);
receive the text content of the plurality of questions to be included in the questionnaire form and entered in the text entry interface (The document 106 may include any file, text, article, or source of data for use in the QAC system 100, see par. [0020]);
generate the questionnaire form according to the text content, the questionnaire form including a plurality of questions that are determined based on parsing of the text content; and display the questionnaire form (The content creator may create 306 more questions based on the content, if applicable. The QAC system 100 also generates candidate questions 216 based on the content that may not have been entered by the content creator, see par. [0031]).
However Jenkins does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions; and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form.
In the same field of endeavor Ross teaches a system for enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001]. Ross teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions (A question parsing module 104 includes a process that parses text in a question and recognizes tag objects 106 that are stored in the tag store 216. These tag objects 106 are identified and associated with the question object 108 for the parsed question, see par. [0039]); and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form (he question store 208 maintains 302 question objects that are associated with hierarchically organized tags. Tag objects are hierarchically organized by topic and sub-topic. Relationships are recognized between topics and sub-topics when tags are created, see par. [0042]; Because tags are organized hierarchically, the questions that are selected include the selected topic as well as sub-topics that are related to the selected topic. The question store 208 executes 312 the request for questions and retrieves question objects based on the selected topic, see par. [0044]).
It would have been obvious to one of ordinary skill in the art to combine the Jenkins invention with the teachings of Ross for the benefit of enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001].
However Jenkins in view of Ross does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types, the plurality of different candidate question types corresponding to different answer collecting manners; obtaining a question type template of each of the plurality of questions based on the question type of the respective question.
In the same field of endeavor Fox teaches systems and methods to enable generation of high-quality summaries of documents that have questions and answers, see abstract. Fox teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types (o better comprehend the text in a question-answer document, it helps to parse the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058]), the plurality of different candidate question types corresponding to different answer collecting manners (classifying each question and each answer according to a category based on dialog acts. Dialog Acts (dialog act) can represent the communicative intention behind a speaker's utterance in a conversation. Identifying the dialog act of each speaker utterance in a conversation thus can help to automatically determine intent and meaning. Specific rules can be developed for each dialog act type to process a conversation question-answer group and transform it into a suitable form for subsequent analysis, see par. [0061]);
obtaining a question type template of each of the plurality of questions based on the question type of the respective question (create generalized templates from summary sentences and leverage the relationships between the summaries and their source conversation transcripts to generate abstract summaries, see par. [0046-0047]; ] Classification of questions and answers based on dialog acts can be done with machine learning methods and other methods as can be appreciated. In some embodiments, classification using machine learning can involve training a classifier and applying the resulting classifier with its trained model, see par. [0062].
It would have been obvious to one of ordinary skill in the art to combine the Jenkins in view of Ross invention with the teachings of Fox for the benefit of parsing the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058].
Regarding claim 20 Jenkins teaches a non-transitory computer-readable storage medium storing instructions which when executed by a processor cause the processor to perform (invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon, see par. [0075]):
displaying a text entry interface, the text entry interface being configured to receive text content of a plurality of questions to be included in a questionnaire form (the QAC system 100 may receive input from the network 102, a corpus of documents 106 or other data, a content creator 108, content users, and other possible sources of input, see par. [0019]);
receiving the text content of the plurality of questions to be included in the questionnaire form and entered in the text entry interface (The document 106 may include any file, text, article, or source of data for use in the QAC system 100, see par. [0020]);
generating the questionnaire form according to the text content, the questionnaire form including a plurality of questions that are determined based on parsing of the text content (The content creator may create 306 more questions based on the content, if applicable. The QAC system 100 also generates candidate questions 216 based on the content that may not have been entered by the content creator, see par. [0031]);
and displaying the questionnaire form (The questions, candidate questions 216, and answers 218 may then be presented 308 on an interface to the content creator for verification, see par. [0033]).
However Jenkins does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions; and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form.
In the same field of endeavor Ross teaches a system for enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001]. Ross teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions (A question parsing module 104 includes a process that parses text in a question and recognizes tag objects 106 that are stored in the tag store 216. These tag objects 106 are identified and associated with the question object 108 for the parsed question, see par. [0039]); and wherein the plurality of questions are arranged in the questionnaire based on a predefined arrangement of the question types in the questionnaire form (he question store 208 maintains 302 question objects that are associated with hierarchically organized tags. Tag objects are hierarchically organized by topic and sub-topic. Relationships are recognized between topics and sub-topics when tags are created, see par. [0042]; Because tags are organized hierarchically, the questions that are selected include the selected topic as well as sub-topics that are related to the selected topic. The question store 208 executes 312 the request for questions and retrieves question objects based on the selected topic, see par. [0044]).
It would have been obvious to one of ordinary skill in the art to combine the Jenkins invention with the teachings of Ross for the benefit of enabling users of a social networking system to answer questions asked by other users of the social networking system, see par. [0001].
However Jenkins in view of Ross does not teach parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types, the plurality of different candidate question types corresponding to different answer collecting manners; obtaining a question type template of each of the plurality of questions based on the question type of the respective question.
In the same field of endeavor Fox teaches systems and methods to enable generation of high-quality summaries of documents that have questions and answers, see abstract. Fox teaches parsing the text content using a natural language processing model to identify a question type for each of the plurality of questions from a plurality of different candidate question types (o better comprehend the text in a question-answer document, it helps to parse the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058]), the plurality of different candidate question types corresponding to different answer collecting manners (classifying each question and each answer according to a category based on dialog acts. Dialog Acts (dialog act) can represent the communicative intention behind a speaker's utterance in a conversation. Identifying the dialog act of each speaker utterance in a conversation thus can help to automatically determine intent and meaning. Specific rules can be developed for each dialog act type to process a conversation question-answer group and transform it into a suitable form for subsequent analysis, see par. [0061]);
obtaining a question type template of each of the plurality of questions based on the question type of the respective question (create generalized templates from summary sentences and leverage the relationships between the summaries and their source conversation transcripts to generate abstract summaries, see par. [0046-0047]; ] Classification of questions and answers based on dialog acts can be done with machine learning methods and other methods as can be appreciated. In some embodiments, classification using machine learning can involve training a classifier and applying the resulting classifier with its trained model, see par. [0062].
It would have been obvious to one of ordinary skill in the art to combine the Jenkins in view of Ross invention with the teachings of Fox for the benefit of parsing the question-answer groups in the question-answer document and transform them to a simple form on which traditional NLP techniques can be used, see par. [0058].
Regarding claim 21 Fox teaches the form generation method according to claim 1, wherein the plurality of candidate question types includes one or more of a single-answer question type, a multiple- answer question type, a true/false question type, a completion question type, a mail question type, or a time question type ( a single answer dialog act category, can each be classified into separate answer dialog act category, can each be categorized into multiple answer dialog act categories, or any other combination of the two or more answers and answer dialog act categories as can be appreciated, see par. [0096]).
Regarding claim 22 Fox teaches the information processing apparatus according to claim 18, wherein the plurality of candidate question types includes one or more of a single-answer question type, a multiple-answer question type, a true/false question type, a completion question type, a mail question type, or a time question type ( a single answer dialog act category, can each be classified into separate answer dialog act category, can each be categorized into multiple answer dialog act categories, or any other combination of the two or more answers and answer dialog act categories as can be appreciated, see par. [0096]).
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jenkins U.S. PAP 2014/0046947 A1, in view of Ross U.S. PAP 2012/0166457 A1, in view of Fox U.S. PAP 2021/0174016 A1 further in view of Zhang U.S. PAP 2018/0121801 A1.
Regarding claim 4 Jenkins teaches the form generation method according to claim 1, wherein the parsing the text content using the natural language processing model comprises:
dividing the text content into a plurality of text fragments, each of the plurality of text fragments corresponding to one question (parsing the content of the documents 106 to identify questions found in the documents 106 and other elements of the content, see par. [0029]);
determining a phrase set of each of the plurality of text fragments based on word segmentation of the plurality of text fragments, each phrase set including at least one phrase (parse documents using document markup to identify questions, see par. [0029]);
and generating the questionnaire form based on each of the plurality of text fragments and the obtained corresponding question type templates (The questions, candidate questions 216, and answers 218 may then be presented 308 on an interface to the content creator for verification, see par. [0033]).
However Jenkins in view of Ross does not teach generating a feature vector of each of the plurality of text fragments according to the phrase set corresponding to the respective text fragment; determining a question type of each of the plurality of text fragments based on the feature vector of the respective text fragment.
In the same field of endeavor Zhang teaches a method and a device for classifying questions based on artificial intelligence. The method may simplify operation steps, reduce interactions between a user and a service center, and improve efficiency of the service center, see abstract. Zhang teaches generating a feature vector of each of the plurality of text fragments according to the phrase set corresponding to the respective text fragment (acquiring text content of a question input by a user, and performing a word segmentation process on the text content to obtain a plurality of segmentations; acquiring hidden representation vectors of the plurality of segmentations, see par. [0006]); determining a question type of each of the plurality of text fragments based on the feature vector of the respective text fragment (generating a first vector of the text content according to the hidden representation vectors; and determining a target responder corresponding to the question according to the first vector and a preset classification model, see par. [0006]).
It would have been obvious to one of ordinary skill in the art to combine the Jenkins in view of Ross invention with the teachings of Zhang for the benefit of simplifying operation steps, reduce interactions between a user and a service center, and improve efficiency of the service center, see abstract.
Regarding claim 5 Zhang teaches form generation method according to claim 4, wherein the determining the question type of each of the plurality of text fragments comprises:
obtaining a training text of each of the plurality of candidate question types (the plurality of segmentations may be trained with a word2vec model , see par. [0025]);
determining a plurality of probability values, according to the training text of each of the plurality of candidate question types, that a target text fragment of the plurality of text fragments belongs to the plurality of question types respectively ( A value of each dimension of the word vector is representative of one feature understood with some semantic meaning and grammar, see par. [0025]);
and determining the candidate question type corresponding to a maximum probability value in the plurality of probability values as corresponding to the target text fragment (a maximum probability is selected among the M probabilities, see par. [0044]).
Regarding claim 6 Zhang teaches the form generation method according to claim 5, wherein the phrase set corresponding to the target text fragment includes Q target phrases, Q being a positive integer, and the target phrase includes at least one character (the number of preset responders is M, where M is a positive integer, see par. [0037]), and the determining the plurality of probability values comprises:
for the training text of each question type: collecting statistics on word frequency information of occurrence of each character in each target phrase in the training text ( a largest classification probability may be determined according to the first vector of the text content and the preset classification model, see par. [0035]);
determining a probability value that each target phrase belongs to the candidate question type based on the word frequency information of each character in each target phrase (in order to satisfy the demands of the user, a plurality of responders may be preset to satisfy the demands of answering the questions of the user., see par. [0038]);
and determining, according to the plurality of probability values that the plurality of target phrases belong to the candidate question type, the probability value that the target text fragment belongs to the candidate question type (the responder corresponding to the maximum probability is determined as the target responder from the preset responders, see par. [0045]).
Claim(s) 10, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jenkins U.S. PAP 2014/0046947 A1, in view of Ross U.S. PAP 2012/0166457 A1, in view of Fox U.S. PAP 2021/0174016 A1, further in view of Kolesov U.S. PAP 2019/0087398 A1.
Regarding claim 10 Jenkins in view of Ross does not teach the form generation method according to claim 1, wherein the receiving the text content comprises: receiving the text content that is copied from another document and pasted into the text entry interface.
In the same field of endeavor Kolesov teaches improved electronic form generation technology, including a simplified text-based interface suitable for both desktop computing and mobile devices and an intelligent backend that can infer an end user's intent regarding desired electronic form elements specified based on natural language input and optional supplemental information that may provide context, see par. [0006]. The form description may be saved in text form as entered by the user and stored in form data storage module 315. The user may use any text editor computer software outside the electronic form generation system 106 to generate and save plain text descriptions of the electronic form. Such descriptions may be copied and pasted into the text input field 202 to generate a new electronic form without loss of proper functionality, see par. [0059].
It would have been obvious to one of ordinary skill in the art to combine the Jenkins in view of Ross invention with the teachings of Kolesov for the benefit of inferring an end user's intent regarding desired electronic form elements specified based on natural language input and optional supplemental information that may provide context, see par. [0006].
Regarding claim 16 Kolesov teaches the form generation method according to claim 1, wherein the displaying the questionnaire form comprises: displaying a preview interface based on a selection of a second graphical element associated with the preview interface, and displaying the questionnaire form in the preview interface ( display on the display screen a preview of the electronic form generated by the remote server computer system based on the information, which includes a text-based description, see par. [0007]).
Regarding claim 17 Kolesov teaches the form generation method according to claim 1, further comprising: publishing the questionnaire form based on a user selection of a publication function ( the distribution form may be generated in a markup language, a computer programming language, and later compiled fully or partially in step 415 before or after being transferred to the user device 104, where it may be rendered in step 416 into visual, audible or another form to be perceptible and actionable upon by the user of the user device 104, see par. [0067]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pertinent prior art available on form 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Ortiz-Sanchez whose telephone number is (571)270-3711. The examiner can normally be reached Monday- Friday 9AM-6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL ORTIZ-SANCHEZ/Primary Examiner, Art Unit 2656