DETAILED ACTION
The action is responsive to the Application filed on 11/20/2024. Claims 1-30 are pending in the case. Claims 1, 11 and 21 are independent claims.
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 .
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. As to claims 1, 11 and 21, the claims recites a system, method and non-transitory computer-readable medium for using AI algorithms to generate answers to questions in a template and replacing the questions in the template with answers to produce a document. The limitation of generating answers to questions in a template and replacing the questions with the answers to produce a document, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation as a manual generating answers to questions in a template and replacing the questions with the answers to produce a document but for the recitation of generic computer components. That is, other than reciting “memory”, “hardware processor” and “AI algorithms” (as recited in the system claim 1, method claim 11 and non-transitory computer-readable medium claim 21), nothing the claims elements precludes the step from practically being performed by a user manually answering questions in a template and replacing the questions with answers to produce a document. For example, but for the “memory”, “hardware processor” and “AI algorithms” language, the generating, replacing and producing in the context of the claims encompasses the user manually answering questions in a template and replacing the questions to create a document. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particularly, the claims only recite three additional elements – using memory, a hardware processor and AI algorithms to perform the generating, replacing and producing steps. The memories, processors and AI algorithms in these steps are recited at a high-level of generality (i.e., as a generic processors and memories performing a generic computer function of generating answers to questions in a template and replacing the questions with answers to create a document) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using processors, memories and algorithms to perform the steps of generating, replacing and producing amounts to no more than mere instruction 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. The claims are not patent eligible.
Claims 2, 12 and 22 depends from claims 1, 11 and 21, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of using reference data to generate answers to questions does not integrate the judicial exception into a practical application. The limitations of using reference data to generate answers to questions merely represents instructions to apply the judicial exceptions on a computer and the user manually using reference data to answer questions in the template. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 2, 12 and 22 are directed to an abstract idea.
Claims 3, 13 and 23 depends from claims 1, 11 and 21, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of generating the template based on received questions does not integrate the judicial exception into a practical application. The limitations of generating the template based on received questions merely represents instructions to apply the judicial exceptions on a computer and the user manually generating a question template based on received questions. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 3, 13 and 23 are directed to an abstract idea.
Claims 4, 14 and 24 depends from claims 3, 13 and 23, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of each question corresponds to content items based on a subject does not integrate the judicial exception into a practical application. The limitations of each question corresponding to content items based on a subject merely represents instructions to apply the judicial exceptions on a computer and the user manually generating questions relating to a particular topic and content item. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 4, 14 and 14 are directed to an abstract idea.
Claims 5, 15 and 25 depends from claims 3, 13 and 23, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of using a second AI algorithm to generate the template does not integrate the judicial exception into a practical application. The limitations of using a second AI algorithm to generate the template merely represents instructions to apply the judicial exceptions on a computer using an algorithm and processor and the user manually generating a template based on the questions. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 5, 15 and 15 are directed to an abstract idea.
Claims 6, 16 and 26 depends from claims 5, 15 and 25, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of using reference data does not integrate the judicial exception into a practical application. The limitations of using reference data merely represents instructions to apply the judicial exceptions on a computer using an algorithm and processor and the user manually generating a template based on the questions and reference data. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 6, 16 and 16 are directed to an abstract idea.
Claims 7, 17 and 27 depends from claims 3, 13 and 23, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of classifying the questions does not integrate the judicial exception into a practical application. The limitations of classifying the questions merely represents instructions to apply the judicial exceptions on a computer and the user manually determining some kind of classification for the questions to generate the template. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 7, 17 and 17 are directed to an abstract idea.
Claims 8, 18 and 28 depends from claims 3, 13 and 23, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of mapping questions into the template based on a variety of data does not integrate the judicial exception into a practical application. The limitations of mapping questions into the template based on a variety of data merely represents instructions to apply the judicial exceptions on a computer and the user manually mapping questions based on data in order to generate the template. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 8, 18 and 18 are directed to an abstract idea.
Claims 9, 10, 19, 20, 29 and 30 depends from claims 1, 11 and 21 and claims 5, 15 and 25, and thus recites a similar limitation of generating answers to questions in a template and replacing the questions in the template with the answers to produce the document. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of using a variety of AI algorithms does not integrate the judicial exception into a practical application. The limitations of using a variety of AI algorithms merely represents instructions to apply the judicial exceptions on a computer using a generic AI algorithm. Thus, the additional elements do not integrate the recited judicial exception into a practical application and claims 9, 10, 19, 20, 29 and 30 are directed to an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Desai et al. (US 20170132198 A1, hereinafter Desai) in view of Yoshida (US 20200243082 A1, hereinafter Yoshida).
As to claim 1, Desai discloses a system for generating a document, comprising:
memory (“The one or more processors execute the document processing application in conjunction with the instructions stored in the memory,” Desai paragraph 0071); and
at least one hardware processor collectively configured to (“The one or more processors execute the document processing application in conjunction with the instructions stored in the memory,” Desai paragraph 0071) at least:
use a first one or more algorithms to generate one or more answers to one or more questions included in a template ("In a diagram 300, a document processing application 304 may present one or more user interfaces (302, 304, or 306) to an editor to request answers to questions to generate a document. A user interface 302 may provide a set of content structure templates 312 to request an input as an intent to create the document. The input may be a selection of one of the presented set of content structure templates. An element 310 may be used to select the content structure template to be used to generate the document. For example, the element 310 may be selected to activate a content structure template 320 to generate a thesis statement," Desai paragraph 0039; "Next, the document processing application 304 may display a user interface 306. The user interface 306 may provide the content structure template 320 which may include a question 322 or other questions. The question 322 may be displayed to capture an answer associated with the document for use in generating the document," Desai paragraph 0040; "Next, the document processing application 304 may display a user interface 308 that may include the question 330 and the answer 332 and other questions and other answers of the content structure template 320. The question 330 and the answer 332 (and other questions and answers) may be customizable by the editor. A customization to the question 330 may be used to modify the content structure template 320. The modified content structure template may be saved for future use. The customization to the answer 332 or other answers may be used to further customize the document or a section of the document. An element 334 may also be provided to generate the document or a section of the document by combining the answer 332 with other answer(s) captured through the content structure template 320," Desai paragraph 0041 "In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043; Desai Figure 3 332 "I think sometimes in winter its dark all day and I wonder how people deal with that?", questions in a template along with user guidance answers which can also be in the form of additional questions where the template is replaced with automatically generated answer content); and
replace the one or more questions in the template with the one or more answers to produce the document ("In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043).
However Desai does not appear to explicitly disclose using a first one or more AI algorithms to generate one or more answers to one or more questions.
Yoshida teaches using a first one or more AI algorithms to generate one or more answers to one or more questions ("The setter 12 sets the query keywords based on the query sentence. For example, the setter 12 sets the entire query sentence including one or more words as one query keyword. The selector 13 selects one or more question-answer pairs including the query keyword from the question-answer table. The generator 14 generates a task scenario according to the number of selected question-answer pairs. The generator 14 outputs the generated task scenario to an external device such as a monitor, a speaker, a printer, etc. These devices output the task scenario so that the task scenario can be recognized by the user," Yoshida paragraph 0051; "The setter 12 may refer to a pre-generated model. For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114, determine a query keyword and generating an answer to the query using an AI algorithm).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Desai to use AI algorithms to help generate answers to questions as taught by Yoshida. One would have been motivated to make such a combination so that the finished product could support more kinds of ways for generating answers to questions thus enhancing the utility of the finished product.
As to claim 2, Desai as modified by Yoshida discloses further discloses the system of claim 1, wherein, in using the first one or more AI algorithms to generate the one or more answers to the one or more questions, the at least one hardware processor makes use of reference data ("The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043; Desai Figure 4 416 "SWEDEN 55 degree north latitude. (Source: 123encyclopedia456.com)).
As to claim 3, Desai as modified by Yoshida discloses further discloses the system of claim 1, wherein the at least one hardware processor is further configured to:
receive the one or more questions ("The question 212 may include an information request associated with the document 220. For example, in response to a student's input to generate a term paper, the interaction template 210 associated with a term paper may be selected," Desai paragraph 0033); and
generate the template based on the one or more questions ("The question 212 may include an information request associated with the document 220. For example, in response to a student's input to generate a term paper, the interaction template 210 associated with a term paper may be selected," Desai paragraph 0033, generating template based off of question that included “term paper” input).
As to claim 4, Desai as modified by Yoshida discloses further discloses the system of claim 3, wherein each of the one or more questions corresponds to one or more requirement content items that are related to one or more aspects of a subject of the document ("The question 212 may include an information request associated with the document 220. For example, in response to a student's input to generate a term paper, the interaction template 210 associated with a term paper may be selected," Desai paragraph 0033, generating template based off of question that included “term paper” input; Desai Figure 3 "What is your Topic?").
As to claim 5, Desai as modified by Yoshida discloses further discloses the system of claim 3, wherein, in generating the template based on the one or more questions, the at least one hardware processor uses a second one or more AI algorithms ("For example, the second memory 22 stores a generic model 120 shown in FIG. 3. The generic model 120 includes multiple scenario templates 121a to 121c. The scenario templates 121a to 121c are used as the base of the task scenarios. The task scenarios include specific information and are used as the responses output from the processor 10. Answer candidate numbers 122a to 122c are set respectively for the scenario templates 121a to 121c. The scenario template to be used is determined according to the answer candidate number," Yoshida paragraph 0046; "For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114, generating a question template using a model).
As to claim 6, Desai as modified by Yoshida discloses further discloses the system of claim 5, wherein, in using the second one or more AI algorithms, the at least one hardware processor makes use of reference data ("For example, the second memory 22 stores a generic model 120 shown in FIG. 3. The generic model 120 includes multiple scenario templates 121a to 121c. The scenario templates 121a to 121c are used as the base of the task scenarios. The task scenarios include specific information and are used as the responses output from the processor 10. Answer candidate numbers 122a to 122c are set respectively for the scenario templates 121a to 121c. The scenario template to be used is determined according to the answer candidate number," Yoshida paragraph 0046; "The scenario templates 121a to 121c are generically written and are independent of the inquiry of the user. Specifically, the scenario templates 121a to 121c each include blanks. As described below, a question, an answer, or a question keyword is plugged into the blanks when generating the task scenario," Yoshida paragraph 0047, generically written templates (i.e., reference data) and filling in the blanks with the model to generate the question template).
As to claim 7, Desai as modified by Yoshida discloses further discloses the system of claim 3, wherein, in generating the template based on the one or more questions, the at least one hardware processor uses a classification procedure ("The content module is further configured to select the content structure template from a set of the content structure templates based on the intent to generate document, where the content structure template includes one or more of: a thesis statement, a project presentation, a how to guide, a story outline, a research conclusion, and a biography," Desai paragraph 0075; Desai Figure 3 "Write a thesis statement" option with "A set of questions to discover something worth writing about" and other options for "presentation", "how to guide", "story and "research conclusion" with their own sets of questions classified based on different document options).
As to claim 8, Desai as modified by Yoshida discloses further discloses the system of claim 3, wherein, in generating the template based on the one or more questions, the at least one hardware processor maps the one or more questions into the template based on at least one of a labelled field, a tagged field, an index, a keyword, a designated symbol, a mapping table, a mapping pointer, a mapping function, and code embedded within the template ("The answer 214 or other answers captured by the interactivity module 206 in relation to the content structure template 210 may be combined to generate the document 220 or a section (222 or 224) of the document 220. The structure used to map the answers to a location on the document may be described in the content structure template 210. For example, the question 212 and the answer 214 may be mapped to a topic of the section 222 such as a paragraph of the document 220. The answer 214 may be inserted into the document 220 as an initial sentence of the section 222," Desai paragraph 0036).
As to claim 9, Desai as modified by Yoshida discloses further discloses the system of claim 1, wherein the first one or more AI algorithms comprises one or more of the following: a large language model; a neural network; a deep neural network; a transformer; a decision tree; a random forest; a decision fern; a support vector machine; and an adversarial generative network (GAN) ("For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114).
As to claim 10, Desai as modified by Yoshida discloses further discloses the system of claim 5, wherein the second one or more AI algorithms comprises one or more of the following: a large language model; a neural network; a deep neural network; a transformer; a decision tree; a random forest; a decision fern; a support vector machine; and an adversarial generative network (GAN) ("For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114).
As to claim 11, Desai discloses a method of generating a document, comprising:
using a first one or more algorithms to generate one or more answers to one or more questions included in a template ("In a diagram 300, a document processing application 304 may present one or more user interfaces (302, 304, or 306) to an editor to request answers to questions to generate a document. A user interface 302 may provide a set of content structure templates 312 to request an input as an intent to create the document. The input may be a selection of one of the presented set of content structure templates. An element 310 may be used to select the content structure template to be used to generate the document. For example, the element 310 may be selected to activate a content structure template 320 to generate a thesis statement," Desai paragraph 0039; "Next, the document processing application 304 may display a user interface 306. The user interface 306 may provide the content structure template 320 which may include a question 322 or other questions. The question 322 may be displayed to capture an answer associated with the document for use in generating the document," Desai paragraph 0040; "Next, the document processing application 304 may display a user interface 308 that may include the question 330 and the answer 332 and other questions and other answers of the content structure template 320. The question 330 and the answer 332 (and other questions and answers) may be customizable by the editor. A customization to the question 330 may be used to modify the content structure template 320. The modified content structure template may be saved for future use. The customization to the answer 332 or other answers may be used to further customize the document or a section of the document. An element 334 may also be provided to generate the document or a section of the document by combining the answer 332 with other answer(s) captured through the content structure template 320," Desai paragraph 0041 "In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043; Desai Figure 3 332 "I think sometimes in winter its dark all day and I wonder how people deal with that?", questions in a template along with user guidance answers which can also be in the form of additional questions where the template is replaced with automatically generated answer content); and
replacing the one or more questions in the template with the one or more answers to produce the document using at least one hardware processor ("In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043).
However Desai does not appear to explicitly disclose using a first one or more AI algorithms to generate one or more answers to one or more questions.
Yoshida teaches using a first one or more AI algorithms to generate one or more answers to one or more questions ("The setter 12 sets the query keywords based on the query sentence. For example, the setter 12 sets the entire query sentence including one or more words as one query keyword. The selector 13 selects one or more question-answer pairs including the query keyword from the question-answer table. The generator 14 generates a task scenario according to the number of selected question-answer pairs. The generator 14 outputs the generated task scenario to an external device such as a monitor, a speaker, a printer, etc. These devices output the task scenario so that the task scenario can be recognized by the user," Yoshida paragraph 0051; "The setter 12 may refer to a pre-generated model. For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114, determine a query keyword and generating an answer to the query using an AI algorithm).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Desai to use AI algorithms to help generate answers to questions as taught by Yoshida. One would have been motivated to make such a combination so that the finished product could support more kinds of ways for generating answers to questions thus enhancing the utility of the finished product.
As to claim 12, it is substantially similar to claim 2 and is therefore rejected using the same rationale as above.
As to claim 13, it is substantially similar to claim 3 and is therefore rejected using the same rationale as above.
As to claim 14, it is substantially similar to claim 4 and is therefore rejected using the same rationale as above.
As to claim 15, it is substantially similar to claim 5 and is therefore rejected using the same rationale as above.
As to claim 16, it is substantially similar to claim 6 and is therefore rejected using the same rationale as above.
As to claim 17, it is substantially similar to claim 7 and is therefore rejected using the same rationale as above.
As to claim 18, it is substantially similar to claim 8 and is therefore rejected using the same rationale as above.
As to claim 19, it is substantially similar to claim 9 and is therefore rejected using the same rationale as above.
As to claim 20, it is substantially similar to claim 10 and is therefore rejected using the same rationale as above.
As to claim 21, Desai discloses a non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for generating a document (“The one or more processors execute the document processing application in conjunction with the instructions stored in the memory,” Desai paragraph 0071), the method comprising:
using a first one or more algorithms to generate one or more answers to one or more questions included in a template ("In a diagram 300, a document processing application 304 may present one or more user interfaces (302, 304, or 306) to an editor to request answers to questions to generate a document. A user interface 302 may provide a set of content structure templates 312 to request an input as an intent to create the document. The input may be a selection of one of the presented set of content structure templates. An element 310 may be used to select the content structure template to be used to generate the document. For example, the element 310 may be selected to activate a content structure template 320 to generate a thesis statement," Desai paragraph 0039; "Next, the document processing application 304 may display a user interface 306. The user interface 306 may provide the content structure template 320 which may include a question 322 or other questions. The question 322 may be displayed to capture an answer associated with the document for use in generating the document," Desai paragraph 0040; "Next, the document processing application 304 may display a user interface 308 that may include the question 330 and the answer 332 and other questions and other answers of the content structure template 320. The question 330 and the answer 332 (and other questions and answers) may be customizable by the editor. A customization to the question 330 may be used to modify the content structure template 320. The modified content structure template may be saved for future use. The customization to the answer 332 or other answers may be used to further customize the document or a section of the document. An element 334 may also be provided to generate the document or a section of the document by combining the answer 332 with other answer(s) captured through the content structure template 320," Desai paragraph 0041 "In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043; Desai Figure 3 332 "I think sometimes in winter its dark all day and I wonder how people deal with that?", questions in a template along with user guidance answers which can also be in the form of additional questions where the template is replaced with automatically generated answer content); and
replacing the one or more questions in the template with the one or more answers to produce the document using at least one hardware processor ("In a diagram 400, a document processing application 404 may present the document generated from answers to questions of a content structure template. The document may include multiple sections. The section 410 may be a title section which may be generated from a question asking about the title of the document. The section 410 may be generated with information from the answer as well as information from external resources. The information from external resources may be provided as a prompt 416 to request a validation of the information and customize the information to prevent issues with plagiarism," Desai paragraph 0043).
However Desai does not appear to explicitly disclose using a first one or more AI algorithms to generate one or more answers to one or more questions.
Yoshida teaches using a first one or more AI algorithms to generate one or more answers to one or more questions ("The setter 12 sets the query keywords based on the query sentence. For example, the setter 12 sets the entire query sentence including one or more words as one query keyword. The selector 13 selects one or more question-answer pairs including the query keyword from the question-answer table. The generator 14 generates a task scenario according to the number of selected question-answer pairs. The generator 14 outputs the generated task scenario to an external device such as a monitor, a speaker, a printer, etc. These devices output the task scenario so that the task scenario can be recognized by the user," Yoshida paragraph 0051; "The setter 12 may refer to a pre-generated model. For example, the model is an artificial neural network model or a recurrent neural network model. The model is pre-trained using training data. The training data includes multiple paired data. Each paired data includes a sentence and information indicating that the sentence is the negative form. The model is trained to output information indicating a negative sentence when a negative sentence is input," Yoshida paragraph 0114, determine a query keyword and generating an answer to the query using an AI algorithm).
Accordingly it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the non-transitory computer-readable medium of Desai to use AI algorithms to help generate answers to questions as taught by Yoshida. One would have been motivated to make such a combination so that the finished product could support more kinds of ways for generating answers to questions thus enhancing the utility of the finished product.
As to claim 22, it is substantially similar to claim 2 and is therefore rejected using the same rationale as above.
As to claim 23, it is substantially similar to claim 3 and is therefore rejected using the same rationale as above.
As to claim 24, it is substantially similar to claim 4 and is therefore rejected using the same rationale as above.
As to claim 25, it is substantially similar to claim 5 and is therefore rejected using the same rationale as above.
As to claim 26, it is substantially similar to claim 6 and is therefore rejected using the same rationale as above.
As to claim 27, it is substantially similar to claim 7 and is therefore rejected using the same rationale as above.
As to claim 28, it is substantially similar to claim 8 and is therefore rejected using the same rationale as above.
As to claim 29, it is substantially similar to claim 9 and is therefore rejected using the same rationale as above.
As to claim 30, it is substantially similar to claim 10 and is therefore rejected using the same rationale as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20240273123 A1 to Costa et al. discloses question-answer reference data set generation for artificial intelligence where a user’s question is rewritten to follow a question template and then determining an answer based on the question template;
US 20060112036 A1 to Zhang et al. discloses a method and system for identifying questions within a discussion thread where identified questions are replaced in a document with a corresponding identified answer; and
US 20140372875 A1 to Sugibuchi et al. discloses an information processing apparatus and non-transitory computer readable medium where question content in a document is replaced with answer content.
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/DANIEL SAMWEL/Primary Examiner, Art Unit 2171
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