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 was filed in this application after a decision by the Patent Trial and Appeal Board, but before the filing of a Notice of Appeal to the Court of Appeals for the Federal Circuit or the commencement of a civil action. 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examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on 08/18/2026 has been entered.
Response to Arguments
Applicant's arguments filed 08/18/2026 have been fully considered but they are not persuasive. Regarding arguments on pages 7-8 of the Remarks, Examiner notes that regarding the second to last limitation, the cited paragraphs still do not support the language of the claims. The cited portions teach that the output may have a specific format, such as a sentence structure from para [0035] or a verbal or textual response” or “provide pictures or display a list” from para [0032]. However, the claims require that the output response includes the response and “one or more response formatting options”. The options of a sentence structure, verbal or textual response, or pictures or a list as options are not included in the response. Therefore, Examiner will continue to interpret the output as having a specific format from one or more formatting options.
Regarding arguments on pages 10-12 of the Remarks, Examiner notes that the characterization of the claims as abstract has already been upheld by the PTAB decision of 06/18/2026. Regarding the “updating” amendment, Examiner notes that the updating is performed on a corpus of unstructured text rather than to a language model as argued. Updating a corpus of text could be as simple as adding a new passage to existing passages, which is a mental process. Since the newly added amendments are abstract and do not integrate the abstract ideas into a practical application, the claims remain rejected under 101 as affirmed by the PTAB decision.
Regarding arguments on page 14 of the Remarks, Examiner notes that both Hoffmeister and Govindan teach the argued limitation of receiving input of a query and a new corpus of unstructured text. The new corpus of unstructured text could be broadly interpreted as the query itself if the query is received as text, or could be the transcribed version of a speech input. Alternatively, the claims do not require the query and corpus of text to be related or even received at the same time, so any teaching in the references of receiving a query and separately receiving a text input would disclose the limitation. In a similar manner, the updating of the corpus at runtime does not have to be related to the processing of the query as currently claimed. Therefore, the current interpretation where the corpus is updated at runtime, and then the response to the query is determined some time in the future using the updated corpus teaches the limitations of the amended claims.
Regarding arguments on pages 14-16 of the Remarks, Examiner notes that the above arguments as well as the PTAB decision address the argued claims.
Claim Objections
Claim 1 objected to because of the following informalities: “using a language model from processing a corpus of unstructured text” in lines 7-8 is unclear as to whether the language model was formed or constructed from the processing, or whether “from” should be “for”. Similarly, the second to last limitation of the output response including one or more response formatting options does not appear supported, and is being interpreted as the output having a specific format. Appropriate correction is required.
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 1-18 and 20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 1-9 are directed to a processor, which is a machine or an article of manufacture; claims 10-18 and 20 are directed to a method, which is a process. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites determining a classification, extracting a text sequence, updating a corpus, and determining a response, which are mental processes. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional limitations of receiving a query and providing the response are mere extrasolution activity, and processing units are generic computing components, and neither integrates the abstract idea into a practical application.
Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself.
Regarding claim 1, determining a classification, extracting a text sequence, updating a corpus, and determining and generating a response are mental processes, which is an abstract idea. Addition limitations of receiving a query and providing the response are mere extrasolution activity, and processing units are generic computing components, and neither integrates the abstract idea into a practical application or constitutes significantly more.
Regarding claims 2, 9, the limitations are further clarifications of the above abstract ideas.
Regarding claims 3, 12, and 20, receiving a response and generating an answer are mental processes, which is an abstract idea. The use of a trained generative neural network model could be interpreted to fall within the range of a mental process, without enough detail to integrate the abstract idea into a practical application and without significantly more.
Regarding claims 4 and 13, receiving and storing data are mere extrasolution activity, and do not constitute integration into a practical application or significantly more.
Regarding claim 5, determining a classification and determining an association are mental processes, which is an abstract idea. Additional limitations of receiving the query and performing a task are mere extrasolution activity and do not constitute integration into a practical application or significantly more.
Regarding claim 6, executing a task is mere extrasolution activity and does not constitute integration into a practical application or significantly more.
Regarding claim 7, determining an identifier is a mental process, which is an abstract idea. Performing a task is mere extrasolution activity and does not constitute integration into a practical application or significantly more.
Regarding claims 8, 11, and 18, using an extractive question and answer model and answering a question could be interpreted to fall within the range of a mental process, without enough detail to integrate the abstract idea into a practical application and without significantly more.
Regarding claim 10, extracting a text sequence, determining facts, and generating a response are mental processes, which is an abstract idea. Addition limitations of receiving a query and storing data are mere extrasolution activity, do not integrate the abstract idea into a practical application or constitute significantly more.
Regarding claim 14, determining a classification and retrieving portions of input are mental processes, which is an abstract idea. Additional limitations of receiving the query and executing a task are mere extrasolution activity and do not constitute integration into a practical application or significantly more.
Regarding claim 15, determining a call is a mental process, which is an abstract idea. Performing an action is mere extrasolution activity and does not constitute integration into a practical application or significantly more.
Regarding claim 16, determining a classification, extracting a segment, and retrieving a portion of an input are mental processes, which is an abstract idea. Addition limitations of receiving data and storing data are mere extrasolution activity, do not integrate the abstract idea into a practical application or constitute significantly more.
Regarding claim 17, determining actions and assigning tags are mental processes, which is an abstract idea without integration into a practical application and without significantly more.
The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp., 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014), Benson, 409 U.S. at 63.
See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618).
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.
Claim(s) 1-2, 4-9, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hoffmeister (US 10,388,274 B1), in view of Govindan (US 2018/0158245 A1).
Regarding claim 1, Hoffmeister teaches:
A processor, comprising:
one or more processing units (Fig. 19 element 1904, col. 30 lines 20-37, where a processor is used) to:
receive, using an interface, audio data representative of a query and a new corpus of unstructured text (col. 21 line 62 - col. 22 line 42, where an audio query is received at a query answering system, and col. 26 lines 6-45, where unstructured search results are obtained, where Examiner notes that the new corpus of unstructured text and the audio data are not necessarily related or received at the same time);
determine a classification for the query corresponds to an information based query for information associated with the environment (col. 21 line 62 - col. 22 line 42, where queries are requests for information, where all queries for information from the interaction environment are necessarily associated with the interaction environment);
extract, using a language model provided with a corpus of unstructured text for a domain of options associated with the virtual interactive environment (col. 9 lines 18-33, where a language model is associated with one of domain options, and col. 21 lines 47-61, where unstructured text is used to populate the data store, which is used by the language model for answering queries), a text sequence from the query (col. 21 line 62 - col. 22 line 42, where a triple is identified from the query, or the text is semantically parsed);
update, at runtime, the corpus of unstructured text with the new corpus of unstructured text to generate an updated corpus of unstructured text (col. 28 line 45 – col. 29 line 54, where association data is stored during runtime, the storage considered updating the corpus as the knowledge base is being added to);
determine, using the language model and based at least in part on the text sequence, a response to the query from the updated corpus of unstructured text (col. 21 lines 47-61, where facts based on unstructured text are stored in the knowledge base, and col. 21 line 62 - col. 22 line 42, where the question is answered using the knowledge base);
generate an output response to the query including the response and one or more response formatting options (col. 21 line 62 - col. 22 line 42, where the response is provided in a natural language format); and
provide the output response to the query (col. 21 line 62 - col. 22 line 42, where the answer is returned to the user).
Hoffmeister does not teach:
receive, using an interface of a virtual interactive environment, audio data representative of a query and a new corpus of unstructured text;
a virtual interactive environment;
provide the output response to the query using a graphical user interface of the virtual interactive environment.
Govindan teaches:
receive, using an interface of a virtual interactive environment, audio data representative of a query and a new corpus of unstructured text (para [0113-114], where the user query is received as a voice input, and para [0100], where text is extracted from an image);
virtual interactive environments (para [0114], where a query is received and report outputted in a VR scenario)
provide the output response to the query using a graphical user interface of the virtual interactive environment (para [0114], where the results are outputted in a GUI rendered via a VR headset).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister by using the VR interface of Govindan (Govindan para [0114]) as the interaction environment of Hoffmeister (Hoffmeister col. 21 line 62 – col. 22 line 42), in order to assist users with decision making by integrating VR in analytics visualizations (Govindan para [0023]).
Regarding claim 2, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the response is identified within the updated corpus of unstructured text (Hoffmeister col. 21 lines 47-61, where facts based on unstructured text are stored in the knowledge base).
Regarding claim 4, Hoffmeister in view of Govindan teaches:
The processor of claim 3, wherein the one or more processing units are further to:
receive an informative statement (Hoffmeister col. 21 lines 47-61, where facts are obtained from unstructured text from a web search); and
store the informative statement as a natural language statement (Hoffmeister col. 21 lines 47-61, where the obtained facts are stored in a knowledge base, and col. 28 line 45 - col. 29 line 18, where the answers are stored with the question text).
Regarding claim 5, Hoffmeister in view of Govindan teaches:
The processor of claim 4, wherein the one or more processing units are further to:
receive a second query to the virtual interactive environment (Hoffmeister col. 22 lines 43-53, where a second query is received, and Govindan para [0114], where a VR environment is used);
determine the classification for the second query is a second information based query (Hoffmeister col. 22 lines 54-63, where the query is classified as a rephrase of the first query or not);
determine the second query is associated with the informative statement (Hoffmeister col. 24 line 41 - col. 25 line 10, where the second query is answered using the knowledge base); and
perform one or more tasks based, at least in part, on the informative statement (Hoffmeister col. 24 line 41 - col. 25 line 10, where the task is the TTS output being performed).
Regarding claim 6, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the one or more processing units are further to execute a task, responsive to the query, based, at least in part, on the response (Hoffmeister col. 21 line 62 - col. 22 line 42, where the task is to perform text-to-speech processing on prompt text of the response).
Regarding claim 7, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the one or more processing units are further to:
determine an identifier associated with the response (Hoffmeister col. 21 line 62 - col. 22 line 42, where the identifier is the ASR confidence, which is used to determine which prompt to use); and
cause one or more actions to be performed based, at least in part, on the identifier (Hoffmeister col. 21 line 62 - col. 22 line 42, where the action is to perform text-to-speech processing on prompt text of the response).
Regarding claim 8, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the one or more processing units are further to execute an extractive question and answer model, and wherein the one or more processing units use the extractive question answer model to determine the response to the query (Hoffmeister col. 21 line 39 - col. 22 line 42, where the answer to the query is extracted from the knowledge base, which contains both structured and unstructured data).
Regarding claim 9, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the query is an auditory input and further includes at least one of a textual input or a selectable input (Hoffmeister col. 21 line 62 - col. 22 line 42, where the input is audio data corresponding to a query, col. 22 line 64 – col. 23 line 6, where the auditory input is processed into a textual input, and col. 24 lines 49-66, where the query further includes a response to a prompt involving selecting a yes or no answer).
Regarding claim 16, Hoffmeister teaches:
A computer-implemented method, comprising:
receiving an informational input from a user in a natural language format (col. 21 line 47 - col. 22 line 42, where facts are obtained from unstructured text from a web search from a first user query);
storing, in the natural language format, the informational input as part of a corpus of unstructured text associated with a domain of a representation in an environment (col. 21 lines 47-61, where the obtained facts are stored in a knowledge base as unstructured text, col. 28 line 45 - col. 29 line 18, where the answers are stored with the question text, and col. 9 lines 18-33, where a language model is associated with one of domain options), the corpus of unstructured text including a plurality of different schema structures, and at least one schema structure of the plurality of schema structures is different from a selected schema structure for the informational input (col. 26 lines 6-45, where unstructured search results come in a variety of forms such as a web page, and col. 3 lines 46-54, where another example is a triple which is used to respond to the query);
receiving, using an interface of the environment audio data representative of a user query and a new corpus of unstructured text, to perform a task (col. 22 lines 43-53, where a second audio query is received, and col. 26 lines 6-45, where unstructured search results are obtained, where Examiner notes that the new corpus of unstructured text and the audio data are not necessarily related or received at the same time);
determining a classification of the user query (col. 22 lines 54-63, where the query is classified as a rephrase of the first query or not);
extracting, from the user query and based on the classification, a segment associated with the task (col. 23 line 58 - col. 24 line 21, where semantic parsing is performed, including extracting triples);
updating, at runtime the corpus of unstructured text with the new corpus of unstructured text to generate an updated corpus of unstructured text (col. 28 line 45 – col. 29 line 54, where association data is stored during runtime, the storage considered updating the corpus as the knowledge base is being added to);
retrieving, based at least in part on the segment, at least a portion of the informational input from the updated corpus of unstructured text associated with the environment (col. 24 line 41 - col. 25 line 10, where the second query is answered using the knowledge base, and col. 21 lines 47-61, where facts based on unstructured text are stored in the knowledge base); and
executing the task based, at least in part, on the portion of the informational input (col. 24 line 41 - col. 25 line 10, where the task is the TTS output being performed).
Hoffmeister does not teach:
receiving, using an interface of the virtual interaction environment audio data representative of a user query and a new corpus of unstructured text, to perform a task;
a virtual interactive environment;
Govindan teaches:
receiving, using an interface of the virtual interaction environment audio data representative of a user query and a new corpus of unstructured text, to perform a task (para [0113-114], where the user query is received as a voice input, and para [0100], where text is extracted from an image);
virtual interactive environments (para [0114], where a query is received and report outputted in a VR scenario)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister by using the VR interface of Govindan (Govindan para [0114]) as the interaction environment of Hoffmeister (Hoffmeister col. 21 line 62 – col. 22 line 42), in order to assist users with decision making by integrating VR in analytics visualizations (Govindan para [0023]).
Regarding claim 17, Hoffmeister in view of Govindan teaches:
The computer-implemented method of claim 16, further comprising:
determining one or more actions associated with the informational input (Hoffmeister col. 21 line 62 - col. 22 line 42, where the action is to perform text-to-speech processing on prompt text of the response); and
assigning one or more tags to the informational input (Hoffmeister col. 10 lines 8-28, where semantic tagging is performed to label the triple).
Regarding claim 18, Hoffmeister in view of Govindan teaches:
The computer-implemented method of claim 16, wherein an extractive question answer model determines the portion of the informational input (Hoffmeister col. 21 line 39 - col. 22 line 42, where the answer to the query is extracted from the knowledge base, which contains both structured and unstructured data).
Claim(s) 3, 10-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hoffmeister, in view of Govindan, and further in view of Podgorny et al. (US 2021/0065018 A1), hereinafter referred to as Podgorny.
Regarding claim 3, Hoffmeister in view of Govindan teaches:
The processor of claim 1, wherein the one or more processing units are further to:
Hoffmeister in view of Govindan does not teach:
receive the response using a trained generative neural network model; and
generate the output response using the trained generative neural network model based, at least in part, on the response and an extracted portion of the query.
Podgorny teaches:
receive the response using a trained generative neural network model (para [0040-41], where the generative answer model receives an answer for optimization, and para [0043], where a neural network is used); and
generate the output response using the trained generative neural network model based, at least in part, on the response and an extracted portion of the query (para [0040-41], where the generative answer model optimizes the answer, and para [0042], where knowledge generated at previous layers is used, such as generated question and answers) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister in view of Govindan by using the generative model of Podgorny (Podgorny para [0040-43]) in the question answering of Hoffmeister in view of Govindan (Hoffmeister col. 21 line 62 - col. 22 line 42), in order to assist a user by generating an answer or optimizing answer, or generating a quality answer quickly (Podgorny para [0040]).
Regarding claim 10, Hoffmeister teaches:
A method, comprising:
storing a plurality of facts as unstructured plain text within a dataset, using a plurality of different schema structures, to define a domain of information for an environment (col. 21 lines 47-61, where facts based on unstructured text are stored in the knowledge base, col. 26 lines 6-45, where unstructured search results come in a variety of forms such as a web page, and col. 9 lines 18-33, where a language model is associated with one of domain options);
receiving a user query and a new corpus of unstructured text, in a natural language format, associated with a parameter for an object in the environment (col. 21 line 62 - col. 22 line 42, where a query is received at a query answering system, where the parameter is the subject "President Obama", and col. 26 lines 6-45, where unstructured search results are obtained, where Examiner notes that the new corpus of unstructured text and the audio data are not necessarily related or received at the same time);
extracting, from the user query, a text sequence (col. 21 line 62 - col. 22 line 42, where a triple is identified from the query, or the text is semantically parsed);
updating, at runtime, the dataset with the new corpus of unstructured text to generate an updated dataset (col. 28 line 45 – col. 29 line 54, where association data is stored during runtime, the storage considered updating the corpus as the knowledge base is being added to);
determining, within the updated dataset, one or more selected facts associated with the text sequence to describe the parameter (col. 21 line 62 - col. 22 line 42, where the question is answered using the knowledge base, and col. 25 line 44 - col. 26 line 5, where the results are found via a web search); and
generating a response based, at least in part, on the one or more selected facts (col. 21 line 62 - col. 22 line 42, where the answer is returned to the user).
Hoffmeister does not teach visual interactive environments, nor visual representation of parameters and objects.
Govindan teaches:
receiving a user query and a new corpus of unstructured text, in a natural language format, associated with a parameter for an object in the virtual interactive environment (para [0113-114], where the user query is received as a voice input, and para [0100], where text is extracted from an image);
virtual interactive environments (para [0114], where a query is received and report outputted in a VR scenario)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister by using the VR interface of Govindan (Govindan para [0114]) as the interaction environment of Hoffmeister (Hoffmeister col. 21 line 62 – col. 22 line 42), in order to assist users with decision making by integrating VR in analytics visualizations (Govindan para [0023]).
Podgorny teaches:
receiving a user query and a new corpus of unstructured text, in a natural language format, associated with a visually represented parameter for a visual representation of an object in an interaction environment (Fig. 3A-C, para [0063-66], where a user interface displays the user query, the query including parameters such as “house” or “new”, the query itself considered an object, and where the retrieved answers are the new corpus of unstructured text);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister in view of Govindan by using the generative model of Podgorny (Podgorny para [0040-43]) in the question answering of Hoffmeister in view of Govindan (Hoffmeister col. 21 line 62 - col. 22 line 42), in order to assist a user by generating an answer or optimizing answer, or generating a quality answer quickly (Podgorny para [0040]).
Regarding claim 11, Hoffmeister in view of Govindan and Podgorny teaches:
The method of claim 10, wherein the text sequence is extracted and the one or more selected facts are determined using a trained extractive question answer model (Hoffmeister col. 21 line 39 - col. 22 line 42, where the answer to the query is extracted from the knowledge base, which contains both structured and unstructured data).
Regarding claim 12, Hoffmeister in view of Govindan and Podgorny teaches:
The method of claim 10, wherein the response is a natural language response based, at least in part, on an output of a generative neural network model (Podgorny para [0040-43], where the generative answer model using a neural network generates an optimized answer in natural language).
Regarding claim 13, Hoffmeister in view of Govindan and Podgorny teaches:
The method of claim 10, further comprising:
receiving an informative input (Hoffmeister col. 21 lines 47-61, where facts are obtained from unstructured text from a web search); and
storing, in a natural language format, the informative input within the dataset (Hoffmeister col. 21 lines 47-61, where the obtained facts are stored in a knowledge base, and col. 28 line 45 - col. 29 line 18, where the answers are stored with the question text).
Regarding claim 14, Hoffmeister in view of Govindan and Podgorny teaches:
The method of claim 13, further comprising:
receiving a second user query (Hoffmeister col. 22 lines 43-53, where a second query is received);
determining a classification of the second user query (Hoffmeister col. 22 lines 54-63, where the query is classified as a rephrase of the first query or not);
retrieving, based at least in part on the classification, one or more portions of the informative input (Hoffmeister col. 24 line 41 - col. 25 line 10, where the second query is answered using the knowledge base); and
executing one or more tasks based, at least in part, on the one or more portions (Hoffmeister col. 24 line 41 - col. 25 line 10, where the task is the TTS output being performed).
Regarding claim 15, Hoffmeister in view of Govindan and Podgorny teaches:
The method of claim 10, further comprising:
determining one or more calls associated with the text sequence, the one or more calls corresponding to an action (Hoffmeister col. 21 line 62 - col. 22 line 42, where the call is to perform text-to-speech processing on prompt text of the response); and
causing the action to be performed based, at least in part, on the response (Hoffmeister col. 21 line 62 - col. 22 line 42, where the text-to-speech processing is performed on prompt text of the response).
Regarding claim 20, Hoffmeister in view of Govindan teaches:
The computer-implemented method of claim 16,
generating an auditory indicator associated with at least the portion of the informational input (col. 24 line 41 - col. 25 line 10, where the TTS output is generated).
Hoffmeister in view of Govindan does not teach:
receiving at least the portion of the informational input at a trained generative neural network model;
Podgorny teaches:
receiving at least the portion of the informational input at a trained generative neural network model (para [0040-41], where the generative answer model receives an answer for optimization, and para [0043], where a neural network is used);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hoffmeister in view of Govindan by using the generative model of Podgorny (Podgorny para [0040-43]) in the question answering of Hoffmeister in view of Govindan (Hoffmeister col. 21 line 62 - col. 22 line 42), in order to assist a user by generating an answer or optimizing answer, or generating a quality answer quickly (Podgorny para [0040]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 11,907,672 B2 col. 12 lines 45-63 teaches updating a topic specific corpus as feedback is received for retraining a classifier.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN S BLANKENAGEL whose telephone number is (571)270-0685. The examiner can normally be reached 8:00am-5:30pm.
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/BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2653
