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 .
DETAILED ACTION
This office action is in response to application 18/954,213, which was filed 11/20/24. Claims 1-23 are pending in the application and have been considered.
Specification
In the abstract, line 3, should “The at least one processor programmed to” be “The at least one processor is programmed to”?
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “receive a request for information about a first individual; retrieve information from one or more data sources based upon the request for information; determine one or more missing items of information based upon the retrieved information and the request for information; execute a GPT model trained with interaction data to generate one or more questions for the first individual to answer in order to provide the one or more missing items of information; and present the one or more questions to the first individual”.
The limitation of “receive a request for information about a first individual”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “receive a request for information about a first individual” in the context of this claim encompasses receiving a written request for information about a first individual written on a sheet of paper.
Similarly, the limitation of “retrieve information from one or more data sources based upon the request for information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “retrieve information from one or more data sources based upon the request for information” in the context of this claim encompasses retrieving a file folder with information about the first individual.
Similarly, the limitation of “determine one or more missing items of information based upon the retrieved information and the request for information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determine one or more missing items of information based upon the retrieved information and the request for information” in the context of this claim encompasses reading through the file folder and mentally determining missing items of information based upon the retrieved information and the request for information.
Similarly, the limitation of “execute a GPT model trained with interaction data to generate one or more questions for the first individual to answer in order to provide the one or more missing items of information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “execute a GPT model trained with interaction data” language, “generate one or more questions for the first individual to answer in order to provide the one or more missing items of information” in the context of this claim encompasses mentally generating one or more questions for the first individual to answer in order to provide the one or more missing items of information.
Similarly, the limitation of “present the one or more questions to the first individual”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “present the one or more questions to the first individual” in the context of this claim encompasses writing down the questions and presenting them to the individual.
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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites four additional elements – “computer system”, “at least one processor”, “memory device” and “GPT model trained with interaction data”. The computing elements in this step are recited at a high-level of generality (i.e., as a generic computer system, a generic at least one processor, a generic memory device and generic GPT model) such that they amount to no more than mere instructions to apply the exception using generic computer elements. While the claim specifies that the GPT model is “trained with interaction data”, this does not distinguish the claimed GPT model from generic, off the shelf GPT models which were well-known to those skilled in the art at filing time to be pre-trained on massive amounts of text including online interactions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does 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 element of using a computing device to perform the receiving, retrieving, determining, generating, and presenting amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Specifically with respect to Step 2A, Prong Two, of the Alice/Mayo test, the judicial exception is not integrated into a practical application. Claim 1 does not recite any limitations that are not mental steps.
Specifically with respect to Step 2B of the Alice/Mayo test, “the claim as a whole does not amount to significantly more than the exception itself (there is no inventive concept in the claim)”. MPEP 2106.05 Il. There are no limitations in claim 1 outside of the judicial exception. As a whole, there does not appear to contain any inventive concept. As discussed above, claim 1 is a mental process that pertains to the mental process of determining information is missing and generating questions for an individual to answer in order to provide the missing information, which can be performed entirely by a human with physical aids.
Dependent claims 2-11 depend from claim 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 above.
Generally, claims 2-11 merely recite additional steps for determining information is missing and generating questions for an individual to answer in order to provide the missing information, all of which either could be performed mentally or by writing down relationships with a pen and paper, or amount to reciting generic computer equipment performing generic computer functions which do not amount to anything more than substantially the same abstract idea as explained with respect to claim 1.
Specifically:
Claim 2 recites “ receive, from the first individual, one or more responses to the one or more questions” which could be performed by receiving, from the first individual, one or more responses to the one or more questions.
Claim 3 recites “generate a response to the request for information based upon the retrieved information and the one or more responses to the one or more questions” which could be performed by mentally generating a response to the request for information based upon the retrieved information and the one or more responses to the one or more questions.
Claim 4 recites “determine a first channel of communication to transmit the one or more questions to a computer device associated with the first individual” which could be performed by mentally determining a first channel of communication to transmit the one or more questions to a computer device associated with the first individual.
Claim 5 recites “ the one or more questions includes a first question and a second question, and wherein the at least one processor is further programmed to: determine a first channel of communication to transmit the first question to a computer device associated with the first individual; and determine a second channel of communication to transmit the second question to a computer device associated with the first individual, wherein the first channel and the second channel are different” which, but for “the at least one processor is further programmed to:”, could be performed by mentally determining a first channel of communication to transmit the first question to a computer device associated with the first individual; and mentally determining a second, different channel of communication to transmit the second question to a computer device associated with the first individual. The “at least one processor” does not amount to significantly more for the same reasons as explained above with respect to claim 1.
Claim 6 recites “transmit the second question a period of time after the first question was answered” which could be performed by speaking the second question a period of time after the first question was answered.
Claim 7 recites “determine an ordering for communicating the first question and the second question” which could be performed by mentally determining an ordering for communicating the first question and the second question.
Claim 8 recites “train the GPT model with a plurality of interaction data associated with a plurality of individuals over a plurality of channels of communication”. This does not distinguish the claimed GPT model from generic, off the shelf GPT models which were well-known to those skilled in the art at filing time to be pre-trained on massive amounts of text including online interactions across multiple channels. 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.
Claim 9 recites “the GPT model is trained with the plurality of interaction data associated with the first individual” which does not integrate the abstract idea into a practical application for reasons similar to those for claim 8.
Claim 10 recites “the GPT model is executed with the plurality of interaction data associated with the first individual as input” which does not integrate the abstract idea into a practical application for reasons similar to those for claim 8.
Claim 11 recites “ generate the one or more questions via natural language processing” which does not integrate the abstract idea into a practical application for reasons similar to those for claim 8.
In sum, claims 2-11 depend from claim 1 and further recite mental processes or generic computer equipment performing generic computer functions which do not amount to anything more than substantially the same abstract idea as explained with respect to claim 1 as explained above. None of the additional limitations recited in claims 2-11 amount to significantly more than the same or a similar abstract idea as recited in claim 1. Nor do any limitations in claims 2-11 (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception. Claims 2-11 are not patent eligible.
Claim 12 is directed to a method that corresponds to the system of claim 1 and is therefore rejected for the same reasons set for the above with respect to claim 1. While claim 12 recites generic computer components (at least one processor, at least one memory device), such generic computing components are recited at a high-level of generality (i.e., as a generic processor and generic memory performing a generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 12 does 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 limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 12 is not patent eligible.
Claims 13-22 depend from claim 12, do not remedy any of the deficiencies of claim 12, and correspond to the subject matter discussed above in claims 2-11, and therefore are rejected on similar grounds as claims 12 and 2-11 above.
Claim 23 is directed to at least one non-transitory computer-readable storage media that corresponds to the system of claim 1 and is therefore rejected for the same reasons set forth above with respect to claim 1. Moreover, while claim 23 recites generic computing components (e.g., instructions, processor), such components are only claimed at a high-level of generality and are not sufficient to render the claim subject matter eligible for the same reasons discussed above with respect to claims 1 and 12.
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 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 of this title, 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 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Goyal et al. (US 20190347319) in view of Mahler-Haug et al. (US 20250069148).
Consider claim 1, Goyal discloses a computer system for coordinating advanced communications (computers implementing conversational chat bots, [0051], [0040]), the system comprising at least one processor in communication with at least one memory device (processor 111 and RAM 113, [0040]), the at least one processor programmed to:
receive a request for information about a first individual (a conversation is initiated by a user by sending a query to the bot client interface, [0076], e.g. “I want to return my shoes”, [0054], received at bot client interface such as Google Home, Amazon Alexa, etc., [0052], or a question about a document, [0070]);
retrieve information from one or more data sources based upon the request for information (matching the query with an intent that corresponds to a particular document and associated conversation model, [0076], e.g. conversational and return form document, [0054]);
determine one or more missing items of information based upon the retrieved information and the request for information (classifying interrogatory text with locations a user should enter information, e.g. areas left blank, [0062-0063], e.g. confirmation number, product identification number, [0054]);
execute a model trained with interaction data to generate one or more questions for the first individual to answer in order to provide the one or more missing items of information (generating questions based on the interrogatory text, [0068-0069], e.g. questions asking the user for confirmation number, product identification number, [0054]); and
present the one or more questions to the first individual (the system asks the sequence of generated questions to the user, [0084], [0068-0069], [0054]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model in order to improve natural language processing, as suggested by Mahler-Haug ([0017]). Doing so would have led to predictable results of improved user experience, as suggested by Mahler-Haug ([0017]). The references cited are analogous art in the same field of natural language processing.
Consider claim 12, Goyal discloses a computer-implemented method for coordinating advanced communications implemented by a computer system including at least one processor in communication with at least one memory device (methods performed by computers implementing conversational chat bots, [0051], including processor 111 and RAM 113, [0040]), the method comprising:
receiving a request for information about a first individual (a conversation is initiated by a user by sending a query to the bot client interface, [0076], e.g. “I want to return my shoes”, [0054], received at bot client interface such as Google Home, Amazon Alexa, etc., [0052], or a question about a document, [0070]);
retrieving information from one or more data sources based upon the request for information (matching the query with an intent that corresponds to a particular document and associated conversation model, [0076], e.g. conversational and return form document, [0054]);
determining one or more missing items of information based upon the retrieved information and the request for information (classifying interrogatory text with locations a user should enter information, e.g. areas left blank, [0062-0063], e.g. confirmation number, product identification number, [0054]);
executing a model trained with interaction data to generate one or more questions for the first individual to answer in order to provide the one or more missing items of information (generating questions based on the interrogatory text, [0068-0069], e.g. questions asking the user for confirmation number, product identification number, [0054]); and
presenting the one or more questions to the first individual (the system asks the sequence of generated questions to the user, [0084], [0068-0069], [0054]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 23, Goyal discloses at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor of a computer system (processor 111 executes control logic stored on RAM 113, [0040]), the computer-executable instructions cause the processor to:
receive a request for information about a first individual (a conversation is initiated by a user by sending a query to the bot client interface, [0076], e.g. “I want to return my shoes”, [0054], received at bot client interface such as Google Home, Amazon Alexa, etc., [0052], or a question about a document, [0070]);
retrieve information from one or more data sources based upon the request for information (matching the query with an intent that corresponds to a particular document and associated conversation model, [0076], e.g. conversational and return form document, [0054]);
determine one or more missing items of information based upon the retrieved information and the request for information (classifying interrogatory text with locations a user should enter information, e.g. areas left blank, [0062-0063], e.g. confirmation number, product identification number, [0054]);
execute a model trained with interaction data to generate one or more questions for the first individual to answer in order to provide the one or more missing items of information (generating questions based on the interrogatory text, [0068-0069], e.g. questions asking the user for confirmation number, product identification number, [0054]); and
present the one or more questions to the first individual (the system asks the sequence of generated questions to the user, [0084], [0068-0069], [0054]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 2, Goyal discloses the at least one processor is further programmed to receive, from the first individual, one or more responses to the one or more questions (answers provided by the user, [0082]; receive response, bock 520, Fig. 5).
Consider claim 3, Goyal discloses the at least one processor is further programmed to generate a response to the request for information based upon the retrieved information and the one or more responses to the one or more questions (system responds to the request for information by retrieving a document, asking the user questions, and filling the document with the answers provided by the user, [0082], [0070], [0062-0063]).
Consider claim 4, Goyal discloses the at least one processor is further programmed to determine a first channel of communication to transmit the one or more questions to a computer device associated with the first individual (e.g. questions are transmitted from NLP service to chat interface on a Google Home, [0052]).
Consider claim 5, Goyal discloses the one or more questions includes a first question and a second question, and wherein the at least one processor is further programmed to: determine a first channel of communication to transmit the first question to a computer device associated with the first individual (chatbot communicates questions of plurality of different communication interfaces, including instant messenger, [0020-0021], [0054], e.g. Facebook Messager, Fig. 3); and determine a second channel of communication to transmit the second question to a computer device associated with the first individual, wherein the first channel and the second channel are different (chatbot communicates questions of plurality of different communication interfaces, including voice-activated personal assistant, [0020-0021], [0054], e.g. Google Home, Fig. 3).
Consider claim 6, Goyal discloses the at least one processor is further programmed to transmit the second question a period of time after the first question was answered (system begins a loop that iterates over the questions in the conversation model, asking the next question after response is received to the previous question, [0084], [0085], Fig. 5; the “period of time” being the amount of time after receiving the answer and before asking the next question).
Consider claim 7, Goyal discloses the at least one processor is further programmed to determine an ordering for communicating the first question and the second question (the order of questions in the iterative loop, [0084]).
Consider claim 8, Goyal discloses the at least one processor is further programmed to train the model with a plurality of interaction data associated with a plurality of individuals over a plurality of channels of communication (training engine updates conversation models, [0052], improving the conversation model as it engages in conversations with users, [0033], over Google Home, Facebook Messenger, Slack, etc., Fig 3, [0052], [0020-0021]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 9, Goyal discloses the model is trained with the plurality of interaction data associated with the first individual (training engine updates conversation models, [0052], improving the conversation model as it engages in conversations with users, [0033]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 10, Goyal discloses the model is executed with the plurality of interaction data associated with the first individual as input (model validates user’s answer and determines whether to provide follow up instructions/queries, Fig. 5, [0090]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 11, Goyal discloses the at least one processor is further programmed to generate the one or more questions via natural language processing (the conversation model, which includes questions to ask a user, is generated via natural language processing techniques, [0033]).
Consider claim 13, Goyal discloses receiving, from the first individual, one or more responses to the one or more questions (answers provided by the user, [0082]; receive response, bock 520, Fig. 5).
Consider claim 14, Goyal discloses generating a response to the request for information based upon the retrieved information and the one or more responses to the one or more questions (system responds to the request for information by retrieving a document, asking the user questions, and filling the document with the answers provided by the user, [0082], [0070], [0062-0063]).
Consider claim 15, Goyal discloses determining a first channel of communication to transmit the one or more questions to a computer device associated with the first individual (e.g. questions are transmitted from NLP service to chat interface on a Google Home, [0052]).
Consider claim 16, Goyal discloses the one or more questions includes a first question and a second question, and wherein the method further comprises: determining a first channel of communication to transmit the first question to a computer device associated with the first individual (chatbot communicates questions of plurality of different communication interfaces, including instant messenger, [0020-0021], [0054], e.g. Facebook Messager, Fig. 3); and determining a second channel of communication to transmit the second question to a computer device associated with the first individual, wherein the first channel and the second channel are different (chatbot communicates questions of plurality of different communication interfaces, including voice-activated personal assistant, [0020-0021], [0054], e.g. Google Home, Fig. 3).
Consider claim 17, Goyal discloses transmitting the second question a period of time after the first question was answered (system begins a loop that iterates over the questions in the conversation model, asking the next question after response is received to the previous question, [0084], [0085], Fig. 5; the “period of time” being the amount of time after receiving the answer and before asking the next question).
Consider claim 18, Goyal discloses determining an ordering for communicating the first question and the second question (the order of questions in the iterative loop, [0084]).
Consider claim 19, Goyal discloses training the model with a plurality of interaction data associated with a plurality of individuals over a plurality of channels of communication (training engine updates conversation models, [0052], improving the conversation model as it engages in conversations with users, [0033], over Google Home, Facebook Messenger, Slack, etc., Fig 3, [0052], [0020-0021]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 20, Goyal discloses the model is trained with the plurality of interaction data associated with the first individual (training engine updates conversation models, [0052], improving the conversation model as it engages in conversations with users, [0033]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 21, Goyal discloses the model is executed with the plurality of interaction data associated with the first individual as input (model validates user’s answer and determines whether to provide follow up instructions/queries, Fig. 5, [0090]).
Goyal does not specifically mention a GPT model.
Mahler-Haug discloses a GPT model (utilizing a GPT to generate queries, [0040], [0017], Fig 6C, [0081]).
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 invention of Goyal by utilizing a GPT model for reasons similar to those for claim 1.
Consider claim 22, Goyal discloses generating the one or more questions via natural language processing (the conversation model, which includes questions to ask a user, is generated via natural language processing techniques, [0033]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Majumber et al. (“Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge”. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4300–4312 June 6–11, 2021) discloses automatically generating clarification questions that identify useful and missing information in a given context
US 12073930 Alperin discloses generating a use profile by asking questions and filling in missing sections
US 12112135 Yuan discloses question answering information completion using machine reading comprehension-based process.
US 20250037084 Kang discloses generating interview questions for missing or incomplete information using AI
US 12456015 Kumar discloses natural language question generation
US 20250078150 Deng discloses using an LLM to generate and answer questions associated with a mortgage application
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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 06/16/26
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