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 .
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 12, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 5 and 12, are dependent on claims 1 and 8.
Claims 1 and 8 recite “determin ..., by the LLM, that the feedback, received from the expert agent, resolves the ambiguity”.
Therefore claims 1 and 8 positively recite that the feedback does resolve the ambiguity.
Claims 5 and 12 recite “verify..., by the LLM, that the feedback fails to resolve the ambiguity”.
Therefore claims 5 and 12 contradict claims 1 and 8 respectively by reciting that the feedback fails to resolve the ambiguity, rendering claim(s) 5 and 12 indefinite.
For examination purposes, the examiner has interpreted claims 5 and 12, by ignoring the positive recitation in claims 1 and 8 that the feedback does resolve the ambiguity.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claim(s) 1-7 and 15-20 is/are method type claim. Claim(s) 8-14 is/are system type claim(s). Therefore, claims 1-20 is/are directed to either a process, machine, manufacture or composition of matter.
Independent claim(s):
Step 2A Prong 1:
Regarding claim(s) 1 and 8, this/these claim(s) recite(s)
identifying an ambiguity during decision processing of first input data;
determining, ..., that the feedback, received from the expert agent, resolves the ambiguity;
generating second input data ..., the second input data comprising the first input data and the feedback determined to resolve the ambiguity;
processing the second input data ... to generate a decision based on processing of the second input data.
The above limitations of appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can manually identify ambiguities, determine whether feedback resolves ambiguity, generate and process second input data, and generate a decision.
Regarding claim(s) 15, this/these claim(s) recite(s)
evaluating the first input data...;
identifying, ..., an ambiguity preventing the decision ML model from satisfying a confidence threshold condition;
providing, ... , second input data determined to resolve the ambiguity from the expert agent;
applying, ... , the second input data determined to resolve the ambiguity to the first input data to complete evaluation of the first input data; and
The above limitations of appears to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can manually evaluate data, identify ambiguities based on conditions, provide data and apply data to perform data evaluation.
Step 2A Prong 2:
Regarding claim(s) 1, 8 and 15, this judicial exception is not integrated into a practical application.
Additional elements:
Regarding claim(s) 8 this/these claim(s) recite(s) a processor and memory to perform the step of the abstract idea (mere instructions stored in a generic memory component to apply the exception using a generic computer component);
Regarding claim(s) 1 and 8, this/these claim(s) further recite(s)
by a decision machine learning (ML) model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a decision ML model to make a decision);
conveying... receiving, ..., (insignificant extra solution activity of mere data gathering or transmission);
the ambiguity to an expert agent for evaluation; feedback regarding the ambiguity from the expert agent (These limitations appear to be directed to the specification of data to be used to perform the abstract idea, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
by a large language model (LLM) (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a large language model);
outputting the decision received from the ML model (These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, see MPEP 2106.05(g)).
Regarding claim(s) 15, this/these claim(s) further recite(s)
receiving ...transmitting (insignificant extra solution activity of mere data gathering or transmission);
customer activity as first input data; information relating to the ambiguity to an expert agent (These limitations appear to be directed to the specification of data to be used to perform the abstract idea, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
by the decision ML model, to the decision ML model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a decision ML model to make a decision);
outputting the completed evaluation of the first input data as a risk assessment decision(These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, see MPEP 2106.05(g)).
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B:
Regarding claim(s) 1, 8 and 15 this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Regarding claim(s) 8 this/these claim(s) recite(s) a processor and memory to perform the step of the abstract idea (mere instructions stored in a generic memory component to apply the exception using a generic computer component);
Regarding claim(s) 1 and 8, this/these claim(s) further recite(s)
by a decision machine learning (ML) model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a decision ML model to make a decision);
conveying... receiving, ..., (insignificant extra solution activity of mere data gathering, Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and transmitting step is well-understood, routine, conventional activity is supported under Berkheimer);
the ambiguity to an expert agent for evaluation; feedback regarding the ambiguity from the expert agent (These limitations appear to be directed to the specification of data to be used to perform the abstract idea, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
by a large language model (LLM) (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a large language model);
outputting the decision received from the ML model (These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, Furthermore, these limitations directed towards outputting information determined by the abstract idea, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim) see Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Regarding claim(s) 15, this/these claim(s) further recite(s)
receiving ...transmitting (insignificant extra solution activity of mere data gathering, Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining and transmitting step is well-understood, routine, conventional activity is supported under Berkheimer);
customer activity as first input data; information relating to the ambiguity to an expert agent (These limitations appear to be directed to the specification of data to be used to perform the abstract idea, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
by the decision ML model, to the decision ML model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a decision ML model to make a decision);
outputting the completed evaluation of the first input data as a risk assessment decision (These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, Furthermore, these limitations directed towards outputting information determined by the abstract idea, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim) see Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Dependent claims 2-7, 9-14 and 16-20 recite at least the abstract idea identified above in the claims 1, 8 and 15 upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Therefore, the claim(s) is/are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-8, 11-14, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (US 20230245651 A1).
Regarding claim 1, Wang teaches a method comprising (Wang Figs. 2, 7 and 19, [5, 33, 117, 413, 501-506] method to request information from user to better understand intents and objectives, AI system (200) has various components that work together (see [117] and Fig. 2 for list of components)):
identifying an ambiguity during decision processing of first input data by a decision machine learning (ML) model (Wang [413-416, 210] AI system determines if there are any gaps or deficiencies (ambiguity) input data (user input and OKB data));
conveying the ambiguity to an expert agent for evaluation (Wang [210, 416, 417, 422, 427] if input data has ambiguity, then AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity);
receiving, by a large language model (LLM), feedback regarding the ambiguity from the expert agent (Wang [210, 413, 416, 417] user feedback may be received by NLU module(s) (large language model(s)), Wang [135, 143, 145, 243] large language model(s) may be used for dialogue between the system and a user, dialogue is performed using Natural Language Understanding (NLU) and Natural Language Generation (NLG) modules, NLG includes Generative AI module);
determining, by the LLM, that the feedback, received from the expert agent, resolves the ambiguity; generating second input data by the LLM, the second input data comprising the first input data and the feedback determined to resolve the ambiguity (Wang [210, 419-423] user feedback is analyzed to determine whether feedback results in high enough confidence level (resolves ambiguity), Wang [415, 419, 420, 424, 481] user feedback in combination with input data for current loop to generate integrated information (second input data) for current loop, Wang [481] NLU and NLG modules may be used for interacting with the user and generating understanding and responses);
processing the second input data by the decision ML model to generate a decision based on processing of the second input data; and outputting, by the LLM, the decision received from the ML model (Wang [199, 203, 284-289, 435] integrated information is analyzed to determine whether to generate a notification based on trends and what type of notification (decision), notifications are sent to appropriate recipients based on type of notification, Wang [66, 67, 277] trends may be determined using mL model(s)).
Regarding claim 4, Wang teaches the invention as claimed in claim 1 above.
Wang further teaches wherein determining that the feedback resolves the ambiguity comprises verifying, by the LLM, that the feedback provides data that resolves the ambiguity (Wang [210, 419-423] user feedback is analyzed to determine whether feedback results in high enough confidence level (resolves ambiguity), Wang [481] NLU and NLG modules may be used for interacting with the user and generating understanding and responses).
Regarding claim 5, Wang teaches the invention as claimed in claim 1 above.
Wang further teaches wherein determining that the feedback resolves the ambiguity comprises: verifying, by the LLM, that the feedback fails to resolve the ambiguity; and notifying the expert agent that additional insight is needed (Wang [210, 419-423] user feedback is analyzed to determine whether feedback results in high enough confidence level (resolves ambiguity), Wang [415, 419, 420, 424, 481] user feedback in combination with input data for current loop to generate integrated information (second input data) for current loop, if integrated information has ambiguity then process is repeated, Wang [210, 416, 417, 420, 422, 424, 427] if input or integration data has ambiguity, then AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity, Wang [481] NLU and NLG modules may be used for interacting with the user and generating understanding and responses).
Regarding claim 6, Wang teaches the invention as claimed in claim 1 above.
Wang further teaches wherein generating the second input data comprises: transforming the feedback determined to resolve the ambiguity into a transformed feedback configured as an input for processing by the decision ML model (Wang [113, 168, 198, 442] user feedback during conversation may be transformed using natural language techniques);
combining the transformed feedback with the first input data to form the second input data; and providing to the decision ML model, the second input data from the LLM (Wang [415, 419, 420, 424, 481] user feedback may be transformed and then used in combination with input data for current loop to generate integrated information (second input data) for current loop, Wang [199, 203, 284-289, 435] integrated information is analyzed to determine whether to generate a notification based on trends and what type of notification (decision), notifications are sent to appropriate recipients based on type of notification, Wang [66, 67, 277] trends may be determined using mL model(s)).
Regarding claim 7, Wang teaches the invention as claimed in claim 1 above.
Wang further teaches wherein: the first input data comprises customer activity data, and the decision is a risk assessment of customer activity based on the customer activity data (Wang [230, 274, 276-279] input data may include user activity, decision may include security risk based on user activity).
Claim 8 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale.
Wang further teaches processing system, comprising: a memory comprising computer-executable instructions; and a processor configured to execute the computer-executable instructions (Wang Figs. 2, 7 and 19, [5, 33, 117, 413, 501-506]).
Claim(s) 11-14 is/are dependent on claim 8 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 4-7 respectively, and is/are rejected under the same rationale.
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 2, 3, 9, 10, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230245651 A1), in view of Konam (US 20230334263 A1).
Regarding claim 2, Wang teaches the invention as claimed in claim 1 above.
Wang further teaches wherein conveying the ambiguity to the expert agent comprises: transmitting, by the decision ML model, ambiguity-related information to the LLM; and processing, by the LLM, the ambiguity-related information to provide ... ambiguity ...information... in a human readable format (Wang [210, 416, 417, 422, 427] AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity, prompt may be generated using questions and conversation in human-readable format and using NLG).
Wang does not specifically teach an ambiguity report.
However Konam teaches processing, by the LLM, the ambiguity-related information to provide an ambiguity report in a human readable format (Konam [87, 90, 140, 144] system may use LLM to facilitate communication with user, communication may be a report of possible options for ambiguity and allow user to select option, report may be a summary of input data that is used to determine ambiguity and key points derived from input data, Also see Konam [42, 76, 77, 98, 123]).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Konam of processing, by the LLM, the ambiguity-related information to provide an ambiguity report in a human readable format, into the invention suggested by Wang; since both inventions are directed towards determining and conveying ambiguity in input data, and incorporating the teaching of Konam into the invention suggested by Wang would provide the added advantage of allowing a user to get insight into the key points of input data analysis by providing a summary report of analysis of input data, and the combination would perform with a reasonable expectation of success (Konam [42, 76, 77, 87, 98, 123, 140, 144]).
Regarding claim 3, Wang and Konam teach the invention as claimed in claim 2 above.
Wang does not specifically teach wherein the ambiguity report comprises: a description of the ambiguity; and a summary of analysis performed on the first input data by the decision ML model prior to detection of the ambiguity.
However Konam teaches wherein the ambiguity report comprises: a description of the ambiguity; and a summary of analysis performed on the first input data by the decision ML model prior to detection of the ambiguity (Konam [87, 90, 140, 144] system may use LLM to facilitate communication with user, communication may be a report of possible options for ambiguity and allow user to select option, report may be a summary of input data that is used to determine ambiguity and key points derived from input data).
Claim(s) 9 and 10 is/are dependent on claim 8 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2 and 3 respectively, and is/are rejected under the same rationale.
Claims 15, 18, 19, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230245651 A1), in view of Allen (US20160259863A1).
Regarding claim 15, Wang teaches a method for providing a risk assessment of customer activity using a decision machine learning (ML) model, comprising (Wang Figs. 2, 7 and 19, [5, 33, 117, 274, 413, 501-506] method to request information from user to better understand intents and objectives and then provide notifications for risk assessments, AI system (200) has various components that work together (see [117] and Fig. 2 for list of components)):
receiving customer activity as first input data (Wang [230, 274, 276-279] input data may include user activity);
evaluating the first input data by the decision ML model; identifying, by the decision ML model, an ambiguity preventing the decision ML model from satisfying a confidence ... condition (Wang [413-416, 210] AI system evaluates if there are any gaps or deficiencies (ambiguity) input data (user input and OKB data), AI system determines whether or not it can determine the user’s intent and objective with a reasonable level of confidence (satisfying a confidence ... condition);
transmitting information relating to the ambiguity to an expert agent (Wang [210, 416, 417, 422, 427] if input data has ambiguity, then AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity);
providing, to the decision ML model, second input data determined to resolve the ambiguity from the expert agent (Wang [210, 415, 419, 420, 424, 481] user feedback that resolves ambiguity in combination with input data for current loop is used to generate integrated information (second input data) for current loop, Wang [66, 67, 277] integrated information may be provided to ML models to determine trends);
applying, by the decision ML model, the second input data determined to resolve the ambiguity to the first input data to complete evaluation of the first input data; and outputting the completed evaluation of the first input data as a risk assessment decision (Wang [199, 203, 284-289, 435] integrated information is analyzed to determine whether to generate a notification based on trends and what type of notification (decision), notifications are sent to appropriate recipients based on type of notification, Wang [66, 67, 277] trends may be determined using mL model(s)).
Wang does not specifically teach satisfying a confidence threshold condition.
However Allen teaches identifying an ambiguity preventing the decision ... model from satisfying a confidence threshold condition (Allen [2, 21, 63-67] ambiguity for input may be based on domain confidence threshold(s), domain(s) may determine context).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Allen of identifying an ambiguity preventing the decision ML model from satisfying a confidence threshold condition,, into the invention suggested by Wang; since both inventions are directed towards determining whether or not the context(s) of input data is/are ambiguous, and incorporating the teaching of Allen into the invention suggested by Wang would provide the added advantage of using well defined criteria for ambiguity by using thresholds as conditions to define ambiguity, and the combination would perform with a reasonable expectation of success (Allen [2, 21, 63-67]).
Regarding claim 18, Wang and Allen teach the invention as claimed in claim 15 above.
Wang further teaches wherein providing the second input data to the decision ML model comprises: receiving, by a large language model (LLM), a feedback from the expert agent (Wang [210, 413, 416, 417] user feedback may be received by NLU module(s) (large language model(s)), Wang [135, 143, 145, 243] large language model(s) may be used for dialogue between the system and a user, dialogue is performed using Natural Language Understanding (NLU) and Natural Language Generation (NLG) modules, NLG includes Generative AI module);
verifying, by the LLM, that the feedback provides data that resolves the ambiguity (Wang [210, 419-423] user feedback is analyzed to determine whether feedback results in high enough confidence level (resolves ambiguity), Wang [481] NLU and NLG modules may be used for interacting with the user and generating understanding and responses);
transforming the feedback verified to resolve the ambiguity into the second input data for the decision ML model; and transmitting, to the decision ML model, the second input data by the LLM (Wang [113, 168, 198, 442] user feedback during conversation may be transformed using natural language techniques, Wang [415, 419, 420, 424, 481] user feedback may be transformed and then used in combination with input data for current loop to generate integrated information (second input data) for current loop, Wang [199, 203, 284-289, 435] integrated information is analyzed to determine whether to generate a notification based on trends and what type of notification (decision), notifications are sent to appropriate recipients based on type of notification, Wang [66, 67, 277] trends may be determined using mL model(s)
Regarding claim 19, Wang and Allen teaches the invention as claimed in claim 15 above.
Wang further teaches wherein providing the second input data to the decision ML model comprises: receiving, by a large language model (LLM), a feedback from the expert agent (Wang [210, 413, 416, 417] user feedback may be received by NLU module(s) (large language model(s)), Wang [135, 143, 145, 243] large language model(s) may be used for dialogue between the system and a user, dialogue is performed using Natural Language Understanding (NLU) and Natural Language Generation (NLG) modules, NLG includes Generative AI module);
verifying, by the LLM, that the feedback fails to resolve the ambiguity; and notifying the expert agent that additional insight is needed Wang [210, 419-423] user feedback is analyzed to determine whether feedback results in high enough confidence level (resolves ambiguity), Wang [415, 419, 420, 424, 481] user feedback in combination with input data for current loop to generate integrated information (second input data) for current loop, if integrated information has ambiguity then process is repeated, Wang [210, 416, 417, 420, 422, 424, 427] if input or integration data has ambiguity, then AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity, Wang [481] NLU and NLG modules may be used for interacting with the user and generating understanding and responses).
Claims 16, 17, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230245651 A1) in view of Allen (US20160259863A1), and further in view of Konam (US 20230334263 A1).
Regarding claim 16, Wang and Allen teaches the invention as claimed in claim 15 above.
Wang further teaches wherein transmitting information relating to the ambiguity to the expert agent comprises: sending, by the decision ML model, ambiguity-related information to a large language model (LLM) (Wang [210, 416, 417, 422, 427] if input data has ambiguity, then AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity, Wang [210, 413, 416, 417] user feedback may be received by NLU module(s) (large language model(s));
processing, by the LLM, the ambiguity-related information to provide ... ambiguity ...information...in a human readable format (Wang [210, 416, 417, 422, 427] AI system may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity, prompt may be generated using questions and conversation in human-readable format and using NLG); and
transmitting, by the LLM, the ... ambiguity ...information... to the expert agent (Wang [210, 416, 417, 422, 427] if input data has ambiguity, then NLG may prompt user (questions or ask for clarification regarding the input data) for feedback for ambiguity);
Wang does not specifically teach an ambiguity report.
However Konam teaches processing, by the LLM, the ambiguity-related information to provide an ambiguity report in a human readable format (Konam [87, 90, 140, 144] system may use LLM to facilitate communication with user, communication may be a report of possible options for ambiguity and allow user to select option, report may be a summary of input data that is used to determine ambiguity and key points derived from input data, Also see Konam [42, 76, 77, 98, 123]).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Konam of processing, by the LLM, the ambiguity-related information to provide an ambiguity report in a human readable format, into the invention suggested by Wang and Allen; since both inventions are directed towards determining and conveying ambiguity in input data, and incorporating the teaching of Konam into the invention suggested by Wang and Allen would provide the added advantage of allowing a user to get insight into the key points of input data analysis by providing a summary report of analysis of input data, and the combination would perform with a reasonable expectation of success (Konam [42, 76, 77, 87, 98, 123, 140, 144]).
Regarding claim 17, Wang, Allen and Konam teach the invention as claimed in claim 16 above.
Wang does not specifically teach wherein the ambiguity report comprises: a description of the ambiguity; and a summary of analysis performed on the first input data by the decision ML model prior to detection of the ambiguity.
However Konam teaches wherein the ambiguity report comprises: a description of the ambiguity; and a summary of analysis performed on the first input data by the decision ML model prior to detection of the ambiguity (Konam [87, 90, 140, 144] system may use LLM to facilitate communication with user, communication may be a report of possible options for ambiguity and allow user to select option, report may be a summary of input data that is used to determine ambiguity and key points derived from input data).
Claims 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230245651 A1) in view of Allen (US20160259863A1), and further in view of Budzik (US 20210158085 A1).
Regarding claim 20 Wang and Allen teaches the invention as claimed in claim 15 above.
Wang does not specifically teach wherein the decision ML model identifies ambiguities based on reason code and counterfactual explanations
However Budzik teaches wherein the decision ML model identifies ambiguities based on reason code and counterfactual explanations (Budzik [20-25,52, 57-60] model documentation data includes model information including missing data (ambiguity), model information may be determined by ML model based on reason codes and counterfactual explanation).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Budzik of wherein the decision ML model identifies ambiguities based on reason code and counterfactual explanations, into the invention suggested by Wang and Allen; since both inventions are directed towards identifying ambiguities in input data, and incorporating the teaching of Budzik into the invention suggested by Wang and Allen would provide the added advantage of identifying causal impact and providing actionable explanations by using counterfactual explanations, and the combination would perform with a reasonable expectation of success (Budzik [20-25,52, 57-60]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANCHITA ROY whose telephone number is (571)272-5310. The examiner can normally be reached Monday-Friday 12-8.
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
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