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 .
This office action is in response to the claimed invention filed on June 05, 2025, in which claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement filed September 17, 2025, December 16, 2025 ns June 25, 2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
Step 1, Statutory Category:
Claims 1-16 are directed to a system.
Claims 16-19 are directed to a method.
Claim 20 is directed to a computer program product embodied in a non-transitory computer readable medium.
Therefore, claims 1-20 fall into at least one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
2A, Prong One (Judicial exception recited)
The limitation “determine that a correctness associated with a derived pattern mapping associated with a webpage or application is greater than a confidence threshold” in claims 1, 16 and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can mentally or manually with the aid of pen and paper determine that a correctness associated with a derived pattern mapping associated with a webpage or application is greater than a confidence threshold.
The limitation “utilize the derived pattern mapping to generate a response for the input” in claims 1, 16 and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can mentally or manually with the aid of pen and paper generate a response for the input based on a derived pattern mapping.
Step 2A, Prong Two (Integrated into a practical application):
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The limitation “receive an input and processed content associated with a tree data structure” amounts to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “obtain the derived pattern mapping that is based on a large language model response” amounts to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “a processor, memory and non-transitory computer readable medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). Note, the mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application.
Step 2B (claim provides an inventive concept):
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to the “receiving ….” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the “obtaining…” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the “processor, memory and non-transitory computer readable medium” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 1 is directed to an abstract idea. The remaining independent claim 16 and 20 fall short the 35 USC 101 requirement under the same rationale.
The dependent claims 2-15 and 17-19 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 2 recites “wherein the input is a structured query”. This additional element is recited at a high level of generality and would function in its ordinary capacity for using structured query as filed of use, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same analysis applies to claim 17.
Claim 3 recites “wherein the input is freeform text”. This additional element is recited at a high level of generality and would function in its ordinary capacity for using freeform text as filed of use, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same analysis applies to claim 18.
Claim 4 recites “store the derived pattern mapping”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing the derived pattern mapping, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 5 recites “provide the response for the input.”. This additional element is recited at a high level of generality and would function in its ordinary capacity for providing the response for the input., this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same analysis applies to claim 19.
Claim 6 recites “determine a plurality of beacon nodes in the tree data structure associated with the processed content”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 7 recites “wherein a beacon node of the plurality of beacon nodes includes a consistent set of attributes across a plurality of instances associated with the webpage or application”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 8 recites “determine in the tree data structure associated with the processed content corresponding paths from the plurality of beacon nodes to target nodes corresponding to the one or more variables associated with the input”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 9 recites “wherein the response is generated by an inference pattern engine utilizing the derived pattern mapping by mapping one or more variables included in the input to one or more elements included in the processed content associated with the tree data structure”. This additional element is recited at a high level of generality and would function in its ordinary capacity for utilizing the derived pattern mapping by mapping one or more variables included in the input to one or more elements included in the processed content associated with the tree data structure, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 10 recites “generate a corresponding prompt based on the input and the processed content associated with the tree data structure; and provide the corresponding prompt to the large language model”. This additional element is recited at a high level of generality and would function in its ordinary capacity for providing the corresponding prompt to the large language model., this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 11 recites “receive from the large language model a corresponding response that maps one or more variables associated with the input to the one or more elements associated with the processed content associated with the tree data structure”. This additional element is recited at a high level of generality and would function in its ordinary capacity for providing the corresponding prompt to the large language model., this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more; and “compare the response generated by the inference pattern engine to the corresponding response received from the large language model”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 12 recites “determine a corresponding correctness associated with the response generated by the inference pattern engine based on the comparison”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 13 recites “determine, based on the corresponding correctness associated with the response generated by the inference pattern engine, that a confidence threshold has been reached for the input and the processed content associated with the tree data structure”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 14 recites “wherein the processor is configured to determine, based on the corresponding correctness associated with the response generated by the inference pattern engine, that a confidence threshold has not been reached for the input and the processed content associated with the tree data structure”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 15 recites “wherein in response to the confidence threshold not being reached, the processor is configured to generate a new pattern”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 16 and 20 recite “determine that a correctness associated with a derived pattern mapping associated with a webpage or application is greater than a confidence threshold”. It is unclear how a correctness associated with a derived pattern would determine that associates with a webpage or application that is greater than a confidence threshold. It is also of what seems to be a confidence threshold. The claims also recite “utilize the derived pattern mapping to generate a response for the input”. It is not clear how one having skill in the art would utilize a derived pattern to generate a response to a query.
Claims 2-15 and 17-19 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Khosla et al., (hereinafter “Khosla”) US 20250005052.
As to claim 1, Khosla discloses a system, comprising:
a processor (see [0020], customer computing devices 122 can be configured to have at least one processor. That processor can be in communication with the memory for maintaining computer-executable instructions. The customer computing devices 122 may be physical or virtual. The customer computing devices 122 may be mobile devices, personal computers, servers, or other types of devices. The customer computing devices 122 have a display and input devices through which a user can interact with the user-interface component) configured to:
receive an input and processed content associated with a tree data structure (see par [0017]-[0020], receive answers from the natural language question answering service based on the natural language question); determine that a correctness associated with a derived pattern mapping associated with a webpage or application is greater than a confidence threshold (see [0010], [0014] and [0025], natural language questions themselves have their own issues. For example, regardless of how accurate a generative AI model is, a natural language query may be entered (e.g., words used, order of words, etc.) in a manner (e.g., faulty, not sufficiently broad enough, etc.) such that a generative AI model generates an answer that is (i) incomplete or incorrect in reference to the question or (ii) fails to capture the intent of the drafter of the question. This can result in inefficient use of resources of a generative AI model (e.g., multiple question and answer turns not resulting in an answer agreeable to the entity which entered the question). Additionally, even if the natural language question is not faulty or too narrow, generative AI models may still generate an answer that is determined to be in error or incorrect (e.g., did not really answer the question, the answer is not justified by training data of the generative AI, a generative AI model generates an answer different from what is expected, etc.), which is also called “hallucination.” Answers generated in error can happen when questions about particular private network-based services are asked because the public internet does not have this private information to train a generative AI model);
obtain the derived pattern mapping that is based on a large language model response (see [0015], the watermark may include a hidden pattern in the regenerated verified answers that is imperceptible by humans while making the hidden pattern algorithmically identifiable as synthetic by a system); and
utilize the derived pattern mapping to generate a response for the input (see [0013], [0014], the natural language question answer service can utilize a verifier to verify the answer generated by the LLM to ensure it was not generated in error (e.g., hallucinated). The verifier may utilize one or more modules to ensure the answer was not generated in error. A textual overlap module of the verifier can determine how much overlap in text there is in the answer and the retrieved passages. A textual natural language inference (NLI) module of the verifier may determine whether the answer generated from the LLM contradicts the retrieved passages from the aggregator by using a premise and hypothesis. A relational NLI module of the verifier may determine whether the answer generated from the LLM is not generated in error in relation to the natural language question by using head, tail, and relation triples. Also, a membership inference attack module of the verifier may determine whether the question is similar to a previous question and is in a training set of the LLM); and
a memory coupled to the processor and configured to provide the processor with one or more instructions (see [0020], a processor is in communication with the memory for maintaining computer-executable instructions).
As to claim 2, Khosla discloses the system of claim 1, wherein the input is a structured query(see par. [0046]).
As to claim 3, Khosla discloses the system of claim 1, wherein the input is freeform text (see par. [0046]).
As to claim 4, Khosla discloses the system of claim 1, wherein the processor is further configured to store the derived is pattern mapping (see par. [0074]).
As to claim 5, Khosla discloses the system of claim 1, wherein the processor is configured to provide the response for the input (see par.[053]).
As to claim 6, Khosla discloses the system of claim 1, wherein to derive the derived pattern mapping associated with the webpage or application, the processor is configured to determine a plurality of beacon nodes in the tree data structure associated with the processed content (see par. [0053]-[0055]).
As to claim 7, Khosla discloses the system of claim 6, wherein a beacon node of the plurality of beacon nodes includes a consistent set of attributes across a plurality of instances associated with the webpage or application (see par. [0053]-[0055]).
As to claim 8, Khosla discloses the system of claim 6, wherein to derive the pattern mappings, the processor is configured to determine in the tree data structure associated with the processed content corresponding paths from the plurality of beacon nodes to target nodes corresponding to the one or more variables associated with the input (see par. [0059]-[0061]).
As to claim 9, Khosla discloses the system of claim 1, wherein the response is generated by an inference pattern engine utilizing the derived pattern mapping by mapping one or more variables included in the input to one or more elements included in the processed content associated with the tree data structure (see par. [0059]-[0061]).
As to claim 10, Khosla discloses the system of claim 9, wherein the processor is configured to: 5 generate a corresponding prompt based on the input and the processed content associated with the tree data structure; and provide the corresponding prompt to the large language model (see par. [0061]-[0063]).
As to claim 11, Khosla discloses the system of claim 10, wherein the processor is configured to: receive from the large language model a corresponding response that maps one or more io variables associated with the input to the one or more elements associated with the processed content associated with the tree data structure (see par. [0046], LLM component may receive the prompt from the aggregator component 104, the user context (optionally) from user context component 105, and generate one or more answers based on the prompt and the user context. The LLM component 106 may be trained on at least QA pairs generated from the search systems 124 or knowledge graphs of customers of network-based services (e.g., a network-based on-demand computing service which is serverless, etc.). The LLM component 106 may take the prompt and the user context received from the aggregator component 104 and utilize a generative AI model (e.g., Retrieval Augmented Generation (RAG) utilizing natural language processing (NLP) architecture) to determine an answer to the natural language question. For example, if the customer computing devices 122 sends a natural language question regarding how to setup a type of network-based storage, the LLM component 106 may utilize a trained generative AI model (e.g., trained on QA pairs from a network-based storage service and customer knowledge graphs) to determine an answer (e.g., where the answer provides the instructions and potential API calls to setup the network-based storage) from the prompt received from the aggregator component); and compare the response generated by the inference pattern engine to the corresponding response received from the large language model (see par. [0014], natural language question answer service can utilize a verifier to verify the answer generated by the LLM to ensure it was not generated in error (e.g., hallucinated). The verifier may utilize one or more modules to ensure the answer was not generated in error. A textual overlap module of the verifier can determine how much overlap in text there is in the answer and the retrieved passages. A textual natural language inference (NLI) module of the verifier may determine whether the answer generated from the LLM contradicts the retrieved passages from the aggregator by using a premise and hypothesis. A relational NLI module of the verifier may determine whether the answer generated from the LLM is not generated in error in relation to the natural language question by using head, tail, and relation triples. Also, a membership inference attack module of the verifier may determine whether the question is similar to a previous question and is in a training set of the LLM).
As to claim 12, Khosla discloses the system of claim 11, wherein the processor is configured to determine a is corresponding correctness associated with the response generated by the inference pattern engine based on the comparison (see par. [0014], natural language question answer service can utilize a verifier to verify the answer generated by the LLM to ensure it was not generated in error (e.g., hallucinated). The verifier may utilize one or more modules to ensure the answer was not generated in error. A textual overlap module of the verifier can determine how much overlap in text there is in the answer and the retrieved passages. A textual natural language inference (NLI) module of the verifier may determine whether the answer generated from the LLM contradicts the retrieved passages from the aggregator by using a premise and hypothesis. A relational NLI module of the verifier may determine whether the answer generated from the LLM is not generated in error in relation to the natural language question by using head, tail, and relation triples. Also, a membership inference attack module of the verifier may determine whether the question is similar to a previous question and is in a training set of the LLM).
As to claim 13, Khosla discloses the system of claim 12, wherein the processor is configured to determine, based on the corresponding correctness associated with the response generated by the inference pattern engine, that a confidence threshold has been reached for the input and the processed content associated with the tree data structure (see [0010], [0014] and [0025], natural language questions themselves have their own issues. For example, regardless of how accurate a generative AI model is, a natural language query may be entered (e.g., words used, order of words, etc.) in a manner (e.g., faulty, not sufficiently broad enough, etc.) such that a generative AI model generates an answer that is (i) incomplete or incorrect in reference to the question or (ii) fails to capture the intent of the drafter of the question. This can result in inefficient use of resources of a generative AI model (e.g., multiple question and answer turns not resulting in an answer agreeable to the entity which entered the question). Additionally, even if the natural language question is not faulty or too narrow, generative AI models may still generate an answer that is determined to be in error or incorrect (e.g., did not really answer the question, the answer is not justified by training data of the generative AI, a generative AI model generates an answer different from what is expected, etc.), which is also called “hallucination.” Answers generated in error can happen when questions about particular private network-based services are asked because the public internet does not have this private information to train a generative AI model).
As to claim 14, Khosla discloses the system of claim 12, wherein the processor is configured to determine, based on the corresponding correctness associated with the response generated by the inference pattern engine, that a confidence threshold has not been reached for the input and the processed content associated with the tree data structure (see [0010], [0014] and [0025], natural language questions themselves have their own issues. For example, regardless of how accurate a generative AI model is, a natural language query may be entered (e.g., words used, order of words, etc.) in a manner (e.g., faulty, not sufficiently broad enough, etc.) such that a generative AI model generates an answer that is (i) incomplete or incorrect in reference to the question or (ii) fails to capture the intent of the drafter of the question. This can result in inefficient use of resources of a generative AI model (e.g., multiple question and answer turns not resulting in an answer agreeable to the entity which entered the question). Additionally, even if the natural language question is not faulty or too narrow, generative AI models may still generate an answer that is determined to be in error or incorrect (e.g., did not really answer the question, the answer is not justified by training data of the generative AI, a generative AI model generates an answer different from what is expected, etc.), which is also called “hallucination.” Answers generated in error can happen when questions about particular private network-based services are asked because the public internet does not have this private information to train a generative AI model);
As to claim 15, Khosla discloses the system of claim 14, wherein in response to the confidence threshold not being reached, the processor is configured to generate a new pattern (see [0010], [0014] and [0025], natural language questions themselves have their own issues. For example, regardless of how accurate a generative AI model is, a natural language query may be entered (e.g., words used, order of words, etc.) in a manner (e.g., faulty, not sufficiently broad enough, etc.) such that a generative AI model generates an answer that is (i) incomplete or incorrect in reference to the question or (ii) fails to capture the intent of the drafter of the question. This can result in inefficient use of resources of a generative AI model (e.g., multiple question and answer turns not resulting in an answer agreeable to the entity which entered the question). Additionally, even if the natural language question is not faulty or too narrow, generative AI models may still generate an answer that is determined to be in error or incorrect (e.g., did not really answer the question, the answer is not justified by training data of the generative AI, a generative AI model generates an answer different from what is expected, etc.), which is also called “hallucination.” Answers generated in error can happen when questions about particular private network-based services are asked because the public internet does not have this private information to train a generative AI model).
As to claims 16-19, claims 16-19 are system for performing the method of claims 1-15 above. They are rejected under the same rationale.
As to claim 20, claim 20 is a non-transitory computer readable medium for executing the method of claims 1-15. It is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240412000 (involved in receiving a user input comprising a request for data through a cloud-based platform. The user input is classified into a set of deterministic-stochastic spectrum classifications based on the user input and a probability of mapping the request for the data to a data location. The data is retrieved from the data location and based on one of the classifications. An input is transmitted to a large language model e.g. publicly accessible large language. A response is presented to the input, where the response is based on a combination of an output of the model and the data).
US 20220414456 (involved in processing a portion of a file in a classification neural network to determine whether a watermark is present in the portion of the file. The processor performs an update associated with the watermark in the file portion based on a result of the processing, and provides the updated file portion, where the processor is configured to check security settings to ensure that the watermarks are permitted to be removed from the file portions before removing the watermarks. Processor compares the watermark information to an information template to determine whether the watermark information matches the information template.)
US 20220138559 (involved in receiving multiple questions in a natural language question and answer system by a computing device. Multiple answers are generated to the questions. A training set with the generated answer is constructed by the computing device. Each answer is compared with a corresponding question. The training set with multiple tokens delimiting a span of generated answers is augmented. A natural language question and answer system with the augmented training set is trained by using the computing device. An answer span of a reader model of the natural language question and answer system is corrected.)
US 20210089594 (involved in receiving a query. The result documents related to the query are retrieved. An evidence graph having links are established between individual result documents. The contextualized semantic representations are obtained for individual words in a second result document by propagating inter-document attention from a first result document that is linked to the second result document in the evidence graph. The contextualized semantic representations of the individual words are processed in the second result document using a machine learning model to obtain an answer to the query. The answer is outputted in response to the query).
US 20150227520 (involved in determining that a question received by a deep question answering system is speculative. A set of candidate answers is generated by multiple predictive algorithms. A score is computed for each candidate answer in the set of candidate answers. A candidate answer of the set of candidate answers is returned as responsive to the speculative question received by the deep question answering system.)
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 July 9, 2026
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