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 .
Response to Amendment
The Amendment filed 5/4/2026 has been entered. Claims 91-92 and 94-95 were amended. Claims 105-106 and 108-110 are canceled. Claims 111-117 are new claims. Claims 1-90, 93, 96-104, and 107 were previously canceled. Claims 91-92, 94-95, and 111-117 are pending. Claims 113-117 stand withdrawn from consideration as being directed to a non-elected invention (see the requirement for restriction by original presentation below). Claims 91-92, 94-95, and 111-112 are under examination and are addressed on the merits in this action.
Priority
Acknowledgment was previously made of Applicant's claim to the benefit of prior-filed provisional applications 62/286,437 (filed January 24, 2016), 62/294,258 (filed February 11, 2016), 62/307,558 (filed March 13, 2016), 62/323,657 (filed April 16, 2016), 62/326,723 (filed April 23, 2016), 62/341,310 (filed May 25, 2016), 62/439,409 (filed December 27, 2016), and 62/449,313 (filed January 23, 2017), through non-provisional application 15/413,666 (filed January 24, 2017).
For purposes of prior art, the effective filing date of examined claims 91-92, 94-95, and 111-112 is December 27, 2016. Entitlement to an earlier date is not supported. Under 35 U.S.C. 112(a), a claim is entitled to the benefit of the filing date of an earlier-filed application only for subject matter the earlier application describes in compliance with the written description requirement (see MPEP 211.05 and 2152.01). The provisional applications filed before December 27, 2016 do not provide written description support for the claimed Lexical Objectivity Mining system. Specifically, provisional 62/286,437 is directed to a Clandestine Machine Intelligence (MACINT) covert-operations system; provisional 62/294,258 and provisional 62/307,558 are directed to a malware-defense platform (LIZARD; Critical Infrastructure Protection and Retribution through Cloud and Tiered Information Security); provisional 62/323,657 is directed to a Critical Thinking Memory and Perception (CTMP) module; and provisional 62/326,723 is directed to a Linear Atomic Quantum Information Transfer (LAQiT) scheme. None of these discloses the claimed Lexical Objectivity Mining modules. The earliest provisional disclosing the Lexical Objectivity Mining subject matter is provisional 62/439,409, filed December 27, 2016. Accordingly, claims 91-92, 94-95, and 111-112 are accorded an effective filing date of December 27, 2016.
Election/Restrictions by Original Presentation
Newly submitted claims 113-117 are directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
Claims 113-117 are directed to a computer-implemented security system for secure communication, comprising a Communication Target Monitoring Protocol (CTMP) module that monitors a plurality of communication targets, a Linguistic Intent Zooming and Analysis of Resultant Data (LIZARD) module that analyzes a linguistic intent of the communication targets, a Mimicry of Artificial Intelligence and Computer Intelligence (MACINT) module that creates a mimicry of the communication targets, and an Ultimate Blockchain Encryption Core (UBEC) module that encrypts communications between the communication targets. This invention is directed to monitoring, analyzing, mimicking, and encrypting machine-to-machine communications.
The originally presented and examined invention, recited in claims 91-92, 94-95, and 111-112, is a Lexical Objectivity Mining (LOM) system that engages a human subject to concede or improve an argument against the stance of the system, comprising the Initial Query Reasoning, Survey Clarification, Assertion Construction, Response Presentation, Hierarchical Mapping, Central Knowledge Retention, Knowledge Validation, Accept Response, Rational Appeal, and Managed Artificially Intelligent Services Provider modules. This invention is directed to deriving an objective answer to a human subject's question or assertion and validating knowledge.
The two inventions are unrelated and independent (see MPEP 802.01 and 806.06): they share no common module, are directed to different problems (human-to-machine argument adjudication versus machine-to-machine communication security), operate by materially different means, and achieve different results. The mere recitation in claim 113 of "a Lexical Objectivity Mining (LOM) module" as an element that performs no recited function does not relate the inventions, because claim 113's recited operation is performed entirely by the Communication Target Monitoring Protocol, Linguistic Intent Zooming and Analysis of Resultant Data, Mimicry of Artificial Intelligence and Computer Intelligence, and Ultimate Blockchain Encryption Core modules. Even if the inventions were considered related, they are distinct: each can be made and used separately, and a serious search and examination burden would result from their joint examination because they fall in different fields of search (the Lexical Objectivity Mining invention in artificial-intelligence knowledge processing and the secure-communication invention in network-communication security and encryption). The newly submitted claims could have been restricted from the claims drawn to the elected invention had they been presented earlier; the practice of MPEP 821.03 therefore applies.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 113-117 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Rejections - 35 USC 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 111 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 111, the claim recites "wherein the MAISP is configured to manage a plurality of LOM instances." The original disclosure does not reasonably convey to one skilled in the art that the inventor had possession of a Managed Artificially Intelligent Services Provider (MAISP) that manages a plurality of (that is, two or more) Lexical Objectivity Mining (LOM) instances as of the filing date.
To satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention at the time the application was filed. See MPEP 2163. Possession may be shown by describing the claimed invention with all of its limitations; an adequate written description requires more than a mere statement that the invention is encompassed by the disclosure, and the level of detail must be commensurate with the scope of the claim.
Here, the specification describes only a single instance of LOM in connection with the MAISP. The summary recites that the MAISP "runs an internet cloud instance of LOM with a master instance of the CKR" (a single instance of LOM), and the corresponding detailed description ([0149], describing Figs. 122-124) likewise states that "MAISP runs an internet cloud instance of LOM with a master instance of Central Knowledge Retention (CKR) 806," again a single instance. The only duplication or plurality described in the LOM architecture is of the CKR database, not of the LOM instance: the specification explains that LOM "would benefit from Central Knowledge Retention (CKR) 806 being centralized in a single (yet duplicated for redundancy and backups) master instance," and that "[t]hird party apps can be facilitated via a paid or free API that connects to such a central master instance." Thus the disclosure depicts a single LOM instance served by a single (redundancy-duplicated) master CKR database. Every other use of the term in the specification is likewise singular, for example "[a]n isolated and secure instance of LOM" and "a customized instance of LOM" used for the policy-making and military deployments. The terms "plurality of LOM instances," "LOM instances," and "plurality of LOM" do not appear anywhere in the disclosure.
Because the original disclosure depicts a single LOM instance (with a single, redundancy-duplicated master CKR database) and nowhere describes the MAISP managing two or more LOM instances, the disclosure does not demonstrate that the inventor had possession of a MAISP "configured to manage a plurality of LOM instances" as recited in claim 111. The duplication of the CKR database for backup and redundancy is not a description of a plurality of LOM instances, and a single described instance does not convey possession of the broader claimed genus of plural instances. See MPEP 2163. Claim 111 therefore fails to comply with the written description requirement.
Appropriate correction or a showing of support in the specification as filed is required. Applicant is reminded that no new matter may be added (35 U.S.C. 132; MPEP 2163.06).
Claim Rejections - 35 USC 112(b)
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 91-92, 94-95, and 111-112 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.
Claim 91 is indefinite for at least the following reasons:
1. Starting line 6 recites "at least one database operating a Lexical Objectivity Mining (LOM) module." As written, it is grammatically unclear whether the "at least one database" performs the operating, which would be inconsistent with a database functioning as a passive storage structure. The reasonable interpretation, the uncertainty not being great, is that the claimed system (that is, the processor executing the programmed instructions) operates the LOM module, and the LOM module utilizes the at least one database. (The previously recited "wherein the system is a computer implemented system being Lexical Objectivity Mining (LOM)" has been deleted; the prior ambiguity as to whether "being LOM" is a verb, noun, or something else is therefore cured, although the new "database operating a ... module" phrasing introduces the ambiguity addressed here.)
2. The preamble recites "A computer-implemented security system based upon artificial intelligence, the computer-implemented system comprising," while the body recites "the computer-implemented security system further comprising." The intervening phrase "the computer-implemented system" omits the word "security" and thus does not exactly correspond to the introduced "a computer-implemented security system." The previous recitation of "the computer system" has been amended, which narrows but does not eliminate the inconsistency. The uncertainty is not great: "the computer-implemented system," "the computer-implemented security system," and "a computer-implemented security system" are each interpreted as referring to the single claimed system. Applicant should conform the terminology.
3. Limitation (d) recites "a Response Presentation (RP) interface for presenting a conclusion drawn by the AC module to both the HS and Rational Appeal (RA) module." It remains unclear whether "presenting a conclusion drawn by" the AC module to both the HS and the RA module requires a visually displayed drawing or interface connection, or instead denotes logical connections within the system. The reasonable interpretation adopted is that "a conclusion drawn by ... the AC module" means a conclusion derived or reached by the AC module, and that the RP is an interface that presents that conclusion as output to both the HS and the RA module.
4. Limitation (c) recites "a proposition in the form of an assertion or question and provides output of the concepts related to said proposition." The terms "the form" and "the concepts" lack express antecedent basis. (The amendment changed "the proposition" to "said proposition" but did not address "the form" or "the concepts.") The reasonable interpretation adopted is that "in the form of" recites the format of the proposition, and that "the concepts" refers to the concepts, related to said proposition, that the AC module provides as output.
5. The Response Separation Logic (RSL) clause recites "associate a relevant and valid response with the initial request, thereby accomplishing the objective of SC." The phrase "the initial request" lacks express antecedent basis. The reasonable interpretation adopted is that "the initial request" refers to the question input by the HS (the original Question/Assertion), and that "the objective of SC" refers to forming the Clarified Question/Assertion (CQ/A).
6. The Concept Compatibility Detection (CCD) clause recites "compares conceptual derivatives from the original Question/Assertion to ascertain the logical compatibility result." The phrases "the original Question/Assertion" and "the logical compatibility result" lack express antecedent basis. The reasonable interpretation adopted is that "the original Question/Assertion" refers to the Question/Assertion input by the HS prior to clarification, and that "the logical compatibility result" refers to the result of the CCD comparison.
7. The Benefit/Risk Calculator (BRC) clause recites "receives the compatibility results from the CCD and weighs the benefits and risks to form a uniform decision that encompasses the gradients of variables implicit in the concept makeup." The phrases "the gradients of variables" and "the concept makeup" lack express antecedent basis. The reasonable interpretation adopted is that these phrases refer to the variable gradients and the conceptual composition of the concepts being weighed by the BRC. ("The compatibility results" now finds reasonable antecedent basis in the "logical compatibility result" ascertained by the CCD).
8. The Concept Interaction (CI) clause recites "assigns attributes that pertain to AC concepts to parts of the information collected from the HS via Survey Clarification (SC)." The phrase "the information collected from the HS" lacks express antecedent basis. The reasonable interpretation adopted is that "the information collected from the HS" refers to the information that the SC collects from the HS.
9. Limitation (e) recites that the Hierarchical Mapping (HM) module "calculates benefits and risks of having a certain stance on a topic," and the BRC clause recites that the BRC "weighs the benefits and risks." It is unclear whether "the benefits and risks" weighed by the BRC are the same benefits and risks calculated by the HM module, or a separate determination. The uncertainty is not great: the reasonable interpretation adopted is that "the benefits and risks" recited in the BRC clause refers to, and finds antecedent basis in, the "benefits and risks" first introduced in limitation (e) (the HM module), and that the BRC weighs those benefits and risks, as informed by the compatibility results from the CCD, to form the uniform decision.
10. Limitation (i) recites that the MAISP "connects the LOM module to Front End Services, Back End Services and third-party application dependencies via an internet cloud connection." The terms "Front End Services" and "Back End Services" remain capitalized terms. The reasonable interpretation adopted is that these terms denote external service categories to which the MAISP connects the LOM module (further defined in claim 92), and not separately claimed structural modules of claim 91.
Claims 92, 94-95, and 111-112 depend from claim 91. They incorporate and do not cure the indefiniteness of claim 91, and are rejected for the same reason.
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 91-92, 94-95, and 111-112 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 91
Step 1
Claim 91 recites "A computer-implemented security system based upon artificial intelligence, the computer-implemented system comprising" a memory, a processor, and at least one database. The claim is therefore directed to a system, which is a machine, and falls within a statutory category of 35 U.S.C. 101. The analysis proceeds to Step 2A.
Step 2A Prong One
The claim recites an abstract idea. Setting aside the generically recited hardware (addressed in Step 2A Prong Two), the substance of the Lexical Objectivity Mining (LOM) module limitations recites a combination of two groupings of abstract ideas: (i) mental processes, that is, concepts that can be performed in the human mind or with the aid of pen and paper, including observation, evaluation, judgment, and opinion; and (ii) certain methods of organizing human activity, that is, managing personal behavior and managing interactions between people. See MPEP 2106.04(a)(2)(III) and MPEP 2106.04(a)(2)(II).
Mental processes. The following recited modules, under their broadest reasonable interpretation in light of the specification, set forth steps that a person can perform in the mind or with pen and paper, and are recited at a high level of generality as labeled functions:
• The Initial Query Reasoning (IQR) module, "which leverages a Central Knowledge Retention (CKR) database to decipher missing details," recites the mental act of reading a question and reasoning out the details that are missing or implied. Referencing remembered or written-down knowledge to fill in the gaps of a question is an act of observation, evaluation, and judgment.
• The Assertion Construction (AC) module, "which receives a proposition in the form of an assertion or question and provides output of the concepts related to said proposition," recites the mental act of taking an assertion or question and identifying the concepts related to it.
• The Hierarchical Mapping (HM) module, "which maps associated concepts to find corroboration or conflict in consistency of the CQ/A, and calculates benefits and risks of having a certain stance on a topic," recites the mental act of comparing concepts to see whether they support or contradict one another and weighing the benefits and risks of taking a position. Comparing ideas and weighing the pros and cons of a stance is classic human evaluation and judgment.
• The Knowledge Validation (KV) module, "which receives knowledge requiring logical separation for query capability and assimilation into the CKR database," recites the mental act of validating knowledge and logically sorting it so that it can be looked up and remembered.
• The Rational Appeal (RA) module, "which criticizes reasons of appeal given by the HS," recites the mental act of evaluating an appeal and critiquing the reasons offered for it. Evaluating and critiquing the reasoning of another is an opinion or evaluation performed in the mind.
• The linguistic and conceptual sub-steps are likewise mental processes. Linguistic Construction (LC) "interprets raw question/assertion input from the Human Subject (HS) to produce a logical separation of linguistic syntax" (mentally parsing the words of a sentence). Concept Discovery (CD) "derives associated concepts" (mentally calling to mind related ideas). Concept Prioritization (CP) "orders them in logical tiers that represent specificity and generality" (mentally ranking ideas from general to specific). Response Separation Logic (RSL) leverages the LC "to associate a relevant and valid response with the initial request" (mentally matching an answer to a question). Concept Compatibility Detection (CCD) "compares conceptual derivatives from the original Question/Assertion to ascertain the logical compatibility result" (mentally comparing ideas for consistency). The Benefit or Risk Calculator (BRC) "weighs the benefits and risks to form a uniform decision" (mentally, or with pen and paper, weighing pros and cons to reach a decision). Concept Interaction (CI) "assigns attributes that pertain to AC concepts to parts of the information collected from the HS" (mentally labeling information with attributes). Each of these is a step of observation, evaluation, or judgment that the human mind performs, or that a person performs with pen and paper.
Certain methods of organizing human activity. The claim further recites a debate and persuasion interaction in which the system engages a Human Subject and the Human Subject is invited to concede or to improve an argument that the Human Subject makes against the stance reached by the system. This is the managing of personal behavior and the managing of an interaction between people. In particular:
• The Survey Clarification (SC) module, "which receives input from and sends output to the HS, and forms a Clarified Question or Assertion (CQ/A)," recites a back and forth exchange between the system and a person to clarify what the person meant. Conducting a clarifying dialogue with a person is the managing of an interaction between people.
• The Response Presentation (RP) interface "for presenting a conclusion drawn by the AC module to both the HS and Rational Appeal (RA) module" recites presenting a conclusion to a person for that person's consideration.
• The Accept Response logic, "configured to receive a selection from the HS to accept a response of the LOM module or to appeal the response with a criticism, wherein if the response is accepted it is processed by the KV module, wherein should the HS not accept the response it is forwarded to the RA module, which criticizes reasons of appeal given by the HS," recites the core debate interaction: a person is given the choice to accept the system's answer or to dispute it with a criticism, and the dispute is then critiqued. Inviting a person to accept or to argue against a position, and responding to that person's argument, is the managing of personal behavior and the managing of an interaction or relationship between people, namely a debate or persuasion exchange.
These techniques are not meaningfully different from concepts the courts have found to be abstract, including considering historical usage information while inputting data, BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018), and filtering or organizing information, BASCOM Global Internet v. AT and T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016). Accordingly, the claim recites an abstract idea, and the analysis proceeds to Step 2A Prong Two.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. Apart from the abstract idea itself, the claim recites the following additional elements: a memory configured to store programmed instructions; a processor coupled to the memory and configured to execute the programmed instructions; at least one database; the "computer-implemented security system based upon artificial intelligence" label of the preamble; the Central Knowledge Retention (CKR) database "serving as a main database for referencing knowledge for the LOM module"; and the Managed Artificially Intelligent Services Provider (MAISP), "which runs a cloud-based instance of the LOM module, and connects the LOM module to Front End Services, Back End Services and third-party application dependencies via an internet cloud connection."
The memory, processor, at least one database, and the "computer-implemented security system" label are recited at a high level of generality and amount to no more than mere instructions to apply the abstract idea using generic computer components. Using a general-purpose computer as a tool to perform an abstract idea does not integrate the exception into a practical application. See MPEP 2106.05(f).
The CKR database, the MAISP cloud-based instance, and the connection of the LOM module to Front End Services, Back End Services, and third-party application dependencies over an internet cloud connection do no more than generally link the use of the abstract idea to a particular technological environment, namely a generic cloud computing and internet environment. A general database for referencing knowledge and a cloud-hosted instance reachable over the internet are recited functionally and generically. Limitations that merely indicate a field of use or technological environment in which to apply a judicial exception do not integrate the exception into a practical application. See MPEP 2106.05(h).
The naming of the system as a "computer-implemented security system" does not change this conclusion. The operations actually performed by the LOM module are the argument-objectivity and linguistic-evaluation operations identified in Step 2A Prong One, that is, deciphering questions, deriving and weighing concepts, validating knowledge, and conducting an accept-or-appeal debate with a Human Subject. A generic label of "security" applied to what is, in operation, abstract argument-objectivity processing does not reflect any improvement to the functioning of a computer or to any other technology or technical field. See MPEP 2106.05(a). The claim does not recite a particular machine beyond a generic computer (MPEP 2106.05(b)), does not effect a transformation of an article (MPEP 2106.05(c)), and does not apply the exception in any other meaningful way beyond generally linking it to a generic computing environment (MPEP 2106.05(e)). The additional elements, considered individually and in combination, do not integrate the judicial exception into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained in Step 2A Prong Two, the additional elements are limited to a generic memory, a generic processor, at least one generic database, a generic database for referencing knowledge, and a generic cloud-based instance connected over the internet. These are well-understood, routine, and conventional computer components and activities, recited at a high level of generality and used only as tools to apply the abstract idea and to link it to a generic computing environment. Storing instructions in a memory, executing instructions on a processor, storing and retrieving data in a database, and running a hosted instance reachable over the internet are well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05(d) and MPEP 2106.05(f) and (h). Considered as an ordered combination, the additional elements add nothing beyond what is present when they are considered individually, and the ordered combination merely carries out the same abstract idea on generic components. Accordingly, the claim does not provide an inventive concept and does not amount to significantly more than the abstract idea itself. Claim 91 is therefore patent ineligible.
Claim 92
Step 1: A machine, for the reasons given for claim 91.
Step 2A Prong One: Claim 92 recites the same abstract idea as claim 91.
Step 2A Prong Two: The additional elements are that the Front End Services include Artificially Intelligent Personal Assistants, Communication Applications and Protocols, Home Automation and Medical Applications; that the Back End Services include online shopping, online transportation, and Medical Prescription ordering; that the Front End and Back End Services interact with the LOM module through a documented application programming interface (API) infrastructure that enables standardization of information transfers and protocols; and that the LOM module retrieves knowledge from external Information Sources through the Automated Research Mechanism (ARM). These additional elements are recited at a high level of generality and do no more than name categories of generic applications and a generic interface and retrieval mechanism through which the abstract idea is reached. They merely generally link the abstract idea to a particular field of use and technological environment and provide generic computer linkage. See MPEP 2106.05(h) and (f). They do not integrate the exception into a practical application.
Step 2B: The additional elements, individually and as an ordered combination, are the same generic computer linkage and field-of-use limitations and are well-understood, routine, and conventional. They do not provide an inventive concept or amount to significantly more than the abstract idea. Claim 92 is patent ineligible.
Claim 94
Step 1: A machine, for the reasons given for claim 91.
Step 2A Prong One: Claim 94 recites, inter alia, that inside the IQR module the LC receives the original Question or Assertion, that the question is linguistically separated and the IQR processes each individual word or phrase one at a time leveraging the CKR, and that by referencing the CKR the IQR considers potential options in view of the ambiguity of the word or phrase. These limitations further describe the mental process already identified for claim 91, namely the mental act of reading a question, breaking it into its words and phrases, and considering, by reference to remembered or written-down knowledge, what each ambiguous word or phrase might mean. This is observation, evaluation, and judgment performed in the mind or with pen and paper.
Step 2A Prong Two: Claim 94 recites no additional element beyond the generic computer environment of claim 91. It merely describes the internal word-by-word processing of the abstract idea and does not integrate the exception into a practical application.
Step 2B: For the same reasons, the claim adds no inventive concept and does not amount to significantly more than the abstract idea. Claim 94 is patent ineligible.
Claim 95
Step 1: A machine, for the reasons given for claim 91.
Step 2A Prong One: Claim 95 recites, inter alia, that the Survey Clarification (SC) receives input from the IQR; that the input contains a series of Requested Clarifications to be answered by the Human Subject so that an objective answer to the original Question or Assertion can be reached; that the provided responses are forwarded to Response Separation Logic (RSL), which correlates the responses with the requests; that, in parallel, a Clarification Linguistic Association is provided to the LC; and that the Association contains the internal relationship between the Requested Clarifications and the language structure, which enables the RSL to amend the original Question or Assertion so that the LC outputs the Clarified Question. These limitations further describe the same abstract idea identified for claim 91. The clarifying dialogue in which a series of clarifying questions is put to a person and the person's answers are matched back to the questions is a method of organizing human activity, namely managing an interaction between people. The accompanying correlating, associating, and amending steps are mental processes of evaluation and judgment, performed in the mind or with pen and paper.
Step 2A Prong Two: Claim 95 recites no additional element beyond the generic computer environment of claim 91. It merely describes the clarification dialogue of the abstract idea in more detail and does not integrate the exception into a practical application.
Step 2B: For the same reasons, the claim adds no inventive concept and does not amount to significantly more than the abstract idea. Claim 95 is patent ineligible.
Claim 111
Step 1: A machine, for the reasons given for claim 91.
Step 2A Prong One: Claim 111 recites the same abstract idea as claim 91.
Step 2A Prong Two: The additional element is that the MAISP is configured to manage an instance of LOM (as best interpreted per the 35 U.S.C. 112(a) rejection set forth above). This further describes the generic cloud element of claim 91 and recites, at a high level of generality, the running of the abstract idea on generic cloud infrastructure. Managing an instance of a hosted service is a generic function of cloud computing and merely generally links the abstract idea to that environment and applies it on generic components. See MPEP 2106.05(f) and (h). It does not integrate the exception into a practical application.
Step 2B: These additional elements provide no inventive concept, whether the additional elements are considered individually or as part of the ordered combination. Claim 111 is patent ineligible.
Claim 112
Step 1: A machine, for the reasons given for claim 91.
Step 2A Prong One: Claim 112 recites that the RA module is configured to evaluate the logic of the Human Subject and of the LOM module, and to decide whether the Human Subject or the LOM module is correct. This further describes the mental process and the debate interaction already identified for claim 91. Evaluating the logic of two competing positions and deciding which one is correct is the adjudication of an argument, an act of evaluation, judgment, and opinion that a person performs in the mind, and it is the management of the persuasion interaction between the system and the Human Subject. It therefore recites the same abstract idea.
Step 2A Prong Two: Claim 112 recites no additional element beyond the generic computer environment of claim 91. It merely describes the appeal-adjudication step of the abstract idea and does not integrate the exception into a practical application.
Step 2B: For the same reasons, the claim adds no inventive concept and does not amount to significantly more than the abstract idea. Claim 112 is patent ineligible.
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 91-92, 94-95, and 111-112 are rejected under 35 U.S.C. 103 over Cook (US 2015/0039536 A1) in view of Lowrance (US 8,438,054 B2), and further in view of IBM '887 (US 2015/0370887 A1).
Regarding independent claim 91, Cook teaches a computer-implemented security system based upon artificial intelligence, the computer-implemented system comprising (Cook: [0045], "Data processing system 200 is an example of a computer, such as server 104 or client 110 in FIG. 1 … FIG. 2 represents a server computing device, such as a server 104, which implements a QA system 100 and QA system pipeline 108"; a server computing device (the computer-implemented system comprising) implements the artificial-intelligence (based upon artificial intelligence) question answering (QA) system 100 and QA system pipeline 108, providing the recited data-processing system which under BRI can answer questions related to security protocols (a computer-implemented security system)):
a memory configured to store programmed instructions (Cook: [0007], "The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of the operations outlined above with regard to the method illustrative embodiment"; a memory (a memory) holds the instructions (programmed instructions) that are executed to operate the system),
a processor coupled to the memory and configured to execute the programmed instructions (Cook: [0007], "The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors"; one or more processors (a processor) coupled to the memory and executing the stored instructions (the programmed instructions)),
and at least one database operating a Lexical Objectivity Mining (LOM) module, the computer-implemented security system further comprising (Cook: [0045], "FIG. 2 represents a server computing device, such as a server 104, which implements a QA system 100 and QA system pipeline 108"; the data-processing server (at least one database) implements and operates the QA system 100 and pipeline 108 (a Lexical Objectivity Mining (LOM) module), such that the claimed system operates the recited QA engine):
an Initial Query Reasoning (IQR) module, to which a question input by a Human Subject (HS) is transferred (Cook: [0015], "a user submits a question in a natural language form, i.e. unstructured form, to the QA system which searches a corpus of information"; a QA-system user submits a natural-language input question that is transferred to the question-analysis logic (an Initial Query Reasoning (IQR) module), the submitted question (a question) being input by the user (a Human Subject (HS))),
and which leverages a Central Knowledge Retention (CKR) database to decipher missing details (Cook: [0015], "a user submitted question often contains implied context that is intuitive to humans but is not intuitive to algorithmic QA systems"; the logic queries the corpus of information (a Central Knowledge Retention (CKR) database) to identify the implied context of the submitted question (missing details));
a Survey Clarification (SC) which receives input from and sends output to the HS (Cook: [0005], "sending, by the data processing system, in response to a determination that clarification of the input question is required, a request for user input to clarify the input question. In addition, the method comprises receiving, in the data processing system, user input from the computing device in response to the request"; the system sends a clarification request to and receives clarifying input from the user (the HS), the request-and-response exchange being the recited clarification (a Survey Clarification (SC)),
and forms a Clarified Question/Assertion (CQ/A) (Cook: [0071], "the context clarification logic 392 formulates one or more user interfaces for requesting user feedback input that further clarifies the implied context of the input question 310"; the context clarification logic builds the clarification interface and collects the user input that further clarifies the input question, producing the clarified question (a Clarified Question/Assertion (CQ/A)));
a Response Presentation (RP) interface for presenting a conclusion drawn by the AC module to both the HS and Rational Appeal (RA) module (Cook: [0040], "The QA system 100 may interpret the question and provide a response to the QA system user, e.g., QA system user 110, containing one or more answers to the question. In some embodiments, the QA system 100 may provide a response to users in a ranked list of candidate answers"; the QA system provides the drawn response/answer (a conclusion) as output to the QA system user (the HS) through a response presentation (a Response Presentation (RP) interface) and forwards the same response within the pipeline for further evaluation);
said CKR database, serving as a main database for referencing knowledge for the LOM module (Cook: [0035], "QA mechanisms operate by accessing information from a corpus of data or information (also referred to as a corpus of content), analyzing it, and then generating answer results based on the analysis of this data. Accessing information from a corpus of data typically includes: a database query that answers questions about what is in a collection of structured records"; the corpus of data or information (said CKR database), queried as a database, is the main knowledge store that the QA engine pipeline (the LOM module) references to generate answers);
a Managed Artificially Intelligent Services Provider (MAISP), which runs a cloud-based instance of the LOM module, and connects the LOM module to Front End Services, Back End Services and third-party application dependencies (Cook: [0038], "The QA system 100 may be implemented on one or more computing devices 104 … connected to the computer network 102. The network 102 may include multiple computing devices 104 in communication with each other and with other devices or components via one or more wired and/or wireless data communication links"; the QA system (the LOM module) is implemented on networked computing devices 104 (a Managed Artificially Intelligent Services Provider (MAISP)) that interconnect the engine with other devices and components (Front End Services, Back End Services and third-party application dependencies)) via an internet cloud connection (Cook: [0029], "the remote computer may be connected to the user's computer through any type of network … or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider)"; the devices connect through the Internet by way of an Internet Service Provider (an internet cloud connection));
wherein Linguistic Construction (LC) interprets raw question/assertion input from the Human Subject (HS) to produce a logical separation of linguistic syntax (Cook: [0056], "the question and topic analysis stage 320, parses the input question using natural language processing (NLP) techniques to extract major features from the input question, classify the major features according to types, e.g., names, dates, or any of a plethora of other defined topics"; the question and topic analysis stage 320 (Linguistic Construction (LC)) parses the natural-language input question from the user (raw question/assertion input from the Human Subject (HS)) and extracts and classifies its major features by syntactic type (a logical separation of linguistic syntax));
wherein Concept Discovery (CD) receives points of interest within the Clarified Question/Assertion (CQ/A) and derives associated concepts by leveraging CKR (Cook: [0058], "The queries being applied to the corpus of data/information at the hypothesis generation stage 340 to generate results identifying potential hypotheses for answering the input question … These hypotheses are also referred to herein as "candidate answers" for the input question"; the hypothesis generation stage 340 (Concept Discovery (CD)) applies queries formulated from the question's extracted features (points of interest within the Clarified Question/Assertion (CQ/A)) to the corpus (CKR) and generates candidate answers (associated concepts));
wherein Concept Prioritization (CP) receives relevant concepts and orders them in logical tiers that represent specificity and generality (Cook: [0061], "The resulting confidence scores or measures are processed by a final confidence merging and ranking stage 370 … The hypotheses/candidate answers may be ranked according to these comparisons to generate a ranked listing of hypotheses/candidate answers"; the final confidence merging and ranking stage 370 (Concept Prioritization (CP)) receives the candidate answers (relevant concepts) and ranks them into a ranked listing (logical tiers that represent specificity and generality));
wherein Response Separation Logic (RSL) leverages the LC to understand the question and supplemental query data and associate a relevant and valid response with the initial request, thereby accomplishing the objective of SC (Cook: [0076], "The user input into the user interface(s) is returned to the QA system pipeline 300 and received by the user collaboration logic 396 which informs the context clarification logic 392 of the user's identification of the correct differentiating factor indicative of the implied context of the input question 310"; the user collaboration logic 396 (Response Separation Logic (RSL)) receives the user input (supplemental query data) and correlates it to the correct differentiating factor for the input question (associate a relevant and valid response with the initial request), thereby clarifying the question (accomplishing the objective of SC));
wherein the LC is optimized during the output phase to amend the original CQ/A to include supplemental information received by the SC (Cook: [0020], "the user interactively clarifies their originally submitted question to thereby enable the QA system to identify which of the potentially "correct" candidate answers is considered to be the most likely correct answer for the originally submitted question"; the originally submitted question (the original CQ/A) is interactively amended with the user's clarifying input (supplemental information received by the SC) during the output phase (the LC is optimized during the output phase)).
Cook does not expressly teach an Assertion Construction (AC) module, which receives a proposition in the form of an assertion or question and provides output of the concepts related to said proposition; a Hierarchical Mapping (HM) module, which maps associated concepts to find corroboration or conflict in consistency of the CQ/A, and calculates benefits and risks of having a certain stance on a topic; a Knowledge Validation (KV) module, which receives knowledge requiring logical separation for query capability and assimilation into the CKR database; wherein Concept Compatibility Detection (CCD) compares conceptual derivatives from the original Question/Assertion to ascertain the logical compatibility result; wherein Benefit/Risk Calculator (BRC) receives the compatibility results from the CCD and weighs the benefits and risks to form a uniform decision that encompasses the gradients of variables implicit in the concept makeup; wherein Concept Interaction (CI) assigns attributes that pertain to AC concepts to parts of the information collected from the HS via Survey Clarification (SC).
However, Lowrance teaches an Assertion Construction (AC) module, which receives a proposition in the form of an assertion or question (Lowrance: [0010], "the present invention is based on the concept of a structured argument having a plurality of questions that are used to assess whether an opportunity or threat of a given type is imminent"; the structured-argument construction (an Assertion Construction (AC) module) receives a situation assessed by the argument's plurality of questions (a proposition in the form of an assertion or question)) and provides output of the concepts related to said proposition (Lowrance: [0012], "an argument server for selecting one of the templates which is most relevant to a particular situation and for receiving input to one or more of the selected template's questions to thereby generate a new argument having an associated conclusion based on such answers"; the argument server generates a new argument having an associated conclusion (the concepts related to said proposition)); a Hierarchical Mapping (HM) module, which maps associated concepts to find corroboration or conflict in consistency of the CQ/A (Lowrance: [0011], "with one end of the scale representing strong support for a particular type of opportunity or threat and the other end representing strong refutation … The links represent support relationships among the questions. Derivative questions are supported by all the derivative and primitive questions linked below it"; the hierarchically organized argument (the Hierarchical Mapping (HM) module) links the derivative and primitive questions (associated concepts) of the argument (the CQ/A) by support relationships (corroboration) or refutation (conflict)), and calculates benefits and risks of having a certain stance on a topic (Lowrance: [0051], "each answer is represented by a different color or shade which in turn represents a different level of risk or opportunity for a particular question … Green represents a highly likely positive outcome; red represents a highly likely negative outcome"; each answer conveys a level of risk or opportunity, that is, a positive or negative outcome, for a question (calculates benefits and risks of having a certain stance on a topic)); a Knowledge Validation (KV) module, which receives knowledge requiring logical separation for query capability (Lowrance: [0006], "Analysis, on the other hand, deals with the examination and separation of a complex situation, its elements, and its relationships"; the analysis function (a Knowledge Validation (KV) module) examines and separates a complex situation into its elements and relationships (knowledge requiring logical separation for query capability)) and assimilation into the CKR database; (Lowrance: [0036], "KB server 210 accesses and retrieves objects from memory 107 via SQL server 212 … The KBMS 211 is generally configured to change slot values and KB frames that represent question answers that are to be stored in memory 107"; the knowledge-base management system assimilates the question answers into KB frames and slots stored in the knowledge-base database (assimilation into the CKR database)) wherein Concept Compatibility Detection (CCD) compares conceptual derivatives from the original Question/Assertion to ascertain the logical compatibility result; (Lowrance: [0040], "the business category 406 includes a hierarchy of questions for answering the business category question "are there signs in the hydrocarbons business environment that are compatible with our strategic intent?""; the hierarchy of derivative questions of the argument (conceptual derivatives from the original Question/Assertion) (Concept Compatibility Detection (CCD)) is evaluated for a "compatible with our strategic intent" determination (to ascertain the logical compatibility result)) wherein Benefit/Risk Calculator (BRC) receives the compatibility results from the CCD and weighs the benefits and risks to form a uniform decision (Lowrance: [0075], "Both the fusion and inference methods determine how answers are combined into a single answer … the answers may be averaged together, a minimum answer may be selected (most negative outcome) or a maximum answer may be selected (most positive outcome)"; the fusion and inference methods (Benefit/Risk Calculator (BRC)) combine the answers into a single answer weighing the most negative against the most positive outcome (weighs the benefits and risks to form a uniform decision)) that encompasses the gradients of variables implicit in the concept makeup; (Lowrance: [0056], "each answer reflects a level of risk or opportunity. That is, each answer indicates a likelihood of a negative or positive outcome for the associated question … each multiple choice question has a categorical scale of likelihood represented by a number of answers"; each answer spans a categorical scale of likelihood of a negative or positive outcome (the gradients of variables implicit in the concept makeup)) wherein Concept Interaction (CI) assigns attributes that pertain to AC concepts (Lowrance: [0070], "a situation descriptor describes what an argument or template is about: who is the actor under discussion, what sort of event is under discussion, where (i.e., region) or when the situation occurs (i.e., time interval), and the perspective from which the situation is being analyzed"; the situation descriptor (Concept Interaction (CI)) assigns descriptive attributes, that is, actor, event, region, time, and perspective, to the argument (assigns attributes that pertain to AC concepts)) to parts of the information collected from the HS via Survey Clarification (SC). (Lowrance: [0071], "Some of the situation descriptor slots … may only be filled by selections from a predefined set of terms … while others may be filled with free-form text"; the descriptor slots are filled, from predefined terms or free-form text, with the collected information (to parts of the information collected from the HS via Survey Clarification (SC))).
Because Cook and Lowrance are analogous art and within the same field of endeavor, specifically artificial-intelligence systems that analyze a user's natural-language question or situation against a stored knowledge base to reach a reasoned conclusion, they address the same problem solving area of producing a structured, corroboration-tested, and risk-weighted conclusion from a user's input rather than a bare ranked answer, accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to combine Lowrance's structured-argument construction, hierarchical support-and-refutation mapping with risk-and-opportunity scoring, analysis-and-assimilation knowledge-base management, compatibility testing, fusion-and-inference benefit-risk decision over a likelihood gradient, and situation-descriptor attribute assignment with Cook's question answering pipeline, with a reasonable expectation of success, such that the corpus-derived concepts and the clarified question that Cook's pipeline already produces are organized into a hierarchically corroborated, benefit/risk-weighted conclusion to teach an Assertion Construction (AC) module, which receives a proposition in the form of an assertion or question and provides output of the concepts related to said proposition; a Hierarchical Mapping (HM) module, which maps associated concepts to find corroboration or conflict in consistency of the CQ/A, and calculates benefits and risks of having a certain stance on a topic; a Knowledge Validation (KV) module, which receives knowledge requiring logical separation for query capability and assimilation into the CKR database; wherein Concept Compatibility Detection (CCD) compares conceptual derivatives from the original Question/Assertion to ascertain the logical compatibility result; wherein Benefit/Risk Calculator (BRC) receives the compatibility results from the CCD and weighs the benefits and risks to form a uniform decision that encompasses the gradients of variables implicit in the concept makeup; wherein Concept Interaction (CI) assigns attributes that pertain to AC concepts to parts of the information collected from the HS via Survey Clarification (SC). This modification would have been motivated by the desire to yield reasoned, corroboration- and conflict-tested, benefit/risk-weighted conclusions rather than bare ranked candidate answers for selecting one of the templates which is most relevant to a particular situation and for receiving input to one or more of the selected template's questions to thereby generate a new argument having an associated conclusion based on such answer (Lowrance: [0012]).
Cook and Lowrance do not expressly teach an Accept Response logic module configured to receive a selection from the HS to accept a response of the LOM module or to appeal the response with a criticism, wherein if the response is accepted, it is processed by the KV module, wherein should the HS not accept the response, it is forwarded to the RA module, which criticizes reasons of appeal given by the HS as that limitation is interpreted under the 35 U.S.C. 112(b) rejection set forth above.
However, Khapra teaches an Accept Response logic module configured to receive a selection from the HS to accept a response of the LOM module or to appeal the response with a criticism, wherein if the response is accepted, it is processed by the KV module, (Khapra: [0003], "people are required to provide convincing claims (and counter claims) in order to persuade the other side"; the user provides a claim accepting the system response or provides a counter claim to contest it (an Accept Response logic module configured to receive a selection from the HS to accept a response of the LOM module or to appeal the response with a criticism); "Persuading can either take the form of influencing someone to take your point of view, agreeing to your opinion"; an accepted response corresponds to the user agreeing to the claim, which is then adopted and retained as the user's position (wherein if the response is accepted, it is processed by the KV module)) wherein should the HS not accept the response, it is forwarded to the RA module, which criticizes reasons of appeal given by the HS (Khapra: [0057], "The first module determines if arguments in a given pair of arguments are semantically equivalent. For this purpose, two arguments are considered as equivalent if the claims of the arguments are semantically equivalent"; the first module (the RA module) compares the user's appealing argument against the system's argument; [0033], "automatically assessing the semantic similarity between each pair of claims"; the module assesses the semantic similarity or difference between the competing claims (criticizes); [0042], "Each argument may include a claim of the set of claims. Optionally, a claim may be associated with supportive evidence"; the appealing user's argument comprises a claim and its supporting evidence (reasons of appeal given by the HS)).
Because Cook, in view of Lowrance, and Khapra are analogous art and within the same field of endeavor, specifically artificial-intelligence systems that generate and evaluate competing natural-language claims or conclusions, they address the same problem solving area of allowing a user to accept or to appeal a system conclusion and having the system critique the reasons of appeal and decide which competing position prevails, accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to combine Khapra's claim-and-counter-claim debate framework, with its semantic equivalence-and-difference scoring and pairwise argument-comparison module, with the question answering and structured-argument analysis of Cook as modified by Lowrance, with a reasonable expectation of success, such that the human subject can accept the system's conclusion or appeal it with a criticism and the system critiques the competing claims to resolve the appeal to teach an Accept Response logic module configured to receive a selection from the HS to accept a response of the LOM module or to appeal the response with a criticism, wherein if the response is accepted, it is processed by the KV module, wherein should the HS not accept the response, it is forwarded to the RA module, which criticizes reasons of appeal given by the HS. This modification would have been motivated by the desire to let the human subject contest a conclusion and have the system adjudicate the human's reasons of appeal against the system's position, improving the reliability of the final answer by determining if arguments in a given pair of arguments are semantically equivalent (Khapra: [0003], [0057]).
Regarding dependent claim 92, Cook, in view of Lowrance and Khapra, teach the computer-implemented security system of claim 91, wherein Front End Services include Artificially Intelligent Personal Assistants, Communication Applications and Protocols, Home Automation and Medical Applications (Cook: [0039], "The various computing devices 104 on the network 102 may include access points for content creators and QA system users"; the networked computing devices 104 include access points for content creators and QA system users (Front End Services), the front-end entry points through which users reach the QA system of which Artificially Intelligent Personal Assistants, Communication Applications and Protocols, Home Automation and Medical Applications are merely design choices recited at a high level of generality), wherein Back End Services include online shopping, online transportation, Medical Prescription ordering (Cook: [0039], "Some of the computing devices 104 may include devices for a database storing the corpus of data 106"; certain of the networked computing devices 104 are the back-end devices for the database storing the corpus of data 106 (Back End Services)), wherein Front End and Back End Services interact with LOM via a documented API infrastructure, which enables standardization of information transfers and protocols (Cook: [0038], "The network 102 may include multiple computing devices 104 in communication with each other and with other devices or components via one or more wired and/or wireless data communication links"; the front-end and back-end computing devices 104 communicate with the QA system 100 (LOM) over one or more wired and/or wireless data communication links (a documented API infrastructure), the data communication links standardizing the information transfers and protocols among the networked devices), wherein LOM retrieves knowledge from external Information Sources via the Automated Research Mechanism (ARM) (Cook: [0002], "QA systems provide automated mechanisms for searching through large sets of sources of content, e.g., electronic documents, and analyze them with regard to an input question to determine an answer to the question"; the QA system (LOM) provides automated mechanisms for searching through large sets of sources of content such as electronic documents (the Automated Research Mechanism (ARM)) to retrieve answers from those external sources of content (external Information Sources)).
Regarding dependent claim 94, Cook, in view of Lowrance and Khapra, teach the system of claim 91, wherein inside the IQR, LC receives the original Question/Assertion (Cook: [0042], "The Watson QA system may receive an input question"; within the QA question-and-context analysis logic (the IQR), the natural language parse (LC) receives the input question (the original Question/Assertion)); the question is linguistically separated and IQR processes each individual word/phrase at a time leveraging the CKR (Cook: [0042], "which it then parses to extract the major features of the question, that in turn are then used to formulate queries that are applied to the corpus of data"; the input question (the question) is parsed by natural language processing into its major features (each individual word/phrase), so that the question is linguistically separated, and the QA question-and-context analysis logic (the IQR) formulates queries from those extracted features that are applied to the corpus of data (the CKR), processing each feature in turn while leveraging the corpus); by referencing CKR, IQR considers potential options considering the ambiguity of the word/phrase (Cook: [0016], "The first president to die in office, as well as many other first presidents for various other contexts, may also be a valid answer to this submitted question as well since the QA system cannot determine from the question itself the implied context"; by referencing the corpus of data (the CKR), the QA question-and-context analysis logic (the IQR) considers the many first presidents for various other contexts as multiple potential valid answers (potential options) because the QA system cannot determine from the question itself the implied context of the term (the ambiguity of the word/phrase)).
Regarding dependent claim 95, Cook, in view of Lowrance and Khapra, teach the system of claim 91, wherein Survey Clarification (SC) receives input from IQR (Cook: [0071], "the context clarification logic 392 formulates one or more user interfaces for requesting user feedback input that further clarifies the implied context of the input question 310"; the context clarification logic 392 (the Survey Clarification (SC)) builds the user-feedback request from the implied context of the input question 310 supplied by the question-and-context analysis (the IQR), receiving that implied-context determination as the basis for the requested clarification), wherein the input contains a series of Requested Clarifications that are to be answered by HS for an objective answer to the original Question/Assertion to be reached (Cook: [0075], "A series of such questions may be presented in the user interface, or a series of user interfaces, until a "Yes" answer is returned by the user"; a series of clarification questions (a series of Requested Clarifications) is presented to and answered by the user (the HS) and explored until a "Yes" answer is returned, resolving the implied context of the input question (the original Question/Assertion) so that an objective answer is reached), wherein provided responses to the requests are forwarded to Response Separation Logic (RSL), which correlates the responses with the requests (Cook: [0076], "The user input into the user interface(s) is returned to the QA system pipeline 300 and received by the user collaboration logic 396 which informs the context clarification logic 392 of the user's identification of the correct differentiating factor indicative of the implied context of the input question 310"; the user input (the provided responses) is returned to and received by the user collaboration logic 396 (the Response Separation Logic (RSL)), which informs the context clarification logic of the user's identification of the correct differentiating factor, thereby correlating each response (the responses) with the clarification request (the requests) that elicited it); wherein in parallel to the Requested Clarifications being processed, Clarification Linguistic Association is provided to LC, wherein the Association contains the internal relationship between Requested Clarifications and the language structure, which enables the RSL to amend the original Question/Assertion whereby LC outputs the Clarified Question (Cook: [0086], "The user response input to the user interface(s) is then received from the submitter (step 570) and the candidate answers are updated based on the user response input (step 580)"; the user response input is received and the candidate answers are updated, the natural-language analysis (the LC) correlating the responses to the question's extracted linguistic features (the Clarification Linguistic Association) so that the user collaboration logic 396 (the RSL) amends the originally submitted input question (the original Question/Assertion); [0020], "the user interactively clarifies their originally submitted question to thereby enable the QA system to identify which of the potentially "correct" candidate answers is considered to be the most likely correct answer for the originally submitted question"; the originally submitted question is interactively refined by the natural-language analysis (the LC) into the most-likely-correct form output as the clarified input question (the Clarified Question)).
Regarding dependent claim 111, Cook, in view of Lowrance and Khapra, teach the system of claim 91, wherein the MAISP is configured to manage a plurality of LOM instances (Cook: [0050], "Data processing system 200 may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit 206"; the data processing system 200 implementing the QA system 100 and pipeline 108 (the MAISP) is a symmetric multiprocessor system having a plurality of processors in processing unit 206 (a plurality of LOM instances), the QA engine executing across the plurality of processors; Cook: [0053], "the processes of the illustrative embodiments may be applied to a multiprocessor data processing system, other than the SMP system mentioned previously, without departing from the spirit and scope of the present invention"; the QA-system processes extend to a multiprocessor data processing system that runs and manages the plural instances). (Claim 111 is also subject to the 35 U.S.C. 112(a) written-description rejection set forth above; for purposes of this prior-art rejection, the recited plurality of LOM instances is mapped to Cook's scalable multiprocessor deployment).
Regarding dependent claim 112, Cook, in view of Lowrance and Khapra, teach the computer-implemented security system of claim 91, wherein the RA module is configured to evaluate logic of HS and LOM module (Khapra: [0058], "Each annotator was presented with a pair of claims and was asked to label whether these claims are semantically the same or not… Several categories were used in order to make the task less subjective: equivalent, A subsumes B, B subsumes A, partial overlap and not equivalent (where A and B represent the two claims)"; the supervised machine learning module (the RA module) evaluates a pair of competing claims, claim A (the logic of the HS) and claim B (the logic of the LOM module), and labels their logical relation as equivalent, A subsumes B, B subsumes A, partial overlap, or not equivalent), and wherein the RA module is configured to decide whether the HS or the LOM module is correct (Khapra: [0051], "In each cluster of claims, a claim may be selected to represent the cluster in the list… The representing argument and/or claim may be selected based on a quality score, if such is assigned to the argument/claim, or based on the centroid of the cluster"; the module selects, from the two competing claims, the representative claim based on its quality score; [0010], "generating a list of non-redundant claims comprising said semantically different claims"; the selected representative claim is output as the single non-redundant result of the comparison).
Response to Arguments
Applicant’s claim amendments and Remarks filed 4/27/2026 with respect to the claim objects set forth in the Office Action dated 10/28/2025 are persuasive and thus the said claim objects are withdrawn.
Applicant’s claim amendments and Remarks filed 4/27/2026 with respect to the 35 U.S.C. 101 rejections set forth in the Office Action dated 10/28/2025 are not persuasive.
Applicant traverses the Section 101 rejection (Remarks pages 9-14) and argues that the claims are not directed to an abstract idea and provide significantly more because of a claimed technical improvement to computer security.
Examiner respectfully disagrees.
First, Applicant's technical improvement arguments rest almost entirely on modules that are not recited in the elected claims. Applicant relies on the LIZARD (Linguistic Intent Zooming and Analysis of Resultant Data) module performing "syntax analysis of non-executable foreign code," the MACINT (Mimicry of Artificial Intelligence and Computer Intelligence) module performing "virtual signal mimicry" and creating "decoy signals," the CTMP module performing "real-time monitoring," and the UBEC (Ultimate Blockchain Encryption Core) module performing "decentralized blockchain encryption." None of LIZARD, MACINT, CTMP, or UBEC is recited in elected independent claim 91 or in any of its elected dependent claims 92, 94, 95, 111, or 112. Those four modules appear only in withdrawn claims 113 through 117, which are directed to a non-elected invention and are not before the Office on the merits. Arguments that are not commensurate in scope with the claims under examination, and that are directed to features that are not recited in those claims, cannot establish eligibility of the claims actually presented. See MPEP 2145. The elected claims must be evaluated on the limitations they actually recite, and those limitations are the LOM argument-objectivity modules analyzed above.
Second, to the extent Applicant points to the modules that are in fact recited in elected claim 91, namely the "unique interplay between the IQR, AC, and HM modules" and the calculation of "Self-Critical Knowledge Density," those modules are themselves the abstract idea, not a technical solution to it. As explained in Step 2A Prong One, deciphering a question (IQR), constructing assertions and deriving related concepts (AC), and mapping concepts and weighing benefits and risks of a stance (HM) are mental processes of observation, evaluation, and judgment. Reciting these mental steps, alone or in combination, and labeling their interplay as inventive does not transform the abstract idea into patent-eligible subject matter, and a claimed advance that lies entirely in the abstract idea itself cannot supply the inventive concept at Step 2B. See MPEP 2106.05(I) and 2106.04.
Third, the argument that the claimed system is a "computer security system" that "improves the functionality of the computer itself" is not supported by the elected claim language. The elected claims do not recite any operation on machine communications, data packets, malicious code, or network traffic. What the LOM module of claim 91 actually does is conduct the linguistic and conceptual argument-objectivity processing and the accept-or-appeal debate with a Human Subject described above. A generic "security system" label placed on what is, in operation, abstract argument-objectivity processing is not an improvement to computer functionality or to any other technology, and reciting the idea as performed by a generic processor, memory, database, and cloud instance is mere instruction to apply the idea on generic computers and to link it to a generic computing environment. See MPEP 2106.05(a) and (f).
Accordingly, the amendments and arguments do not overcome the rejection, and the rejection of claims 91, 92, 94, 95, 111, and 112 under 35 U.S.C. 101 is maintained.
Applicant’s claim amendments and Remarks filed 4/27/2026 with respect to the rejections under 35 U.S.C. 102/103 have been considered but are moot because the
new ground of rejection, necessitated by Applicant’s amendment, does not rely on any references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUANG FU CHEN whose telephone number is (571)272-1393. The examiner can normally be reached M-F 9:00-5:30pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on (571) 272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KC CHEN/Primary Patent Examiner, Art Unit 2143
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