DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 08/05/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments/Amendments
3. With respect to 101 Abstract idea towards Claim 1, Applicant argues that the amended claim 1 does not recite a mental process. Instead, the claim recites a specific sequence of computer-implemented operations in which preprocessed natural language text is received following lexical disambiguation, knowledge propositions are retrieved from an explicit knowledge graph. With Step 2A, Prong Two, the Applicant argues that amended claim 1 applies any alleged abstract idea through a specific computer-implemented architecture that performs semantic interpretation using structured knowledge representations and coordinated computational processing. The claim therefore integrates any alleged judicial exception into a practical application. With Step 2B, the Applicant argues that it defines a particular computational framework in which structured knowledge representations, context frames, and associated computational states cooperate throughout the semantic interpretation process to produce structured semantic representations through coordinated computational processing.
In response, Examiner respectfully notes that Claim 1 recites mental processes. For example, human could receive (e.g., listen, read) the natural language text, analyze and detect a viewpoint, intensity, veracity and audience of the natural language text. The human could retrieve knowledge propositions, classify the knowledge propositions, analyze viewpoint and bias, analyze intensity and exaggeration, analyze veracity and agenda and analyze audience and social proximity by looking at profile information of context. Step 2A, Prong One: Yes. Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluation those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites “at a deep language understanding module of an interpreter running on a computing device”. The limitations are recited as being performed by a computing device. In the limitation, the computer is used as a tool to perform an abstract idea of analyzing and detecting a viewpoint, intensity, veracity and audience of the text, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Step 2A, Prong Two: NO, and the claim is directed to the judicial exception. Step 2A: YES. Step 2B: This part of the eligibility analysis evaluates the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional elements, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05(f). As explained with respect to Step 2A, Prong Two, there is one additional element. The additional element of “at a deep language understanding module of an interpreter running on a computing device” is recited in Claim 1. As discussed in Step 2A, Prong Two above, the recitation of a computer to perform limitations amounts to no more than mere instruction to apply the exception using a generic computer component. Even when considered in combination, the additional element represents mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. (Step 2B: NO). Claim 1 is ineligible.
With respect to Claim 2, Applicant argues that the amended claim 2 is directed to a
specific computer-implemented inference architecture rather than a mental process and therefore does not recite a judicial exception under Step 2A, Prong One. With respect to Step 2A, Prong two, the Applicant argues that the claimed architecture organizes semantic information within structured context frames containing named attributes and candidate knowledge propositions. The architecture further employs heuristic probability adjustment together with crossover and mutation operations that iteratively modify candidate confidence values until one or more candidates satisfy a defined emergence threshold. These coordinated computational operations collectively define a specific implementation for computerized semantic inference rather than merely evaluating textual information. With respect to Step 2B, the Applicant argues that the claimed architecture does not merely automate human language analysis. Rather, it defines a particular computational framework that combines structured semantic representations, heuristic probability adjustment, emergence-based candidate selection, and evolutionary processing to determine intended meaning through coordinated computational operations. The ordered combination of these limitations defines a specific technological solution for computerized semantic inference and represents significantly more than the alleged abstract idea identified by the Office Action.
In response, Examiner respectfully notes that Claim 2 recites mental processes. For example, human could combine the input text and commonsense knowledge in a plurality of context to determine which answer is the best answer for the question. Applying heuristic procedures to adjust weight for each candidate answer is a mental process. Applying logical reasoning with common sense knowledge to determining meaning of the input text is a mental process. Using genetic algorithms to apply crossover and mutation inputs to adjust confidence is a mental process. For crossover, the human could combine impact of two or more knowledge propositions to a single candidate to adjust confidence. For mutation, the human could apply the independent impact of a single knowledge proposition to a single candidate to adjust the confidence. Step 2A, Prong One: Yes. There are no additional elements presented in Claim 2. Step 2A Prong Two: NO. Step 2A: YES. Step 2B: NO. Claim 2 is ineligible.
With respect to Claim 3, Applicant argues that the amended claim 3 does not recite a mental process under Step 2A, Prong One. Instead, the claim recites a specific sequence of computer-implemented operations in which crossover and mutation heuristic processes are applied to candidate knowledge propositions distributed among defined attributes of defined context frames. The claim further recites identifying emergent or top-scoring candidates as surviving individuals within respective populations and validating correctness of the inferred association with the input text. With respect to Step 2A, Prong two, the Applicant argues that the claim applies crossover and mutation heuristic processes to candidate knowledge propositions organized within defined attributes of defined context frames, identifies emergent or top-scoring candidate populations through evolutionary selection, and validates the resulting inferred semantic association with the input text. These operations define a particular computational framework that employs heuristic optimization techniques to generate semantic inferences through structured computational processing rather than through conventional textual analysis. With respect to Step 2B, Applicant argues that viewed as an ordered combination, amended claim 3 recites a specialized computational framework in which candidate knowledge propositions are iteratively processed using crossover and mutation heuristic operations, organized within defined context frames and attributes, evaluated through evolutionary selection to identify emergent or top-scoring surviving individuals, and validated against the input text to infer semantic intent.
In response, Examiner respectfully notes that Claim 3 recites mental processes. For example, humans could apply crossover and mutation heuristic processes to rank candidate knowledge propositions in each of a plurality of attributes in a context, identify top scoring candidates and validate correctness. For crossover, the human could combine impact of two or more knowledge propositions to a single candidate to infer intent. For mutation, the human could apply the independent impact of a single knowledge proposition to a single candidate to infer intent. Next, the human could rank the candidates to select top scoring candidate and finally validate correctness. Step 2A, Prong One: Yes. There are no additional elements presented in Claim 3. Step 2A Prong Two: NO. Step 2A: YES. Step 2B: NO. Claim 3 is ineligible.
With respect to Claim 4, Applicant argues that the amended claim 4 does not recite a mental process under Step 2A, Prong One. Rather, the claim recites a specific sequence of computer-implemented operations in which digital content is processed to read natural language text, disambiguate its meaning, infer structured knowledge propositions reflecting the digital content, search an existing knowledge graph for previously learned knowledge propositions corresponding to the inferred knowledge propositions, determine whether matching knowledge propositions already exist, and, in the absence of matching knowledge propositions, automatically queue newly inferred knowledge propositions for validation and potential incorporation into a core knowledge graph. With respect to Step 2A, Prong two, the Applicant argues that the claimed architecture automatically infers structured knowledge propositions from natural language text, compares those knowledge propositions against previously learned knowledge propositions maintained within a structured knowledge graph, determines whether corresponding knowledge already exists, and selectively queues only newly inferred knowledge propositions for subsequent validation before incorporation into the core knowledge graph. These coordinated operations define a specific technological implementation for continuously maintaining and expanding structured machine knowledge rather than merely storing or organizing information. With respect to Step 2B, Applicant argues that viewed as an ordered combination, amended claim 4 recites a specialized continuous learning architecture in which natural language text extracted from digital content is processed to infer structured knowledge propositions, compared against previously learned knowledge propositions maintained within a knowledge graph, selectively queued for validation when corresponding knowledge is absent, and thereafter managed for potential incorporation into a continuously evolving core knowledge graph. The ordered combination of these operations defines a particular computational framework for acquiring, validating, and maintaining structured machine knowledge.
In response, Examiner respectfully notes that Claim 4 recites mental processes. For example, the human could read the digital content presented on the computer, infer knowledge propositions of the digital content, determine whether the inferred knowledge propositions match any previous learned knowledge propositions, if not, using pen and paper to add the inferred knowledge propositions to a queue. Step 2A, Prong One: Yes. The claim recites an additional machine learning algorithm. The machine learning algorithm is recited at high level of generality. The claim does not include any technical details about how the machine learning algorithm reads the natural language text, disambiguates the meaning of the natural language text, and infers knowledge propositions in the digital contents. Step 2A Prong Two: NO. Step 2A: YES. Step 2B: This part of the eligibility analysis evaluates the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional elements, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05(f). As explained with respect to Step 2A, Prong Two, there is one additional element. The additional element of “a machine learning algorithm” is recited in Claim 4. As discussed in Step 2A, Prong Two above, machine learning algorithm is recited at high level of generality. The claim does not include any technical details about how the machine learning algorithm reads the natural language text, disambiguates the meaning of the natural language text, and infers knowledge propositions in the digital contents. Even when considered in combination, the additional element represents mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. (Step 2B: NO). Claim 4 is ineligible.
Claim 5 depends on Claim 4. Claim 5 recites a mental process of searching any accessible digital content.
The Applicant’s arguments are not persuasive, and thus for these reasons, Examiner respectfully disagrees. The 101 Abstract idea rejections are maintained.
With respect to 112(b), the amended claims 1-5 overcome the rejection. Thus, the rejection has been withdrawn.
With respect to 102 rejection towards Claims 4-5, Applicant argues on page 18 of the Remarks that “Roushar does not disclose the claimed conditional validation workflow recited in amended claim 4.”
In response, Examiner respectfully does not agree. Roushar discloses using the existing knowledge graph to identify a received knowledge proposition (e.g., new word, new phrase) by interpreting the received knowledge proposition and matching the received knowledge proposition with knowledge graph, if do not match, the received knowledge proposition is determined as a new and the received knowledge proposition is added (See paragraphs [0048, 00121, 00148 and 00183]. The Applicant’s argument is not persuasive. The 102 rejection is maintained.
With respect to 103 rejection towards Claim 3, the Applicant’s argument is persuasive. Thus, the 103 rejection towards Claim 3 has been withdrawn.
Claim Rejections - 35 USC § 101
4. 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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The limitations recited in Claim 1 as drafted cover mental processes. For example, human could receive (e.g., listen, read) the natural language text, analyze and detect a viewpoint, intensity, veracity and audience of the natural language text. The human could retrieve knowledge propositions, classify the knowledge propositions, analyze viewpoint and bias, analyze intensity and exaggeration, analyze veracity and agenda and analyze audience and social proximity by looking at profile information of context.
The limitations recited in Claim 2 as drafted cover mental processes. For example, human could combine the input text and commonsense knowledge in a plurality of context to determine which answer is the best answer for the question. Applying heuristic procedures to adjust weight for each candidate answer is a mental process. Applying logical reasoning with common sense knowledge to determining meaning of the input text is a mental process. Using genetic algorithms to apply crossover and mutation inputs to adjust confidence is a mental process. For crossover, the human could combine impact of two or more knowledge propositions to a single candidate to adjust confidence. For mutation, the human could apply the independent impact of a single knowledge proposition to a single candidate to adjust the confidence.
The limitations recited in Claim 3 as drafted cover mental processes. For example, humans could apply crossover and mutation heuristic processes to rank candidate knowledge propositions in each of a plurality of attributes in a context, identify top scoring candidates and validate correctness. For crossover, the human could combine impact of two or more knowledge propositions to a single candidate to infer intent. For mutation, the human could apply the independent impact of a single knowledge proposition to a single candidate to infer intent. Next, the human could rank the candidates to select top scoring candidate and finally validate correctness.
The limitations recited in Claim 4 as drafted cover mental processes. For example, the human could read the digital content presented on the computer, infer knowledge propositions of the digital content, determine whether the inferred knowledge propositions match any previous learned knowledge propositions, if not, using pen and paper to add the inferred knowledge propositions to a queue.
The judicial exception is not integrated into a practical application. In particular, claims recite the additional limitations of a computing device, a continuous learning system. The additional element(s) or combination of elements such as a computing device, a continuous learning system in the claim(s) other than the abstract idea per se amount(s) to no more than (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. The mere recitation of a computer and a system and/or the like is akin of adding the word “apply it” and/or “use it” with a computer in conjunction with the abstract idea. The paragraph [0008] of the specification discloses “[0008] FIG. 1 depicts a schematic diagram of one illustrated embodiment of a deep intent analyzer system running on networked computing device.”
As filed in the specification, the computer is listed as a general-purpose computer and is mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The machine learning algorithm is recited at high level of generality. The claim does not include any technical details about how the machine learning algorithm reads the natural language text, disambiguates the meaning of the natural language text, and infers knowledge propositions in the digital contents.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims 5-18 further do not remedy the issues noted above. More specifically, claim 5 recites a mental process of searching for any accessible digital content.
Claim 6 recites a mental process of matching each word with a corresponding lexicon object and generating an ordered word list and an order sentence matrix. Claim 7 merely defines components. Claim 8 recites a mental process of classifying the retrieved knowledge propositions. Claim 9 recites a mental process of assigning candidate knowledge propositions. Claim 10 recites a mental process of analyzing morphology of words within the preprocessed natural language text. Claim 11 recites a mental process of resolving ambiguity among a plurality of candidate interpretations. Claim 12 recites a mental process of searching for one or more sources. Claim 13 recites a mental process of searching and analyzing one or more public web resources. Claim 14 recites a mental process of generating an explanation. Claim 15 recites a mental process of scanning and reading digital materials to determine meanings of portions of the digital materials. Claim 16 recites a mental process of iteratively modifying confidence vectors. Claim 17 recites a mental process of determining whether the preprocessed natural language text is associated with answering a question. Claim 18 recites a mental process of integrating deep natural language understanding, the commonsense knowledge, and heuristic inference.
For at least the supra provided reasons, claims 1-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
6. The following is a quotation 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.
7. Claims 6-18 are 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.
Claims 6-18 do not have support in the present specification. There is nowhere in the specification disclose features claimed in newly added claims 6-18.
Claim Rejections - 35 USC § 102
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
9. Claims 4-5 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Joseph Roushar (WO 2021/092099 A1.)
With respect to Claim 4, Joseph Roushar discloses
A continuous learning system in which digital content of any format is presented to a machine learning algorithm that reads natural language text in the digital content, disambiguates its meaning (Joseph Roushar [00121] describes the IKE interpreter disambiguate words or phrase that have unique meanings in the user’s context), and infers knowledge propositions reflecting the digital contents (Joshep Roushar [00183] describes inferring knowledge propositions), searches a knowledge graph for previously learned knowledge propositions matching the inferred knowledge propositions (Joseph Roushar [00183] describes searching a plurality of named sources for information to be used in the creation of a knowledge propositions to build a knowledge graph for use in causal reasoning and natural language understanding), and in a case of no matches, adds newly inferred knowledge propositions to a queue for validation and potential addition to a core knowledge graph (Joseph Roushar [0048] describes the natural language (NL) interpreter invokes machine learning to add new words, phrases and other tokens to the lexicon to represent knowledge that is new to the system or new to the language, [00148] using matching to infer new knowledge propositions).
With respect to Claim 5, Joseph Roushar discloses
wherein each newly inferred knowledge proposition in a validation queue is analyzed by searching any accessible digital content for corroborating evidence (Joseph Roushar [00106, 00183] describes knowledge propositions is searched and used to process an input. A validation data set includes a list of named sources to search to corroborate a solution.)
Allowable Subject Matter
10. Claims 1-3 and 6-18 are allowed in view of the prior art of record. The claims stand rejected under 101 Abstract idea and 112(a), and for the application to pass to allowance these rejections need to be overcome. Any amendments to overcome the rejections that results in any change in scope require further search and/or consideration in order to determine it allowability.
The following is a statement of reasons for the indication of allowable subject matter: the prior art(s) taken alone or in combination fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“analyzing viewpoint and bias based on word placement on topic-based opinion arcs;
analyzing intensity and exaggeration based on quantitative and qualitative statements vis-à-vis normative descriptions;
analyzing veracity and agenda by identifying fictitious statements and biased assertions;
analyzing audience and social proximity by extracting as much profile information as is available about a context in which the text was created, its audience and author.” as recited in Claim 1.
“apply heuristic procedures to increase or decrease a probability value or weight associated with each candidate until one or more candidates reach a threshold value defined as a emergence level;
apply logical reasoning with commonsense knowledge to determine an intended meaning of the speaker or writer and an accuracy and objectivity of a text; and
use genetic algorithms to apply crossover and mutation inputs to heuristics to contribute to increasing and decreasing confidence values to accelerate candidate differentiation toward the threshold value.” as recited in Claim 2.
“applying crossover and mutation heuristic processes to sort candidate knowledge propositions in each of a plurality of defined attributes each in a defined context frame to infer discreet elements of intent with a process to:
identify emergent or top scoring candidates as surviving individuals in each population; and
validate correctness of the inferred association with an input text.” as recited in Claim 3.
The closest prior arts found as follows.
a. Joseph Roushar (WO 2021/092099 A1). In this reference, Joseph Roushar discloses a method of building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains (e.g., causality, taxonomy, meronomy, time, space, identity, language, symbols and mathematical formulas). The method also includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences in natural language understanding, using the knowledge graph in conjunction with natural language understanding and logical inference. The method also includes generating a response to the input text, as to why and/or how unknown factors resulted in a known outcome, or what outcomes are likely given known causal factors. Joseph Roushar discloses viewpoint in paragraph [00195] (Joseph Roushar [00195] Context is a snapshot of the universe from a specific point of view to a specific depth. If the viewpoint is that of an astronomer at work, it could begin at her desk and include a radius of many thousands of light years. If the viewpoint is that of an electron in an inert substance, the context would encompass a very small distance. Context includes locations in space, points in time, activities, ideas, intentions, communications, motion, change, stasis, and any describable thing closely associated with the person place or thing to which the context applies. Higher or superior levels of context may be described as domains.) Joshep Roushar describes combining input text and knowledge proposition in a context to understanding of causal relations in paragraphs [0006 and 0008] describes the causal relation is used to answer a question in paragraphs [0003, 0005]. Joseph Roushar describes applying heuristic algorithm to modify a value of a weight component for each candidate in paragraph [00167], describes apply logical reasoning with knowledge propositions to determine intend of input text in [0010-0011], describes accurate interpretation of input human language text or utterance in [00197]. However, Joshep Roushar does not teach and/or suggest analyzing viewpoint and bias based on word placement on topic-based opinion arcs, analyzing intensity and exaggeration based on quantitative and qualitative statements vis-à-vis normative descriptions, analyzing veracity and agenda by identifying fictitious statements and biased assertions, analyzing audience and social proximity by extracting as much profile information as is available about the context in which the text was created, its audience and author as recited in Claim 1. Joshep Roushar does not teach and/or suggest using genetic algorithms to apply crossover and mutation inputs to the heuristics to contribute to increasing and decreasing the confidence values to accelerate candidate differentiation toward the threshold value as recited in Claim 2. Josep Roushar does not teach applying crossover and mutation heuristic processes in identifying and validating as recited in Claim 3. Thus, Joseph Roushar fails to teach and/or suggest the allowable subject matter noted above.
b. Karadogan (US 2026/0050879 A1.) In this reference, Karadogan disclose applying crossover and mutation to calculate probabilities for user intention in the contextual integration and selecting a subset of the candidate topologies based on a fitness score that reflects model accuracy, vendor ROI, or delivery efficiency (Karadogan [0025] applying crossover by combining edge connections and node structures from two selected parent topologies to form offspring models; and applying mutation by randomly altering node weights, adding new nodes, or introducing new edges to the offspring models to introduce variation and prevent premature convergence; monitoring, by the request handler, a new request for a product by continuously polling or subscribing to data updates from the at least one electronic device; receiving, by an intention handler executed by the computing system, intention data associated with at least one user, wherein the intention data includes application interaction events, product browsing time, or cart additions; identifying, by the intention handler, an intention of the at least one user by applying a pattern recognition algorithm including: extracting temporal and frequency-based features from the intention data; encoding the features as numerical vectors; comparing the numerical vectors to labeled training data using a classification model; and assigning the intention data to one of a plurality of predefined intent categories including purchase intent, browsing-only intent, or deferred interest intent; determining, by the intention handler, a probability that the at least one user will follow through on the identified intention by accessing historical user activity data and computing a statistical likelihood using a trained decision model; updating the probability in real time based on subsequent intention data collected by the intention handler, including changes in interaction patterns or abandonment signals; transmitting the intention data, including the computed probability, to the NE engine.) Karadogan applies crossover and mutation to calculate probabilities. However, Karadogan does not teach and/or suggest applying crossover and mutation inputs to the heuristics to contribute to increasing and decreasing the confidence values to accelerate candidate differentiation toward the threshold value as recited in Claim 2. Karadogan does not teach and/or suggest analyzing viewpoint and bias based on word placement on topic-based opinion arcs, analyzing intensity and exaggeration based on quantitative and qualitative statements vis-à-vis normative descriptions, analyzing veracity and agenda by identifying fictitious statements and biased assertions, analyzing audience and social proximity by extracting as much profile information as is available about the context in which the text was created, its audience and author as recited in Claim 1. Karadogan does not teach applying crossover and mutation heuristic processes in identifying and validating as recited in Claim 3. Thus, Karadogan fails to teach and/or suggest the allowable subject matter as noted above.
c. Cho (US 2024/0311723 A1.) In this reference, Cho disclose a method of using a heuristic evaluation value calculated on the basis of a causal action network, calculating heuristic evaluation values of search tree nodes for an action space on the basis of a causal action network, creating a search tree related to completion or achievement of a task, and generating an action plan of an autonomous thing on the basis of the search tree, and includes a processor configured to create an action space search tree (Cho [0017] The automated task planning system may further include a knowledge base system to be used in creating a search tree related to a task or generating an action plan on the basis of the search tree, [0018] The knowledge base system may be a system for processing precondition and postcondition data stored in a knowledge base, and the knowledge base may be a device in which action knowledge including a precondition representing knowledge for determining whether an action is executable in a specific situation and a postcondition representing how the situation changes after the action is executed is stored, [0021] To expand the action space search tree, the processor may select a front action node with a smallest heuristic evaluation value representing a distance or cost for a target node from among front action nodes. Here, the front action nodes may be actions which are executable at a current point in time and have preconditions satisfied in a situation of the current point in time, and the selection of the front action node may mean reflecting a postcondition of the action in a knowledge base of a current situation, [0023] Each of the causal relationships may be a directional relationship between two actions, “A” action and “B” action. When some preconditions of “A” action correspond to some postconditions of “B” action, there may be a causal relationship from “B” action to “A” action. The preconditions and the postconditions may be conjunctions between units of knowledge, and when some of the preconditions correspond to some of the postconditions, a unit of knowledge constituting the conjunctions of the preconditions may correspond to a unit of knowledge constituting the conjunctions of the postconditions.) Cho disclose creating a search tree for finding a target state. Cho indicates that the preconditions and the postconditions may be conjunctions between units of knowledge (i.e., propositions) in [0135]. Cho also disclose causal relationships with the target action node of the first-level layer in [0024]. However, Cho does not teach and/or disclose analyzing viewpoint and bias based on word placement on topic-based opinion arcs, analyzing intensity and exaggeration based on quantitative and qualitative statements vis-à-vis normative descriptions, analyzing veracity and agenda by identifying fictitious statements and biased assertions, analyzing audience and social proximity by extracting as much profile information as is available about the context in which the text was created, its audience and author as recited in Claim 1. Cho does not teach and/or suggest using genetic algorithms to apply crossover and mutation inputs to the heuristics to contribute to increasing and decreasing the confidence values to accelerate candidate differentiation toward the threshold value as recited in Claim 2. Cho does not teach applying crossover and mutation heuristic processes in identifying and validating as recited in Claim 3. Thus, Cho fails to teach and/or suggest the allowable subject matter noted above.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Stetson et al. (US 2023/0202513 A1.) In this reference, Stetson et al. disclose encoding a set of training data into a knowledge graph, generating a manifold based on the knowledge graph, and training an AI model by traversing the manifold.
b. Adibi et al. (US 2021/0136205 A1.) In this reference, Adibi et al. disclose a knowledge graph engine. The knowledge graph engine gathers information from multiple sources and makes it available to the virtual agent engine.
c. Trim et al. (US 2020/0380377 A1.) In this reference, Trim et al. disclose determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph.
12. 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.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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/THUYKHANH LE/Primary Examiner, Art Unit 2655
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