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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
1. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a Knowledge Base (KB) containing data on various types of cognitive biases, cognitive distortions, and logical fallacies, including definitions, examples, and patterns in claim 1
a Cognitive Error and Logical Fallacy Detector (CELFD), configured to process and analyze input to identify potential cognitive errors and logical fallacies within said input, informed by said Knowledge Base in claim 1
a Rational Advisor (RA), configured to diagnose and generate suggestions to mitigate said potential cognitive errors and logical fallacies identified by said CELFD in claim 1
a User Dashboard (UD), adapt to present and generate reports based on said potential cognitive errors and logical fallacies and said suggestions in claim 1
a User Interface (UI), adapted to allow a user to submit content for evaluation of arguments and reasoning in claim 1.
and a Data Storage Component (DS), configured to serve as a repository for all data entered into the detector, as well as output generated by the detector in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
2. Claims 1, 2, and 4 are objected to because of the following informalities:
Claim 1: The first limitation (regarding the KB) and the fourth limitation (regarding the UD) each end with a period. These periods should be replaced be semicolons to ensure that claim 1 recites exactly one sentence.
Claim 2: “aspects of the logical reasoning and rational thinking” should instead be “aspects of logical reasoning and rational thinking”, as antecedent basis is not provided for this term in claim 1.
Claim 4: “said knowledge base” should instead be “said Knowledge Base” to match capitalization recited in independent claim 1.
Appropriate correction is required.
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.
3. Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, “A digital detector” is recited, which is directed to one of the four statutory categories of invention (machine; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
knowledge… containing data on various types of cognitive biases, cognitive distortions, and logical fallacies, including definitions, examples, and patterns: a person keeps a written list of fallacies, cognitive biases, and cognitive distortions with definitions, examples, and patterns
…process and analyze input to identify potential cognitive errors and logical fallacies within said input, informed by said Knowledge…: a person analyzes the text and identifies possible cognitive errors/logically fallacies (e.g. identifies circular reasoning or ad hominem attacks)
…diagnose and generate suggestions to mitigate said potential cognitive errors and logical fallacies identified …: a person determines suggestions based on the identified error/fallacy (e.g. rewriting a sentence to remove a circular reasoning from occurring)
…generate reports based on said potential cognitive errors and logical fallacies and said suggestions: a person writes down a written report about the errors/fallacies and the suggestions
… allow a user to submit content for evaluation of arguments and reasoning: a user gives a person content to analyze by talking/writing to them
… a repository for all data entered into the detector, as well as output generated by the detector: a person writes down inputs (e.g. user provided content) and output (detected errors/fallacies + suggestions) on a piece of paper
Claim 1 does not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are “a Knowledge Base”, “a Cognitive Error and Logical Fallacy Detector (CELFD), configured to process…informed by said Knowledge Base”, “a Rational Advisor…based on said potential cognitive errors and logical fallacies identified by said CELFD”, “a User Dashboard (UD)”, “a User Interface (UI)”, and “a Data Storage Component (DS)”. These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claim 1 is directed to an abstract idea.
Claim 1 does not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claim 1 is not patent eligible.
Regarding claims 2-23, “The detector” is recited, which is directed to one of the four statutory categories of invention (machine; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite further mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
Claim 2:
…comprises information on various aspects of the logical reasoning and rational thinking, including definitions, examples, and patterns: a person uses information on logical reasoning and rational thinking to identify cognitive errors and logical fallacies
Claim 2 contains the additional limitation “said Knowledge Base comprises information…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 3:
…further comprises a database of world facts, including statistics and evidence-based findings, which is collected from external sources: a person uses facts/statistics/and evidence from external sources (e.g. books) to identify cognitive errors and logical fallacies
Claim 3 contains the additional limitation “said Knowledge Base further comprises”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 4:
Claim 4 recites “wherein information within said knowledge base is sourced and regularly updated.”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 5:
…perform NLP tasks on said input after converting it to text: a person listens to a user speak, writes down text, then processes the natural language in some way (e.g. detects sentiment)
Claim 5 contains the additional limitation “said CELFD is adapted to perform…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 6:
wherein said NLP tasks comprise at least one of tokenization, named entity recognition, and sentiment analysis: a person can tokenize (write down separate words they hear/read), detected named entities (i.e. identify names, places, etc.), and determine sentiment (i.e. tell if user is happy, sad, etc.)
Claim 7:
…apply information …to said content, evaluate arguments and reasoning presented, and determine if there are any cognitive errors or logical fallacies present in said processed input: a person uses the information to evaluation the content and determine if there are logical fallacies or cognitive errors
Claim 7 contains the additional limitation “said CELFD is adapted to apply information…” and “information from said Knowledge Base”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 8:
Claim 8 recites “wherein said CELFD is adapted to utilize pre-trained and fine-tuned custom AI models to detect and highlight said potential cognitive errors and logical fallacies in said input”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 9:
Claim 9 recites “wherein said RA is adapted to employ artificial intelligence models and prescriptive analytics to furnish comprehensive insights regarding said potential cognitive errors and logical fallacies identified by said CELFD.”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 10:
highlight possible consequences of said potential cognitive errors and logical fallacies: a person can write down consequence of the error/fallacy (e.g. circular reasoning can make argument illogical)
Claim 10 contains the limitation “said RA is adapted to highlight…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 11:
…offer strategic recommendations for addressing, managing, and mitigating said potential cognitive errors and logical fallacies: a person explains on paper how to mitigate/address/manage the error/fallacy (e.g. rewrite it a certain way to remove error/fallacy)
Claim 11 contains the additional limitation “said RA is adapted to offer…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 12:
…a feedback mechanism: a person writes down their feedback on a piece of paper
Claim 12 contains the additional limitation “said UD further comprises…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 13:
wherein said feedback mechanism is adapted to provide text-based feedback comprising a written summary of findings: a person writes down a summary
Claim 14:
wherein said feedback mechanism is adapted to provide visual representations which map out structure of arguments: a person draws a diagram to explain the arguments
Claim 15:
wherein said feedback mechanism is adapted to provide audio feedback: a person talks to the user about their results
Claim 16:
accept said submitted content in textual, vocal, and visual formats: a person reads/listens/interprets drawing from user to analyze
Claim 16 contains the additional limitation “said User Interface is adapted to accept…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 17:
Claim 17 contains the additional limitation “wherein said User Interface is text-based.”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 18:
Claim 18 contains the additional limitation “wherein said User Interface comprises a graphical user interface.”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 19:
Claim 19 contains the additional limitation “wherein said graphical user interface incorporates visual elements and interactive components that enhance user experience.”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 20:
store a record of user interactions with said detector: a person writes down a record of their conversation with a user
Claim 20 contains the additional limitation “said DS is further adapted to store…”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 21:
wherein said record comprises information on the users: a person writes information about a user
Claim 22:
wherein said information on the users comprises at least one of: individual preferences, historical usage patterns, or custom settings configured by the users: a person writes down preferences, a history, or settings of a user
Claim 23:
Claim 23 contains the additional limitation “wherein said DS is adapted to utilize both cloud servers and local servers”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 2-23 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claims as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claims 2-23 are directed to an abstract idea.
Claims 2-23 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claims as a whole, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 2-23 are not patent eligible.
Regarding claim 24, “A method” is recited, which is directed to one of the four statutory categories of invention (process; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
collecting and labeling a large dataset of texts related to all domains where the system could be used: a person labels data relating to cognitive errors and logical fallacies
… to identify and categorize cognitive biases and logical fallacies in text form: a person can analyze text to identify cognitive errors or logical fallacies (e.g. detect ad hominem attack)
analyzing user-written texts to identify potential cognitive biases and logical fallacies by users: a person can analyze user-written text to identify cognitive biases and logical fallacies
providing feedback and suggestions to the users on how to improve the user-written texts and reduce the presence of cognitive biases and logical fallacies: a person can write down the errors/fallacies they detected and suggestions for reducing them on a piece of paper to show the user
continuously monitoring the system's performance and improving it based on feedback and user behavior: a person can continuously improve how they identify errors by listening to the user’s feedback and behavior
Claim 24 does not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are “training a machine learning model on said dataset to identify and categorize cognitive biases and logical fallacies in text form” and “integrating said machine learning model with existing writing platforms”. These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claim 24 is directed to an abstract idea.
Claim 24 does not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claim 24 is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claim 24 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Su et al. (US 2025/0068843 A1, hereinafter Su).
Regarding claim 24, Su discloses A method for using a cognitive error and logical fallacy checking system, comprising to steps of: collecting and labeling a large dataset of texts related to all domains where the system could be used (para. 0055-0056 “In certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to automatically identify and correct contextual and logical errors in content. That is, in certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to perform one or more operations of the server ICLP bot code 212. [0056] In certain embodiments, the training data 218 may include a set of samples, where each sample includes a pair made of a tagged error type and corrected content.”; training data text is labeled (for a particular text, a corrected text is given, see para. 0057-0058)); training a machine learning model on said dataset to identify and categorize cognitive biases and logical fallacies in text form (para. 0055-0056 “In certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to automatically identify and correct contextual and logical errors in content. That is, in certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to perform one or more operations of the server ICLP bot code 212. [0056] In certain embodiments, the training data 218 may include a set of samples, where each sample includes a pair made of a tagged error type and corrected content.”); integrating said machine learning model with existing writing platforms (para. 0107 “For example, a first user may get the automated correction feature on content from word processing applications, emails, chat applications, etc., while a second user may get the automated correction feature for word processing applications.”); analyzing user-written texts to identify potential cognitive biases and logical fallacies by users (para. 0124 “Control begins at block 500 with the client ICLP bot code 222 receiving content in an input buffer 550. In certain embodiments, a user provides the content. In certain embodiments, the user is typing content in real time (e.g., in a chat application)…”; para. 0127 “In block 512, the ICLP parser 430 receives the content and parses the content. In block 514, the ICLP analyzer 432 builds and/or updates knowledge graphs 565 using the parsed content. In block 516, the ICLP selector 450 uses the knowledge graphs 565 and custom ICLP criteria in the user profile 416 to select one or more machine learning models 570. In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”); providing feedback and suggestions to the users on how to improve the user-written texts and reduce the presence of cognitive biases and logical fallacies (para. 0129 “FIG. 7 illustrates an example of correcting a contradiction error in accordance with certain embodiments. In this example, the ICLP bot code 200 detects the contradiction error, displays the contradiction error in a pop up dialog window 700, and corrects that error with text 710. The ICLP bot code 200 determines that “I confirm that our GUI does have the problem” should be corrected to “I confirm that our GUI does not have the problem”.”); continuously monitoring the system’s performance and improving it based on feedback and user behavior (para. 0030 “Example 6: The limitations of any of Examples 1-5 and 7, wherein each machine learning model of the plurality of machine learning models is retrained using user feedback on the correction. The user feedback may provide changes to suggested corrections and is used to improve the accuracy of corrections output by the plurality of machine learning models.”).
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.
5. Claims 1-2, 9-13, and 17-23 are rejected under 35 U.S.C. 103 as being unpatentable over Su in view of Dowden (NPL Internet Encyclopedia of Philosophy: Fallacies, hereinafter Dowden).
Regarding claim 1, Su discloses A digital detector for cognitive bias, comprising: a Knowledge Base (KB) (Fig. 5B, ‘knowledge graphs 565’; para. 0127 “In block 512, the ICLP parser 430 receives the content and parses the content. In block 514, the ICLP analyzer 432 builds and/or updates knowledge graphs 565 using the parsed content.”)…a Cognitive Error and Logic Fallacy Detector (CELFD) (Fig. 5B, ‘machine learning models 570’), configured to process and analyze input to identify potential cognitive errors and logical fallacies within said input, informed by said Knowledge Base (para. 0127 “In block 516, the ICLP selector 450 uses the knowledge graphs 565 and custom ICLP criteria in the user profile 416 to select one or more machine learning models 570. In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”; para. 0122 “The ICLP selector 450 selects an appropriate ICLP model 452 (e.g., a machine learning model) for identifying different contextual and logical errors according to the knowledge graphs and any combination of universal ICLP criteria in the ICLP service profile 412 and custom ICLP criteria in the user profile 416.”; Figs. 3A, errors identified include logical fallacies and cognitive errors (e.g. factual errors)); a Rational Advisor (RA) (Fig. 4, ‘ICLP identifier 454’), configured to diagnose and generate suggestions to mitigate said potential cognitive errors and logical fallacies identified by said CELFD (para. 0127 “In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”); a User Dashboard (UD) (Fig. 5A, ‘display screen 555’; see also para. 0122 “With embodiments, the ICLP monitor module 490 monitors the input buffer in the current active application window and sends the data in the input buffer to the server ICLP bot code 212 (i.e., to the ICLP parser 430 and the ICLP adjuster 470). The ICLP parser 430 parses the content from the input buffer into different elements.”), adapted to allow a user to submit content for evaluation of arguments and reasoning (para. 0122 “With embodiments, the ICLP monitor module 490 monitors the input buffer in the current active application window and sends the data in the input buffer to the server ICLP bot code 212 (i.e., to the ICLP parser 430 and the ICLP adjuster 470). The ICLP parser 430 parses the content from the input buffer into different elements.”; para. 0124 “Control begins at block 500 with the client ICLP bot code 222 receiving content in an input buffer 550. In certain embodiments, a user provides the content. In certain embodiments, the user is typing content in real time (e.g., in a chat application), while in other embodiments, the user selects a document (e.g., a patent document) as content. In block 502, the client ICLP bot code 222 processes the content in the input buffer. For example, the input buffer may store content in an editing box for a short message application, where the typed text content hasn't been sent to the receiver yet.”); and a Data Storage Component (DS) (memory is disclosed (Fig. 1, 112, 113); further disclosed are data structures for storing data: para. 0100 “In certain embodiments, the server ICLP bot code 212 generates an ICLP data structure 414 from the ICLP service profile 412, and the ICLP data structure 414 stores related data.”), configured to serve as a repository for all data entered into the detector, as well as output generated by the detector (Fig. 6A-6B, data structure stores information entered (see Fig. 6B, input buffer) and output generated (see Fig. 6B, ‘error type’, ‘suggested content’, ‘proofreading comments’)).
Su does not specifically disclose wherein the Knowledge Base (KB) is containing data on various types of cognitive biases, cognitive distortions, and logical fallacies, including definitions, examples, and patterns.
Dowden teaches containing data on various types of cognitive biases, cognitive distortions, and logical fallacies, including definitions, examples, and patterns (Su discloses building knowledge graph (which constitutes a knowledge base) from parsing input content; Dowden teaches an Internet encyclopedia giving definitions, examples, and patterns of various logical fallacies/cognitive distortions/cognitive biases: see, for example, pg. 6 “Ad Hominem”, containing a definition, example, and patterns (e.g. define what ad hominem means, gives example, and shows common ways this fallacy occurs)).
Su and Dowden are considered to be analogous to the claimed invention as
they both are in the same field of detecting logical fallacies. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su to incorporate the teachings of Dowden in order to specifically have the knowledge base contain data on various types of cognitive biases, cognitive distortions, and logical fallacies. Doing so would be beneficial, as this would provide domain specific knowledge (in this case, knowledge regarding logical fallacies and cognitive errors) to the knowledge graph, aiding in the logical fallacy/cognitive error detection.
Regarding claim 2, Su in view of Dowden discloses wherein said Knowledge Base comprises information on various aspects of the logical reasoning and rational thinking, including definitions, examples, and patterns (Su discloses building knowledge graph (which constitutes a knowledge base) from parsing input content; Dowden teaches an Internet encyclopedia giving definitions, examples, and patterns of various logical fallacies/cognitive distortions/cognitive biases: see, for example, pg. 6 “Ad Hominem”, containing a definition, example, and patterns (e.g. define what ad hominem means, gives example, and shows common ways this fallacy occurs)).
Su and Dowden are considered to be analogous to the claimed invention as
they both are in the same field of detecting logical fallacies. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su to incorporate the teachings of Dowden in order to specifically have the knowledge base contain data on various types of cognitive biases, cognitive distortions, and logical fallacies. Doing so would be beneficial, given the same rationale as claim 1.
Regarding claim 9, Su in view of Dowden discloses wherein said RA is adapted to employ artificial intelligence models and prescriptive analytics to furnish comprehensive insights regarding said potential cognitive errors and logical fallacies identified by said CELFD (Su, para. 0127 “In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”).
Regarding claim 10, Su in view of Dowden discloses wherein said RA is adapted to highlight possible consequences of said potential cognitive errors and logical fallacies (Su, e.g. Fig. 7 and Fig. 9, message identifies specific type of error (e.g. inconsistency or contradiction) to user, highlighting consequences of the errors identified (e.g. specifying the error is a contradiction tells the user the consequence of the error is that there are conflicting ideas)).
Regarding claim 11, Su in view of Dowden discloses wherein said RA is adapted to offer strategic recommendations for addressing, managing, and mitigating said potential cognitive errors and logical fallacies (Su, e.g., Fig. 7 and Fig. 9, recommendations are identified and displayed to user to address/manage/mitigate errors (e.g. Fig. 7, user is suggested to change ‘does have’ to ‘does NOT have’ to address the contradiction error)).
Regarding claim 12, Su in view of Dowden discloses wherein said UD comprises a feedback mechanism (Su, para. 0129 “FIG. 7 illustrates an example of correcting a contradiction error in accordance with certain embodiments. In this example, the ICLP bot code 200 detects the contradiction error, displays the contradiction error in a pop up dialog window 700”).
Regarding claim 13, Su in view of Dowden discloses wherein said feedback mechanism is adapted to provide text-based feedback comprising a written summary of findings (Su, para. 0129 “FIG. 7 illustrates an example of correcting a contradiction error in accordance with certain embodiments. In this example, the ICLP bot code 200 detects the contradiction error, displays the contradiction error in a pop up dialog window 700”).
Regarding claim 17, Su in view of Dowden discloses where said User Interface is text-based (Su, e.g. Fig. 7, display window 700 shows text output; input buffer receives text: para. 0124 “Control begins at block 500 with the client ICLP bot code 222 receiving content in an input buffer 550. In certain embodiments, a user provides the content. In certain embodiments, the user is typing content in real time (e.g., in a chat application)…”).
Regarding claim 18, Su in view of Dowden discloses wherein said User Interface comprises a graphical user interface (Su, Fig. 7, pop-up window 700 displayed to user on application window, which reads on GUI; para. 0129 “FIG. 7 illustrates an example of correcting a contradiction error in accordance with certain embodiments. In this example, the ICLP bot code 200 detects the contradiction error, displays the contradiction error in a pop up dialog window 700”; para. 0122 “With embodiments, the ICLP monitor module 490 monitors the input buffer in the current active application window and sends the data in the input buffer to the server ICLP bot code 212 (i.e., to the ICLP parser 430 and the ICLP adjuster 470).”).
Regarding claim 19, Su in view of Dowden discloses wherein said graphical user interface incorporates visual elements and interactive components that enhance user experience (Su, Fig. 7, pop-up window 700 visually provides information to user, which they can interact with (user feedback 560) to enhance user experience (i.e. remove errors)).
Regarding claim 20, Su in view of Dowden discloses wherein said DS is further adapted to store a record of user interactions with said detector (Su, Figs. 6A-6B, data structure 600 stores interactions (e.g. ‘input buffer’ and ‘suggested content’); para. 0128 “FIGS. 6A and 6B illustrate an example ICLP data structure 600, 650 in accordance with certain embodiments.”).
Regarding claim 21, Su in view of Dowden discloses wherein said record comprises information on the users (Su, e.g. Fig. 6A, “UserID”; see also stored user profile: para. 0109 “In certain embodiments, the user profile 416 stores user customizations and user preferences as custom ICLP criteria. The custom ICLP criteria includes a set of rules for defining an ICLP service. The custom ICLP criteria may identify one or more monitored applications (“Apps”), identify one or more types of errors to detect, and specify how to process the errors.”).
Regarding claim 22, Su in view of Dowden discloses wherein said information on the users comprises at least one of: individual preferences, historical usage patterns, or custom settings configured by the users (Su, individual preferences: para. 0109 “In certain embodiments, the user profile 416 stores user customizations and user preferences as custom ICLP criteria. The custom ICLP criteria includes a set of rules for defining an ICLP service. The custom ICLP criteria may identify one or more monitored applications (“Apps”), identify one or more types of errors to detect, and specify how to process the errors.”).
Regarding claim 23, Su in view of Dowden discloses wherein said DS is adapted to utilize both cloud servers and local servers (Su, para. 0038 “Computing environment 100 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as Intelligent Contextual and Logical Proofreading (ICLP) bot code 200. In addition to block 200, computing environment 100 includes, for example, computer 101, wide area network (WAN) 102, end user device (EUD) 103, remote server 104, public cloud 105, and private cloud 106. In this embodiment, computer 101 includes processor set 110 (including processing circuitry 120 and cache 121), communication fabric 111, volatile memory 112, persistent storage 113 (including operating system 122 and block 200, as identified above), peripheral device set 114 (including user interface (UI) device set 123, storage 124, and Internet of Things (IoT) sensor set 125), and network module 115. Remote server 104 includes remote database 130. Public cloud 105 includes gateway 140, cloud orchestration module 141, host physical machine set 142, virtual machine set 143, and container set 144.”; para. 0047 “In some embodiments, the WAN 102 may be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network.”).
6. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Su in view of Dowden and further in view of Williams et al. (US 2019/0220474 A1, hereinafter Williams)
Regarding claim 3, Su in view of Dowden does not specifically disclose wherein said Knowledge Base further comprises a database of world facts, including statistics and evidence-based findings, which is collected from external sources.
Williams teaches wherein said Knowledge Base further comprises a database of world facts, including statistics and evidence-based findings, which is collected from external sources wherein said Knowledge Base further comprises a database of world facts, including statistics and evidence-based findings, which is collected from external sources (knowledge base with knowledge collected from external sources: para. 0069 “Each of the content sources 16-1 through 16-N includes any source of content, where the content includes one or more of data files, a data stream, a tech stream, a text file, an audio stream, an audio file, a video stream, a video file, etc. ...” para. 0070 “For example, the AI servers 20-1 through 20-N ingest content from the content sources 16-1 through 16-N by receiving, via the core network 24 content messages 28-1 through 28-N as AI messages 32-1 through 32-N, extract the knowledge from the ingested content, and interact with the various user devices to utilize the extracted knowledge”; this knowledge comprises facts, statistics, and evidence based findings (e.g. facts based on real world): e.g. para. 0292 “The plurality of AI servers 20-1 through 20-N include a knowledge database associated with Mario Andretti, a general sports knowledge database, an auto racing database, a database of sports statistics, a database of media articles, a database of Mario Andretti interview content, etc.”).
Su, Dowden, and Williams are considered to be analogous to the claimed invention as Su and Dowden are in the same field of detecting logical fallacies and Williams is in the same field of knowledge bases. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Williams in order to specifically have the knowledge base comprise a database of world facts, including statistics and evidence-based findings, which is collected from external sources. Doing so would be beneficial, as this would allow for trusted (factual) sources to be utilized for the knowledge base disclosed in Su, with the data having a high quality level (Williams, para. 0085).
7. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Su in view of Dowden and further in view of Jamrog et al. (US 2016/0110459 A1, hereinafter Jamrog)
Regarding claim 4, Su in view of Dowden does not specifically disclose wherein information within said knowledge base is sourced and regularly updated.
Jamrog teaches wherein information within said knowledge base is sourced and regularly updated (para. 0037 “Process 320 ingests information from documents, such as newspapers, periodicals, etc., and the information is added to the corpora, or knowledge base 106, that is utilized by the QA system to answer user questions.”; para. 0039 “An update to all or part of the input is published (e.g., daily, weekly, monthly, etc.) as new documents 330. These new documents are ingested using the QA system's update ingestion process 340….”).
Su, Dowden, and Jamrog are considered to be analogous to the claimed invention as Su and Dowden are in the same field of detecting logical fallacies and Jamrog is in the same field of knowledge bases. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Williams in order to specifically have the knowledge base be source and regularly updated. Doing so would be beneficial, as this would ensure that up-to-date information is stored in the knowledge base, allowing for the model disclosed in Su to operate using the newest information available, which might be more accurate if the information has changed over time (Jamrog, para. 0041).
8. Claims 5-8 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Su in view of Dowden and further in view of Myslinski (US 2013/0151240 A1).
Regarding claim 5, Su in view of Dowden discloses wherein said CELFD is adapted to perform NLP tasks on said input (para. 0127 “In block 516, the ICLP selector 450 uses the knowledge graphs 565 and custom ICLP criteria in the user profile 416 to select one or more machine learning models 570. In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”; para. 0122 “The ICLP selector 450 selects an appropriate ICLP model 452 (e.g., a machine learning model) for identifying different contextual and logical errors according to the knowledge graphs and any combination of universal ICLP criteria in the ICLP service profile 412 and custom ICLP criteria in the user profile 416.”; Figs. 3A, errors identified include logical fallacies and cognitive errors (e.g. factual errors)). However, Su in view of Dowden does not specifically disclose performing NLP tasks on said input after converting it to text.
Myslinski teaches to perform NLP tasks on said input after converting it to text (para. 0102 “In the step 102, the information is processed. In some embodiments, processing includes converting the information into a searchable format. During or after the information is monitored, the information is converted into a searchable format. Processing is able to include many aspects including, but not limited to, converting audio into text, formatting, parsing data, determining context and/or any other aspect that enables the information to be fact checked.”).
Su, Dowden, and Myslinski are considered to be analogous to the claimed invention as they are in the same field of detecting logical fallacies/factual errors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Myslinski in order to specifically have the input converted to text. Doing so would be beneficial, as this would enable an additional modality (audio) to be utilized for logical fallacy/cognitive error detection, extending functionality of the system and improving user experience.
Regarding claim 6, Su in view of Dowden and Myslinski discloses wherein the NLP tasks comprise at least one of tokenization, named entity recognition, and sentiment analysis (Su, para. 0115 “The ICLP models 452 may include: a named entity error module, a temporal relation module, and an event error module. The named entity error module identifies and extracts temporal entities (e.g., dates, times, and durations) from the text. This is used to check that the timeline is accurate and consistent.”).
Regarding claim 7, Su in view of Dowden and Myslinski discloses wherein said CELFD is adapted to apply information from said Knowledge Base to said content, evaluate arguments and reasoning presented, and determine if there are any cognitive errors or logical fallacies present in said processed input (Su, para. 0127 “In block 516, the ICLP selector 450 uses the knowledge graphs 565 and custom ICLP criteria in the user profile 416 to select one or more machine learning models 570. In block 518, the ICLP identifier 454 uses the one or more selected machine learning models 570 to search for contextual and logical errors and recommendations for correcting any contextual and/or logical errors that are found.”; para. 0122 “The ICLP selector 450 selects an appropriate ICLP model 452 (e.g., a machine learning model) for identifying different contextual and logical errors according to the knowledge graphs and any combination of universal ICLP criteria in the ICLP service profile 412 and custom ICLP criteria in the user profile 416.”).
Regarding claim 8, Su in view of Dowden and Myslinski discloses wherein said CELFD is adapted to utilize pre-trained and fine-tuned custom AI models to detect and highlight said potential cognitive errors and logical fallacies in said input (Su, models are pre-trained: para. 0055-0056 “In certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to automatically identify and correct contextual and logical errors in content. That is, in certain embodiments, the ICLP bot code 200 trains the one or more machine learning models 214 to perform one or more operations of the server ICLP bot code 212. [0056] In certain embodiments, the training data 218 may include a set of samples, where each sample includes a pair made of a tagged error type and corrected content.”; these models are fine-tuned via user feedback: para. 0030 “Example 6: The limitations of any of Examples 1-5 and 7, wherein each machine learning model of the plurality of machine learning models is retrained using user feedback on the correction. The user feedback may provide changes to suggested corrections and is used to improve the accuracy of corrections output by the plurality of machine learning models.”).
Regarding claim 15, Su in view of Dowden does not specifically disclose wherein said feedback mechanism is adapted to provide audio feedback.
Myslinski teaches wherein said feedback mechanism is adapted to provide audio feedback (para. 0213 “In some embodiments, the fact checker is used to inform a person (e.g. a host) that he made a mistake. For example, a host states the U.S. is $15 Billion in debt, and a chime and/or other audio is emitted in the host's earpiece, letting the host know that he made a mistake. In some embodiments, the chime is merely just a short chime where the host has to figure out what the mistake was, and in some embodiments, the audio is a correction (e.g. "Trillion" in this example) or a chime linked to a teleprompter that could display accurate information or incorrect statement. In some embodiments, the indicator to the person is visual (e.g. a flashing red light), tactile (e.g. vibration), or any other indicator.”).
Su, Dowden, and Myslinski are considered to be analogous to the claimed invention as they are in the same field of detecting logical fallacies/factual errors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Myslinski in order to specifically have the feedback mechanism be adapted to provide audio feedback. Doing so would be beneficial, as this would a different modality for users to receive the output feedback in, aiding users who either cannot read or are otherwise impaired.
Regarding claim 16, Su in view of Dowden discloses wherein said User Interface is adapted to accept said content in textual…formats (Su, para. 0124 “Control begins at block 500 with the client ICLP bot code 222 receiving content in an input buffer 550. In certain embodiments, a user provides the content. In certain embodiments, the user is typing content in real time (e.g., in a chat application), while in other embodiments, the user selects a document (e.g., a patent document) as content. In block 502, the client ICLP bot code 222 processes the content in the input buffer. For example, the input buffer may store content in an editing box for a short message application, where the typed text content hasn't been sent to the receiver yet.”). However, Su in view of Dowden does not specifically disclose [wherein said interface is adapted to accept said content in textual], vocal, and visual formats.
Myslinski teaches [wherein said interface is adapted to accept said content in textual], vocal, and visual formats (vocal: para. 0102 “In the step 102, the information is processed. In some embodiments, processing includes converting the information into a searchable format. During or after the information is monitored, the information is converted into a searchable format. Processing is able to include many aspects including, but not limited to, converting audio into text, formatting, parsing data, determining context and/or any other aspect that enables the information to be fact checked.”; visual: para. 0253 “In some embodiments, the fact checker is used for fact checking sports' rules and the implementation of the rules. For example, the fact checker is used for determining if the umpire/referee made the correct call. The fact checker is able to analyze video or images of the sport, determine the applicable rule, analyze the facts and the rule, and produce a judgment.”).
Su, Dowden, and Myslinski are considered to be analogous to the claimed invention as they are in the same field of detecting logical fallacies/factual errors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Myslinski in order to specifically have User Interface be adapted to accept said submitted content in textual, vocal, and visual formats. Doing so would be beneficial, as this would allow for speech inputs and visual formats to be analyzed, extending the functionality of the model to incorporate inputs in the audio and video/image modalities and covering a wider variety of inputs, extending functionality of the system and improving user experience.
9. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Su in view of Dowden and further in view of Baum (US 2018/0322407 A1, hereinafter Baum).
Regarding claim 14, Su in view of Dowden does not specifically disclose wherein said feedback mechanism is adapted to provide visual representations which map out structure of arguments.
Baum teaches wherein said feedback mechanism is adapted to provide visual representations which map out structure of arguments (para. 0009 “To that end, the system may support the ability of users to construct argument trees in which arguments may be entered in natural language text (or possibly including data, for example images or graphs or links). These arguments may expand into graph-like structures as do the formalized proof structures in mathematics, because a user may enter some statements of axioms or assumptions, and some arguments that may depend on these assumptions and that may be said to be arguments for the validity of some other statements—analogous to lemmas in formal mathematics, and so on finally giving an argument or arguments for the final result.”; see for example, Fig. 14A, mapping a structure of arguments; para. 0073 “For example, as the user locally edits the graph, the system may locally (1.8, 1.4) run a graph arranging algorithm to display the graph in a pleasing and informative layout, while it may perform the calculations of rating updates and belief value score updates for the nodes on the graph at the central computer 1.7 and send the updated information to the local computer 1.8 for display.”).
Su, Dowden, and Baum are considered to be analogous to the claimed invention as they are in the same field of detecting logical fallacies/factual errors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Su in view of Dowden to incorporate the teachings of Baum in order to specifically have the feedback mechanism be adapted to provide visual representations which map out structure of arguments. Doing so would be beneficial, as this would provide a way of enabling users to see if the arguments that they have entered have been established based on unchallenged arguments that are not disputed (Baum, para. 0009) in a pleasing and informative layout (Baum, para. 0073).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Namazifar et al. (US 12,651,599 B2): generating answers to user input text data using knowledge bases (Fig. 1A)
Tensmeyer et al. (US 11,880,655 B2): fact correction system (Fig. 1)
Wong et al. (US 10,380,708 B1): utilizing knowledge base containing real world facts regarding various domains (Col. 6 Lines 40-45)
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/CODY DOUGLAS HUTCHESON/ Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/ Supervisory Patent Examiner, Art Unit 2659
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