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 .
DETAILED ACTION
Claims 1-20 are pending.
This action is response to the application filed on April 26, 2024.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dalli et al (US 20220012591 A1).
With respect to claims 1, 11 and 16, Dalli et al teaches
obtaining conversation data derived from a conversation associated with at least one user device and at least one artificial intelligence model ([0002] artificial intelligence. [0003] AI systems in Machine learning models built through some statistical process which ends up generally biasing the learnt model towards the data on which the model has been trained on);
generating at least one bias detection determination attributable to the at least one artificial intelligence model by processing at least a portion of the conversation data using at least a first of multiple artificial intelligence-based agents ([0008] Bias in AI models answers from the model differ from the true underlying features. Bias detected locally for a specific sample or group of data. Alternatively, global bias may also be extracted by aggregating bias results from multiple samples. Alternatively, global bias may also be detected for the entire data and model by analyzing the white-box model itself in its entirety, using the population sample and/or synthetic data methods to increase the samples to cover the whole model ability to perform bias detection);
generating an adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model by processing, using at least a second of the multiple artificial intelligence-based agents, the at least a portion of the conversation data, the at least one bias detection determination, and contextual data related to the conversation ([0011] Various detecting the bias and weakness of the data sets and also the resulting models. second method makes use of local feature importance extracted from the rule-based model coefficients in order to identify any potential bias. [0042] detect this type of bias, and in some cases, may be able to correct or adjust the model using techniques such as human knowledge injection. [0202] extended versions of bias detection and mitigation methods and strength and weakness detection);
transmitting, to at least one of the at least one user device and one or more additional user devices, at least a portion of the adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model (FIG. 2, [0084] FIG. 2 for extracting rules for an explainable white-box model of a machine learning algorithm from a black-box machine learning algorithm); and
performing one or more automated actions based at least in part on the at least a portion of the adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model ([0060] bias information be causing bias, bias detection may be applied to a specific sample. The answer, along with its explanation coefficients, serve as the basis for localized bias detection utilize bias detection objectives to search for XAI models that minimize bias of an unacceptable type. basis of an AutoXAI system that incorporates bias detection);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory ([0198] In an exemplary embodiment utilizing FPGAs, a bias and/or strength and weakness detection and mitigation system may perform and execute while some form of local or remote memory be utilized to save interim results, configurations and results).
With respect to claims 2 ,12 and 17, Dalli et al teaches a user categorization bias category, a gamification bias category, a hidden intentions bias category, and a sided information bias category ([0093] category bias due to the distinct set of categories represented in a dataset collection. The size of the training multi-dimensional datasets used to build the AI mode).
With respect to claims 3, 13 and 18, Dalli et al teaches assigning, using the at least a first of multiple artificial intelligence-based agents, at least one bias detection score to the at least one artificial intelligence model and generating a text-based rational for the at least one bias detection score (AI mode) ( [0035] In step 1008, the neural network may compute the feature attribution of each transformed feature, which is activated by the associated partition. The relevance attribution works by multiplying the result of the computed coefficient with the transformed feature. The output of layer 1008 serves the basis of explanation generation. The values from this layer may be used to generate feature attribution graphs, heatmaps, textual explanations or other form of explanations).
With respect to claims 4, 14 and 19, Dalli et al teaches adjusting the at least one bias detection score based at least in part on processing the at least a portion of the conversation data using the using at least a second of the multiple artificial intelligence-based agents ([0008] Bias in AI models occurs when the expected or actual reported values/answers from the model differ from the true underlying features or parameters being estimated or analyzed).
With respect to claims 5, 15 and 20, Dalli et al teaches incorporating, into the at least one bias detection determination, at least one of one or more community standards, one or more legal requirements, and one or more geographic-based specificities by processing, using the at least a second of the multiple artificial intelligence-based agents, the at least a portion of the conversation data, the at least one bias detection determination, and the contextual data related to the conversation ([0008] Bias in AI models occurs when the expected or actual reported values/answers from the model differ from the true underlying features or parameters being estimated).
With respect to claim 6, Dalli et al teaches incorporating, into the adjusted version of the at least one bias detection determination, multiple adjustments to at least a portion of the at least one bias detection determination, each of the multiple adjustments carried out by a distinct additional one of the multiple artificial intelligence-based agents ([0008] Bias in AI models occurs when the expected or actual reported values/answers from the model differ from the true underlying features or parameters being estimated).
With respect to claim 7, Dalli et al teaches language model and at least one chatbot ([0023] FIG. 7 is a general architecture of an Interpretable Neural Network).
With respect to claim 8, Dalli et al teaches automatically training at least a portion of the at least a first of the multiple artificial intelligence-based agents using feedback related to the at least a portion of the adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model ([0023] FIG. 7 is a general architecture of an Interpretable Neural Network).
With respect to claim 9, Dalli et al teaches automatically training at least a portion of the at least a second of the multiple artificial intelligence-based agents using feedback related to the at least a portion of the adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model ([0023] FIG. 7 is a general architecture of an Interpretable Neural Network).
With respect to claim 10, Dalli et al teaches automatically training at least a portion of the at least one artificial intelligence model using feedback related to the at least a portion of the adjusted version of the at least one bias detection determination attributable to the at least one artificial intelligence model ([0002] artificial intelligence. [0003] AI systems in Machine learning models built through some statistical process which ends up generally biasing the learnt model towards the data on which the model has been trained on).
Conclusion
The prior arty made of record and not relied upon is considered pertinent to applicant’s disclosure.
Naufel (US 20240362208 A1) GRAPH-BASED NATURAL LANGUAGE PROCESSING (NLP) FOR QUERYING, ANALYZING, AND VISUALIZING COMPLEX DATA STRUCTURES
Considered for teaching generally system with graph-based Natural Language Processing (NLP) for querying, analyzing, and visualizing complex data structures is described. Such a system executes a generalized AI language model; defines and migrates a training dataset into a graph database by exposing data sources to an executing AI language model that self-defines a structure and self-writes an executable script to query the original data sources and self-writes code to load the extracted data into a graph database in the form of new nodes and new relationships with directionality between the nodes. The system further includes means for loading the extracted data into the graph database; condensing the information stored within the graph database into a condensed data structure representing the full architecture of the data in a natural language format; and responding to human language inquiries with responsive text, speech, and visualizations using the data loaded into the graph database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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